Serum Neurofilament Light Protein as a Marker for Diffuse Axonal Injury: Results from a Case Series Study

Diffuse axonal injury (DAI) is an important cause of morbidity in patients with traumatic brain injury (TBI). There is currently no simple and reliable technique for early identification of patients with DAI, or to prognosticate long-term outcome in this patient group. In the present study, we examined acute serum concentrations of neurofilament light (NFL) in nine patients with severe TBI and DAI using a novel ultrasensitive single molecule array (Simoa) assay. The relationships between the NFL concentrations and MRI in the acute stage as well as clinical outcome and magnetic resonance diffusion tensor imaging (MR-DTI) parameters at 12 months were analyzed. We found that the mean NFL concentrations among the patients displayed a 30-fold increase compared with controls, and that NFL completely discriminated between the patients and controls. We also found a relationship between serum NFL and MR-DTI parameters, with higher NFL concentrations in patients with higher trace (R2 = 0.79) and lower fractional anisotropy (FA) (R 2 = 0.83). These results suggest that serum NFL may be a valuable blood biomarker for TBI, reflecting the severity of DAI

Survival and Recruitment of Rehabilitated Caspian Terns in Southern California

Thousands of birds are taken in by animal care centers each year for rehabilitation. Birds returned to health by some centers are banded for later identification, but very few are ever reencountered following their return to the wild. We report here, information on the post-release survival of Caspian Terns (Hydroprogne caspia) in southern California as well as on their recruitment into a local breeding population and colony site fidelity

Impact of functional group types in ion exchange resins on rare earth element recovery from treated acid mine waters

Ion-exchange (IX) resins incorporating single functional groups (sulfonic or amino-phosphonic) and two functional groups (sulfonic and phosphonic) were evaluated for selective recovery of Rare Earth Elements (REEs) from acidic mine waters (AMW). The composition of AMW solution, complexing properties of the functional group, and acidity were investigated as key parameters for concentration and separation of REEs from transition elements (TEs). Fe has to be removed from AMW to enable REE recovery and here the AMW was treated with NaOH solutions to reach pH 3.9 where Fe(III) was selectively removed (≤99%) by precipitation of schwertmannite. Single functional IX resin containing a sulfonic group displayed a higher REE recovery efficiency and separation ratio than observed for the bi-functional resin (sulfonic/phosphonic). Concentration factors for REEs between 30 and 40 were achieved using regeneration cycles with H2SO4. The performance of the aminophosphonic resin showed lower separation factors for REEs from TEs than the two resins containing sulfonic groups. IX resins performance was improved by tuning the acidity to match the functional group reactivity, where pH adjustment to the range of 0.5-2.0 provided the highest REE/TE separation factor for the single sulfonic resin followed by the bifunctional resin. The integration of an elution cycle using Na2-EDTA/NH4Cl mixtures strongly increases the concentration factors of REE and Light REE (LREE) concentration factors of up to 260 were achieved for the single functional sulfonic resin

Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization

To date, no "information-theoretic" frameworks for reasoning about generalization error have been shown to establish minimax rates for gradient descent in the setting of stochastic convex optimization. In this work, we consider the prospect of establishing such rates via several existing information-theoretic frameworks: input-output mutual information bounds, conditional mutual information bounds and variants, PAC-Bayes bounds, and recent conditional variants thereof. We prove that none of these bounds are able to establish minimax rates. We then consider a common tactic employed in studying gradient methods, whereby the final iterate is corrupted by Gaussian noise, producing a noisy "surrogate" algorithm. We prove that minimax rates cannot be established via the analysis of such surrogates. Our results suggest that new ideas are required to analyze gradient descent using information-theoretic techniques.Comment: 49 pages, 2 figures. To appear, Proc. International Conference on Algorithmic Learning Theory (ALT), 202