Recovery Metrics Research Articles

Cardioembolic Stroke Risk and Recovery After Anticoagulation-Related Intracerebral Hemorrhage.

Background and Purpose- Whether to resume oral anticoagulation treatment after intracerebral hemorrhage (ICH) remains an unresolved question. Previous studies focused primarily on recurrent stroke after ICH. We sought to investigate the association between cardioembolic stroke risk, oral anticoagulation therapy resumption, and functional recovery among ICH survivors in the absence of recurrent stroke. Methods- We conducted a joint analysis of 3 observational studies: (1) the multicenter RETRACE study (German-Wide Multicenter Analysis of Oral Anticoagulation Associated Intracerebral Hemorrhage); (2) the Massachusetts General Hospital ICH study (n=166); and (3) the ERICH study (Ethnic/Racial Variations of Intracerebral Hemorrhage; n=131). We included 941 survivors of ICH in the setting of active oral anticoagulation therapy for prevention of cardioembolic stroke because of nonvalvular atrial fibrillation and without evidence of ischemic stroke and recurrent ICH at 1 year from the index event. We created univariable and multivariable models to explore associations between cardioembolic stroke risk (based on CHA2DS2-VASc scores) and functional recovery after ICH, defined as achieving modified Rankin Scale score of ≤3 at 1 year for participants with modified Rankin Scale score of >3 at discharge. Results- In multivariable analyses, the CHA2DS2-VASc score was associated with a decreased likelihood of functional recovery (odds ratio, 0.83 per 1 point increase; 95% CI, 0.79-0.86) at 1 year. Anticoagulation resumption was independently associated with a higher likelihood of recovery, regardless of CHA2DS2-VASc score (odds ratio, 1.89; 95% CI, 1.32-2.70). We found an interaction between CHA2DS2-VASc score and anticoagulation resumption in terms of association with increased likelihood of functional recovery (interaction P=0.011). Conclusions- Increasing cardioembolic stroke risk is associated with a decreased likelihood of functional recovery at 1 year after ICH, but this association was weaker among participants resuming oral anticoagulation therapy. These findings support, including recovery metrics, in future studies of anticoagulation resumption after ICH.

Ground truthing spatial disaster recovery metrics with participatory mapping in post-Katrina Mississippi

Quantitative approximations of community-level recovery are a mainstay in disaster science. These longitudinal spatial metrics, which chart the status of housing stock, economic indices, or demographic change post-disaster, aim to make the complex recovery process more understandable. The question remains, however, whether these technical approaches often imposed by outside researchers draw the same conclusions about recovery as do assessments made by community insiders who have lived through the process. To address this question, this study uses participatory mapping as a means to ground truth spatial disaster recovery metrics compiled from secondary data sources. Techniques for collecting, aggregating, and transforming qualitatively mapped data are detailed. Clustering techniques to ready longitudinal recovery metrics for comparison with the participant-derived data are also described. Acknowledging that the best recovery measurements, whether qualitative or quantitative, should validate one another, this paper contributes to geographical scholarship on disasters by providing a method for conducting such cross-validation.

Quantitative Susceptibility Mapping after Sports-Related Concussion.

Quantitative susceptibility mapping using MR imaging can assess changes in brain tissue structure and composition. This report presents preliminary results demonstrating changes in tissue magnetic susceptibility after sports-related concussion. Longitudinal quantitative susceptibility mapping metrics were produced from imaging data acquired from cohorts of concussed and control football athletes. One hundred thirty-six quantitative susceptibility mapping datasets were analyzed across 3 separate visits (24 hours after injury, 8 days postinjury, and 6 months postinjury). Longitudinal quantitative susceptibility mapping group analyses were performed on stability-thresholded brain tissue compartments and selected subregions. Clinical concussion metrics were also measured longitudinally in both cohorts and compared with the measured quantitative susceptibility mapping. Statistically significant increases in white matter susceptibility were identified in the concussed athlete group during the acute (24 hour) and subacute (day 8) period. These effects were most prominent at the 8-day visit but recovered and showed no significant difference from controls at the 6-month visit. The subcortical gray matter showed no statistically significant group differences. Observed susceptibility changes after concussion appeared to outlast self-reported clinical recovery metrics at a group level. At an individual subject level, susceptibility increases within the white matter showed statistically significant correlations with return-to-play durations. The results of this preliminary investigation suggest that sports-related concussion can induce physiologic changes to brain tissue that can be detected using MR imaging-based magnetic susceptibility estimates. In group analyses, the observed tissue changes appear to persist beyond those detected on clinical outcome assessments and were associated with return-to-play duration after sports-related concussion.

Survival analysis: Informing recovery of Sierra Nevada bighorn sheep

ABSTRACTSurvival in small populations (e.g., Sierra Nevada bighorn sheep or Sierra bighorn [Ovis canadensis sierrae]) is often highly variable. External selective pressures vary in the degree to which they regulate survival by sex and age class. Understanding the important factors and risks for different demographic classes helps managers design strategies that enhance the recovery of endangered species, including Sierra bighorn. Our goal was to determine what population‐level factors (e.g., climate, habitat, population size, predation) affect survival and whether there are interactions between these factors by age and sex, and then apply our findings to recovery strategies. To this end, we conducted a known‐fate survival analysis for female and male Sierra bighorn with data collected over 12 years, and used model selection to evaluate models with spatial, environmental, and other population‐level factors hypothesized to be related to survival. Survival of adult Sierra bighorn declined continuously with age for both sexes; survival was generally higher for females than males, and there were no interactions between age and any environmental or population‐level factors. The top model for both sexes included the date of peak value of normalized difference vegetation index (NDVI) from the previous summer; NDVI had a similar positive relationship with survival for both sexes, which indicates that the later the growing season persists into the summer, the better survival the subsequent year. For females, survival also was negatively related to an index of abundance for mountain lions (Puma concolor), whereas the relationship was less apparent for males. Instead, top models for males indicated elevated survival during warm wet years, but years with late peaks in NDVI the previous year ameliorated the effect of a cold, dry winter. Finally, competitive models for males and females included a variable representing avalanche risk, indicating reduced survival in areas with increased avalanche risk. From a recovery management perspective, the lack of any interaction between age and other covariates suggests that although we may still select younger female Sierra bighorn for translocations (an essential recovery action) because they have higher reproductive value than old females, there were no additional negative synergies between age and other factors to consider. All variables are of value in guiding expectations for newly established populations and established source populations and some may help fine tune the selection of translocation areas. In addition, including predation, weather covariates, and catastrophic effects, such as avalanche risk, in projection models is important for realistic estimation of the time required to meet recovery goals and predicting population trajectories under likely climate change scenarios. Our approach is generalizable to other systems; we demonstrated how survival analyses can inform endangered species recovery management by indicating ideal areas for translocations and provided realistic estimates of time‐to‐recovery or other recovery metrics. © 2018 The Wildlife Society.

Concussion recovery trajectories among United States Service Academy Members

PURPOSE: Describe concussion/mild traumatic brain injury (mTBI) recovery durations by sex and injury-related activities in US Service Academy cadets. METHODS: Injury data (2014-2017) from three US Military Service Academies were examined to describe concussion/mTBI characteristics (n=800 injuries, 301 female). Of the documented injuries, there were 738 injured individuals, of which, 679 had one injury, 56 individuals had two injuries and three individuals had three injuries. The recovery trajectories for all injuries were examined across sex and injury activity. Three durations were examined: days until asymptomatic, duration of return to activity (RTA) protocol, and days of total time lost. Duration was examined using Kaplan-Meier and log-rank tests. Due to the right skewed distribution, medians and interquartile range (IQR) are reported. RESULTS: Across all cadets, the median days until asymptomatic was 9 (IQR: 5-16). The median duration of RTA protocol was 5 days (IQR: 5-7). The total time lost due to concussion/mTBI was 20.68 days (IQR: 12.78-33.12). There was a significant effect of sex for days until asymptomatic and total time lost (p’s < 0.01). Across all recovery metrics, females were more likely to have more days until asymptomatic (11 vs. 8.0 median days) and total time lost (24.6 vs 18.8 days). A significant effect of injury activity was observed for days until asymptomatic and total time lost (p’s < 0.05). Injuries occurring during free-time activities had the greatest median number of days until asymptomatic (13 days IQR: 10-15) while injuries occurring during varsity athletics or academy specific training had the fewest median days until asymptomatic (both 6 days). Injuries occurring during varsity athletics had the shortest median total time loss of 12.9 days (IQR: 11.95-15.73). All other injury mechanisms were associated with median time loss of more than 20 days. CONCLUSIONS: These analyses show significant effects of sex and injury activity on recovery duration. Differences may also reflect varying approaches to injury management by the medical provider. Further investigation is needed to determine an individualized approach to clinical care.

Deterministic Lateral Displacement: The Next-Generation CAR T-Cell Processing?

Reliable cell recovery and expansion are fundamental to the successful scale-up of chimeric antigen receptor (CAR) T cells or any therapeutic cell-manufacturing process. Here, we extend our previous work in whole blood by manufacturing a highly parallel deterministic lateral displacement (DLD) device incorporating diamond microposts and moving into processing, for the first time, apheresis blood products. This study demonstrates key metrics of cell recovery (80%) and platelet depletion (87%), and it shows that DLD T-cell preparations have high conversion to the T-central memory phenotype and expand well in culture, resulting in twofold greater central memory cells compared to Ficoll-Hypaque (Ficoll) and direct magnetic approaches. In addition, all samples processed by DLD converted to a majority T-central memory phenotype and did so with less variation, in stark contrast to Ficoll and direct magnetic prepared samples, which had partial conversion among all donors (<50%). This initial comparison of T-cell function infers that cells prepared via DLD may have a desirable bias, generating significant potential benefits for downstream cell processing. DLD processing provides a path to develop a simple closed system that can be automated while simultaneously addressing multiple steps when there is potential for human error, microbial contamination, and other current technical challenges associated with the manufacture of therapeutic cells.

Abstract TP158: Neural Function and Injury Combined Best Predict Behavioral Gains During Inpatient Stroke Rehabilitation

Background: A better understanding of the mechanisms of recovery during rehabilitation could inform treatment decision-making. We tested two hypotheses: [1] a combination of neural function and injury measures is better than either measure alone for predicting motor gains during inpatient rehabilitation facility (IRF) admission; and [2] performance of prediction measures varies according to severity of baseline impairment. Methods: Fifteen patients with subacute stroke (56±12 yr, 16 days post-stroke) admitted to an IRF underwent EEG [3-min, resting-state, dense-array (256-lead)] and MRI [anatomical and diffusion tensor] at IRF admission; and serial behavioral testing. Neural function was assessed using EEG measures of coherence and power from electrodes overlying ipsilesional (M1 i ) and contralesional (M1 c ) primary motor cortex, in the Delta (1-3 Hz) and high Beta (20-30 Hz) frequency bands. Neural injury was assessed as integrity of white matter in corpus callosum (CC). Change in arm Fugl-Meyer (FM) and Functional Independent Measurement motor (FIM-m) scores served as primary and secondary behavioral recovery metrics, respectively. Results: In subjects with moderate or severe impairment (FM &lt;55, N=11), neither neural function (M1 i -M1 c Delta coherence) nor neural injury (CC integrity) alone significantly predicted FIM-m score change. However, when combined into a single model, these measures did significantly predicted FIM-m score change (R 2 = 0.85, p=0.024); note that baseline behavior was not a significant predictor. An identical neural injury+function model approached significance at predicting FM score change (R 2 = 0.72 p=0.08). These models failed, however, when applied to all 15 patients (R 2 = 0.06, p=0.81). Conclusions: Results thus far in this ongoing study suggest that recovery during inpatient rehabilitation is best predicted by combining neural function and injury measures, and not by behavioral assessments. Performance of recovery predictors varies according to severity of baseline deficits, as adding mild strokes to moderate/severe strokes increased the sample size but diluted the model. These findings could potentially inform patient selection, treatment decisions, and discharge planning in an IRF setting.

Analyzing spatial and temporal variability in short-term rates of post-fire vegetation return from Landsat time series

The disturbance and recovery cycles of Canadian boreal forests result in highly dynamic landscapes, requiring continued monitoring to observe and characterize environmental change over time. Well-established remote sensing methods capture change over forested ecosystems, however the return of forest vegetation in disturbed locations is infrequently documented and not well understood. Landsat time-series data allows for both the capture of the initial disturbance and the ability to monitor the subsequent vegetation regeneration with spectral vegetation indices. In this research, we used three spectral recovery metrics derived from an annual Landsat-based per-pixel Normalized Burn Ratio time series to determine trends in the short-term rates of spectral recovery for areas disturbed by wildfire (1986–2006), as assessed using a series of 5-year post-disturbance windows to observe forest recovery trends. Our results indicated that rates of spectral forest recovery vary over time and space in the Taiga and Boreal Shield ecozones. We found evidence that post-fire spectral forest recovery rates have accelerated over time in both the East and West Taiga Shield ecozones, with a consistent, positive, and significant trend measured using a Mann-Kendall test for monotonicity and Theil-Sen slope estimation. Over the analysis period (1986–2011), relative rates of spectral forest recovery increased by 18% in the Taiga Shield East and 9% in the Taiga Shield West. In contrast, spectral forest recovery rates in the Boreal Shield varied temporally, and were not consistently positive or negative. These results demonstrate that post-fire spectral recovery rates are not fixed over time and that spectral trends are dependent upon spatial location in the Canadian boreal. This retrospective baseline information on trends in spectral recovery rates highlights the value of, and continued need for detailed monitoring of vegetation regeneration in boreal forest ecosystems, particularly in the context of a changing climate.

Stochastic post-disaster functionality recovery of community building portfolios I: Modeling

Understanding the process of post-disaster recovery is a critical step toward assessing and achieving community resilience. However, the recovery process of a community building portfolio as a spatially distributed system is intrinsically complex and highly uncertain, and is conditional on the resourcefulness and social-economic characteristics of the community as well as the various decisions made by numerous community stakeholders and building owners at different phases of the recovery. We propose a simulation-based building portfolio recovery model (BPRM) to predict the functionality recovery time and recovery trajectory of a community building portfolio following natural scenario hazard events, in two steps: (1) modeling individual building-level restoration as a discrete state, continuous time Markov Chain (CTMC); and (2) modeling building portfolio-level recovery through aggregating the CTMC restoration processes of individual buildings across the domain of the community and over the entire recovery time horizon. We propagate uncertainties associated with the recovery process in a consistent manner in order to quantify portfolio recovery metrics probabilistically. The proposed building portfolio recovery model is intended to support risk-informed community resilience planning and hazard mitigation.

Post-harvest recovery dynamics depend on predator specialization in size-selective fisheries

Author(s): Aalto, EA; Baskett, ML | Abstract: Fisheries typically truncate target species' size distributions through an increase in mortality, especially if harvest is size-selective. Such truncation can push a harvested species' size distribution into classes most vulnerable to gape-limited predation, such that predator-prey dynamics might affect the rate of recovery from fishing. Understanding this rate of recovery is crucial to adaptive management of no-take reserves and fisheries closures. We used a 2-species size-structured model to examine how gape-limited predation alters post-harvest recovery for 3 example prey species, viz. cod, haddock, and whiting. We found little difference in recovery patterns of prey abundance and size structure between systems with gape-limited generalist predation and those with no size-dependent predation. However, gape-limited obligate predation can lead to fluctuations, with transient declines, that delay recovery to the pre-harvest equilibrium for both biomass and mean size due to a time lag in predator recovery. Fluctuating recovery dynamics under obligate predation were most likely for predators that experienced greater declines due to prey loss during harvest and for slow-growing prey with an adult size refuge. We conclude that through these delays, the presence of a strong dynamically linked predator can alter the expec - tations of the time scale for different recovery metrics used in adaptive management.

Availability modeling and analysis of a disaster-recovery-as-a-service solution

In modern corporate environments, having a disaster recovery solution is no longer a luxury but a business necessity. The adoption of a disaster recovery solution, however, can be costly and often only affordable by large enterprises. Disaster-Recovery-as-a-Service (DRaaS) is a cloud-based solution that small and medium-sized businesses have been adopting to guarantee availability even in catastrophic situations. It offers lower acquisition and operational costs than traditional solutions. This work presents availability models for evaluating a DRaaS solution taking into account crucial disaster recovery metrics, such as downtime, costs, recovery time objective, and transaction loss. Furthermore, we performed sensitivity analysis on the DRaaS model to determine the parameters that cause the greatest impact on the system availability. Based on these analyses, disaster recovery coordinators can determine the optimum point to recover the damaged system by balancing the cost of system downtime against the cost of resources required for restoring the system. Our numerical results show the effectiveness of a DRaaS solution in terms of downtime, cost, and transition loss.

Electroshock Induced Seizures in Adult C. elegans.

The nematode Caenorhabditis elegans is a useful model organism for dissecting molecular mechanisms of neurological diseases. While hermaphrodite C. elegans contains only 302 neurons, the conserved homologous neurotransmitters, simpler neuronal circuitry, and fully mapped connectome make it an appealing model system for neurological research. Here we developed an assay to induce an electroconvulsive seizure in C. elegans which can be used as a behavioral method of analyzing potential anti-epileptic therapeutics and novel genes involved in seizure susceptibility. In this assay, worms are suspended in an aqueous solution as current is passed through the liquid. At the onset of the shock, worms will briefly paralyze and twitch, and shortly after regain normal sinusoidal locomotion. The time to locomotor recovery is used as a metric of recovery from a seizure which can be reduced or extended by incorporating drugs that alter neuronal and muscular excitability.

Predictors of At-Home Arterial Oxygen Desaturation Events in Ambulatory Surgical Patients.

Little is known about the early recovery phase occurring at-home after anesthesia and surgery in ambulatory surgical patients. We studied quantitative oximetry and quality-of-life metrics in the first 48 hours after same-day orthopedic surgery examining the association between the recovery metrics and specific patient and procedural factors. We used the STOP-Bang score to quantify patient risk for obstructive sleep apnea in 50 adult patients at 2 centers using continuous portable oximetry and patient journaling. Parametric statistical procedures were used to assess relationships among patient and procedural factors and desaturation events. Higher STOP-Bang scores were predictive of the number and duration of desaturation events below mild and severe thresholds for arterial oxygen saturation during their first 48 hours after discharge from ambulatory surgery. Older patients and patients with higher BMI in particular were at an increased risk of mild and severe arterial oxygen desaturation. Using a home CPAP reduced the number of desaturation events. Of interest, taking opiate analgesics decreased the number of desaturation events. Given the absence of systematic research of early ambulatory anesthesia/surgery recovery at home and concerns of postoperative respiratory events, our results have clear implications for patient safety. Our results imply that screening based on noninvasive STOP-Bang scores may allow for suggestions for recovery from ambulatory surgery, such as encouraging patients with high scores to use home CPAP and aggressive education regarding use of opiates.

Recovery of floristic diversity and basal area in natural forest regeneration and planted plots in a Costa Rican wet forest

AbstractWhen compared to planted reforestation, natural unassisted regeneration is often reported to result in slow recovery of biomass and biodiversity, especially early in succession. In some cases, naturally regenerating forests are not comparable to the community structure of primary forests after many decades. However, direct comparison of the outcomes of tropical forest restoration and natural regeneration is hindered by differences in metrics of forest recovery, inconsistency in land use histories, and dissimilarities in experimental design. We present the results of a replicated reforestation experiment comparing natural regeneration and polyculture tree planting at multiple diversity levels (3, 6, 9, or 12 native tree species), with uniform land use history and initial edaphic conditions. We compare the recovery of basal area and floristic diversity in these treatments after 5 yr of succession. Total basal area was higher in planted plots than in naturally regenerating plots, but it but did not vary among the different planted diversity levels. The basal area of woody recruits did not differ among treatments. The diversity of woody recruits increased substantially over time but did not vary among planting treatments. Species composition trajectories showed directional turnover over time, with no consistent differences among treatments. The convergence of restoration trajectories and similarity of floristic community diversity and composition across all treatments, after only 5 yr, provides evidence of the viability of natural regeneration for rapid restoration of forest biodiversity.

Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale.

OverviewNotions of community quality underlie the clustering of networks. While studies surrounding network clustering are increasingly common, a precise understanding of the realtionship between different cluster quality metrics is unknown. In this paper, we examine the relationship between stand-alone cluster quality metrics and information recovery metrics through a rigorous analysis of four widely-used network clustering algorithms—Louvain, Infomap, label propagation, and smart local moving. We consider the stand-alone quality metrics of modularity, conductance, and coverage, and we consider the information recovery metrics of adjusted Rand score, normalized mutual information, and a variant of normalized mutual information used in previous work. Our study includes both synthetic graphs and empirical data sets of sizes varying from 1,000 to 1,000,000 nodes.Cluster Quality MetricsWe find significant differences among the results of the different cluster quality metrics. For example, clustering algorithms can return a value of 0.4 out of 1 on modularity but score 0 out of 1 on information recovery. We find conductance, though imperfect, to be the stand-alone quality metric that best indicates performance on the information recovery metrics. Additionally, our study shows that the variant of normalized mutual information used in previous work cannot be assumed to differ only slightly from traditional normalized mutual information.Network Clustering AlgorithmsSmart local moving is the overall best performing algorithm in our study, but discrepancies between cluster evaluation metrics prevent us from declaring it an absolutely superior algorithm. Interestingly, Louvain performed better than Infomap in nearly all the tests in our study, contradicting the results of previous work in which Infomap was superior to Louvain. We find that although label propagation performs poorly when clusters are less clearly defined, it scales efficiently and accurately to large graphs with well-defined clusters.

MAPPING DISTURBANCE DYNAMICS IN WET SCLEROPHYLL FORESTS USING TIME SERIES LANDSAT

Abstract. In this study, we characterised the temporal-spectral patterns associated with identifying acute-severity disturbances and low-severity disturbances between 1985 and 2011 with the objective to test whether different disturbance agents within these categories can be identified with annual Landsat time series data. We analysed a representative State forest within the Central Highlands which has been exposed to a range of disturbances over the last 30 years, including timber harvesting (clearfell, selective and thinning) and fire (wildfire and prescribed burning). We fitted spectral time series models to annual normal burn ratio (NBR) and Tasseled Cap Indices (TCI), from which we extracted a range of disturbance and recovery metrics. With these metrics, three hierarchical random forest models were trained to 1) distinguish acute-severity disturbances from low-severity disturbances; 2a) attribute the disturbance agents most likely within the acute-severity class; 2b) and attribute the disturbance agents most likely within the low-severity class. Disturbance types (acute severity and low-severity) were successfully mapped with an overall accuracy of 72.9 %, and the individual disturbance types were successfully attributed with overall accuracies ranging from 53.2 % to 64.3 %. Low-severity disturbance agents were successfully mapped with an overall accuracy of 80.2 %, and individual agents were successfully attributed with overall accuracies ranging from 25.5 % to 95.1. Acute-severity disturbance agents were successfully mapped with an overall accuracy of 95.4 %, and individual agents were successfully attributed with overall accuracies ranging from 94.2 % to 95.2 %. Spectral metrics describing the disturbance magnitude were more important for distinguishing the disturbance agents than the post-disturbance response slope. Spectral changes associated with planned burning disturbances had generally lower magnitudes than selective harvesting. This study demonstrates the potential of landsat time series mapping for fire and timber harvesting disturbances at the agent level and highlights the need for distinguishing between agents to fully capture their impacts on ecosystem processes.

MAPPING DISTURBANCE DYNAMICS IN WET SCLEROPHYLL FORESTS USING TIME SERIES LANDSAT

In this study, we characterised the temporal-spectral patterns associated with identifying acute-severity disturbances and low-severity disturbances between 1985 and 2011 with the objective to test whether different disturbance agents within these categories can be identified with annual Landsat time series data. We analysed a representative State forest within the Central Highlands which has been exposed to a range of disturbances over the last 30 years, including timber harvesting (clearfell, selective and thinning) and fire (wildfire and prescribed burning). We fitted spectral time series models to annual normal burn ratio (NBR) and Tasseled Cap Indices (TCI), from which we extracted a range of disturbance and recovery metrics. With these metrics, three hierarchical random forest models were trained to 1) distinguish acute-severity disturbances from low-severity disturbances; 2a) attribute the disturbance agents most likely within the acute-severity class; 2b) and attribute the disturbance agents most likely within the low-severity class. Disturbance types (acute severity and low-severity) were successfully mapped with an overall accuracy of 72.9&amp;thinsp;%, and the individual disturbance types were successfully attributed with overall accuracies ranging from 53.2&amp;thinsp;% to 64.3&amp;thinsp;%. Low-severity disturbance agents were successfully mapped with an overall accuracy of 80.2&amp;thinsp;%, and individual agents were successfully attributed with overall accuracies ranging from 25.5&amp;thinsp;% to 95.1. Acute-severity disturbance agents were successfully mapped with an overall accuracy of 95.4&amp;thinsp;%, and individual agents were successfully attributed with overall accuracies ranging from 94.2&amp;thinsp;% to 95.2&amp;thinsp;%. Spectral metrics describing the disturbance magnitude were more important for distinguishing the disturbance agents than the post-disturbance response slope. Spectral changes associated with planned burning disturbances had generally lower magnitudes than selective harvesting. This study demonstrates the potential of landsat time series mapping for fire and timber harvesting disturbances at the agent level and highlights the need for distinguishing between agents to fully capture their impacts on ecosystem processes.

Assessing postfire recovery of chamise chaparral using multi-temporal spectral vegetation index trajectories derived from Landsat imagery

Abstract Land managers and ecologists working in Mediterranean-type ecosystems require information on postfire recovery of shrublands as a means of identifying long-term changes in these sensitive systems. This study evaluates the utility of postfire regrowth trajectories, derived from multi-temporal Landsat satellite surface reflectance imagery, as a basis for estimating postfire recovery of chamise chaparral in southern California. Postfire recovery metrics are applied to time series trajectories of spectral vegetation indices (SVIs) including NDVI, EVI, SAVI, MSAVI, NBR, and NBR2. Shrub fractional cover changes following postfire recovery are estimated at 74 plots using multi-date high spatial resolution orthoimagery, enabling stratification of the recovery outcomes. The sensitivity of Landsat-based regrowth trajectories to postfire shrub recovery is assessed by ANOVA statistical testing based on the stratified recovery outcomes. We evaluate several combinations of postfire recovery metrics and SVIs as postfire recovery indicators. We compare SVI trends extending 19 to 29 years postfire with field-collected data on postfire recovery trends reported in previous studies, in order to assess the sensitivity of the SVIs to short- and long-term phases of regrowth. The utility and necessity of normalizing SVI time series based on unburned control plots to reduce moisture effects is also evaluated. A primary finding is that the Scaled Recovery Metric (SRM), a variant of the Regeneration Index which incorporates the pixel-specific SVI value immediately after fire, is a particularly useful indicator of postfire recovery based on time-sequential SVI trajectories and facilitates inter-site comparisons. Time series of several SVIs (especially NDVI and NBR2) provide statistically significant (p

Artificial selection on chill-coma recovery time in Drosophila melanogaster: Direct and correlated responses to selection

Artificial selection can be used to create populations with extreme phenotypic responses to environmental stressors. When artificial selection is applied to a single component of a stress response, this selection may result in correlated responses in other stress responses, a phenomenon called cross-tolerance, which is ultimately controlled by the genetic correlations among traits. We selected for extreme responses to cold tolerance by selecting for chill-coma recovery time from a single temperate population of Drosophila melanogaster. Chill-coma recovery time is a common metric of low, but non-lethal, cold temperature tolerance. Replicated divergent artificial selection was applied to a genetically variable base population for 31 generations, resulting in two cold resistant, two cold susceptible, and two unselected control lines. To quantify the relationship between selection on chill-coma recovery and other metrics of thermal performance, we also measured survivorship after acute cold exposure, survivorship after chronic cold exposure, survivorship after cold exposure following a pre-treatment period (rapid cold hardening), starvation tolerance, and heat tolerance. We find that chill-coma recovery time is heritable within this population and that there is an asymmetric response to increased and decreased chill-coma recovery time. Surprisingly, we found no cross-tolerances between selection on chill-coma recovery time and the other environmental stress response traits. These results suggest that although artificial selection has dramatically altered chill-coma recovery time, the correlated response to selection on other stress response phenotypes has been negligible. The lack of a correlated response suggests that chill-coma recovery time in these selection lines is likely genetically independent from measures of cold survivorship tested here.

Abstract WP174: Prestroke Selective Serotonin Reuptake Inhibitor Use and Functional Outcomes in Ischemic Stroke

Introduction: Selective serotonin reuptake inhibitors (SSRIs) have been implicated in contributing to recovery after acute ischemic stroke. In particular, post stroke initiation of an SSRI has been demonstrated to improve motor recovery. The role of prestroke SSRI use on functional outcomes and stroke recovery is less clear. We aimed to examine the effect of prestroke SSRI use on metrics of hospitalization and functional recovery. Hypothesis: Pre-event SSRI therapy improves functional outcome in acute ischemic stroke. Methods: We included 4968 consecutive patients from January 2006 to June 2015 in our local Get with the Guidelines-Stroke registry in whom the preadmission admission drug list could be extracted from an administrative research data registry. Univariate and multivariable analyses were performed to identify predictors of functional outcomes. Results: Among 4698 ischemic strokes (740 SSRI users and 3948 nonusers) univariate analysis of SSRI use before acute ischemic stroke did not impact admission NIHSS, length of stay or rate of symptomatic hemorrhage (Table 1). Patients using SSRIs prior to their stroke were more likely to present with weakness (57% vs 47.3%, p&lt;0.001) and have hospitalizations complicated by pneumonia (7.6% vs 5.7%, p&lt;0.001). Moreover, pre-stroke SSRI use was associated with a negative impact on ambulatory status at discharge and discharge to home. On multivariable regression analysis, SSRI use was associated with lower likelihood of discharge to home (aOR 0.79, 95%CI 0.62, 0.997; p&lt;0.05). Conclusions: SSRI use preceding an acute ischemic stroke is associated with lower rates of discharge to home despite no significant increase in length of stay or NIHSS. Further studies evaluating potential confounding factors, the influence of prior SSRI use on infarct size, and longer term outcomes are warranted.