The Emerging Scholarly Brain

It is now a commonplace observation that human society is becoming a coherent super-organism, and that the information infrastructure forms its emerging brain. Perhaps, as the underlying technologies are likely to become billions of times more powerful than those we have today, we could say that we are now building the lizard brain for the future organism.Comment: to appear in Future Professional Communication in Astronomy-II (FPCA-II) editors A. Heck and A. Accomazz

Whole Earth Telescope discovery of a strongly distorted quadrupole pulsation in the largest amplitude rapidly oscillating Ap star

We present a new analysis of the rapidly oscillating Ap (roAp) star, 2MASS J19400781 − 4420093 (J1940; V = 13.1). The star was discovered using SuperWASP broadband photometry to have a frequency of 176.39 d−1 (2041.55 µHz; P = 8.2 min; Holdsworth et al. 2014a) and is shown here to have a peak-to-peak amplitude of 34 mmag. J1940 has been observed during three seasons at the South African Astronomical Ob- servatory, and has been the target of a Whole Earth Telescope campaign. The observations reveal that J1940 pulsates in a distorted quadrupole mode with unusual pulsational phase variations. A higher signal-to-noise ratio spectrum has been obtained since J1940’s first announcement, which allows us to classify the star as A7 Vp Eu(Cr). The observing campaigns presented here reveal no pulsations other than the initially detected frequency. We model the pulsation in J1940 and conclude that the pulsation is distorted by a magnetic field of strength 1.5 kG. A difference in the times of rotational maximum light and pulsation maximum suggests a significant offset between the spots and pulsation axis, as can be seen in roAp stars

Clustering and Alignment of Polymorphic Sequences for HLA-DRB1 Genotyping

Located on Chromosome 6p21, classical human leukocyte antigen genes are highly polymorphic. HLA alleles associate with a variety of phenotypes, such as narcolepsy, autoimmunity, as well as immunologic response to infectious disease. Moreover, high resolution genotyping of these loci is critical to achieving long-term survival of allogeneic transplants. Development of methods to obtain high resolution analysis of HLA genotypes will lead to improved understanding of how select alleles contribute to human health and disease risk. Genomic DNAs were obtained from a cohort of n = 383 subjects recruited as part of an Ulcerative Colitis study and analyzed for HLA-DRB1. HLA genotypes were determined using sequence specific oligonucleotide probes and by next-generation sequencing using the Roche/454 GSFLX instrument. The Clustering and Alignment of Polymorphic Sequences (CAPSeq) software application was developed to analyze next-generation sequencing data. The application generates HLA sequence specific 6-digit genotype information from next-generation sequencing data using MUMmer to align sequences and the R package diffusionMap to classify sequences into their respective allelic groups. The incorporation of Bootstrap Aggregating, Bagging to aid in sorting of sequences into allele classes resulted in improved genotyping accuracy. Using Bagging iterations equal to 60, the genotyping results obtained using CAPSeq when compared with sequence specific oligonucleotide probe characterized 4-digit genotypes exhibited high rates of concordance, matching at 759 out of 766 (99.1%) alleles. © 2013 Ringquist et al

Global parameter identification of stochastic reaction networks from single trajectories

We consider the problem of inferring the unknown parameters of a stochastic biochemical network model from a single measured time-course of the concentration of some of the involved species. Such measurements are available, e.g., from live-cell fluorescence microscopy in image-based systems biology. In addition, fluctuation time-courses from, e.g., fluorescence correlation spectroscopy provide additional information about the system dynamics that can be used to more robustly infer parameters than when considering only mean concentrations. Estimating model parameters from a single experimental trajectory enables single-cell measurements and quantification of cell--cell variability. We propose a novel combination of an adaptive Monte Carlo sampler, called Gaussian Adaptation, and efficient exact stochastic simulation algorithms that allows parameter identification from single stochastic trajectories. We benchmark the proposed method on a linear and a non-linear reaction network at steady state and during transient phases. In addition, we demonstrate that the present method also provides an ellipsoidal volume estimate of the viable part of parameter space and is able to estimate the physical volume of the compartment in which the observed reactions take place.Comment: Article in print as a book chapter in Springer's "Advances in Systems Biology

Finite-size and correlation-induced effects in Mean-field Dynamics

The brain's activity is characterized by the interaction of a very large number of neurons that are strongly affected by noise. However, signals often arise at macroscopic scales integrating the effect of many neurons into a reliable pattern of activity. In order to study such large neuronal assemblies, one is often led to derive mean-field limits summarizing the effect of the interaction of a large number of neurons into an effective signal. Classical mean-field approaches consider the evolution of a deterministic variable, the mean activity, thus neglecting the stochastic nature of neural behavior. In this article, we build upon two recent approaches that include correlations and higher order moments in mean-field equations, and study how these stochastic effects influence the solutions of the mean-field equations, both in the limit of an infinite number of neurons and for large yet finite networks. We introduce a new model, the infinite model, which arises from both equations by a rescaling of the variables and, which is invertible for finite-size networks, and hence, provides equivalent equations to those previously derived models. The study of this model allows us to understand qualitative behavior of such large-scale networks. We show that, though the solutions of the deterministic mean-field equation constitute uncorrelated solutions of the new mean-field equations, the stability properties of limit cycles are modified by the presence of correlations, and additional non-trivial behaviors including periodic orbits appear when there were none in the mean field. The origin of all these behaviors is then explored in finite-size networks where interesting mesoscopic scale effects appear. This study leads us to show that the infinite-size system appears as a singular limit of the network equations, and for any finite network, the system will differ from the infinite system

Evaluation of the zucker diabetic fatty (ZDF) rat as a model for human disease based on urinary peptidomic profiles

Representative animal models for diabetes-associated vascular complications are extremely relevant in assessing potential therapeutic drugs. While several rodent models for type 2 diabetes (T2D) are available, their relevance in recapitulating renal and cardiovascular features of diabetes in man is not entirely clear. Here we evaluate at the molecular level the similarity between Zucker diabetic fatty (ZDF) rats, as a model of T2D-associated vascular complications, and human disease by urinary proteome analysis. Urine analysis of ZDF rats at early and late stages of disease compared to age- matched LEAN rats identified 180 peptides as potentially associated with diabetes complications. Overlaps with human chronic kidney disease (CKD) and cardiovascular disease (CVD) biomarkers were observed, corresponding to proteins marking kidney damage (eg albumin, alpha-1 antitrypsin) or related to disease development (collagen). Concordance in regulation of these peptides in rats versus humans was more pronounced in the CVD compared to the CKD panels. In addition, disease-associated predicted protease activities in ZDF rats showed higher similarities to the predicted activities in human CVD. Based on urinary peptidomic analysis, the ZDF rat model displays similarity to human CVD but might not be the most appropriate model to display human CKD on a molecular level

Group interventions to improve health outcomes : a framework for their design and delivery

Peer reviewedPublisher PD

26Postoperative diagnosis and outcome in patients with revision arthroplasty for aseptic loosening

BACKGROUND: The most common cause of implant failure is aseptic loosening (AL), followed by prosthetic joint infection (PJI). This study evaluates the incidence of PJI among patients operated with suspected AL and whether the diagnosis of PJI was predictive of subsequent implant failure including re-infection, at 2 years of follow up. METHODS: Patients undergoing revision hip or knee arthroplasty due to presumed AL from February 2009 to September 2011 were prospectively evaluated. A sonication fluid of prosthesis and tissue samples for microbiology and histopathology at the time of the surgery were collected. Implant failure include recurrent or persistent infection, reoperation for any reason or need for chronic antibiotic suppression. RESULTS: Of 198 patients with pre-and intraoperative diagnosis of AL, 24 (12.1 %) had postoperative diagnosis of PJI. After a follow up of 31 months (IQR: 21 to 38 months), 9 (37.5 %) of 24 patients in the PJI group had implant failure compared to only 1 (1.1 %) in the 198 of AL group (p 20 CFU) and peri-prosthetic tissue culture were 87.5 % vs 66.7 %, respectively. Specificities were 100 % for both techniques (95 % CI, 97.9-100 %). A greater number of patients with PJI (79.1 %) had previous partial arthroplasty revisions than those patients in the AL group (56.9 %) (p = 0.04). In addition, 5 (55.5 %) patients with PJI and implant failure had more revision arthroplasties during the first year after the last implant placement than those patients with PJI without implant failure (1 patient; 6.7 %) (RR 3.8; 95 % CI 1.4-10.1; p = 0.015). On the other hand, 6 (25 %) patients finally diagnosed of PJI were initially diagnosed of AL in the first year after primary arthroplasty, whereas it was only 16 (9.2 %) patients in the group of true AL (RR 2.7; 95 % CI 1.2-6.1; p = 0.03). CONCLUSIONS: More than one tenth of patients with suspected AL are misdiagnosed PJI. Positive histology and positive peri-implant tissue and sonicate fluid cultures are highly predictive of implant failure in patients with PJI. Patients with greater number of partial hip revisions for a presumed AL had more risk of PJI. Early loosening is more often caused by hidden PJI than late loosening

International Evidence Based Reappraisal of Genes Associated With Arrhythmogenic Right Ventricular Cardiomyopathy Using the Clinical Genome Resource Framework

Background - Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited disease characterized by ventricular arrhythmias and progressive ventricular dysfunction. Genetic testing is recommended and a pathogenic variant in an ARVC-associated gene is a major criterion for diagnosis according to the 2010 Task Force Criteria (TFC). As incorrect attribution of a gene to ARVC can contribute to misdiagnosis, we assembled an international multidisciplinary ARVC ClinGen Gene Curation Expert Panel to reappraise all reported ARVC genes. / Methods - Following a comprehensive literature search, six two-member teams conducted blinded independent curation of reported ARVC genes using the semi-quantitative ClinGen framework. /Results - Of 26 reported ARVC genes, only six (PKP2, DSP, DSG2, DSC2, JUP, TMEM43) had strong evidence and were classified as definitive for ARVC causation. There was moderate evidence for two genes, DES and PLN. The remaining 18 genes had limited or no evidence. RYR2 was refuted as an ARVC gene since clinical data and model systems exhibited a catecholaminergic polymorphic ventricular tachycardia (CPVT) phenotype. In ClinVar, only 5 pathogenic / likely pathogenic (P/LP) variants (1.1%) in limited evidence genes had been reported in ARVC cases in contrast to 450 desmosome gene variants (97.4%). / Conclusions - Using the ClinGen approach to gene-disease curation, only eight genes, (PKP2, DSP, DSG2, DSC2, JUP, TMEM43, PLN, DES) had definitive or moderate evidence for ARVC and these genes accounted for nearly all P/LP ARVC variants in ClinVar. Therefore, only P/LP variants in these eight genes should yield a major criterion for ARVC diagnosis. P/LP variants identified in other genes in a patient should prompt further phenotyping as variants in many of these genes are associated with other cardiovascular conditions

The Promise and Challenge of Therapeutic MicroRNA Silencing in Diabetes and Metabolic Diseases

MicroRNAs (miRNAs) are small, non-coding, RNA molecules that regulate gene expression. They have a long evolutionary history and are found in plants, viruses, and animals. Although initially discovered in 1993 in Caenorhabditis elegans, they were not appreciated as widespread and abundant gene regulators until the early 2000s. Studies in the last decade have found that miRNAs confer phenotypic robustness in the face of environmental perturbation, may serve as diagnostic and prognostic indicators of disease, underlie the pathobiology of a wide array of complex disorders, and represent compelling therapeutic targets. Pre-clinical studies in animal models have demonstrated that pharmacologic manipulation of miRNAs, mostly in the liver, can modulate metabolic phenotypes and even reverse the course of insulin resistance and diabetes. There is cautious optimism in the field about miRNA-based therapies for diabetes, several of which are already in various stages of clinical trials. This review will highlight both the promise and the most pressing challenges of therapeutic miRNA silencing in diabetes and related conditions