Feasibility and Safety of Multicenter Tissue and Biofluid Sampling for α-Synuclein in Parkinson's Disease: The Systemic Synuclein Sampling Study (S4)

BACKGROUND: α-synuclein is a lead Parkinson's disease (PD) biomarker. There are conflicting reports regarding accuracy of α-synuclein in different tissues and biofluids as a PD biomarker, and the within-subject anatomical distribution of α-synuclein is not well described. The Systemic Synuclein Sampling Study (S4) aims to address these gaps in knowledge. The S4 is a multicenter, cross-sectional, observational study evaluating α-synuclein in multiple tissues and biofluids in PD and healthy controls (HC). OBJECTIVE: To describe the baseline characteristics of the S4 cohort and safety and feasibility of this study. METHODS: Participants underwent motor and non-motor clinical assessments, dopamine transporter SPECT, biofluid collection (cerebrospinal fluid, saliva, and blood), and tissue biopsies (skin, sigmoid colon, and submandibular gland). Biopsy adequacy was determined based on presence of adequate target tissue. Tissue sections were stained with the 5C12 monoclonal antibody against unmodified α-synuclein. All specimens were acquired and processed in a standardized manner. Adverse events were systematically recorded. RESULTS: The final cohort consists of 82 participants (61 PD, 21 HC). In 68 subjects (83%), all types of specimens were obtained but only 50 (61%) of subjects had all specimens both collected and evaluable for α-synuclein. Mild adverse events were common, especially for submandibular gland biopsy, but only 1 severe adverse event occurred. CONCLUSION: Multicenter tissue and biofluid sampling for α-synuclein is feasible and generally safe. S4 will inform understanding of the concurrent distribution of α-synuclein pathology and biomarkers in biofluids and peripheral nervous system in PD

The impact of XENON100 and the LHC on Supersymmetric Dark Matter

The effect of 2010 and 2011 LHC data are discussed in connection to the potential for the direct detection of supersymmetric dark matter. The impact of the recent XENON100 results are contrasted to these predictions.Comment: 14 pages, 23 figures, To be published in the Proceedings of the 7th DSU Conference, Beijing Chin

Analysis of Endocrine Disruption in Southern California Coastal Fish Using an Aquatic Multispecies Microarray

BackgroundEndocrine disruptors include plasticizers, pesticides, detergents, and pharmaceuticals. Turbot and other flatfish are used to characterize the presence of chemicals in the marine environment. Unfortunately, there are relatively few genes of turbot and other flatfish in GenBank, which limits the use of molecular tools such as microarrays and quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR) to study disruption of endocrine responses in sentinel fish captured by regulatory agencies.ObjectivesWe fabricated a multigene cross-species microarray as a diagnostic tool to screen the effects of environmental chemicals in fish, for which there is minimal genomic information. The array included genes that are involved in the actions of adrenal and sex steroids, thyroid hormone, and xenobiotic responses. This microarray will provide a sensitive tool for screening for the presence of chemicals with adverse effects on endocrine responses in coastal fish species.MethodsWe used a custom multispecies microarray to study gene expression in wild hornyhead turbot (Pleuronichthys verticalis) collected from polluted and clean coastal waters and in laboratory male zebrafish (Danio rerio) after exposure to estradiol and 4-nonylphenol. We measured gene-specific expression in turbot liver by qRT-PCR and correlated it to microarray data.ResultsMicroarray and qRT-PCR analyses of livers from turbot collected from polluted areas revealed altered gene expression profiles compared with those from nonaffected areas.ConclusionsThe agreement between the array data and qRT-PCR analyses validates this multispecies microarray. The microarray measurement of gene expression in zebrafish, which are phylogenetically distant from turbot, indicates that this multispecies microarray will be useful for measuring endocrine responses in other fish

Identifiability, Improper Priors and Gibbs Sampling for Generalized Linear Models

Markov chain Monte Carlo algorithms are widely used in the fitting of generalized linear models (GLM). Such model fitting is somewhat of an art form requiring suitable trickery and tuning to obtain results one can have confidence in. A wide range of practical issues arise. The focus here is on parameter identifiability and posterior propriety. In particular, we clarify that non-identifiability arises for usual GLM's and discuss its implications for simulation based model fitting. Since often, some part of the prior specification is vague we consider whether the resulting posterior is proper, providing rather general and easy to check results for GLM's. We also show that if a Gibbs sampler is run with an improper posterior, it may be possible to use the output to obtain meaningful inference for certain model unknowns. Key words and phrases: Convergence; Embedded Posterior; Estimability; Integrability; Non-full rank models. 1 Introduction Currently, simulation-based methods offer the be..