Fluctuation Test Research Articles

Estimating Rates of Switching Between Cell States Using a Single Time Point Measurement

In isogenic cell populations, cells can switch between different gene expression states. These expression states can be biologically relevant. For example, a certain expression state may cause a tumor cell to be resistant to treatment, while another state may leave it vulnerable to treatment. Estimating the rates of state-switching can be difficult, because experimentally measuring a cell's transcriptome often involves destroying the cell, so it can only be measured once. We propose a computational method to estimate the rate of switching between expression states, given data from a Luria-Delbrück style fluctuation test that requires only a single time point measurement. We then benchmark this method using simulated data to test its efficacy, with varying assumptions made about the cell cycle timing distribution in the simulations.

Evolutionary Dynamics of Asexual Hypermutators Adapting to a Novel Environment.

How microbes adapt to a novel environment is a central question in evolutionary biology. Although adaptive evolution must be fueled by beneficial mutations, whether higher mutation rates facilitate the rate of adaptive evolution remains unclear. To address this question, we cultured Escherichia coli hypermutating populations, in which a defective methyl-directed mismatch repair pathway causes a 140-fold increase in single-nucleotide mutation rates. In parallel with wild-type E. coli, populations were cultured in tubes containing Luria-Bertani broth, a complex medium known to promote the evolution of subpopulation structure. After 900 days of evolution, in three transfer schemes with different population-size bottlenecks, hypermutators always exhibited similar levels of improved fitness as controls. Fluctuation tests revealed that the mutation rates of hypermutator lines converged evolutionarily on those of wild-type populations, which may have contributed to the absence of fitness differences. Further genome-sequence analysis revealed that, although hypermutator populations have higher rates of genomic evolution, this largely reflects strong genetic linkage. Despite these linkage effects, the evolved population exhibits parallelism in fixed mutations, including those potentially related to biofilm formation, transcription regulation, and mutation-rate evolution. Together, these results are generally inconsistent with a hypothesized positive relationship between the mutation rate and the adaptive speed of evolution, and provide insight into how clonal adaptation occurs in novel environments.

An explicit split point procedure in model-based trees allowing for a quick fitting of GLM trees and GLM forests

Classification and regression trees (CART) prove to be a true alternative to full parametric models such as linear models (LM) and generalized linear models (GLM). Although CART suffer from a biased variable selection issue, they are commonly applied to various topics and used for tree ensembles and random forests because of their simplicity and computation speed. Conditional inference trees and model-based trees algorithms for which variable selection is tackled via fluctuation tests are known to give more accurate and interpretable results than CART, but yield longer computation times. Using a closed-form maximum likelihood estimator for GLM, this paper proposes a split point procedure based on the explicit likelihood in order to save time when searching for the best split for a given splitting variable. A simulation study for non-Gaussian response is performed to assess the computational gain when building GLM trees. We also propose a benchmark on simulated and empirical datasets of GLM trees against CART, conditional inference trees and LM trees in order to identify situations where GLM trees are efficient. This approach is extended to multiway split trees and log-transformed distributions. Making GLM trees possible through a new split point procedure allows us to investigate the use of GLM in ensemble methods. We propose a numerical comparison of GLM forests against other random forest-type approaches. Our simulation analyses show cases where GLM forests are good challengers to random forests.

Benzalkonium chloride antagonises aminoglycoside antibiotics and promotes evolution of resistance.

BackgroundBiocide disinfectants are essential tools in infection control, but their use can inadvertently contribute to emergence of antibiotic-resistant bacteria. In this study we systematically examine the effect of the biocide benzalkonium chloride, which is primarily used for surface disinfection but is also present as a preservative in many consumer products, on the activity of aminoglycoside antibiotics in Acinetobacter baumannii.MethodsThe effect of subinhibitory BAC on aminoglycoside treatment of A. baumannii ATCC17978 was investigated using time-to-kill assays, MIC determination, directed evolution experiments, fluctuation tests and labelled gentamicin accumulation assays. Further MIC determinations and directed evolution experiments were performed with additional Gram-negative ESKAPE pathogens.FindingsIn A. baumannii ATCC17978, BAC prevents gentamicin killing and drastically increases the frequency at which resistant mutants emerge, through reducing intracellular antibiotic accumulation. BAC also increases the MIC of multiple aminoglycoside antibiotics (kanamycin, tobramycin, streptomycin, gentamicin and amikacin). BAC promotes the emergence of mutants with reduced gentamicin susceptibility in other Gram-negative ESKAPE pathogens but does not always alter the MIC. These effects occur at BAC concentrations which are similar to residual levels in high-use environments, and just below the concentration range for BAC when used as a preservative in eye drops and ear drops.InterpretationOur results suggest that subinhibitory BAC has the potential to antagonise aminoglycoside activity and promote the emergence of bacterial mutants with reduced susceptibility. We suggest that the extremely widespread use of BAC in clinical and home settings and its long half-life mean there is potential for these interactions to occur in the environment, or in patients who use BAC-containing products while taking aminoglycosides to treat skin, eye or ear infections, although such co-exposure is likely to be rare. We suggest that biocide stewardship is needed to prevent the types of exposure that can contribute to antibiotic resistance.FundingThis work was funded by the National Health and Medical Research Council of Australia. The funders had no role in study design, interpretation or decision to publish.

In Vitro and In Vivo Sequestration of Methamphetamine by a Sulfated Acyclic CB[n]-Type Receptor.

We report the synthesis of two new acyclic sulfated acyclic CB[n]-type receptors (TriM0 and Me4 TetM0) and investigations of their binding properties toward a panel of drugs of abuse (1-13) by a combination of 1 H NMR spectroscopy and isothermal titration calorimetry. TetM0 is the most potent receptor with Ka ≥106 M-1 toward methamphetamine, fentanyl, MDMA and mephedrone. TetM0 is not cytotoxic toward HepG2 and HEK 293 cells below 100 μM according to MTS metabolic and adenylate kinase release assays and is well tolerated in vivo when dosed at 46 mg kg-1 . TetM0 does not inhibit the hERG ion channel and is not mutagenic based on the Ames fluctuation test. Finally, in vivo efficacy studies show that the hyperlocomotion of mice treated with methamphetamine can be greatly reduced by treatment with TetM0 up to 5 minutes later. TetM0 has potential as a broad spectrum in vivo sequestrant for drugs of abuse.

Forecasting crude oil real prices with averaging time-varying VAR models

The aim of this research is to discuss the ability to forecast real crude oil price by the use of Time-Varying Vector Autoregression (TVP-VAR) models. In particular, model averaging and model selection schemes over several TVP-VAR models are performed. These methods address the problem of variable uncertainty. Indeed, several previous studies indicate that explanatory variables for crude oil prices can be different in different periods of time. Further, the strength of the relationship between crude oil price and its determinants can vary in time. The applied model combination schemes are an extension of Dynamic Model Averaging, which has already been found viable. Moreover, geopolitical risk is included in each model as an endogenous variable, and the model combination scheme is constructed in a way to tackle the joint forecasting ability (with respect to crude oil real price and geopolitical risk). It is found that, indeed, the Vector Autoregression approach results in more accurate forecasts than single equation a Time-Varying Regression or the standard Dynamic Model Averaging. Also, the model combination scheme of several Vector Autoregression models outperforms a single Vector Autoregression model approach. However, forecast accuracy is tested with some novel tools, such as Giacomini and Rossi fluctuation test and Murphy diagrams, which are able to capture time-varying predictive ability significances and several scoring functions.

Mutagenicity and genotoxicity of water boiled in aluminum pots of different duration of use using SOS chromotest and Ames fluctuation test.

Boiling water before drinking or using it for cooking is a general practice especially in areas where portable water is not readily available. However, boiling water in an aluminum pot could be a route of entry of heavy metals into humans. This study assessed the genotoxic and mutagenic potential of boiled water samples from aluminum pots of different duration of use using the SOS chromotest on Escherichia coli PQ37 and the Ames fluctuation test on Salmonella typhimurium strains TA98 and TA100, respectively. Three aluminum pots from the same manufacturer but of different years of use (6-year-old, 3-year-old, and new aluminum pots) were used for the experiment. Six selected heavy metals (Cadmium, Copper, Arsenic, Nickel, Lead, and Aluminum) were also analyzed in the samples using an Atomic Absorption Spectrophotometer (AAS Buck, Scientific model 210 VGP). Cadmium, Copper, Arsenic, Nickel, Lead, and Aluminum were present in all the test water samples at concentrations that were higher than the maximum limit allowable by standard regulatory organizations. The concentrations of these metals in the samples also increased as the duration of use of the aluminum pots increased. The results further showed that the water boiled in the three aluminum pots is mutagenic and genotoxic in both Ames fluctuation and SOS chromotests. The 6-year-old aluminum pot induced the highest mutagenicity and genotoxicity followed by the 3-year-old aluminum pot. The metals in the tested samples were believed to be responsible for the observed mutagenicity and genotoxicity in the microbial assays. The findings of this study revealed that cooking with Aluminum pots could lead to the leaching of heavy metals into food, and pose mutagenic and genotoxic risks to consumers.

A modified fluctuation test for elucidating drug resistance in microbial and cancer cells

Clonal populations of microbial and cancer cells are often driven into a drug-tolerant persister state in response to drug therapy, and these persisters can subsequently adapt to the new drug environment via genetic and epigenetic mechanisms. Estimating the frequency with which drug-tolerance states arise, and its transition to drug-resistance, is critical for designing efficient treatment schedules. Here we study a stochastic model of cell proliferation where drug-tolerant persister cells transform into a drug-resistant state with a certain adaptation rate, and the resistant cells can then proliferate in the presence of the drug. Assuming a random number of persisters to begin with, we derive an exact analytical expression for the statistical moments and the distribution of the total cell count (i.e., colony size) over time. Interestingly, for Poisson initial conditions the noise in the colony size (as quantified by the Fano factor) becomes independent of the initial condition and only depends on the adaptation rate. Thus, experimentally quantifying the fluctuations in the colony sizes provides an estimate of the adaptation rate, which then can be used to infer the starting persister numbers from the mean colony size. Overall, our analysis introduces a modification of the classical Luria–Delbrück experiment, also called the “Fluctuation Test”, providing a valuable tool to quantify the emergence of drug resistance in cell populations.

Comparing conventional and green fracturing fluids by chemical characterisation and effect-based screening

There is public and scientific concern about air, soil and water contamination and possible adverse environmental and human health effects as a result of hydraulic fracturing activities. The use of greener chemicals in fracturing fluid aims to mitigate these effects.This study compares fracturing fluids marketed as either ‘conventional’ or ‘green’, as assessed by their chemical composition and their toxicity in bioassays. Chemical composition was analysed via non-target screening using liquid chromatography - high resolution mass spectrometry, while toxicity was evaluated by the Ames fluctuation test to assess mutagenicity and CALUX reporter gene assays to determine specific toxicity.Overall, the results do not indicate that the ‘green’ fluids are less harmful than the ‘conventional’ ones. First, there is no clear indication that the selected green fluids contain chemicals present at lower concentrations than the selected conventional fluids. Second, the predicted environmental fate of the identified compounds does not seem to be clearly distinct between the ‘green’ and ‘conventional’ fluids, based on the available data for the top five chemicals based on signal intensity that were tentatively identified. Furthermore, Ames fluctuation test results indicate that the green fluids have a similar genotoxic potential than the conventional fluids. Results of the CALUX reporter gene assays add to the evidence that there is no clear difference between the green and conventional fluids.These results do not support the claim that currently available and tested green-labeled fracturing fluids are environmentally more friendly alternatives to conventional fracturing fluids.

Age-dependent branching processes and applications to the Luria-Delbruck experiment

Microbial populations adapt to their environment by acquiring advantageous mutations, but in the early twentieth century, questions about how these organisms acquire mutations arose. The experiment of Salvador Luria and Max Delbrück that won them a Nobel Prize in 1969 confirmed that mutations don't occur out of necessity, but instead can occur many generations before there is a selective advantage, and thus organisms follow Darwinian evolution instead of Lamarckian. Since then, new areas of research involving microbial evolution have spawned as a result of their experiment. Determining the mutation rate of a cell is one such area. Probability distributions that determine the number of mutants in a large population have been derived by Lea, Coulson, and Haldane. However, not much work has been done when time of cell division is dependent on the cell age, and even less so when cell division is asymmetric, which is the case in most microbial populations. Using probability generating function methods, we rigorously construct a probability distribution for the cell population size given a life-span distribution for both mother and daughter cells, and then determine its asymptotic growth rate. We use this to construct a probability distribution for the number of mutants in a large cell population, which can be used with likelihood methods to estimate the cell mutation rate.
 For more information see https://ejde.math.txstate.edu/Volumes/2021/56/abstr.html

Forecasting selected energy commodities prices with Bayesian dynamic finite mixtures

In this paper selected energy commodities spot prices are forecasted with the help of Bayesian dynamic finite mixtures. In particular, crude oil, natural gas, and coal spot prices are analyzed. Due to the availability of data, crude oil is analyzed between 1988 and 2019, natural gas between 1990 and 2019, and coal between 1987 and 2019. Monthly data are used. The dynamic mixtures used herein are a novel methodological tool in forecasting. Their first important feature is that regression coefficients are estimated in a recursive on-line way, allowing for real-time performance. Secondly, the switching between mixture components is also allowed to vary in time. Thirdly, the algorithms used herein are based on explicit solutions, allowing for the fully Bayesian inference approach, whereas approximations are only on the numerical level of the pdfs (probability density functions) statistics. In other words, the evolution of prior to posterior pdfs has fixed functional form; only the numerical statistics of those pdfs are evolving in time. Both normal regression components and state-space models are considered as mixture components, which makes this study a generalization of previous research with Bayesian approaches to model averaging techniques. Indeed, those mixtures are compared with other benchmark models, such as Dynamic Model Averaging, Time-Varying Parameter regression, ARIMA, and the naïve method, with the Diebold-Mariano test, and are found to generate significantly more accurate forecasts. Additionally, the Giacomini-Rossi fluctuation test and Model Confidence Set are applied for more thorough examination of forecasting performances.

Multiplexing mutation rate assessment: determining pathogenicity of Msh2 variants in Saccharomyces cerevisiae.

Despite the fundamental importance of mutation rate as a driving force in evolution and disease risk, common methods to assay mutation rate are time-consuming and tedious. Established methods such as fluctuation tests and mutation accumulation experiments are low-throughput and often require significant optimization to ensure accuracy. We established a new method to determine the mutation rate of many strains simultaneously by tracking mutation events in a chemostat continuous culture device and applying deep sequencing to link mutations to alleles of a DNA-repair gene. We applied this method to assay the mutation rate of hundreds of Saccharomyces cerevisiae strains carrying mutations in the gene encoding Msh2, a DNA repair enzyme in the mismatch repair pathway. Loss-of-function mutations in MSH2 are associated with hereditary nonpolyposis colorectal cancer, an inherited disorder that increases risk for many different cancers. However, the vast majority of MSH2 variants found in human populations have insufficient evidence to be classified as either pathogenic or benign. We first benchmarked our method against Luria-Delbrück fluctuation tests using a collection of published MSH2 missense variants. Our pooled screen successfully identified previously characterized nonfunctional alleles as high mutators. We then created an additional 185 human missense variants in the yeast ortholog, including both characterized and uncharacterized alleles curated from ClinVar and other clinical testing data. In a set of alleles of known pathogenicity, our assay recapitulated ClinVar's classification; we then estimated pathogenicity for 157 variants classified as uncertain or conflicting reports of significance. This method is capable of studying the mutation rate of many microbial species and can be applied to problems ranging from the generation of high-fidelity polymerases to measuring the frequency of antibiotic resistance emergence.

Is dairy wastewater safe for production of edible microbial biomass: A case study of Saras dairy plant at Jaipur, India

Mismanagement of wastewater at large scale may lead to catastrophic environmental and health consequences. Microbial remediation of wastewater is one of the most effective low-cost solutions. There are also initiatives to use wastewater for production edible biomass as an alternative for protein diets. While much researches were oriented towards maximum recovery of biomass and applications, less were focused on mutagenicity of dairy wastewater. In this study, we examined wastewater of one of the largest dairy industries in Rajasthan for its suitability for microbial biomass production and mutagenicity. Influent wastewater was collected from Saras dairy plant, Jaipur, for over a week. Physiochemical properties of wastewater were examined, such as; temperature, pH, salinity, TSS, TDS, turbidity, conductivity, BOD, COD, total carbon, and total nitrogen. SOS chromotest and Salmonella fluctuation test (TA 98, TA 100 and TA 102) were carried out at variable concentration of wastewater to assess mutagenic activity. Results indicated ideal pH, temperature and salinity, for microbial remediation. High TOC and TKN were also observed in the investigated wastewater, which is few of the prerequisites for single cell production. The ratio of BOD and COD was between 0.3-0.4, making the wastewater ideal for microbial growth. No mutagenic activity was observed by SOS chromotest, all three concentrations (C 0.01, C 0.1, and C 0.2) investigated in this study were <1.5 IF. Likewise, mutagenic ratio for all three types of Salmonella revertants were below 1.2 threshold, for investigated concentrations (C 0.5, C 1, and C 10) of wastewater. Conclusively, examined influent wastewater is less likely to induce mutagenic activity at the investigated concentration. Through physiochemical analysis, the investigated wastewater assumed to be candidate substrate for microbial biomass production.

A transient heritable memory regulates HIV reactivation from latency

SummaryReactivation of human immunodeficiency virus 1 (HIV-1) from latently infected T cells is a critical barrier to cure patients. It remains unknown whether reactivation of individual latent cells occurs stochastically in response to latency reversal agents (LRAs) or is a deterministic outcome of an underlying cell state. To characterize these single-cell responses, we leverage the classical Luria-Delbrück fluctuation test where single cells are isolated from a clonal population and exposed to LRAs after colony expansion. Data show considerable colony-to-colony fluctuations with the fraction of reactivating cells following a skewed distribution. Modeling systematic measurements of fluctuations over time uncovers a transient heritable memory that regulates HIV-1 reactivation, where single cells are in an LRA-responsive state for a few weeks before switching back to an irresponsive state. These results have enormous implications for designing therapies to purge the latent reservoir and further utilize fluctuation-based assays to uncover hidden transient cellular states underlying phenotypic heterogeneity.

Deuterium-depletion has no significant impact on the mutation rate of Escherichia coli, deuterium abundance therefore has a probabilistic, not deterministic effect on spontaneous mutagenesis.

Deuterium (D), the second most abundant isotope of hydrogen is present in natural waters at an approximate concentration of 145-155 ppm (ca. 1.5E-4 atom/atom). D is known to influence various biological processes due to its physical and chemical properties, which significantly differ from those of hydrogen. For example, increasing D-concentration to >1000-fold above its natural abundance has been shown to increase the frequency of genetic mutations in several species. An interesting deterministic hypothesis, formulated with the intent of explaining the mechanism of D-mutagenicity is based on the calculation that the theoretical probability of base pairs to comprise two adjacent D-bridges instead of H-bridges is 2.3E-8, which is equal to the mutation rate of certain species. To experimentally challenge this hypothesis, and to infer the mutagenicity of D present at natural concentrations, we investigated the effect of a nearly 100-fold reduction of D concentration on the bacterial mutation rate. Using fluctuation tests, we measured the mutation rate of three Escherichia coli genes (cycA, ackA and galK) in media containing D at either <2 ppm or 150 ppm concentrations. Out of 15 pair-wise fluctuation analyses, nine indicated a significant decrease, while three marked the significant increase of the mutation/culture value upon D-depletion. Overall, growth in D-depleted minimal medium led to a geometric mean of 0.663-fold (95% confidence interval: 0.483-0.911) change in the mutation rate. This falls nowhere near the expected 10,000-fold reduction, indicating that in our bacterial systems, the effect of D abundance on the formation of point mutations is not deterministic. In addition, the combined results did not display a statistically significant change in the mutation/culture value, the mutation rate or the mutant frequency upon D-depletion. The potential mutagenic effect of D present at natural concentrations on E. coli is therefore below the limit of detection using the indicated methods.

Subgroup identification for differential cardio-respiratory fitness effect on cardiovascular disease risk factors: A model-based recursive partitioning approach

The goal of this study is twofold: i) identification of features associated with three cardiovascular disease (CVD) risk factors, and (ii) identification of subgroups with differential treatment effects. Multivariate analysis is performed to identify the features associated with the CVD risk factors: hypertension, diabetes, and dyslipidemia. For subgroup identification, we applied model-based recursive partitioning approach. This method fits a local model in each subgroup of the population rather than fitting one global model for the whole population. The method starts with a model for the overall effect of treatment and checks whether this effect is equally applicable for all individuals under the study based on parameter instability of M fluctuation test over a set of partitioning variables. The procedure produces a segmented model with a differential effect of cardio-respiratory fitness (CRF) corresponding to each subgroup. The subgroups are linked to predictive factors learned by the recursive partitioning approach. This approach is applied to the data from the Ball State Adult Fitness Program Longitudinal Lifestyle Study (BALL ST), where we considered the level of CRF as a treatment variable. The overall results indicate that CRF is inversely associated with hypertension, diabetes and dyslipidemia. The partitioning factors that are selected are related to these risk factors. The subgroup-specific results indicate that for each subgroup, the chance of hypertension, diabetes and dyslipidemia increases with low CRF.

Is dairy wastewater safe for production of edible microbial biomass: A case study of Saras Dairy plant at Jaipur, India

Mismanagement of wastewater at large scale may lead to catastrophic environmental and health consequences. Microbial remediation of wastewater is one of the most effective low-cost solutions. There are also initiatives to use wastewater for production edible biomass as an alternative for protein diets. While much researches were oriented towards maximum recovery of biomass and applications, less were focused on mutagenicity of dairy wastewater. In this study, we examined wastewater of one of the largest dairy industries in Rajasthan for its suitability for microbial biomass production and mutagenicity. Influent wastewater was collected from Saras dairy plant, Jaipur, for over a week. Physiochemical properties of wastewater were examined, such as; temperature, pH, salinity, TSS, TDS, turbidity, conductivity, BOD, COD, total carbon, and total nitrogen. SOS chromotest and Salmonella fluctuation test (TA 98, TA 100 and TA 102) were carried out at variable concentration of wastewater to assess mutagenic activity. Results indicated ideal pH, temperature and salinity, for microbial remediation. High TOC and TKN were also observed in the investigated wastewater, which is few of the prerequisites for single cell production. The ratio of BOD and COD was between 0.3-0.4, making the wastewater ideal for microbial growth. No mutagenic activity was observed by SOS chromotest, all three concentrations (C 0.01, C 0.1, and C 0.2) investigated in this study were &lt;1.5 IF. Likewise, mutagenic ratio for all three types of Salmonella revertants were below 1.2 threshold, for investigated concentrations (C 0.5, C 1, and C 10) of wastewater. Conclusively, examined influent wastewater is less likely to induce mutagenic activity at the investigated concentration. Through physiochemical analysis, the investigated wastewater assumed to be candidate substrate for microbial biomass production.

An Experiment for Outdoor GPS Localization Enhancement using Kalman Filter with Multiantenna Consumer-Grade Sensors

Consumer-Grade global positioning system (GPS) is widely used in many domains. The obvious issue of this consumer-grade device is low accuracy and reading fluctuation results. In terms of using an application that requires a more precise location, the output could be difficult. In this study, the authors deploy various methods to reduce the global positioning system data fluctuation and present field test results. Two main types of the device worked together to collect data from global positioning systems, such as Microcontroller for algorithm processing and presenting data and global positioning system receivers for receiving data from a satellite. We combine three global positioning system modules to received signals in a single device and test calculated data compared with the Kalman filtering methods in many cases, including moving and static devices. Implementing the Standard Kalman Filter to multiple global positioning system Modules has improved the constancy of cheap global positioning system equipment. The experiment algorithm is presented significant improvement to overcome the retrieved data fluctuation problem. This study's contribution will enable creating a cheap global positioning system locator device for various applications that require more accuracy than the standard consumer-grade receiver.

Global economic policy uncertainty and stock volatility: evidence from emerging economies

ABSTRACT We investigate the impact of the global economic policy uncertainty (GEPU) on stock volatility for nine emerging economies (Brazil, Russia, India, China, South Africa, Mexico, Indonesia, South Korea, and Turkey). We employ an expanded GARCH-MIDAS approach to connect low-frequency GEPU data and high-frequency stock data, assuming that GEPU affects stock volatility via the long-run component of total volatility. We not only use DM and SPA tests to statically evaluate the out-of-sample forecasting ability of the extended model, taking traditional GARCH model and GARCH-MIDAS model as benchmarks, but also use the Fluctuation test to examine the time-varyingly relative forecasting performance in the presence of potential instability. From the in-sample estimation results, we find that GEPU has empirically significant impact on stock volatility for the nine emerging economies. The out-of-sample forecasting results show that the GEPU-based model can improve forecasting performance of stock volatility for emerging markets, especially in unstable environments.

The Identification of Precursor Regulation Impact on the Methamphetamine Market and Public Health Indicators in the Czech Republic: Time Series Structural Break Analysis.

Background: This study provides insight into the impact of methamphetamine precursor regulation, which is considered to be one of the most important tools of supply reduction and a tool with potential public health impact. Methods: It is based on a longitudinal and quasi-experimental design and it investigates the changes of methamphetamine precursor regulation in Czech Republic, which is treated as a natural experiment. The statistical analysis uses features from the generalized fluctuation test framework as well as from the F test framework to estimate structural changes in the methamphetamine-related arrests and nonfatal intoxications time series. Results: The analysis identified structural breaks in the majority of the methamphetamine drug market-related time series in the period related to the tightening of regulation. The results of this study show that methamphetamine precursor regulation was associated with the proliferation of international and organized crime groups and with no change in the overall number of arrests and nonfatal intoxications. Conclusions: The precursor regulation ceteris paribus plausibly leads to the change in drug supply towards more organized groups and to an increasing involvement of foreign nationals at the drug market and is not effective in suppressing the methamphetamine market and in reducing the public health indicator of nonfatal methamphetamine intoxications.