Toxicity (-lgEC_(50)) Measurement of the Fluorobenzene Derivants against Vibrio Qinghaiensis (Q67) and Their 2D,3D-QSAR Study

Toxicities （-lgEC50） of 16 fluorobenzene derivants against vibrio qinghaiensis （Q67） were measured systematically,and their quantum chemistry parameters were calculated at the B3LYP/6-311G＊＊ level. Based on the experimental toxicity data and quantum chemistry parameters,2D-QSAR model was proposed,which was validated by variance inflation factors （VIF）,t-value and cross-validation method. At the mean time,comparative molecular force field （CoMFA） based on molecular simulation was used to investigate the toxicity of fluorobenzene derivants. Furthermore,the intoxicating mechanism of fluorobenzene derivants was discussed. To our interest,2D-QSAR and CoMFA models exhibit good prediction ability,with which the toxicity of similar compounds can be predicted. Finally,toxicities （-lgEC50） of 12 fluorobenzene derivants against vibrio qinghaiensis （Q67） were predicted with these models.

Crystal Structure of the Coordination Polymer of Sodium with Hydrazone Derivant of Acetoacetanilide

One new polymer, [Na（NPHSNPAB）（CH3OH）]n, where NPHSNPAB stands for Nphenyl-2-[2-hydroxy-3-sulfo-5-nitrophenylhydrazone]butadione-1,3, has been synthesized and characterized by ^1H NMR and FTIR spectroscopy and single-crystal X-ray diffraction. For this complex： C（17）H（17）N4NaO9S, Mr = 476.39, triclinic system, space group P1, a = 8.8741（18）, b = 10.942（2）, c = 12.039（2） A, α = 65.74（3）, β = 77.49（3）, γ = 84.30（3）o, V = 1040.3（4） A3, Z = 2, Dc = 1.521 g/cm^3, λ = 0.71073 A, F（000） = 492, S = 1.106, R = 0.0614 and w R = 0.1423 for 2945 observed reflections with I 〉 2（I）. X-ray structural analysis revealed that the structure of NPHSNPAB framework was almost planar by C–H···O, N–H···O, O–H···O, and O–H···S hydrogen bonds. Moreover, sodium（I） center was bound by six O and one N atoms, forming the coordination polymer. The molecular packing diagram showed complicated hydrogen bonds and π···π stacking interaction in the polymer. The average bond distance of the two dicyclic units（3.768 A） indicated strong π···π stacking interaction. The complex displays greenyellow emission at room temperature.

Fullerenes and derivatives as electrocatalysts: Promises and challenges

Carbon-based metal-free nanomaterials are promising alternatives to precious metals as electrocatalysts of key energy storage and conversion technologies.Of paramount significance are the establishment of design principles by understanding the catalytic mechanisms and identifying the active sites.Distinct from sp2-conjugated graphene and carbon nanotube,fullerene possesses unique characteristics that are growingly being discovered and exploited by the electrocatalysis community.For instance,the well-defined atomic and molecular structures,the good electron affinity to tune the electronic structures of other substances,the intermolecular self-assembly into superlattices,and the on-demand chemical modification have endowed fullerene with incomparable advantages as electrocatalysts that are otherwise not applicable to other carbon ma-terials.As increasing studies are being reported on this intriguing topic,it is necessary to provide a state-of-the-art overview of the recent progress.This review takes such an initiative by summarizing the promises and challenges in the electrocatalytic applications of fullerene and its derivatives.The content is structured according to the composition and structure of fullerene,including intact fullerene(e.g.,fullerene composite and superlattices)and fullerene derivatives(e.g.,doped,endohedral,and disintegrated fullerene).The synthesis,characterization,catalytic mechanisms,and deficiencies of these fullerene-based materials are explicitly elaborated.We conclude it by sharing our perspectives on the key aspects that future efforts shall consider.

New Second-Level-Discrete Zeroing Neural Network for Solving Dynamic Linear System

Dear Editor,This letter deals with a new second-level-discretization method with higher precision than the traditional first-level-discretization method.Specifically,the traditional discretization method utilizes the first-order time derivative information,and it is termed first-level-discretization method.By contrast,the new discretization method not only utilizes the first-order time derivative information,but also makes use of the second-order derivative information.By combining the new second-level-discretization method with zeroing neural network(ZNN),the second-level-discrete ZNN(SLDZNN)model is proposed to solve dynamic(i.e.,time-variant or time-dependent)linear system.Numerical experiments and application to angle-of-arrival(AoA)localization show the effectiveness and superiority of the SLDZNN model.

Input-to-State Stability of Impulsive Switched Systems Involving Uncertain Impulse-Switching Moments

Dear Editor,This letter studies the input-to-state stability(ISS)for a class of impulsive switched systems,where uncertain impulse-switching moments are involved.The robustness of ISS with respect to the perturbations of the occurrence time of impulse-switching moments is revealed by several less conservative dwell-time conditions for the uncertain impulse-switching moments combined with Lyapunov conditions.Moreover,the Lyapunov conditions have multiple coefficients at discrete time so as to handle the hybrid effect of impulse-switching moments,and the case that time derivative of Lyapunov function is indefinite is also taken into account.Finally,a numerical example is proposed to illustrate the effectiveness of the theoretical results.

Organic solar cells with D18 or derivatives offer efficiency over 19%

In recent years,organic solar cells(OSCs)have garnered significant attention due to their distinctive attributes,such as flexibility,lightweight,and solution processing,which position them as alternatives for next-generation solar technologies[1−5].Thanks to breakthroughs in materials development,the power conversion efficiency(PCE)for single-junction OSCs has already surpassed 19%[6−13].The development of photoactive materials is pivotal in enhancing the PCEs,and several reviews have provided insights into materials design[14−18].Herein,we highlight single-junction OSCs based on D18 and its derivatives[19,20].

Time-dependent behavior and permeability evolution of limestone under hydro-mechanical coupling

Excessively high pore water pressure presents unpredictable risks to the safety of rock tunnels in mountainous regions that are predominantly composed of limestone. Investigating the creep characteristics and permeability evolution of limestone under varying hydrated conditions is crucial for a better understanding of the delayed deformation mechanisms of limestone rock tunnels. To this end, this paper initially conducts a series of multi-stage triaxial creep tests on limestone samples under varying pore water pressures. The experiment examines how pore water pressure affects limestone’s creep strain, strain rate, long-term strength, lifespan, and permeability, all within the context of hydraulicmechanical(HM) coupling. To better describe the creep behavior associated with pore water pressure, this paper proposes a new nonlinear fractional creep constitutive model. This constitutive model depicts the initial, steady-state, and accelerated phases of limestone’s creep behavior. Finally, the proposed model is applied to the numerical realization of deformation in limestone tunnel, validating the effectiveness of the proposed constitutive model in predicting tunnel’s creep deformation. This research enhances our understanding of limestone’s creep characteristics and permeability evolution under HM coupling, laying a foundation for assessing the longterm stability of mountain tunnels.

Strong metal-support interactions between highly dispersed Cu^(+) species and ceria via mix-MOF pyrolysis toward promoted water-gas shift reaction

The modulation of metal-support interfacial interaction is significant but challenging in the design of high-efficiency and high-stability supported catalysts.Here,we report a synthetic strategy to upgrade Cu-CeO_(2)interfacial interaction by the pyrolysis of mixed metal-organic framework(MOF)structure.The obtained highly dispersed Cu/CeO_(2)-MOF catalyst via this strategy was used to catalyze water-gas shift reaction(WGSR),which exhibited high activity of 40.5μmolCOgcat^(-1).s^(-1)at 300℃and high stability of about 120 h.Based on comprehensive studies of electronic structure,pyrolysis strategy has significant effect on enhancing metal-support interaction and then stabilizing interfacial Cu^(+)species under reaction conditions.Abundant Cu^(+)species and generated oxygen vacancies over Cu/CeO_(2)-MOF catalyst played a key role in CO molecule activation and H2O molecule dissociation,respectively.Both collaborated closely and then promoted WGSR catalytic performance in comparison with traditio nal supported catalysts.This study shall offer a robust approach to harvest highly dispersed catalysts with finely-tuned metal-support interactions for stabilizing the most interfacial active metal species in diverse heterogeneous catalytic reactions.

Discovery and structure-activity relationship studies of novel tetrahydro-β-carboline derivatives as apoptosis initiators for treating bacterial infections

Developing and excavating new agrochemicals with highly active and safe is an important tactic for protecting crop health and food safety.In this paper,to discover the new bactericide candidates,we designed,prepared a new type of1,2,3,4-tetrahydro-β-carboline(THC)derivatives and evaluated the in vitro and in vivo bioactivities against the Xanthomonas oryzae pv.oryzae(Xoo),Xanthomonas axonopodis pv.citri(Xac),and Pseudomonas syringae pv.actinidiae(Psa).The in vitro bioassay results exhibited that most title molecules possessed good activity toward the three plant pathogenic bacteria,the compound A17 showed the most active against Xoo and Xac with EC50 values of 7.27 and 4.89 mg mL^(-1)respectively,and compound A8 exhibited the best inhibitory activity against Psa with EC50value of 4.87 mg mL^(-1).Pot experiments showed that compound A17 exhibited excellent in vivo antibacterial activities to manage rice bacterial leaf blight and citrus bacterial canker,with protective efficiencies of 52.67 and 79.79%at 200 mgmL^(-1),respectively.Meanwhile,compound A8 showed good control efficiency(84.31%)against kiwifruit bacterial canker at 200 mg mL^(-1).Antibacterial mechanism suggested that these compounds could interfere with the balance of the redox system,damage the cell membrane,and induce the apoptosis of Xoo cells.Taken together,our study revealed that tetrahydro-β-carboline derivatives could be a promising candidate model for novel broadspectrum bactericides.

Identification of key genes regulating the synthesis of quercetin derivatives in Rosa roxburghii through integrated transcriptomics and metabolomics

Rosa roxburghii fruit is rich in flavonoids, but little is known about their biosynthetic pathways. In this study, we employed transcriptomics and metabolomics to study changes related to the flavonoids at five different stages of R. roxburghii fruit development. Flavonoids and the genes related to their biosynthesis were found to undergo significant changes in abundance across different developmental stages, and numerous quercetin derivatives were identified. We found three gene expression modules that were significantly associated with the abundances of the different flavonoids in R. roxburghii and identified three structural UDP-glycosyltransferase genes directly involved in the synthesis of quercetin derivatives within these modules. In addition, we found that RrBEH4, RrLBD1 and RrPIF8could significantly increase the expression of downstream quercetin derivative biosynthesis genes. Taken together,these results provide new insights into the metabolism of flavonoids and the accumulation of quercetin derivatives in R. roxburghii.

Development and identification of two novel wheat-rye 6R derivative lines with adult-plant resistance to powdery mildew and high-yielding potential

Powdery mildew,caused by Blumeria graminis f.sp.tritici(Bgt),is a devastating disease that seriously threatens wheat yield and quality.To control this disease,host resistance is the most effective measure.Compared with the resistance genes from common wheat,alien resistance genes can better withstand infection of this highly variable pathogen.Development of elite alien germplasm resources with powdery mildew resistance and other key breeding traits is an attractive strategy in wheat breeding.In this study,three wheat-rye germplasm lines YT4-1,YT4-2,and YT4-3 were developed through hybridization between octoploid triticale and common wheat,out of which the lines YT4-1 and YT4-2 conferred adult-plant resistance(APR)to powdery mildew while the line YT4-3 was susceptible to powdery mildew during all of its growth stages.Using genomic in situ hybridization,multi-color fluorescence in situ hybridization,multi-color GISH,and molecular marker analysis,YT4-1,YT4-2,and YT4-3 were shown to be cytogenetically stable wheat-rye 6R addition and T1RS.1BL translocation line,6RL ditelosomic addition and T1RS.1BL translocation line,and T1RS.1BL translocation line,respectively.Compared with previously reported wheat-rye derivative lines carrying chromosome 6R,YT4-1 and YT4-2 showed stable APR without undesirable pleiotropic effects on agronomic traits.Therefore,these novel wheat-rye 6R derivative lines are expected to be promising bridge resources in wheat disease breeding.

Characterizations of semihoops based on derivations

In this paper,we discuss the related properties of some particular derivations in semihoops and give some characterizations of them.Then,we prove that every Heyting algebra is isomorphic to the algebra of all multiplicative derivations and show that every Boolean algebra is isomorphic to the algebra of all implicative derivations.Finally,we show that the sets of multiplicative and implicative derivations on bounded regular idempotent semihoops are in oneto-one correspondence.

A New Scheme of the ARA Transform for Solving Fractional-Order Waves-Like Equations Involving Variable Coefficients

The goal of this research is to develop a new,simplified analytical method known as the ARA-residue power series method for obtaining exact-approximate solutions employing Caputo type fractional partial differential equations(PDEs)with variable coefficient.ARA-transform is a robust and highly flexible generalization that unifies several existing transforms.The key concept behind this method is to create approximate series outcomes by implementing the ARA-transform and Taylor’s expansion.The process of finding approximations for dynamical fractional-order PDEs is challenging,but the ARA-residual power series technique magnifies this challenge by articulating the solution in a series pattern and then determining the series coefficients by employing the residual component and the limit at infinity concepts.This approach is effective and useful for solving a massive class of fractional-order PDEs.Five appealing implementations are taken into consideration to demonstrate the effectiveness of the projected technique in creating solitary series findings for the governing equations with variable coefficients.Additionally,several visualizations are drawn for different fractional-order values.Besides that,the estimated findings by the proposed technique are in close agreement with the exact outcomes.Finally,statistical analyses further validate the efficacy,dependability and steady interconnectivity of the suggested ARA-residue power series approach.

A creep model for ultra-deep salt rock considering thermal-mechanical damage under triaxial stress conditions

To investigate the specific creep behavior of ultra-deep buried salt during oil and gas exploitation,a set of triaxial creep experiments was conducted at elevated temperatures with constant axial pressure and unloading confining pressure conditions.Experimental results show that the salt sample deforms more significantly with the increase of applied temperature and deviatoric loading.The accelerated creep phase is not occurring until the applied temperature reaches 130℃,and higher temperature is beneficial to the occurrence of accelerated creep.To describe the specific creep behavior,a novel three-dimensional(3D)creep constitutive model is developed that incorporates the thermal and mechanical variables into mechanical elements.Subsequently,the standard particle swarm optimization(SPSO)method is adopted to fit the experimental data,and the sensibility of key model parameters is analyzed to further illustrate the model function.As a result,the model can accurately predict the creep behavior of salt under the coupled thermo-mechanical effect in deep-buried condition.Based on the research results,the creep mechanical behavior of wellbore shrinkage is predicted in deep drilling projects crossing salt layer,which has practical implications for deep rock mechanics problems.

Multivariate form of Hermite sampling series

In this paper,we establish a new multivariate Hermite sampling series involving samples from the function itself and its mixed and non-mixed partial derivatives of arbitrary order.This multivariate form of Hermite sampling will be valid for some classes of multivariate entire functions,satisfying certain growth conditions.We will show that many known results included in Commun Korean Math Soc,2002,17:731-740,Turk J Math,2017,41:387-403 and Filomat,2020,34:3339-3347 are special cases of our results.Moreover,we estimate the truncation error of this sampling based on localized sampling without decay assumption.Illustrative examples are also presented.

Prediction and Output Estimation of Pattern Moving in Non-Newtonian Mechanical Systems Based on Probability Density Evolution

A prediction framework based on the evolution of pattern motion probability density is proposed for the output prediction and estimation problem of non-Newtonian mechanical systems,assuming that the system satisfies the generalized Lipschitz condition.As a complex nonlinear system primarily governed by statistical laws rather than Newtonian mechanics,the output of non-Newtonian mechanics systems is difficult to describe through deterministic variables such as state variables,which poses difficulties in predicting and estimating the system’s output.In this article,the temporal variation of the system is described by constructing pattern category variables,which are non-deterministic variables.Since pattern category variables have statistical attributes but not operational attributes,operational attributes are assigned to them by posterior probability density,and a method for analyzing their motion laws using probability density evolution is proposed.Furthermore,a data-driven form of pattern motion probabilistic density evolution prediction method is designed by combining pseudo partial derivative(PPD),achieving prediction of the probability density satisfying the system’s output uncertainty.Based on this,the final prediction estimation of the system’s output value is realized by minimum variance unbiased estimation.Finally,a corresponding PPD estimation algorithm is designed using an extended state observer(ESO)to estimate the parameters to be estimated in the proposed prediction method.The effectiveness of the parameter estimation algorithm and prediction method is demonstrated through theoretical analysis,and the accuracy of the algorithm is verified by two numerical simulation examples.

Characterization of blueberry exosome-like nanoparticles and miRNAs with potential cross-kingdom human gene targets

Edible plant derived exosome-like nanoparticles(ELNs)have been shown to have multiple nutraceutical functions.However,the diversity of plant materials makes the plant derived ELN study challenging.More efforts are still needed to explore the feasible isolation methods of edible plant derived ELNs and the possible roles of food-derived ELNs in improving human health.In this study,a size exclusion chromatography based method was compared with the traditional ultracentrifugation method to isolate blueberry derived ELNs(B-ELNs),and the miRNA profile of B-ELNs was analyzed by high-throughput sequencing.A total of 36 miRNAs were found to be enriched in B-ELNs compared with berry tissue,and their potential cross-kingdom human gene targets were further predicted.Results showed that size exclusion chromatography was effective for B-ELN isolation.The most abundant miRNAs in B-ELNs mainly belonged to the miR166 family and miR396 family.Target gene prediction indicated that B-ELNs could potentially regulate pathways related to the human digestive system,immune system and infectious diseases.

High-Value-Added Utilization of Turpentine:Screening of Anti-Influenza Virus Agents fromβ-Pinene Derivatives

Turpentine is a renewable and resourceful forest product.The deep processing and utilization of turpentine,particularly its primary componentβ-pinene,has garnered widespread attention.This study aimed to synthesize 40 derivatives ofβ-pinene,including nopinone,3-cyanopyridines of nopinone,myrtanyl acid,myrtanyl acylthioureas,and myrtanyl amides.We assessed the antiviral activities of theseβ-pinene derivatives against influenza virus A/Puerto Rico/8/34(H1N1)using the 3-(4,5-dimetylthiazol-2-yl)-2,5-diphenyltetrazolium bromide method.Theβ-pinene derivatives were used before and after cellular infection with the influenza virus to evaluate their preventive and therapeutic effects against the H1N1 virus.The results showed that only compound 10o exhibited a preventive effect against the H1N1 virus with a half-maximal inhibitory concentration(IC50)value of 47.6μmol/L.Among the compounds,4e,4i,and 4l demonstrated therapeutic effects against cellular infection,with compound 4e displaying the most potent therapeutic effect(IC50=17.5μmol/L),comparable to the positive control ribavirin.These findings indicated that certainβ-pinene derivatives exhibited in vitro antiviral activity against the H1N1 influenza A virus,warranting further investigation as potential anti-influenza agents.

Sepsis Prediction Using CNNBDLSTM and Temporal Derivatives Feature Extraction in the IoTMedical Environment

Background:Sepsis,a potentially fatal inflammatory disease triggered by infection,carries significant healthimplications worldwide.Timely detection is crucial as sepsis can rapidly escalate if left undetected.Recentadvancements in deep learning(DL)offer powerful tools to address this challenge.Aim:Thus,this study proposeda hybrid CNNBDLSTM,a combination of a convolutional neural network(CNN)with a bi-directional long shorttermmemory(BDLSTM)model to predict sepsis onset.Implementing the proposed model provides a robustframework that capitalizes on the complementary strengths of both architectures,resulting in more accurate andtimelier predictions.Method:The sepsis prediction method proposed here utilizes temporal feature extraction todelineate six distinct time frames before the onset of sepsis.These time frames adhere to the sepsis-3 standardrequirement,which incorporates 12-h observation windows preceding sepsis onset.All models were trained usingthe Medical Information Mart for Intensive Care III(MIMIC-III)dataset,which sourced 61,522 patients with 40clinical variables obtained from the IoT medical environment.The confusion matrix,the area under the receiveroperating characteristic curve(AUCROC)curve,the accuracy,the precision,the F1-score,and the recall weredeployed to evaluate themodels.Result:The CNNBDLSTMmodel demonstrated superior performance comparedto the benchmark and other models,achieving an AUCROC of 99.74%and an accuracy of 99.15%one hour beforesepsis onset.These results indicate that the CNNBDLSTM model is highly effective in predicting sepsis onset,particularly within a close proximity of one hour.Implication:The results could assist practitioners in increasingthe potential survival of the patient one hour before sepsis onset.