116 research outputs found
Inclusion of Host Quality Data Improves Predictions of Herbivore Phenology
Understanding the correspondence between ambient temperature and insect development is necessary to forecast insect phenology under novel environments. In the face of climate change, both conservation and pest control efforts require accurate phenological predictions. Here, we compare a suite of degree-day models to assess their ability to predict the phenology of a common, oligophagous butterfly, the silver-spotted skipper, Epargyreus clarus (Cramer) (Lepidoptera: Hesperiidae). To estimate model parameters, we used development time of eggs and larvae reared in the laboratory at six constant temperatures ranging from 8 to 38 °C and on two host plants of contrasting quality (kudzu and wisteria). We employed three approaches to determine the base temperature to calculate degree days: linear regression, modified reduced major axis regression, and application of a generic base temperature value of 10 °C, which is commonly used in the absence of laboratory data. To calculate the number of degree days required to complete a developmental stage, we used data from caterpillars feeding on high- and low-quality hosts, both in the field and in the laboratory. To test model accuracy, we predicted development time of seven generations of larvae reared in the field on the same host plants across 3 years (2014–2016). To compare performance among models, we regressed predicted vs. observed development time, and found that r2 values were significantly larger when accounting for host plant quality. The accuracy of development time predictions varied across the season, with estimates of the first two generations being more accurate than estimates of the third generation, when ambient temperatures dropped outside the range in which development rate and temperature have a linear relationship. Overall, we show that accounting for variation in host plant quality when calculating development time in the field is more important than the choice of the base temperature for calculating degree days
Variants in the fetal genome near FLT1 are associated with risk of preeclampsia.
Preeclampsia, which affects approximately 5% of pregnancies, is a leading cause of maternal and perinatal death. The causes of preeclampsia remain unclear, but there is evidence for inherited susceptibility. Genome-wide association studies (GWAS) have not identified maternal sequence variants of genome-wide significance that replicate in independent data sets. We report the first GWAS of offspring from preeclamptic pregnancies and discovery of the first genome-wide significant susceptibility locus (rs4769613; P = 5.4 × 10-11) in 4,380 cases and 310,238 controls. This locus is near the FLT1 gene encoding Fms-like tyrosine kinase 1, providing biological support, as a placental isoform of this protein (sFlt-1) is implicated in the pathology of preeclampsia. The association was strongest in offspring from pregnancies in which preeclampsia developed during late gestation and offspring birth weights exceeded the tenth centile. An additional nearby variant, rs12050029, associated with preeclampsia independently of rs4769613. The newly discovered locus may enhance understanding of the pathophysiology of preeclampsia and its subtypes
Comprehensive Research Synopsis and Systematic Meta-Analyses in Parkinson's Disease Genetics: The PDGene Database
More than 800 published genetic association studies have implicated dozens of potential risk loci in Parkinson's disease (PD). To facilitate the interpretation of these findings, we have created a dedicated online resource, PDGene, that comprehensively collects and meta-analyzes all published studies in the field. A systematic literature screen of ∼27,000 articles yielded 828 eligible articles from which relevant data were extracted. In addition, individual-level data from three publicly available genome-wide association studies (GWAS) were obtained and subjected to genotype imputation and analysis. Overall, we performed meta-analyses on more than seven million polymorphisms originating either from GWAS datasets and/or from smaller scale PD association studies. Meta-analyses on 147 SNPs were supplemented by unpublished GWAS data from up to 16,452 PD cases and 48,810 controls. Eleven loci showed genome-wide significant (P<5×10−8) association with disease risk: BST1, CCDC62/HIP1R, DGKQ/GAK, GBA, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, and SYT11/RAB25. In addition, we identified novel evidence for genome-wide significant association with a polymorphism in ITGA8 (rs7077361, OR 0.88, P = 1.3×10−8). All meta-analysis results are freely available on a dedicated online database (www.pdgene.org), which is cross-linked with a customized track on the UCSC Genome Browser. Our study provides an exhaustive and up-to-date summary of the status of PD genetics research that can be readily scaled to include the results of future large-scale genetics projects, including next-generation sequencing studies
Genome-wide meta-analysis associates HLA-DQA1/DRB1 and LPA and lifestyle factors with human longevity
Genomic analysis of longevity offers the potential to illuminate the biology of human aging. Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA). We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity. Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated. We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD. Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan
Readout of a quantum processor with high dynamic range Josephson parametric amplifiers
We demonstrate a high dynamic range Josephson parametric amplifier (JPA) in
which the active nonlinear element is implemented using an array of rf-SQUIDs.
The device is matched to the 50 environment with a Klopfenstein-taper
impedance transformer and achieves a bandwidth of 250-300 MHz, with input
saturation powers up to -95 dBm at 20 dB gain. A 54-qubit Sycamore processor
was used to benchmark these devices, providing a calibration for readout power,
an estimate of amplifier added noise, and a platform for comparison against
standard impedance matched parametric amplifiers with a single dc-SQUID. We
find that the high power rf-SQUID array design has no adverse effect on system
noise, readout fidelity, or qubit dephasing, and we estimate an upper bound on
amplifier added noise at 1.6 times the quantum limit. Lastly, amplifiers with
this design show no degradation in readout fidelity due to gain compression,
which can occur in multi-tone multiplexed readout with traditional JPAs.Comment: 9 pages, 8 figure
Measurement-Induced State Transitions in a Superconducting Qubit: Within the Rotating Wave Approximation
Superconducting qubits typically use a dispersive readout scheme, where a
resonator is coupled to a qubit such that its frequency is qubit-state
dependent. Measurement is performed by driving the resonator, where the
transmitted resonator field yields information about the resonator frequency
and thus the qubit state. Ideally, we could use arbitrarily strong resonator
drives to achieve a target signal-to-noise ratio in the shortest possible time.
However, experiments have shown that when the average resonator photon number
exceeds a certain threshold, the qubit is excited out of its computational
subspace, which we refer to as a measurement-induced state transition. These
transitions degrade readout fidelity, and constitute leakage which precludes
further operation of the qubit in, for example, error correction. Here we study
these transitions using a transmon qubit by experimentally measuring their
dependence on qubit frequency, average photon number, and qubit state, in the
regime where the resonator frequency is lower than the qubit frequency. We
observe signatures of resonant transitions between levels in the coupled
qubit-resonator system that exhibit noisy behavior when measured repeatedly in
time. We provide a semi-classical model of these transitions based on the
rotating wave approximation and use it to predict the onset of state
transitions in our experiments. Our results suggest the transmon is excited to
levels near the top of its cosine potential following a state transition, where
the charge dispersion of higher transmon levels explains the observed noisy
behavior of state transitions. Moreover, occupation in these higher energy
levels poses a major challenge for fast qubit reset
Overcoming leakage in scalable quantum error correction
Leakage of quantum information out of computational states into higher energy
states represents a major challenge in the pursuit of quantum error correction
(QEC). In a QEC circuit, leakage builds over time and spreads through
multi-qubit interactions. This leads to correlated errors that degrade the
exponential suppression of logical error with scale, challenging the
feasibility of QEC as a path towards fault-tolerant quantum computation. Here,
we demonstrate the execution of a distance-3 surface code and distance-21
bit-flip code on a Sycamore quantum processor where leakage is removed from all
qubits in each cycle. This shortens the lifetime of leakage and curtails its
ability to spread and induce correlated errors. We report a ten-fold reduction
in steady-state leakage population on the data qubits encoding the logical
state and an average leakage population of less than
throughout the entire device. The leakage removal process itself efficiently
returns leakage population back to the computational basis, and adding it to a
code circuit prevents leakage from inducing correlated error across cycles,
restoring a fundamental assumption of QEC. With this demonstration that leakage
can be contained, we resolve a key challenge for practical QEC at scale.Comment: Main text: 7 pages, 5 figure
Phenome-wide association analysis of LDL-cholesterol lowering genetic variants in PCSK9
Abstract: Background: We characterised the phenotypic consequence of genetic variation at the PCSK9 locus and compared findings with recent trials of pharmacological inhibitors of PCSK9. Methods: Published and individual participant level data (300,000+ participants) were combined to construct a weighted PCSK9 gene-centric score (GS). Seventeen randomized placebo controlled PCSK9 inhibitor trials were included, providing data on 79,578 participants. Results were scaled to a one mmol/L lower LDL-C concentration. Results: The PCSK9 GS (comprising 4 SNPs) associations with plasma lipid and apolipoprotein levels were consistent in direction with treatment effects. The GS odds ratio (OR) for myocardial infarction (MI) was 0.53 (95% CI 0.42; 0.68), compared to a PCSK9 inhibitor effect of 0.90 (95% CI 0.86; 0.93). For ischemic stroke ORs were 0.84 (95% CI 0.57; 1.22) for the GS, compared to 0.85 (95% CI 0.78; 0.93) in the drug trials. ORs with type 2 diabetes mellitus (T2DM) were 1.29 (95% CI 1.11; 1.50) for the GS, as compared to 1.00 (95% CI 0.96; 1.04) for incident T2DM in PCSK9 inhibitor trials. No genetic associations were observed for cancer, heart failure, atrial fibrillation, chronic obstructive pulmonary disease, or Alzheimer’s disease – outcomes for which large-scale trial data were unavailable. Conclusions: Genetic variation at the PCSK9 locus recapitulates the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and MI. While indicating an increased risk of T2DM, no other possible safety concerns were shown; although precision was moderate
Measurement-induced entanglement and teleportation on a noisy quantum processor
Measurement has a special role in quantum theory: by collapsing the
wavefunction it can enable phenomena such as teleportation and thereby alter
the "arrow of time" that constrains unitary evolution. When integrated in
many-body dynamics, measurements can lead to emergent patterns of quantum
information in space-time that go beyond established paradigms for
characterizing phases, either in or out of equilibrium. On present-day NISQ
processors, the experimental realization of this physics is challenging due to
noise, hardware limitations, and the stochastic nature of quantum measurement.
Here we address each of these experimental challenges and investigate
measurement-induced quantum information phases on up to 70 superconducting
qubits. By leveraging the interchangeability of space and time, we use a
duality mapping, to avoid mid-circuit measurement and access different
manifestations of the underlying phases -- from entanglement scaling to
measurement-induced teleportation -- in a unified way. We obtain finite-size
signatures of a phase transition with a decoding protocol that correlates the
experimental measurement record with classical simulation data. The phases
display sharply different sensitivity to noise, which we exploit to turn an
inherent hardware limitation into a useful diagnostic. Our work demonstrates an
approach to realize measurement-induced physics at scales that are at the
limits of current NISQ processors
Iron Behaving Badly: Inappropriate Iron Chelation as a Major Contributor to the Aetiology of Vascular and Other Progressive Inflammatory and Degenerative Diseases
The production of peroxide and superoxide is an inevitable consequence of
aerobic metabolism, and while these particular "reactive oxygen species" (ROSs)
can exhibit a number of biological effects, they are not of themselves
excessively reactive and thus they are not especially damaging at physiological
concentrations. However, their reactions with poorly liganded iron species can
lead to the catalytic production of the very reactive and dangerous hydroxyl
radical, which is exceptionally damaging, and a major cause of chronic
inflammation. We review the considerable and wide-ranging evidence for the
involvement of this combination of (su)peroxide and poorly liganded iron in a
large number of physiological and indeed pathological processes and
inflammatory disorders, especially those involving the progressive degradation
of cellular and organismal performance. These diseases share a great many
similarities and thus might be considered to have a common cause (i.e.
iron-catalysed free radical and especially hydroxyl radical generation). The
studies reviewed include those focused on a series of cardiovascular, metabolic
and neurological diseases, where iron can be found at the sites of plaques and
lesions, as well as studies showing the significance of iron to aging and
longevity. The effective chelation of iron by natural or synthetic ligands is
thus of major physiological (and potentially therapeutic) importance. As
systems properties, we need to recognise that physiological observables have
multiple molecular causes, and studying them in isolation leads to inconsistent
patterns of apparent causality when it is the simultaneous combination of
multiple factors that is responsible. This explains, for instance, the
decidedly mixed effects of antioxidants that have been observed, etc...Comment: 159 pages, including 9 Figs and 2184 reference
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