δ13C and δ15N in the endangered Kemp’s ridley sea turtle Lepidochelys kempii after the Deepwater Horizon oil spill

The Deepwater Horizon explosion in April 2010 and subsequent oil spill released 3.19 × 106 barrels (5.07 × 108 l) of MC252 crude oil into important foraging areas of the endangered Kemp’s ridley sea turtle Lepidochelys kempii (Lk) in the northern Gulf of Mexico (GoM). We measured δ13C and δ15N in scute biopsy samples from 33 Lk nesting in Texas during the period 2010 to 2012. Of these, 27 were equipped with satellite transmitters and were tracked to traditional foraging areas in the northern GoM after the spill. Differences in δ13C between the oldest and newest scute layers from 2010 nesters were not significant, but δ13C in the newest layers from 2011 and 2012 nesters was significantly lower compared to 2010. δ15N differences were not statis- tically significant. Collectively, the stable isotope and tracking data indicate that the lower δ13C values reflect the incorporation of oil rather than changes in diet or foraging area. Discriminant analysis indicated that 51.5% of the turtles sampled had isotope signatures indicating oil exposure. Growth of the Lk population slowed in the years following the spill. The involvement of oil exposure in recent population trends is unknown, but long-term effects may not be evident for many years. Our results indicate that C isotope signatures in scutes may be useful biomarkers of sea turtle exposure to oil

Exploiting the noise: improving biomarkers with ensembles of data analysis methodologies.

BackgroundThe advent of personalized medicine requires robust, reproducible biomarkers that indicate which treatment will maximize therapeutic benefit while minimizing side effects and costs. Numerous molecular signatures have been developed over the past decade to fill this need, but their validation and up-take into clinical settings has been poor. Here, we investigate the technical reasons underlying reported failures in biomarker validation for non-small cell lung cancer (NSCLC).MethodsWe evaluated two published prognostic multi-gene biomarkers for NSCLC in an independent 442-patient dataset. We then systematically assessed how technical factors influenced validation success.ResultsBoth biomarkers validated successfully (biomarker #1: hazard ratio (HR) 1.63, 95% confidence interval (CI) 1.21 to 2.19, P = 0.001; biomarker #2: HR 1.42, 95% CI 1.03 to 1.96, P = 0.030). Further, despite being underpowered for stage-specific analyses, both biomarkers successfully stratified stage II patients and biomarker #1 also stratified stage IB patients. We then systematically evaluated reasons for reported validation failures and find they can be directly attributed to technical challenges in data analysis. By examining 24 separate pre-processing techniques we show that minor alterations in pre-processing can change a successful prognostic biomarker (HR 1.85, 95% CI 1.37 to 2.50, P < 0.001) into one indistinguishable from random chance (HR 1.15, 95% CI 0.86 to 1.54, P = 0.348). Finally, we develop a new method, based on ensembles of analysis methodologies, to exploit this technical variability to improve biomarker robustness and to provide an independent confidence metric.ConclusionsBiomarkers comprise a fundamental component of personalized medicine. We first validated two NSCLC prognostic biomarkers in an independent patient cohort. Power analyses demonstrate that even this large, 442-patient cohort is under-powered for stage-specific analyses. We then use these results to discover an unexpected sensitivity of validation to subtle data analysis decisions. Finally, we develop a novel algorithmic approach to exploit this sensitivity to improve biomarker robustness

A supervised learning regression method for the analysis of oral sensitivity of healthy individuals and patients with chemosensory loss

The gustatory, olfactory, and trigeminal systems are anatomically separated. However, they interact cognitively to give rise to oral perception, which can significantly affect health and quality of life. We built a Supervised Learning (SL) regression model that, exploiting participants' features, was capable of automatically analyzing with high precision the self-ratings of oral sensitivity of healthy participants and patients with chemosensory loss, determining the contribution of its components: gustatory, olfactory, and trigeminal. CatBoost regressor provided predicted values of the self-rated oral sensitivity close to experimental values. Patients showed lower predicted values of oral sensitivity, lower scores for measured taste, spiciness, astringency, and smell sensitivity, higher BMI, and lower levels of well-being. CatBoost regressor defined the impact of the single components of oral perception in the two groups. The trigeminal component was the most significant, though astringency and spiciness provided similar contributions in controls, while astringency was most important in patients. Taste was more important in controls while smell was the least important in both groups. Identifying the significance of the oral perception components and the differences between the two groups provide important information to allow for more targeted examinations supporting both patients and healthcare professionals in clinical practice

Usefulness of a simple sleep-deprived EEG protocol for epilepsy diagnosis in de novo subjects.

OBJECTIVE: In case series concerning the role of EEG after sleep deprivation (SD-EEG) in epilepsy, patients' features and protocols vary dramatically from one report to another. In this study, we assessed the usefulness of a simple SD-EEG method in well characterized patients. METHODS: Among the 963 adult subjects submitted to SD-EEG at our Center, in the period 2003-2010, we retrospectively selected for analysis only those: (1) evaluated for suspected epileptic seizures; (2) with a normal/non-specific baseline EEG; (3) still drug-free at the time of SD-EEG; (4) with an MRI analysis; (5) with at least 1year follow-up. SD-EEG consisted in SD from 2:00 AM and laboratory EEG from 8:00 AM to 10:30 AM. We analyzed epileptic interictal abnormalities (IIAs) and their correlations with patients' features. RESULTS: Epilepsy was confirmed in 131 patients. SD-EEG showed IIAs in 41.2% of all patients with epilepsy, and a 91.1% specificity for epilepsy diagnosis; IIAs types observed during SD-EEG are different in generalized versus focal epilepsies; for focal epilepsies, the IIAs yield in SD-EEG is higher than in second routine EEG. CONCLUSIONS: This simple SD-EEG protocol is very useful in de novo patients with suspected seizures. SIGNIFICANCE: This study sheds new light on the role of SD-EEG in specific epilepsy populations