arXiv subjects

K. Zhou

Publications and source records attributed to K. Zhou.

5 recordsLinked to original sources

arXiv2020

Nature of the charge-density wave excitations in cuprates

The discovery of charge-density wave (CDW)-related effects in the resonant inelastic x-ray scattering (RIXS) spectra of cuprates holds the tantalizing promise of clarifying the interactions that stabilize the electronic order. Here, we report a comprehensive RIXS study of La2-xSrxCuO4 (LSCO) finding that CDW effects persist up to a remarkably high doping level of x = 0.21 before disappearing at x = 0.25. The inelastic excitation spectra remain essentially unchanged with doping despite crossing a topological transition in the Fermi surface. This indicates that the spectra contain little or no direct coupling to electronic excitations near the Fermi surface, rather they are dominated by the resonant cross-section for phonons and CDW-induced phonon-softening. We interpret our results in terms of a CDW that is generated by strong correlations and a phonon response that is driven by the CDW-induced modification of the lattice.

cond-mat.supr-con↗

arXiv2018

Deep learning framework DNN with conditional WGAN for protein solubility prediction

Protein solubility plays a critical role in improving production yield of recombinant proteins in biocatalyst and pharmaceutical field. To some extent, protein solubility can represent the function and activity of biocatalysts which are mainly composed of recombinant proteins. Highly soluble proteins are more effective in biocatalytic processes and can reduce the cost of biocatalysts. Screening proteins by experiments in vivo is time-consuming and expensive. In literature, large amounts of machine learning models have been investigated, whereas parameters of those models are underdetermined with insufficient data of protein solubility. A data augmentation algorithm that can enlarge the dataset of protein solubility and improve the performance of prediction model is highly desired, which can alleviate the common issue of insufficient data in biotechnology applications for developing machine learning models. We first implemented a novel approach that a data augmentation algorithm, conditional WGAN was used to improve prediction performance of DNN for protein solubility from protein sequence by generating artificial data. After adding mimic data produced from conditional WGAN, the prediction performance represented by $R^{2}$ was improved compared with the $R^{2}$ without data augmentation. After tuning the hyperparameters of two algorithms and organizing the dataset, we achieved a $R^{2}$ value of $45.04\%$, which enhanced $R^{2}$ about $10\%$ compared with the previous study using the same dataset. Data augmentation opens the door to applications of machine learning models on biological data, as machine learning models always fail to be well trained by small datasets.

q-bio.QM↗

arXiv2018

Develop machine learning based predictive models for engineering protein solubility

Protein activity is a significant characteristic for recombinant proteins which can be used as biocatalysts. High activity of proteins reduces the cost of biocatalysts. A model that can predict protein activity from amino acid sequence is highly desired, as it aids experimental improvement of proteins. However, only limited data for protein activity are currently available, which prevents the development of such models. Since protein activity and solubility are correlated for some proteins, the publicly available solubility dataset may be adopted to develop models that can predict protein solubility from sequence. The models could serve as a tool to indirectly predict protein activity from sequence. In literature, predicting protein solubility from sequence has been intensively explored, but the predicted solubility represented in binary values from all the developed models was not suitable for guiding experimental designs to improve protein solubility. Here we propose new machine learning models for improving protein solubility in vivo. We first implemented a novel approach that predicted protein solubility in continuous numerical values instead of binary ones. After combing it with various machine learning algorithms, we achieved a prediction accuracy of 76.28 percent when Support Vector Machine algorithm was used. Continuous values of solubility are more meaningful in protein engineering, as they enable researchers to choose proteins with higher predicted solubility for experimental validation, while binary values fail to distinguish proteins with the same value. There are only two possible values so many proteins have the same one.

q-bio.QM↗

arXiv2016

Heavy ions at the Future Circular Collider

The Future Circular Collider (FCC) Study is aimed at assessing the physics potential and the technical feasibility of a new collider with centre-of-mass energies, in the hadron-hadron collision mode, seven times larger than the nominal LHC energies. Operating such machine with heavy ions is an option that is being considered in the accelerator design studies. It would provide, for example, Pb-Pb and p-Pb collisions at sqrt{s_NN} = 39 and 63 TeV, respectively, per nucleon-nucleon collision, with integrated luminosities above 30 nb^-1 per month for Pb-Pb. This is a report by the working group on heavy-ion physics of the FCC Study. First ideas on the physics opportunities with heavy ions at the FCC are presented, covering the physics of the Quark-Gluon Plasma, of gluon saturation, of photon-induced collisions, as well as connections with other fields of high-energy physics.

hep-ph↗

arXiv2015

Undersaturation of quarks at early stages of relativistic nuclear collisions: the hot glue initial scenario and its observable signatures

The early stage of high multiplicity nuclear collisions is represented by a nearly quarkless, hot, deconfined pure gluon plasma. This new scenario should be characterized by a suppression of high $p_T$ photons and dileptons as well as by reduced baryon to meson ratios. We present the numerical results for central Pb+Pb collisions at the LHC energies by using the ideal Bjorken hydrodynamics with time-dependent quark fugacity. It is shown that about 25\% of final total entropy is generated during the hydrodynamic evolution of chemically undersaturated quark-gluon plasma.

hep-ph↗