Electrically driven spin resonance in a bent disordered carbon nanotube

Resonant manipulation of carbon nanotube valley-spin qubits by an electric field is investigated theoretically. We develop a new analysis of electrically driven spin resonance exploiting fixed physical characteristics of the nanotube: a bend and inhomogeneous disorder. The spectrum is simulated for an electron valley-spin qubit coupled to a hole valley-spin qubit and an impurity electron spin, and features that coincide with a recent measurement are identified. We show that the same mechanism allows resonant control of the full four-dimensional spin-valley space.Comment: 11 pages, 7 figure

Assessing agonistic potential of a candidate therapeutic anti-IL21R antibody

<p>Abstract</p> <p>Background</p> <p>Selective neutralization of the IL21/IL21R signaling pathway is a promising approach for the treatment of a variety of autoimmune diseases. Ab-01 is a human neutralizing anti-IL21R antibody. In order to ensure that the activities of Ab-01 are restricted to neutralization even under <it>in vitro </it>cross-linking and <it>in vivo </it>conditions, a comprehensive assessment of agonistic potential of Ab-01 was undertaken.</p> <p>Methods</p> <p><it>In vitro </it>antibody cross-linking and cell culture protocols reported for studies with a human agonistic antibody, TGN1412, were followed for Ab-01. rhIL21, the agonist ligand of the targeted receptor, and cross-linked anti-CD28 were used as positive controls for signal transduction. <it>In vivo </it>agonistic potential of Ab-01 was assessed by measuring expression levels of cytokine storm-associated and IL21 pathway genes in blood of cynomolgus monkeys before and after IV administration of Ab-01.</p> <p>Results</p> <p>Using a comprehensive set of assays that detected multiple activation signals in the presence of the positive control agonists, <it>in vitro </it>Ab-01-dependent activation was not detected in either PBMCs or the rhIL21-responsive cell line Daudi. Furthermore, no difference in gene expression levels was detected in blood before and after <it>in vivo </it>Ab-01 dosing of cynomolgus monkeys.</p> <p>Conclusions</p> <p>Despite efforts to intentionally force an agonistic signal from Ab-01, none could be detected.</p

Machine learning enables completely automatic tuning of a quantum device faster than human experts

Variability is a problem for the scalability of semiconductor quantum devices. The parameter space is large, and the operating range is small. Our statistical tuning algorithm searches for specific electron transport features in gate-defined quantum dot devices with a gate voltage space of up to eight dimensions. Starting from the full range of each gate voltage, our machine learning algorithm can tune each device to optimal performance in a median time of under 70 minutes. This performance surpassed our best human benchmark (although both human and machine performance can be improved). The algorithm is approximately 180 times faster than an automated random search of the parameter space, and is suitable for different material systems and device architectures. Our results yield a quantitative measurement of device variability, from one device to another and after thermal cycling. Our machine learning algorithm can be extended to higher dimensions and other technologies

Radio-frequency characterization of a supercurrent transistor made of a carbon nanotube

A supercurrent transistor is a superconductor–semiconductor hybrid device in which the Josephson supercurrent is switched on and off using a gate voltage. While such devices have been studied using DC transport, radio-frequency measurements allow for more sensitive and faster experiments. Here a supercurrent transistor made from a carbon nanotube is measured simultaneously via DC conductance and radio-frequency reflectometry. The radio-frequency measurement resolves all the main features of the conductance data across a wide range of bias and gate voltage, and many of these features are seen more clearly. These results are promising for measuring other kinds of hybrid superconducting devices, in particular for detecting the reactive component of the impedance, which a DC measurement can never detect

Efficiently measuring a quantum device using machine learning

Scalable quantum technologies such as quantum computers will require very large numbers of quantum devices to be characterised and tuned. As the number of devices on chip increases, this task becomes ever more time-consuming, and will be intractable on a large scale without efficient automation. We present measurements on a quantum dot device performed by a machine learning algorithm in real time. The algorithm selects the most informative measurements to perform next by combining information theory with a probabilistic deep-generative model that can generate full-resolution reconstructions from scattered partial measurements. We demonstrate, for two different current map configurations that the algorithm outperforms standard grid scan techniques, reducing the number of measurements required by up to 4 times and the measurement time by 3.7 times. Our contribution goes beyond the use of machine learning for data search and analysis, and instead demonstrates the use of algorithms to automate measurements. This works lays the foundation for learning-based automated measurement of quantum devices

Assessing the carcinogenic potential of low-dose exposures to chemical mixtures in the environment: the challenge ahead.

Lifestyle factors are responsible for a considerable portion of cancer incidence worldwide, but credible estimates from the World Health Organization and the International Agency for Research on Cancer (IARC) suggest that the fraction of cancers attributable to toxic environmental exposures is between 7% and 19%. To explore the hypothesis that low-dose exposures to mixtures of chemicals in the environment may be combining to contribute to environmental carcinogenesis, we reviewed 11 hallmark phenotypes of cancer, multiple priority target sites for disruption in each area and prototypical chemical disruptors for all targets, this included dose-response characterizations, evidence of low-dose effects and cross-hallmark effects for all targets and chemicals. In total, 85 examples of chemicals were reviewed for actions on key pathways/mechanisms related to carcinogenesis. Only 15% (13/85) were found to have evidence of a dose-response threshold, whereas 59% (50/85) exerted low-dose effects. No dose-response information was found for the remaining 26% (22/85). Our analysis suggests that the cumulative effects of individual (non-carcinogenic) chemicals acting on different pathways, and a variety of related systems, organs, tissues and cells could plausibly conspire to produce carcinogenic synergies. Additional basic research on carcinogenesis and research focused on low-dose effects of chemical mixtures needs to be rigorously pursued before the merits of this hypothesis can be further advanced. However, the structure of the World Health Organization International Programme on Chemical Safety 'Mode of Action' framework should be revisited as it has inherent weaknesses that are not fully aligned with our current understanding of cancer biology

Acute effects of fine particulate air pollution on ST segment height: A longitudinal study

Background The mechanisms for the relationship between particulate air pollution and cardiac disease are not fully understood. Air pollution-induced myocardial ischemia is one of the potentially important mechanisms. Methods We investigate the acute effects and the time course of fine particulate pollution (PM2.5) on myocardium ischemic injury as assessed by ST-segment height in a community-based sample of 106 healthy non-smokers. Twenty-four hour beat-to-beat electrocardiogram (ECG) data were obtained using a high resolution 12-lead Holter ECG system. After visually identifying and removing all the artifacts and arrhythmic beats, we calculated beat-to-beat ST-height from ten leads (inferior leads II, III, and aVF; anterior leads V3 and V4; septal leads V1 and V2; lateral leads I, V5, and V6,). Individual-level 24-hour real-time PM2.5 concentration was obtained by a continuous personal PM2.5 monitor. We then calculated, on a 30-minute basis, the corresponding time-of-the-day specific average exposure to PM2.5 for each participant. Distributed lag models under a linear mixed-effects models framework were used to assess the regression coefficients between 30-minute PM2.5 and ST-height measures from each lead; i.e., one lag indicates a 30-minute separation between the exposure and outcome. Results The mean (SD) age was 56 (7.6) years, with 41% male and 74% white. The mean (SD) PM2.5 exposure was 14 (22) μg/m3. All inferior leads (II, III, and aVF) and two out of three lateral leads (I and V6), showed a significant association between higher PM2.5 levels and higher ST-height. Most of the adverse effects occurred within two hours after PM2.5 exposure. The multivariable adjusted regression coefficients β (95% CI) of the cumulative effect due to a 10 μg/m3 increase in Lag 0-4 PM2.5 on ST-I, II, III, aVF and ST-V6 were 0.29 (0.01-0.56) μV, 0.79 (0.20-1.39) μV, 0.52 (0.01-1.05) μV, 0.65 (0.11-1.19) μV, and 0.58 (0.07-1.09) μV, respectively, with all p < 0.05. Conclusions Increased PM2.5 concentration is associated with immediate increase in ST-segment height in inferior and lateral leads, generally within two hours. Such an acute effect of PM2.5 may contribute to increased potential for regional myocardial ischemic injury among healthy individuals

Insights into hominid evolution from the gorilla genome sequence.

Gorillas are humans' closest living relatives after chimpanzees, and are of comparable importance for the study of human origins and evolution. Here we present the assembly and analysis of a genome sequence for the western lowland gorilla, and compare the whole genomes of all extant great ape genera. We propose a synthesis of genetic and fossil evidence consistent with placing the human-chimpanzee and human-chimpanzee-gorilla speciation events at approximately 6 and 10 million years ago. In 30% of the genome, gorilla is closer to human or chimpanzee than the latter are to each other; this is rarer around coding genes, indicating pervasive selection throughout great ape evolution, and has functional consequences in gene expression. A comparison of protein coding genes reveals approximately 500 genes showing accelerated evolution on each of the gorilla, human and chimpanzee lineages, and evidence for parallel acceleration, particularly of genes involved in hearing. We also compare the western and eastern gorilla species, estimating an average sequence divergence time 1.75 million years ago, but with evidence for more recent genetic exchange and a population bottleneck in the eastern species. The use of the genome sequence in these and future analyses will promote a deeper understanding of great ape biology and evolution