Length Of Lag Research Articles

Adjusting the timing of inseminations to the time lag on luteolysis alerts results in higher conception in dairy cattle

Dairy cow fertility is a complex trait that depends on the cow's physiological status, the farm's environmental and management conditions, and their interactions. Already the slightest improvement in fertility can positively impact a farm's profitability and sustainability. In research, milk progesterone (P4) has often been used as an accurate and feasible way to identify a dairy cow's reproduction status. Moreover, in Europe and Canada, it has been used to improve fertility management on commercial farms as it allows to accurately identify reproduction issues, pregnancy and the optimal insemination window. An on-farm P4 device (OPD) automatically samples, measures and monitors the milk P4 concentration of individual cows. To this end, the P4 data is smoothed to be robust for measurement errors and outliers, and fixed thresholds are used to estimate the time of luteolysis preceding ovulation, thereby generating a luteolysis alert (LA). By smoothing the P4 data, the OPD introduces a time lag on the LA. Variation in this time lag is not considered in the estimation of the optimal insemination window that is advised to the farmer. Ignoring this variation might decrease the accuracy of the optimal insemination window and, therefore, decreases the likelihood of conception. We hypothesize that considering the length of the time lag and adapting the advice accordingly improves the conception rate. This observational retrospective study uses an extensive data set from 17 commercial dairy farms that are equipped with an OPD. We estimated the time lag on the alerts and evaluated their relationship with the interval from LA to insemination for successful (n = 3721) and unsuccessful inseminations (n = 3896) separately. Results showed that the probability of conception increases when a longer LA time lag is compensated with a shorter interval from LA to insemination and vice versa. In addition, for successful inseminations, we found a clear negative relation between the time lag and the interval from LA to insemination and the interval was significantly shorter when the time lag of the LA was longer. This negative relation between time lag and interval from LA to insemination was less pronounced for unsuccessful inseminations. Additionally, we evaluated the conception rates for inseminations that are performed too early, in time or too late with respect to the optimal insemination window advised by the OPD, in function of their associated time lags. We found that, for inseminations that were preceded by a short time lag (<8 h), the conception rate was 17.5 percentage points higher when cows were inseminated later than advised. Likewise, when inseminations were preceded by a long time lag (≥24 h), we found that the conception rate was 13 percentage points higher when cows were inseminated earlier than advised. Our results suggest that farmers using an OPD could potentially increase their conception success by compensating the variable time lag on the LA by adapting the interval from alert to insemination accordingly. This could be used to develop reproductive management strategies to improve reproductive performance on those farms, which can positively impact their sustainability.

Spatiotemporal variations of groundwater and gully impact in two peatland watersheds in the Upper Yellow River, Qinghai‐Tibet Plateau

AbstractThe spatiotemporal variability of groundwater level is an important property of peatland hydrology that directly alternates water storage. Nonetheless, the current understanding of the variations of groundwater level over long periods of time remains limited. In this study, we investigated two peatland watersheds (0.151 km2 for Watershed 1 and 0.844 km2 for Watershed 2) in the Zoige Basin in the upper watershed of the Yellow River to monitor temporal variability of groundwater level using self‐recorded water loggers over 4 years (2017–2021). The main results demonstrate that (1) groundwater level variations were controlled by gully drainage in sites adjacent to the gully but were more affected by rainfall in sites distant from the gully. The groundwater level near the gully downcut was lower than that near the gully without complete downcutting through the pear layer, with a maximum difference of 58.3 cm, indicating the longitudinal effect of groundwater level in the watershed. (2) Because the rainfall had a lag effect on the groundwater level, the length of lag gradually decreased with increased rainfall intensity (i.e., the lag time for sites distant from the gully was about 18 min shorter than that of sites close to the gully in Watershed 1). (3) The peak values of the groundwater level occurred simultaneously with the maximum and minimum rainfall in Watershed 2, and the peak occurrence time was related to the ratio of precipitation to evaporation. In the downstream sites, the groundwater level fluctuated more than the upstream ones in Watershed 2. Moreover, the average groundwater level in the upstream sites was 14.3 cm higher than that of the middle ones, indicating a decreasing trend of water storage along the gully. (4) The differences in groundwater level between wet and dry seasons were clear, but the difference was smaller in the upstream sites due to limited gully incision and higher water storage within the peat layer. Additionally, groundwater level changes were more extreme on rainy days during both the wet and dry seasons, but the different intensities of rainfall resulted in stable groundwater in the dry season and an oscillating groundwater level in the wet season in Watershed 2. This study uncovers the groundwater dynamics in the two peatland watersheds, which is of great significance for understanding runoff variation, ecohydrological processes, and wetland shrinkage.

A combined method of autoregressive model and matrix factorization for recovery and forecasting of cyclic structural health monitoring data

Structures such as bridges, offshore platforms, wind power facilities, and drilling rigs generate a corresponding periodic structural response under cyclic loads such as solar radiation, tides and sea-land breeze. Data recovery and forecasting are major parts of the work to ensure structural safety using periodic structural health monitoring data. In this context, a combined method of autoregressive (AR) model and matrix factorization (MF) method is presented to reaching the tasks of data imputation and prediction. The combined method with a graph-based temporal regularizer can well capture the cyclic characteristics and random character. The verification of the method is conducted using an in-situ temperature monitoring dataset of a high-speed railway steel bridge. The application of the method for both data recovery and forecasting with structured missing entries is carried out, which indicates the combined method owns outstanding performance in accurately recover missing entries and making predictions. In the last part, the analysis of rank order and length of time lag is made to show its influence on imputation accuracy.

Wave after wave: determining the temporal lag in Covid-19 infections and deaths using spatial panel data from Germany

The Covid-19 pandemic requires a continuous evaluation of whether current policies and measures taken are sufficient to protect vulnerable populations. One quantitative indicator of policy effectiveness and pandemic severity is the case fatality ratio, which relies on the lagged number of infections relative to current deaths. The appropriate length of the time lag to be used, however, is heavily debated. In this article, I contribute to this debate by determining the temporal lag between the number of infections and deaths using daily panel data from Germany’s 16 federal states. To account for the dynamic spatial spread of the virus, I rely on different spatial econometric models that allow not only to consider the infections in a given state but also spillover effects through infections in neighboring federal states. My results suggest that a wave of infections within a given state is followed by increasing death rates 12 days later. Yet, if the number of infections in other states rises, the number of death cases within that given state subsequently decreases. The results of this article contribute to the better understanding of the dynamic spatio-temporal spread of the virus in Germany, which is indispensable for the design of effective policy responses.

Growth of ɣ-Proteobacteria in Low Salt Cucumber Fermentation Is Prevented by Lactobacilli and the Cover Brine Ingredients.

ABSTRACTThis study investigated the ability of ɣ-proteobacteria, indigenous to fresh cucumber, to grow in the expressed fruit juice (CJM) and fermentation. It was hypothesized that fresh cucumbers can support prolific growth of ɣ-proteobacteria but that the cover brine composition and acid production by the competing lactobacilli in the fermentation of the fruit act as inhibitory agents. The ɣ-proteobacteria proliferated in CJM with an average maximum growth rate (μmax) of 0.3895 ± 0.0929 and doubling time (Td) of 1.885 ± 0.465/h. A significant difference was found between the ɣ-proteobacteria μmax and Td relative to Lactiplantibacillus pentosus LA0445 (0.2319 ± 0.019; 2.89/h) and Levilactobacillus brevis 7.2.43 (0.221 ± 0.015; 3.35/h) but not Lactiplantibacillus plantarum 3.2.8 (0.412 ± 0.119; 1.87/h). While inoculation level insignificantly altered the μmax and Td of the bacteria tested; it impacted the length of lag and stationary phases for the lactobacilli. Unlike the lactobacilli, the ɣ-proteobacteria were inhibited in CJM supplemented with a low salt fermentation cover brine containing calcium chloride, acetic acid and potassium sorbate. The ɣ-proteobacteria, P. agglomerans, was unable to proliferate in cucumber fermentations brined with calcium chloride at a pH of 6.0 ± 0.1 and the population of Enterobacteriaceae was outcompeted by the lactobacilli within 36 h. Together these observations demonstrate that the prolific growth of ɣ-proteobacteria in CJM is not replicated in cucumber fermentation. While the ɣ-proteobacteria growth rate is faster that most lactobacilli in CJM, their growth in cucumber fermentation is prevented by the cover brine and the acid produced by the indigenous lactobacilli. Thus, the lactobacilli indigenous to cucumber and cover brine composition influence the safety and quality of fermented cucumbers.IMPORTANCE While the abundance of specific ɣ-proteobacteria species varies among vegetable type, several harbor Enterobacteriaceae and Pseudomonadaceae that benefit the plant system. It is documented that such bacterial populations decrease in density early in vegetable fermentations. Consequently, it is assumed that they do not contribute to the quality of finished products. This study explored the viability of ɣ-proteobacteria in CJM, used as a model system, CJM supplemented with fermentation cover brine and cucumber fermentation, which are characterized by an extremely acidic endpoint pH (3.23 ± 0.17; n = 391). The data presented demonstrates that fresh cucumbers provide the nutrients needed by ɣ-proteobacteria to proliferate and reduce pH to 4.47 ± 0.12. However, ɣ-proteobacteria are unable to proliferate in cucumber fermentation. Control of ɣ-proteobacteria in fermentations depends on the cover brine constituents and the indigenous competing lactobacilli. This knowledge is of importance when developing guidelines for the safe fermentation of vegetables, particularly with low salt.

The natural environment and social cohesion: Tree planting is associated with increased voter turnout in Portland, Oregon

Research suggests that social cohesion partially mediates the health benefits of being exposed to the natural environment. However, past studies have relied on self-reported measures of social cohesion that have well-documented limitations. Therefore, we evaluate the impact of tree planting on social cohesion using voter-turnout data as an objective metric of social cohesion. Our study area is the 141 Census tracts in Multnomah County, Oregon that fall within (>50% area) the City of Portland. We used biennial primary and general-election turnout data from 2002 to 2020, and tree-planting data from the Portland based non-profit Friends of Trees. We estimated mixed models of voter turnout including tract-level random effects and controlling for socioeconomic status. Voter turnout and tree planting may be codetermined, so lagged tree-planting variables were used in all regression models. We found that each tree planted 10–11 years before an election was associated with a 0.020% (95% CI: 0.013–0.027) increase in general election turnout and a 0.016% (95% CI: 0.0089–0.023) increase in primary turnout. As the length of the planting lag decreased, the association between tree planting and voter turnout, in both primary and general elections, declined in significance and magnitude. Our results add to the body of evidence suggesting that social cohesion partially mediates the health benefits of exposure to the natural environment.

Time lag effects of COVID-19 policies on transportation systems: A comparative study of New York City and Seattle.

The unprecedented challenges caused by the COVID-19 pandemic demand timely action. However, due to the complex nature of policy making, a lag may exist between the time a problem is recognized and the time a policy has its impact on a system. To understand this lag and to expedite decision making, this study proposes a change point detection framework using likelihood ratio, regression structure and a Bayesian change point detection method. The objective is to quantify the time lag effect reflected in transportation systems when authorities take action in response to the COVID-19 pandemic. Using travel patterns as an indicator of policy effectiveness, the length of policy lag and magnitude of policy impacts on the road system, mass transit, and micromobility are investigated through the case studies of New York City (NYC), and Seattle-two U.S. cities significantly affected by COVID-19. The quantitative findings show that the National declaration of emergency had no policy lag while stay-at-home and reopening policies had a lead effect on mobility. The magnitude of impact largely depended on the land use and sociodemographic characteristics of the area, as well as the type of transportation system.

On the relationship between download and citation counts: An introduction of Granger-causality inference

Studies on the relationship between the numbers of citations and downloads of scientific publications is beneficial for understanding the mechanism of citation patterns and research evaluation. However, seldom studies have considered directionality issues between downloads and citations or adopted a case-by-case time lag length between the download and citation time series of each individual publication. In this paper, we introduce the Granger-causal inference strategy to study the directionality between downloads and citations and set up the length of time lag between the time series for each case. By researching the publications on the Lancet, we find that publications have various directionality patterns, but highly cited publications tend to feature greater possibilities to have Granger causality. We apply a step-by-step manner to introduce the Granger-causal inference method to information science as four steps, namely conducting stationarity tests, determining time lag between time series, establishing cointegration test, and implementing Granger-causality inference. We hope that this method can be applied by future information scientists in their own research contexts.

Dynamics of the Market for Corporate Tax‐Avoidance Advice*

AbstractThis paper addresses the debate over aggressive tax‐planning models and analyzes the role of tax consultants. It focuses on the dynamic interaction between innovation and imitation of aggressive tax‐planning products and governmental tax regulation, and it highlights the importance of the length of regulatory lag in comparison with the time it takes the tax‐consulting industry to imitate newly innovated tax‐avoidance products. It reveals an alignment of interests between highly innovative tax‐consulting firms and the governmental tax legislator/regulator. The conclusions are also relevant for the policy debate on mandatory disclosure rules about aggressive tax‐planning models.

Long lead-time daily and monthly streamflow forecasting using machine learning methods

Long lead-time streamflow forecasting is of great significance for water resources planning and management in both the short and long terms. Despite of some studies using machine learning methods in streamflow forecasting, only few studies have been conducted to explore long lead-time forecasting capabilities of these methods, and gain an insight into systematic comparison of model forecasting performance in both the short and long terms. In this work, an artificial neural network (ANN) and a long short term memory (LSTM), a powerful tool for learning long-term temporal dependencies and capturing nonlinear relationship, have been adopted to forecast streamflow at daily and monthly scales for a long lead-time period. For long lead-time streamflow forecasting, a recursive forecasting procedure, which takes the last one-step-ahead forecast as a new input for the next-step-ahead forecast, is used in the ANN and LSTM forecasting systems. Two models are trained and validated for streamflow forecasting using the rainfall and runoff datasets collected from the Nan River Basin and Ping River Basin, Thailand, covering the period 1974 to 2014. To further explore the impact of parameter settings on model performance, two parameters, i.e. the length of time lag and the number of maximum epochs, are examined in the ANN and LSTM models. The main findings are highlighted here. First, with an optimal setting up of model parameters, both the ANN and LSTM model can provide accurate daily forecasting (up to 20 days ahead). Second, in comparison to the ANN model, the LSTM model exhibits better model performance in long lead-time daily forecasting, but less satisfactory in multi-monthly forecasting due to lack of large monthly training dataset. Third, the selection of the length of the time lag and number of maximum epochs used in both ANN and LSTM modelling are the key for long lead-time streamflow forecasting at daily and monthly scales. These findings suggest that the LSTM could be advance in daily streamflow forecasting and thus would be helpful to assist in strategy decisions in water resource management.

Microbial lag phase can be indicative of, or independent from, cellular stress

Measures of microbial growth, used as indicators of cellular stress, are sometimes quantified at a single time-point. In reality, these measurements are compound representations of length of lag, exponential growth-rate, and other factors. Here, we investigate whether length of lag phase can act as a proxy for stress, using a number of model systems (Aspergillus penicillioides; Bacillus subtilis; Escherichia coli; Eurotium amstelodami, E. echinulatum, E. halophilicum, and E. repens; Mrakia frigida; Saccharomyces cerevisiae; Xerochrysium xerophilum; Xeromyces bisporus) exposed to mechanistically distinct types of cellular stress including low water activity, other solute-induced stresses, and dehydration-rehydration cycles. Lag phase was neither proportional to germination rate for X. bisporus (FRR3443) in glycerol-supplemented media (r2 = 0.012), nor to exponential growth-rates for other microbes. In some cases, growth-rates varied greatly with stressor concentration even when lag remained constant. By contrast, there were strong correlations for B. subtilis in media supplemented with polyethylene-glycol 6000 or 600 (r2 = 0.925 and 0.961), and for other microbial species. We also analysed data from independent studies of food-spoilage fungi under glycerol stress (Aspergillus aculeatinus and A. sclerotiicarbonarius); mesophilic/psychrotolerant bacteria under diverse, solute-induced stresses (Brochothrix thermosphacta, Enterococcus faecalis, Pseudomonas fluorescens, Salmonella typhimurium, Staphylococcus aureus); and fungal enzymes under acid-stress (Terfezia claveryi lipoxygenase and Agaricus bisporus tyrosinase). These datasets also exhibited diversity, with some strong- and moderate correlations between length of lag and exponential growth-rates; and sometimes none. In conclusion, lag phase is not a reliable measure of stress because length of lag and growth-rate inhibition are sometimes highly correlated, and sometimes not at all.

A New Deep Learning Algorithm for Detecting the Lag Effect of Fine Particles on Hospital Emergency Visits for Respiratory Diseases

There exists a time lag between short-term exposure to fine particulate matter (PM2.5) and incidence of respiratory diseases. The quantification of length of the time lag is significant for preparation and allocation of relevant medical resources. Several classic lag analysis methods have been applied to determine this length. However, different models often lead to distinct results and which one is better is subtle. The prerequisite of obtaining the reliable length is that the model can truly reveal the underlying pattern hidden in the above relationship. Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning, whose strong capability makes it widely applied in many fields. In this study, we manage to exploit it to acquire the time-lag length in the exposure-response relationship. The relationship between exposure and response is assumed as linear and non-linear, and models with and without confounding factors are performed under these two assumptions. Results of DLNM model show that the best hospital emergency visit prediction appears in 3 lag days, with the maximum RR value of 1.004357 (95% CI: 1.000938-1.009563). Then, a vary of LSTM models with different time steps are performed, which are evaluated by mean absolute error (MAE), the mean absolute percentage error (MAPE), the root of mean square error (RMSE) and R square (R 2 ). The results show that LSTM of time step 3 achieves the lowest MAE (33), MAPE (9.86), RMSE (42) and the highest R 2 (0.78), consistent with the result of DLNM model. Also, the proposed model is compared with ARIMA model, one of the commonly used forecasting models, showing better accuracy. This demonstrates that LSTM can be used as a new method to detect the lag effect of PM2.5 on respiratory diseases.

How old are the towns and villages in Central Europe? Archaeological data reveal the size of bias in dating obtained from traditional historical sources

In various research fields, from archaeology to landscape history and ecology, it is important to know the date of the origin of historical settlements (i.e. towns, villages, hamlets, isolated farms) as precisely as possible. In Central Europe, there are two primary ways to obtain the date when a settlement was founded: “historical dating” (based on historical written sources) and “archaeological dating” (based on archaeological findings). Historical dating usually does not reflect the real time of origin, since the first reference to a settlement in written sources can be recorded many years after the real origin of the settlement. However, the time lag is unknown. Until now, no study has attempted to show exactly how the time lag differs in different centuries, or whether the time lag has been affected by any geographical factors.This paper compares the dates of origin from archaeological data and from written sources of medieval and early modern settlements (n = 527, AD 850–1600) in the present-day Czech Republic. We also tested the influence of local environmental conditions on the time lag. Our comparison shows that the time lag has been decreasing with the passing of calendar years (from a time lag of 250 years for AD 1000 to approx. 80 years for AD 1400). Towns and places close to major towns also have a shorter time lag in their historical dating (the difference is almost 100 years).These results make an interpretation of the historical dating of medieval towns and villages more complicated. The length of the time lag and its dispersion means that, for the purposes of settlement dating, historical dating needs to be combined with other dating methods (especially in the medieval period). Our results also identify a possible bias in the chronology of landscape transformation.

Dynamics of the Market for Corporate Tax-Avoidance Advice

This paper addresses the current debate about mandatory disclosure rules for aggressive tax-planning models as a means to shorten regulatory delay. It focuses on the dynamic interaction of innovation and imitation of aggressive tax-planning products and governmental tax regulation and highlights the importance of the length of regulatory lag in comparison to the time it takes the tax-consulting industry to imitate newly innovated tax-avoidance products. It reveals synergies between highly innovative tax-consulting firms and the governmental tax legislator/regulator. It suggests that innovative tax-consulting firms may benefit from governmental regulation and may actively try to inform and influence the regulator to shorten but not eliminate the regulatory delay.

Bomb-pulse Radiocarbon Dating of Modern Paintings on Canvas

ABSTRACTRadiocarbon (14C) dating has previously been applied to modern paintings on canvas from the 20th century to identify potential modern forgeries, and dates indicate a time lag of several years between the harvesting of plant fibers for making canvas, and completion of a painting. This study investigated both the length of this time lag and the potential of 14C dating to inform about an individual artist’s mode of working (for example long-term storage or reuse of canvases, or extended reworking on a single canvas) and/or to establish a chronology for a corpus of work. Two pre-bomb and 16 post-bomb artworks by 17 mid-20th-century Scandinavian artists were 14C dated. The majority of post-bomb samples indicated a time lag of 2–5 years between the harvesting of the plants and completion of a painting, but some samples recorded lags of up to 10 years, and others produced much earlier results, potentially indicating the use of much older canvases or challenges removing contamination prior to dating. The importance of thorough pre-screening of canvas samples for both synthetic fibers and contaminants prior to dating, and selection of the most suitable calibration curve, are highlighted.

Capacity optimization under uncertainty: The impact of operational time lags

Time lags in switching operational modes are typical in the manufacturing and power sectors but are not treated in most real options models. In this paper, we consider a firm that has the opportunity to suspend and to resume production infinitely many times subject to a time lag after each startup decision. We contribute to the literature by allowing the firm to determine its level of installed capacity in conjunction with its optimal investment timing. We find that an increase in the length of the time lag results in an increase in the optimal capacity level. Capacity optimization also interacts with the length of the time lag to affect investment timing and the triggers to suspend and resume production, thereby weakening the result about hysteresis from a standard real options model. Under the assumption of a fixed level of capacity, a longer lag speeds up the decision to resume operations due to a positive upside to the revenue but delays the suspension of operations. By contrast, with capacity optimization, a longer time lag results in a larger capacity choice, which can indirectly delay the investment decision and the timing to resume operations. This indirect effect dominates when the level of market uncertainty is low and the time lag is initially small.

Evaluation of environmentally driven models for early warning of malaria: an exploratory study

BackgroundRemotely-sensed earth observation data have the potential to inform early warning systems for malaria and other environmentally mediated diseases due to the lagged relation between environmental conditions at present and disease transmission potential in the near future. A common approach is to use total rainfall and mean temperature at a specific time lag to forecast malaria incidence, without justification for the choice of predictor variables or the length of the time lag. Here we explore a number of commonly used formulations of models to forecast malaria incidences across multiple districts using a variety of environmental indices derived from near-real-time satellite remote-sensing data and summarized using polynomial distributed lags. MethodsWe used weekly malaria incidence data from 2012 to 2017 in 47 districts in the Amhara Region of Ethiopia, a region of unstable malaria transmission where epidemics are driven by environmental conditions, combined with district-level rainfall, land surface temperature, and vegetation greenness, and vegetation water content summarised at lags of 1–16 weeks. We fit models containing various combinations of parameters related to trend, seasonality, and transformations of the environmental indices. We assessed model accuracy using time series cross validation. FindingsModels containing a linear effect of trend, Fourier components for seasonality, and climatological anomalies of rainfall, daytime land surface temperature, and normalised difference water index (NDWI) produced the most useful forecasts. Optimal distributed lag functions varied geographically, but generally showed the most positive correlations with malaria incidence at lags of 4–6 weeks. InterpretationTime series models used for early warning of malaria outbreaks are improved by incorporating other environmental indices in addition to rainfall and temperature, by using distributed lags for summarising environmental effects, and by using Fourier components for seasonality combined with environmental anomalies rather than raw environmental indices. FundingNational Institute of Allergy and Infectious Diseases (grant number R01AI079411).

Negative risk of cold on lag 0 day in distributed lag pattern can be due to preceding mortality peak before the temperature trough

Introduction One of the puzzling results of the distributed lag pattern of the temperature-mortality relation has been that the lag 0 risk of cold temperature range was negative in many of the locations including London and Japanese cities. Here, through simulation, we tried to show that this puzzling phenomenon can be due to time difference between mortality peak and temperature trough on this puzzling phenomenon. Methods We set the annual mortality pattern as follows: The baseline mortality follows Poisson distribution with the mean 150; the additional seasonal deaths are from 0 for July and August up to 20 for January and February; to see the offset results, we added 30 deaths to the latter half of January. We set the temperature (degC) gradient is as follows: 30 for July and August down to 5 for January and February. I added random variation for each day using normal distribution of mean 0 and sd 3. To see the offset results, we set additional deaths to be 50 during the latter half of January and set the temperature of early February to be -5. With and without the offset, we evaluated the lag patterns using distributed lag non-linear model. Results Without temperature trough- mortality peak offset, we observed no negative cold risk on lag 0 day but did observe negative cold risk on lag 0 day with the offset Conclusions The results of this simulation suggests that the preceding mortality peak before the temperature trough can create the negative cold risk on lag 0 day. Other factors inherent with the model or package, such as length of lag, degree of freedoms for the non-linear regression, should also be considered, but this simulation clearly shows that the offset created the pattern. Acknowledgements This study was supported by the Environment Research and Technology Development Fund (S-10 &amp; S14) of the Ministry of the Environment, Japan and by the Global Research Laboratory (grant K21004000001-10A0500-00710) through the National Research Foundation of Korea.

Optimal Management of Environmental Externalities with Time Lags and Uncertainty

Many environmental externalities occur with time lags that can range from a few days to several centuries in length, and many of these externalities are also subject to uncertainty. In this paper, we examine the key features of an optimal policy to manage environmental externalities that are both lagged and stochastic. We develop a two-period, two-polluter model and obtain closed-form solutions for optimal emissions levels under different combinations of damage functions and stochastic processes. These solutions show that it is not obvious whether greater control should be exerted on polluters that generate externalities with longer lags or on polluters that generate externalities with shorter lags. We find that the optimal ranking of polluters with respect to the length of the time lag associated with their externality will depend on (a) the discount rate, (b) conditional expectations of future states of the polluted resource, (c) persistence of the pollutant, and (d) initial conditions.

In-Vivo Quantitative Proteomics Reveals a Key Contribution of Post-Transcriptional Mechanisms to the Circadian Regulation of Liver Metabolism

Circadian clocks are endogenous oscillators that drive the rhythmic expression of a broad array of genes, orchestrating metabolism and physiology. Recent evidence indicates that post-transcriptional and post-translational mechanisms play essential roles in modulating temporal gene expression for proper circadian function, particularly for the molecular mechanism of the clock. Due to technical limitations in large-scale, quantitative protein measurements, it remains unresolved to what extent the circadian clock regulates metabolism by driving rhythms of protein abundance. Therefore, we aimed to identify global circadian oscillations of the proteome in the mouse liver by applying in vivo SILAC mouse technology in combination with state of the art mass spectrometry. Among the 3000 proteins accurately quantified across two consecutive cycles, 6% showed circadian oscillations with a defined phase of expression. Interestingly, daily rhythms of one fifth of the liver proteins were not accompanied by changes at the transcript level. The oscillations of almost half of the cycling proteome were delayed by more than six hours with respect to the corresponding, rhythmic mRNA. Strikingly we observed that the length of the time lag between mRNA and protein cycles varies across the day. Our analysis revealed a high temporal coordination in the abundance of proteins involved in the same metabolic process, such as xenobiotic detoxification. Apart from liver specific metabolic pathways, we identified many other essential cellular processes in which protein levels are under circadian control, for instance vesicle trafficking and protein folding. Our large-scale proteomic analysis reveals thus that circadian post-transcriptional and post-translational mechanisms play a key role in the temporal orchestration of liver metabolism and physiology.