Showing papers by "Louisiana State University published in 2021"

Population Properties of Compact Objects from the Second LIGO-Virgo Gravitational-Wave Transient Catalog

Abstract: We report on the population of 47 compact binary mergers detected with a false-alarm rate of 0.01 are dynamically assembled. Third, we estimate merger rates, finding RBBH = 23.9-+8.614.3 Gpc-3 yr-1 for BBHs and RBNS = 320-+240490 Gpc-3 yr-1 for binary neutron stars. We find that the BBH rate likely increases with redshift (85% credibility) but not faster than the star formation rate (86% credibility). Additionally, we examine recent exceptional events in the context of our population models, finding that the asymmetric masses of GW190412 and the high component masses of GW190521 are consistent with our models, but the low secondary mass of GW190814 makes it an outlier.

Observation of Gravitational Waves from Two Neutron Star–Black Hole Coalescences

Abstract: We report the observation of gravitational waves from two compact binary coalescences in LIGO’s and Virgo’s third observing run with properties consistent with neutron star–black hole (NSBH) binaries. The two events are named GW200105_162426 and GW200115_042309, abbreviated as GW200105 and GW200115; the first was observed by LIGO Livingston and Virgo and the second by all three LIGO–Virgo detectors. The source of GW200105 has component masses 8.9−1.5+1.2 and 1.9−0.2+0.3M⊙ , whereas the source of GW200115 has component masses 5.7−2.1+1.8 and 1.5−0.3+0.7M⊙ (all measurements quoted at the 90% credible level). The probability that the secondary’s mass is below the maximal mass of a neutron star is 89%–96% and 87%–98%, respectively, for GW200105 and GW200115, with the ranges arising from different astrophysical assumptions. The source luminosity distances are 280−110+110 and 300−100+150Mpc , respectively. The magnitude of the primary spin of GW200105 is less than 0.23 at the 90% credible level, and its orientation is unconstrained. For GW200115, the primary spin has a negative spin projection onto the orbital angular momentum at 88% probability. We are unable to constrain the spin or tidal deformation of the secondary component for either event. We infer an NSBH merger rate density of 45−33+75Gpc−3yr−1 when assuming that GW200105 and GW200115 are representative of the NSBH population or 130−69+112Gpc−3yr−1 under the assumption of a broader distribution of component masses.

Capturing Causal Complexity: Heuristics for Configurational Theorizing

Abstract: Management scholars study phenomena marked by complex interdependencies where multiple explanatory factors combine to bring about an outcome of interest. Yet, theorizing about causal complexity can prove challenging for the correlational theorizing that is predominant in the field of management, given its “net effects thinking” that emphasizes the unique contribution of individual explanatory factors. In contrast, configurational theories and thinking are well-suited to explaining causally complex phenomena. In this article, we seek to advance configurational theorizing by providing a model of the configurational theorizing process which consists of three iterative stages—scoping, linking and naming. In each stage, we develop and offer several heuristics aimed at stimulating configurational theorizing. That is, these theorizing heuristics are intended to help scholars discover configurations of explanatory factors, probe the connections among these factors, and articulate the orchestrating themes that underpin their coherence. We conclude with a discussion of how configurational theorizing advances theory development in the field of management and organizations, and beyond.

Multisystem Inflammatory Syndrome in Children - Initial Therapy and Outcomes.

Abstract: Background The assessment of real-world effectiveness of immunomodulatory medications for multisystem inflammatory syndrome in children (MIS-C) may guide therapy. Methods We analyzed surve...

Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction

Abstract: Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84–5.29) for men of European ancestry to 3.74 (95% CI, 3.36–4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14–2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71–0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction.

A gravitational-wave measurement of the Hubble constant following the second observing run of Advanced LIGO and Virgo

Abstract: This paper presents the gravitational-wave measurement of the Hubble constant (H 0) using the detections from the first and second observing runs of the Advanced LIGO and Virgo detector network. The presence of the transient electromagnetic counterpart of the binary neutron star GW170817 led to the first standard-siren measurement of H 0. Here we additionally use binary black hole detections in conjunction with galaxy catalogs and report a joint measurement. Our updated measurement is H 0 = km s−1 Mpc−1 (68.3% of the highest density posterior interval with a flat-in-log prior) which is an improvement by a factor of 1.04 (about 4%) over the GW170817-only value of km s−1 Mpc−1. A significant additional contribution currently comes from GW170814, a loud and well-localized detection from a part of the sky thoroughly covered by the Dark Energy Survey. With numerous detections anticipated over the upcoming years, an exhaustive understanding of other systematic effects are also going to become increasingly important. These results establish the path to cosmology using gravitational-wave observations with and without transient electromagnetic counterparts.

Interim Estimates of Vaccine Effectiveness of Pfizer-BioNTech and Moderna COVID-19 Vaccines Among Health Care Personnel - 33 U.S. Sites, January-March 2021.

Abstract: Throughout the COVID-19 pandemic, health care personnel (HCP) have been at high risk for exposure to SARS-CoV-2, the virus that causes COVID-19, through patient interactions and community exposure (1). The Advisory Committee on Immunization Practices recommended prioritization of HCP for COVID-19 vaccination to maintain provision of critical services and reduce spread of infection in health care settings (2). Early distribution of two mRNA COVID-19 vaccines (Pfizer-BioNTech and Moderna) to HCP allowed assessment of the effectiveness of these vaccines in a real-world setting. A test-negative case-control study is underway to evaluate mRNA COVID-19 vaccine effectiveness (VE) against symptomatic illness among HCP at 33 U.S. sites across 25 U.S. states. Interim analyses indicated that the VE of a single dose (measured 14 days after the first dose through 6 days after the second dose) was 82% (95% confidence interval [CI] = 74%-87%), adjusted for age, race/ethnicity, and underlying medical conditions. The adjusted VE of 2 doses (measured ≥7 days after the second dose) was 94% (95% CI = 87%-97%). VE of partial (1-dose) and complete (2-dose) vaccination in this population is comparable to that reported from clinical trials and recent observational studies, supporting the effectiveness of mRNA COVID-19 vaccines against symptomatic disease in adults, with strong 2-dose protection.

Upper limits on the isotropic gravitational-wave background from Advanced LIGO and Advanced Virgo’s third observing run

Abstract: We report results of a search for an isotropic gravitational-wave background (GWB) using data from Advanced LIGO’s and Advanced Virgo’s third observing run (O3) combined with upper limits from the earlier O1 and O2 runs. Unlike in previous observing runs in the advanced detector era, we include Virgo in the search for the GWB. The results of the search are consistent with uncorrelated noise, and therefore we place upper limits on the strength of the GWB. We find that the dimensionless energy density Ω GW ≤ 5.8 × 10 − 9 at the 95% credible level for a flat (frequency-independent) GWB, using a prior which is uniform in the log of the strength of the GWB, with 99% of the sensitivity coming from the band 20–76.6 Hz; Ω GW ( f ) ≤ 3.4 × 10 − 9 at 25 Hz for a power-law GWB with a spectral index of 2 / 3 (consistent with expectations for compact binary coalescences), in the band 20–90.6 Hz; and Ω GW ( f ) ≤ 3.9 × 10 − 10 at 25 Hz for a spectral index of 3, in the band 20–291.6 Hz. These upper limits improve over our previous results by a factor of 6.0 for a flat GWB, 8.8 for a spectral index of 2 / 3 , and 13.1 for a spectral index of 3. We also search for a GWB arising from scalar and vector modes, which are predicted by alternative theories of gravity; we do not find evidence of these, and place upper limits on the strength of GWBs with these polarizations. We demonstrate that there is no evidence of correlated noise of magnetic origin by performing a Bayesian analysis that allows for the presence of both a GWB and an effective magnetic background arising from geophysical Schumann resonances. We compare our upper limits to a fiducial model for the GWB from the merger of compact binaries, updating the model to use the most recent data-driven population inference from the systems detected during O3a. Finally, we combine our results with observations of individual mergers and show that, at design sensitivity, this joint approach may yield stronger constraints on the merger rate of binary black holes at z ≳ 2 than can be achieved with individually resolved mergers alone.

Effect of Bamlanivimab vs Placebo on Incidence of COVID-19 Among Residents and Staff of Skilled Nursing and Assisted Living Facilities: A Randomized Clinical Trial

Abstract: Importance Preventive interventions are needed to protect residents and staff of skilled nursing and assisted living facilities from COVID-19 during outbreaks in their facilities. Bamlanivimab, a neutralizing monoclonal antibody against SARS-CoV-2, may confer rapid protection from SARS-CoV-2 infection and COVID-19. Objective To determine the effect of bamlanivimab on the incidence of COVID-19 among residents and staff of skilled nursing and assisted living facilities. Design, Setting, and Participants Randomized, double-blind, single-dose, phase 3 trial that enrolled residents and staff of 74 skilled nursing and assisted living facilities in the United States with at least 1 confirmed SARS-CoV-2 index case. A total of 1175 participants enrolled in the study from August 2 to November 20, 2020. Database lock was triggered on January 13, 2021, when all participants reached study day 57. Interventions Participants were randomized to receive a single intravenous infusion of bamlanivimab, 4200 mg (n = 588), or placebo (n = 587). Main Outcomes and Measures The primary outcome was incidence of COVID-19, defined as the detection of SARS-CoV-2 by reverse transcriptase–polymerase chain reaction and mild or worse disease severity within 21 days of detection, within 8 weeks of randomization. Key secondary outcomes included incidence of moderate or worse COVID-19 severity and incidence of SARS-CoV-2 infection. Results The prevention population comprised a total of 966 participants (666 staff and 300 residents) who were negative at baseline for SARS-CoV-2 infection and serology (mean age, 53.0 [range, 18-104] years; 722 [74.7%] women). Bamlanivimab significantly reduced the incidence of COVID-19 in the prevention population compared with placebo (8.5% vs 15.2%; odds ratio, 0.43 [95% CI, 0.28-0.68];P Conclusions and Relevance Among residents and staff in skilled nursing and assisted living facilities, treatment during August-November 2020 with bamlanivimab monotherapy reduced the incidence of COVID-19 infection. Further research is needed to assess preventive efficacy with current patterns of viral strains with combination monoclonal antibody therapy. Trial Registration ClinicalTrials.gov Identifier:NCT04497987

Research on image classification method based on improved multi-scale relational network

Abstract: Small sample learning aims to learn information about object categories from a single or a few training samples. This learning style is crucial for deep learning methods based on large amounts of data. The deep learning method can solve small sample learning through the idea of meta-learning "how to learn by using previous experience." Therefore, this paper takes image classification as the research object to study how meta-learning quickly learns from a small number of sample images. The main contents are as follows: After considering the distribution difference of data sets on the generalization performance of measurement learning and the advantages of optimizing the initial characterization method, this paper adds the model-independent meta-learning algorithm and designs a multi-scale meta-relational network. First, the idea of META-SGD is adopted, and the inner learning rate is taken as the learning vector and model parameter to learn together. Secondly, in the meta-training process, the model-independent meta-learning algorithm is used to find the optimal parameters of the model. The inner gradient iteration is canceled in the process of meta-validation and meta-test. The experimental results show that the multi-scale meta-relational network makes the learned measurement have stronger generalization ability, which further improves the classification accuracy on the benchmark set and avoids the need for fine-tuning of the model-independent meta-learning algorithm.

Improving Visual Reasoning Through Semantic Representation

Abstract: In visual reasoning, the achievement of deep learning significantly improved the accuracy of results. Image features are primarily used as input to get answers. However, the image features are too redundant to learn accurate characterizations within a limited complexity and time. While in the process of human reasoning, abstract description of an image is usually to avoid irrelevant details. Inspired by this, a higher-level representation named semantic representation is introduced. In this paper, a detailed visual reasoning model is proposed. This new model contains an image understanding model based on semantic representation, feature extraction and process model refined with watershed and u-distance method, a feature vector learning model using pyramidal pooling and residual network, and a question understanding model combining problem embedding coding method and machine translation decoding method. The feature vector could better represent the whole image instead of overly focused on specific characteristics. The model using semantic representation as input verifies that more accurate results can be obtained by introducing a high-level semantic representation. The result also shows that it is feasible and effective to introduce high-level and abstract forms of knowledge representation into deep learning tasks. This study lays a theoretical and experimental foundation for introducing different levels of knowledge representation into deep learning in the future.

Phylogenomics Reveals Ancient Gene Tree Discordance in the Amphibian Tree of Life.

Abstract: Molecular phylogenies have yielded strong support for many parts of the amphibian Tree of Life, but poor support for the resolution of deeper nodes, including relationships among families and orders. To clarify these relationships, we provide a phylogenomic perspective on amphibian relationships by developing a taxon-specific Anchored Hybrid Enrichment protocol targeting hundreds of conserved exons which are effective across the class. After obtaining data from 220 loci for 286 species (representing 94% of the families and 44% of the genera), we estimate a phylogeny for extant amphibians and identify gene tree-species tree conflict across the deepest branches of the amphibian phylogeny. We perform locus-by-locus genealogical interrogation of alternative topological hypotheses for amphibian monophyly, focusing on interordinal relationships. We find that phylogenetic signal deep in the amphibian phylogeny varies greatly across loci in a manner that is consistent with incomplete lineage sorting in the ancestral lineage of extant amphibians. Our results overwhelmingly support amphibian monophyly and a sister relationship between frogs and salamanders, consistent with the Batrachia hypothesis. Species tree analyses converge on a small set of topological hypotheses for the relationships among extant amphibian families. These results clarify several contentious portions of the amphibian Tree of Life, which in conjunction with a set of vetted fossil calibrations, support a surprisingly younger timescale for crown and ordinal amphibian diversification than previously reported. More broadly, our study provides insight into the sources, magnitudes, and heterogeneity of support across loci in phylogenomic data sets.[AIC; Amphibia; Batrachia; Phylogeny; gene tree-species tree discordance; genomics; information theory.].

Joint embedding VQA model based on dynamic word vector.

Abstract: The existing joint embedding Visual Question Answering models use different combinations of image characterization, text characterization and feature fusion method, but all the existing models use static word vectors for text characterization. However, in the real language environment, the same word may represent different meanings in different contexts, and may also be used as different grammatical components. These differences cannot be effectively expressed by static word vectors, so there may be semantic and grammatical deviations. In order to solve this problem, our article constructs a joint embedding model based on dynamic word vector-none KB-Specific network (N-KBSN) model which is different from commonly used Visual Question Answering models based on static word vectors. The N-KBSN model consists of three main parts: question text and image feature extraction module, self attention and guided attention module, feature fusion and classifier module. Among them, the key parts of N-KBSN model are: image characterization based on Faster R-CNN, text characterization based on ELMo and feature enhancement based on multi-head attention mechanism. The experimental results show that the N-KBSN constructed in our experiment is better than the other 2017-winner (glove) model and 2019-winner (glove) model. The introduction of dynamic word vector improves the accuracy of the overall results.

Emergence in late 2020 of multiple lineages of SARS-CoV-2 Spike protein variants affecting amino acid position 677

Abstract: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein (S) plays critical roles in host cell entry. Non-synonymous substitutions affecting S are not uncommon and have become fixed in a number of SARS-CoV-2 lineages. A subset of such mutations enable escape from neutralizing antibodies or are thought to enhance transmission through mechanisms such as increased affinity for the cell entry receptor, angiotensin-converting enzyme 2 (ACE2). Independent genomic surveillance programs based in New Mexico and Louisiana contemporaneously detected the rapid rise of numerous clade 20G (lineage B.1.2) infections carrying a Q677P substitution in S. The variant was first detected in the US on October 23, yet between 01 Dec 2020 and 19 Jan 2021 it rose to represent 27.8% and 11.3% of all SARS-CoV-2 genomes sequenced from Louisiana and New Mexico, respectively. Q677P cases have been detected predominantly in the south central and southwest United States; as of 03 Feb 2021, GISAID data show 499 viral sequences of this variant from the USA. Phylogenetic analyses revealed the independent evolution and spread of at least six distinct Q677H sub-lineages, with first collection dates ranging from mid-August to late November 2020. Four 677H clades from clade 20G (B.1.2), 20A (B.1.234), and 20B (B.1.1.220, and B.1.1.222) each contain roughly 100 or fewer sequenced cases, while a distinct pair of clade 20G clusters are represented by 754 and 298 cases, respectively. Although sampling bias and founder effects may have contributed to the rise of S:677 polymorphic variants, the proximity of this position to the polybasic cleavage site at the S1/S2 boundary are consistent with its potential functional relevance during cell entry, suggesting parallel evolution of a trait that may confer an advantage in spread or transmission. Taken together, our findings demonstrate simultaneous convergent evolution, thus providing an impetus to further evaluate S:677 polymorphisms for effects on proteolytic processing, cell tropism, and transmissibility.

LIGO Detector Characterization in the Second and Third Observing Runs

Abstract: The characterization of the Advanced LIGO detectors in the second and third observing runs has increased the sensitivity of the instruments, allowing for a higher number of detectable gravitational-wave signals, and provided confirmation of all observed gravitational-wave events. In this work, we present the methods used to characterize the LIGO detectors and curate the publicly available datasets, including the LIGO strain data and data quality products. We describe the essential role of these datasets in LIGO–Virgo Collaboration analyses of gravitational-waves from both transient and persistent sources and include details on the provenance of these datasets in order to support analyses of LIGO data by the broader community. Finally, we explain anticipated changes in the role of detector characterization and current efforts to prepare for the high rate of gravitational-wave alerts and events in future observing runs.

Prospects for beyond the Standard Model physics searches at the Deep Underground Neutrino Experiment: DUNE Collaboration.

Abstract: The Deep Underground Neutrino Experiment (DUNE) will be a powerful tool for a variety of physics topics. The high-intensity proton beams provide a large neutrino flux, sampled by a near detector system consisting of a combination of capable precision detectors, and by the massive far detector system located deep underground. This configuration sets up DUNE as a machine for discovery, as it enables opportunities not only to perform precision neutrino measurements that may uncover deviations from the present three-flavor mixing paradigm, but also to discover new particles and unveil new interactions and symmetries beyond those predicted in the Standard Model (SM). Of the many potential beyond the Standard Model (BSM) topics DUNE will probe, this paper presents a selection of studies quantifying DUNE’s sensitivities to sterile neutrino mixing, heavy neutral leptons, non-standard interactions, CPT symmetry violation, Lorentz invariance violation, neutrino trident production, dark matter from both beam induced and cosmogenic sources, baryon number violation, and other new physics topics that complement those at high-energy colliders and significantly extend the present reach.

Rheological Aspects of Cellulose Nanomaterials: Governing Factors and Emerging Applications

Abstract: Cellulose nanomaterials (CNMs), mainly including nanofibrillated cellulose (NFC) and cellulose nanocrystals (CNCs), have attained enormous interest due to their sustainability, biodegradability, biocompatibility, nanoscale dimensions, large surface area, facile modification of surface chemistry, as well as unique optical, mechanical, and rheological performance. One of the most fascinating properties of CNMs is their aqueous suspension rheology, i.e., CNMs helping create viscous suspensions with the formation of percolation networks and chemical interactions (e.g., van der Waals forces, hydrogen bonding, electrostatic attraction/repulsion, and hydrophobic attraction). Under continuous shearing, CNMs in an aqueous suspension can align along the flow direction, producing shear-thinning behavior. At rest, CNM suspensions regain some of their initial structure immediately, allowing rapid recovery of rheological properties. These unique flow features enable CNMs to serve as rheological modifiers in a wide range of fluid-based applications. Herein, the dependence of the rheology of CNM suspensions on test protocols, CNM inherent properties, suspension environments, and postprocessing is systematically described. A critical overview of the recent progress on fluid applications of CNMs as rheology modifiers in some emerging industrial sectors is presented as well. Future perspectives in the field are outlined to guide further research and development in using CNMs as the next generation rheological modifiers.

An Augmented q-Factor Model with Expected Growth

Abstract: In the investment theory, firms with high expected investment growth earn higher expected returns than firms with low expected investment growth, holding investment and expected profitability constant. Building on cross-sectional growth forecasts with Tobin’s q, operating cash flows, and change in return on equity as predictors, an expected growth factor earns an average premium of 0.84% per month (t = 10.27) in the 1967–2018 sample. The q5 model, which augments the Hou-Xue-Zhang (2015) q-factor model with the expected growth factor, shows strong explanatory power in the cross section and outperforms the Fama-French (2018) 6-factor model.

Tunable magnetoresistance of core-shell structured polyaniline nanocomposites with 0-, 1-, and 2-dimensional nanocarbons

Abstract: Core-shell structured polyaniline (PANI) nanocomposites with tunable magnetoresistance (MR) were obtained through the facial surface-initiated polymerization method with assistance of zero-, one-, and two-dimensional nanocarbons (carbon black, carbon fiber, carbon tube, and graphene). The improved dielectric properties and typical semiconducting behavior were observed in the PANI nanocomposites. And the quasi 3D electron conduction mechanism was observed in all the samples through Mott variable range hopping model, indicating that dimension of the nanocarbons does not affect the charge transport mechanism. Meanwhile, positive MR was observed in all the samples, and the MR value can be controlled by nanocarbons. When nanocarbon loading is 10.0 wt%, MR of graphene/PANI, carbon fiber/PANI, carbon black/PANI, and carbon tube/PANI were 15.6%, 14.7%, 9.5%, and 1.5%, respectively. The positive MR phenomenon was analyzed by the wave functional shrinkage model. The magnetic field and nanocarbons’ effects on the localization length, density of state at the Fermi level, average hopping length, and hopping energy were systematically studied. This work provides the guideline for the fabrication of tunable magnetic sensor or information storage device. Tunable magnetoresistance was reported in the polyaniline nanocomposites with zero-, one-, and two-dimensional nanocarbons as fillers.

Mechanical quantum sensing in the search for dark matter

Abstract: Numerous astrophysical and cosmological observations are best explained by the existence of dark matter, a mass density which interacts only very weakly with visible, baryonic matter. Searching for the extremely weak signals produced by this dark matter strongly motivate the development of new, ultra-sensitive detector technologies. Paradigmatic advances in the control and readout of massive mechanical systems, in both the classical and quantum regimes, have enabled unprecedented levels of sensitivity. In this overview paper, we outline recent ideas in the potential use of a range of solid-state mechanical sensing technologies to aid in the search for dark matter in a number of energy scales and with a variety of coupling mechanisms.

Self-Recovery, Fatigue-Resistant, and Multifunctional Sensor Assembled by a Nanocellulose/Carbon Nanotube Nanocomplex-Mediated Hydrogel.

Abstract: Flexible sensors have attracted great research interest due to their applications in artificial intelligence, wearable electronics, and personal health management. However, due to the inherent brittleness of common hydrogels, preparing a hydrogel-based sensor integrated with excellent flexibility, self-recovery, and antifatigue properties still remains a challenge to date. In this study, a type of physically and chemically dual-cross-linked conductive hydrogels based on 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO)-oxidized cellulose nanofiber (TOCN)-carrying carbon nanotubes (CNTs) and polyacrylamide (PAAM) matrix via a facial one-pot free-radical polymerization is developed for multifunctional wearable sensing application. Inside the hierarchical gel network, TOCNs not only serve as the nanoreinforcement with a toughening effect but also efficiently assist the homogeneous distribution of CNTs in the hydrogel matrix. The optimized TOCN-CNT/PAAM hydrogel integrates high compressive (∼2.55 MPa at 60% strain) and tensile (∼0.15 MPa) strength, excellent intrinsic self-recovery property (recovery efficiency >92%), and antifatigue capacity under both cyclic stretching and pressing. The multifunctional sensors assembled by the hydrogel exhibit both high strain sensitivity (gauge factor ≈11.8 at 100-200% strain) and good pressure sensing ability over a large pressure range (0-140 kPa), which can effectively detect the subtle and large-scale human motions through repeatable and stable electrical signals even after 100 loading-unloading cycles. The comprehensive performance of the TOCN-CNT/PAAM hydrogel-based sensor is superior to those of most gel-based sensors previously reported, indicating its potential applications in multifunctional sensing devices for healthcare systems and human motion monitoring.