Determining containment policy impacts on public sentiment during the pandemic using social media data.

Stringent containment and closure policies have been widely implemented by governments to prevent the transmission of COVID-19. Yet, such policies have significant impacts on people's emotions and mental well-being. Here, we study the effects of pandemic containment policies on public sentiment in Singapore. We computed daily sentiment values scaled from −1 to 1, using high-frequency data of ∼240,000 posts from highly followed public Facebook groups during January to November 2020. The lockdown in April saw a 0.1 unit rise in daily average sentiment, followed by a 0.2 unit increase with partially lifting of lockdown in June, and a 0.15 unit fall after further easing of restrictions in August. Regarding the impacts of specific containment measures, a 0.13 unit fall in sentiment was associated with travel restrictions, whereas a 0.18 unit rise was related to introducing a facial covering policy at the start of the pandemic. A 0.15 unit fall in sentiment was linked to restrictions on public events, post lock-down. Virus infection, wearing masks, salary, and jobs were the chief concerns found in the posts. A 2 unit increase in these concerns occurred even when some restrictions were eased in August 2020. During pandemics, monitoring public sentiment and concerns through social media supports policymakers in multiple ways. First, the method given here is a near real-time scalable solution to study policy impacts. Second, it aids in data-driven and evidence-based revision of existing policies and implementation of similar policies in the future. Third, it identifies public concerns following policy changes, addressing which can increase trust in governments and improve public sentiment.

Safety and effectiveness of SGLT2 inhibitors in a UK population with type 2 diabetes and aged over 70 years: an instrumental variable approach.

AIMS/HYPOTHESIS: Older adults are under-represented in trials, meaning the benefits and risks of glucose-lowering agents in this age group are unclear. The aim of this study was to assess the safety and effectiveness of sodium-glucose cotransporter 2 inhibitors (SGLT2i) in people with type 2 diabetes aged over 70 years using causal analysis. METHODS: Hospital-linked UK primary care data (Clinical Practice Research Datalink, 2013-2020) were used to compare adverse events and effectiveness in individuals initiating SGLT2i compared with dipeptidyl peptidase-4 inhibitors (DPP4i). Analysis was age-stratified: <70 years (SGLT2i n=66,810, DPP4i n=76,172), ≥70 years (SGLT2i n=10,419, DPP4i n=33,434). Outcomes were assessed using the instrumental variable causal inference method and prescriber preference as the instrument. RESULTS: Risk of diabetic ketoacidosis was increased with SGLT2i in those aged ≥70 (incidence rate ratio compared with DPP4i: 3.82 [95% CI 1.12, 13.03]), but not in those aged <70 (1.12 [0.41, 3.04]). However, incidence rates with SGLT2i in those ≥70 was low (29.6 [29.5, 29.7]) per 10,000 person-years. SGLT2i were associated with similarly increased risk of genital infection in both age groups (incidence rate ratio in those <70: 2.27 [2.03, 2.53]; ≥70: 2.16 [1.77, 2.63]). There was no evidence of an increased risk of volume depletion, poor micturition control, urinary frequency, falls or amputation with SGLT2i in either age group. In those ≥70, HbA1c reduction was similar between SGLT2i and DPP4i (-0.3 mmol/mol [-1.6, 1.1], -0.02% [0.1, 0.1]), but in those <70, SGLT2i were more effective (-4 mmol/mol [4.8, -3.1], -0.4% [-0.4, -0.3]). CONCLUSIONS/INTERPRETATION: Causal analysis suggests SGLT2i are effective in adults aged ≥70 years, but increase risk for genital infections and diabetic ketoacidosis. Our study extends RCT evidence to older adults with type 2 diabetes.

Unveiling Microbial Nitrogen Metabolism in Rivers using a Machine Learning Approach.

Microbial nitrogen metabolism is a complicated and key process in mediating environmental pollution and greenhouse gas emissions in rivers. However, the interactive drivers of microbial nitrogen metabolism in rivers have not been identified. Here, we analyze the microbial nitrogen metabolism patterns in 105 rivers in China driven by 26 environmental and socioeconomic factors using an interpretable causal machine learning (ICML) framework. ICML better recognizes the complex relationships between factors and microbial nitrogen metabolism than traditional linear regression models. Furthermore, tipping points and concentration windows were proposed to precisely regulate microbial nitrogen metabolism. For example, concentrations of dissolved organic carbon (DOC) below tipping points of 6.2 and 4.2 mg/L easily reduce bacterial denitrification and nitrification, respectively. The concentration windows for NO3--N (15.9-18.0 mg/L) and DOC (9.1-10.8 mg/L) enabled the highest abundance of denitrifying bacteria on a national scale. The integration of ICML models and field data clarifies the important drivers of microbial nitrogen metabolism, supporting the precise regulation of nitrogen pollution and river ecological management.

Connectivity of neural signals to the primary motor area during preparatory periods for movement following external and internal cues.

PURPOSE: We investigated the connectivity of neural signals from movement-related cortical areas to the primary motor area (M1) in the hemisphere contralateral to the movement side during the period of movement-related magnetic fields before movement. MATERIALS AND METHODS: Participants were 13 healthy adults, and nerual signals were recorded using magnetoencephalography. Spontaneous extension of the right wrist was performed at the participant's own pace and following a visual cue in internal (IC) and external (EC) cue tasks. The connectivity of neural signals to M1 from each movement-related motor area was assessed by Granger causality analysis (GCA). The GCA was performed on the neural activity elicited in a frequency band between 7.8 and 46.9 Hz during the pre-movement periods, which occurred durng the readiness field (RF) and the negative slope prime (NSp). F-values, as connectivity values obtained by GCA, were compared between the EC and IC cue tasks. RESULTS: For NSp periods, the connectivity of neural signals from the left superior frontal area (SF-L) to M1 was dominant in the IC task, whereas that from the left superior parietal area (SP-L) to M1 was dominant in the EC task. The F value in the GCA from SP-L to M1 was greater in the EC task during RF than in the IC task during equivalent periods. CONSLUSIONS: In the present study, there were differences in the connectivity of neural signals to M1 between IC and EC tasks. The present results suggested that the pattern of pre-movement neural activity that resulted in a movement was not uniform but differed between movement tasks just before the movement.

Movement-related cortical magnetic fields were assessed with Granger causality analysisConnectivity of neural signals to M1 was different between internal and external cue tasks.Connectivity of neural signals from the frontal area was dominant to M1 in the internal cue task.Connectivity of neural signals to M1 from the parietal area was observed in the external cue task.

Genetic screening for anticancer genes highlights FBLN5 as a synthetic lethal partner of MYC.

BACKGROUND: When ectopically overexpressed, anticancer genes, such as TRAIL, PAR4 and ORCTL3, specifically destroy tumour cells without harming untransformed cells. Anticancer genes can not only serve as powerful tumour specific therapy tools but studying their mode of action can reveal mechanisms underlying the neoplastic transformation, sustenance and spread. METHODS: Anticancer gene discovery is normally accidental. Here we describe a systematic, gain of function, forward genetic screen in mammalian cells to isolate novel anticancer genes of human origin. Continuing with over 30,000 transcripts from our previous study, 377 cell death inducing genes were subjected to screening. FBLN5 was chosen, as a proof of principle, for mechanistic gene expression profiling, comparison pathways analyses and functional studies. RESULTS: Sixteen novel anticancer genes were isolated; these included non-coding RNAs, protein-coding genes and novel transcripts, such as ZNF436-AS1, SMLR1, TMEFF2, LINC01529, HYAL2, NEIL2, FBLN5, YPEL4 and PHKA2-processed transcript. FBLN5 selectively caused inhibition of MYC in COS-7 (transformed) cells but not in CV-1 (normal) cells. MYC was identified as synthetic lethality partner of FBLN5 where MYC transformed CV-1 cells experienced cell death upon FBLN5 transfection, whereas FBLN5 lost cell death induction in MCF-7 cells upon MYC knockdown. CONCLUSIONS: Sixteen novel anticancer genes are present in human genome including FBLN5. MYC is a synthetic lethality partner of FBLN5. Video Abstract.

A high-dimensional mediation model for a neuroimaging mediator: Integrating clinical, neuroimaging, and neurocognitive data to mitigate late effects in pediatric cancer.

Pediatric cancer treatment, especially for brain tumors, can have profound and complicated late effects. With the survival rates increasing because of improved detection and treatment, a more comprehensive understanding of the impact of current treatments on neurocognitive function and brain structure is critically needed. A frontline medulloblastoma clinical trial (SJMB03) has collected data, including treatment, clinical, neuroimaging, and cognitive variables. Advanced methods for modeling and integrating these data are critically needed to understand the mediation pathway from the treatment through brain structure to neurocognitive outcomes. We propose an integrative Bayesian mediation analysis approach to model jointly a treatment exposure, a high-dimensional structural neuroimaging mediator, and a neurocognitive outcome and to uncover the mediation pathway. The high-dimensional imaging-related coefficients are modeled via a binary Ising-Gaussian Markov random field prior (BI-GMRF), addressing the sparsity, spatial dependency, and smoothness and increasing the power to detect brain regions with mediation effects. Numerical simulations demonstrate the estimation accuracy, power, and robustness. For the SJMB03 study, the BI-GMRF method has identified white matter microstructure that is damaged by cancer-directed treatment and impacts late neurocognitive outcomes. The results provide guidance on improving treatment planning to minimize long-term cognitive sequela for pediatric brain tumor patients.

Using Explainable Machine Learning to Interpret the Effects of Policies on Air Pollution: COVID-19 Lockdown in London.

Activity changes during the COVID-19 lockdown present an opportunity to understand the effects that prospective emission control and air quality management policies might have on reducing air pollution. Using a regression discontinuity design for causal analysis, we show that the first UK national lockdown led to unprecedented decreases in road traffic, by up to 65%, yet incommensurate and heterogeneous responses in air pollution in London. At different locations, changes in air pollution attributable to the lockdown ranged from -50% to 0% for nitrogen dioxide (NO2), 0% to +4% for ozone (O3), and -5% to +0% for particulate matter with an aerodynamic diameter less than 10 µm (PM10), and there was no response for PM2.5. Using explainable machine learning to interpret the outputs of a predictive model, we show that the degree to which NO2 pollution was reduced in an area was correlated with spatial features (including road freight traffic and proximity to a major airport and the city center), and that existing inequalities in air pollution exposure were exacerbated: pollution reductions were greater in places with more affluent residents and better access to public transport services.

A novel deep learning ensemble model based on two-stage feature selection and intelligent optimization for water quality prediction.

Accurate prediction of effluent total nitrogen (E-TN) can assist in feed-forward control of wastewater treatment plants (WWTPs) to ensure effluent compliance with standards while reducing energy consumption. However, multivariate time series prediction of E-TN is a challenge due to the complex nonlinearity of WWTPs. This paper proposes a novel prediction framework that combines a two-stage feature selection model, the Golden Jackal Optimization (GJO) algorithm, and a hybrid deep learning model, CNN-LSTM-TCN (CLT), aiming to effectively capture the nonlinear relationships of multivariate time series in WWTPs. Specifically, convolutional neural network (CNN), long short-term memory (LSTM), and temporal convolutional network (TCN) combined to build a hybrid deep learning model CNN-LSTM-TCN (CLT). A two-stage feature selection method is utilized to determine the optimal feature subset to reduce the complexity and improve the accuracy of the prediction model, and then, the feature subset is input into the CLT. The hyperparameters of the CLT are optimized using GJO to further improve the prediction performance. Experiments indicate that the two-stage feature selection model learns the optimal feature subset to predict best, and the GJO-CLT achieves the best performance for different backtracking windows and prediction steps. These results demonstrate that the prediction system excels in the task of multivariate water quality time series prediction of WWTPs.

The effect of smoking on latent hazard classes of metabolic syndrome using latent class causal analysis method in the Iranian population.

BACKGROUND: The prevalence of metabolic syndrome is increasing worldwide. Clinical guidelines consider metabolic syndrome as an all or none medical condition. One proposed method for classifying metabolic syndrome is latent class analysis (LCA). One approach to causal inference in LCA is using propensity score (PS) methods. The aim of this study was to investigate the causal effect of smoking on latent hazard classes of metabolic syndrome using the method of latent class causal analysis. METHODS: In this study, we used data from the Tehran Lipid and Glucose Cohort Study (TLGS). 4857 participants aged over 20 years with complete information on exposure (smoking) and confounders in the third phase (2005-2008) were included. Metabolic syndrome was evaluated as outcome and latent variable in LCA in the data of the fifth phase (2014-2015). The step-by-step procedure for conducting causal inference in LCA included: (1) PS estimation and evaluation of overlap, (2) calculation of inverse probability-of-treatment weighting (IPTW), (3) PS matching, (4) evaluating balance of confounding variables between exposure groups, and (5) conducting LCA using the weighted or matched data set. RESULTS: Based on the results of IPTW which compared the low, medium and high risk classes of metabolic syndrome (compared to a class without metabolic syndrome), no association was found between smoking and the metabolic syndrome latent classes. PS matching which compared low and moderate risk classes compared to class without metabolic syndrome, showed that smoking increases the probability of being in the low-risk class of metabolic syndrome (OR: 2.19; 95% CI: 1.32, 3.63). In the unadjusted analysis, smoking increased the chances of being in the low-risk (OR: 1.45; 95% CI: 1.01, 2.08) and moderate-risk (OR: 1.68; 95% CI: 1.18, 2.40) classes of metabolic syndrome compared to the class without metabolic syndrome. CONCLUSIONS: Based on the results, the causal effect of smoking on latent hazard classes of metabolic syndrome can be different based on the type of PS method. In adjusted analysis, no relationship was observed between smoking and moderate-risk and high-risk classes of metabolic syndrome.

Impact of Early Tracheostomy on Clinical Outcomes in Trauma Patients Admitted to the Intensive Care Unit: A Retrospective Causal Analysis.

OBJECTIVES: To assess the indications, timing, and clinical outcomes that result from the early tracheostomy (ET) administration, by causal inference models. DESIGN: A retrospective observational study. SETTING: Multiinstitutional intensive care unit in the United States PARTICIPANTS: The study comprised 626 trauma patients. INTERVENTIONS: An ET versus late tracheostomy (LT). MEASUREMENTS AND MAIN RESULTS: Trauma patients with tracheostomy were identified from 2 public databases named Medical Information Mart for the Intensive Care-IV and eICU Collaborative Research Database. Tracheostomy was defined as early (≤7 days) or late (>7 days) from intensive care unit admission. A marginal structural Cox model (MSCM) with inverse probability weighting was employed. For comparison, the authors also used time-dependent propensity-score matching (PSM) to account for differences in the probability of receiving an ET or LT. A total of 626 eligible patients were enrolled in the study, of whom 321 (51%) received a ET. The MSCM and time-dependent PSM indicated that the ET group was associated with reduced ventilation-associated pneumonia (VAP) and a shorter mechanical ventilation (MV) duration than the LT group. Yet, mortality did not show any difference between the two groups. CONCLUSIONS: The authors' study observed that ET was not associated with reduced mortality in trauma patients, but it was associated with reduced VAP risk and MV duration. The results warrant further validation in randomized controlled trials.

Reply to Rourk, C. Comment on "Albantakis et al. Computing the Integrated Information of a Quantum Mechanism. Entropy 2023, 25, 449".

In response to a comment by Chris Rourk on our article Computing the Integrated Information of a Quantum Mechanism, we briefly (1) consider the role of potential hybrid/classical mechanisms from the perspective of integrated information theory (IIT), (2) discuss whether the (Q)IIT formalism needs to be extended to capture the hypothesized hybrid mechanism, and (3) clarify our motivation for developing a QIIT formalism and its scope of applicability.

Comparison of Bootstrap Methods for Estimating Causality in Linear Dynamic Systems: A Review.

In this study, we present a thorough comparison of the performance of four different bootstrap methods for assessing the significance of causal analysis in time series data. For this purpose, multivariate simulated data are generated by a linear feedback system. The methods investigated are uncorrelated Phase Randomization Bootstrap (uPRB), which generates surrogate data with no cross-correlation between variables by randomizing the phase in the frequency domain; Time Shift Bootstrap (TSB), which generates surrogate data by randomizing the phase in the time domain; Stationary Bootstrap (SB), which calculates standard errors and constructs confidence regions for weakly dependent stationary observations; and AR-Sieve Bootstrap (ARSB), a resampling method based on AutoRegressive (AR) models that approximates the underlying data-generating process. The uPRB method accurately identifies variable interactions but fails to detect self-feedback in some variables. The TSB method, despite performing worse than uPRB, is unable to detect feedback between certain variables. The SB method gives consistent causality results, although its ability to detect self-feedback decreases, as the mean block width increases. The ARSB method shows superior performance, accurately detecting both self-feedback and causality across all variables. Regarding the analysis of the Impulse Response Function (IRF), only the ARSB method succeeds in detecting both self-feedback and causality in all variables, aligning well with the connectivity diagram. Other methods, however, show considerable variations in detection performance, with some detecting false positives and others only detecting self-feedback.

Computing the Integrated Information of a Quantum Mechanism.

Originally conceived as a theory of consciousness, integrated information theory (IIT) provides a theoretical framework intended to characterize the compositional causal information that a system, in its current state, specifies about itself. However, it remains to be determined whether IIT as a theory of consciousness is compatible with quantum mechanics as a theory of microphysics. Here, we present an extension of IIT's latest formalism to evaluate the mechanism integrated information (φ) of a system subset to discrete, finite-dimensional quantum systems (e.g., quantum logic gates). To that end, we translate a recently developed, unique measure of intrinsic information into a density matrix formulation and extend the notion of conditional independence to accommodate quantum entanglement. The compositional nature of the IIT analysis might shed some light on the internal structure of composite quantum states and operators that cannot be obtained using standard information-theoretical analysis. Finally, our results should inform theoretical arguments about the link between consciousness, causation, and physics from the classical to the quantum.

A principled approach to mediation analysis in perinatal epidemiology.

For many research questions in perinatal epidemiology, gestational age is a mediator that features the causal pathway between exposure and outcome. A mediator is an intermediate variable between an exposure and outcome, which is influenced by the exposure on the causal pathway to the outcome. Therefore, conventional analyses that adjust, stratify, or match for gestational age or its proxy (eg, preterm vs term deliveries) are problematic. This practice, which is entrenched in perinatal research, induces an overadjustment bias. Depending on the causal question, it may be inappropriate to adjust (or condition) for a mediator, such as gestational age, by either design or statistical analysis, but its effect can be quantified through causal mediation analysis. In an exposition of such methods, we demonstrated the relationship between the exposure and outcome and provided a formal analytical framework to quantify the extent to which a causal effect is influenced by a mediator. We reviewed concepts of confounding and causal inference, introduced the concept of a mediator and illustrated the perils of adjusting for a mediator in an exposure-outcome paradigm for a given causal question, adopted causal methods that call for an evaluation of a mediator in a causal exposure effect on the outcome, and discussed unmeasured confounding assumptions in mediation analysis. Furthermore, we reviewed other developments in the causal mediation analysis literature, including decomposition of a total effect when the mediator interacts with the exposure (4-way decomposition), methods for multiple mediators, mediation methods for case-control studies, mediation methods for time-to-event outcomes, sample size and power analysis for mediation analysis, and available software to apply these methods. To illustrate these methods, we provided a clinical example to estimate the risk of perinatal mortality (outcome) concerning placental abruption (exposure) and to determine the extent to which preterm delivery (mediator; a proxy for gestational age) plays a role in this causal effect. We hoped that the adoption of mediation methods described in this review will move research in perinatal epidemiology away from biased adjustments of mediators toward a more nuanced quantification of effects that pose unique challenges and provide unique insights in our field.

Parental divorce is not uniformly disruptive to children's educational attainment.

Children whose parents divorce tend to have worse educational outcomes than children whose parents stay married. However, not all children respond identically to their parents divorcing. We focus on how the impact of parental divorce on children's education varies by how likely or unlikely divorce was for those parents. We find a significant negative effect of parental divorce on educational attainment, particularly college attendance and completion, among children whose parents were unlikely to divorce. Families expecting marital stability, unprepared for disruption, may experience considerable adjustment difficulties when divorce occurs, leading to negative outcomes for children. By contrast, we find no effect of parental divorce among children whose parents were likely to divorce. Children of high-risk marriages, who face many social disadvantages over childhood irrespective of parental marital status, may anticipate or otherwise accommodate to the dissolution of their parents' marriage. Our results suggest that family disruption does not uniformly disrupt children's attainment.

Identifying causal mechanisms in psychotherapy: What can we learn from causal mediation analysis?

Despite widespread interest in the development of process-based psychotherapies, little is still known about the underlying processes that underpin our most effective therapies. Statistical mediation analysis is a commonly used analytical method to evaluate how, or by which processes, a therapy causes change in an outcome. Causal mediation analysis (CMA) represents a new advancement in mediation analysis that employs causally defined direct and indirect effects based on potential outcomes. These novel ideas and analytical techniques have been characterized as revolutionary in epidemiology and biostatistics, although they are not (yet) widely known among researchers in clinical psychology. In this paper, I outline the fundamental concepts underlying CMA, clarify the differences between the CMA approach and the traditional approach to mediation, and identify two important data analytical aspects that have been emphasized as a result of these recent advancements. To illustrate the key ideas, assumptions, and mathematical definitions intuitively, an applied clinical example from a previously published randomized controlled trial is used. CMA's main contributions are discussed, as well as some of the key challenges. Finally, it is argued that the most significant contribution of CMA is the formalization of mediation in a unified causal framework with clear assumptions.

Estimating the Marginal Causal Effect and Potential Impact of Waterpipe Smoking on Risk of Multiple Sclerosis Using the Targeted Maximum Likelihood Estimation Method: A Large, Population-Based Incident Case-Control Study.

There are few if any reports regarding the role of lifetime waterpipe smoking in the etiology of multiple sclerosis (MS). In a population-based incident case-control study conducted in Tehran, Iran, we investigated the association between waterpipe smoking and MS, adjusted for confounders. Cases (n = 547) were patients aged 15-50 years identified from the Iranian Multiple Sclerosis Society between 2013 and 2015. Population-based controls (n = 1,057) were persons aged 15-50 years recruited through random digit telephone dialing. A doubly robust estimation method, the targeted maximum likelihood estimator (TMLE), was used to estimate the marginal risk ratio and odds ratio for the association between waterpipe smoking and MS. The estimated risk ratio and odds ratio were both 1.70 (95% confidence interval: 1.34, 2.17). The population attributable fraction was 21.4% (95% confidence interval: 4.0, 38.8). Subject to the limitations of case-control studies in interpreting associations causally, these results suggest that waterpipe use, or strongly related but undetermined factors, increases the risk of MS. Further epidemiologic studies, including nested case-control studies, are needed to confirm these findings.

Effects of Time-Varying Parent Input on Children's Language Outcomes Differ for Vocabulary and Syntax.

Early linguistic input is a powerful predictor of children's language outcomes. We investigated two novel questions about this relationship: Does the impact of language input vary over time, and does the impact of time-varying language input on child outcomes differ for vocabulary and for syntax? Using methods from epidemiology to account for baseline and time-varying confounding, we predicted 64 children's outcomes on standardized tests of vocabulary and syntax in kindergarten from their parents' vocabulary and syntax input when the children were 14 and 30 months old. For vocabulary, children whose parents provided diverse input earlier as well as later in development were predicted to have the highest outcomes. For syntax, children whose parents' input substantially increased in syntactic complexity over time were predicted to have the highest outcomes. The optimal sequence of parents' linguistic input for supporting children's language acquisition thus varies for vocabulary and for syntax.

Effects of prenatal alcohol exposure on cognitive and behavioral development: Findings from a hierarchical meta-analysis of data from six prospective longitudinal U.S. cohorts.

BACKGROUND: Cognitive and behavioral sequelae of prenatal alcohol exposure (PAE) continue to be prevalent in the United States and worldwide. Because these sequelae are also common in other neurodevelopmental disorders, researchers have attempted to identify a distinct neurobehavioral profile to facilitate the differential diagnosis of fetal alcohol spectrum disorders (FASD). We used an innovative, individual participant meta-analytic technique to combine data from six large U.S. longitudinal cohorts to provide a more comprehensive and reliable characterization of the neurobehavioral deficits seen in FASD than can be obtained from smaller samples. METHODS: Meta-analyses were performed on data from 2236 participants to examine effects of PAE (measured as oz absolute alcohol/day (AA/day)) on IQ, four domains of cognition function (learning and memory, executive function, reading achievement, and math achievement), sustained attention, and behavior problems, after adjusting for potential confounders using propensity scores. RESULTS: The effect sizes for IQ and the four domains of cognitive function were strikingly similar to one another and did not differ at school age, adolescence, or young adulthood. Effect sizes were smaller in the more middle-class Seattle cohort and larger in the three cohorts that obtained more detailed and comprehensive assessments of AA/day. PAE effect sizes were somewhat weaker for parent- and teacher-reported behavior problems and not significant for sustained attention. In a meta-analysis of five aspects of executive function, the strongest effect was on set-shifting. CONCLUSIONS: The similarity in the effect sizes for the four domains of cognitive function suggests that PAE affects an underlying component or components of cognition involving learning and memory and executive function that are reflected in IQ and academic achievement scores. The weaker effects in the more middle-class cohort may reflect a more cognitively stimulating environment, a different maternal drinking pattern (lower alcohol dose/occasion), and/or better maternal prenatal nutrition. These findings identify two domains of cognition-learning/memory and set-shifting-that are particularly affected by PAE, and one, sustained attention, which is apparently spared.

Evaluating natural experiments in ecology: using synthetic controls in assessments of remotely sensed land treatments.

Many important ecological phenomena occur on large spatial scales and/or are unplanned and thus do not easily fit within analytical frameworks that rely on randomization, replication, and interspersed a priori controls for statistical comparison. Analyses of such large-scale, natural experiments are common in the health and econometrics literature, where techniques have been developed to derive insight from large, noisy observational data sets. Here, we apply a technique from this literature, synthetic control, to assess landscape change with remote sensing data. The basic data requirements for synthetic control include (1) a discrete set of treated and untreated units, (2) a known date of treatment intervention, and (3) time series response data that include both pre- and post-treatment outcomes for all units. Synthetic control generates a response metric for treated units relative to a no-action alternative based on prior relationships between treated and unexposed groups. Using simulations and a case study involving a large-scale brush-clearing management event, we show how synthetic control can intuitively infer treatment effect sizes from satellite data, even in the presence of confounding noise from climate anomalies, long-term vegetation dynamics, or sensor errors. We find that accuracy depends on the number and quality of potential control units, highlighting the importance of selecting appropriate control populations. Although we consider the synthetic control approach in the context of natural experiments with remote sensing data, we expect the methodology to have wider utility in ecology, particularly for systems with large, complex, and poorly replicated experimental units.