Site-Specific Fluorescence Polarization for Studying the Disaggregation of α-Synuclein Fibrils by Small Molecules

Fibrillar aggregates of the protein α-synuclein (αS) are one of the hallmarks of Parkinson’s disease. Here, we show that measuring the fluorescence polarization (FP) of labels at several sites on αS allows one to monitor changes in the local dynamics of the protein after binding to micelles or vesicles, and during fibril formation. Most significantly, these site-specific FP measurements provide insight into structural remodeling of αS fibrils by small molecules and have the potential for use in moderate-throughput screens to identify small molecules that could be used to treat Parkinson’s disease. © 2016 American Chemical Society

Levels and equivalence in credit and qualifications frameworks: Contrasting the prescribed and enacted curriculum in school and college

Drawing on data from an empirical study of three matched subjects in upper secondary school and further education college in Scotland, this article explores some of the factors that result in differences emerging from the translation of the prescribed curriculum into the enacted curriculum. We argue that these differences raise important questions about equivalences which are being promoted through the development of credit and qualifications frameworks. The article suggests that the standardisation associated with the development of a rational credit and qualifications framework and an outcomes-based prescribed curriculum cannot be achieved precisely because of the multiplicity that emerges from the practices of translation

Technetium chemistry, oxidation states and species

Pertechnetic and perrhenic acids behave as very strong acids, Ka ~ 108. Their extensive dehydration to M2O7 in such media as 7 M sulphuric acid complicates a spectrophotometric comparison of acid strengths. Chloroform extractable TcO3Cl forms from pertechnetate in the presence of chloride ion and concentrated sulphuric acid. The (VII) state of this compound is confirmed and its spectrum described. No evidence of unusual technetium (VII) species, in aqueous media of 1 N base to 1 N acid, has been found. The red colour of concentrated aqueous HTcO4 is ascribed to a lower (VI) or (V) state. The existence in alkaline media of a technetate, TcO42-, species has been re-examined. Some (IV) and (III) state species are partially characterized, but no Tc2O3 could be isolated.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/33360/1/0000758.pd

Risk Factors for Nipah Virus Encephalitis in Bangladesh1

Patients in Goalando were likely infected by direct contact with fruit bats or their secretions, rather than through contact with an intermediate host

Neuroimaging-based classification of PTSD using data-driven computational approaches: a multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for D-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.Stress-related psychiatric disorders across the life spa