Stabilized entanglement of massive mechanical oscillators.

Quantum entanglement is a phenomenon whereby systems cannot be described independently of each other, even though they may be separated by an arbitrarily large distance 1 . Entanglement has a solid theoretical and experimental foundation and is the key resource behind many emerging quantum technologies, including quantum computation, cryptography and metrology. Entanglement has been demonstrated for microscopic-scale systems, such as those involving photons2-5, ions 6 and electron spins 7 , and more recently in microwave and electromechanical devices8-10. For macroscopic-scale objects8-14, however, it is very vulnerable to environmental disturbances, and the creation and verification of entanglement of the centre-of-mass motion of macroscopic-scale objects remains an outstanding goal. Here we report such an experimental demonstration, with the moving bodies being two massive micromechanical oscillators, each composed of about 10 12 atoms, coupled to a microwave-frequency electromagnetic cavity that is used to create and stabilize the entanglement of their centre-of-mass motion15-17. We infer the existence of entanglement in the steady state by combining measurements of correlated mechanical fluctuations with an analysis of the microwaves emitted from the cavity. Our work qualitatively extends the range of entangled physical systems and has implications for quantum information processing, precision measurements and tests of the limits of quantum mechanics.

Various advanced wastewater treatment methods to remove microplastics and prevent transmission of SARS-CoV-2 to airborne microplastics.

Microplastics (MPs) and SARS-CoV-2 interact due to their widespread presence in our environment and affect the virus' behaviour indoors and outdoors. Therefore, it is necessary to study the interaction between MPs and SARS-CoV-2. The environmental damage caused by MPs is increasing globally. Emerging pollutants may adversely affect organisms, especially sewage, posing a threat to human health, animal health, and the ecological system. A significant concern with MPs in the air is that they are a vital component of MPs in the other environmental compartments, such as water and soil, which may affect human health through ingesting or inhaling. This work introduces the fundamental knowledge of various methods in advanced water treatment, including membrane bioreactors, advanced oxidation processes, adsorption, etc., are highly effective in removing MPs; they can still serve as an entrance route due to their constantly being discharged into aquatic environments. Following that, an analysis of each process for MPs' removal and mitigation or prevention of SARS-CoV-2 contamination is discussed. Next, an airborne microplastic has been reported in urban areas, raising health concerns since aerosols are considered a possible route of SARS-CoV-2 disease transmission and bind to airborne MP surfaces. The MPs can be removed from wastewater through conventional treatment processes with physical processes such as screening, grit chambers, and pre-sedimentation.

Strategies for safe management of hospital wastewater during the COVID-19 pandemic.

Management of hospital wastewater is a challenging task, particularly during the situations like coronavirus 2019 (COVID-19) pandemic. The hospital effluent streams are likely to contain many known and unknown contaminants including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) along with a variety of pollutants arising from pharmaceuticals, life-style chemicals, drugs, radioactive species, and human excreta from the patients. The effluents are a mixed bag of contaminants with some of them capable of infecting through contact. Hence, it is essential to identify appropriate treatment strategies for hospital waste streams. In this work, various pollutants emerging in the context of COVID-19 are examined. A methodical review is conducted on the occurrence and disinfection methods of SARS-CoV-2 in wastewater. An emphasis is given to the necessity of addressing the challenges of handling hospital effluents dynamically involved during the pandemic scenario to ensure human and environmental safety. A comparative evaluation of disinfection strategies makes it evident that the non-contact methods like ultraviolet irradiation, hydrogen peroxide vapor, and preventive approaches such as the usage of antimicrobial surface coating offer promise in reducing the chance of disease transmission. These methods are also highly efficient in comparison with other strategies. Chemical disinfection strategies such as chlorination may lead to further disinfection byproducts, complicating the treatment processes. An overall analysis of various disinfection methods is presented here, including developing methods such as membrane technologies, highlighting the merits and demerits of each of these processes. Finally, the wastewater surveillance adopted during the COVID-19 outbreak is discussed. Supplementary Information: The online version contains supplementary material available at 10.1007/s13762-023-04803-1.

Novel adsorptive PVC nanofibrous/thiol-functionalized TNT composite UF membranes for effective dynamic removal of heavy metal ions.

The development of efficient strategies for the removal of heavy metal ions from aqueous solutions is rapidly demanded as these contaminants are very toxic and carcinogenic and show detrimental effects on the living creatures. The main focus of the current study is on the preparation and assessment of electrospun adsorptive nanofiber membranes for the removal of toxic Ni(II) and Cu(II) from wastewater in the ultrafiltration process. Hydrothermally synthesized titanate nanotubes (TNT) was modified with thiol functional groups and then directly incorporated to the polyvinyl chloride nanofiber matrices via electrospinning process to fabricate an adsorptive membrane. The as-prepared electrospun nanofiber membranes and the nanoadsorbents were characterized with respect to the physiochemical properties, surface structure and morphology, applying XPS, FTIR, FESEM, EDX and TEM analysis and then, the membranes were evaluated in terms of the removal of the heavy metal ions in a continuous ultrafiltration mode. In adsorptive filtration of the metal ions, the effective factors including nanoadsorbents loading (0.5-1.5 wt%), initial metal ion concentration (60-150 mg/L), feed temperature (~25 °C-45 °C), presence of competing ion and reusability were investigated in the UF system where the membranes containing 1.5 wt% thiol-modified TNT and virgin TNT adsorbents demonstrated excellent removal efficiency compared to the other membranes. The Cu(II) and Ni(II) removal efficiency of the membrane containing 1.5 wt% functionalized TNT was 90% and 86.7%, respectively which was the highest ones. As was expected and due to the uniform dispersion and less aggregation of the modified TNT adsorbents on the large surface area of the electrospun nanofibers, more adsorption capacity of the nanoparticles can be exploited. Moreover, the strong affinity of the thiol functional groups toward the metal cations, these membranes removed metal contaminants more efficiently. Besides, the Cu(II) removal efficiency of the fabricated membranes didn't show any drastic changes in the presence of the competing ions. Furthermore, acceptable performance was achieved for the prepared membranes even after four adsorption/regeneration cycles in the continuous UF experiments, demonstrating the feasibility and effectiveness of the prepared adsorptive nanofiber membranes for the removal of heavy metal ions.

Hybrid circuit cavity quantum electrodynamics with a micromechanical resonator.

Hybrid quantum systems with inherently distinct degrees of freedom have a key role in many physical phenomena. Well-known examples include cavity quantum electrodynamics, trapped ions, and electrons and phonons in the solid state. In those systems, strong coupling makes the constituents lose their individual character and form dressed states, which represent a collective form of dynamics. As well as having fundamental importance, hybrid systems also have practical applications, notably in the emerging field of quantum information control. A promising approach is to combine long-lived atomic states with the accessible electrical degrees of freedom in superconducting cavities and quantum bits (qubits). Here we integrate circuit cavity quantum electrodynamics with phonons. Apart from coupling to a microwave cavity, our superconducting transmon qubit, consisting of tunnel junctions and a capacitor, interacts with a phonon mode in a micromechanical resonator, and thus acts like an atom coupled to two different cavities. We measure the phonon Stark shift, as well as the splitting of the qubit spectral line into motional sidebands, which feature transitions between the dressed electromechanical states. In the time domain, we observe coherent conversion of qubit excitation to phonons as sideband Rabi oscillations. This is a model system with potential for a quantum interface, which may allow for storage of quantum information in long-lived phonon states, coupling to optical photons or for investigations of strongly coupled quantum systems near the classical limit.

Unusual behavior of MgFe2O4 during regeneration: desorption versus specific adsorption.

The reusability of spent adsorbents is the most important characteristic for their practical application. The process of MgFe2O4 regeneration after methylene blue (MB) adsorption was studied. The effect of the nature (HCl, HNO3, and MgCl2) and the concentration (10-3-10-1 M) of regeneration agents was established. All the regeneration agents at 10-3 and 10-2 M had high efficiency and adsorption capacity recovery reached 80-90%, whereas for 10-1 M concentration the adsorption efficiency was in the range of 4.5-36.2%. It was shown that the concentration of desorbed MB was much less than what had been previously adsorbed and did not correlate with regeneration efficiency. The unusual behavior of MgFe2O4 during regeneration could be due to different mechanisms of regeneration by OH3 + and Mg2+ ions: (i) for acidic regeneration the main process was the non-specific adsorption of OH3 + ions in a diffusion layer and the substitution of adsorbed MB due to electrostatic forces; (ii) in the case of Mg2+ as a regeneration agent, there was specific adsorption due to the completion of a crystal lattice of MgFe2O4 nanoparticles by Mg2+ ions (according to the rules of Fayans-Pannet) with the formation of new Mg-OH adsorption sites and the super-equivalent adsorption of Mg2+ ions (according to DLVO (Derjaguin, Landau, Verwey, and Overbeek) theory) accompanied by a recharge of the MgFe2O4 surface. These phenomena of MgFe2O4 regeneration using Mg2+ ions must be taken into account in the theory and practice of adsorption.

Revealing Hidden Quantum Correlations in an Electromechanical Measurement.

Under a strong quantum measurement, the motion of an oscillator is disturbed by the measurement backaction, as required by the Heisenberg uncertainty principle. When a mechanical oscillator is continuously monitored via an electromagnetic cavity, as in a cavity optomechanical measurement, the backaction is manifest by the shot noise of incoming photons that becomes imprinted onto the motion of the oscillator. Following the photons leaving the cavity, the correlations appear as squeezing of quantum noise in the emitted field. Here we observe such "ponderomotive" squeezing in the microwave domain using an electromechanical device made out of a superconducting resonator and a drumhead mechanical oscillator. Under a strong measurement, the emitted field develops complex-valued quantum correlations, which in general are not completely accessible by standard homodyne measurements. We recover these hidden correlations, using a phase-sensitive measurement scheme employing two local oscillators. The utilization of hidden correlations presents a step forward in the detection of weak forces, as it allows us to fully utilize the quantum noise reduction under the conditions of strong force sensitivity.

Noiseless Quantum Measurement and Squeezing of Microwave Fields Utilizing Mechanical Vibrations.

A process which strongly amplifies both quadrature amplitudes of an oscillatory signal necessarily adds noise. Alternatively, if the information in one quadrature is lost in phase-sensitive amplification, it is possible to completely reconstruct the other quadrature. Here we demonstrate such a nearly perfect phase-sensitive measurement using a cavity optomechanical scheme, characterized by an extremely small noise less than 0.2 quanta. The device also strongly squeezes microwave radiation by 8 dB below vacuum. A source of bright squeezed microwaves opens up applications in manipulations of quantum systems, and noiseless amplification can be used even at modest cryogenic temperatures.

Genetic heterogeneity underlying variation in a locally adaptive clinal trait in Pinus sylvestris revealed by a Bayesian multipopulation analysis.

Local adaptation is a common feature of plant and animal populations. Adaptive phenotypic traits are genetically differentiated along environmental gradients, but the genetic basis of such adaptation is still poorly known. Genetic association studies of local adaptation combine data over populations. Correcting for population structure in these studies can be problematic since both selection and neutral demographic events can create similar allele frequency differences between populations. Correcting for demography with traditional methods may lead to eliminating some true associations. We developed a new Bayesian approach for identifying the loci underlying an adaptive trait in a multipopulation situation in the presence of possible double confounding due to population stratification and adaptation. With this method we studied the genetic basis of timing of bud set, a surrogate trait for timing of yearly growth cessation that confers local adaptation to the populations of Scots pine (Pinus sylvestris). Population means of timing of bud set were highly correlated with latitude. Most effects at individual loci were small. Interestingly, we found genetic heterogeneity (that is, different sets of loci associated with the trait) between the northern and central European parts of the cline. We also found indications of stronger stabilizing selection toward the northern part of the range. The harsh northern conditions may impose greater selective pressure on timing of growth cessation, and the relative importance of different environmental cues used for tracking the seasons might differ depending on latitude of origin.

Microwave amplification with nanomechanical resonators.

The sensitive measurement of electrical signals is at the heart of modern technology. According to the principles of quantum mechanics, any detector or amplifier necessarily adds a certain amount of noise to the signal, equal to at least the noise added by quantum fluctuations. This quantum limit of added noise has nearly been reached in superconducting devices that take advantage of nonlinearities in Josephson junctions. Here we introduce the concept of the amplification of microwave signals using mechanical oscillation, which seems likely to enable quantum-limited operation. We drive a nanomechanical resonator with a radiation pressure force, and provide an experimental demonstration and an analytical description of how a signal input to a microwave cavity induces coherent stimulated emission and, consequently, signal amplification. This generic scheme, which is based on two linear oscillators, has the advantage of being conceptually and practically simpler than the Josephson junction devices. In our device, we achieve signal amplification of 25 decibels with the addition of 20 quanta of noise, which is consistent with the expected amount of added noise. The generality of the model allows for realization in other physical systems as well, and we anticipate that near-quantum-limited mechanical microwave amplification will soon be feasible in various applications involving integrated electrical circuits.

Quantum Backaction Evading Measurement of Collective Mechanical Modes.

The standard quantum limit constrains the precision of an oscillator position measurement. It arises from a balance between the imprecision and the quantum backaction of the measurement. However, a measurement of only a single quadrature of the oscillator can evade the backaction and be made with arbitrary precision. Here we demonstrate quantum backaction evading measurements of a collective quadrature of two mechanical oscillators, both coupled to a common microwave cavity. The work allows for quantum state tomography of two mechanical oscillators, and provides a foundation for macroscopic mechanical entanglement and force sensing beyond conventional quantum limits.

Squeezing of Quantum Noise of Motion in a Micromechanical Resonator.

A pair of conjugate observables, such as the quadrature amplitudes of harmonic motion, have fundamental fluctuations that are bound by the Heisenberg uncertainty relation. However, in a squeezed quantum state, fluctuations of a quantity can be reduced below the standard quantum limit, at the cost of increased fluctuations of the conjugate variable. Here we prepare a nearly macroscopic moving body, realized as a micromechanical resonator, in a squeezed quantum state. We obtain squeezing of one quadrature amplitude 1.1±0.4 dB below the standard quantum limit, thus achieving a long-standing goal of obtaining motional squeezing in a macroscopic object.

A robust multiple-locus method for quantitative trait locus analysis of non-normally distributed multiple traits.

Linear regression-based quantitative trait loci/association mapping methods such as least squares commonly assume normality of residuals. In genetics studies of plants or animals, some quantitative traits may not follow normal distribution because the data include outlying observations or data that are collected from multiple sources, and in such cases the normal regression methods may lose some statistical power to detect quantitative trait loci. In this work, we propose a robust multiple-locus regression approach for analyzing multiple quantitative traits without normality assumption. In our method, the objective function is least absolute deviation (LAD), which corresponds to the assumption of multivariate Laplace distributed residual errors. This distribution has heavier tails than the normal distribution. In addition, we adopt a group LASSO penalty to produce shrinkage estimation of the marker effects and to describe the genetic correlation among phenotypes. Our LAD-LASSO approach is less sensitive to the outliers and is more appropriate for the analysis of data with skewedly distributed phenotypes. Another application of our robust approach is on missing phenotype problem in multiple-trait analysis, where the missing phenotype items can simply be filled with some extreme values, and be treated as outliers. The efficiency of the LAD-LASSO approach is illustrated on both simulated and real data sets.

Graphene optomechanics realized at microwave frequencies.

Cavity optomechanics has served as a platform for studying the interaction between light and micromechanical motion via radiation pressure. Here we observe such phenomena with a graphene mechanical resonator coupled to an electromagnetic mode. We measure thermal motion and backaction cooling in a bilayer graphene resonator coupled to a microwave on-chip cavity. We detect the lowest flexural mode at 24 MHz down to 60 mK, corresponding to 50±6 mechanical quanta, which represents a phonon occupation that is nearly 3 orders of magnitude lower than that which has been recorded to date with graphene resonators.

Combined linkage disequilibrium and linkage mapping: Bayesian multilocus approach.

Quantitative trait loci (QTL) affecting the phenotype of interest can be detected using linkage analysis (LA), linkage disequilibrium (LD) mapping or a combination of both (LDLA). The LA approach uses information from recombination events within the observed pedigree and LD mapping from the historical recombinations within the unobserved pedigree. We propose the Bayesian variable selection approach for combined LDLA analysis for single-nucleotide polymorphism (SNP) data. The novel approach uses both sources of information simultaneously as is commonly done in plant and animal genetics, but it makes fewer assumptions about population demography than previous LDLA methods. This differs from approaches in human genetics, where LDLA methods use LA information conditional on LD information or the other way round. We argue that the multilocus LDLA model is more powerful for the detection of phenotype-genotype associations than single-locus LDLA analysis. To illustrate the performance of the Bayesian multilocus LDLA method, we analyzed simulation replicates based on real SNP genotype data from small three-generational CEPH families and compared the results with commonly used quantitative transmission disequilibrium test (QTDT). This paper is intended to be conceptual in the sense that it is not meant to be a practical method for analyzing high-density SNP data, which is more common. Our aim was to test whether this approach can function in principle.

Using the unified relationship matrix adjusted by breed-wise allele frequencies in genomic evaluation of a multibreed population.

The observed low accuracy of genomic selection in multibreed and admixed populations results from insufficient linkage disequilibrium between markers and trait loci. Failure to remove variation due to the population structure may also hamper the prediction accuracy. We verified if accounting for breed origin of alleles in the calculation of genomic relationships would improve the prediction accuracy in an admixed population. Individual breed proportions derived from the pedigree were used to estimate breed-wise allele frequencies (AF). Breed-wise and across-breed AF were estimated from the currently genotyped population and also in the base population. Genomic relationship matrices (G) were subsequently calculated using across-breed (GAB) and breed-wise (GBW) AF estimated in the currently genotyped and also in the base population. Unified relationship matrices were derived by combining different G with pedigree relationships in the evaluation of genomic estimated breeding values (GEBV) for genotyped and ungenotyped animals. The validation reliabilities and inflation of GEBV were assessed by a linear regression of deregressed breeding value (deregressed proofs) on GEBV, weighted by the reliability of deregressed proofs. The regression coefficients (b1) from GAB ranged from 0.76 for milk to 0.90 for protein. Corresponding b1 terms from GBW ranged from 0.72 to 0.88. The validation reliabilities across 4 evaluations with different G were generally 36, 40, and 46% for milk, protein, and fat, respectively. Unexpectedly, validation reliabilities were generally similar across different evaluations, irrespective of AF used to compute G. Thus, although accounting for the population structure in GBW tends to simplify the blending of genomic- and pedigree-based relationships, it appeared to have little effect on the validation reliabilities.

The estimation of genomic relationships using breedwise allele frequencies among animals in multibreed populations.

Different approaches of calculating genomic measures of relationship were explored and compared with pedigree relationships (A) within and across base breeds in a crossbreed population, using genotypes for 38,194 loci of 4,106 Nordic Red dairy cattle. Four genomic relationship matrices (G) were calculated using either observed allele frequencies (AF) across breeds or within-breed AF. The G matrices were compared separately when the AF were estimated in the observed and in the base population. Breedwise AF in the current and base population were estimated using linear regression models of individual genotypes on breed composition. Different G matrices were further used to predict direct estimated genomic values using a genomic BLUP model. Higher variability existed in the diagonal elements of G across breeds (standard deviation=0.06, on average) compared with A (0.01). The use of simple observed AF across base breeds to compute G increased coefficients for individuals in distantly related populations. Estimated breedwise AF reduced differences in coefficients similarly within and across populations. The variability of the current adjusted G matrix decreased from 0.055 to 0.035 when breedwise AF were estimated from the base breed population. The direct estimated genomic values and their validation reliabilities were, however, unaffected by AF used to compute G when estimated with a genomic BLUP model, due to inclusion of breed means in the model. In multibreed populations, G adjusted with breedwise AF from the founder population may provide more consistency among relationship coefficients between genotyped and ungenotyped individuals in an across-breed single-step evaluation.

Genetic parameters for claw health and feet and leg conformation traits in Finnish Ayrshire cows.

Genetic parameters for different claw disorders, overall claw health and feet and leg conformation traits were estimated for Finnish Ayrshire cows. The merged data set with records of claw health and feet and leg conformation traits consisted of 105,000 observations from 52,598 Finnish Ayrshire cows between 2000 and 2010. The binary claw health data and the linearly scored conformation data were analysed using an animal model and restricted maximum likelihood method by applying the statistical package ASReml. Binomial logistic models with mixed effects were used to estimate genetic parameters for sole haemorrhages, chronic laminitis, white-line separation, sole ulcer, interdigital dermatitis, heel horn erosion, digital dermatitis, corkscrew claw and overall claw health. Estimated heritabilities for different claw disorders using a binomial logistic model ranged from 0.01 to 0.20. Estimated heritability for overall claw health using a binomial logistic model was 0.08. Estimated heritabilities for feet and leg conformation traits ranged from 0.07 to 0.39. The genetic correlations between claw health and feet and leg conformation traits ranged from -0.40 to 0.42. All phenotypic correlations were close to zero. The moderate genetic correlation, together with higher heritability of feet and leg conformation traits, showed that RLSV (rear leg side view) is a useful indicator trait to be used together with claw trimming information to increase the accuracy of breeding values for claw health in genetic evaluation.

Across breed multi-trait random regression genomic predictions in the Nordic Red dairy cattle.

The current study evaluates reliability of genomic predictions in selection candidates using multi-trait random regression model, which accounts for interactions between marker effects and breed of origin in the Nordic Red dairy cattle (RDC). The population structure of the RDC is admixed. Data consisted of individual animal breed proportions calculated from the full pedigree, deregressed proofs (DRP) of published estimated breeding values (EBV) for yield traits and genotypic data for 37,595 single nucleotide polymorphic markers. The analysed data included 3330 bulls in the reference population and 812 bulls that were used for validation. Direct genomic breeding values (DGV) were estimated using the model under study, which accounts for breed effects and also with GBLUP, which assume uniform population. Validation reliability was calculated as a coefficient of determination from weighted regression of DRP on DGV (rDRP,DGV 2), scaled by the mean reliability of DRP. Using the breed-specific model increased the reliability of DGV by 2 and 3% for milk and protein, respectively, when compared to homogeneous population GBLUP. The exception was for fat, where there was no gain in reliability. Estimated validation reliabilities were low for milk (0.32) and protein (0.32) and slightly higher (0.42) for fat.

Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters.

Accurate and fast estimation of genetic parameters that underlie quantitative traits using mixed linear models with additive and dominance effects is of great importance in both natural and breeding populations. Here, we propose a new fast adaptive Markov chain Monte Carlo (MCMC) sampling algorithm for the estimation of genetic parameters in the linear mixed model with several random effects. In the learning phase of our algorithm, we use the hybrid Gibbs sampler to learn the covariance structure of the variance components. In the second phase of the algorithm, we use this covariance structure to formulate an effective proposal distribution for a Metropolis-Hastings algorithm, which uses a likelihood function in which the random effects have been integrated out. Compared with the hybrid Gibbs sampler, the new algorithm had better mixing properties and was approximately twice as fast to run. Our new algorithm was able to detect different modes in the posterior distribution. In addition, the posterior mode estimates from the adaptive MCMC method were close to the REML (residual maximum likelihood) estimates. Moreover, our exponential prior for inverse variance components was vague and enabled the estimated mode of the posterior variance to be practically zero, which was in agreement with the support from the likelihood (in the case of no dominance). The method performance is illustrated using simulated data sets with replicates and field data in barley.