Structural Domain Imaging and Direct Determination of Crystallographic Orientation in Noncentrosymmetric Ca3 Ru2O7 Using Polarized Light Reflectance

The noncentrosymmetricity of a prototypical correlated electron system Ca3Ru2O7 renders extensive interest in the possible polar metallic state,along with multiple other closely competing interactions.However,the structural domain formation in this material often complicates the study of intrinsic material properties.It is crucial to fully characterize the structural domains for unrevealing underlying physics.Here,we report the domain imaging on Ca3Ru2O7 crystal using the reflection of polarized light at normal incidence.The reflection anisotropy measurement utilizes the relative orientation between electric field component of the incident polarized light and the principal axis of the crystal,and gives rise to a peculiar contrast.The domain walls are found to be the interfaces between 90° rotated twin crystals by complementary magnetization measurements.A distinct contrast in reflectance is also found in the opposite cleavage surfaces,owing to the polar mode of the RuO6 octahedra.More importantly,the analysis of the contrast between all inequivalent cleavage surfaces enables a direct determination of the crystallographic orientation of each domain.Such an approach provides an efficient yet feasible method for structural domain characterization,which can also find applications in noncentrosymmetric crystals in general.

Clustering in the Wireless Channel with a Power Weighted Statistical Mixture Model in Indoor Scenario

Cluster-based channel model is the main stream of fifth generation mobile communications, thus the accuracy of clustering algorithm is important. Traditional Gaussian mixture model (GMM) does not consider the power information which is important for the channel multipath clustering. In this paper, a normalized power weighted GMM (PGMM) is introduced to model the channel multipath components (MPCs). With MPC power as a weighted factor, the PGMM can fit the MPCs in accordance with the cluster-based channel models. Firstly, expectation maximization (EM) algorithm is employed to optimize the PGMM parameters. Then, to further increase the searching ability of EM and choose the optimal number of components without resort to cross-validation, the variational Bayesian (VB) inference is employed. Finally, 28 GHz indoor channel measurement data is used to demonstrate the effectiveness of the PGMM clustering algorithm.

Pavement structure and materials design for sea-crossing bridges and tunnel:Case study of the Hong Kong-Zhuhai-Macao Bridge

With the continuous development of bridge and tunnel construction technologies,large-scale sea-crossing bridges and tunnels have gradually become the preferred choice for regional traffic.The construction technology of Hong Kong-Zhuhai-Macao Bridge(HZMB),one of the most representative sea-crossing passageways,is instructive for the construction of other large sea-crossing infrastructures.At present,the pavement design method of sea-crossing passageways lacks pertinence as it still refers to specifications for design of common pavement.Therefore,it is necessary to consider the bridge and tunnel pavement of HZMB as a typical example to analyze key technical problems encountered in its design,construction and operation.Novel solutions for material selection and structural design built upon the analysis of such critical problems should thus follow up.Based on comprehensive literature research,it can be found that environmental variability,tunnel closure,structural differential settlement and expansion deformation are the key technical problems faced by pavement of sea-crossing passageways.In view of the environmental variability,the steel deck-paving material and structure design of GMA-10 t SMA-13 is innovatively proposed.As for the closure of immersed tube tunnel,warm-mix flame retardant asphalt mixture is used to control pavement design through key indexes such as temperature and limit oxygen index.Regarding the deformation of immersed pipe joints,BJ200 asphalt seamless expansion joint material is introduced,which effectively satisfies the multi-directional deformation between pipe joints and ensures the smoothness of the road surface and driving comfort.For segmental joints,double-layer waterproof-coiled material is used to effectively prevent reflective cracks while ensuring the continuity of asphalt concrete pavement.Therefore,this paper provides a panel of ideas and methods for the pavement design of the same type of sea-crossing passageways.

Chemical Constituents from the Flowers of Dendrobium chrysanthum

This study aimed to investigate the chemical constituents from the flowers of Dendrobium chrysanthum and determine its bioactive compounds. [Method] The compounds were extracted by 95% alcohol and isolated by column chromatography on silica gel and Sephadex LH-20. Their structures were identified by spectroscopic analysis （1H NMR, 13CNMR and ElMS）. [Result] Nine compounds were obtained and identified as stigmasterol , β-sitosterol , linoleic acid , stigmast-4-en-3-one , dibutyl phthalate , moscatilin , ergosterol , ergosterol peroxide and daucosterol . [Conclusion] Al compounds were isolated from its flowers for the first time.

Extraction and Structure Elucidation of Phenols from Dendrobium thyrsiflorum

[Objective] This study aimed to investigate the chemical constituents of Dendrobium thyrsiflorum and determine its bioactive compounds. [Method] The compounds were extracted by 95% alcohol and isolated by column chromatography on silica gel and Sephadex LH-20. Their structures were identified by spectroscopic analysis(1H NMR and13 C NMR). [Result] Seven compounds were obtained and identified as 4, 7-dihydroxy-2-methoxy-9, 10-dihydrophenan-threne(1), syringaldehyde(2),moscatin(3), gigantol(4), batatasin III(5), tristin(6) and stigmasterol(7). [Conclusion] Compounds 1 and 2 were isolated from this medicinal plant for the first time.

Detecting compromised accounts caused by phone number recycling on e-commerce platforms: taking Meituan as an example

Phone number recycling(PNR)refers to the event wherein a mobile operator collects a disconnected number and reassigns it to a new owner.It has posed a threat to the reliability of the existing authentication solution for e-commerce platforms.Specifically,a new owner of a reassigned number can access the application account with which the number is associated,and may perform fraudulent activities.Existing solutions that employ a reassigned number database from mobile operators are costly for e-commerce platforms with large-scale users.Thus,alternative solutions that depend on only the information of the applications are imperative.In this work,we study the problem of detecting accounts that have been compromised owing to the reassignment of phone numbers.Our analysis on Meituan's real-world dataset shows that compromised accounts have unique statistical features and temporal patterns.Based on the observations,we propose a novel model called temporal pattern and statistical feature fusion model(TSF)to tackle the problem,which integrates a temporal pattern encoder and a statistical feature encoder to capture behavioral evolutionary interaction and significant operation features.Extensive experiments on the Meituan and IEEE-CIS datasets show that TSF significantly outperforms the baselines,demonstrating its effectiveness in detecting compromised accounts due to reassigned numbers.

Characteristics of wind-sand transportation along railways in the eastern fringe of the Taklimakan Desert and sand control system

This paper focuses on the Korla-Ruoqiang desert railway line,utilizing ERAS(ERA5 is the fifth generation of ECMWF(European Centre for Medium-Range Weather Forecasts)atmospheric reanalysis global climate data)wind data to conduct a comprehensive analysis of the wind energy environment along the line in all aspects,reveals the characteristics of wind-sand transport,and puts forward the countermeasures to prevent and control wind-sand disasters.The results of the study indicate that:(1)the wind-sand along the line mainly occurs in the spring and summer seasons,and the dominant sand-moving directions are easterly(ENE,E and NNE)and northeasterly(NE).(2)The average annual sand-moving wind speed ranges from 5.80 m/s to 7.25 m/s,and the annual frequency of sand-moving ranges from 11.99%to 37.26%.(3)The annual sand drift potential(DP)along the line ranges from 69.20 VU to 607.24 VU,with three types of wind energy environments:low,medium and high.The resultant drift potential(RDP)ranges from 45.52 VU to 547.49 VU,and the wind variability index(RDP/DP)is between 0.54 and 0.90.(4)The average sand transport quantity along the line ranges from 2.92 m^(3)/m/a to 9.09 m3/m/a.Based on these results,we optimize the sand blocking,sand fixing and wind erosion prevention measures for different types of wind-sand environments,establish a scientific and efficient wind-sand protection and control system to solve the wind-sand problems and provide a scientific foundation for the prevention and control of wind-sand disasters along the line.

Autophagy and cancer treatment: four functional forms of autophagy and their therapeutic applications

Cancer is the leading cause of death worldwide. Drugs play a pivotal role in cancer treatment, but the complex biological processes of cancer cells seriously limit the efficacy of various anticancer drugs. Autophagy, a self-degradative system that maintains cellular homeostasis, universally operates under normal and stress conditions in cancer cells. The roles of autophagy in cancer treatment are still controversial because both stimulation and inhibition of autophagy have been reported to enhance the effects of anticancer drugs. Thus, the important question arises as to whether we should try to strengthen or suppress autophagy during cancer therapy. Currently, autophagy can be divided into four main forms according to its different functions during cancer treatment: cytoprotective(cell survival), cytotoxic(cell death), cytostatic(growth arrest), and nonprotective(no contribution to cell death or survival). In addition, various cell death modes, such as apoptosis, necrosis, ferroptosis, senescence, and mitotic catastrophe, all contribute to the anticancer effects of drugs. The interaction between autophagy and these cell death modes is complex and can lead to anticancer drugs having different or even completely opposite effects on treatment. Therefore, it is important to understand the underlying contexts in which autophagy inhibition or activation will be beneficial or detrimental.That is, appropriate therapeutic strategies should be adopted in light of the different functions of autophagy. This review provides an overview of recent insights into the evolving relationship between autophagy and cancer treatment.

An artificial-neural-network-based surrogate modeling workflow for reactive transport modeling

Process-based reactive transport modeling(RTM)integrates thermodynamic and kinetically controlled fluid-rock interactions with fluid flow through porous media in the subsurface and surface environment.RTM is usually conducted through numerical programs based on the first principle of physical processes.However,the calculation for complex chemical reactions in most available programs is an iterative process,where each iteration is in general computationally intensive.A workflow of neural networkbased surrogate model as a proxy for process-based reactive transport simulation is established in this study.The workflow includes(1)base case RTM design,(2)development of training experiments,(3)surrogate model construction based on machine learning,(4)surrogate model validation,and(5)prediction with the calibrated model.The training experiments for surrogate modeling are generated and run prior to the predictions using RTM.The results show that the predictions from the surrogate model agree well with those from processes-based RTM but with a significantly reduced computational time.The well-trained surrogate model is especially useful when a large number of realizations are required,such as the sensitivity analysis or model calibration,which can significantly reduce the computational time compared to that required by RTM.The benefits are(1)it automatizes the experimental design during the sensitivity analysis to get sufficient numbers and coverage of the training cases;(2)it parallelizes the calculations of RTM training cases during the sensitivity analysis to reduce the simulation time;(3)it uses the neural network algorithm to rank the sensitivity of the parameters and to search the optimal solution for model calibration.

腹腔镜胆囊切除术难度评分系统在急性胆囊炎治疗中的应用价值

目的:探讨腹腔镜胆囊切除术难度评分系统(DiLCs)在急性胆囊炎腹腔镜胆囊切除术(LC)治疗中的应用价值。方法:回顾性分析2016年5月至2019年1月在新疆维吾尔自治区人民医院行LC的158例急性胆囊炎患者临床资料。其中男88例,女70例;平均年龄(60±18)岁。患者均签署知情同意书,符合医学伦理学规定。分别采用DiLCs评分系统和急性胆囊炎京东指南2018(TG18)分级系统对患者围手术期指标进行分析,两个分级系统与临床指标相关性分析采用单因素方差分析、秩和检验等;采用ROC曲线下面积(AUC)对两个系统进行比较。结果:随着两种评分系统评分增加,WBC、手术时间、术中出血量、住院时间、胆囊造瘘率、中转开腹率等急性胆囊炎相关指标增加,差异有统计学意义(P<0.05)。TG18和DiLCs评估并发症的AUC分别为0.889(95%CI:0.829~0.933)和0.509(95%CI:0.428~0.598),TG18明显优于DiLCs(Z=3.795,P<0.05)。DiLCs和TG评估中转开腹率的AUC分别为0.697(95%CI:0.619~0.768)和0.746(95%CI:0.670~0.811),差异无统计学意义(Z=0.829,P>0.05)。DiLCs和TG评估胆囊造瘘率的AUC分别为0.664(95%CI:0.584~0.737)和0.770(95%CI:0.697~0.833),差异无统计学意义(Z=1.639,P>0.05)。结论:DiLCs可有效评估患者手术难度和治疗方式选择,其评估能力与TG18一致,而在手术风险预测等方面弱于TG18。

Integrate intere-well connectivity data with static reservoir models based on Bayesian formalism

The inter-well connectivity calculated from reservoir dynamic production data reflects formation heterogeneity quantitatively.Currently,the calculated inter-well connectivity between pair wells is mainly used as a tool for water flood management but not for quantitative reservoir characterization.This study proposes an innovative,dynamic data integration workflow that can integrate inter-well connectivity with a static reservoir model.In the workflow,the first step is calculating the inter-well connectivity vectors from the reservoir pairwise injector and producer wells.The second step covers interpolation in the domain of interest.The third step is to update the permeability model based on the Bayesian updating method.The result of this study shows that integrating the calculated inter-well connectivity with the static models enhances model reliability and it also provides an insight to deeper geological understanding reflected from dynamic data integration in reservoir modeling.

A review on room-temperature self-healing polyurethane:synthesis,self-healing mechanism and application

Polyurethanes have been widely used in many fields due to their remarkable features such as excellent mechanical strength, good abrasion resistance, toughness, low temperature flexibility, etc. In recent years, room-temperature self-healing polyurethanes have been attracting broad and growing interest because under mild conditions, room- temperature self-healing polyurethanes can repair damages, thereby extending their lifetimes and reducing maintenance costs. In this paper, the recent advances of room-temperature self-healing polyurethanes based on dynamic covalent bonds, noncovalent bonds and combined dual or triple dynamic bonds are reviewed, focusing on their synthesis methods and self-healing mechanisms, and their mechanical properties, healing efficiency and healing time are also described in detial. In addition, the latest applications of room-temperature self-healing polyurethanes in the fields of leather coatings, photoluminescence materials, flexible electronics and biomaterials are summarized. Finally, the current challenges and future development directions of the room-temprature self-healing polyurethanes are highlighted. Overall, this review is expected to provide a valuable reference for the prosperous development of room- temperature self-healing polyurethanes.

Gaussian Backbone-Based Spherical Evolutionary Algorithm with Cross-search for Engineering Problems

In recent years,with the increasing demand for social production,engineering design problems have gradually become more and more complex.Many novel and well-performing meta-heuristic algorithms have been studied and developed to cope with this problem.Among them,the Spherical Evolutionary Algorithm(SE)is one of the classical representative methods that proposed in recent years with admirable optimization performance.However,it tends to stagnate prematurely to local optima in solving some specific problems.Therefore,this paper proposes an SE variant integrating the Cross-search Mutation(CSM)and Gaussian Backbone Strategy(GBS),called CGSE.In this study,the CSM can enhance its social learning ability,which strengthens the utilization rate of SE on effective information;the GBS cooperates with the original rules of SE to further improve the convergence effect of SE.To objectively demonstrate the core advantages of CGSE,this paper designs a series of global optimization experiments based on IEEE CEC2017,and CGSE is used to solve six engineering design problems with constraints.The final experimental results fully showcase that,compared with the existing well-known methods,CGSE has a very significant competitive advantage in global tasks and has certain practical value in real applications.Therefore,the proposed CGSE is a promising and first-rate algorithm with good potential strength in the field of engineering design.

Integrated single-cell RNA sequencing analysis reveals distinct cellular and transcriptional modules associated with survival in lung cancer

Lung adenocarcinoma(LUAD)and squamous carcinoma(LUSC)are two major subtypes of non-small cell lung cancer with distinct pathologic features and treatment paradigms.The heterogeneity can be attributed to genetic,transcriptional,and epigenetic parameters.Here,we established a multi-omics atlas,integrating 52 single-cell RNA sequencing and 2342 public bulk RNA sequencing.We investigated their differences in genetic amplification,cellular compositions,and expression modules.We revealed that LUAD and LUSC contained amplifications occurring selectively in subclusters of AT2 and basal cells,and had distinct cellular composition modules associated with poor survival of lung cancer.Malignant and stage-specific gene analyses further uncovered critical transcription factors and genes in tumor progression.Moreover,we identified subclusters with proliferating and differentiating properties in AT2 and basal cells.Overexpression assays of ten genes,including sub-cluster markers AQP5 and KPNA2,further indicated their functional roles,providing potential targets for early diagnosis and treatment in lung cancer.

Negative magnetoresistance in Weyl semimetals NbAs and NbP： Intrinsic chiral anomaly and extrinsic effects

Chiral anomaly-induced negative magnetoresistance （NMR） has been widely used as critical transport evidence for the existence of Weyl fermions in topological semimetals. In this mini-review, we discuss the general observation of NMR phenomena in non-centrosymmetric NbP and NbAs. We show that NMR can arise from the intrinsic chiral anomaly of Weyl fermions and/or extrinsic effects, such as the superimposition of Hall signals; field-dependent inhomogeneous current flow in the bulk, i.e., current jetting; and weak localization （WL） of coexistent trivial carriers. The WLmcontrolled NMR is heavily dependent on sample quality and is characterized by a pronounced crossover from positive to negative MR growth at elevated temperatures, resulting from the competition between the phase coherence time and the spin-orbital scattering constant of the bulk trivial pockets. Thus, the correlation between the NMR and the chiral anomaly need to be scrutinized without the support of complimentary techniques. Because of the lifting of spin degeneracy, the spin orientations of Weyl fermions are either parallel or antiparallel to the momentum, which is a unique physical property known as helicity. The conservation of helicity provides strong protection for the transport of Weyl fermions, which can only be effectively scattered by magnetic impurities. Chemical doping with magnetic and non-magnetic impurities is thus more convincing than the NMR method for detecting the existence of Weyl fermions.

The continuing shrinkage of snow cover in High Mountain Asia over the last four decades

High Mountain Asia (HMA), known as Earth's "Third Pole" and"Asia's water tower",. is the largest glacier and snow reservoir onEarth except for the polar ice sheets (Text S1 and Fig. S1 online)。Snow is an important component of the HMA cryosphere, and itsvariability directly affects the water and energy balances in theregion [1,2] The average warming rate in the HMA region in recentdecades is approximately twofold higher than the average warm-ing rate in China and the world in the same period. The climatologyand trends of snow cover in the HMA have been investigated basedon station and satellite observations. However, these methodshave some limitations, resulting in large uncertainties or limita-tions in assessing long-term snow cover changes in the HMA.Snowpack changes in the HMA region analyzed over a short timeseries may also lead to conclusions that are inconsistent with con-ventional assertions. Several studies of the temporal and spatialvariability of snow cover in the HMA region using short-termModerate Resolution lmaging Spectroradiometer data revealed nosignificant shrinkage in the snow cover area (SCA) [3].

Crystal design of bismuth oxyiodide with highly exposed (110) facets on curved carbon nitride for the photocatalytic degradation of pollutants in wastewater

Crystalline materials with specific facet atomic arrangements and crystal facet structures exhibit unique functions according to their facet effects, quantum size effects and physical and chemical properties. In this study, a novel high-exposure (110) facet of bismuth oxyiodide (BiOI) was prepared (denoted as BiOI-110), and designed as nanosheets rich in oxygen vacancies by crystal facet design and regulation. Graphitic carbon nitride was designed as curved carbon nitride with dibromopyrazine, denoted as DCN, which contributed to a significant structural distortion in plane symmetry and improved the separation of charge carriers. Novel heterostructured BiOI-110/DCN nanosheets with a high-exposure (110) facet and abundant oxygen vacancies were successfully designed to enhance the photocatalytic degradation of organic pollutants. It was demonstrated that complete and tight contact between BiOI-110 and DCN was achieved by changing the size and crystal facet of BiOI. Oxytetracycline (OTC) and methyl blue dyes were used as targets for pollutant degradation, and 85.6% and 96.5% photocatalytic degradation efficiencies, respectively, were observed in the optimal proportion of 7% BiOI-110/DCN. The experimental results and electron spin resonance analysis showed that •O_(2)^(–) and h^(+) played a major role in the process of pollutant degradation. Additionally, high-resolution liquid chromatography-mass spectrography was used to identify the reaction intermediates of OTC, and the possible degradation pathway of this pollutant was proposed. Finally, the excellent reusability of BiOI-110/DCN nanomaterials was confirmed, providing a new approach for the removal of antibiotics that are difficult to biodegrade. Overall, crystal facet design has been proven to have broad prospects in improving the water environment.

DeepNitro: Prediction of Protein Nitration and Nitrosylation Sites by Deep Learning

Protein nitration and nitrosylation are essential post-translational modifications （PTMs）involved in many fundamental cellular processes. Recent studies have revealed that excessive levels of nitration and nitrosylation in some critical proteins are linked to numerous chronic diseases.Therefore, the identification of substrates that undergo such modifications in a site-specific manner is an important research topic in the community and will provide candidates for targeted therapy. In this study, we aimed to develop a computational tool for predicting nitration and nitrosylation sites in proteins. We first constructed four types of encoding features, including positional amino acid distributions, sequence contextual dependencies, physicochemical properties, and position-specific scoring features, to represent the modified residues. Based on these encoding features, we established a predictor called DeepNitro using deep learning methods for predicting protein nitration and nitrosylation. Using n-fold cross-validation, our evaluation shows great AUC values for DeepNitro, 0.65for tyrosine nitration, 0.80 for tryptophan nitration, and 0.70 for cysteine nitrosylation, respectively,demonstrating the robustness and reliability of our tool. Also, when tested in the independent dataset, DeepNitro is substantially superior to other similar tools with a 7%à42%improvement in the prediction performance. Taken together, the application of deep learning method and novel encoding schemes, especially the position-specific scoring feature, greatly improves the accuracy of nitration and nitrosylation site prediction and may facilitate the prediction of other PTM sites. DeepNitro is implemented in JAVA and PHP and is freely available for academic research at http：//deepnitro.renlab.org.

Anisotropic gapping of topological Weyl rings in the charge-density-wave superconductor In_(x)TaSe_(2)

Topological materials and topological phases have recently become a hot topic in condensed matter physics.In this work,we report an In-intercalated transition-metal dichalcogenide In_(x)TaSe_(2)(named 112 system),a topological nodal-line semimetal in the prep seffiffinffi ce of both charge density wave(CDW)and superconductivity.In the x=0.58 sample,the 2×√3 commensurate CDW(CCDW)and the 2×2 CCDW are observed below 116 and 77 K,respectively.Consistent with theoretical calculations,the spin–orbital coupling gives rise to two twofold-degenerate nodal rings(Weyl rings)connected by drumhead surface states,confirmed by angle-resolved photoemission spectroscopy.Our results suggest that the 2×2 CCDW ordering gaps out one Weyl ring in accordance with the CDW band folding,while the other Weyl ring remains gapless with intact surface states.In addition,superconductivity emerges at 0.91 K,with the upper critical field deviating from the s-wave behavior at low temperature,implying possibly unconventional superconductivity.Therefore,we think this type of the 112 system may possess abundant physical states and offer a platform to investigate the interplay between CDW,nontrivial band topology and superconductivity.

Mechanical properties of nanocomposites reinforced by carbon nanotube sponges

Carbon nanotube(CNT)sponge exhibits unique porous and hierarchical structure that are beneficial to the design of ultralight and tough composites.In this study,CNT sponges(undoped and boron doped)reinforced polydimethylsiloxane(PDMS)composites were fabricated.Mechanical properties of the composite,including compressive modulus,rate-dependent modulus,stress relaxation behaviors,dynamic viscoelastic properties,and their dependency on temperature,were systematically investigated.A micromechanical model,Mori-Tanaka model,was validated to describe the mechanical behaviors of CNT sponge reinforced composites.By coupling with boron-doped CNT sponge,PDMS composites showed remarkable improvement of mechanical properties,including compressive modulus(70%),viscous modulus(243%)and damping capacity(50%).Such reinforcement effects can be controlled by the morphology of CNT sponges,as the boron-doped and undoped nanocomposites showed distinct viscoelastic behaviors.The results proved that CNT sponge reinforcement is a promising strategy to develop engineering composites with both outstanding mechanical stiffness and controllable viscoelastic performances.