Role of long non-coding RNAs in non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease(NAFLD)is emerging as a common cause of chronic liver disease in children and adults.NAFLD can progress to steatohepa-titis and potentially even hepatocellular carcinoma.Early identification of pati-ents at risk for progressive disease is crucial for managing NAFLD.Recent studies have identified long noncoding RNAs(lncRNAs),circular RNAs,and microRNAs as playing important roles in the pathogenesis of NAFLD.These noncoding RNAs are involved in modulating several metabolic pathways such as hepatic glucose and lipid metabolism,oxidative stress,and even carcinogenesis.Elevated levels of lncARSR and lncRNA nuclear-enriched abundant transcript 1 have been found in patients with NAFLD.In addition,lncRNAs such as PRYP4-3 and RP11-128N14.5 can distinguish patients with NAFLD from healthy indi-viduals.Increased MEG3 expression has been observed in both NAFLD and non-alcoholic steatohepatitis,suggesting that it may help predict patients at risk for disease progression.With advances in transcriptomics,we may discover additional targets to help in the identification and prognostication of NAFLD.

Clinical Effects of the Follicular Phase Long Regimen and Luteal Phase Long Regimen on Ovulation Induction in IVF-ET Treatment: A Meta-Analysis

Objective:To systematically evaluate the clinical effects of the follicular phase long regimen and the luteal phase long regimen on ovulation induction in IVF-ET treatment.Methods:Databases including PubMed,Embase,Cochrane Library,CNKI,Chinese Biomedical Literature(CBM),VIP,Wanfang,and others were searched up to January 2021.Clinical studies on ovulation induction using the follicular phase long regimen and luteal phase long regimen in IVF-ET treatment were identified.Literature screening,data extraction,and quality evaluation were conducted based on inclusion and exclusion criteria.Meta-analysis was performed using RevMan 5.3 software.Results:After screening,a total of 11 studies were included,comprising 21,544 patients:9,974 in the follicular phase long regimen group and 11,570 in the luteal phase long regimen group.The meta-analysis results were as follows:(1)The number of Gn days and the total amount of Gn in the follicular phase long regimen were higher than those in the luteal phase long regimen(P<0.05);(2)The number of eggs obtained in the follicular phase long regimen was higher than that in the luteal phase long regimen(P<0.05).There were no significant differences in the rate of embryo optimization and cycle cancellation between the two groups(P>0.05);(3)The embryo implantation rate and clinical pregnancy rate in the follicular phase long regimen were higher than those in the luteal phase long regimen(P<0.05),while the abortion rate in the follicular phase long regimen was lower than that in the luteal phase long regimen(P<0.05).Conclusion:Compared to the luteal phase long regimen,the follicular phase long regimen involves more Gn days and a higher total amount of Gn.The optimal embryo rate and cycle cancellation rate were similar between the regimens,but the follicular phase long regimen resulted in more eggs,significantly improved the implantation and clinical pregnancy rates,and reduced the abortion rate.However,these conclusions require further validation through more multicenter,large-sample RCT studies.

Construction and validation of somatic mutation-derived long noncoding RNAs signatures of genomic instability to predict prognosis of hepatocellular carcinoma

BACKGROUND Long non-coding RNAs(LncRNAs)have been found to be a potential prognostic factor for cancers,including hepatocellular carcinoma(HCC).Some LncRNAs have been confirmed as potential indicators to quantify genomic instability(GI).Nevertheless,GI-LncRNAs remain largely unexplored.This study established a GI-derived LncRNA signature(GILncSig)that can predict the prognosis of HCC patients.AIM To establish a GILncSig that can predict the prognosis of HCC patients.METHODS Identification of GI-LncRNAs was conducted by combining LncRNA expression and somatic mutation profiles.The GI-LncRNAs were then analyzed for functional enrichment.The GILncSig was established in the training set by Cox regression analysis,and its predictive ability was verified in the testing set and TCGA set.In addition,we explored the effects of the GILncSig and TP53 on prognosis.RESULTS A total of 88 GI-LncRNAs were found,and functional enrichment analysis showed that their functions were mainly involved in small molecule metabolism and GI.The GILncSig was constructed by 5 LncRNAs(miR210HG,AC016735.1,AC116351.1,AC010643.1,LUCAT1).In the training set,the prognosis of high-risk patients was significantly worse than that of low-risk patients,and similar results were verified in the testing set and TCGA set.Multivariate Cox regression analysis and stratified analysis confirmed that the GILncSig could be used as an independent prognostic factor.Receiver operating characteristic curve analysis of the GILncSig showed that the area under the curve(0.773)was higher than the two LncRNA signatures published recently.Furthermore,the GILncSig may have a better predictive performance than TP53 mutation status alone.CONCLUSION We established a GILncSig that can predict the prognosis of HCC patients,which will help to guide prognostic evaluation and treatment decisions.

Numerical analysis of downward progressive landslides in long natural slopes with sensitive clay

Landslides occurring in sensitive clay often result in widespread destruction,posing a significant risk to human lives and property due to the substantial decrease in undrained shear strength during deformation.Assessing the consequences of these landslides is challenging and necessitates robust numerical methods to comprehensively investigate their failure mechanisms.While studies have extensively explored upward progressive landslides in sensitive clays,understanding downward progressive cases remains limited.In this study,we utilised the nodal integration-based particle finite element method(NPFEM)with a nonlinear strain-softening model to analyse downward progressive landslides in sensitive clay on elongated slopes,induced by surcharge loads near the crest.We focused on elucidating the underlying failure mechanisms and evaluating the effects of different soil parameters and strainsoftening characteristics.The simulation results revealed the typical pattern for downward landslides,which typically start with a localised failure in proximity to the surcharge loads,followed by a combination of different types of failure mechanisms,including single flow slides,translational progressive landslides,progressive flow slides,and spread failures.Additionally,inclined shear bands occur within spread failures,often adopting distinctive ploughing patterns characterised by triangular shapes.The sensitive clay thickness at the base,the clay strength gradient,the sensitivity,and the softening rate significantly influence the failure mechanisms and the extent of diffused displacement.Remarkably,some of these effects mirror those observed in upward progressive landslides,underscoring the interconnectedness of these phenomena.This study contributes valuable insights into the complex dynamics of sensitive clay landslides,shedding light on the intricate interplay of factors governing their behaviour and progression.

Prevalence and risk factors associated with long COVID symptoms in children and adolescents in a southern province of Vietnam

Objective:To investigate the prevalence and risk factors associated with long COVID symptoms among children and adolescents who have recovered from COVID-19.Methods:This study applied a cross-sectional approach within community settings in a southern province of Vietnam.A structured questionnaire featuring socio-demographic information and common long COVID symptoms was employed.Phi correlation coefficients assessed associations among pairs of long COVID symptoms.Additionally,multivariable logistic regression models were performed to investigate the risk factors of long COVID in recovered COVID-19 children and adolescents.Results:Among 422 participants,39.3%reported long COVID symptoms,with a prevalence of 45.2%(SD=0.5)in children and 22.2%(SD=0.4)in adolescents.Common symptoms reported were cough 34.6%(SD=0.5),fatigue 20.6%(SD=0.4),shortness of breath 10.9%(SD=0.3),and lack of appetite 6.6%(SD=0.3).Concerning risk factors of long COVID,a higher risk was observed among demographic groups,including girls(OR 1.25,95%CI 1.15-1.37;P<0.001,reference:boys),children compared to adolescents(OR 1.24,95%CI 1.12-1.37;P<0.001),overweight individuals(OR 1.14,95%CI 1.02-1.27;P=0.018,reference:healthy weight),and participants without any COVID-19 vaccination(OR 1.36,95%CI 1.20-1.54;P<0.001),or have received only one single dose(OR 1.35,95%CI 1.10-1.64;P=0.004)compared to those who have received two doses.Besides,patients with a COVID-19 treatment duration exceeding two weeks also had a higher risk of long COVID(OR 1.32,95%CI 1.09-1.60;P=0.003)than those who recovered less than seven days.Conclusions:The insights from this study provide crucial guidance for predicting the factors associated with the occurrence of long COVID in pediatric patients,contributing to strategic interventions aimed at mitigating the long COVID risks among children and adolescents in Vietnam.

Failure mechanism and simulation for long run-out of the catastrophic rock landslide in the Shanyang Vanadium Mine,China

On 12th August 2015,a massive rapid long run-out rock landslide occurred in the Shanyang Vanadium Mine in Shaanxi Province,China,which claimed the lives of 65 miners.No heavy rainfalls,earthquakes,and mining blasts were recorded before the incident.Therefore,the failure mechanism and the cause of the long run-out movement are always in arguments.In this paper,we conducted a detailed field investigation,laboratory tests,block theory analysis,and numerical simulation to investigate the failure and long run-out mechanisms of the landslide.The field investigation results show that the source material of the rock landslide is a huge dolomite wedge block bedding on siliceous shale layers.Uniaxial compression tests indicate that the uniaxial compression strength of the intact dolomite is 130-140MPa and the dolomite shows a brittle failure mode.Due to the progressive downward erosion of the gully,the dolomite rock bridge at the slope toe became thinner.As the compression stress in the dolomite bridge increased to surpass its strength,the brittle failure of the bridge occurred.Then huge potential energy was released following the disintegration of the landslide,which led to the high acceleration of this rock landslide.The 3D discrete element simulation results suggest that the low intergranular friction contributes to the long run-out movement of this rock landslide.

High-modulus solid electrolyte interphase layer with gradient composition enables long-cycle all-solid-state lithium-sulfur batteries

All-solid-state lithium-sulfur batteries(ASSLSBs) have become one of the most potential candidates for the next-generation high-energy systems due to their intrinsic safety and high theoretical energy density.However, PEO-based ASSLSBs face the dilemma of insufficient Coulombic efficiency and long-term stability caused by the coupling problems of dendrite growth of anode and polysulfide shuttle of cathode. In this work, 1,3,5-trioxane(TOX) is used as a functional additive to design a PEO-based composite solidstate electrolyte(denoted as TOX-CSE), which realizes the stable long-term cycle of an ASSLSB. The results show that TOX can in-situ decompose on the anode to form a composite solid electrolyte interphase(SEI) layer with rich-organic component. It yields a high average modulus of 5.0 GPa, greatly improving the mechanical stability of the SEI layer and thus inhibiting the growth of dendrites. Also,the robust SEI layer can act as a barrier to block the side reaction between polysulfides and lithium metal.As a result, a Li-Li symmetric cell assembled with a TOX-CSE exhibits prolonged cycling stability over 2000 h at 0.2 m A cm^(-2). The ASSLSB also shows a stable cycling performance of 500 cycles at 0.5 C.This work reveals the structure–activity relationship between the mechanical property of interface layer and the battery's cycling stability.

Non-flammable long chain phosphate ester based electrolyte via competitive solventized structures for high-performance lithium metal batteries

Safety remains a persistent challenge for high-energy-density lithium metal batteries(LMBs).The development of safe and non-flammable electrolytes is especially important in harsh conditions such as high temperatures.Herein,a flame-retardant,low-cost and thermally stable long chain phosphate ester based(tributyl phosphate,TBP)electrolyte is reported,which can effectively enhance the cycling stability of highly loaded high-nickel LMBs with high safety through co-solvation strategy.The interfacial compatibility between TBP and electrode is effectively improved using a short-chain ether(glycol dimethyl ether,DME),and a specially competitive solvation structure is further constructed using lithium borate difluorooxalate(LiDFOB)to form the stable and inorganic-rich electrode interphases.Benefiting from the presence of the cathode electrolyte interphase(CEI)and solid electrolyte interphase(SEI)enriched with LiF and Li_(x)PO_(y)F_(z),the electrolyte demonstrates excellent cycling stability assembled using a 50μm lithium foil anode in combination with a high loading NMC811(15.4 mg cm^(-2))cathode,with 88%capacity retention after 120 cycles.Furthermore,the electrolyte exhibits excellent high-temperature characteristics when used in a 1-Ah pouch cell(N/P=0.26),and higher thermal runaway temperature(238℃)in the ARC(accelerating rate calorimeter)demonstrating high safety.This novel electrolyte adopts long-chain phosphate as the main solvent for the first time,and would provide a new idea for the development of extremely high safety and high-temperature electrolytes.

Lipid metabolism-related long noncoding RNAs:A potential prognostic biomarker for hepatocellular carcinoma

The incidence rates of hepatocellular carcinoma(HCC)have increased in recent decades.Despite advancements in therapy and early diagnosis improving shortterm prognosis,long-term outcomes remain poor.Long noncoding RNAs(lncRNAs)and lipid metabolism play crucial roles in the development and progression of HCC.Enhanced lipid synthesis promotes HCC progression,and lncRNAs can reprogram the expression of lipogenic enzymes.Consequently,lipid metabolism-related(LMR)-lncRNAs regulate lipid anabolism,accelerating the onset and progression of HCC.This suggests that LMR-lncRNAs could serve as novel prognostic biomarkers and therapeutic targets.

Efficacy and Safety of Lianhua Qingke Tablets in the Treatment of Long Coronavirus Disease (COVID) Cough: A Randomized, Double-Blind, Placebo-Controlled, Multicenter Clinical Study

Lianhua Qingke tablets,a patented traditional Chinese medicine that has validated clinical efficacy for treating cough caused by severe acute respiratory syndrome coronavirus 2 infection,lack rigorous evidence-based research evaluating their effect on long coronavirus disease(COVID)cough.A randomized,double-blind,placebo-controlled,multicenter clinical study was conducted among patients with long COVID cough from 19 hospitals and 23 community health centers in China.Patients were randomized 1:1 to receive either Lianhua Qingke tablets or placebo orally for 14 days(four tablets,1.84 g,three times a day).The primary endpoint indicator was the disappearance of cough,with the remission of cough also considered.Among 482 randomized patients,480(full analysis set 480;per-protocol set 470;safety set 480)were included in the primary analysis.According to the full analysis,the time until cough disappearance was significantly shorter in the trial group than in the control group,with a significant increase in the 14-day cough disappearance rate.Accordingly,the time to cough remission was significantly shorter in the trial group than in the control group.The change in the total symptom score was significantly greater in the trial group than in the control group on days 7 and 14,consistent with the results indicated by the visual analog scale(VAS)and cough evaluation test(CET)scores.No serious adverse events were recorded during the study.Lianhua Qingke tablets significantly improved the clinical symptoms of patients with long COVID cough.

Long wavelength infrared metalens fabricated by photolithography

Metasurfaces in the long wave infrared(LWIR)spectrum hold great potential for applications in ther-mal imaging,atmospheric remote sensing,and target identification,among others.In this study,we designed and experimentally demonstrated a 4 mm size,all-silicon metasurface metalens with large depth of focus opera-tional across a broadband range from 9µm to 11.5µm.The experimental results confirm effective focusing and imaging capabilities of the metalens in LWIR region,thus paving the way for practical LWIR applications of met-alens technology.

A three-dimensional co-continuous network structure polymer electrolyte with efficient ion transport channels enabling ultralong-life all solid-state lithium metal batteries

Solid polymer electrolytes(SPEs)have emerged as one of the most promising candidates for the construction of solid-state lithium batteries due to their excellent flexibility,scalability,and interface compatibility with electrodes.Herein,a novel all-solid polymer electrolyte(PPLCE)was fabricated by the copolymer network of liquid crystalline monomers and poly(ethylene glycol)dimethacrylate(PEGDMA)acts as a structural frame,combined with poly(ethylene glycol)diglycidyl ether short chain interspersed serving as mobile ion transport entities.The preparaed PPLCEs exhibit excellent mechanical property and out-standing electrochemical performances,which is attributed to their unique three-dimensional cocontinuous structure,characterized by a cross-linked semi-interpenetrating network and an ionic liquid phase,resulting in a distinctive nanostructure with short-range order and long-range disorder.Remarkably,the addition of PEGDMA is proved to be critical to the comprehensive performance of the PPLCEs,which effectively modulates the microscopic morphology of polymer networks and improves the mechanical properties as well as cycling stability of the solid electrolyte.When used in a lithiumion symmetrical battery configuration,the 6 wt%-PPLCE exhibites super stability,sustaining operation for over 2000 h at 30 C,with minimal and consistent overpotential of 50 mV.The resulting Li|PPLCE|LFP solid-state battery demonstrates high discharge specific capacities of 160.9 and 120.1 mA h g^(-1)at current densities of 0.2 and 1 C,respectively.Even after more than 300 cycles at a current density of 0.2 C,it retaines an impressive 73.5%capacity.Moreover,it displayes stable cycling for over 180 cycles at a high current density of 0.5C.The super cycle stability may promote the application for ultralong-life all solid-state lithium metal batteries.

Characterization of N6-methyladenosine long non-coding RNAs in sporadic congenital cataract and age-related cataract

AIM:To characterize the N6-methyladenosine(m6A)modification patterns in long non-coding RNAs(lncRNAs)in sporadic congenital cataract(CC)and age-related cataract(ARC).METHODS:Anterior capsule of the lens were collected from patients with CC and ARC.Methylated RNA immunoprecipitation with next-generation sequencing and RNA sequencing were performed to identify m6A-tagged lncRNAs and lncRNAs expression.Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses and Gene Ontology annotation were used to predict potential functions of the m6A-lncRNAs.RESULTS:Large amount of m6A peaks within lncRNA were identified for both CC and ARC,while the level was much higher in ARC(49870 peaks)than that in CC(18688 peaks),yet those difference between ARC in younger age group(ARC-1)and ARC in elder age group(ARC-2)was quite slight.A total of 1305 hypermethylated and 1178 hypomethylated lncRNAs,as well as 182 differential expressed lncRNAs were exhibited in ARC compared with CC.On the other hand,5893 hypermethylated and 5213 hypomethylated lncRNAs,as well as 155 significantly altered lncRNA were identified in ARC-2 compared with ARC-1.Altered lncRNAs in ARC were mainly associated with the organization and biogenesis of intracellular organelles,as well as nucleotide excision repair.CONCLUSION:Our results for the first time present an overview of the m6A methylomes of lncRNA in CC and ARC,providing a solid basis and uncovering a new insight to reveal the potential pathogenic mechanism of CC and ARC.

Long non-coding RNAs with essential roles in neurodegenerative disorders

Recently,with the advent of high-resolution and high-throughput sequencing technologies,an increasing number of long non-coding RNAs(lncRNAs)have been found to be involved in the regulation of neuronal function in the central nervous system with specific spatiotemporal patterns,across different neurodegenerative diseases.However,the underlying mechanisms of lncRNAs during neurodegeneration remain poorly understood.This review provides an overview of the current knowledge of the biology of lncRNAs and focuses on introducing the latest identified roles,regulatory mechanisms,and research status of lncRNAs in Alzheimer's disease,Parkinson's disease,Huntington's disease,and amyotrophic lateral sclerosis.Finally,this review discusses the potential values of lncRNAs as diagnostic biomarkers and therapeutic targets for neurodegenerative diseases,hoping to provide broader implications for developing effective treatments.

State-of-health estimation for fast-charging lithium-ion batteries based on a short charge curve using graph convolutional and long short-term memory networks

A fast-charging policy is widely employed to alleviate the inconvenience caused by the extended charging time of electric vehicles. However, fast charging exacerbates battery degradation and shortens battery lifespan. In addition, there is still a lack of tailored health estimations for fast-charging batteries;most existing methods are applicable at lower charging rates. This paper proposes a novel method for estimating the health of lithium-ion batteries, which is tailored for multi-stage constant current-constant voltage fast-charging policies. Initially, short charging segments are extracted by monitoring current switches,followed by deriving voltage sequences using interpolation techniques. Subsequently, a graph generation layer is used to transform the voltage sequence into graphical data. Furthermore, the integration of a graph convolution network with a long short-term memory network enables the extraction of information related to inter-node message transmission, capturing the key local and temporal features during the battery degradation process. Finally, this method is confirmed by utilizing aging data from 185 cells and 81 distinct fast-charging policies. The 4-minute charging duration achieves a balance between high accuracy in estimating battery state of health and low data requirements, with mean absolute errors and root mean square errors of 0.34% and 0.66%, respectively.

An Enhanced Ensemble-Based Long Short-Term Memory Approach for Traffic Volume Prediction

With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality.Traffic volume is an influential parameter for planning and operating traffic structures.This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems.A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process.The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships.Firstly,a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition model.The second aspect involves predicting traffic volume using the long short-term memory algorithm.Next,the study employs a trial-and-error approach to select a set of optimal hyperparameters,including the lookback window,the number of neurons in the hidden layers,and the gradient descent optimization.Finally,the fusion of the obtained results leads to a final traffic volume prediction.The experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures,including mean absolute error,root mean squared error,mean absolute percentage error,and R-squared.The achieved R-squared value reaches an impressive 98%,while the other evaluation indices surpass the competing.These findings highlight the accuracy of traffic pattern prediction.Consequently,this offers promising prospects for enhancing transportation management systems and urban infrastructure planning.

Navigating the labyrinth of long non-coding RNAs in colorectal cancer:From chemoresistance to autophagy

Long non-coding RNAs(lncRNAs),with transcript lengths exceeding 200 nucleotides and little or no protein-coding capacity,have been found to impact colorectal cancer(CRC)through various biological processes.LncRNA expression can regulate autophagy,which plays dual roles in the initiation and progression of cancers,including CRC.Abnormal expression of lncRNAs is associated with the emergence of chemoresistance.Moreover,it has been confirmed that targeting autophagy through lncRNA regulation could be a viable approach for combating chemoresistance.Two recent studies titled“Human β-defensin-1 affects the mammalian target of rapamycin pathway and autophagy in colon cancer cells through long non-coding RNA TCONS_00014506”and“Upregulated lncRNA PRNT promotes progression and oxaliplatin resistance of colorectal cancer cells by regulating HIPK2 transcription”revealed novel insights into lncRNAs associated with autophagy and oxaliplatin resistance in CRC,respectively.In this editorial,we particularly focus on the regulatory role of lncRNAs in CRC-related autophagy and chemoresistance since the regulation of chemotherapeutic sensitivity by intervening with the lncRNAs involved in the autophagy process has become a promising new approach for cancer treatment.

Crystalline and amorphous metal sulfide composite electrode materials with long cycle life:Preparation and performance of hybrid capacitors

Crystalline@amorphous NiCo_(2)S_(4)@MoS_(2)(v-NCS@MS)nanostructures were designed and constructed via an ethylene glycol-induced strategy with hydrothermal synthesis and solvothermal method,which simultaneously realized the defect regulation of crystal NiCo_(2)S_(4) in the core.Taking advantage of the flexible protection of an amor-phous shell and the high capacity of a conductive core with defects,the v-NCS@MS electrode exhibited high specif-ic capacity(1034 mAh·g^(-1) at 1 A·g^(-1))and outstanding rate capability.Moreover,a hybrid supercapacitor was assembled with v-NCS@MS as cathode and activated carbon(AC)as anode,which can achieve remarkably high specific energy of 111 Wh·kg^(-1) at a specific power of 219 W·kg^(-1) and outstanding capacity retention of 80.5%after 15000 cycling at different current densities.

Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire population.With recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining properties.Early cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival rates.The analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection systems.The upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and prediction.Initially,the histopathology biopsy images are taken from standard data sources.Further,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are acquired.Subsequently,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer disorder.The efficacy of the model is evaluated using divergent metrics.When compared with other methods,the proposed work reveals that it offers impressive results for detection.

Adaptive Successive POI Recommendation via Trajectory Sequences Processing and Long Short-Term Preference Learning

Point-of-interest(POI)recommendations in location-based social networks(LBSNs)have developed rapidly by incorporating feature information and deep learning methods.However,most studies have failed to accurately reflect different users’preferences,in particular,the short-term preferences of inactive users.To better learn user preferences,in this study,we propose a long-short-term-preference-based adaptive successive POI recommendation(LSTP-ASR)method by combining trajectory sequence processing,long short-term preference learning,and spatiotemporal context.First,the check-in trajectory sequences are adaptively divided into recent and historical sequences according to a dynamic time window.Subsequently,an adaptive filling strategy is used to expand the recent check-in sequences of users with inactive check-in behavior using those of similar active users.We further propose an adaptive learning model to accurately extract long short-term preferences of users to establish an efficient successive POI recommendation system.A spatiotemporal-context-based recurrent neural network and temporal-context-based long short-term memory network are used to model the users’recent and historical checkin trajectory sequences,respectively.Extensive experiments on the Foursquare and Gowalla datasets reveal that the proposed method outperforms several other baseline methods in terms of three evaluation metrics.More specifically,LSTP-ASR outperforms the previously best baseline method(RTPM)with a 17.15%and 20.62%average improvement on the Foursquare and Gowalla datasets in terms of the Fβmetric,respectively.