Frailty in Chronic Obstructive Pulmonary Disease and Risk of Exacerbations and Hospitalizations

Background: Frailty is a complex clinical syndrome associated with vulnerability to adverse health outcomes. While frailty is thought to be common in chronic obstructive pulmonary disease (COPD), the relationship between frailty and COPD-related outcomes such as risk of acute exacerbations of COPD (AE-COPD) and hospitalizations is unclear.Purpose: To examine the association between physical frailty and risk of acute exacerbations, hospitalizations, and mortality in patients with COPD.Methods: A longitudinal analysis of data from a cohort of 280 participants was performed. Baseline frailty measures included exhaustion, weakness, low activity, slowness, and undernutrition. Outcome measures included AE-COPD, hospitalizations, and mortality over 2 years. Negative binomial regression and Cox proportional hazard modeling were used.Results: Sixty-two percent of the study population met criteria for pre-frail and 23% were frail. In adjusted analyses, the frailty syndrome was not associated with COPD exacerbations. However, among the individual components of the frailty syndrome, weakness measured by handgrip strength was associated with increased risk of COPD exacerbations (IRR 1.46, 95% CI 1.09– 1.97). The frailty phenotype was not associated with all-cause hospitalizations but was associated with increased risk of non-COPD-related hospitalizations.Conclusion: This longitudinal cohort study shows that a high proportion of patients with COPD are pre-frail or frail. The frailty phenotype was associated with an increased risk of non-COPD hospitalizations but not with all-cause hospitalizations or COPD exacerbations. Among the individual frailty components, low handgrip strength was associated with increased risk of COPD exacerbations over a 2-year period. Measuring handgrip strength may identify COPD patients who could benefit from programs to reduce COPD exacerbations

Out-of-distribution generalization for learning quantum dynamics

Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution generalization of quantum neural networks (QNNs), where training and testing data are assumed to be drawn from the same data distribution. However, there are currently no results on out-of-distribution generalization in QML, where we require a trained model to perform well even on data drawn from a distribution different from the training distribution. In this work, we prove out-of-distribution generalization for the task of learning an unknown unitary using a QNN and for a broad class of training and testing distributions. In particular, we show that one can learn the action of a unitary on entangled states using only product state training data. We numerically illustrate this by showing that the evolution of a Heisenberg spin chain can be learned using only product training states. Since product states can be prepared using only single-qubit gates, this advances the prospects of learning quantum dynamics using near term quantum computers and quantum experiments, and further opens up new methods for both the classical and quantum compilation of quantum circuits.Comment: 7 pages (main body) + 14 pages (references and appendix); 4+1 figure

Dynamical simulation via quantum machine learning with provable generalization

Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated. Here we develop a framework for using QML methods to simulate quantum dynamics on near-term quantum hardware. We use generalization bounds, which bound the error a machine learning model makes on unseen data, to rigorously analyze the training data requirements of an algorithm within this framework. Our algorithm is thus resource efficient in terms of qubit and data requirements. Furthermore, our preliminary numerics for the XY model exhibit efficient scaling with problem size, and we simulate 20 times longer than Trotterization on IBMQ-Bogota

Dynamical simulation via quantum machine learning with provable generalization

Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated. Here we develop a framework for using QML methods to simulate quantum dynamics on near-term quantum hardware. We use generalization bounds, which bound the error a machine learning model makes on unseen data, to rigorously analyze the training data requirements of an algorithm within this framework. This provides a guarantee that our algorithm is resource-efficient, both in terms of qubit and data requirements. Our numerics exhibit efficient scaling with problem size, and we simulate 20 times longer than Trotterization on IBMQ-Bogota.Comment: Main text: 5 pages & 3 Figures. Supplementary Information: 12 pages & 2 Figure

Photometric Redshifts in the North Ecliptic Pole Wide Field based on a Deep Optical Survey with Hyper Suprime-Cam

The $AKARI$ space infrared telescope has performed near- to mid-infrared (MIR) observations on the North Ecliptic Pole Wide (NEPW) field (5.4 deg$^2$) for about one year. $AKARI$ took advantage of its continuous nine photometric bands, compared with NASA's $Spitzer$ and WISE space telescopes, which had only four filters with a wide gap in the MIR. The $AKARI$ NEPW field lacked deep and homogeneous optical data, limiting the use of nearly half of the IR sources for extra-galactic studies owing to the absence of photometric redshifts (photo-zs). To remedy this, we have recently obtained deep optical imaging over the NEPW field with 5 bands ($g$, $r$, $i$, $z$, and $Y$) of the Hyper Suprime-Camera (HSC) on the Subaru 8m telescope. We optically identify AKARI-IR sources along with supplementary $Spitzer$ and WISE data as well as pre-existing optical data. In this work, we derive new photo-zs using a $\chi^2$ template-fitting method code ($Le$ $Phare$) and reliable photometry from 26 selected filters including HSC, $AKARI$, CFHT, Maidanak, KPNO, $Spitzer$ and WISE data. We take 2026 spectroscopic redshifts (spec-z) from all available spectroscopic surveys over the NEPW to calibrate and assess the accuracy of the photo-zs. At z < 1.5, we achieve a weighted photo-z dispersion of $\sigma_{\Delta{z/(1+z)}}$ = 0.053 with $\eta$ = 11.3% catastrophic errors.Comment: 20 pages, 13 figures, accepted for publication in MNRAS. For summary video, please see http://youtu.be/hjNJRCoBIg

Ultrasensitive force and displacement detection using trapped ions

The ability to detect extremely small forces is vital for a variety of disciplines including precision spin-resonance imaging, microscopy, and tests of fundamental physical phenomena. Current force-detection sensitivity limits have surpassed 1 $aN/\sqrt{Hz}$ (atto $=10^{-18}$) through coupling of micro or nanofabricated mechanical resonators to a variety of physical systems including single-electron transistors, superconducting microwave cavities, and individual spins. These experiments have allowed for probing studies of a variety of phenomena, but sensitivity requirements are ever-increasing as new regimes of physical interactions are considered. Here we show that trapped atomic ions are exquisitely sensitive force detectors, with a measured sensitivity more than three orders of magnitude better than existing reports. We demonstrate detection of forces as small as 174 $yN$ (yocto $=10^{-24}$), with a sensitivity 390$\pm150$ $yN/\sqrt{Hz}$ using crystals of $n=60$ $^{9}$Be$^{+}$ ions in a Penning trap. Our technique is based on the excitation of normal motional modes in an ion trap by externally applied electric fields, detection via and phase-coherent Doppler velocimetry, which allows for the discrimination of ion motion with amplitudes on the scale of nanometers. These experimental results and extracted force-detection sensitivities in the single-ion limit validate proposals suggesting that trapped atomic ions are capable of detecting of forces with sensitivity approaching 1 $yN/\sqrt{Hz}$. We anticipate that this demonstration will be strongly motivational for the development of a new class of deployable trapped-ion-based sensors, and will permit scientists to access new regimes in materials science.Comment: Expanded introduction and analysis. Methods section added. Subject to press embarg

A statistical learning strategy for closed-loop control of fluid flows

This work discusses a closed-loop control strategy for complex systems utilizing scarce and streaming data. A discrete embedding space is first built using hash functions applied to the sensor measurements from which a Markov process model is derived, approximating the complex system’s dynamics. A control strategy is then learned using reinforcement learning once rewards relevant with respect to the control objective are identified. This method is designed for experimental configurations, requiring no computations nor prior knowledge of the system, and enjoys intrinsic robustness. It is illustrated on two systems: the control of the transitions of a Lorenz’63 dynamical system, and the control of the drag of a cylinder flow. The method is shown to perform well

Bird and bat predation services in tropical forests and agroforestry landscapes

Understanding distribution patterns and multitrophic interactions is critical for managing batâ and birdâ mediated ecosystem services such as the suppression of pest and nonâ pest arthropods. Despite the ecological and economic importance of bats and birds in tropical forests, agroforestry systems, and agricultural systems mixed with natural forest, a systematic review of their impact is still missing. A growing number of bird and bat exclosure experiments has improved our knowledge allowing new conclusions regarding their roles in food webs and associated ecosystem services. Here, we review the distribution patterns of insectivorous birds and bats, their local and landscape drivers, and their effects on trophic cascades in tropical ecosystems. We report that for birds but not bats community composition and relative importance of functional groups changes conspicuously from forests to habitats including both agricultural areas and forests, here termed â forestâ agriâ habitats, with reduced representation of insectivores in the latter. In contrast to previous theory regarding trophic cascade strength, we find that birds and bats reduce the density and biomass of arthropods in the tropics with effect sizes similar to those in temperate and boreal communities. The relative importance of birds versus bats in regulating pest abundances varies with season, geography and management. Birds and bats may even suppress tropical arthropod outbreaks, although positive effects on plant growth are not always reported. As both bats and birds are major agents of pest suppression, a better understanding of the local and landscape factors driving the variability of their impact is needed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134094/1/brv12211_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134094/2/brv12211.pd

Preparation and CO2 adsorption of amine modified Mg-Al LDH via exfoliation route

In response to the recent focus on reducing carbon dioxide emission, the preparation and characterization of organically functionalized materials for use in carbon capture have received considerable attention. In this paper the synthesis of amine modified layered double hydroxides (LDHs) via an exfoliation and grafting synthetic route is reported. The materials were characterized by elemental analysis (EA), powder x-ray diffraction (PXRD), diffuse reflectance infrared Fourier transform spectrometer (DRIFTS) and thermogravimetric analysis (TGA). Adsorption of carbon dioxide on modified layered double hydroxides was investigated by TGA at 25–80 °C. 3-[2-(2-Aminoethylamino) ethylamino]propyl-trimethoxysilane modified MgAl LDH showed a maximum CO2 adsorption capacity of 1.76 mmol g−1 at 80 °C. The influence of primary and secondary amines on carbon dioxide adsorption is discussed. The carbon dioxide adsorption isotherms presented were closely fitted to the Avrami kinetic model