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Age of diagnosis does not alter the presentation or progression of robustly defined adult onset type 1 diabetes

This is the author accepted manuscript. The final version is available from the American Diabetes Association via the DOI in this record OBJECTIVE: To determine whether presentation, progression, and genetic susceptibility of robustly defined adult-onset type 1 diabetes (T1D) are altered by diagnosis age. RESEARCH DESIGN AND METHODS: We compared the relationship between diagnosis age and presentation, C-peptide loss (annual change in urine C-peptide-creatinine ratio [UCPCR]), and genetic susceptibility (T1D genetic risk score [GRS]) in adults with confirmed T1D in the prospective StartRight study, 1,798 adults with new-onset diabetes. T1D was defined in two ways: two or more positive islet autoantibodies (of GAD antibody, IA-2 antigen, and ZnT8 autoantibody) irrespective of clinical diagnosis (n = 385) or one positive islet autoantibody and a clinical diagnosis of T1D (n = 180). RESULTS: In continuous analysis, age of diagnosis was not associated with C-peptide loss for either definition of T1D (P > 0.1), with mean (95% CI) annual C-peptide loss in those diagnosed before and after 35 years of age (median age of T1D defined by two or more positive autoantibodies): 39 (31-46) vs. 44% (38-50) with two or more positive islet autoantibodies and 43 (33-51) vs. 39% (31-46) with clinician diagnosis confirmed by one positive islet autoantibody (P > 0.1). Baseline C-peptide and T1D GRS were unaffected by age of diagnosis or T1D definition (P > 0.1). In T1D defined by two or more autoantibodies, presentation severity was similar in those diagnosed before and after 35 years of age: unintentional weight loss, 80 (95% CI 74-85) vs. 82% (76-87); ketoacidosis, 24 (18-30) vs. 19% (14-25); and presentation glucose, 21 (19-22) vs. 21 mmol/L (20-22) (all P ≥ 0.1). Despite similar presentation, older adults were less likely to be diagnosed with T1D, insulin-treated, or admitted to hospital. CONCLUSIONS: When adult-onset T1D is robustly defined, the presentation characteristics, progression, and T1D genetic susceptibility are not altered by age of diagnosis.Diabetes UKNational Institute for Health Research (NIHR)European Foundation for the Study of Diabete

A genome-wide association study identifies protein quantitative trait loci (pQTLs)

There is considerable evidence that human genetic variation influences gene expression. Genome-wide studies have revealed that mRNA levels are associated with genetic variation in or close to the gene coding for those mRNA transcripts - cis effects, and elsewhere in the genome - trans effects. The role of genetic variation in determining protein levels has not been systematically assessed. Using a genome-wide association approach we show that common genetic variation influences levels of clinically relevant proteins in human serum and plasma. We evaluated the role of 496,032 polymorphisms on levels of 42 proteins measured in 1200 fasting individuals from the population based InCHIANTI study. Proteins included insulin, several interleukins, adipokines, chemokines, and liver function markers that are implicated in many common diseases including metabolic, inflammatory, and infectious conditions. We identified eight Cis effects, including variants in or near the IL6R (p = 1.8×10 -57), CCL4L1 (p = 3.9×10-21), IL18 (p = 6.8×10-13), LPA (p = 4.4×10-10), GGT1 (p = 1.5×10-7), SHBG (p = 3.1×10-7), CRP (p = 6.4×10-6) and IL1RN (p = 7.3×10-6) genes, all associated with their respective protein products with effect sizes ranging from 0.19 to 0.69 standard deviations per allele. Mechanisms implicated include altered rates of cleavage of bound to unbound soluble receptor (IL6R), altered secretion rates of different sized proteins (LPA), variation in gene copy number (CCL4L1) and altered transcription (GGT1). We identified one novel trans effect that was an association between ABO blood group and tumour necrosis factor alpha (TNF-alpha) levels (p = 6.8×10-40), but this finding was not present when TNF-alpha was measured using a different assay , or in a second study, suggesting an assay-specific association. Our results show that protein levels share some of the features of the genetics of gene expression. These include the presence of strong genetic effects in cis locations. The identification of protein quantitative trait loci (pQTLs) may be a powerful complementary method of improving our understanding of disease pathways. © 2008 Melzer et al

Optimal learning rules for familiarity detection

It has been suggested that the mammalian memory system has both familiarity and recollection components. Recently, a high-capacity network to store familiarity has been proposed. Here we derive analytically the optimal learning rule for such a familiarity memory using a signalto- noise ratio analysis. We find that in the limit of large networks the covariance rule, known to be the optimal local, linear learning rule for pattern association, is also the optimal learning rule for familiarity discrimination. The capacity is independent of the sparseness of the patterns, as long as the patterns have a fixed number of bits set. The corresponding information capacity is 0.057 bits per synapse, less than typically found for associative networks

The Quantum Internet

Quantum networks offer a unifying set of opportunities and challenges across exciting intellectual and technical frontiers, including for quantum computation, communication, and metrology. The realization of quantum networks composed of many nodes and channels requires new scientific capabilities for the generation and characterization of quantum coherence and entanglement. Fundamental to this endeavor are quantum interconnects that convert quantum states from one physical system to those of another in a reversible fashion. Such quantum connectivity for networks can be achieved by optical interactions of single photons and atoms, thereby enabling entanglement distribution and quantum teleportation between nodes.Comment: 15 pages, 6 figures Higher resolution versions of the figures can be downloaded from the following link: http://www.its.caltech.edu/~hjkimble/QNet-figures-high-resolutio

The Relative Influence of Competition and Prey Defenses on the Phenotypic Structure of Insectivorous Bat Ensembles in Southern Africa

Deterministic filters such as competition and prey defences should have a strong influence on the community structure of animals such as insectivorous bats that have life histories characterized by low fecundity, low predation risk, long life expectancy, and stable populations. We investigated the relative influence of these two deterministic filters on the phenotypic structure of insectivorous bat ensembles in southern Africa. We used null models to simulate the random phenotypic patterns expected in the absence of competition or prey defences and analysed the deviations of the observed phenotypic pattern from these expected random patterns. The phenotypic structure at local scales exhibited non-random patterns consistent with both competition and prey defense hypotheses. There was evidence that competition influenced body size distribution across ensembles. Competition also influenced wing and echolocation patterns in ensembles and in functional foraging groups with high species richness or abundance. At the same time, prey defense filters influenced echolocation patterns in two species-poor ensembles. Non-random patterns remained evident even after we removed the influence of body size from wing morphology and echolocation parameters taking phylogeny into account. However, abiotic filters such as geographic distribution ranges of small and large-bodied species, extinction risk, and the physics of flight and sound probably also interacted with biotic filters at local and/or regional scales to influence the community structure of sympatric bats in southern Africa. Future studies should investigate alternative parameters that define bat community structure such as diet and abundance to better determine the influence of competition and prey defences on the structure of insectivorous bat ensembles in southern Africa

The Influence of Markov Decision Process Structure on the Possible Strategic Use of Working Memory and Episodic Memory

Researchers use a variety of behavioral tasks to analyze the effect of biological manipulations on memory function. This research will benefit from a systematic mathematical method for analyzing memory demands in behavioral tasks. In the framework of reinforcement learning theory, these tasks can be mathematically described as partially-observable Markov decision processes. While a wealth of evidence collected over the past 15 years relates the basal ganglia to the reinforcement learning framework, only recently has much attention been paid to including psychological concepts such as working memory or episodic memory in these models. This paper presents an analysis that provides a quantitative description of memory states sufficient for correct choices at specific decision points. Using information from the mathematical structure of the task descriptions, we derive measures that indicate whether working memory (for one or more cues) or episodic memory can provide strategically useful information to an agent. In particular, the analysis determines which observed states must be maintained in or retrieved from memory to perform these specific tasks. We demonstrate the analysis on three simplified tasks as well as eight more complex memory tasks drawn from the animal and human literature (two alternation tasks, two sequence disambiguation tasks, two non-matching tasks, the 2-back task, and the 1-2-AX task). The results of these analyses agree with results from quantitative simulations of the task reported in previous publications and provide simple indications of the memory demands of the tasks which can require far less computation than a full simulation of the task. This may provide a basis for a quantitative behavioral stoichiometry of memory tasks

Protective effect of geranylgeranylacetone, an inducer of heat shock protein 70, against drug-induced lung injury/fibrosis in an animal model

<p>Abstract</p> <p>Background</p> <p>To determine whether oral administration of geranylgeranylacetone (GGA), a nontoxic anti-ulcer drug that is an inducer of heat shock protein (HSP) 70, protects against drug-induced lung injury/fibrosis <it>in vivo</it>.</p> <p>Methods</p> <p>We used a bleomycin (BLM)-induced lung fibrosis model in which mice were treated with oral 600 mg/kg of GGA before and after BLM administration. Inflammation and fibrosis were evaluated by histological scoring, hydroxyproline content in the lung and inflammatory cell count, and quantification by ELISA of macrophage inflammatory protein-2 (MIP-2) in bronchoalveolar lavage fluid. Apoptosis was evaluated by the TUNEL method. The induction of HSP70 in the lung was examined with western blot analysis and its localization was determined by immunohistochemistry.</p> <p>Results</p> <p>We confirmed the presence of inflammation and fibrosis in the BLM-induced lung injury model and induction of HSP70 by oral administration of GGA. GGA prevented apoptosis of cellular constituents of lung tissue, such as epithelial cells, most likely related to the <it>de novo </it>induction of HSP70 in the lungs. GGA-treated mice also showed less fibrosis of the lungs, associated with the findings of suppression of both production of MIP-2 and inflammatory cell accumulation in the injured lung, compared with vehicle-treated mice.</p> <p>Conclusion</p> <p>GGA had a protective effect on drug-induced lung injury/fibrosis. Disease-modifying antirheumatic drugs such as methotrexate, which are indispensable for the treatment of rheumatoid arthritis, often cause interstitial lung diseases, an adverse event that currently cannot be prevented. Clinical use of GGA for drug-induced pulmonary fibrosis might be considered in the future.</p

Global circulation patterns of seasonal influenza viruses vary with antigenic drift.

Understanding the spatiotemporal patterns of emergence and circulation of new human seasonal influenza virus variants is a key scientific and public health challenge. The global circulation patterns of influenza A/H3N2 viruses are well characterized, but the patterns of A/H1N1 and B viruses have remained largely unexplored. Here we show that the global circulation patterns of A/H1N1 (up to 2009), B/Victoria, and B/Yamagata viruses differ substantially from those of A/H3N2 viruses, on the basis of analyses of 9,604 haemagglutinin sequences of human seasonal influenza viruses from 2000 to 2012. Whereas genetic variants of A/H3N2 viruses did not persist locally between epidemics and were reseeded from East and Southeast Asia, genetic variants of A/H1N1 and B viruses persisted across several seasons and exhibited complex global dynamics with East and Southeast Asia playing a limited role in disseminating new variants. The less frequent global movement of influenza A/H1N1 and B viruses coincided with slower rates of antigenic evolution, lower ages of infection, and smaller, less frequent epidemics compared to A/H3N2 viruses. Detailed epidemic models support differences in age of infection, combined with the less frequent travel of children, as probable drivers of the differences in the patterns of global circulation, suggesting a complex interaction between virus evolution, epidemiology, and human behaviour.T.B. was supported by a Newton International Fellowship from the Royal Society and through NIH U54 GM111274. S.R. was supported by MRC (UK, Project MR/J008761/1), Wellcome Trust (UK, Project 093488/Z/10/Z), Fogarty International Centre (USA, R01 TW008246‐01), DHS (USA, RAPIDD program), NIGMS (USA, MIDAS U01 GM110721‐01) and NIHR (UK, Health Protection Research Unit funding). The Melbourne WHO Collaborating Centre for Reference and Research on Influenza was supported by the Australian Government Department of Health and thanks N. Komadina and Y.‐M. Deng. The Atlanta WHO Collaborating Center for Surveillance, Epidemiology and Control of Influenza was supported by the U.S. Department of 13 Health and Human Services. NIV thanks A.C. Mishra, M. Chawla‐Sarkar, A.M. Abraham, D. Biswas, S. Shrikhande, AnuKumar B, and A. Jain. Influenza surveillance in India was expanded, in part, through US Cooperative Agreements (5U50C1024407 and U51IP000333) and by the Indian Council of Medical Research. M.A.S. was supported through NSF DMS 1264153 and NIH R01 AI 107034. Work of the WHO Collaborating Centre for Reference and Research on Influenza at the MRC National Institute for Medical Research was supported by U117512723. P.L., A.R. & M.A.S were supported by EU Seventh Framework Programme [FP7/2007‐2013] under Grant Agreement no. 278433-­‐PREDEMICS and ERC Grant agreement no. 260864. C.A.R. was supported by a University Research Fellowship from the Royal Society.This is the author accepted manuscript. It is currently under infinite embargo pending publication of the final version

Temporal-Difference Reinforcement Learning with Distributed Representations

Temporal-difference (TD) algorithms have been proposed as models of reinforcement learning (RL). We examine two issues of distributed representation in these TD algorithms: distributed representations of belief and distributed discounting factors. Distributed representation of belief allows the believed state of the world to distribute across sets of equivalent states. Distributed exponential discounting factors produce hyperbolic discounting in the behavior of the agent itself. We examine these issues in the context of a TD RL model in which state-belief is distributed over a set of exponentially-discounting “micro-Agents”, each of which has a separate discounting factor (γ). Each µAgent maintains an independent hypothesis about the state of the world, and a separate value-estimate of taking actions within that hypothesized state. The overall agent thus instantiates a flexible representation of an evolving world-state. As with other TD models, the value-error (δ) signal within the model matches dopamine signals recorded from animals in standard conditioning reward-paradigms. The distributed representation of belief provides an explanation for the decrease in dopamine at the conditioned stimulus seen in overtrained animals, for the differences between trace and delay conditioning, and for transient bursts of dopamine seen at movement initiation. Because each µAgent also includes its own exponential discounting factor, the overall agent shows hyperbolic discounting, consistent with behavioral experiments