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BACKGROUND Spontaneous thrombosis of a developmental venous abnormality (DVA) is a rare complication associated with hypercoagulability. The objective of this case report is to describe an association between DVA thrombosis and mild coronavirus disease 2019 (COVID-19) infection in a vaccinated patient. OBSERVATIONS A 28-year-old male with hypertension presented with severe headache and left-sided hemiparesis. Five weeks prior to presentation, the patient experienced mild respiratory symptoms and tested positive for COVID-19. Admission brain computed tomography (CT) showed a large right parieto-occipital intracerebral hemorrhage with surrounding edema. CT venography and catheter angiography showed a thrombosed DVA with associated venous infarction as the hemorrhage etiology. He was treated with decompressive hemicraniectomy, external ventricular drain placement, and systemic anticoagulation. The patient was functionally independent (modified Rankin Scale score, 2) at 4-month follow-up. Hypercoagulability work-up was unremarkable. LESSONS Delayed DVA thrombosis after the COVID-19 infectious period may represent an association between the infection and a protracted systemic viral-induced hypercoagulable state. The severity of COVID-19 symptomatology does not appear to correlate with risk of DVA thrombosis. Young patients with a recent history of COVID-19 infection who present with venous infarction should be evaluated for an underlying thrombosed DVA.

Current healthcare practices are reactive and use limited physiological and clinical information, often collected months or years apart. Moreover, the discovery and profiling of blood biomarkers in clinical and research settings are constrained by geographical barriers, the cost and inconvenience of in-clinic venepuncture, low sampling frequency and the low depth of molecular measurements. Here we describe a strategy for the frequent capture and analysis of thousands of metabolites, lipids, cytokines and proteins in 10 μl of blood alongside physiological information from wearable sensors. We show the advantages of such frequent and dense multi-omics microsampling in two applications: the assessment of the reactions to a complex mixture of dietary interventions, to discover individualized inflammatory and metabolic responses; and deep individualized profiling, to reveal large-scale molecular fluctuations as well as thousands of molecular relationships associated with intra-day physiological variations (in heart rate, for example) and with the levels of clinical biomarkers (specifically, glucose and cortisol) and of physical activity. Combining wearables and multi-omics microsampling for frequent and scalable omics may facilitate dynamic health profiling and biomarker discovery.

Summary The increasing global prevalence of SARS‐CoV‐2 and the resulting COVID‐19 disease pandemic pose significant concerns for clinical management of solid organ transplant recipients (SOTR). Wearable devices that can measure physiologic changes in biometrics including heart rate, heart rate variability, body temperature, respiratory, activity (such as steps taken per day) and sleep patterns, and blood oxygen saturation show utility for the early detection of infection before clinical presentation of symptoms. Recent algorithms developed using preliminary wearable datasets show that SARS‐CoV‐2 is detectable before clinical symptoms in >80% of adults. Early detection of SARS‐CoV‐2, influenza, and other pathogens in SOTR, and their household members, could facilitate early interventions such as self‐isolation and early clinical management of relevant infection(s). Ongoing studies testing the utility of wearable devices such as smartwatches for early detection of SARS‐CoV‐2 and other infections in the general population are reviewed here, along with the practical challenges to implementing these processes at scale in pediatric and adult SOTR, and their household members. The resources and logistics, including transplant‐specific analyses pipelines to account for confounders such as polypharmacy and comorbidities, required in studies of pediatric and adult SOTR for the robust early detection of SARS‐CoV‐2, and other infections are also reviewed.

Longitudinal studies increasingly collect rich ‘omics’ data sampled frequently over time and across large cohorts to capture dynamic health fluctuations and disease transitions. However, the generation of longitudinal omics data has preceded the development of analysis tools that can efficiently extract insights from such data. In particular, there is a need for statistical frameworks that can identify not only which omics features are differentially regulated between groups but also over what time intervals. Additionally, longitudinal omics data may have inconsistencies, including nonuniform sampling intervals, missing data points, subject dropout, and differing numbers of samples per subject. In this work, we developed a statistical method that provides robust identification of time intervals of temporal omics biomarkers. The proposed method is based on a semi-parametric approach, in which we use smoothing splines to model longitudinal data and infer significant time intervals of omics features based on an empirical distribution constructed through a permutation procedure. We benchmarked the proposed method on five simulated datasets with diverse temporal patterns, and the method showed specificity greater than 0.99 and sensitivity greater than 0.72. Applying the proposed method to the Integrative Personal Omics Profiling (iPOP) cohort revealed temporal patterns of amino acids, lipids, and hormone metabolites that are differentially regulated in male versus female subjects following a respiratory infection. In addition, we applied the longitudinal multi-omics dataset of pregnant women with and without preeclampsia, and the method identified potential lipid markers that are temporally significantly different between the two groups. We provide an open-source R package, OmicsLonDA (Omics Longitudinal Differential Analysis): https://bioconductor.org/packages/OmicsLonDA to enable widespread use.

This paper discusses contributions that industrial organization economists have made to our understanding of energy markets and environmental regulation. We emphasize the substantive contributions of recent papers while also highlighting how this literature has adopted and sometimes augmented theoretical and empirical tools from industrial organization. Many of the topics examined by this literature—especially auctions, investment, productivity and innovation, and regulation—also apply to a variety of settings beyond energy and the environment. We also indicate areas where future research is likely to be fruitful, with an emphasis on how industrial organization economists can help inform energy and environmental policies.

Consumer wearable devices that continuously measure vital signs have been used to monitor the onset of infectious disease. Here, we show that data from consumer smartwatches can be used for the pre-symptomatic detection of coronavirus disease 2019 (COVID-19). We analysed physiological and activity data from 32 individuals infected with COVID-19, identified from a cohort of nearly 5,300 participants, and found that 26 of them (81%) had alterations in their heart rate, number of daily steps or time asleep. Of the 25 cases of COVID-19 with detected physiological alterations for which we had symptom information, 22 were detected before (or at) symptom onset, with four cases detected at least nine days earlier. Using retrospective smartwatch data, we show that 63% of the COVID-19 cases could have been detected before symptom onset in real time via a two-tiered warning system based on the occurrence of extreme elevations in resting heart rate relative to the individual baseline. Our findings suggest that activity tracking and health monitoring via consumer wearable devices may be used for the large-scale, real-time detection of respiratory infections, often pre-symptomatically.

Wearable devices digitally measuring vital signs have been used for monitoring health and illness onset and have high potential for real-time monitoring and disease detection. As such they are potentially useful during public health crises, such as the current COVID-19 global pandemic. Using smartwatch data from 31 infected individuals identified from a cohort of over 5000 participants, we investigated the use of wearables for early, presymptomatic detection of COVID-19. From physiological and activity data, we first demonstrate that COVID-19 infections are associated with alterations in heart rate, steps and sleep in 80% of COVID-19 infection cases. Failure to detect these changes in the remaining patients often occurred in those with chronic respiratory/lung disease. Importantly the physiological alterations were detected prior to, or at, symptom onset in over 85% of the positive cases (21/24), in some cases nine or more days before symptoms. Through daily surveys we can track physiological changes with symptom onset and severity. Finally, we develop a method to detect onset of COVID-19 infection in real-time which detects 67% of infection cases at or before symptom onset. Our study provides a roadmap to a rapid and universal diagnostic method for the large-scale detection of respiratory viral infections in advance of symptoms, highlighting a useful approach for managing epidemics using digital tracking and health monitoring.

Oil and gas leases between mineral owners and extraction firms ubiquitously include royalty and primary term clauses. The royalty denotes the share of revenue that is paid to the mineral owner, and the primary term specifies the date by which the firm must complete a well, lest it lose the lease. Using data from the Louisiana shale boom, we first show that wells' drilling timing is substantially bunched just before lease expiration, raising the question of why leases include development deadlines that distort drilling decisions. We then develop a contracting model in which mineral owners face firms with private information and have the ability to contract on both realized revenue and drilling timing. We show that primary terms can increase both the owner's expected revenue and total surplus because they counteract the delay incentives imposed by the royalty. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

OBJECTIVE In recent years, hyperoxemia in the intensive care unit has received attention as potentially contributing to negative outcomes in the setting of cardiac arrest, ischemic stroke, and traumatic brain injury. The authors sought to evaluate whether hyperoxemia contributes to worse outcomes in the setting of aneurysmal subarachnoid hemorrhage (aSAH) and to summarize suggested pathophysiological mechanisms. METHODS A systematic literature review was conducted without date restrictions on the PubMed and Web of Science databases on September 15, 2021. All studies that assessed the relationship between patients treated for aSAH and hyperoxemia were eligible independent of the criteria used to define hyperoxemia. All nonclinical studies and studies that did not report outcome data specific to patients with aSAH were excluded. A total of 102 records were found and screened, resulting in assessment of 10 full-text studies, of which 7 met eligibility criteria. Risk of bias was assessed using the Downs and Black checklist. A meta-analysis on the pooled 2602 patients was performed, and forest plots were constructed. Additionally, a review of the literature was performed to summarize available data regarding the pathophysiology of hyperoxemia. RESULTS The included studies demonstrated an association between hyperoxemia and increased morbidity and mortality following aSAH. The criteria used to determine hyperoxemia varied among studies. Pooling of univariate data showed hyperoxemia to be associated with poor neurological outcome (OR 2.26, 95% CI 1.66–3.07; p < 0.001), delayed cerebral ischemia (DCI) (OR 1.91, 95% CI 1.31–2.78; p < 0.001), and increased incidence of poor neurological outcome or mortality as a combined endpoint (OR 2.36, 95% CI 1.87–2.97; p < 0.001). Pooling of multivariable effect sizes showed the same relationship for poor neurological outcome (OR 1.28, 95% CI 1.07–1.55; p = 0.01) and poor neurological outcome and mortality as a combined endpoint (OR 1.17, 95% CI 1.11–1.23; p < 0.001). Additionally, review of preclinical studies underlined the contribution of oxidative stress due to hyperoxemia to acute secondary brain injury and DCI. CONCLUSIONS Reported outcomes from the available studies have indicated that hyperoxemia is associated with worse neurological outcome, mortality, and DCI. These findings provide a general guideline toward avoiding hyperoxemia in the acute setting of aSAH. Further studies are needed to determine the optimal ventilation and oxygenation parameters for acute management of this patient population.

We show that oil production from existing wells in Texas does not respond to price incentives. Drilling activity and costs, however, do respond strongly to prices. To explain these facts, we reformulate Hotelling's (1931) classic model of exhaustible resource extraction as a drilling problem: firms choose when to drill, but production from existing wells is constrained by reservoir pressure, which decays as oil is extracted. The model implies a modified Hotelling rule for drilling revenues net of costs and explains why production is typically constrained. It also rationalizes regional production peaks and observed patterns of price expectations following demand shocks.

What are the implications of gasoline price volatility for the design of fuel economy policies? I show that this problem has a strong parallel to Weitzman's (1974) classic model of using price or quantity controls to regulate an externality. Changes in fuel prices act as shocks to the marginal cost of complying with the standard. Assuming constant marginal damages from fuel consumption, an application of Weitzman (1974) implies that a fixed fuel economy standard reduces expected welfare relative to a “price” policy such as a feebate or, equivalently, a fuel economy standard that is indexed to the price of gasoline. When the regulator is constrained to use a fixed standard, I show that the usual approach to setting the standard—equate expected marginal compliance cost to marginal damage—is likely to be sub-optimal because the standard may not bind if the realized gasoline price is sufficiently high. Instead, the optimal fixed standard will be relatively relaxed and may be non-binding even at the expected gasoline price. Finally, I show that although an attribute-based standard allows vehicle choices to flexibly respond to gasoline price shocks, the resulting distortions imply that the optimal fuel economy standard is not attribute-based.

The convergence of scientific capability and technology that generates vast health data at diminishing cost has generated opportunities, challenges, and anticipation surrounding future data-centric healthcare models. Individualized health data spanning biomolecular, physiological, and environmental dimensions comprise a personal omics profile. Here, we discuss methods and opportunities to bridge genome and dynamic physiology, detect disease at an early stage, and uncover lifestyle and environmental patterns associated with the disease. Significant challenges exist to aggregate, integrate, and protect personal omics data to advance our understanding of the disease, enable data-driven clinical decisions, and motivate individuals to sustain behavioral change. Since the first sequencing of the human genome in 2003, the relationship between genetic variants and phenotypes has remained a central challenge in medicine. Many diseases including coronary atherosclerosis are polygenic or indeed omnigenic wherein many variants work together to impact a phenotype.1 Potentially confounding factors and small study population size in comparison to the size of the human genome make it challenging to decipher genetic risk for complex and heterogeneous diseases. To better understand how genetic variation maps to complex traits, simultaneous measurements that bridge genotype and phenotype are required. This deep phenotyping is the goal of personal omics profiling,2 which combines measures of the genome, epigenome, transcriptome, proteome, metabolome, and additional omes (Figure [A]). Rapid advances in sequencing and mass spectrometry drive continued improvement in cost, accuracy, and throughput.3,4 Mobile and wearable technologies enable physiological, contextual, and environmental measurements. As we learn more about the symbiotic functions of the microbiome in human health, we also apply multiomic profiling to microbial populations (Figure [B]). Together these measurements provide a holistic profile of dynamic health and facilitate personalized, precision interventions based on predictive models (Figure [E]). Figure. Overview of personal omics. A , Omic measures span …

Background Wearable sensors (wearables) have been commonly integrated into a wide variety of commercial products and are increasingly being used to collect and process raw physiological parameters into salient digital health information. The data collected by wearables are currently being investigated across a broad set of clinical domains and patient populations. There is significant research occurring in the domain of algorithm development, with the aim of translating raw sensor data into fitness- or health-related outcomes of interest for users, patients, and health-care providers. Objectives The aim of this review is to highlight a selected group of fitness- and health-related indicators from wearables data and to describe several algorithmic approaches used to generate these higher order indicators. Methods A systematic search of the PubMed database was performed with the following search terms (number of records in parentheses): Fitbit algorithm (18), Apple Watch algorithm (3), Garmin algorithm (5), Microsoft Band algorithm (8), Samsung Gear algorithm (2), Xiaomi MiBand algorithm (1), Huawei Band (Watch) algorithm (2), photoplethysmography algorithm (465), accelerometry algorithm (966), electrocardiogram (ECG) algorithm (8287), and continuous glucose monitor algorithm (343). The search terms chosen for this review are focused on algorithms for wearable devices that dominated the commercial wearables market between 2014 and 2017 and that were highly represented in the biomedical literature. A second set of search terms included categories of algorithms for fitness-related and health-related indicators that are commonly used in wearable devices (e.g. accelerometry, PPG, ECG). These articles covered the following domain areas: fitness; exercise; movement; physical activity; step count; walking; running; swimming; energy expenditure; atrial fibrillation; arrhythmia; cardiovascular; autonomic nervous system; neuropathy; heart rate variability; fall detection; trauma; behavior change; diet; eating; stress detection; serum glucose monitoring; continuous glucose monitoring; diabetes mellitus type 1; and diabetes mellitus type 2. All studies uncovered through this search on commercially available device algorithms and pivotal studies on sensor algorithm development were summarized, and a summary table was constructed using references generated by the literature review as described ( Table 1 ). Conclusions Wearable health technologies aim to collect and process raw physiological or environmental parameters into salient digital health information. Much of the current and future utility of wearables lies in the signal processing steps and algorithms used to analyze large volumes of data. Continued algorithmic development and advances in machine learning techniques will further increase analytic capabilities. In the context of these advances, our review aims to highlight a range of advances in fitness- and other health-related indicators provided by current wearable technologies.

Technological innovations in horizontal drilling and hydraulic fracturing have enabled tremendous amounts of natural gas to be extracted profitably from underground shale formations that were long thought to be uneconomical. In this paper, we provide the first estimates of broad-scale welfare and distributional implications of this supply boom. We provide new estimates of supply and demand elasticities, which we use to estimate the drop in natural gas prices that is attributable to the supply expansion. We calculate large, positive welfare impacts for four broad sectors of gas consumption (residential, commercial, industrial, and electric power), and a negative impact for producers, with variation across regions. We then examine the evidence for a gas-led "manufacturing renaissance" and for pass-through to prices of products such as retail natural gas, retail electricity, and commodity chemicals. We conclude with a discussion of environmental externalities from unconventional natural gas, including limitations of the current regulatory environment. Overall, we find that between 2007 and 2013 the shale gas revolution led to an increase in welfare for natural gas consumers and producers of $48 billion per year, but more data are needed on the extent and valuation of the environmental impacts of shale gas production.

Diabetes is an increasing problem worldwide; almost 30 million people, nearly 10% of the population, in the United States are diagnosed with diabetes. Another 84 million are prediabetic, and without intervention, up to 70% of these individuals may progress to type 2 diabetes. Current methods for quantifying blood glucose dysregulation in diabetes and prediabetes are limited by reliance on single-time-point measurements or on average measures of overall glycemia and neglect glucose dynamics. We have used continuous glucose monitoring (CGM) to evaluate the frequency with which individuals demonstrate elevations in postprandial glucose, the types of patterns, and how patterns vary between individuals given an identical nutrient challenge. Measurement of insulin resistance and secretion highlights the fact that the physiology underlying dysglycemia is highly variable between individuals. We developed an analytical framework that can group individuals according to specific patterns of glycemic responses called "glucotypes" that reveal heterogeneity, or subphenotypes, within traditional diagnostic categories of glucose regulation. Importantly, we found that even individuals considered normoglycemic by standard measures exhibit high glucose variability using CGM, with glucose levels reaching prediabetic and diabetic ranges 15% and 2% of the time, respectively. We thus show that glucose dysregulation, as characterized by CGM, is more prevalent and heterogeneous than previously thought and can affect individuals considered normoglycemic by standard measures, and specific patterns of glycemic responses reflect variable underlying physiology. The interindividual variability in glycemic responses to standardized meals also highlights the personal nature of glucose regulation. Through extensive phenotyping, we developed a model for identifying potential mechanisms of personal glucose dysregulation and built a webtool for visualizing a user-uploaded CGM profile and classifying individualized glucose patterns into glucotypes.

We document a strong correlation in the brand of automobile chosen by parents and their adult children, using data from the Panel Study of Income Dynamics. This correlation could represent transmission of brand preferences across generations, or it could result from correlation in family characteristics that determine brand choice. We present a variety of empirical specifications that lend support to the former interpretation and to a mechanism that relies at least in part on state dependence. We then discuss implications of intergenerational brand preference transmission for automakers’ product-line strategies and for the strategic pricing of vehicles to different age groups.

SummaryCells must respond sensitively to time-varying inputs in complex signaling environments. To understand how signaling networks process dynamic inputs into gene expression outputs and the role of noise in cellular information processing, we studied the immune pathway NF-κB under periodic cytokine inputs using microfluidic single-cell measurements and stochastic modeling. We find that NF-κB dynamics in fibroblasts synchronize with oscillating TNF signal and become entrained, leading to significantly increased NF-κB oscillation amplitude and mRNA output compared to non-entrained response. Simulations show that intrinsic biochemical noise in individual cells improves NF-κB oscillation and entrainment, whereas cell-to-cell variability in NF-κB natural frequency creates population robustness, together enabling entrainment over a wider range of dynamic inputs. This wide range is confirmed by experiments where entrained cells were measured under all input periods. These results indicate that synergy between oscillation and noise allows cells to achieve efficient gene expression in dynamically changing signaling environments.

The recent large-scale use of railroads to transport crude oil out of newly discovered shale formations has no recent precedent in the U.S. oil industry. This paper addresses the question of whether crude-by-rail is simply a transient phenomenon, owing to delays in pipeline construction, or whether it will be a durable presence in the industry by reducing investment in pipeline infrastructure. We develop a model of crude oil transportation that highlights how railroads generate option value by: (1) giving shippers the ability to flexibly increase or decrease volumes shipped in response to price shocks; and (2) allowing shippers to opportunistically send oil to multiple destinations. In contrast, pipelines have low amortized costs but lock shippers into debt-like ship-or-pay contracts to a single destination. We calibrate this model to the recently constructed Dakota Access Pipeline and find that the elasticity of pipeline capacity to railroad transportation costs lies between 0.24 and 0.61, depending on parameters such as the upstream oil supply elasticity. These values are likely conservative because they neglect economies of scale in pipeline construction and the presence of cost-saving contracting in rail. Our results imply that crude-by-rail is an economically significant long-run substitute for pipeline transportation and that regulatory policies targeting environmental and accident externalities from rail transportation would likely substantially affect pipeline investments.

Cells receive a multitude of signals from the environment, but how they process simultaneous signaling inputs is not well understood. Response to infection, for example, involves parallel activation of multiple Toll-like receptors (TLRs) that converge on the nuclear factor κB (NF-κB) pathway. Although we increasingly understand inflammatory responses for isolated signals, it is not clear how cells process multiple signals that co-occur in physiological settings. We therefore examined a bacterial infection scenario involving co-stimulation of TLR4 and TLR2. Independent stimulation of these receptors induced distinct NF-κB dynamic profiles, although surprisingly, under co-stimulation, single cells continued to show ligand-specific dynamic responses characteristic of TLR2 or TLR4 signaling rather than a mixed response, comprising a cellular decision that we term “non-integrative” processing. Iterating modeling and microfluidic experiments revealed that non-integrative processing occurred through interaction of switch-like NF-κB activation, receptor-specific processing timescales, cell-to-cell variability, and TLR cross-tolerance mediated by multilayer negative feedback., Cell Reports, 19 (1), ISSN:2666-3864, ISSN:2211-1247

Time-dependent analysis of dynamic processes in single live cells is a revolutionary technique for the quantitative studies of signaling networks. Here we describe an experimental pipeline and associated protocol that incorporate microfluidic cell culture, precise stimulation of cells with signaling molecules or drugs, live-cell microscopy, computerized cell tracking, on-chip staining of key proteins and subsequent retrieval of cells for high-throughput gene expression analysis using microfluidic quantitative PCR (qPCR). Compared with traditional culture dish approaches, this pipeline enhances experimental precision and throughput by orders of magnitude and introduces much-desired new capabilities in cell and fluid handling, thus representing a major step forward in dynamic single-cell analysis. A combination of microfluidic membrane valves, automation and a streamlined protocol now enables a single researcher to generate 1 million data points on single-cell protein localization within 1 week, in various cell types and densities, under 48 predesigned experimental conditions selected from different signaling molecules or drugs, their doses, timings and combinations.

This paper estimates the response of investment to changes in uncertainty using data on oil drilling in Texas and the expected volatility of the future price of oil. Using a dynamic model of firms’ investment problem, I find that: (i) the response of drilling activity to changes in price volatility has a magnitude consistent with the optimal response prescribed by theory, (ii) the cost of failing to respond to volatility shocks is economically significant, and (iii) implied volatility data derived from futures options prices yields a better fit to firms’ investment behavior than backward-looking volatility measures such as GARCH. (JEL C58, D92, G13, G31, L71, Q31) The real options literature, beginning with Marschak (1949) and Arrow (1968) and developed in Bernanke (1983), Pindyck (1991), and Dixit and Pindyck (1994), explains how firms should make decisions about investments that involve sunk costs. Real options theory views such investments as options in that, at any point in time, a firm may choose to either invest immediately or delay and observe the evolution of the investment’s payoff. A key insight is that the option to delay has value when future states of the world with positive returns to investing and states with negative returns are both possible, even holding the expected future return constant at its present level. Thus, in the presence of irreversibility and uncertainty, a naive investment timing rule—proceed with an investment if its expected benefit even slightly exceeds its cost—is suboptimal because it does not account for the value of continuing to hold the option. Instead, firms should delay irreversible investments until a significant gap develops between the investments’ expected benefits and costs. Moreover, as uncertainty increases, real options theory tells us that the incentive to delay should grow stronger and the gap between the expected benefit and cost necessary to trigger investment should widen. While real options theory therefore prescribes how firms should carry out irreversible investments in uncertain environments, it is not empirically well-known

A full understanding of how gasoline prices affect consumer behavior frequently requires information on how consumers forecast future gasoline prices. We provide the first evidence on the nature of these forecasts by analyzing two decades of data on gasoline price expectations from the Michigan Survey of Consumers. We find that average consumer beliefs are typically indistinguishable from a no-change forecast, justifying an assumption commonly made in the literature on consumer valuation of energy efficiency. We also provide evidence on circumstances in which consumer forecasts are likely to deviate from no-change and on significant cross-consumer forecast heterogeneity.

Managers, research administrators, and policy makers need a greater understanding of the factors that drive employment preferences of foreign science, technology, engineering, and mathematics (STEM) doctoral graduates of U.S. universities. To address this need, the authors report the results of a large multischool conjoint survey of return-migration preferences among U.S. STEM doctoral students from China. The survey presents the respondents with potential job offers and yields individual-level estimates of each respondent's indirect utility of a job as a function of location, job status, and salary. The authors use a delayed follow-up choice task to demonstrate stability of the preference estimates both over time and across response modalities. The estimated preferences imply that Chinese doctoral graduates tend to remain in the United States because of a large salary disparity between the two countries rather than because of an inherent preference for locating in the United States. Given these estimated preferences, the authors conduct several policy-relevant, counterfactual simulations of return-migration choice and outline effective targeting and positioning strategy for attracting Chinese STEM talent.

Oscillations and noise drive many processes in biology, but how both affect the activity of the transcription factor nuclear factor κB (NF-κB) is not understood. Here, we observe that when NF-κB oscillations are entrained by periodic tumor necrosis factor (TNF) inputs in experiments, NF-κB exhibits jumps between frequency modes, a phenomenon we call “cellular mode-hopping.” By comparing stochastic simulations of NF-κB oscillations to deterministic simulations conducted inside and outside the chaotic regime of parameter space, we show that noise facilitates mode-hopping in all regimes. However, when the deterministic system is driven by chaotic dynamics, hops between modes are erratic and short-lived, whereas in experiments, the system spends several periods in one entrainment mode before hopping and rarely visits more than two modes. The experimental behavior matches our simulations of noise-induced mode-hopping outside the chaotic regime. We suggest that mode-hopping is a mechanism by which different NF-κB-dependent genes under frequency control can be expressed at different times., Cell Systems, 3 (6), ISSN:2405-4720

We study how incentive conflicts known as ‘career concerns’ can generate inefficiencies not only within firms but also in market outcomes. Career concerns may lead agents to avoid actions that, while valueincreasing in expectation, could potentially be associated with a bad outcome. We apply this theory to natural gas procurement by regulated public utilities and show that career concerns may lead to a reduction in surplus-increasing market transactions during periods when the benefits of trade are likely to be greatest. We show that data from natural gas markets are consistent with this prediction and difficult to explain using alternative theories.

Production in many industries, such as construction and heavy manufacturing, relies on inputs from both lead firms and contractors. These firms’ joint productivity often hinges on their ability to share information and coordinate activities, suggesting that they have strong incentives to learn about each other’s personnel, procedures, and expertise. This learning differs from standard learning-by-doing in that it is relationship-specific: its benefits are not appropriable outside the relationship in which the learning takes place. In this paper, I empirically examine the importance of relationship-specific learning using high-frequency data from oil and gas drilling. I find that the joint productivity of a lead firm and its drilling contractor is enhanced significantly as they accumulate experience working together. This result is robust to other relationship specificities. I also find that firms appear to recognize the benefits of joint experience: controlling for other specificities, lead firms are more likely to work with contractors with which they have substantial prior experience than those with which they have worked relatively little.

This paper analyzes the predictive power of a new data set of consumer gasoline price forecasts taken from the Michigan Survey of Consumers (MSC). MSC data generally perform as well as a no-change forecast in predicting future gasoline prices, and they substantially out-perform the no-change forecast during the recent economic crisis, during which time they track futures market prices. Finally, the cross-respondent dispersion of the MSC forecasts increases substantially during the economic crisis, paralleling the large increase in price volatility at this time.

This paper examines the effects of U.S. gasoline content regulations on groundlevel ozone pollution. These regulations are costly and have been shown to fragment gasoline markets and raise prices paid by consumers. We provide the first comprehensive empirical estimates of the regulations’ air quality benefits. We exploit the fact that gasoline regulations vary by time and place of introduction, using both difference-in-difference and regression discontinuity designs. We show that federal regulations targeting the emissions of volatile organic compounds (VOCs), one of the two main precursors to ozone, do not substantially improve air quality. This outcome is driven by the response of refiners to the regulation: minimizing the cost of abatement involves removing a type of VOC from gasoline that is not an important determinant of ozone pollution. In California, however, we show that precisely targeted regulations requiring the removal of VOCs particularly prone to forming ozone caused a significant improvement in air quality.

Several countries are considering using daylight saving time (DST) as a tool for energy conservation and reduction of greenhouse gas emissions, and the United States extended DST in 2007 with the goal of reducing electricity consumption. This paper assesses DST’s impact on electricity demand by examining a quasi-experiment in which parts of Australia extended DST in 2000 to facilitate the Sydney Olympics. Using detailed panel data and a difference-in-difference-in-difference framework, we show that the extension did not reduce overall electricity consumption, but did cause a substantial intraday shift in demand consistent with activity patterns that are tied to the clock rather than sunrise and sunset.

Digital signaling enhances robustness of cellular decisions in noisy environments, but it is unclear how digital systems transmit temporal information about a stimulus. To understand how temporal input information is encoded and decoded by the NF-κB system, we studied transcription factor dynamics and gene regulation under dose- and duration-modulated inflammatory inputs. Mathematical modeling predicted and microfluidic single-cell experiments confirmed that integral of the stimulus (or area, concentration × duration) controls the fraction of cells that activate NF-κB in the population. However, stimulus temporal profile determined NF-κB dynamics, cell-to-cell variability, and gene expression phenotype. A sustained, weak stimulation lead to heterogeneous activation and delayed timing that is transmitted to gene expression. In contrast, a transient, strong stimulus with the same area caused rapid and uniform dynamics. These results show that digital NF-κB signaling enables multidimensional control of cellular phenotype via input profile, allowing parallel and independent control of single-cell activation probability and population heterogeneity., eLife, 4, ISSN:2050-084X

Context Initiatives that convene community stakeholders to implement policy, systems, environment, and infrastructure (PSEI) change have become a standard approach for promoting community health. Objective To assess the PSEI changes brought about by the King County, Washington, Communities Putting Prevention to Work initiative and describe how initiative structures and processes contributed to making changes. Design The impact evaluation used a logic model design, linking PSEI changes to longer-term behavioral impacts in healthy eating active living and tobacco use and exposure. Qualitative methods, including stakeholder interviews and surveys, were used to identify initiative success factors. Setting Communities Putting Prevention to Work activities occurred throughout King County, with a focus on 7 low-income communities in South Seattle/King County. Participants The focus communities had a combined population of 652 000, or 35% of the county total, with lower incomes and higher rates of physical inactivity, tobacco use, poor diet, and chronic disease. Intervention Twenty-four PSEI strategies were pursued by organizations in sectors including schools, local governments, and community organizations, supported by the public health department. There were 17 healthy eating active living strategies (eg, enhancements to school menus, city planning policies) and 7 tobacco strategies (eg, smoke-free policies in schools, housing, and hospitals). Main outcome measure PSEI changes made and numbers of residents reached. Results Twenty-two of the 24 strategies achieved significant progress toward implementing PSEI changes. The most common success factor was a "dyad" consisting of a dedicated technical assistance provider-either an outside consultant or public health department staff-working closely with a champion from the participating organizations to bring about PSEI changes. Conclusions An initiative structure that creates and supports external consultant/internal organizational champion dyads in key community sectors offers a promising approach that may be adopted by similar community health efforts in the future.

We show that oil production from existing wells in Texas does not respond to price incentives. Drilling activity and costs, however, do respond strongly to prices. To explain these facts, we reformulate Hotelling's (1931) classic model of exhaustible resource extraction as a drilling problem: firms choose when to drill, but production from existing wells is constrained by reservoir pressure, which decays as oil is extracted. The model implies a modified Hotelling rule for drilling revenues net of costs and explains why production is typically constrained. It also rationalizes regional production peaks and observed patterns of price expectations following demand shocks.

We document a strong correlation in the brand of automobile chosen by parents and their adult children, using data from the Panel Study of Income Dynamics. This correlation could represent transmission of brand preferences across generations, or it could result from correlation in family characteristics that determine brand choice. We present a variety of empirical specifications that lend support to the former interpretation and to a mechanism that relies at least in part on state dependence. We then discuss implications of intergenerational brand preference transmission for automakers' product-line strategies and for the strategic pricing of vehicles to different age groups.