Long-term trends in grassland bird relative abundance on focal grassland landscapes in Missouri.

North American grassland birds have widely declined over the past 50 years, largely due to anthropogenic-driven loss of native prairie habitat. In response to these declines, many conservation programs have been implemented to help secure wildlife habitat on private and public lands. The Grasslands Coalition is one such initiative established to advance the conservation of grassland birds in Missouri. The Missouri Department of Conservation conducted annual point count surveys for comparison of grassland bird relative abundance between focal grassland areas and nearby paired (i.e., containing no targeted management) sites. We analyzed 17 years of point count data with a generalized linear mixed model in a Bayesian framework to estimate relative abundance and trends across focal or paired sites for nine bird species of management interest that rely on grasslands: barn swallow (Hirundo rustica), brown-headed cowbird (Molothrus ater), dickcissel (Spiza americana), eastern meadowlark (Sturnella magna), grasshopper sparrow (Ammodramus savannarum), Henslow's sparrow (A. henslowii), horned lark (Eremophila alpestris), northern bobwhite (Colinus virginianus), and red-winged blackbird (Agelaius phoeniceus). Relative abundance of all species except eastern meadowlarks declined regionally. Relative abundance of barn swallows, brown-headed cowbirds, dickcissels, eastern meadowlarks, Henslow's sparrows, and northern bobwhites was higher in focal than paired sites, though relative abundance trends were only improved in focal vs. paired areas for dickcissels and Henslow's sparrows. Relative abundance increased with increasing grassland cover at the local (250-m radius) scale for all species except horned larks and red-winged blackbirds and at the landscape (2,500-m radius) scale for all species except dickcissels, eastern meadowlarks, and northern bobwhites. Our results suggest focal areas contained greater relative abundances of several grassland species of concern, likely due to increased availability of grassland habitat at local and landscape scales. Further efforts to decrease landscape-scale fragmentation and improve habitat quality may be needed to achieve conservation goals.

Vision, Aphasia, Neglect Assessment for Large Vessel Occlusion Stroke.

BACKGROUND AND PURPOSE: Vision, Aphasia, Neglect (VAN) is a large vessel occlusion (LVO) screening tool that was initially tested in a small study where emergency department (ED) nurses were trained to perform VAN assessment on stroke code patients. We aimed to validate the VAN assessment in a larger inpatient dataset. METHODS: We utilized a large dataset and used National Institute of Health Stroke Scale (NIHSS) performed by physicians to extrapolate VAN. VAN was compared to NIHSS greater than or equal to 6 and established prehospital LVO screening tools including Rapid Arterial Occlusion Evaluation scale (RACE), Field Assessment Stroke Triage for Emergency Destination (FAST-ED), and Cincinnati Pre-hospital Stroke Scale (CPSS). Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under receiver operating characteristics curve was calculated to estimate the predictive value of LVO. RESULTS: VAN was comparable in sensitivity (79% versus 80%) and NPV (88% versus 87%) to NIHSS greater than or equal to 6. It was superior in specificity (69% versus 57%), PPV (53% versus 46%) and accuracy to NIHSS greater than or equal to 6 (72% versus 64%) with significant receiver operating curve (.74 versus .69, P = .02). VAN also had comparable area under the curve when compared to RACE, FAST-ED, and CPSS however slightly lower accuracy (69%-73%) compared to RACE (76%), FAST-ED (77%), and CPSS (75%). VAN had the highest NPV among all screening assessments (88%). CONCLUSIONS: VAN is a simple screening tool that can identify LVOs with adequate accuracy in hospital setting. Future studies need to be conducted in prehospital setting to validate its utility to detect LVOs in the field.

Vision, Aphasia, Neglect Assessment to Predict Neurosurgical Intervention in Patients with Nontraumatic Intracerebral Hemorrhage.

BACKGROUND AND PURPOSE: The Vision, Aphasia, and Neglect (VAN) screening tool is a simple bedside test developed to identify patients with large vessel occlusion stroke. In the setting of intracerebral hemorrhage (ICH), there are very few bedside predictors of need for neurosurgical interventions other than age and Glasgow Coma Scale (GCS). We aimed to assess the utility of the VAN screening tool in predicting the need for neurosurgical intervention in patients with ICH. METHODS: We accessed sensitivity, specificity, positive predictive value, negative predictive value (NPV), and area under receiver operating characteristics curve of VAN for identifying ICH patients who require neurosurgical intervention. RESULTS: Among 228 ICH patients, 176 were VAN positive and 52 were VAN negative. On unadjusted analyses, VAN positive patients had a significantly higher ICH volume, GCS score, and National Institutes of Health Stroke Scale score (P < .001 for all). As compared to VAN negative patients, significantly higher proportion of VAN positive ICH patients (15.4% versus 32.4%) underwent a neurosurgical procedure such as external ventricular drain (EVD) and/or hematoma evacuation with craniotomy or craniectomy. The VAN screening tool had high sensitivity and NPV (100%) in predicting the need for craniectomy or hematoma evacuation, but had lower sensitivity (87.7%) for any neurosurgical procedure, as 15.4% of VAN negative patients received EVD. CONCLUSIONS: Our study suggests that VAN screening tool can identify high-risk ICH patients who are more likely to undergo craniotomy or craniectomy but is less sensitive to rule out need for EVD.

Application and interpretation of functional data analysis techniques to differential scanning calorimetry data from lupus patients.

BACKGROUND: DSC is used to determine thermally-induced conformational changes of biomolecules within a blood plasma sample. Recent research has indicated that DSC curves (or thermograms) may have different characteristics based on disease status and, thus, may be useful as a monitoring and diagnostic tool for some diseases. Since thermograms are curves measured over a range of temperature values, they are considered functional data. In this paper we apply functional data analysis techniques to analyze differential scanning calorimetry (DSC) data from individuals from the Lupus Family Registry and Repository (LFRR). The aim was to assess the effect of lupus disease status as well as additional covariates on the thermogram profiles, and use FD analysis methods to create models for classifying lupus vs. control patients on the basis of the thermogram curves. METHODS: Thermograms were collected for 300 lupus patients and 300 controls without lupus who were matched with diseased individuals based on sex, race, and age. First, functional regression with a functional response (DSC) and categorical predictor (disease status) was used to determine how thermogram curve structure varied according to disease status and other covariates including sex, race, and year of birth. Next, functional logistic regression with disease status as the response and functional principal component analysis (FPCA) scores as the predictors was used to model the effect of thermogram structure on disease status prediction. The prediction accuracy for patients with Osteoarthritis and Rheumatoid Arthritis but without Lupus was also calculated to determine the ability of the classifier to differentiate between Lupus and other diseases. Data were divided 1000 times into separate 2/3 training and 1/3 test data for evaluation of predictions. Finally, derivatives of thermogram curves were included in the models to determine whether they aided in prediction of disease status. RESULTS: Functional regression with thermogram as a functional response and disease status as predictor showed a clear separation in thermogram curve structure between cases and controls. The logistic regression model with FPCA scores as the predictors gave the most accurate results with a mean 79.22% correct classification rate with a mean sensitivity = 79.70%, and specificity = 81.48%. The model correctly classified OA and RA patients without Lupus as controls at a rate of 75.92% on average with a mean sensitivity = 79.70% and specificity = 77.6%. Regression models including FPCA scores for derivative curves did not perform as well, nor did regression models including covariates. CONCLUSION: Changes in thermograms observed in the disease state likely reflect covalent modifications of plasma proteins or changes in large protein-protein interacting networks resulting in the stabilization of plasma proteins towards thermal denaturation. By relating functional principal components from thermograms to disease status, our Functional Principal Component Analysis model provides results that are more easily interpretable compared to prior studies. Further, the model could also potentially be coupled with other biomarkers to improve diagnostic classification for lupus.