Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

Data-Driven Adaptive Predictive Control Method With Autotuned Weighting Factor for Nonlinear Systems Using Triangular Dynamic Linearization

Dear Editor,In this letter,a novel data-driven adaptive predictive control method is proposed using the triangular dynamic linearization technique.The proposed method only contains one time-varying parameter with explicit physical meaning,which can prevent severe deviation in parameter estimation.Specifically,a triangular dynamic linearization(TDL)data model is employed to predict future system outputs,and then to correct inaccurate predictive outputs,a feedback regulator is designed.An autotuned weighing factor is introduced to alleviate the computational burden in practical applications and further improve output tracking performance.Closed-loop stability conditions are derived by rigorous analysis.Simulation results are provided to demonstrate the efficacy of the proposed method.

State Estimation Method for GNSS/INS/Visual Multi-sensor Fusion Based on Factor Graph Optimization for Unmanned System

With the development of unmanned driving technology,intelligent robots and drones,high-precision localization,navigation and state estimation technologies have also made great progress.Traditional global navigation satellite system/inertial navigation system(GNSS/INS)integrated navigation systems can provide high-precision navigation information continuously.However,when this system is applied to indoor or GNSS-denied environments,such as outdoor substations with strong electromagnetic interference and complex dense spaces,it is often unable to obtain high-precision GNSS positioning data.The positioning and orientation errors will diverge and accumulate rapidly,which cannot meet the high-precision localization requirements in large-scale and long-distance navigation scenarios.This paper proposes a method of high-precision state estimation with fusion of GNSS/INS/Vision using a nonlinear optimizer factor graph optimization as the basis for multi-source optimization.Through the collected experimental data and simulation results,this system shows good performance in the indoor environment and the environment with partial GNSS signal loss.

Overview of Factors Affecting Dust Deposition on Photovoltaic Cells and Cleaning Methods

Dust deposition on the surface of photovoltaic (PV) cells poses a significant challenge to their efficiency, especially in arid regions characterized by desert and semi-desert conditions. Despite the pronounced impact of dust accumulation, these regions offer optimal solar radiation and minimal cloud cover, making them ideal candidates for widespread PV cell deployment. Various surface cleaning methods exist, each employing distinct approaches. Choosing an appropriate cleaning method requires a comprehensive understanding of the mechanisms involved in both dust deposition on module surfaces and dust adhesion to PV cell surfaces. The mechanisms governing dust deposition and adhesion are complex and multifaceted, influenced by factors such as the nature and properties of the dust particles, environmental climatic conditions, characteristics of protective coatings, and the specific location of the PV installation. These factors exhibit regional variations, necessitating the implementation of diverse cleaning approaches tailored to the unique conditions of each location. The first part of this article explores the factors influencing dust deposition on PV cell surfaces, delving into the intricate interplay of environmental variables and particle characteristics. Subsequently, the second part addresses various cleaning methods, offering an analysis of their respective advantages and disadvantages. By comprehensively examining the factors influencing dust accumulation and evaluating the effectiveness of different cleaning strategies, this article aims to contribute valuable insights to the ongoing efforts to optimize the performance and longevity of photovoltaic systems in diverse geographical contexts.

Factorized Smith Method for A Class of High-Ranked Large-Scale T-Stein Equations

We introduce a factorized Smith method(FSM)for solving large-scale highranked T-Stein equations within the banded-plus-low-rank structure framework.To effectively reduce both computational complexity and storage requirements,we develop techniques including deflation and shift,partial truncation and compression,as well as redesign the residual computation and termination condition.Numerical examples demonstrate that the FSM outperforms the Smith method implemented with a hierarchical HODLR structured toolkit in terms of CPU time.

Hybrid Multi-Infeed Interaction Factor Calculation Method Considering Voltage Regulation Control Characteristics of Voltage Source Converter

Voltage source converter based high voltage direct current(VSC-HVDC)can participate in voltage regulation by flexible control to help maintain the voltage stability of the power grid.In order to quantitatively evaluate its influence on the voltage interaction between VSC-HVDC and line commutated converter based high voltage direct current(LCC-HVDC),this paper proposes a hybrid multi-infeed interaction factor(HMIIF)calculation method considering the voltage regulation control characteristics of VSC-HVDC.Firstly,for a hybrid multi-infeed high voltage direct current system,an additional equivalent operating admittance matrix is constructed to characterize HVDC equipment characteristics under small disturbance.Secondly,based on the characteristic curve between the reactive power and the voltage of a certain VSC-HVDC project,the additional equivalent operating admittance of VSC-HVDC is derived.The additional equivalent operating admittance matrix calculation method is proposed.Thirdly,the equivalent bus impedance matrix is obtained by modifying the alternating current(AC)system admittance matrix with the additional equivalent operating admittance matrix.On this basis,the HMIIF calculation method based on the equivalent bus impedance ratio is proposed.Finally,the effectiveness of the proposed method is verified in a hybrid dual-infeed high voltage direct current system constructed in Power Systems Computer Aided Design(PSCAD),and the influence of voltage regulation control on HMIIF is analyzed.

Application of Choquet Integral-Importance-Performance Analysis and TOPSIS Methods in Approaching the Preference Factors of Calligraphy and Seal Engraving Imagery

Classical Chinese characters,presented through calligraphy,seal engraving,or painting,can exhibit different aesthetics and essences of Chinese characters,making them the most important asset of the Chinese people.Calligraphy and seal engraving,as two closely related systems in traditional Chinese art,have developed through the ages.Due to changes in lifestyle and advancements in modern technology,their original functions of daily writing and verification have gradually diminished.Instead,they have increasingly played a significant role in commercial art.This study utilizes the Evaluation Grid Method(EGM)and the Analytic Hierarchy Process(AHP)to research the key preference factors in the application of calligraphy and seal engraving imagery.Different from the traditional 5-point equal interval semantic questionnaire,this study employs a non-equal interval semantic questionnaire with a golden ratio scale,distinguishing the importance ratio of adjacent semantic meanings and highlighting the weighted emphasis on visual aesthetics.Additionally,the study uses Importance-Performance Analysis(IPA)and Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)to obtain the key preference sequence of calligraphy and seal engraving culture.Plus,the Choquet integral comprehensive evaluation is used as a reference for IPA comparison.It is hoped that this study can provide cultural imagery references and research methods,injecting further creativity into industrial design.

A RELAXED INERTIAL FACTOR OF THE MODIFIED SUBGRADIENT EXTRAGRADIENT METHOD FOR SOLVING PSEUDO MONOTONE VARIATIONAL INEQUALITIES IN HILBERT SPACES

In this paper,we investigate pseudomonotone and Lipschitz continuous variational inequalities in real Hilbert spaces.For solving this problem,we propose a new method that combines the advantages of the subgradient extragradient method and the projection contraction method.Some very recent papers have considered different inertial algorithms which allowed the inertial factor is chosen in[0;1].The purpose of this work is to continue working in this direction,we propose another inertial subgradient extragradient method that the inertial factor can be chosen in a special case to be 1.Under suitable mild conditions,we establish the weak convergence of the proposed algorithm.Moreover,linear convergence is obtained under strong pseudomonotonicity and Lipschitz continuity assumptions.Finally,some numerical illustrations are given to confirm the theoretical analysis.

Crude oil cracking in deep reservoirs:A review of the controlling factors and estimation methods

The natural cracking of crude oils in deep reservoirs has gained great interest due to continuously increasing depth of petroleum exploration and exploitation.Complex oil compositions and surroundings as well as complicated geological evolutions make oil cracking in nature much more complex than industrial pyrolysis.So far,numerous studies,focused on this topic,have made considerable progress although there still exist some drawbacks.However,a comprehensive review on crude oil cracking is yet to be conducted.This article systematically reviews the controlling factors of oil cracking from six aspects,namely,oil compositions,temperature and time,pressure,water,minerals and solid organic matter.We compare previous experimental and modelling results and present new field cases.In the following,we evaluate the prevailing estimation methods for the extent of oil cracking,and elucidate other factors that may interfere with the application of these estimation methods.This review will be helpful for further investigations of crude oil cracking and provides a guide for estimation of the cracking extent of crude oils.

A Credit Card Fraud Model Prediction Method Based on Penalty Factor Optimization AWTadaboost

With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detection has started to use this method in large numbers, but thetraditional Adaboost is prone to overfitting in the presence of noisy samples.Therefore, in order to alleviate this phenomenon, this paper proposes a newidea: using the number of consecutive sample misclassifications to determinethe noisy samples, while constructing a penalty factor to reconstruct thesample weight assignment. Firstly, the theoretical analysis shows that thetraditional Adaboost method is overfitting in a noisy training set, which leadsto the degradation of classification accuracy. To this end, the penalty factorconstructed by the number of consecutive misclassifications of samples isused to reconstruct the sample weight assignment to prevent the classifierfrom over-focusing on noisy samples, and its reasonableness is demonstrated.Then, by comparing the penalty strength of the three different penalty factorsproposed in this paper, a more reasonable penalty factor is selected.Meanwhile, in order to make the constructed model more in line with theactual requirements on training time consumption, the Adaboost algorithmwith adaptive weight trimming (AWTAdaboost) is used in this paper, so thepenalty factor-based AWTAdaboost (PF_AWTAdaboost) is finally obtained.Finally, PF_AWTAdaboost is experimentally validated against other traditionalmachine learning algorithms on credit card fraud datasets and otherdatasets. The results show that the PF_AWTAdaboost method has betterperformance, including detection accuracy, model recall and robustness, thanother methods on the credit card fraud dataset. And the PF_AWTAdaboostmethod also shows excellent generalization performance on other datasets.From the experimental results, it is shown that the PF_AWTAdaboost algorithmhas better classification performance.

A Review on Sources,Extractions and Analysis Methods of a Sustainable Biomaterial:Tannins

Condensed and hydrolysable tannins are non-toxic natural polyphenols that are a commercial commodity industrialized for tanning hides to obtain leather and for a growing number of other industrial applications mainly to substitute petroleum-based products.They are a definite class of sustainable materials of the forestry industry.They have been in operation for hundreds of years to manufacture leather and now for a growing number of applications in a variety of other industries,such as wood adhesives,metal coating,pharmaceutical/medical applications and several others.This review presents the main sources,either already or potentially commercial of this forestry by-materials,their industrial and laboratory extraction systems,their systems of analysis with their advantages and drawbacks,be these methods so simple to even appear primitive but nonetheless of proven effectiveness,or very modern and instrumental.It constitutes a basic but essential summary of what is necessary to know of these sustainable materials.In doing so,the review highlights some of the main challenges that remain to be addressed to deliver the quality and economics of tannin supply necessary to fulfill the industrial production requirements for some materials-based uses.

Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning

BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective preventive measures.AIM To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment.METHODS Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission.Relevant data from the initial 14 d of ICU stay,such as age,comorbidities,sedative dosage,vasopressor dosage,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,were gathered.The relationships between these variables and ICU-AW were examined.Utilizing iterative machine learning techniques,a multilayer perceptron neural network model was developed,and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve.RESULTS Within the ICU-AW group,age,duration of mechanical ventilation,lorazepam dosage,adrenaline dosage,and length of ICU stay were significantly higher than in the non-ICU-AW group.Additionally,sepsis,multiple organ dysfunction syndrome,hypoalbuminemia,acute heart failure,respiratory failure,acute kidney injury,anemia,stress-related gastrointestinal bleeding,shock,hypertension,coronary artery disease,malignant tumors,and rehabilitation therapy ratios were significantly higher in the ICU-AW group,demonstrating statistical significance.The most influential factors contributing to ICU-AW were identified as the length of ICU stay(100.0%)and the duration of mechanical ventilation(54.9%).The neural network model predicted ICU-AW with an area under the curve of 0.941,sensitivity of 92.2%,and specificity of 82.7%.CONCLUSION The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation.A primary preventive strategy,when feasible,involves minimizing both ICU stay and mechanical ventilation duration.

The Factors of the Failure in the Treatment of Congenital Equinus Clubfoot Varus by the Ponseti Method at the Reference Health Care Center (CSREF) of Commune III of Bamako

Clubfoot malformation is the most common serious congenital anomaly affecting the foot in children. Its treatment by the Ponseti method is simple, profitable and widely used in the world. Objective: The objective of this study was to identify the factors of the failure of its treatment by the Ponseti method. Material and Method: We carried out a retrospective and descriptive study of cases of congenital equinus clubfoot varus at the Reference Health Care Center of Commune III of Bamako over 26 months from September 2020 to November 2022. Data were treated with the utmost anonymity. Result: This study was performed on 44 children seen for clubfoot: male (68%) and female (32%), with a sex ratio of 2.1. We obtained 13 cases of recidivism including 7 boys and 6 girls. We found 21 cases of unilateral and 23 bilateral;among which 9 recurrences were found against 4 in the unilateral forms. 85% of recurrences did not have good adherence to the splint and 62% did not come regularly for follow-up consultation. We obtained 33 children with idiopathic clubfeet (75%) with a recurrence of 24%, and 7 children with secondary clubfeet with 71 % recurrence. There was no recurrence in the postural type. Among the recurrences, 38.5% started treatment between 1 and 6 months, 23.1% from 0 to 1 month and 15.4% at 2 years and more. 85% of recurrences had a Pirani score between 4.5 to 6 at the start of treatment and 15% at a score of 2.5 to 4. Conclusion: The factors of the failure of the Ponseti method are mainly non-compliance with treatment, secondary clubfeet, and a high Pirani score at the start of treatment.

Are TrkB receptor agonists the right tool to fulfill the promises for a therapeutic value of the brain-derived neurotrophic factor?

Brain-derived neurotrophic factor signaling via its receptor tro pomyosin receptor kinase B regulates several crucial physiological processes.It has been shown to act in the brain,promoting neuronal survival,growth,and plasticity as well as in the rest of the body where it is involved in regulating for instance aspects of the metabolism.Due to its crucial and very pleiotro pic activity,reduction of brain-derived neurotrophic factor levels and alterations in the brain-derived neurotrophic factor/tropomyosin receptor kinase B signaling have been found to be associated with a wide spectrum of neurological diseases.Howeve r,because of its poor bioavailability and pharmacological properties,brain-derived neurotrophic factor itself has a very low therapeutic value.Moreover,the concomitant binding of exogenous brain-derived neurotrophic factor to the p75 neurotrophin receptor has the potential to elicit several unwanted and deleterious side effects.Therefo re,developing tools and approaches to specifically promote tropomyosin receptor kinase B signaling has become an important goal of translational research.Among the newly developed tools are different categories of tropomyosin receptor kinase B receptor agonist molecules.In this review,we give a comprehensive description of the diffe rent tro pomyosin receptor kinase B receptor agonist drugs developed so far and of the res ults of their application in animal models of several neurological diseases.Moreover,we discuss the main benefits of tropomyosin receptor kinase B receptor agonists,concentrating especially on the new tropomyosin receptor kinase B agonist antibodies.The benefits observed both in vitro and in vivo upon application of tropomyosin receptor kinase B receptor agonist drugs seem to predominantly depend on their general neuroprotective activity and their ability to promote neuronal plasticity.Moreover,tro pomyosin receptor kinase B agonist antibodies have been shown to specifically bind the tropomyosin receptor kinase B receptor and not p75 neurotrophin receptor.Therefore,while,based on the current knowledge,the tropomyosin receptor kinase B receptor agonists do not seem to have the potential to reve rse the disease pathology per se,promoting brainderived neurotrophic factor/tro pomyosin receptor kinase B signaling still has a very high therapeutic relevance.

Hepatocyte growth factor promotes retinal pigment epithelium cell activity through MET/AKT signaling pathway

AIM:To explore the effects of hepatocyte growth factor(HGF)on retinal pigment epithelium(RPE)cell behaviors.METHODS:The human adult retinal pigment epithelial cell line-19(ARPE-19)were treated by HGF or mesenchymalepithelial transition factor(MET)inhibitor SU11274 in vitro.Cell viability was detected by a Cell Counting Kit-8 assay.Cell proliferation and motility was detected by a bromodeoxyuridine incorporation assay and a wound healing assay,respectively.The expression levels of MET,phosphorylated MET,protein kinase B(AKT),and phosphorylated AKT proteins were determined by Western blot assay.The MET and phosphorylated MET proteins were also determined by immunofluorescence assay.RESULTS:HGF increased ARPE-19 cells’viability,proliferation and migration,and induced an increase of phosphorylated MET and phosphorylated AKT proteins.SU11274 significantly reduced cell viability,proliferation,and migration and decreased the expression of MET and AKT proteins.SU11274 suppressed HGF-induced increase of viability,proliferation,and migration in ARPE-19 cells.Additionally,SU11274 also blocked HGF-induced phosphorylation of MET and AKT proteins.CONCLUSION:HGF enhances cellular viability,proliferation,and migration in RPE cells through the MET/AKT signaling pathway,whereas this enhancement is suppressed by the MET inhibitor SU11274.HGF-induced MET/AKT signaling might be a vital contributor of RPE cells survival.

The roles of macrophage migration inhibitory factor in retinal diseases

Macrophage migration inhibitory factor(MIF),a multifunctional cytokine,is secreted by various cells and participates in inflammatory reactions,including innate and adaptive immunity.There are some evidences that MIF is involved in many vitreoretinal diseases.For example,MIF can exacerbate many types of uveitis;measurements of MIF levels can be used to monitor the effectiveness of uveitis treatment.MIF also alleviates trauma-induced and glaucoma-induced optic nerve damage.Furthermore,MIF is critical for retinal/choroidal neovascularization,especially complex neovascularization.MIF exacerbates retinal degeneration;thus,anti-MIF therapy may help to mitigate retinal degeneration.MIF protects uveal melanoma from attacks by natural killer cells.The mechanism underlying the effects of MIF in these diseases has been demonstrated:it binds to cluster of differentiation 74,inhibits the c-Jun N-terminal kinase pathway,and triggers mitogen-activated protein kinases,extracellular signal-regulated kinase-1/2,and the phosphoinositide-3-kinase/Akt pathway.MIF also upregulates Toll-like receptor 4 and activates the nuclear factor kappa-B signaling pathway.This review focuses on the structure and function of MIF and its receptors,including the effects of MIF on uveal inflammation,retinal degeneration,optic neuropathy,retinal/choroidal neovascularization,and uveal melanoma.

Drilling-based measuring method for the c-φ parameter of rock and its field application

The technology of drilling tests makes it possible to obtain the strength parameter of rock accurately in situ. In this paper, a new rock cutting analysis model that considers the influence of the rock crushing zone(RCZ) is built. The formula for an ultimate cutting force is established based on the limit equilibrium principle. The relationship between digital drilling parameters(DDP) and the c-φ parameter(DDP-cφ formula, where c refers to the cohesion and φ refers to the internal friction angle) is derived, and the response of drilling parameters and cutting ratio to the strength parameters is analyzed. The drillingbased measuring method for the c-φ parameter of rock is constructed. The laboratory verification test is then completed, and the difference in results between the drilling test and the compression test is less than 6%. On this basis, in-situ rock drilling tests in a traffic tunnel and a coal mine roadway are carried out, and the strength parameters of the surrounding rock are effectively tested. The average difference ratio of the results is less than 11%, which verifies the effectiveness of the proposed method for obtaining the strength parameters based on digital drilling. This study provides methodological support for field testing of rock strength parameters.

Biological factors driving colorectal cancer metastasis

Approximately 20%of colorectal cancer(CRC)patients present with metastasis at diagnosis.Among Stage I-III CRC patients who undergo surgical resection,18%typically suffer from distal metastasis within the first three years following initial treatment.The median survival duration after the diagnosis of metastatic CRC(mCRC)is only 9 mo.mCRC is traditionally considered to be an advanced stage malignancy or is thought to be caused by incomplete resection of tumor tissue,allowing cancer cells to spread from primary to distant organs;however,increa-sing evidence suggests that the mCRC process can begin early in tumor development.CRC patients present with high heterogeneity and diverse cancer phenotypes that are classified on the basis of molecular and morphological alterations.Different genomic and nongenomic events can induce subclone diversity,which leads to cancer and metastasis.Throughout the course of mCRC,metastatic cascades are associated with invasive cancer cell migration through the circulatory system,extravasation,distal seeding,dormancy,and reactivation,with each step requiring specific molecular functions.However,cancer cells presenting neoantigens can be recognized and eliminated by the immune system.In this review,we explain the biological factors that drive CRC metastasis,namely,genomic instability,epigenetic instability,the metastatic cascade,the cancer-immunity cycle,and external lifestyle factors.Despite remarkable progress in CRC research,the role of molecular classification in therapeutic intervention remains unclear.This review shows the driving factors of mCRC which may help in identifying potential candidate biomarkers that can improve the diagnosis and early detection of mCRC cases.

A comparison study on structure-function relationship of polysaccharides obtained from sea buckthorn berries using different methods:antioxidant and bile acid-binding capacity

In this study,the structural characters,antioxidant activities and bile acid-binding ability of sea buckthorn polysaccharides(HRPs)obtained by the commonly used hot water(HRP-W),pressurized hot water(HRP-H),ultrasonic(HRP-U),acid(HRP-C)and alkali(HRP-A)assisted extraction methods were investigated.The results demonstrated that extraction methods had significant effects on extraction yield,monosaccharide composition,molecular weight,particle size,triple-helical structure,and surface morphology of HRPs except for the major linkage bands.Thermogravimetric analysis showed that HRP-U with filamentous reticular microstructure exhibited better thermal stability.The HRP-A with the lowest molecular weight and highest arabinose content possessed the best antioxidant activities.Moreover,the rheological analysis indicated that HRPs with higher galacturonic acid content and molecular weight showed higher viscosity and stronger crosslinking network(HRP-C,HRP-W and HRP-U),which exhibited stronger bile acid binding capacity.The present findings provide scientific evidence in the preparation technology of sea buckthorn polysaccharides with good antioxidant and bile acid binding capacity which are related to the structure affected by the extraction methods.

Sorl1 knockout inhibits expression of brain-derived neurotrophic factor:involvement in the development of late-onset Alzheimer's disease

Sortilin-related receptor 1(SORL1)is a critical gene associated with late-onset Alzheimer’s disease.SORL1 contributes to the development and progression of this neurodegenerative condition by affecting the transport and metabolism of intracellularβ-amyloid precursor protein.To better understand the underlying mechanisms of SORL1 in the pathogenesis of late-onset Alzheimer s disease,in this study,we established a mouse model of SorI1 gene knockout using cluste red regularly inters paced short palindro mic repeats-associated protein 9 technology.We found that Sorl1-knocko ut mice displayed deficits in learning and memory.Furthermore,the expression of brain-derived neurotrophic factor was significantly downregulated in the hippocampus and co rtex,and amyloidβ-protein deposits were observed in the brains of 5orl1-knockout mice.In vitro,hippocampal neuronal cell synapses from homozygous Sorl1-knockout mice were impaired.The expression of synaptic proteins,including Drebrin and NR2B,was significantly reduced,and also their colocalization.Additionally,by knocking out the Sorl1 gene in N2a cells,we found that expression of the N-methyl-D-aspartate receptor,NR2B,and cyclic adenosine monophosphate-response element binding protein was also inhibited.These findings suggest that SORL1 participates in the pathogenesis of late-onset Alzheimer s disease by regulating the N-methyl-D-aspartate receptor NR2B/cyclic adenosine monophosphate-response element binding protein signaling axis.