A Once and Future Gulf of Mexico Ecosystem: Restoration Recommendations of an Expert Working Group

The Deepwater Horizon (DWH) well blowout released more petroleum hydrocarbons into the marine environment than any previous U.S. oil spill (4.9 million barrels), fouling marine life, damaging deep sea and shoreline habitats and causing closures of economically valuable fisheries in the Gulf of Mexico. A suite of pollutants — liquid and gaseous petroleum compounds plus chemical dispersants — poured into ecosystems that had already been stressed by overfishing, development and global climate change. Beyond the direct effects that were captured in dramatic photographs of oiled birds in the media, it is likely that there are subtle, delayed, indirect and potentially synergistic impacts of these widely dispersed, highly bioavailable and toxic hydrocarbons and chemical dispersants on marine life from pelicans to salt marsh grasses and to deep-sea animals. As tragic as the DWH blowout was, it has stimulated public interest in protecting this economically, socially and environmentally critical region. The 2010 Mabus Report, commissioned by President Barack Obama and written by the secretary of the Navy, provides a blueprint for restoring the Gulf that is bold, visionary and strategic. It is clear that we need not only to repair the damage left behind by the oil but also to go well beyond that to restore the anthropogenically stressed and declining Gulf ecosystems to prosperity-sustaining levels of historic productivity. For this report, we assembled a team of leading scientists with expertise in coastal and marine ecosystems and with experience in their restoration to identify strategies and specific actions that will revitalize and sustain the Gulf coastal economy. Because the DWH spill intervened in ecosystems that are intimately interconnected and already under stress, and will remain stressed from global climate change, we argue that restoration of the Gulf must go beyond the traditional “in-place, in-kind” restoration approach that targets specific damaged habitats or species. A sustainable restoration of the Gulf of Mexico after DWH must: 1. Recognize that ecosystem resilience has been compromised by multiple human interventions predating the DWH spill; 2. Acknowledge that significant future environmental change is inevitable and must be factored into restoration plans and actions for them to be durable; 3. Treat the Gulf as a complex and interconnected network of ecosystems from shoreline to deep sea; and 4. Recognize that human and ecosystem productivity in the Gulf are interdependent, and that human needs from and effects on the Gulf must be integral to restoration planning. With these principles in mind, we provide the scientific basis for a sustainable restoration program along three themes: 1. Assess and repair damage from DWH and other stresses on the Gulf; 2. Protect existing habitats and populations; and 3. Integrate sustainable human use with ecological processes in the Gulf of Mexico. Under these themes, 15 historically informed, adaptive, ecosystem-based restoration actions are presented to recover Gulf resources and rebuild the resilience of its ecosystem. The vision that guides our recommendations fundamentally imbeds the restoration actions within the context of the changing environment so as to achieve resilience of resources, human communities and the economy into the indefinite future

Once and Future Gulf of Mexico Ecosystem: Restoration Recommendations of an Expert Working Group

The Deepwater Horizon (DWH) well blowout released more petroleum hydrocarbons into the marine environment than any previous U.S. oil spill (4.9 million barrels), fouling marine life, damaging deep sea and shoreline habitats and causing closures of economically valuable fisheries in the Gulf of Mexico. A suite of pollutants—liquid and gaseous petroleum compounds plus chemical dispersants—poured into ecosystems that had already been stressed by overfishing, development and global climate change. Beyond the direct effects that were captured in dramatic photographs of oiled birds in the media, it is likely that there are subtle, delayed, indirect and potentially synergistic impacts of these widely dispersed, highly bioavailable and toxic hydrocarbons and chemical dispersants on marine life from pelicans to salt marsh grasses and to deep-sea animals. As tragic as the DWH blowout was, it has stimulated public interest in protecting this economically, socially and environmentally critical region. The 2010 Mabus Report, commissioned by President Barack Obama and written by the secretary of the Navy, provides a blueprint for restoring the Gulf that is bold, visionary and strategic. It is clear that we need not only to repair the damage left behind by the oil but also to go well beyond that to restore the anthropogenically stressed and declining Gulf ecosystems to prosperity-sustaining levels of historic productivity. For this report, we assembled a team of leading scientists with expertise in coastal and marine ecosystems and with experience in their restoration to identify strategies and specific actions that will revitalize and sustain the Gulf coastal economy. Because the DWH spill intervened in ecosystems that are intimately interconnected and already under stress, and will remain stressed from global climate change, we argue that restoration of the Gulf must go beyond the traditional "in-place, in-kind" restoration approach that targets specific damaged habitats or species. A sustainable restoration of the Gulf of Mexico after DWH must: 1. Recognize that ecosystem resilience has been compromised by multiple human interventions predating the DWH spill; 2. Acknowledge that significant future environmental change is inevitable and must be factored into restoration plans and actions for them to be durable; 3. Treat the Gulf as a complex and interconnected network of ecosystems from shoreline to deep sea; and 4. Recognize that human and ecosystem productivity in the Gulf are interdependent, and that human needs from and effects on the Gulf must be integral to restoration planning. With these principles in mind, the authors provide the scientific basis for a sustainable restoration program along three themes: 1. Assess and repair damage from DWH and other stresses on the Gulf; 2. Protect existing habitats and populations; and 3. Integrate sustainable human use with ecological processes in the Gulf of Mexico. Under these themes, 15 historically informed, adaptive, ecosystem-based restoration actions are presented to recover Gulf resources and rebuild the resilience of its ecosystem. The vision that guides our recommendations fundamentally imbeds the restoration actions within the context of the changing environment so as to achieve resilience of resources, human communities and the economy into the indefinite future

A genomic analysis of the archaeal system Ignicoccus hospitalis-Nanoarchaeum equitans

Sequencing of the complete genome of Ignicoccus hospitalis gives insight into its association with another species of Archaea, Nanoarchaeum equitans

Estimating treated prevalence and service utilization rates: Assessing disparities in mental health

Abstract available at publisher's website.http://dx.doi.org/10.1002/sim.390

Utilization of machine learning for identifying symptom severity military-related PTSD subtypes and their biological correlates

We sought to find clinical subtypes of posttraumatic stress disorder (PTSD) in veterans 6–10 years post-trauma exposure based on current symptom assessments and to examine whether blood biomarkers could differentiate them. Samples were males deployed to Iraq and Afghanistan studied by the PTSD Systems Biology Consortium: a discovery sample of 74 PTSD cases and 71 healthy controls (HC), and a validation sample of 26 PTSD cases and 36 HC. A machine learning method, random forests (RF), in conjunction with a clustering method, partitioning around medoids, were used to identify subtypes derived from 16 self-report and clinician assessment scales, including the clinician-administered PTSD scale for DSM-IV (CAPS). Two subtypes were identified, designated S1 and S2, differing on mean current CAPS total scores: S2 = 75.6 (sd 14.6) and S1 = 54.3 (sd 6.6). S2 had greater symptom severity scores than both S1 and HC on all scale items. The mean first principal component score derived from clinical summary scales was three times higher in S2 than in S1. Distinct RFs were grown to classify S1 and S2 vs. HCs and vs. each other on multi-omic blood markers feature classes of current medical comorbidities, neurocognitive functioning, demographics, pre-military trauma, and psychiatric history. Among these classes, in each RF intergroup comparison of S1, S2, and HC, multi-omic biomarkers yielded the highest AUC-ROCs (0.819–0.922); other classes added little to further discrimination of the subtypes. Among the top five biomarkers in each of these RFs were methylation, micro RNA, and lactate markers, suggesting their biological role in symptom severity

Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors

Active-duty Army personnel can be exposed to traumatic warzone events and are at increased risk for developing post-traumatic stress disorder (PTSD) compared with the general population. PTSD is associated with high individual and societal costs, but identification of predictive markers to determine deployment readiness and risk mitigation strategies is not well understood. This prospective longitudinal naturalistic cohort study-the Fort Campbell Cohort study-examined the value of using a large multidimensional dataset collected from soldiers prior to deployment to Afghanistan for predicting post-deployment PTSD status. The dataset consisted of polygenic, epigenetic, metabolomic, endocrine, inflammatory and routine clinical lab markers, computerized neurocognitive testing, and symptom self-reports. The analysis was computed on active-duty Army personnel (N = 473) of the 101st Airborne at Fort Campbell, Kentucky. Machine-learning models predicted provisional PTSD diagnosis 90-180 days post deployment (random forest: AUC = 0.78, 95% CI = 0.67-0.89, sensitivity = 0.78, specificity = 0.71; SVM: AUC = 0.88, 95% CI = 0.78-0.98, sensitivity = 0.89, specificity = 0.79) and longitudinal PTSD symptom trajectories identified with latent growth mixture modeling (random forest: AUC = 0.85, 95% CI = 0.75-0.96, sensitivity = 0.88, specificity = 0.69; SVM: AUC = 0.87, 95% CI = 0.79-0.96, sensitivity = 0.80, specificity = 0.85). Among the highest-ranked predictive features were pre-deployment sleep quality, anxiety, depression, sustained attention, and cognitive flexibility. Blood-based biomarkers including metabolites, epigenomic, immune, inflammatory, and liver function markers complemented the most important predictors. The clinical prediction of post-deployment symptom trajectories and provisional PTSD diagnosis based on pre-deployment data achieved high discriminatory power. The predictive models may be used to determine deployment readiness and to determine novel pre-deployment interventions to mitigate the risk for deployment-related PTSD