Mendelian randomization study of the association between telomere length and risk of cancer and non-neoplastic diseases

$\textbf{Importance}$ The causal direction and magnitude of the association between telomere length and incidence of cancer and non-neoplastic diseases is uncertain, due to the susceptibility of observational studies to confounding and reverse causation. $\textbf{Objective}$ To conduct a Mendelian randomization study, using germline genetic variants as instrumental variables, to appraise the causal relevance of telomere length for risk of cancer and non-neoplastic diseases. $\textbf{Data Sources}$ Genome-wide association studies (GWAS) published up to January 15 2015. $\textbf{Study Selection}$ GWAS of non-communicable diseases that assayed germline genetic variation and did not select cohort or control participants on the basis of pre-existing diseases. Of 163 GWAS of non-communicable diseases identified, summary data from 103 were available. $\textbf{Data Extraction}$ Summary association statistics for single nucleotide polymorphisms (SNPs) that are strongly associated with telomere length in the general population. $\textbf{Main Outcomes}$ Odds ratios (ORs) for disease per standard deviation (SD) higher telomere length due to germline genetic variation. $\textbf{Results}$ Summary data were available for 35 cancers and 48 non-neoplastic diseases, corresponding to 420,081 cases (median 2,526 per disease) and 1,093,105 controls (median 6,789 per disease). Increased telomere length due to germline genetic variation was generally associated with increased risk for site-specific cancers. The strongest associations were observed for (ORs per 1-SD change in genetically increased telomere length): glioma 5.27 (3.15-8.81), serous low-malignant-potential ovarian cancer 4.35 (2.39-7.94), lung adenocarcinoma 3.19 (2.40-4.22), neuroblastoma 2.98 (1.92-4.62) , bladder cancer 2.19 (1.32-3.66), melanoma 1.87 (1.55-2.26), testicular cancer 1.76 (1.02- 3.04), kidney cancer 1.55 (1.08-2.23) and endometrial cancer 1.31 (1.07-1.61). Associations were stronger for rarer cancers and at tissue sites with lower rates of stem cell division (P<0.05). There was generally little evidence of association between genetically increased telomere length and risk of psychiatric, autoimmune, inflammatory, diabetic and other non-neoplastic diseases, except for coronary heart disease (0.78 [0.67-0.90]), abdominal aortic aneurysm (0.63 [0.49-0.81]), celiac disease (0.42 [0.28-0.61]) and interstitial lung disease (0.09 [0.05- 0.15]). $\textbf{Conclusions}$ It is likely that longer telomeres increase risk for several cancers but reduce risk for some non-neoplastic diseases, including cardiovascular diseases.This work was supported by CRUK grant number C18281/A19169 (the Integrative Cancer Epidemiology Programme). Dr Haycock is supported by CRUK Population Research Postdoctoral Fellowship C52724/A20138. The MRC Integrative Epidemiology Unit is supported by grants MC_UU_12013/1 and MC_UU_12013/2. Dr Martin is supported by the National Institute for Health Research (NIHR), the Bristol Nutritional Biomedical Research Unit and the University of Bristol

Optimizing Echo State Networks for Static Pattern Recognition

Static pattern recognition requires a machine to classify an object on the basis of a combination of attributes and is typically performed using machine learning techniques such as support vector machines and multilayer perceptrons. Unusually, in this study, we applied a successful time-series processing neural network architecture, the echo state network (ESN), to a static pattern recognition task. The networks were presented with clamped input data patterns, but in this work, they were allowed to run until their output units delivered a stable set of output activations, in a similar fashion to previous work that focused on the behaviour of ESN reservoir units. Our aim was to see if the short-term memory developed by the reservoir and the clamped inputs could deliver improved overall classification accuracy. The study utilized a challenging, high dimensional, real-world plant species spectroradiometry classification dataset with the objective of accurately detecting one of the world’s top 100 invasive plant species. Surprisingly, the ESNs performed equally well with both unsettled and settled reservoirs. Delivering a classification accuracy of 96.60%, the clamped ESNs outperformed three widely used machine learning techniques, namely support vector machines, extreme learning machines and multilayer perceptrons. Contrary to past work, where inputs were clamped until reservoir stabilization, it was found that it was possible to obtain similar classification accuracy (96.49%) by clamping the input patterns for just two repeats. The chief contribution of this work is that a recurrent architecture can get good classification accuracy, even while the reservoir is still in an unstable state

Resolved-sideband Raman cooling to the ground state of an optical lattice

We trap neutral Cs atoms in a two-dimensional optical lattice and cool them close to the zero-point of motion by resolved-sideband Raman cooling. Sideband cooling occurs via transitions between the vibrational manifolds associated with a pair of magnetic sublevels and the required Raman coupling is provided by the lattice potential itself. We obtain mean vibrational excitations \bar{n}_x \approx \bar{n}_y \approx 0.01, corresponding to a population \sim 98% in the vibrational ground state. Atoms in the ground state of an optical lattice provide a new system in which to explore quantum state control and subrecoil laser coolingComment: PDF file, 13 pages including 3 figure

Fault Detection in Steel-Reinforced Concrete Using Echo State Networks

The cost of maintaining and repairing the world's ageing reinforced concrete infrastructure continues to increase, and is expected to cost the United States economy alone $58 billion by 2020. Consequently, the use of non-destructive testing technologies for the early identification of faults in roads and bridges is becoming increasingly important. One such technology is the Electromagnetic Anomaly Detection (EMAD) technique, which exploits non-destructive magnetic flux leakage to detect defects in steel reinforcing meshes embedded in concrete. Despite the increasing need for such techniques, the data analysis options currently in use are limited. This paper presents an application of Echo State Networks, a recurrent neural network from the field of reservoir computing that features a short-term memory, to data obtained using the EMAD technique. Having been trained to discern real defect signals from other anomalous magnetic features, the performance of the ESNs was then compared to that of an analytical data analysis technique that is currently used to process EMAD data. It was found that average ESN performance was comparable in terms of AUC, while the optimal threshold was more consistent, greatly aiding application in the `real-world'. A qualitative analysis of the output of both methods on an unseen testing dataset also demonstrated the superiority of ESNs for practical use as a real time tool for on-site inspections

Heterogeneous data fusion for the improved non-destructive detection of steel-reinforcement defects using echo state networks

The degradation of roads is an expensive problem: in the UK alone, £27 billion was spent on road repairs between 2013 and 2019. One potential cost-saver is the early, non-destructive detection of faults. There are many available techniques, each with its own benefits and drawbacks. This paper builds upon the successful processing of Magnetic Flux Leakage (MFL) data by Echo State Networks (ESNs) for damage diagnostics, by augmenting ESNs with the depth of concrete cover as part of a data fusion approach. This fusion-based ESN outperformed a number of non fusion ESN comparators and a previously used analytical technique. Additionally, the fusion ESN had an optimal threshold value whose standard deviation was three times smaller than that of the nearest alternative technique, potentially prompting a move towards automated defect detection in ‘real-world’ applications

Measuring the Quantum State of a Large Angular Momentum

We demonstrate a general method to measure the quantum state of an angular momentum of arbitrary magnitude. The (2F+1) x (2F+1) density matrix is completely determined from a set of Stern-Gerlach measurements with (4F+1) different orientations of the quantization axis. We implement the protocol for laser cooled Cesium atoms in the 6S_{1/2}(F=4) hyperfine ground state and apply it to a variety of test states prepared by optical pumping and Larmor precession. A comparison of input and measured states shows typical reconstruction fidelities of about 0.95.Comment: 4 pages, 6 figures, submitted to PR

Exploiting horizontal pleiotropy to search for causal pathways within a Mendelian randomization framework

In Mendelian randomization (MR) analysis, variants that exert horizontal pleiotropy are typically treated as a nuisance. However, they could be valuable in identifying alternative pathways to the traits under investigation. Here, we developed MR-TRYX, a framework that exploits horizontal pleiotropy to discover putative risk factors for disease. We begin by detecting outliers in a single exposure-outcome MR analysis, hypothesising they are due to horizontal pleiotropy. We search across hundreds of complete GWAS summary datasets to systematically identify other (candidate) traits that associate with the outliers. We developed a multi-trait pleiotropy model of the heterogeneity in the exposure-outcome analysis due to pathways through candidate traits. Through detailed investigation of several causal relationships, many pleiotropic pathways are uncovered with already established causal effects, validating the approach, but also alternative putative causal pathways. Adjustment for pleiotropic pathways reduces the heterogeneity across the analyses

Foraging time and temperature affected birth timing of Rhinolophus ferrumequinum and predicted year-to-year changes in a population in West Wales. U.K.

Movements of Rhinolophus ferrumequinum in and out of the nursery roost at Stackpole were monitored automatically from 1994 to 2018 with simultaneous measurements of roost and external air temperatures. Pups were counted manually in June–July and mean birth dates calculated. Maximum foraging times of the population between 16.00 h and 08.00 h and temperatures at midnight showed three types of activity. These types of activity explained why warmer springs were followed by earlier birth dates. When April was warmer the number of degree days, linked to the activity of night-flying insects, was higher so the maximum foraging times were longer. Hence, mean birth dates were earlier due to faster gestation. The indirect effect of degree days on the birth date, measured by the partial regression coefficient (ß = -0.321), was weaker than the direct effect (ß = - 0.628) and the mediating effect of maximum foraging time was significant (p < .001). During May-June and June-July bats foraged mainly from dusk to dawn so there was little variation in the maximum foraging times of the population, and it did not significantly mediate the effect of temperature on birth date. Birth dates were later when the external temperatures in June-July were higher (ß = 0.309), but the effect was small (R2 = 9.5%). Path analysis further revealed that longer maximum foraging times of the population in April predicted the year-to-year changes in the number of births and subsequently the number of adult females. Maximal foraging times of the population in April were a major influence on birth timing and ultimately determined whether the population grew or declined

Mesoscopic quantum coherence in an optical lattice

We observe the quantum coherent dynamics of atomic spinor wavepackets in the double well potentials of a far-off-resonance optical lattice. With appropriate initial conditions the system Rabi oscillates between the left and right localized states of the ground doublet, and at certain times the wavepacket corresponds to a coherent superposition of these mesoscopically distinguishable quantum states. The atom/optical double well potential is a flexible and powerful system for further study of mesoscopic quantum coherence, quantum control and the quantum/classical transition.Comment: 12 pages, 4 figures, submitted to Physical Review Letter

Human metabolism and elimination of the anthocyanin, cyanidin-3-glucoside: a 13C-tracer study

BACKGROUND: Evidence suggests that the consumption of anthocyanin-rich foods beneficially affects cardiovascular health; however, the absorption, distribution, metabolism, and elimination (ADME) of anthocyanin-rich foods are relatively unknown. OBJECTIVE: We investigated the ADME of a (13)C5-labeled anthocyanin in humans. DESIGN: Eight male participants consumed 500 mg isotopically labeled cyanidin-3-glucoside (6,8,10,3',5'-(13)C5-C3G). Biological samples were collected over 48 h, and (13)C and (13)C-labeled metabolite concentrations were measured by using isotope-ratio mass spectrometry and liquid chromatography-tandem mass spectrometry. RESULTS: The mean +/- SE percentage of (13)C recovered in urine, breath, and feces was 43.9 +/- 25.9% (range: 15.1-99.3% across participants). The relative bioavailability was 12.38 +/- 1.38% (5.37 +/- 0.67% excreted in urine and 6.91 +/- 1.59% in breath). Maximum rates of (13)C elimination were achieved 30 min after ingestion (32.53 +/- 14.24 mug(13)C/h), whereas (13)C-labeled metabolites peaked (maximum serum concentration: 5.97 +/- 2.14 mumol/L) at 10.25 +/- 4.14 h. The half-life for (13)C-labeled metabolites ranged between 12.44 +/- 4.22 and 51.62 +/- 22.55 h. (13)C elimination was greatest between 0 and 1 h for urine (90.30 +/- 15.28 mug/h), at 6 h for breath (132.87 +/- 32.23 mug/h), and between 6 and 24 h for feces (557.28 +/- 247.88 mug/h), whereas the highest concentrations of (13)C-labeled metabolites were identified in urine (10.77 +/- 4.52 mumol/L) and fecal samples (43.16 +/- 18.00 mumol/L) collected between 6 and 24 h. Metabolites were identified as degradation products, phenolic, hippuric, phenylacetic, and phenylpropenoic acids. CONCLUSION: Anthocyanins are more bioavailable than previously perceived, and their metabolites are present in the circulation fo