Research on the Freezing Phenomenon of Quantum Correlation by Machine Learning

Quantum correlation shows a fascinating nature of quantum mechanics and plays an important role in some physics topics,especially in the field of quantum information.Quantum correlations of the composite system can be quantified by resorting to geometric or entropy methods,and all these quantification methods exhibit the peculiar freezing phenomenon.The challenge is to find the characteristics of the quantum states that generate the freezing phenomenon,rather than only study the conditions which generate this phenomenon under a certain quantum system.In essence,this is a classification problem.Machine learning has become an effective method for researchers to study classification and feature generation.In this work,we prove that the machine learning can solve the problem of X form quantum states,which is a problem of physical significance.Subsequently,we apply the density-based spatial clustering of applications with noise(DBSCAN)algorithm and the decision tree to divide quantum states into two different groups.Our goal is to classify the quantum correlations of quantum states into two classes:one is the quantum correlation with freezing phenomenon for both Rènyi discord(α=2)and the geometric discord(Bures distance),the other is the quantum correlation of non-freezing phenomenon.The results demonstrate that the machine learning method has reasonable performance in quantum correlation research.

Comparison of Coarse Graining DEM Models Based on Exact Scaling Laws

The simulation of a large number of particles requires unacceptable computational time that is the most criticalproblem existing in the industrial application of the DEM. Coarse graining is a promising approach to facilitatethe application of DEM to industrial problems. While the current coarse graining framework is often developedin an ad-hoc manner, leading to different formulations and different solution accuracy and efficiency. Therefore,in this paper, existing coarse graining techniques have been carefully analysed by the exact scaling law which canprovide the theory basis for the upscaling method. A proper scaling rule for the size of particles and samples as wellas interaction laws have been proposed. The scaling rule is applied to a series simulations of biaxial compressiontests with different scale factors to investigate the precision of the coarse graining system. The error between theoriginal system and the coarse system shows a growing tendency as the scale factor increases. It can be concludedthat the precision of the coarse graining system is accepted when applying scaling rules based on the exact scalinglaws.

Biomass-based N doped carbon as metal-free catalyst for selective oxidation of D-xylose into D-xylonic acid

Rational design and facile preparation of low-cost and efficient catalysts for the selective converting of biomass-derived monosaccharides into high value-added chemicals is highly demanded,yet challenging.Herein,we first demonstrate a N doped defect-rich carbon(NC-800-5)as metal-free catalyst for the selective oxidation of D-xylose into D-xylonic acid in alkaline aqueous solution at 100℃ for 30 min,with 57.4%yield.The doped graphitic N is found to be the active site and hydroxyl ion participating in the oxidation of D-xylose.Hydroxyl ion and D-xylose first adsorb on NC-800-5 surface,and the aldehyde group of D-xylose is catalyzed to form germinal diols ion.Then,C–H bond break to yield carboxylic group.Furthermore,NC-800-5 catalyst shows high stability in recycled test.

Multi-Layer Graph Generative Model Using AutoEncoder for Recommendation Systems

Given the glut of information on the web,it is crucially important to have a system,which will parse the information appropriately and recommend users with relevant information,this class of systems is known as Recommendation Systems(RS)-it is one of the most extensively used systems on the web today.Recently,Deep Learning(DL)models are being used to generate recommendations,as it has shown state-of-the-art(SoTA)results in the field of Speech Recognition and Computer Vision in the last decade.However,the RS is a much harder problem,as the central variable in the recommendation system’s environment is the chaotic nature of the human’s purchasing/consuming behaviors and their interest.These user-item interactions cannot be fully represented in the Euclidean-Space,as it will trivialize the interaction and undermine the implicit interactions patterns.So to preserve the implicit as well as explicit interactions of user and items,we propose a new graph based recommendation framework.The fundamental idea behind this framework is not only to generate the recommendations in the unsupervised fashion but to learn the dynamics of the graph and predict the short and long term interest of the users.In this paper,we propose the first step,a heuristic multi-layer high-dimensional graph which preserves the implicit and explicit interactions between users and items using SoTA Deep Learning models such as AutoEncoders.To generate recommendation from this generated graph a new class of neural network architecture-Graph Neural Network-can be used.

Electrical Characteristics of Plasma Plume During Pulse Wave Laser Welding

To detect the real-time plasma plume during pulse wave Nd:YAG laser welding, experimental studies were conducted based on asynchronous signal acquisition system. The electrical signals of the laser-induced plasma plume were obtained by a passive acquisition system. The plume was directly observed and recorded using synchronous high-speed camera. The results showed that the waveform of the signals was in accordance with the periodical laser power. The signals decreased after the laser was turned on and fl uctuated relatively steadily on the stable stage and then increased to 0 V after the laser beam was turned off . The decreasing time of the electrical signals was roughly 1.0 ms, and it decreased with the increasing peak power. However, the average power had insignifi cant eff ect on the signal decreasing time.

The Controllability of Quantum Correlation under Geometry and Entropy Discords

Quantum correlation plays a critical role in the maintenance of quantum information processing and nanometer device design.In the past two decades,several quantitative methods had been proposed to study the quantum correlation of certain open quantum systems,including the geometry and entropy style discord methods.However,there are differences among these quantification methods,which promote a deep understanding of the quantum correlation.In this paper,a novel time-dependent three environmental open system model is established to study the quantum correlation.This system model interacts with two independent spin-environments(two spin-environments are connected to the other spin-environment)respectively.We have calculated and compared the changing properties of the quantum correlation under three kinds of geometry and two entropy discords,especially for the freezing phenomenon.At the same time,some original and novel changing behaviors of the quantum correlation under different timedependent parameters are studied,which is helpful to achieve the optimal revival of the quantum discord and the similar serrated form of the freezing phenomenon.Finally,it shows the controllability of the freezing correlation and the robustness of these methods by adjusting time-dependent parameters.This work provides a new way to control the quantum correlation and design nanospintronic devices.

Quantum Generative Model with Variable-Depth Circuit

In recent years,an increasing number of studies about quantum machine learning not only provide powerful tools for quantum chemistry and quantum physics but also improve the classical learning algorithm.The hybrid quantum-classical framework,which is constructed by a variational quantum circuit(VQC)and an optimizer,plays a key role in the latest quantum machine learning studies.Nevertheless,in these hybrid-framework-based quantum machine learning models,the VQC is mainly constructed with a fixed structure and this structure causes inflexibility problems.There are also few studies focused on comparing the performance of quantum generative models with different loss functions.In this study,we address the inflexibility problem by adopting the variable-depth VQC model to automatically change the structure of the quantum circuit according to the qBAS score.The basic idea behind the variable-depth VQC is to consider the depth of the quantum circuit as a parameter during the training.Meanwhile,we compared the performance of the variable-depth VQC model based on four widely used statistical distances set as the loss functions,including Kullback-Leibler divergence(KL-divergence),Jensen-Shannon divergence(JS-divergence),total variation distance,and maximum mean discrepancy.Our numerical experiment shows a promising result that the variable-depth VQC model works better than the original VQC in the generative learning tasks.

PlantCFG:A comprehensive database with web tools for analyzing candidate flowering genes in multiple plants

Dear Editor,The transition from the vegetative to the reproductive phase of plant development,known as the floral transition,plays important roles in the plant life cycle.Flowering is a vulnerable but crucial phase for building crop yield,and proper timing of this period is therefore vital for obtaining optimal yields(Jung and Muller,2009).Analysis of flowering time is currently an important research topic in several fields,including plant molecular genetics,evolutionary biology,ecology,and crop breeding(Huang et al.,2011).

Denitration of Nitroarenes Under Ambient Physiological Conditions Catalyzed by Graphdiyne-Supported Palladium

The direct cleavage of C–NO_(2)bonds for reductive denitration of nitroarenes remains a challenging transformation in synthetic organic chemistry.Herein,we report a biocompatible palladium-deposited graphdiyne nanocatalyst(Pd@GDY/DSPE-PEG)that can catalyze reductive denitration of nitroarenes under ambient physiological conditions.Mechanistic studies support this transformation via the oxidative addition of nitroarenes with Pd(0)and subsequent ligand exchange to form arylpalladium hydride.This one-step reductive denitration via Pd@GDY/DSPE-PEG successfully facilitates the repair of the nitrated proteins arising from endogenic ONOO−and restores their physiological function,including blocking the apoptosis pathway in living cells.Moreover,Pd@GDY/DSPE-PEG was further successfully applied for catalytic denitration to reduce the level of 3-nitrotyrosine residues of proteins located in the mouse brain hippocampus in vivo.This study provides an ideal strategy for designing highly active enzymatic mimicking synthetic catalysts for the regulation of the nitrated protein level and the detoxification of nitrative damage of living cells and tissues.

Self-Assembled Protein Hybrid Nanofibrils for Photosynthetic Hydrogen Evolution

In artificial photosynthesis systems,synthetic diiron complexes are popular[FeFe]-hydrogenase mimics,which are attractive for the fabrication of photocatalyst-protein hybrid structures to amplify hydrogen(H2)generation capability.However,constructing a highly bionic and efficient catalytic hybrid system is a major challenge.Notably,we designed an ideal hybrid nanofibrils system that incorporates the crucial components:(1)a[FeFe]-H2ase mimic,which has a three-arm architecture(named triFeFe)for more interaction sites and higher catalytic activity and(2)uniform hybrid nanofibrils as the biological environment in which cysteine-catalyst coordination and the hydrogen-bonding network play a vital role in both catalyst binding and hydrogen evolution reaction activity.The assembled hybrid nanofibrils achieve efficient H2 generation with a turnover number of 2.3×103,outperforming previously reported diiron catalyst-protein hybrid systems.Additionally,the hybrid nanofibrils work with photosynthetic thylakoids to produce H2,without extra photosensitizers or electron shuttle proteins,which advances the bioengineering of living systems for solar-driven biofuel production.

共轭聚合物生物电子体系研究进展

共轭聚合物类的有机共轭分子由于其特殊的电子离域效应,具有优异的光电性能.另外该类材料骨架结构丰富、能带可调、易于修饰与制备,使得这类材料除了在发光二极管、场效应晶体管和光伏电池等方面有广泛的研究和应用之外,在生物成像、生物传感、疾病诊断和治疗中也发挥了重要作用.本文综述了近年来基于水溶性共轭聚合物的生物电子体系构建和应用,主要介绍了水溶性共轭聚合物在光合作用、生物燃料电池、生物光伏、生物电子催化等方面的最新研究进展.

BnlR:A multi-omics database with various tools for Brassica napus research and breeding

In the post-genome-wide association study era,multi-omics techniques have shown great power and poten-tial for candidate gene mining and functional genomics research.However,due to the lack of effective data integration and multi-omics analysis platforms,such techniques have not still been applied widely in rape-seed,an important oil crop worldwide.Here,we report a rapeseed multi-omics database(BnlR;http:/l yanglab.hzau.edu.cn/BnlR),which provides datasets of six omics including genomics,transcriptomics,variomics,epigenetics,phenomics,and metabolomics,as well as numerous"variation-gene expression-phenotype"associations by using multiple statistical methods.In addition,a series of multi-omics search and analysis tools are integrated to facilitate the browsing and application of these datasets.BnlR is the most comprehensive multi-omics database for rapeseed so far,and two case studies demonstrated its power to mine candidate genes associated with specific traits and analyze their potential regulatory mechanisms.

Solar-Driven Producing of Value-Added Chemicals with Organic Semiconductor-Bacteria Biohybrid System

Photosynthetic biohybrid systems exhibit promising performance in biosynthesis;however,these systems can only produce a single metabolite and cannot further transform carbon sources into highly valuable chemical production.Herein,a photosynthetic biohybrid system integrating biological and chemical cascade synthesis was developed for solar-driven conversion of glucose to value-added chemicals.A new ternary cooperative biohybrid system,namely bacterial factory,was constructed by self-assembling of enzyme-modified light-harvesting donor-acceptor conjugated polymer nanoparticles(D-A CPNs)and genetically engineered Escherichia coli(E.coli).The D-A CPNs coating on E.coli could effectively generate electrons under light irradiation,which were transferred into E.coli to promote the 37%increment of threonine production by increasing the ratio of nicotinamide adenine dinucleotide phosphate(NADPH).Subsequently,the metabolized threonine was catalyzed by threonine deaminase covalently linking with D-A CPNs to obtain 2-oxobutyrate,which is an important precursor of drugs and chemicals.The 2-oxobutyrate yield under light irradiation is increased by 58%in comparison to that in dark.This work provides a new organic semiconductor-microorganism photosynthetic biohybrid system for biological and chemical cascade synthesis of highly valuable chemicals by taking advantage of renewable carbon sources and solar energy.

Living Bacteria-Mediated Aerobic Photoinduced Radical Polymerization for in Situ Bacterial Encapsulation and Differentiation

Conventional polymerizations mediated by living cells typically require synthetic transition-metal complexes or photoredox catalysts.Herein,we report an alternative photoinduced polymerization strategy for preparing functional polymer hydrogels through bacteria-initiated radical polymerization of acrylamides in ordinary culture media.Upon light irradiation under ambient conditions,polyacrylamides were obtained with molecular weights of over 150 kDa using various bacteria.

Unexpected Photocatalytic Degeneration of NAD^(+) for Inducing Apoptosis of Hypoxia Cancer Cells

Developing customized chemical reactions that could regulate a specific biological process on demand is regarded as an advanced and promising strategy for treating diseases.However,conventional chemical reactions become challenging in complex physiological environments,which demand mild reaction conditions,high efficiency,good biocompatibility,and strong controllability.Moreover,the effects of the achieved reactions on the real biological system are usually further lessened.Herein,we describe an advanced photocatalytic reaction that irreversibly converted nicotinamide adenine dinucleotide(NAD+)to nicotinamide and adenosine diphosphate(ADP)-ribose by the cationic conjugated poly(fluorene-co-phenylene)(PFP).This reaction was introduced to tumor cells and triggered cell apoptosis.Under white-light illumination,the photocatalytic reaction decreased the NAD+ratio in tumor cells,disrupted the mitochondrial membrane potential,inhibited the synthesis of adenosine triphosphate(ATP),and effectively induced apoptosis.We propose a mechanism of the reaction where PFP is photoexcited to PFP*,and the obtained photoelectrons are transferred from PFP*to NAD+to produce nicotinamide and another unstable intermediate,ADP-ribosyl radical.ADP-ribosyl radical quickly reacts with triethanolamine to form ADP-ribose.This intracellular utilization of a specific photocatalytic reaction could offer a new approach to affect biological function for efficient cancer treatment.

Polymer Materials Synthesized through Cell-Mediated Polymerization Strategies for Regulation of Biological Functions

CONSPECTUS:As essential components of living organisms,biomacromolecules construct cell scaffolds and regulate cell activities and biological functions through chemical transformations in biological systems.Inspired by the functional evolution in the formation of natural structures,in situ polymerization methods have been developed to create functional synthetic macromolecules inside or on the surface of living cells.Given the diversity of cell species and the complexity of biological pathways,selected strategies can be employed to control the synthesis of functional polymers that utilize the dynamic cellular microenvironment.In this Account,we summarize recent work in the field of designing cell-mediated in situ polymerization methods,with which we demonstrate their application prospects including tumor cell labeling and treatment,microbial photosynthetic efficiency regulation,and hydrogel generation.The purpose of these efforts is to design polymerization reactions in response to endogenous or exogenous stimuli and to describe the underlying response mechanisms.By reasonable design of molecular structures,in situ synthesized polymers in the cell microenvironment implement regulation of biological functions.For example,using specific redox activity combined with light irradiation,bacteria can mediate the generation of functional polymers as the encapsulating matrix or with antibacterial effects.Conjugated polymers synthesized on the microalgae surface expanded the spectral absorption and improved photosynthetic efficiency.Meanwhile,characteristics of the cellular microenvironment could initiate various polymerization reactions inside living cells,including oxidative thiol cross-linking,condensation polymerization,and free radical polymerization.These reactions can be selectively conducted with reactive species generated in tumor cells,and the resulting polymers showed prolonged retention inside cells for modulating cell behaviors.Further development of cell-mediated polymerization strategies would provide an innovative platform for research and applications of multifunctional biomaterials and engineered biohybrid systems.

Electrochemical Regulation of Antibacterial Activity Using Ferrocene-Containing Antibiotics

The widespread use of antibiotics causes the accumulation of a large amount of antibiotics in the environment.Excessively active antibiotics in the environment results in the emergence of bacterial resistance.Building smart antibiotics capable of reversible regulation between active and inactive states on demand is a promising approach to address this issue.Herein,a ferrocene-containing quaternary ammonium compound has been developed for electrochemical redox-controlled bacterial inhibition.The reversible switch of the reduced and oxidized ferrocene groups between hydrophobic and hydrophilic states triggers the assembly and disassembly of the micelles while modulating the interactions of antibiotic molecules with the bacteria membrane,providing a new way to regulate antibacterial activity.In addition,the alternate use of reduced and oxidized antibiotics exhibits a favorable effect in preventing bacterial resistance.Thus,an unconventional strategy is offered to prevent the build-up of active bactericide in the environment and decrease bacterial resistance.

LncRNA RP11-307C12.11 promotes the growth of hepatocellular carcinoma by acting as a molecular sponge of miR-138

Background:Abnormal expression of long non-coding RNAs(lncRNAs)has been found in almost all tumors in humans,providing numerous potential diagnostic and prognostic biomarkers,and therapeutic targets.Materials and methods:The Cancer Genome Atlas(TCGA)database was used to screen potential LncRNAs,and 30 paired hepatocellular carcinoma(HCC)tissues were used to investigate RP11-307C12.11 expression levels by qRT-PCR and another 105 HCC tissues by in situ hybridizsation(ISH).RP11-307C12.11 overexpression and knockdown experiments were performed to investigate the effects of RP11-307C12.11 on HCC growth through in vitro and in vivo assays(MTT assay,colony formation assay,EdU assay,and xenograft model).The molecular mechanism underlying these effects was confirmed by MS2-RIP-assay,RIP assay,luciferase assay,and rescue experiments.Results:RP11-307C12.11 expression level was significantly higher in tumor tissues than in the adjacent normal tissues.Elevated RP11-307C12.11 expression level was associated with poor prognosis of HCC patients,and it may be represented as an independent prognostic biomarker in patients with HCC.Functionally,RP11-307C12.11 overexpression promoted HCC growth both in vitro and in vivo;however,its knockdown reversed these effects.Mechanistically,we found that RP11-307C12.11 expressed predominantly in the cytoplasm and sponged microRNA(miR)-138 to regulate its common target CCND1 and PDK1.Conclusions:Thus,we found that RP11-307C12.11 acts as an oncogene in HCC by binding to miR-138,which might provide a novel target for HCC therapy.

Synthesis of easily-processable collagen bio-inks using ionic liquid for 3D bioprinted liver tissue models with branched vascular networks

Bioprinting has been a flouring way to fabricate complex tissue and organ mimics via precisely depositing printable cell-laden biomaterials.However,there is a limited number of biomaterials that fulfill the mechanical property of printing while meeting the responsive environment desired for the cells.Despite excellent cell compatibility and bioactivity,collagen suffers from difficulties in processing and printability which inhibited its utilization in three-dimensional(3D)bioprinting.Herein,we address this limitation by using ionic liquid as the solvent in the modification process,enabling collagens modified with quantified norbornene for chemical crosslink and extrusion-based 3D printing.With improved solubility and rheological properties,norbornene-functionalized collagen(Col-Nor)exhibited better shape fidelity in extrusion-based 3D printing compared with the one before modification.Photo-crosslinked Col-Nor hydrogel provided structural support and promoted the adhesion,proliferation,and differentiation of various types of cells,which afforded a centimeter-scale liver tissue model.This highly generalizable methodology expands printable,versatile,and tunable hydrogels developed from the natural extracellular matrix,allowing the biofabrication of 3D liver tissue model with branched vascular networks.

A knowledge integration strategy for the selection of a robust multi-stress biomarkers panel for Bacillus subtilis

One challenge in the engineering of biological systems is to be able to recognise the cellular stress states of bacterial hosts,as these stress states can lead to suboptimal growth and lower yields of target products.To enable the design of genetic circuits for reporting or mitigating the stress states,it is important to identify a relatively reduced set of gene biomarkers that can reliably indicate relevant cellular growth states in bacteria.Recent advances in high-throughput omics technologies have enhanced the identification of molecular biomarkers specific states in bacteria,motivating computational methods that can identify robust biomarkers for experimental characterisation and verification.Focused on identifying gene expression biomarkers to sense various stress states in Bacillus subtilis,this study aimed to design a knowledge integration strategy for the selection of a robust biomarker panel that generalises on external datasets and experiments.We developed a recommendation system that ranks the candidate biomarker panels based on complementary information from machine learning model,gene regulatory network and co-expression network.We identified a recommended biomarker panel showing high stress sensing power for a variety of conditions both in the dataset used for biomarker identification(mean f1-score achieved at 0.99),as well as in a range of independent datasets(mean f1-score achieved at 0.98).We discovered a significant correlation between stress sensing power and evaluation metrics such as the number of associated regulators in a B.subtilis gene regulatory network(GRN)and the number of associated modules in a B.subtilis co-expression network(CEN).GRNs and CENs provide information relevant to the diversity of biological processes encoded by biomarker genes.We demonstrate that quantitatively relating meaningful evaluation metrics with stress sensing power has the potential for recognising biomarkers that show better sensitivity and robustness to an extended set of stress conditions and enable a more reliable biomarker panel selection.