An improved method is proposed for the extraction of the symmetry energy coefficient relative to the temperature,a_(sym)/T,in the heavy-ion reactions near the Fermi energy region,based on the modified Fisher Model.Thi...An improved method is proposed for the extraction of the symmetry energy coefficient relative to the temperature,a_(sym)/T,in the heavy-ion reactions near the Fermi energy region,based on the modified Fisher Model.This method is applied to the primary fragments of antisymmetrized molecular dynamics(AMD)simulations for ^(46)Fe+^(46)Fe,^(40)Ca+^(40)Ca and ^(48)Ca+^(48)Ca at 35 MeV/nucleon,in order to make direct comparison to the results from the K(N,Z)method of Ono et al.In our improved method,the extracted values of a_(sym)/T increase as the size of isotopes increases whereas,in the K(N,Z)method,the results show rather constant behavior.This increase in our result is attributed to the surface contribution of the symmetry energy in finite nuclei.In order to evaluate the surface contribution,the relation a_(sym)/T=[a_(sym)^((V))(1-k_(S/V) A^(-1/3))]/T is applied and k_(S/V)=1.20~1.25 was extracted.This value is smaller than those extracted from the mass table,reflecting the weakened surface contribution at higher temperature regime.Δμ/T,the difference of the neutron-proton chemical potentials relative to the temperature,is also extracted in this method at the same time.The average values of the extractedΔμ/T,Δμ/T show a linear dependence on the proton-neutron a_(sym)metry parameter of the system,δ_(sys),andΔμ/T=(15.1±0.2)δ_(sys)-(0.5±0.1)is obtained.展开更多
Since antiquity,humans have been involved in designing materials through alloying strategies to meet the ever-growing technological demands.In 2004,this endeavor witnessed a significant breakthrough with the discovery...Since antiquity,humans have been involved in designing materials through alloying strategies to meet the ever-growing technological demands.In 2004,this endeavor witnessed a significant breakthrough with the discovery of high-entropy alloys(HEAs)comprising multi-principal elements.Owing to the four“core-effects”,these alloys exhibit exceptional properties including better structural stability,high strength and ductility,improved fatigue/fracture toughness,high corrosion and oxidation resistance,superconductiv-ity,magnetic properties,and good thermal properties.Different synthesis routes have been designed and used to meet the properties of interest for particular applications with varying dimensions.How-ever,HEAs are providing new opportunities and challenges for computational modelling of the complex structure-property correlations and in predictions of phase stability necessary for optimum performance of the alloy.Several attempts have been made to understand these alloys by empirical and computa-tional models,and data-driven approaches to accelerate the materials discovery with a desired set of properties.The present review discusses advances and inferences from simulations and models spanning multiple length and time scales explaining a comprehensive set of structure-properties relations.Addi-tionally,the role of machine learning approaches is also reviewed,underscoring the transformative role of computational modelling in unravelling the multifaceted properties and applications of HEAs,and the scope for future efforts in this direction.展开更多
Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials.Traditional methods based on manually crafted features and graph-based me...Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials.Traditional methods based on manually crafted features and graph-based methods have shown promising results in molecular property prediction.However,traditional methods rely on expert knowledge and often fail to capture the complex structures and interactions within molecules.Similarly,graph-based methods typically overlook the chemical structure and function hidden in molecular motifs and struggle to effectively integrate global and local molecular information.To address these limitations,we propose a novel fingerprint-enhanced hierarchical graph neural network(FH-GNN)for molecular property prediction that simultaneously learns information from hierarchical molecular graphs and fingerprints.The FH-GNN captures diverse hierarchical chemical information by applying directed message-passing neural networks(D-MPNN)on a hierarchical molecular graph that integrates atomic-level,motif-level,and graph-level information along with their relationships.Addi-tionally,we used an adaptive attention mechanism to balance the importance of hierarchical graphs and fingerprint features,creating a comprehensive molecular embedding that integrated hierarchical mo-lecular structures with domain knowledge.Experiments on eight benchmark datasets from MoleculeNet showed that FH-GNN outperformed the baseline models in both classification and regression tasks for molecular property prediction,validating its capability to comprehensively capture molecular informa-tion.By integrating molecular structure and chemical knowledge,FH-GNN provides a powerful tool for the accurate prediction of molecular properties and aids in the discovery of potential drug candidates.展开更多
Tin is a critical metal for various industries,making its recovery from low-grade cassiterite ores crucial.This study aimed to optimize the flotation recovery of cassiterite using multi-component collector systems.Sev...Tin is a critical metal for various industries,making its recovery from low-grade cassiterite ores crucial.This study aimed to optimize the flotation recovery of cassiterite using multi-component collector systems.Several collectors were initially selected through micro-flotation tests,leading to the identification of optimal proportions for a four-component collector system(SHA-OHA-SPA-DBIA in a 4:3:2:1 ratio).Molecular dynamics simulations and surface tension tests were used to investigate the micellar behavior of these collectors in aqueous solution.The adsorption characteristics were quantified using microcalorimetry,enabling the determination of collection entropy and changes in Gibbs free energy.The four-component collector system showed the highest entropy change and the most favorable Gibbs free energy,leading to a cassiterite recovery of above 90%at a concentration of 8.0×10^(5)mol/L.Various analytical techniques were employed to systematically characterize the adsorption mechanism.The findings revealed a positive correlation between the adsorption products formed by the multicomponent collectors on the cassiterite surface and the entropy changes.Industrial-scale testing of the high-entropy collector system produced a tin concentrate with an Sn grade of 6.17%and an Sn recovery of 82.43%,demonstrating its substantial potential for practical applications in cassiterite flotation.展开更多
Colorectal cancer(CRC)ranks among the top causes of cancer-related fatalities globally.Recent progress in genomics,proteomics,and bioinformatics has greatly improved our comprehension of the molecular underpinnings of...Colorectal cancer(CRC)ranks among the top causes of cancer-related fatalities globally.Recent progress in genomics,proteomics,and bioinformatics has greatly improved our comprehension of the molecular underpinnings of CRC,paving the way for targeted therapies and immunotherapies.Nonetheless,obstacles such as tumor heterogeneity and drug resistance persist,hindering advancements in treatment efficacy.In this context,the integration of artificial intelligence(AI)and organoid technology presents promising new avenues.AI can analyze genetic and clinical data to forecast disease risk,prognosis,and treatment responses,thereby expediting drug development and tailoring treatment plans.Organoids replicate the genetic traits and biological behaviors of tumors,acting as platforms for drug testing and the formulation of personalized treatment approaches.Despite notable strides in CRC research and treatment-from genetic insights to therapeutic innovations-numerous challenges endure,including the intricate tumor microen-vironment,tumor heterogeneity,adverse effects of immunotherapies,issues related to AI data quality and privacy,and the need for standardization in organoid culture.Future initiatives should concentrate on clarifying the pathogenesis of CRC,refining AI algorithms and organoid models,and creating more effective therapeutic strategies to alleviate the global impact of CRC.展开更多
Advancements in molecular approaches have been utilized to breed crops with a wide range of economically valuable traits to develop superior cultivars.This review provides a concise overview of modern breakthroughs in...Advancements in molecular approaches have been utilized to breed crops with a wide range of economically valuable traits to develop superior cultivars.This review provides a concise overview of modern breakthroughs in molecular plant production.Genotyping and high-throughput phenotyping methods for predictive plant breeding are briefly discussed.In this study,we explore contemporary molecular breeding techniques for producing desirable crop varieties.These techniques include cisgenesis,clustered regularly interspaced short palindromic repeat(CRISPR/Cas9)gene editing,haploid induction,and de novo domestication.We examine the speed breeding approach-a strategy for cultivating plants under controlled conditions.We further highlight the significance of modern breeding technologies in efficiently utilizing agricultural resources for crop production in urban areas.The deciphering of crop genomes has led to the development of extensive DNA markers,quantitative trait loci(QTLs),and pangenomes associated with various desirable crop traits.This shift to the genotypic selection of crops considerably expedites the plant breeding process.Based on the plant population used,the connection between genotypic and phenotypic data provides several genetic elements,including genes,markers,and alleles that can be used in genomic breeding and gene editing.The integration of speed breeding with genomic-assisted breeding and cutting-edge genome editing tools has made it feasible to rapidly manipulate and generate multiple crop cycles and accelerate the plant breeding process.Breakthroughs in molecular techniques have led to substantial improvements in modern breeding methods.展开更多
Ethiprole is widely used as a second-generation phenyl pyrazole insecticide.Previous studies indicated that ethiprole exhibited thyroid toxicity while two main metabolites(ethiprole sulfone(M1)and ethiprole sulfide(M2...Ethiprole is widely used as a second-generation phenyl pyrazole insecticide.Previous studies indicated that ethiprole exhibited thyroid toxicity while two main metabolites(ethiprole sulfone(M1)and ethiprole sulfide(M2))of ethiprole showed higher acute toxicity than ethiprole.Therefore,assessing the thyroid toxicity of its metabolites is crucial for safety assessment.In this study,the thyroid toxicity and underlying mechanisms of ethiprole and its metabolites were explored using in silico,in vitro,and in vivo assays,with the aim of conducting a comparative study on thyroid toxicity.Molecular docking analysis showed that ethiprole,M1 and M2 could bind with thyroid receptor isoforms and exhibited higher binding affinity compared to 3,3,5-triiodothyronine(T3).GH3 cell proliferation assays revealed that ethiprole,M1 and M2 all served as thyroid hormone antagonists to hinder the T3-induced cell proliferation.Using the zebrafish model,we further investigated that exposure to ethiprole,M1,and M2 disrupted thyroid hormone levels and the transcriptional expressions of hypothalamus-pituitary-thyroid(HPT)axis-related genes.Ethiprole induced thyroid disrupting effects by binding with the thyroid receptor beta,M1 mainly through binding with the corticotropin releasing factor receptor-1,and M2 exposure firstly inhibited the thyroid peroxidase enzyme activity.M2 showed the highest developmental toxicity and thyroid disrupting effects,which significantly reducing hatching rates,increasing deformity rates,exhibiting the lowest lethal concentration 50 value and showing the most serious transcription inhibitory effects on the HPT axis.This study suggested the risk assessment of metabolites should be considered in assessing potential environmental risk of ethiprole.展开更多
Recently,diffusion models have emerged as a promising paradigm for molecular design and optimization.However,most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geom-etries,with limited ...Recently,diffusion models have emerged as a promising paradigm for molecular design and optimization.However,most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geom-etries,with limited research on molecular sequence diffusion models.The International Union of Pure and Applied Chemistry(IUPAC)names are more akin to chemical natural language than the simplified molecular input line entry system(SMILES)for organic compounds.In this work,we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language(SMILES)and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language.We propose DiffIUPAC,a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings.Evaluation results demonstrate that our model out-performs existing methods and successfully captures the semantic rules of both chemical languages.Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints.Additionally,to illustrate the model’s applicability in drug design,we conducted case studies in functional group editing,analogue design and linker design.展开更多
Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the...Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.展开更多
Lake Baiyangdian is one of China’s largest macrophyte-derived lakes,facing severe challenges related to water quality maintenance and eutrophication prevention.Dissolved organic matter(DOM)was a huge carbon pool and ...Lake Baiyangdian is one of China’s largest macrophyte-derived lakes,facing severe challenges related to water quality maintenance and eutrophication prevention.Dissolved organic matter(DOM)was a huge carbon pool and its abundance,property,and transformation played important roles in the biogeochemical cycle and energy flow in lake ecosystems.In this study,Lake Baiyangdian was divided into four distinct areas:Unartificial Area(UA),Village Area(VA),Tourism Area(TA),and Breeding Area(BA).We examined the diversity of DOM properties and sources across these functional areas.Our findings reveal that DOM in this lake is predominantly composed of protein-like substances,as determined by excitation-emission matrix and parallel factor analysis(EEM-PARAFAC).Notably,the exogenous tyrosine-like component C1 showed a stronger presence in VA and BA compared to UA and TA.Ultrahigh-resolution mass spectrometry(FT-ICR MS)unveiled a similar DOM molecular composition pattern across different functional areas due to the high relative abundances of lignan compounds,suggesting that macrophytes significantly influence the material structure of DOM.DOM properties exhibited specific associations with water quality indicators in various functional areas,as indicated by the Mantel test.The connections between DOM properties and NO_(3)-N andNH3-Nwere more pronounced in VA and BA than in UA and TA.Our results underscore the viability of using DOM as an indicator for more precise and scientific water quality management.展开更多
Aqueous zinc-ion batteries(AZIBs)have regained interest due to their inherent safety and costeffectiveness.However,the zinc anode is notorious for side reactions and dendrite growth,which plague the practical applicat...Aqueous zinc-ion batteries(AZIBs)have regained interest due to their inherent safety and costeffectiveness.However,the zinc anode is notorious for side reactions and dendrite growth,which plague the practical application of AZIBs.Adjusting the interfacial pH to reduce the by-products has been proven to be effective in protecting the zinc anode.Nevertheless,the dynamic regulation of the inherently unstable zinc interface during prolonged cycling remains a significant challenge.Herein,zwitterionic N-tris(hydroxymethyl)methylglycine(TMG)integrated with negative-COO^(-)and positive NH_(2)^(+)groups is proposed to stabilize the Zn anode and extend the lifespan as a self-regulating interfacial additive.The anionic portion serves as a trapping site to balance the interfacial pH and thus mitigate the unintended side reactions.Simultaneously,the NH_(2)^(+)cations are anchored on the zinc surface,forming a water-shielding,zincophilic molecular layer that guides three-dimensional diffusion and promotes uniform electro-deposition.Thus,an average plating efficiency of 99.74%over 3300 cycles at a current density of2 mA cm^(-2)is achieved.Notably,the TMG additive actualizes ultralong life in Zn‖Zn symmetrical cells(5500 h,exceeding 229 days,1 mA cm^(-2)/1 mA h cm^(-2)),and enables the Zn‖I_(2)cells to reach capacity retention rate of 89.4%after 1000 cycles at 1 A g^(-1).展开更多
The stable nanobubbles adhered to mineral surfaces may facilitate their efficient separation via flotation in the mining industry.However,the state of nanobubbles on mineral solid surfaces is still elusive.In this stu...The stable nanobubbles adhered to mineral surfaces may facilitate their efficient separation via flotation in the mining industry.However,the state of nanobubbles on mineral solid surfaces is still elusive.In this study,molecular dynamics(MD)simulations are employed to examine mineral-like model surfaces with varying degrees of hydrophobicity,modulated by surface charges,to elucidate the adsorption behavior of nanobubbles at the interface.Our findings not only contribute to the fundamental understanding of nanobubbles but also have potential applications in the mining industry.We observed that as the surface charge increases,the contact angle of the nanobubbles increases accordingly with shape transformation from a pancake-like gas film to a cap-like shape,and ultimately forming a stable nanobubble upon an ordered water monolayer.When the solid–water interactions are weak with a small partial charge,the hydrophobic gas(N_(2))molecules accumulate near the solid surfaces.However,we have found,for the first time,that gas molecules assemble a nanobubble on the water monolayer adjacent to the solid surfaces with large partial charges.Such phenomena are attributed to the formation of a hydrophobic water monolayer with a hydrogen bond network structure near the surface.展开更多
BACKGROUND Colorectal cancer(CRC)is the third most common cancer globally,causing over 900000 deaths annually.Risk factors include aging,diet,obesity,sedentary lifestyle,tobacco use,genetic predisposition,and inflamma...BACKGROUND Colorectal cancer(CRC)is the third most common cancer globally,causing over 900000 deaths annually.Risk factors include aging,diet,obesity,sedentary lifestyle,tobacco use,genetic predisposition,and inflammatory bowel disease.Despite current treatments,survival rates for advanced CRC remain low,highlighting the need for better therapeutic strategies.AIM To evaluate both the clinical significance and the pathological implications of the Kinesin family member 14(KIF14)expression within CRC specimens.Additionally,this study aims to investigate the interaction between nitidine chloride(NC)and KIF14,considering their potential as therapeutic targets.METHODS The expression of the KIF14 protein in CRC was analyzed using immunohistochemical staining.The integration of multicenter high-throughput data facilitated the calculation of the standardized mean difference(SMD)for KIF14 mRNA levels.The assessment of clinical and pathological impact was enhanced by analyzing combined receiver operating characteristic curves,along with measures of sensitivity,specificity,and likelihood ratios.Additionally,clustered regularly interspaced short palindromic repeats knockout screening for cell growth and single-cell sequencing were employed to validate the significance of KIF14 expression in CRC.Survival analysis established the prognostic value of KIF14 in CRC.The molecular mechanism of NC against CRC was elucidated through whole-genome sequencing and enrichment analysis,and molecular docking was utilized to explore the targeting affinity between NC and KIF14.RESULTS KIF14 was highly expressed in 208 CRC patients.Data from 17 platforms involving 2436 CRC samples and 1320 noncancerous colorectal tissue controls indicated that KIF14 expression was significantly higher in CRC samples,with an SMD of 1.92(95%CI:1.49-2.35).The area under the curve was 0.94(95%CI:0.92-0.96),with a sensitivity of 0.85(95%CI:0.78-0.90)and a specificity of 0.90(95%CI:0.85-0.93).The positive and negative likelihood ratios were 8.38(95%CI:5.39-13.02)and 0.17(95%CI:0.11-0.26),respectively.At the single-cell level,significant overexpression of KIF14 was observed in CRC cells(P<0.001),with 35 CRC cell lines dependent on KIF14 for growth.The K-M plots demonstrated that KIF14 possesses prognostic value in CRC patients within the GSE71187 and GSE103679 datasets(P<0.05).Binding energy calculations indicated that KIF14 is a potential target for NC(binding energy:10.3 kcal/mol).CONCLUSION KIF14 promotes the growth of CRC cells and acts as an oncogenic factor,potentially serving as a therapeutic target for NC in the treatment of CRC.展开更多
Guided by molecular networking,nine novel curvularin derivatives(1-9)and 16 known analogs(10-25)were isolated from the hydrothermal vent sediment fungus Penicillium sp.HL-50.Notably,compounds 5-7 represented a hybrid ...Guided by molecular networking,nine novel curvularin derivatives(1-9)and 16 known analogs(10-25)were isolated from the hydrothermal vent sediment fungus Penicillium sp.HL-50.Notably,compounds 5-7 represented a hybrid of curvularin and purine.The structures and absolute configurations of compounds 1-9 were elucidated via nuclear magnetic resonance(NMR)spectroscopy,X-ray diffraction,electronic circular dichroism(ECD)calculations,^(13)C NMR calculation,modified Mosher's method,and chemical derivatization.Investigation of anti-inflammatory activities revealed that compounds 7-9,11,12,14,15,and 18 exhibited significant suppressive effects against lipopolysaccharide(LPS)-induced nitric oxide(NO)production in murine macrophage RAW264.7 cells,with IC_(50)values ranging from 0.44 to 4.40μmol·L^(-1).Furthermore,these bioactive compounds were found to suppress the expression of inflammation-related proteins,including inducible NO synthase(i NOS),cyclooxygenase-2(COX-2),NLR family pyrin domain-containing protein 3(NLRP3),and nuclear factor kappa-B(NF-κB).Additional studies demonstrated that the novel compound 7 possessed potent antiinflammatory activity by inhibiting the transcription of inflammation-related genes,downregulating the expression of inflammation-related proteins,and inhibiting the release of inflammatory cytokines,indicating its potential application in the treatment of inflammatory diseases.展开更多
Background:Gastric cancer(GC)remains a global health burden and is often characterized by heterogeneous molecular profiles and resistance to conventional therapies.The phosphoinositide 3-kinase and PI3K and Janus kina...Background:Gastric cancer(GC)remains a global health burden and is often characterized by heterogeneous molecular profiles and resistance to conventional therapies.The phosphoinositide 3-kinase and PI3K and Janus kinase(JAK)signal transducer and activator of transcription(JAK-STAT)pathways play pivotal roles in GC progression,making them attractive targets for therapeutic interventions.Methods:This study applied a computational and molecular dynamics simulation approach to identify and characterize SBL-JP-0004 as a potential dual inhibitor of JAK2 and PI3KCD kinases.KATOIII and SNU-5 GC cells were used for in vitro evaluation.Results:SBL-JP-0004 exhibited a robust binding affinity for JAK2 and PI3KCD kinases,as evidenced by molecular docking scores and molecular dynamics simulations.Binding interactions and Gibbs binding free energy estimates confirmed stable and favorable interactions with target proteins.SBL-JP-0004 displayed an half-maximal inhibitory concentration(IC_(50))value of 118.9 nM against JAK2 kinase and 200.9 nM against PI3KCD enzymes.SBL-JP-0004 exhibited potent inhibition of cell proliferation in KATOIII and SNU-5 cells,with half-maximal growth inhibitory concentration(GI50)values of 250.8 and 516.3 nM,respectively.A significant elevation in the early phase apoptosis(28.53%in KATOIII cells and 26.85%in SNU-5 cells)and late phase apoptosis(17.37%in KATOIII cells and 10.05%in SNU-5 cells)were observed with SBL-JP-0004 treatment compared to 2.1%and 2.83%in their respective controls.Conclusion:The results highlight SBL-JP-0004 as a promising dual inhibitor targeting JAK2 and PI3KCD kinases for treating GC and warrant further preclinical and clinical investigations to validate its utility in clinical settings.展开更多
The global energy demand is increasing rapidly,and it is imperative to develop shale hydrocarbon re-sources vigorously.The prerequisite for enhancing the exploitation efficiency of shale reservoirs is the systematic e...The global energy demand is increasing rapidly,and it is imperative to develop shale hydrocarbon re-sources vigorously.The prerequisite for enhancing the exploitation efficiency of shale reservoirs is the systematic elucidation of the occurrence characteristics,flow behavior,and enhanced oil recovery(EOR)mechanisms of shale oil within commonly developed nanopores.Molecular dynamics(MD)technique can simulate the occurrence,flow,and extraction processes of shale oil at the nanoscale,and then quantitatively characterize various fluid properties,flow characteristics,and action mechanisms under different reservoir conditions by calculating and analyzing a series of MD parameters.However,the existing review on the application of MD simulation in shale oil reservoirs is not systematic enough and lacks a summary of technical challenges and solutions.Therefore,recent MD studies on shale oil res-ervoirs were summarized and analyzed.Firstly,the applicability of force fields and ensembles of MD in shale reservoirs with different reservoir conditions and fluid properties was discussed.Subsequently,the calculation methods and application examples of MD parameters characterizing various properties of fluids at the microscale were summarized.Then,the application of MD simulation in the study of shale oil occurrence characteristics,flow behavior,and EOR mechanisms was reviewed,along with the elucidation of corresponding micro-mechanisms.Moreover,influencing factors of pore structure,wall properties,reservoir conditions,fluid components,injection/production parameters,formation water,and inorganic salt ions were analyzed,and some new conclusions were obtained.Finally,the main challenges associated with the application of MD simulations to shale oil reservoirs were discussed,and reasonable prospects for future MD research directions were proposed.The purpose of this review is to provide theoretical basis and methodological support for applying MD simulation to study shale oil reservoirs.展开更多
One of agriculture’s major challenges is the low efficiency of phosphate(Pi)use,which leads to increased costs,harmful environmental impacts,and the depletion of phosphorus(P)resources.The TaPHT1;6 gene,which encodes...One of agriculture’s major challenges is the low efficiency of phosphate(Pi)use,which leads to increased costs,harmful environmental impacts,and the depletion of phosphorus(P)resources.The TaPHT1;6 gene,which encodes a high-affinity Pi transporter(PHT),plays a crucial role in Pi absorption and transport.In this study,the promoter and coding regions of three TaPHT1;6 gene copies on chromosomes 5A,5B,and 5D were individually amplified and sequenced from 167 common wheat(Triticum aestivum L.)cultivars.Sequence analysis revealed 16 allelic variation sites within the promoters of TaPHT1;6-5B among these cultivars,forming three distinct haplotypes:Hap1,Hap2,and Hap3.Field trials were conducted over two years to compare wheat genotypes with these haplotypes,focusing on assessing plant dry weight,grain yield,P content,Pi fertilizer absorption efficiency,and Pi fertilizer utilization efficiency.Results indicated that Hap3 represented the favored Pi-efficient haplotype.Dual-luciferase reporter assay demonstrated that the Hap3 promoter,carrying the identified allelic variation sites,exhibited higher gene-driven capability,leading to increased expression levels of the TaPHT1;6-5B gene.We developed a distributed cleaved amplified polymorphic site marker(dCAPS-571)to distinguish Hap3 from the other two haplotypes based on these allelic variation sites,presenting an opportunity for breeding Pi-efficient wheat cultivars.This study successfully identified polymorphic sites on TaPHT1;6-5B associated with Pi efficiency and developed a functional molecular marker to facilitate future breeding endeavors.展开更多
This study identified castor oil and phosphate ester as effective retarders through setting time,tensile,and flexural tests,and determined their optimal dosages.The mechanism by which phosphate ester affects the setti...This study identified castor oil and phosphate ester as effective retarders through setting time,tensile,and flexural tests,and determined their optimal dosages.The mechanism by which phosphate ester affects the setting time of polyurethane was further investigated using molecular dynamics simulations.Fourier transform infrared spectroscopy was also employed to systematically study the physical and chemical interactions between phosphate esters and polyurethane materials.The results demonstrate that a 1%concentration of phosphate ester provides the most effective retarding effect with minimal impact on the strength of polyurethane.When phosphate ester is added to the B component of the two-component polyurethane system,its interaction energy with component A decreases,as do the diffusion coefficient and aggregation degree of component B on the surface of component A.This reduction in interaction slows the setting time.Additionally,the addition of phosphate ester to polyurethane leads to the disappearance or weakening of functional groups,indicating competitive interactions within the phosphate ester components that inhibit the reaction rate.展开更多
As light metals,aluminum and magnesium have been widely used in automotive manufacturing,but the welding of Al/Mg joints is facing challenges.However,it is difficult to obtain high-quality aluminum/magnesium joints wi...As light metals,aluminum and magnesium have been widely used in automotive manufacturing,but the welding of Al/Mg joints is facing challenges.However,it is difficult to obtain high-quality aluminum/magnesium joints with traditional arc welding methods.As a solid-phase welding technology,ultrasonic metal welding has the characteristics of high welding efficiency and less welded defects.It is also suitable for welding sound metal bonds.Aluminum and magnesium ultrasonic welding has become a research hotspot.Therefore,the evolution of microstructures and mechanical performance of Al/Mg and multi-layer Al/Mg ultrasonic welding,and the new study works,including the molecular dynamic simulation of Al/Mg ultrasonic welding and hybrid based on ultrasonic welding are summarized.Furthermore,several promising research directions are proposed to guide in-depth investigations into the ultrasonic welding of Al/Mg dissimilar joints.展开更多
文摘An improved method is proposed for the extraction of the symmetry energy coefficient relative to the temperature,a_(sym)/T,in the heavy-ion reactions near the Fermi energy region,based on the modified Fisher Model.This method is applied to the primary fragments of antisymmetrized molecular dynamics(AMD)simulations for ^(46)Fe+^(46)Fe,^(40)Ca+^(40)Ca and ^(48)Ca+^(48)Ca at 35 MeV/nucleon,in order to make direct comparison to the results from the K(N,Z)method of Ono et al.In our improved method,the extracted values of a_(sym)/T increase as the size of isotopes increases whereas,in the K(N,Z)method,the results show rather constant behavior.This increase in our result is attributed to the surface contribution of the symmetry energy in finite nuclei.In order to evaluate the surface contribution,the relation a_(sym)/T=[a_(sym)^((V))(1-k_(S/V) A^(-1/3))]/T is applied and k_(S/V)=1.20~1.25 was extracted.This value is smaller than those extracted from the mass table,reflecting the weakened surface contribution at higher temperature regime.Δμ/T,the difference of the neutron-proton chemical potentials relative to the temperature,is also extracted in this method at the same time.The average values of the extractedΔμ/T,Δμ/T show a linear dependence on the proton-neutron a_(sym)metry parameter of the system,δ_(sys),andΔμ/T=(15.1±0.2)δ_(sys)-(0.5±0.1)is obtained.
基金the Science and Engineering Re-search Board(SERB),India for providing the financial assistance to support this work(Project No.SRG/2020/002449).
文摘Since antiquity,humans have been involved in designing materials through alloying strategies to meet the ever-growing technological demands.In 2004,this endeavor witnessed a significant breakthrough with the discovery of high-entropy alloys(HEAs)comprising multi-principal elements.Owing to the four“core-effects”,these alloys exhibit exceptional properties including better structural stability,high strength and ductility,improved fatigue/fracture toughness,high corrosion and oxidation resistance,superconductiv-ity,magnetic properties,and good thermal properties.Different synthesis routes have been designed and used to meet the properties of interest for particular applications with varying dimensions.How-ever,HEAs are providing new opportunities and challenges for computational modelling of the complex structure-property correlations and in predictions of phase stability necessary for optimum performance of the alloy.Several attempts have been made to understand these alloys by empirical and computa-tional models,and data-driven approaches to accelerate the materials discovery with a desired set of properties.The present review discusses advances and inferences from simulations and models spanning multiple length and time scales explaining a comprehensive set of structure-properties relations.Addi-tionally,the role of machine learning approaches is also reviewed,underscoring the transformative role of computational modelling in unravelling the multifaceted properties and applications of HEAs,and the scope for future efforts in this direction.
基金supported by Macao Science and Technology Development Fund,Macao SAR,China(Grant No.:0043/2023/AFJ)the National Natural Science Foundation of China(Grant No.:22173038)Macao Polytechnic University,Macao SAR,China(Grant No.:RP/FCA-01/2022).
文摘Accurate prediction of molecular properties is crucial for selecting compounds with ideal properties and reducing the costs and risks of trials.Traditional methods based on manually crafted features and graph-based methods have shown promising results in molecular property prediction.However,traditional methods rely on expert knowledge and often fail to capture the complex structures and interactions within molecules.Similarly,graph-based methods typically overlook the chemical structure and function hidden in molecular motifs and struggle to effectively integrate global and local molecular information.To address these limitations,we propose a novel fingerprint-enhanced hierarchical graph neural network(FH-GNN)for molecular property prediction that simultaneously learns information from hierarchical molecular graphs and fingerprints.The FH-GNN captures diverse hierarchical chemical information by applying directed message-passing neural networks(D-MPNN)on a hierarchical molecular graph that integrates atomic-level,motif-level,and graph-level information along with their relationships.Addi-tionally,we used an adaptive attention mechanism to balance the importance of hierarchical graphs and fingerprint features,creating a comprehensive molecular embedding that integrated hierarchical mo-lecular structures with domain knowledge.Experiments on eight benchmark datasets from MoleculeNet showed that FH-GNN outperformed the baseline models in both classification and regression tasks for molecular property prediction,validating its capability to comprehensively capture molecular informa-tion.By integrating molecular structure and chemical knowledge,FH-GNN provides a powerful tool for the accurate prediction of molecular properties and aids in the discovery of potential drug candidates.
基金supported by Yunnan Science and Technology Leading Talent Project(No.202305AB350005)。
文摘Tin is a critical metal for various industries,making its recovery from low-grade cassiterite ores crucial.This study aimed to optimize the flotation recovery of cassiterite using multi-component collector systems.Several collectors were initially selected through micro-flotation tests,leading to the identification of optimal proportions for a four-component collector system(SHA-OHA-SPA-DBIA in a 4:3:2:1 ratio).Molecular dynamics simulations and surface tension tests were used to investigate the micellar behavior of these collectors in aqueous solution.The adsorption characteristics were quantified using microcalorimetry,enabling the determination of collection entropy and changes in Gibbs free energy.The four-component collector system showed the highest entropy change and the most favorable Gibbs free energy,leading to a cassiterite recovery of above 90%at a concentration of 8.0×10^(5)mol/L.Various analytical techniques were employed to systematically characterize the adsorption mechanism.The findings revealed a positive correlation between the adsorption products formed by the multicomponent collectors on the cassiterite surface and the entropy changes.Industrial-scale testing of the high-entropy collector system produced a tin concentrate with an Sn grade of 6.17%and an Sn recovery of 82.43%,demonstrating its substantial potential for practical applications in cassiterite flotation.
基金Supported by the National Human Genetic Resources Sharing Service Platform,No.PT-2024-0303Qingdao Medical and Health Research Guidance Project,No.2023-WJZD202.
文摘Colorectal cancer(CRC)ranks among the top causes of cancer-related fatalities globally.Recent progress in genomics,proteomics,and bioinformatics has greatly improved our comprehension of the molecular underpinnings of CRC,paving the way for targeted therapies and immunotherapies.Nonetheless,obstacles such as tumor heterogeneity and drug resistance persist,hindering advancements in treatment efficacy.In this context,the integration of artificial intelligence(AI)and organoid technology presents promising new avenues.AI can analyze genetic and clinical data to forecast disease risk,prognosis,and treatment responses,thereby expediting drug development and tailoring treatment plans.Organoids replicate the genetic traits and biological behaviors of tumors,acting as platforms for drug testing and the formulation of personalized treatment approaches.Despite notable strides in CRC research and treatment-from genetic insights to therapeutic innovations-numerous challenges endure,including the intricate tumor microen-vironment,tumor heterogeneity,adverse effects of immunotherapies,issues related to AI data quality and privacy,and the need for standardization in organoid culture.Future initiatives should concentrate on clarifying the pathogenesis of CRC,refining AI algorithms and organoid models,and creating more effective therapeutic strategies to alleviate the global impact of CRC.
基金funded by the United Arab Emirates UniversityResearch Officegrant number 12F041 to KM。
文摘Advancements in molecular approaches have been utilized to breed crops with a wide range of economically valuable traits to develop superior cultivars.This review provides a concise overview of modern breakthroughs in molecular plant production.Genotyping and high-throughput phenotyping methods for predictive plant breeding are briefly discussed.In this study,we explore contemporary molecular breeding techniques for producing desirable crop varieties.These techniques include cisgenesis,clustered regularly interspaced short palindromic repeat(CRISPR/Cas9)gene editing,haploid induction,and de novo domestication.We examine the speed breeding approach-a strategy for cultivating plants under controlled conditions.We further highlight the significance of modern breeding technologies in efficiently utilizing agricultural resources for crop production in urban areas.The deciphering of crop genomes has led to the development of extensive DNA markers,quantitative trait loci(QTLs),and pangenomes associated with various desirable crop traits.This shift to the genotypic selection of crops considerably expedites the plant breeding process.Based on the plant population used,the connection between genotypic and phenotypic data provides several genetic elements,including genes,markers,and alleles that can be used in genomic breeding and gene editing.The integration of speed breeding with genomic-assisted breeding and cutting-edge genome editing tools has made it feasible to rapidly manipulate and generate multiple crop cycles and accelerate the plant breeding process.Breakthroughs in molecular techniques have led to substantial improvements in modern breeding methods.
基金supported by the National Natural Science Foundation of China(Nos.42207320 and 22076214).
文摘Ethiprole is widely used as a second-generation phenyl pyrazole insecticide.Previous studies indicated that ethiprole exhibited thyroid toxicity while two main metabolites(ethiprole sulfone(M1)and ethiprole sulfide(M2))of ethiprole showed higher acute toxicity than ethiprole.Therefore,assessing the thyroid toxicity of its metabolites is crucial for safety assessment.In this study,the thyroid toxicity and underlying mechanisms of ethiprole and its metabolites were explored using in silico,in vitro,and in vivo assays,with the aim of conducting a comparative study on thyroid toxicity.Molecular docking analysis showed that ethiprole,M1 and M2 could bind with thyroid receptor isoforms and exhibited higher binding affinity compared to 3,3,5-triiodothyronine(T3).GH3 cell proliferation assays revealed that ethiprole,M1 and M2 all served as thyroid hormone antagonists to hinder the T3-induced cell proliferation.Using the zebrafish model,we further investigated that exposure to ethiprole,M1,and M2 disrupted thyroid hormone levels and the transcriptional expressions of hypothalamus-pituitary-thyroid(HPT)axis-related genes.Ethiprole induced thyroid disrupting effects by binding with the thyroid receptor beta,M1 mainly through binding with the corticotropin releasing factor receptor-1,and M2 exposure firstly inhibited the thyroid peroxidase enzyme activity.M2 showed the highest developmental toxicity and thyroid disrupting effects,which significantly reducing hatching rates,increasing deformity rates,exhibiting the lowest lethal concentration 50 value and showing the most serious transcription inhibitory effects on the HPT axis.This study suggested the risk assessment of metabolites should be considered in assessing potential environmental risk of ethiprole.
基金supported by the Yonsei University graduate school Department of Integrative Biotechnology.
文摘Recently,diffusion models have emerged as a promising paradigm for molecular design and optimization.However,most diffusion-based molecular generative models focus on modeling 2D graphs or 3D geom-etries,with limited research on molecular sequence diffusion models.The International Union of Pure and Applied Chemistry(IUPAC)names are more akin to chemical natural language than the simplified molecular input line entry system(SMILES)for organic compounds.In this work,we apply an IUPAC-guided conditional diffusion model to facilitate molecular editing from chemical natural language to chemical language(SMILES)and explore whether the pre-trained generative performance of diffusion models can be transferred to chemical natural language.We propose DiffIUPAC,a controllable molecular editing diffusion model that converts IUPAC names to SMILES strings.Evaluation results demonstrate that our model out-performs existing methods and successfully captures the semantic rules of both chemical languages.Chemical space and scaffold analysis show that the model can generate similar compounds with diverse scaffolds within the specified constraints.Additionally,to illustrate the model’s applicability in drug design,we conducted case studies in functional group editing,analogue design and linker design.
基金supported in part by National Institute of Health(NIH),USA(Grant Nos.:R01GM126189,R01AI164266,and R35GM148196)the National Science Foundation,USA(Grant Nos.DMS2052983,DMS-1761320,and IIS-1900473)+3 种基金National Aero-nautics and Space Administration(NASA),USA(Grant No.:80NSSC21M0023)Michigan State University(MSU)Foundation,USA,Bristol-Myers Squibb(Grant No.:65109)USA,and Pfizer,USAsupported by the National Natural Science Foundation of China(Grant Nos.:11971367,12271416,and 11972266).
文摘Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.
基金supported by the National Key Research and Development Program of China(No.2022YFC3204000).
文摘Lake Baiyangdian is one of China’s largest macrophyte-derived lakes,facing severe challenges related to water quality maintenance and eutrophication prevention.Dissolved organic matter(DOM)was a huge carbon pool and its abundance,property,and transformation played important roles in the biogeochemical cycle and energy flow in lake ecosystems.In this study,Lake Baiyangdian was divided into four distinct areas:Unartificial Area(UA),Village Area(VA),Tourism Area(TA),and Breeding Area(BA).We examined the diversity of DOM properties and sources across these functional areas.Our findings reveal that DOM in this lake is predominantly composed of protein-like substances,as determined by excitation-emission matrix and parallel factor analysis(EEM-PARAFAC).Notably,the exogenous tyrosine-like component C1 showed a stronger presence in VA and BA compared to UA and TA.Ultrahigh-resolution mass spectrometry(FT-ICR MS)unveiled a similar DOM molecular composition pattern across different functional areas due to the high relative abundances of lignan compounds,suggesting that macrophytes significantly influence the material structure of DOM.DOM properties exhibited specific associations with water quality indicators in various functional areas,as indicated by the Mantel test.The connections between DOM properties and NO_(3)-N andNH3-Nwere more pronounced in VA and BA than in UA and TA.Our results underscore the viability of using DOM as an indicator for more precise and scientific water quality management.
基金supported by the open research fund of Songshan Lake Materials Laboratory(2023SLABFN18)the Anhui Provincial Natural Science Foundation(2308085QB46)+2 种基金the Scientific Research Foundation of Education Department of Anhui Province of China(2022AH010025,2023AH051109)the Key Research and Development Program of Anhui Province of China(2022l07020011)The open research fund of the Anhui Key Lab of Metal Material and Processing(RZ2200002901)。
文摘Aqueous zinc-ion batteries(AZIBs)have regained interest due to their inherent safety and costeffectiveness.However,the zinc anode is notorious for side reactions and dendrite growth,which plague the practical application of AZIBs.Adjusting the interfacial pH to reduce the by-products has been proven to be effective in protecting the zinc anode.Nevertheless,the dynamic regulation of the inherently unstable zinc interface during prolonged cycling remains a significant challenge.Herein,zwitterionic N-tris(hydroxymethyl)methylglycine(TMG)integrated with negative-COO^(-)and positive NH_(2)^(+)groups is proposed to stabilize the Zn anode and extend the lifespan as a self-regulating interfacial additive.The anionic portion serves as a trapping site to balance the interfacial pH and thus mitigate the unintended side reactions.Simultaneously,the NH_(2)^(+)cations are anchored on the zinc surface,forming a water-shielding,zincophilic molecular layer that guides three-dimensional diffusion and promotes uniform electro-deposition.Thus,an average plating efficiency of 99.74%over 3300 cycles at a current density of2 mA cm^(-2)is achieved.Notably,the TMG additive actualizes ultralong life in Zn‖Zn symmetrical cells(5500 h,exceeding 229 days,1 mA cm^(-2)/1 mA h cm^(-2)),and enables the Zn‖I_(2)cells to reach capacity retention rate of 89.4%after 1000 cycles at 1 A g^(-1).
基金supported by the National Natural Science Foundation of China(Grant Nos.12022508,12074394,and 22125604)Shanghai Supercomputer Center of ChinaShanghai Snowlake Technology Co.Ltd.
文摘The stable nanobubbles adhered to mineral surfaces may facilitate their efficient separation via flotation in the mining industry.However,the state of nanobubbles on mineral solid surfaces is still elusive.In this study,molecular dynamics(MD)simulations are employed to examine mineral-like model surfaces with varying degrees of hydrophobicity,modulated by surface charges,to elucidate the adsorption behavior of nanobubbles at the interface.Our findings not only contribute to the fundamental understanding of nanobubbles but also have potential applications in the mining industry.We observed that as the surface charge increases,the contact angle of the nanobubbles increases accordingly with shape transformation from a pancake-like gas film to a cap-like shape,and ultimately forming a stable nanobubble upon an ordered water monolayer.When the solid–water interactions are weak with a small partial charge,the hydrophobic gas(N_(2))molecules accumulate near the solid surfaces.However,we have found,for the first time,that gas molecules assemble a nanobubble on the water monolayer adjacent to the solid surfaces with large partial charges.Such phenomena are attributed to the formation of a hydrophobic water monolayer with a hydrogen bond network structure near the surface.
基金Natural Science Foundation of Shandong Province,No.ZR2020QH185Scientific Research Nurturing Fund of The First Affiliated Hospital of Shandong First Medical University&Shandong Provincial Qianfoshan Hospital,No.QYPY2020NSFC0803+2 种基金Guangxi Zhuang Autonomous Region Health Commission Scientific Research Project,No.Z-A20220415Guangxi Medical University Teacher Teaching Ability Development Project,No.2022JFA02Guangxi Medical University Undergraduate Education and Teaching Reform Project,No.2023Y05.
文摘BACKGROUND Colorectal cancer(CRC)is the third most common cancer globally,causing over 900000 deaths annually.Risk factors include aging,diet,obesity,sedentary lifestyle,tobacco use,genetic predisposition,and inflammatory bowel disease.Despite current treatments,survival rates for advanced CRC remain low,highlighting the need for better therapeutic strategies.AIM To evaluate both the clinical significance and the pathological implications of the Kinesin family member 14(KIF14)expression within CRC specimens.Additionally,this study aims to investigate the interaction between nitidine chloride(NC)and KIF14,considering their potential as therapeutic targets.METHODS The expression of the KIF14 protein in CRC was analyzed using immunohistochemical staining.The integration of multicenter high-throughput data facilitated the calculation of the standardized mean difference(SMD)for KIF14 mRNA levels.The assessment of clinical and pathological impact was enhanced by analyzing combined receiver operating characteristic curves,along with measures of sensitivity,specificity,and likelihood ratios.Additionally,clustered regularly interspaced short palindromic repeats knockout screening for cell growth and single-cell sequencing were employed to validate the significance of KIF14 expression in CRC.Survival analysis established the prognostic value of KIF14 in CRC.The molecular mechanism of NC against CRC was elucidated through whole-genome sequencing and enrichment analysis,and molecular docking was utilized to explore the targeting affinity between NC and KIF14.RESULTS KIF14 was highly expressed in 208 CRC patients.Data from 17 platforms involving 2436 CRC samples and 1320 noncancerous colorectal tissue controls indicated that KIF14 expression was significantly higher in CRC samples,with an SMD of 1.92(95%CI:1.49-2.35).The area under the curve was 0.94(95%CI:0.92-0.96),with a sensitivity of 0.85(95%CI:0.78-0.90)and a specificity of 0.90(95%CI:0.85-0.93).The positive and negative likelihood ratios were 8.38(95%CI:5.39-13.02)and 0.17(95%CI:0.11-0.26),respectively.At the single-cell level,significant overexpression of KIF14 was observed in CRC cells(P<0.001),with 35 CRC cell lines dependent on KIF14 for growth.The K-M plots demonstrated that KIF14 possesses prognostic value in CRC patients within the GSE71187 and GSE103679 datasets(P<0.05).Binding energy calculations indicated that KIF14 is a potential target for NC(binding energy:10.3 kcal/mol).CONCLUSION KIF14 promotes the growth of CRC cells and acts as an oncogenic factor,potentially serving as a therapeutic target for NC in the treatment of CRC.
基金funded by the National Key Research and Development Program of China(No.2022YFC2804101)the Guangdong Provincial Key R&D Program(No.2023B1111050011)+2 种基金the Guangdong Basic and Applied Basic Research Foundation(No.2023A1515010432)the Guangzhou Basic and Applied Basic Research Foundation(No.202201010305)the High-Level Talents Special Program of Zhejiang(No.2022R52036)。
文摘Guided by molecular networking,nine novel curvularin derivatives(1-9)and 16 known analogs(10-25)were isolated from the hydrothermal vent sediment fungus Penicillium sp.HL-50.Notably,compounds 5-7 represented a hybrid of curvularin and purine.The structures and absolute configurations of compounds 1-9 were elucidated via nuclear magnetic resonance(NMR)spectroscopy,X-ray diffraction,electronic circular dichroism(ECD)calculations,^(13)C NMR calculation,modified Mosher's method,and chemical derivatization.Investigation of anti-inflammatory activities revealed that compounds 7-9,11,12,14,15,and 18 exhibited significant suppressive effects against lipopolysaccharide(LPS)-induced nitric oxide(NO)production in murine macrophage RAW264.7 cells,with IC_(50)values ranging from 0.44 to 4.40μmol·L^(-1).Furthermore,these bioactive compounds were found to suppress the expression of inflammation-related proteins,including inducible NO synthase(i NOS),cyclooxygenase-2(COX-2),NLR family pyrin domain-containing protein 3(NLRP3),and nuclear factor kappa-B(NF-κB).Additional studies demonstrated that the novel compound 7 possessed potent antiinflammatory activity by inhibiting the transcription of inflammation-related genes,downregulating the expression of inflammation-related proteins,and inhibiting the release of inflammatory cytokines,indicating its potential application in the treatment of inflammatory diseases.
文摘Background:Gastric cancer(GC)remains a global health burden and is often characterized by heterogeneous molecular profiles and resistance to conventional therapies.The phosphoinositide 3-kinase and PI3K and Janus kinase(JAK)signal transducer and activator of transcription(JAK-STAT)pathways play pivotal roles in GC progression,making them attractive targets for therapeutic interventions.Methods:This study applied a computational and molecular dynamics simulation approach to identify and characterize SBL-JP-0004 as a potential dual inhibitor of JAK2 and PI3KCD kinases.KATOIII and SNU-5 GC cells were used for in vitro evaluation.Results:SBL-JP-0004 exhibited a robust binding affinity for JAK2 and PI3KCD kinases,as evidenced by molecular docking scores and molecular dynamics simulations.Binding interactions and Gibbs binding free energy estimates confirmed stable and favorable interactions with target proteins.SBL-JP-0004 displayed an half-maximal inhibitory concentration(IC_(50))value of 118.9 nM against JAK2 kinase and 200.9 nM against PI3KCD enzymes.SBL-JP-0004 exhibited potent inhibition of cell proliferation in KATOIII and SNU-5 cells,with half-maximal growth inhibitory concentration(GI50)values of 250.8 and 516.3 nM,respectively.A significant elevation in the early phase apoptosis(28.53%in KATOIII cells and 26.85%in SNU-5 cells)and late phase apoptosis(17.37%in KATOIII cells and 10.05%in SNU-5 cells)were observed with SBL-JP-0004 treatment compared to 2.1%and 2.83%in their respective controls.Conclusion:The results highlight SBL-JP-0004 as a promising dual inhibitor targeting JAK2 and PI3KCD kinases for treating GC and warrant further preclinical and clinical investigations to validate its utility in clinical settings.
基金supported by the National Natural Science Foundation of China(52304021,52104022,52204031)the Natural Science Foundation of Sichuan Province(2022NSFSC0205,2024NSFSC0201,2023NSFSC0947)the National Science and Technology Major Projects of China(2017ZX05049006-010).
文摘The global energy demand is increasing rapidly,and it is imperative to develop shale hydrocarbon re-sources vigorously.The prerequisite for enhancing the exploitation efficiency of shale reservoirs is the systematic elucidation of the occurrence characteristics,flow behavior,and enhanced oil recovery(EOR)mechanisms of shale oil within commonly developed nanopores.Molecular dynamics(MD)technique can simulate the occurrence,flow,and extraction processes of shale oil at the nanoscale,and then quantitatively characterize various fluid properties,flow characteristics,and action mechanisms under different reservoir conditions by calculating and analyzing a series of MD parameters.However,the existing review on the application of MD simulation in shale oil reservoirs is not systematic enough and lacks a summary of technical challenges and solutions.Therefore,recent MD studies on shale oil res-ervoirs were summarized and analyzed.Firstly,the applicability of force fields and ensembles of MD in shale reservoirs with different reservoir conditions and fluid properties was discussed.Subsequently,the calculation methods and application examples of MD parameters characterizing various properties of fluids at the microscale were summarized.Then,the application of MD simulation in the study of shale oil occurrence characteristics,flow behavior,and EOR mechanisms was reviewed,along with the elucidation of corresponding micro-mechanisms.Moreover,influencing factors of pore structure,wall properties,reservoir conditions,fluid components,injection/production parameters,formation water,and inorganic salt ions were analyzed,and some new conclusions were obtained.Finally,the main challenges associated with the application of MD simulations to shale oil reservoirs were discussed,and reasonable prospects for future MD research directions were proposed.The purpose of this review is to provide theoretical basis and methodological support for applying MD simulation to study shale oil reservoirs.
基金supported by the Shennong Laboratory Project of Henan Province,China(SN01-2022-01)the China Postdoctoral Science Foundation(2023M731006)the Project of Science and Technology of Henan Province,China(232102111104)。
文摘One of agriculture’s major challenges is the low efficiency of phosphate(Pi)use,which leads to increased costs,harmful environmental impacts,and the depletion of phosphorus(P)resources.The TaPHT1;6 gene,which encodes a high-affinity Pi transporter(PHT),plays a crucial role in Pi absorption and transport.In this study,the promoter and coding regions of three TaPHT1;6 gene copies on chromosomes 5A,5B,and 5D were individually amplified and sequenced from 167 common wheat(Triticum aestivum L.)cultivars.Sequence analysis revealed 16 allelic variation sites within the promoters of TaPHT1;6-5B among these cultivars,forming three distinct haplotypes:Hap1,Hap2,and Hap3.Field trials were conducted over two years to compare wheat genotypes with these haplotypes,focusing on assessing plant dry weight,grain yield,P content,Pi fertilizer absorption efficiency,and Pi fertilizer utilization efficiency.Results indicated that Hap3 represented the favored Pi-efficient haplotype.Dual-luciferase reporter assay demonstrated that the Hap3 promoter,carrying the identified allelic variation sites,exhibited higher gene-driven capability,leading to increased expression levels of the TaPHT1;6-5B gene.We developed a distributed cleaved amplified polymorphic site marker(dCAPS-571)to distinguish Hap3 from the other two haplotypes based on these allelic variation sites,presenting an opportunity for breeding Pi-efficient wheat cultivars.This study successfully identified polymorphic sites on TaPHT1;6-5B associated with Pi efficiency and developed a functional molecular marker to facilitate future breeding endeavors.
基金Funded by the National Natural Science Foundation of China(No.52370128)the Fundamental Research Funds for the Central Universities(No.2572022AW54)。
文摘This study identified castor oil and phosphate ester as effective retarders through setting time,tensile,and flexural tests,and determined their optimal dosages.The mechanism by which phosphate ester affects the setting time of polyurethane was further investigated using molecular dynamics simulations.Fourier transform infrared spectroscopy was also employed to systematically study the physical and chemical interactions between phosphate esters and polyurethane materials.The results demonstrate that a 1%concentration of phosphate ester provides the most effective retarding effect with minimal impact on the strength of polyurethane.When phosphate ester is added to the B component of the two-component polyurethane system,its interaction energy with component A decreases,as do the diffusion coefficient and aggregation degree of component B on the surface of component A.This reduction in interaction slows the setting time.Additionally,the addition of phosphate ester to polyurethane leads to the disappearance or weakening of functional groups,indicating competitive interactions within the phosphate ester components that inhibit the reaction rate.
基金supported by Key Projects of Science and Technology Research Plan of Hubei Provincial Department of Education(D20221306)the National Natural Science Foundation of China(51605103)Key Project of Hubei Provincial Science and Technology Department(2020BAB055).
文摘As light metals,aluminum and magnesium have been widely used in automotive manufacturing,but the welding of Al/Mg joints is facing challenges.However,it is difficult to obtain high-quality aluminum/magnesium joints with traditional arc welding methods.As a solid-phase welding technology,ultrasonic metal welding has the characteristics of high welding efficiency and less welded defects.It is also suitable for welding sound metal bonds.Aluminum and magnesium ultrasonic welding has become a research hotspot.Therefore,the evolution of microstructures and mechanical performance of Al/Mg and multi-layer Al/Mg ultrasonic welding,and the new study works,including the molecular dynamic simulation of Al/Mg ultrasonic welding and hybrid based on ultrasonic welding are summarized.Furthermore,several promising research directions are proposed to guide in-depth investigations into the ultrasonic welding of Al/Mg dissimilar joints.
