154 research outputs found
The causal relationship between air pollution, obesity, and COVID-19 risk: a large-scale genetic correlation study
ObjectiveObservational evidence reported that air pollution is a significant risk element for numerous health problems, such as obesity and coronavirus disease 2019 (COVID-19), but their causal relationship is currently unknown. Our objective was to probe the causal relationship between air pollution, obesity, and COVID-19 and to explore whether obesity mediates this association.MethodsWe obtained instrumental variables strongly correlated to air pollutants [PM2.5, nitrogen dioxide (NO2) and nitrogen oxides (NOx)], 9 obesity-related traits (abdominal subcutaneous adipose tissue volume, waist-to-hip ratio, body mass index, hip circumference, waist circumference, obesity class 1-3, visceral adipose tissue volume), and COVID-19 phenotypes (susceptibility, hospitalization, severity) from public genome-wide association studies. We used clinical and genetic data from different public biological databases and performed analysis by two-sample and two-step Mendelian randomization.ResultsPM2.5 genetically correlated with 5 obesity-related traits, which obesity class 1 was most affected (beta = 0.38, 95% CI = 0.11 - 0.65, p = 6.31E-3). NO2 genetically correlated with 3 obesity-related traits, which obesity class 1 was also most affected (beta = 0.33, 95% CI = 0.055 - 0.61, p = 1.90E-2). NOx genetically correlated with 7 obesity-related traits, which obesity class 3 was most affected (beta = 1.16, 95% CI = 0.42-1.90, p = 2.10E-3). Almost all the obesity-related traits genetically increased the risks for COVID-19 phenotypes. Among them, body mass index, waist circumference, hip circumference, waist-to-hip ratio, and obesity class 1 and 2 mediated the effects of air pollutants on COVID-19 risks (p < 0.05). However, no direct causal relationship was observed between air pollution and COVID-19.ConclusionOur study suggested that exposure to heavy air pollutants causally increased risks for obesity. Besides, obesity causally increased the risks for COVID-19 phenotypes. Attention needs to be paid to weight status for the population who suffer from heavy air pollution, as they are more likely to be susceptible and vulnerable to COVID-19
Generating Giant and Tunable Nonlinearity in a Macroscopic Mechanical Resonator from Chemical Bonding Force
Nonlinearity in macroscopic mechanical system plays a crucial role in a wide
variety of applications, including signal transduction and processing,
synchronization, and building logical devices. However, it is difficult to
generate nonlinearity due to the fact that macroscopic mechanical systems
follow the Hooke's law and response linearly to external force, unless strong
drive is used. Here we propose and experimentally realize a record-high
nonlinear response in macroscopic mechanical system by exploring the
anharmonicity in deforming a single chemical bond. We then demonstrate the
tunability of nonlinear response by precisely controlling the chemical bonding
interaction, and realize a cubic elastic constant of \mathversion{bold}, many orders of magnitude larger in strength
than reported previously. This enables us to observe vibrational bistate
transitions of the resonator driven by the weak Brownian thermal noise at 6~K.
This method can be flexibly applied to a variety of mechanical systems to
improve nonlinear responses, and can be used, with further improvements, to
explore macroscopic quantum mechanics
Quantum Computing for MIMO Beam Selection Problem: Model and Optical Experimental Solution
Massive multiple-input multiple-output (MIMO) has gained widespread
popularity in recent years due to its ability to increase data rates, improve
signal quality, and provide better coverage in challenging environments. In
this paper, we investigate the MIMO beam selection (MBS) problem, which is
proven to be NP-hard and computationally intractable. To deal with this
problem, quantum computing that can provide faster and more efficient solutions
to large-scale combinatorial optimization is considered. MBS is formulated in a
quadratic unbounded binary optimization form and solved with Coherent Ising
Machine (CIM) physical machine. We compare the performance of our solution with
two classic heuristics, simulated annealing and Tabu search. The results
demonstrate an average performance improvement by a factor of 261.23 and 20.6,
respectively, which shows that CIM-based solution performs significantly better
in terms of selecting the optimal subset of beams. This work shows great
promise for practical 5G operation and promotes the application of quantum
computing in solving computationally hard problems in communication.Comment: Accepted by IEEE Globecom 202
Universal Gaussian Elimination Hardware for Cryptographic Purposes
In this paper, we investigate the possibility of performing Gaussian elimination for arbitrary binary matrices on hardware. In particular, we presented a generic approach for hardware-based Gaussian elimination, which is able to process both non-singular and singular matrices. Previous works on hardware-based Gaussian elimination can only process non-singular ones. However, a plethora of cryptosystems, for instance, quantum-safe key encapsulation mechanisms based on rank-metric codes, ROLLO and RQC, which are among NIST post-quantum cryptography standardization round-2 candidates, require performing Gaussian elimination for random matrices regardless of the singularity. We accordingly implemented an optimized and parameterized Gaussian eliminator for (singular) matrices over binary fields, making the intense computation of linear algebra feasible and efficient on hardware. To the best of our knowledge, this work solves for the first time eliminating a singular matrix on reconfigurable hardware and also describes the a generic hardware architecture for rank-code based cryptographic schemes. The experimental results suggest hardware-based Gaussian elimination can be done in linear time regardless of the matrix type
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