酶诱导碳酸钙沉淀(EICP)固化砂土酸蚀宏细观机理

二氧化碳地质封存是控制二氧化碳排放的有效措施,因此二氧化碳注入后地质安全问题备受关注。当前,酶诱导碳酸钙沉淀(EICP)技术能够有效地解决二氧化碳泄露问题。二氧化碳注入后储层流体呈酸性,对胶结物碳酸钙具有酸蚀作用。因此,亟需对EICP固化砂土耐酸蚀性开展试验研究。本研究将EICP试样浸泡在不同浓度酸溶液中,通过质量损失、表观分析、无侧限抗压强度、SEM和XCT等对其力学强度和宏细观结构进行了研究。结果表明:酸溶液对EICP试样的腐蚀作用随着溶液pH的降低明显增强,并且碳酸钙胶结物酸蚀导致试样结构破坏,试样外层逐渐脱落,从而使试样无侧限抗压强度近似线性下降。同时,酸蚀反应后EICP试样出现大量粒间孔隙。本研究揭示了EICP试样酸蚀演化机理,为EICP耐腐蚀性提供参考。

Wnt/β-catenin Signaling Cascades in Cardiovascular Diseases

Cardiovascular diseases are a group of disorders of the heart and blood vessels,primarily including coronary heart disease,stroke,and other diseases.It is the world’s leading cause of death,and its incidence is increasing yearly.Hypertension is a major risk factor for cardiovascular disease.Wnt signaling comprises a series of highly conservative cascading events controlling fundamental biological processes.Wnt signaling pathways include the canonical Wnt pathway(or Wnt/β-catenin pathway),the non canonical planar cell-polarity pathway,and the non-canonical calcium-dependent pathways.Abnormal Wnt signaling promotes cell proliferation and differentiation,cardiac malformations,various malignancies,so drugs targeting Wnt signaling play a great therapeutic potential.Wnt/β-catenin pathway is involved in the occurrence and development of cardiovascular diseases such as atherosclerosis and stroke by regulating cell proliferation,migration,apoptosis,blood-brain barrier permeability,inflammation,oxidative stress,and immune response.Based on the latest research progress,this review summarizes the role of Wnt/β-catenin signaling in cardiovascular diseases,in order to provide new ideas for the prevention and treatment of cardiovascular diseases.

人工智能在化学逆合成中的应用

近年来,人们对通过人工智能(AI)技术解决化学逆合成预测问题产生了巨大的兴趣。与化学家和基于规则的专家系统进行的逆合成预测不同,AI驱动的逆合成预测自动从现成的实验数据集中学习化学知识,以预测反应和逆合成路径。这提供了解决许多传统挑战的机会,包括专业知识过于繁杂、路线的长度过大和计算成本高。本文描述了当前AI驱动的逆合成预测的概况。我们首先讨论了化学逆合成的数学定义,并回顾了这个问题中的研究挑战。然后,我们回顾相关的AI技术和最新进度,以实现逆合成预测。此外,我们提供了逆合成系统不同部分的全面分类,并调查了AI方法如何重塑每个子模块。最后,我们讨论了未来有希望的研究领域。

MRI insight on multiphase flow in hydrate-bearing sediment and development mechanism of hydrate seal

Gas and water migration through the hydrate-bearing sediment are characteristic features in marine gas hydrate reservoirs worldwide.However,there are few experimental investigations on the effect of water-gas flow on the gas hydrate reservoir.In this study,gas-water migration in gas hydrate stability zone(GHSZ)was investigated visually employing a high-resolution magnetic resonance imaging(MRI)apparatus,and the formation of hydrate seal was experimentally investigated.Results revealed that normal flow of gas-water at the low flow rate of 1–0.25 mL/min will induce the hydrate reformation.Conversely,higher gas-water flow rates(at 2–0.5 and 4–1 mL/min)need higher reservoir pressure to induce the hydrate reformation.In addition,the hydrate reformation during the gas-water flow process produced the hydrate seal,which can withstand an over 9.0 MPa overpressure.This high overpressure provides the development condition for the underlying gas and/or water reservoir.A composite MRI image of the whole hydrate seal was obtained through the MRI.The pore difference between hydrate zone and coexistence zone produces a capillary sealing effect for hydrate seal.The hydrate saturation of hydrate seal was more than 51.6%,and the water saturation was more than 19.3%.However,the hydrate seal can be broken through when the overpressure exceeded the capillary pressure of the hydrate seal,which induced the sudden drop of reservoir pressure.This study provides a scientific explanation for the existence of high-pressure underlying gas below the hydrate layer and is significant for the safe exploitation of these common typical marine hydrate reservoirs.

Evolution of chemistry and selection technology for DNA-encoded library

DNA-encoded chemical library(DEL)links the power of amplifiable genetics and the nonself-replicating chemical phenotypes,generating a diverse chemical world.In analogy with the biological world,the DEL world can evolve by using a chemical central dogma,wherein DNA replicates using the PCR reactions to amplify the genetic codes,DNA sequencing transcripts the genetic information,and DNA-compatible synthesis translates into chemical phenotypes.Importantly,DNA-compatible synthesis is the key to expanding the DEL chemical space.Besides,the evolution-driven selection system pushes the chemicals to evolve under the selective pressure,i.e.,desired selection strategies.In this perspective,we summarized recent advances in expanding DEL synthetic toolbox and panning strategies,which will shed light on the drug discovery harnessing in vitro evolution of chemicals via DEL.

N = 2 SCVA's FROM A GENERALIZED CALABI-YAU MANIFOLD AND MIRROR SYMMETRY

We construct an N = 2 superconformal vertex algebra（SCVA） from a generalized Calabi-Yau manifold and compute the BRST cohomology of its associated topological vertex algebras. We show that the BRST cohomology coincides with the generalized Dobeault cohomology. We show that the two topological vertex algebras constructed from the N = 2 SCVA by A and B twist respectively are mirror pairs.

Pomalidomide improves the function of CD133-or HER2-specific CAR T cells

Chimeric antigen receptor(CAR)T-cell therapy is mostly limited to hematological malignancies and has a poor effect on solid tumors.CAR T cells as a kind of immune cell may be affected by some immunomodulatory drugs such as pomalidomide,so the use of pomalidomide may improve the effect of CAR T cells on solid tumors.In this study,CD133-or HER2-specific CAR T cells were chosen to investigate whether pomalidomide can regulate the function of CAR T cells in vitro.We found that pomalidomide can significantly enhance the ability of CD133-CAR T cells and HER2-CAR T cells to kill tumor cells and increase the cytokine secretion of CD133-CAR T cells and HER2-CAR T cells.Also,pomalidomide was shown to induce down-regulation of protein levels of IL-2 transcriptional repressors Aiolos and Ikaros in CAR T cells.This study suggests that the combination of pomalidomide and CAR T cells may be a new strategy for the treatment of solid tumors.

A pH-responsive nanoparticle delivery system containing dihydralazine and doxorubicin-based prodrug for enhancing antitumor efficacy

The efficacy of nanoparticle(NP)-based drug delivery technology is hampered by aberrant tumor stromal microenvironments(TSMs)that hinder NP transportation.Therefore,the promotion of NP permeation into deep tumor sites via the regulation of tumor microenvironments is of critical importance.Herein,we propose a potential solution using a dihydralazine(HDZ)-loaded nanoparticle drug delivery system containing a pH-responsive,cyclic RGD peptide-modified prodrug based on doxorubicin(cRGD-Dex-DOX).With a combined experimental and theoretical approach,we find that the designed NP system can recognize the acid tumor environments and precisely release the encapsulated HDZ into tumor tissues.HDZ can notably downregulate the expression levels of hypoxia-inducible factor 1α(HIF1α),α-smooth muscle actin,and fibronectin through the dilation of tumor blood vessels.These changes in the TSMs enhance the enrichment and penetration of NPs and also unexpectedly promote the infiltration of activated T cells into tumors,suggesting that such a system may offer an effective“multifunctional therapy”through both improving the chemotherapeutic effect and enhancing the immune response to tumors.In vivo experiments on 4T1 breast cancer bearing mice indeed validate that this therapy has the most outstanding antitumor effects over all the other tested control regimens,with the lowest side effects as well.

Transfer learning enhanced graph neural network for aldehyde oxidase metabolism prediction and its experimental application

Aldehyde oxidase(AOX)is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other xenobiotics.AOX-mediated metabolism can result in unexpected outcomes,such as the production of toxic metabolites and high metabolic clearance,which can lead to the clinical failure of novel therapeutic agents.Computational models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism liability.In this study,we developed a novel graph neural network called AOMP for predicting AOX-mediated metabolism.AOMP integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction,while utilizing transfer learning from 13C nuclear magnetic resonance data to enhance its performance on both tasks.AOMP significantly outperformed the benchmark methods in both cross-validation and external testing.Using AOMP,we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability,which were validated through in vitro experiments.Furthermore,for the convenience of the community,we established the first online service for AOX metabolism prediction based on AOMP,which is freely available at https://aomp.alphama.com.cn.

Identification of exosomal miRNAs as diagnostic biomarkers for cholangiocarcinoma and gallbladder carcinoma

Dear Editor,Extracellular vesicles,especially exosomes,have emerged as promising diagnostic sources for cancers due to their easy and quick accessibility.1,2 Additionally,it has been demonstrated that circulating miRNAs serve as promising biomarkers for the diagnosis of multiple diseases.3,4 Cholangiocarcinoma(CCA)and gallbladder carcinoma(GBC)are two malignant biliary tract cancers.It has been estimated that patients with unresectable GBC have a 5-year survival of only less than 5%,while CCA patients suffer from an overall 5-year survival of~10%.5 Hence,circulating diagnostic biomarkers would benefit the diagnosis and treatment of GBC and CCA patients.We sequenced exosomal small RNA from five CCA patients and four GBC patients before and after surgery and enrolled 40 healthy individuals,45 more CCA patients and 24 more GBC patients to validate the sequencing results to identify diagnostic biomarkers for GBC and CCA.

Study of biomass gasification in an industrial-scale dual circulating fluidized bed(DCFB)using the Eulerian-Lagrangian method

Dual circulating fluidized bed(DCFB)has emerged as an efficient reactor for biomass gasification due to its unique feature of high gas-solid contact efficiency and separated reactions in two reactors,yet the understanding of complex in-furnace phenomena is still lacking.In this study,biomass gasification in an industrial-scale DCFB system was numerically studied using a multiphase particle-in-cell(MP-PIC)method featuring thermochemical sub-models(e.g.,heat transfer,heterogeneous reactions,and homogeneous reactions)under the Eulerian-Lagrangian framework.After model validation,the hydrodynamics and thermochemical characteristics(i.e.,pressure,temperature,and species)in the DCFB are comprehensively investigated.The results show that size-/density-induced segregation makes solid fuels concentrate on the bed surface.Interphase momentum exchange leads to the continuous decrease of the gas pressure axially.In the gasifier and combustor,the lower heating value(LHV)of the gas products is 5.56 MJ/Nm^(3)and 0.2 MJ/Nm^(3)and the combustible gas concentration(CGC)is 65.5%and 1.86%,respectively.The temperature in the combustor is about 100 K higher than that in the gasifier.A higher solid concentration results in a smaller value of particle heat transfer coefficient(HTC).The HTCs range from 50 to 150 W/(m^(2) K)for a solid concentration larger than 0.3 in the combustor while the HTCs range from 100 to 200 W/(m^(2 )K)in the gasifier.The Reynolds number of biomass particles is two orders of magnitude larger than that of the sand particle.The numerical results shed light on the reactor design and process optimization of biomass gasification in DCFBs.

A mild phenoxysilyl linker for self-immolative release of antibody-drug conjugates

Self-immolative linkers have been widely used to construct prodrugs to improve their efficacy and safety.In this study,we report the use of phenoxysilyl linker as a self-immolative unit to prepare antibody-drug conjugates(ADCs).Phenoxysily based ADC Ate-PPS-CA4 was prepared and its release was systematically investigated by mass spectrometry.Biological evaluation showed that Ate-PPS-CA4 displayed the ability to target delivery and self-immolative release the active payload CA4 on PD-L1 positive cells MDA-MB-231.As the same with its payload CA4,it could arrest the cell cycle to the G2/M phase and induced changes in cell morphology at the dose of its IC_(50).The development of this linker with novel drug release mechanisms will expand the methodology to construct ADCs,especially for non-internalizing ADCs by extracellular cleavage.

A novel approach for the optimal arrangement of tube bundles in a 1000-MW condenser

1Introduction The condenser plays a vital role in the operation of a thermal power generation unit.Its primary function is to remove the heat from the steam that is exhausted from the steam turbine,thereby condensing the steam into water.Additionally,it establishes and maintains a specific degree of vacuum at the exhaust port of the steam turbine,facilitating efficient operation of the turbine(Keshvarparast et al.,2020).The vacuum degree of the condenser is affected by physical factors such as steam-side resistance and heat-transfer efficiency.

Answer for questions of repeated measurements of variance analysis and distribution test of data—Authors’reply

Thank the journal for sending us the Correspondence of Dr.Jie Wei for our article“Effects of Shuanghuanglian oral liquids on patients with COVID-19:a randomized,open-label,parallel-controlled,multicenter clinical trial”published in Frontiers of Medicine[1].We appreciate Dr.Jie Wei for agreeing with most of the conclusions in our paper and we address Dr.Jie Wei’s comments carefully as below.

Cryo-EM structures for the Mycobacterium tuberculosis iron-loaded siderophore transporter IrtAB

The adenosine 5'-triphosphate(ATP)-binding cassette(ABC)transporter,IrtAB,plays a vital role in the replication and viability of Mycobacterium tuberculosis(Mtb),where its function is to import iron-loaded siderophores.Unusually,it adopts the canonical type IV exporter fold.Herein,we report the structure of unliganded Mtb IrtAB and its structure in complex with ATP,ADP,or ATP analogue(AMP-PNP)at resolutions ranging from 2.8 to 3.5Å.The structure of IrtAB bound ATP-Mg2+shows a“head-to-tail”dimer of nucleotide-binding domains(NBDs),a closed amphipathic cavity within the transmembrane domains(TMDs),and a metal ion liganded to three histidine residues of IrtA in the cavity.Cryo-electron microscopy(Cryo-EM)structures and ATP hydrolysis assays show that the NBD of IrtA has a higher affinity for nucleotides and increased ATPase activity compared with IrtB.Moreover,the metal ion located in the TM region of IrtA is critical for the stabilization of the conformation of IrtAB during the transport cycle.This study provides a structural basis to explain the ATP-driven conformational changes that occur in IrtAB.

Structure basis for allosteric regulation of lymphocytic choriomeningitis virus polymerase function by Z matrix protein

DearEditor,The Arenaviridae family(recently assigned to the Bunyavirales order)is a group of emerging viruses that include causative agents of severe hemorrhagic fevers with high mortality in humans(de la Torre,2009).Lymphocytic choriomeningitis virus(LCMV)is the prototypic member of the Arenaviridae family and belongs to the Old World(OW)arenavirus together with Lassa virus(LASV),which are distinct from the New World(NW)arenavirus[e.g.Machupo virus(MACV)and Junin virus(JUNV)].LCMV infection in the fetus and newborm results in severe impairment of brain development associated with sensory loss and mental retardation and is also known to be associated with severe systemic infection with high mortality in transplantation patients(Palacios et al.,2008).

SARS-CoV-2 immunity in animal models

The COVID-19 pandemic,which was caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),has become a worldwide health crisis due to its transmissibility.SARS-CoV-2 infection results in severe respiratory illness and can lead to significant complications in affected individuals.These complications encompass symptoms such as coughing,respiratory distress,fever,infectious shock,acute respiratory distress syndrome(ARDS),and even multiple-organ failure.Animal models serve as crucial tools for investigating pathogenic mechanisms,immune responses,immune escape mechanisms,antiviral drug development,and vaccines against SARS-CoV-2.Currently,various animal models for SARS-CoV-2 infection,such as nonhuman primates(NHPs),ferrets,hamsters,and many different mouse models,have been developed.Each model possesses distinctive features and applications.In this review,we elucidate the immune response elicited by SARS-CoV-2 infection in patients and provide an overview of the characteristics of various animal models mainly used for SARS-CoV-2 infection,as well as the corresponding immune responses and applications of these models.A comparative analysis of transcriptomic alterations in the lungs from different animal models revealed that the K18-hACE2 and mouse-adapted virus mouse models exhibited the highest similarity with the deceased COVID-19 patients.Finally,we highlighted the current gaps in related research between animal model studies and clinical investigations,underscoring lingering scientific questions that demand further clarification.

Antigen-induced chimeric antigen receptor multimerization amplifies on-tumor cytotoxicity

Ligand-induced receptor dimerization or oligomerization is a widespread mechanism for ensuring communication specificity,safeguarding receptor activation,and facilitating amplification of signal transduction across the cellular membrane.However,cell-surface antigeninduced multimerization(dubbed AIM herein)has not yet been consciously leveraged in chimeric antigen receptor(CAR)engineering for enriching T cell-based therapies.We co-developed ciltacabtagene autoleucel(cilta-cel),whose CAR incorporates two B-cell maturation antigen(BCMA)-targeted nanobodies in tandem,for treating multiple myeloma.Here we elucidated a structural and functional model in which BCMA-induced cilta-cel CAR multimerization amplifies myeloma-targeted T cell-mediated cytotoxicity.Crystallographic analysis of BCMA–nanobody complexes revealed atomic details of antigen–antibody hetero-multimerization whilst analytical ultracentrifugation and small-angle X-ray scattering characterized interdependent BCMA apposition and CAR juxtaposition in solution.BCMA-induced nanobody CAR multimerization enhanced cytotoxicity,alongside elevated immune synapse formation and cytotoxicity-mediating cytokine release,towards myeloma-derived cells.Our results provide a framework for contemplating the AIM approach in designing next-generation CARs.

Component modeling and updating method of integrated energy systems based on knowledge distillation

Amid the backdrop of carbon neutrality, traditional energy production is transitioning towards integrated energy systems (IES), where model-based scheduling is key in scenarios with multiple uncertainties on both supply and demand sides. The development of artificial intelligence algorithms, has resolved issues related to model accuracy. However, under conditions of high proportion renewable energy integration, component load adjustments require increased flexibility, so the mathematical model of the component must adapt to constantly changing operating conditions. Therefore, the identification of operating condition changes and rapid model updating are pressing issues. This study proposes a modeling and updating method for IES components based on knowledge distillation. The core of this modeling method is the light weighting of the model, which is achieved through a knowledge distillation method, using a teacher-student mode to compress complex neural network models. The triggering of model updates is achieved through principal component analysis. The study also analyzes the impact of model errors caused by delayed model updates on the overall scheduling of IES. Case studies are conducted on critical components in IES, including coal-fired boilers and turbines. The results show that the time consumption for model updating is reduced by 76.67 % using the proposed method. Under changing conditions, compared with two traditional models, the average deviation of this method is reduced by 12.61 % and 3.49 %, respectively, thereby improving the model's adaptability. The necessity of updating the component model is further analyzed, as a 1.00 % mean squared error in the component model may lead to a power deviation of 0.075 MW. This method provides real-time, adaptable support for IES data modeling and updates.

Cross-level steam load smoothing and optimization in industrial parks using data-driven approaches

This study focuses on the integrated energy production system in industrial parks, addressing the problem of stable load dispatch of equipment under demand fluctuations. A cross-level method for steam load smoothing and optimization is proposed, aiming to achieve stable production and optimal economic performance through three levels of integration: load forecasting, load dispatch, and load regulation. Unlike traditional methods that directly use load forecasting values, heat network elasticity is presented as a buffer between demand and supply. Constraints for minimal changes in equipment load and operational parameters are established for smooth regulation. Industrial cases demonstrate that the load forecasting model has mean absolute percentage errors of 2.44% and 1.68% for medium-pressure and low-pressure steam, respectively, meeting accuracy requirements. The modified supply-side load smoothness is effectively improved by considering heat network elasticity. The method increases boiler efficiency by 1.92%, reducing average coal consumption by 0.92 t/h. Compared to manual operation, the proposed model leads to an average increase of 5.69 MW in power generation and an average reduction of 10.81% in coal-to-electricity ratio. This study verifies the importance of smooth integration across different levels and analyzes the effective response of the proposed method to the uncertainty in load forecasting. The method demonstrates the enormous potential of data-driven methods in achieving safe, economical, and sustainable production in industrial parks.