Associations between diffusion MRI microstructure and cerebrospinal fluid markers of Alzheimer's disease pathology and neurodegeneration along the Alzheimer's disease continuum

INTRODUCTION: White matter (WM) degeneration is a critical component of early Alzheimer's disease (AD) pathophysiology. Diffusion-weighted imaging (DWI) models, including diffusion tensor imaging (DTI), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator MRI (MAP-MRI), have the potential to identify early neurodegenerative WM changes associated with AD. METHODS: We imaged 213 (198 cognitively unimpaired) aging adults with DWI and used tract-based spatial statistics to compare 15 DWI metrics of WM microstructure to 9 cerebrospinal fluid (CSF) markers of AD pathology and neurodegeneration treated as continuous variables. RESULTS: We found widespread WM injury in AD, as indexed by robust associations between DWI metrics and CSF biomarkers. MAP-MRI had more spatially diffuse relationships with Aβ42/40 and pTau, compared with NODDI and DTI. DISCUSSION: Our results suggest that WM degeneration may be more pervasive in AD than is commonly appreciated and that innovative DWI models such as MAP-MRI may provide clinically viable biomarkers of AD-related neurodegeneration in the earliest stages of AD progression

From Big Data to Precision Medicine.

For over a decade the term "Big data" has been used to describe the rapid increase in volume, variety and velocity of information available, not just in medical research but in almost every aspect of our lives. As scientists, we now have the capacity to rapidly generate, store and analyse data that, only a few years ago, would have taken many years to compile. However, "Big data" no longer means what it once did. The term has expanded and now refers not to just large data volume, but to our increasing ability to analyse and interpret those data. Tautologies such as "data analytics" and "data science" have emerged to describe approaches to the volume of available information as it grows ever larger. New methods dedicated to improving data collection, storage, cleaning, processing and interpretation continue to be developed, although not always by, or for, medical researchers. Exploiting new tools to extract meaning from large volume information has the potential to drive real change in clinical practice, from personalized therapy and intelligent drug design to population screening and electronic health record mining. As ever, where new technology promises "Big Advances," significant challenges remain. Here we discuss both the opportunities and challenges posed to biomedical research by our increasing ability to tackle large datasets. Important challenges include the need for standardization of data content, format, and clinical definitions, a heightened need for collaborative networks with sharing of both data and expertise and, perhaps most importantly, a need to reconsider how and when analytic methodology is taught to medical researchers. We also set "Big data" analytics in context: recent advances may appear to promise a revolution, sweeping away conventional approaches to medical science. However, their real promise lies in their synergy with, not replacement of, classical hypothesis-driven methods. The generation of novel, data-driven hypotheses based on interpretable models will always require stringent validation and experimental testing. Thus, hypothesis-generating research founded on large datasets adds to, rather than replaces, traditional hypothesis driven science. Each can benefit from the other and it is through using both that we can improve clinical practice.Wellcome Trus

Strong Ultraviolet Pulse From a Newborn Type Ia Supernova

Type Ia supernovae are destructive explosions of carbon oxygen white dwarfs. Although they are used empirically to measure cosmological distances, the nature of their progenitors remains mysterious, One of the leading progenitor models, called the single degenerate channel, hypothesizes that a white dwarf accretes matter from a companion star and the resulting increase in its central pressure and temperature ignites thermonuclear explosion. Here we report observations of strong but declining ultraviolet emission from a Type Ia supernova within four days of its explosion. This emission is consistent with theoretical expectations of collision between material ejected by the supernova and a companion star, and therefore provides evidence that some Type Ia supernovae arise from the single degenerate channel.Comment: Accepted for publication on the 21 May 2015 issue of Natur

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

Correction to: Cluster identification, selection, and description in Cluster randomized crossover trials: the PREP-IT trials

An amendment to this paper has been published and can be accessed via the original article

Inhibition of Aβ42 Peptide Aggregation by a Binuclear Ruthenium(II)−Platinum(II) Complex: Potential for Multimetal Organometallics as Anti-amyloid Agents

Design of inhibitors for amyloid-β (Aβ) peptide aggregation has been widely investigated over the years toward developing viable therapeutic agents for Alzheimer’s disease (AD). The biggest challenge seems to be inhibiting Aβ aggregation at the early stages possibly at the monomeric level, because oligomers are known to be neurotoxic. In this regard, exploiting the metal-chelating property of Aβ to generate molecules that can overcome this impediment presents some promise. Recently, one such metal complex containing PtII ([Pt(BPS)Cl2]) was reported to effectively inhibit Aβ42 aggregation and toxicity (Barnham, et al. (2008) Proc. Natl. Acad. Sci. U.S.A.105, 6813). This complex was able bind to Aβ42 at the N-terminal part of the peptide and triggered a conformational change resulting in effective inhibition. In the current report, we have generated a mixed-binuclear metal complex containing PtII and RuII metal centers that inhibited Aβ42 aggregation at an early stage and seemed to have different modes of interaction than the previously reported PtII complex, suggesting an important role of the second metal center. This ‘proof-of-concept’ compound will help in developing more effective molecules against Aβ aggregation by modifying the two metal centers as well as their bridging ligands, which will open doors to new rationale for Aβ inhibition

The Long-term Illinois, Mississippi, Ohio, And Wabash Rivers Fish Population Monitoring Program 2013

This report presents a summary of those data collected during segment 25(2013-14) of the Long-term Illinois, Mississippi, Ohio, and Wabash Rivers Fish Population Monitoring Program(LTEF), an annual survey executed by members of the Illinois Natural History Survey with funds administered by the U.S. Fish and Wildlife Service and the Illinois Department of Natural Resources. Sampling for the LTEF program was conducted throughout the state’s largest rivers: six reaches of the Illinois River Waterway, six segments or pools of the Mississippi River, four segmentsor pools of the Ohio River,five segments of the Wabash River, and navigable portions of the Iroquois and Kankakee Rives. In all segments of the LTEF program, all fish species collected were accurately identified, tallied, measured, and weighed. The catchrates of sportfishspecies were calculated as the number of individuals collected per hour (CPUEN± standard error).Structural indices [Proportional Size Distribution (PSD) and Relative Weight (Wr)] were also calculated for species of interest to regional managers. Catch rates and species richness varied greatly among all sampling locationsand sampling periods. Emerald shiners and gizzard shad comprised the majority of the individuals caught, while silver carp and common carp accounted for the greatest proportion of the biomass collected in most sampling areas of the survey. The analysis of CPUEN and PSD trends in sportfish populations sampled by the program may indicate inter-annual recruitment patterns in sportfish populations around the state. Both shovelnose sturgeon and blue catfish were the two species most commonly encountered in the gill net surveys.Illinois Department of Natural Resources, Division of Fisheriesunpublishednot peer reviewe

Cord blood DNA methylation modifications in infants are associated with white matter microstructure in the context of prenatal maternal depression and anxiety

Abstract Maternal and environmental factors influence brain networks and architecture via both physiological pathways and epigenetic modifications. In particular, prenatal maternal depression and anxiety symptoms appear to impact infant white matter (WM) microstructure, leading us to investigate whether epigenetic modifications (i.e., DNA methylation) contribute to these WM differences. To determine if infants of women with depression and anxiety symptoms exhibit epigenetic modifications linked to neurodevelopmental changes, 52 umbilical cord bloods (CBs) were profiled. We observed 219 differentially methylated genomic positions (DMPs; FDR p  0.5), which were annotated to 98 and 81 genes, respectively. Together, these findings suggest that umbilical CB DNA methylation levels at birth are associated with 1-month WM microstructure