Alzheimer's disease,a progressively degenerative neurological disorder,is the most common cause of dementia in the elderly.While its precise etiology remains unclear,researchers have identified diverse pathologica...Alzheimer's disease,a progressively degenerative neurological disorder,is the most common cause of dementia in the elderly.While its precise etiology remains unclear,researchers have identified diverse pathological characteristics and molecular pathways associated with its progression.Advances in scientific research have increasingly highlighted the crucial role of non-coding RNAs in the progression of Alzheimer's disease.These non-coding RNAs regulate several biological processes critical to the advancement of the disease,offering promising potential as therapeutic targets and diagnostic biomarkers.Therefore,this review aims to investigate the underlying mechanisms of Alzheimer's disease onset,with a particular focus on microRNAs,long non-coding RNAs,and circular RNAs associated with the disease.The review elucidates the potential pathogenic processes of Alzheimer's disease and provides a detailed description of the synthesis mechanisms of the three aforementioned non-coding RNAs.It comprehensively summarizes the various non-coding RNAs that have been identified to play key regulatory roles in Alzheimer's disease,as well as how these noncoding RNAs influence the disease's progression by regulating gene expression and protein functions.For example,miR-9 targets the UBE4B gene,promoting autophagy-mediated degradation of Tau protein,thereby reducing Tau accumulation and delaying Alzheimer's disease progression.Conversely,the long non-coding RNA BACE1-AS stabilizes BACE1 mRNA,promoting the generation of amyloid-βand accelerating Alzheimer's disease development.Additionally,circular RNAs play significant roles in regulating neuroinflammatory responses.By integrating insights from these regulatory mechanisms,there is potential to discover new therapeutic targets and potential biomarkers for early detection and management of Alzheimer's disease.This review aims to enhance the understanding of the relationship between Alzheimer's disease and non-coding RNAs,potentially paving the way for early detection and novel treatment strategies.展开更多
Myalgic encephalomyelitis/chronic fatigue syndrome-an insidious disease:The recent COVID-19 pandemic has brought substantial attention to the overlapping symptoms between long COVID and myalgic encephalomyelitis/chron...Myalgic encephalomyelitis/chronic fatigue syndrome-an insidious disease:The recent COVID-19 pandemic has brought substantial attention to the overlapping symptoms between long COVID and myalgic encephalomyelitis/chronic fatigue syndrome(ME/CFS),a chronic and poorly understood neurological disorder(Shankar et al.,2024).展开更多
Hepatocellular carcinoma(HCC)remains one of the most prevalent and lethal malignancies worldwide.Long non-coding RNAs(lncRNAs)have emerged as crucial regulators of gene expression and cancer progression,yet the functi...Hepatocellular carcinoma(HCC)remains one of the most prevalent and lethal malignancies worldwide.Long non-coding RNAs(lncRNAs)have emerged as crucial regulators of gene expression and cancer progression,yet the functional diversity of RP11-derived lncRNAs—originally mapped to bacterial artificial chromosome(BAC)clones from the Roswell Park Cancer Institute—has only recently begun to be appreciated.This mini-review aims to systematically synthesize current findings on RP11-derived lncRNAs in HCC,outlining their genomic origins,molecular mechanisms,and biological significance.We highlight their roles in metabolic reprogramming,microRNA network modulation,and tumor progression,as well as their diagnostic and prognostic value in tissue and serum-based analyses.Finally,we discuss therapeutic opportunities and propose future directions to translate RP11-derived lncRNAs into clinically actionable biomarkers and targets for precision liver cancer therapy.展开更多
Ischemic stroke is a serious medical event that cannot be predicted in advance and can have longlasting effects on patients,families,and communities.A deeper understanding of the changes in gene expression and the fun...Ischemic stroke is a serious medical event that cannot be predicted in advance and can have longlasting effects on patients,families,and communities.A deeper understanding of the changes in gene expression and the fundamental molecular mechanisms involved could help address this critical issue.In recent years,research into regulatory long non-coding(lnc)RNAs,a diverse group of RNA molecules with regulatory functions,has emerged as a promising direction in the study of cerebral infarction.This review paper aims to provide a comprehensive exploration of the roles of regulatory lncRNAs in cerebral infarction,as well as potential strategies for their application in clinical settings.LncRNAs have the potential to act as“sponges”that attract specific microRNAs,thereby regulating the expression of microRNA target genes.These interactions influence various aspects of ischemic stroke,including reperfusion-induced damage,cell death,immune responses,autophagy,angiogenesis,and the generation of reactive oxygen species.We highlight several regulatory lncRNAs that have been utilized in animal model treatments,including lncRNA NKILA,lncRNA Meg8,and lncRNA H19.Additionally,we discuss lncRNAs that have been used as biomarkers for the diagnosis and prognosis of cerebral infarction,such as lncRNA FOXO3,lncRNA XIST,and lncRNA RMST.The lncRNAs hold potential for genetic-level treatments in patients.However,numerous challenges,including inefficiency,low targeting accuracy,and side effects observed in preliminary studies,indicate the need for thorough investigation.The application of lncRNAs in ischemic stroke presents challenges that require careful and extensive validation.展开更多
With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contex...With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contextual understanding,sequential dependencies,and/or data imbalance.This makes distinction between genuine and fabricated news a challenging task.To address this problem,we propose a novel hybrid architecture,T5-SA-LSTM,which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attentionenhanced(SA)Long Short-Term Memory(LSTM).The LSTM is trained using the Adam optimizer,which provides faster and more stable convergence compared to the Stochastic Gradient Descend(SGD)and Root Mean Square Propagation(RMSProp).The WELFake and FakeNewsPrediction datasets are used,which consist of labeled news articles having fake and real news samples.Tokenization and Synthetic Minority Over-sampling Technique(SMOTE)methods are used for data preprocessing to ensure linguistic normalization and class imbalance.The incorporation of the Self-Attention(SA)mechanism enables the model to highlight critical words and phrases,thereby enhancing predictive accuracy.The proposed model is evaluated using accuracy,precision,recall(sensitivity),and F1-score as performance metrics.The model achieved 99%accuracy on the WELFake dataset and 96.5%accuracy on the FakeNewsPrediction dataset.It outperformed the competitive schemes such as T5-SA-LSTM(RMSProp),T5-SA-LSTM(SGD)and some other models.展开更多
Recent evidence suggests that ferroptosis plays a crucial role in the occurrence and development of white matter lesions.However,the mechanisms and regulatory pathways involved in ferroptosis within white matter lesio...Recent evidence suggests that ferroptosis plays a crucial role in the occurrence and development of white matter lesions.However,the mechanisms and regulatory pathways involved in ferroptosis within white matter lesions remain unclear.Long non-coding RNAs(lnc RNAs)have been shown to influence the occurrence and development of these lesions.We previously identified lnc_011797 as a biomarker of white matter lesions by high-throughput sequencing.To investigate the mechanism by which lnc_011797 regulates white matter lesions,we established subjected human umbilical vein endothelial cells to oxygenglucose deprivation to simulate conditions associated with white matter lesions.The cells were transfected with lnc_011797 overexpression or knockdown lentiviruses.Our findings indicate that lnc_011797 promoted ferroptosis in these cells,leading to the formation of white matter lesions.Furthermore,lnc_011797 functioned as a competitive endogenous RNA(ce RNA)for mi R-193b-3p,thereby regulating the expression of WNK1 and its downstream ferroptosis-related proteins.To validate the role of lnc_011797 in vivo,we established a mouse model of white matter lesions through bilateral common carotid artery stenosis.The results from this model confirmed that lnc_011797 regulates ferroptosis via WNK1 and promotes the development of white matter lesions.These findings clarify the mechanism by which lnc RNAs regulate white matter lesions,providing a new target for the diagnosis and treatment of white matter lesions.展开更多
The existing knowledge regarding the interfacial forces,lubrication,and wear of bearings in real-world operation has significantly improved their designs over time,allowing for prolonged service life.As a result,self-...The existing knowledge regarding the interfacial forces,lubrication,and wear of bearings in real-world operation has significantly improved their designs over time,allowing for prolonged service life.As a result,self-lubricating bearings have become a viable alternative to traditional bearing designs in industrial machines.However,wear mechanisms are still inevitable and occur progressively in self-lubricating bearings,as characterized by the loss of the lubrication film and seizure.Therefore,monitoring the stages of the wear states in these components will help to impart the necessary countermeasures to reduce the machine maintenance downtime.This article proposes a methodology for using a long short-term memory(LSTM)-based encoder-decoder architecture on interfacial force signatures to detect abnormal regimes,aiming to provide early predictions of failure in self-lubricating sliding contacts even before they occur.Reciprocating sliding experiments were performed using a self-lubricating bronze bushing and steel shaft journal in a custom-built transversally oscillating tribometer setup.The force signatures corresponding to each cycle of the reciprocating sliding motion in the normal regime were used as inputs to train the encoder-decoder architecture,so as to reconstruct any new signal of the normal regime with the minimum error.With this semi-supervised training exercise,the force signatures corresponding to the abnormal regime could be differentiated from the normal regime,as their reconstruction errors would be very high.During the validation procedure for the proposed LSTM-based encoder-decoder model,the model predicted the force signals corresponding to the normal and abnormal regimes with an accuracy of 97%.In addition,a visualization of the reconstruction error across the entire force signature showed noticeable patterns in the reconstruction error when temporally decoded before the actual critical failure point,making it possible to be used for early predictions of failure.展开更多
Acute ischemic stroke is a clinical emergency and a condition with high morbidity,mortality,and disability.Accurate predictive,diagnostic,and prognostic biomarkers and effective therapeutic targets for acute ischemic ...Acute ischemic stroke is a clinical emergency and a condition with high morbidity,mortality,and disability.Accurate predictive,diagnostic,and prognostic biomarkers and effective therapeutic targets for acute ischemic stroke remain undetermined.With innovations in high-throughput gene sequencing analysis,many aberrantly expressed non-coding RNAs(ncRNAs)in the brain and peripheral blood after acute ischemic stroke have been found in clinical samples and experimental models.Differentially expressed ncRNAs in the post-stroke brain were demonstrated to play vital roles in pathological processes,leading to neuroprotection or deterioration,thus ncRNAs can serve as therapeutic targets in acute ischemic stroke.Moreover,distinctly expressed ncRNAs in the peripheral blood can be used as biomarkers for acute ischemic stroke prediction,diagnosis,and prognosis.In particular,ncRNAs in peripheral immune cells were recently shown to be involved in the peripheral and brain immune response after acute ischemic stroke.In this review,we consolidate the latest progress of research into the roles of ncRNAs(microRNAs,long ncRNAs,and circular RNAs)in the pathological processes of acute ischemic stroke–induced brain damage,as well as the potential of these ncRNAs to act as biomarkers for acute ischemic stroke prediction,diagnosis,and prognosis.Findings from this review will provide novel ideas for the clinical application of ncRNAs in acute ischemic stroke.展开更多
Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowad...Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowadays,the groundwater vulnerability assessment(GVA)has become an essential task to identify the current status and development trend of groundwater quality.In this study,the Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism.The study firstly builds the CNN-LSTM modelwith self-attention(SA)mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine(SVM),Random Forest(RF),and Extreme Gradient Boosting(XGBoost).The results indicate that the CNNLSTM model outperforms thesemodels,demonstrating its significance in groundwater vulnerability assessment.It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years.This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities.Moreover,the overall groundwater vulnerability risk in the entire region has increased,evident fromboth the notably high value and standard deviation.This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities.The model can be optimized for diverse applications across regional environmental assessment,pollution prediction,and risk statistics.This study holds particular significance for ecological protection and groundwater resource management.展开更多
This investigation focuses on the utilization of cucurbit[6]uril(Q[6])as the host compound for the development of long-lasting afterglow materials.By strategically manipulating the outer surface interactions of Q[6],c...This investigation focuses on the utilization of cucurbit[6]uril(Q[6])as the host compound for the development of long-lasting afterglow materials.By strategically manipulating the outer surface interactions of Q[6],classical aggregation-caused quenching(ACQ)compounds such as fluorescein sodium(FluNa)and calcein sodium(CalNa)were transformed into afterglow materials with varying colors and durations upon exposure to ultraviolet light.This transformation was facilitated through a host-vip doping method combined with coordination with metal ions.Even at a reduced doping concentration of 5×10^(-5)wt%,the materials exhibit remarkable afterglow properties,lasting up to 2 s,with a phosphorescence lifetime of up to 150 ms.Moreover,by adjusting the concentration of the vip compound,the persistent luminescence color of the materials could be easily transitioned from orange to yellow and subsequently to green.These findings suggest that the developed afterglow materials hold significant potential for multilevel anti-counterfeiting and information encryption applications when exposed to ultraviolet light.The supramolecular assembly strategy,which relies on the outer surface interactions of cucurbit[n]uril,offers a simpler and more efficient approach to crafting multi-color luminescent materials.Additionally,this method opens avenues for enhancing the application potential of aggregation-caused quenching(ACQ)compounds in various technological domains.展开更多
Real-time prediction and precise control of sinter quality are pivotal for energy saving,cost reduction,quality improvement and efficiency enhancement in the ironmaking process.To advance,the accuracy and comprehensiv...Real-time prediction and precise control of sinter quality are pivotal for energy saving,cost reduction,quality improvement and efficiency enhancement in the ironmaking process.To advance,the accuracy and comprehensiveness of sinter quality prediction,an intelligent flare monitoring system for sintering machine tails that combines hybrid neural networks integrating convolutional neural network with long short-term memory(CNN-LSTM)networks was proposed.The system utilized a high-temperature thermal imager for image acquisition at the sintering machine tail and employed a zone-triggered method to accurately capture dynamic feature images under challenging conditions of high-temperature,high dust,and occlusion.The feature images were then segmented through a triple-iteration multi-thresholding approach based on the maximum between-class variance method to minimize detail loss during the segmentation process.Leveraging the advantages of CNN and LSTM networks in capturing temporal and spatial information,a comprehensive model for sinter quality prediction was constructed,with inputs including the proportion of combustion layer,porosity rate,temperature distribution,and image features obtained from the convolutional neural network,and outputs comprising quality indicators such as underburning index,uniformity index,and FeO content of the sinter.The accuracy is notably increased,achieving a 95.8%hit rate within an error margin of±1.0.After the system is applied,the average qualified rate of FeO content increases from 87.24%to 89.99%,representing an improvement of 2.75%.The average monthly solid fuel consumption is reduced from 49.75 to 46.44 kg/t,leading to a 6.65%reduction and underscoring significant energy saving and cost reduction effects.展开更多
Hepatocellular carcinoma(HCC)is a highly lethal malignancy with limited treatment options,particularly for patients with advanced stages of the disease.Sorafenib,the standard first-line therapy,faces significant chall...Hepatocellular carcinoma(HCC)is a highly lethal malignancy with limited treatment options,particularly for patients with advanced stages of the disease.Sorafenib,the standard first-line therapy,faces significant challenges due to the development of drug resistance.Yu et al explored the mechanisms by which lncRNA KIF9-AS1 regulates the stemness and sorafenib resistance in HCC using a combination of cell culture,transfection,RNA immunoprecipitation,co-immunoprecipitation,and xenograft tumor models.They demonstrate that N6-methyladenosine-modified long non-coding RNA KIF9-AS1 acts as an oncogene in HCC.This modification involves methyltransferase-like 3 and insulin-like growth factor 2 mRNA-binding protein 1,which play critical roles in regulating KIF9-AS1.Furthermore,KIF9-AS1 stabilizes and upregulates short stature homeobox 2 by promoting its deubiquitination through ubiquitin-specific peptidase 1,thereby enhancing stemness and contributing to sorafenib resistance in HCC cells.These findings provide a theoretical basis for KIF9-AS1 as a diagnostic marker and therapeutic target for HCC,highlighting the need for further investigation into its clinical application potential.展开更多
The Internet of Things(IoT)has orchestrated various domains in numerous applications,contributing significantly to the growth of the smart world,even in regions with low literacy rates,boosting socio-economic developm...The Internet of Things(IoT)has orchestrated various domains in numerous applications,contributing significantly to the growth of the smart world,even in regions with low literacy rates,boosting socio-economic development.This study provides valuable insights into optimizing wireless communication,paving the way for a more connected and productive future in the mining industry.The IoT revolution is advancing across industries,but harsh geometric environments,including open-pit mines,pose unique challenges for reliable communication.The advent of IoT in the mining industry has significantly improved communication for critical operations through the use of Radio Frequency(RF)protocols such as Bluetooth,Wi-Fi,GSM/GPRS,Narrow Band(NB)-IoT,SigFox,ZigBee,and Long Range Wireless Area Network(LoRaWAN).This study addresses the optimization of network implementations by comparing two leading free-spreading IoT-based RF protocols such as ZigBee and LoRaWAN.Intensive field tests are conducted in various opencast mines to investigate coverage potential and signal attenuation.ZigBee is tested in the Tadicherla open-cast coal mine in India.Similarly,LoRaWAN field tests are conducted at one of the associated cement companies(ACC)in the limestone mine in Bargarh,India,covering both Indoor-toOutdoor(I2O)and Outdoor-to-Outdoor(O2O)environments.A robust framework of path-loss models,referred to as Free space,Egli,Okumura-Hata,Cost231-Hata and Ericsson models,combined with key performance metrics,is employed to evaluate the patterns of signal attenuation.Extensive field testing and careful data analysis revealed that the Egli model is the most consistent path-loss model for the ZigBee protocol in an I2O environment,with a coefficient of determination(R^(2))of 0.907,balanced error metrics such as Normalized Root Mean Square Error(NRMSE)of 0.030,Mean Square Error(MSE)of 4.950,Mean Absolute Percentage Error(MAPE)of 0.249 and Scatter Index(SI)of 2.723.In the O2O scenario,the Ericsson model showed superior performance,with the highest R^(2)value of 0.959,supported by strong correlation metrics:NRMSE of 0.026,MSE of 8.685,MAPE of 0.685,Mean Absolute Deviation(MAD)of 20.839 and SI of 2.194.For the LoRaWAN protocol,the Cost-231 model achieved the highest R^(2)value of 0.921 in the I2O scenario,complemented by the lowest metrics:NRMSE of 0.018,MSE of 1.324,MAPE of 0.217,MAD of 9.218 and SI of 1.238.In the O2O environment,the Okumura-Hata model achieved the highest R^(2)value of 0.978,indicating a strong fit with metrics NRMSE of 0.047,MSE of 27.807,MAPE of 27.494,MAD of 37.287 and SI of 3.927.This advancement in reliable communication networks promises to transform the opencast landscape into networked signal attenuation.These results support decision-making for mining needs and ensure reliable communications even in the face of formidable obstacles.展开更多
Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a vi...Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a viewpoint in DoDAF2.0,the operational viewpoint(OV)describes operational activities,nodes,and resource flows.The OV models are important for SoS architecture development.However,as the SoS complexity increases,constructing OV models with traditional methods exposes shortcomings,such as inefficient data collection and low modeling standards.Therefore,we propose an intelligent modeling method for five OV models,including operational resource flow OV-2,organizational relationships OV-4,operational activity hierarchy OV-5a,operational activities model OV-5b,and operational activity sequences OV-6c.The main idea of the method is to extract OV architecture data from text and generate interoperable OV models.First,we construct the OV meta model based on the DoDAF2.0 meta model(DM2).Second,OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field(BiLSTM-CRF)model.And OV architecture relationships are collected with relationship extraction rules.Finally,we define the generation rules for OV models and develop an OV modeling tool.We use unmanned surface vehicles(USV)swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.展开更多
Metalens technology has been applied extensively in miniaturized and integrated infrared imaging systems.However,due to the high phase dispersion of unit structures,metalens often exhibits chromatic aberration,making ...Metalens technology has been applied extensively in miniaturized and integrated infrared imaging systems.However,due to the high phase dispersion of unit structures,metalens often exhibits chromatic aberration,making broadband achromatic infrared imaging challenging to achieve.In this paper,six different unit structures based on chalcogenide glass are constructed,and their phase-dispersion parameters are analyzed to establish a database.On this basis,using chromatic aberration compensation and parameterized adjoint topology optimization,a broadband achromatic metalens with a numerical aperture of 0.5 is designed by arranging these six unit structures in the far-infrared band.Simulation results show that the metalens achieves near diffraction-limited focusing within the operating wavelength range of 9−11μm,demonstrating the good performance of achromatic aberration with flat focusing efficiency of 54%−58%across all wavelengths.展开更多
This paper mainly studies the well-posedness of steady incompressible impinging jet flow problem through a 3D axisymmetric finitely long nozzle.This problem originates from the physical phenomena encountered in practi...This paper mainly studies the well-posedness of steady incompressible impinging jet flow problem through a 3D axisymmetric finitely long nozzle.This problem originates from the physical phenomena encountered in practical engineering fields,such as in short take-off and vertical landing(STOVL)aircraft.Nowadays many intricate phenomena associated with impinging jet flows remain inadequately elucidated,which limits the ability to optimize aircraft design.Given a boundary condition in the inlet,the impinging jet problem is transformed into a Bernoulli-type free boundary problem according to the stream function.Then the variational method is used to study the corresponding variational problem with one parameter,thereby the wellposedness is established.The main conclusion is as follows.For a 3D axisymmetric finitely long nozzle and an infinitely long vertical wall,given an axial velocity in the inlet of nozzle,there exists a unique smooth incom‑pressible impinging jet flow such that the free boundary initiates smoothly at the endpoint of the nozzle and extends to infinity along the vertical wall at far fields.The key point is to investigate the regularity of the corner where the nozzle and the vertical axis intersect.展开更多
文摘Alzheimer's disease,a progressively degenerative neurological disorder,is the most common cause of dementia in the elderly.While its precise etiology remains unclear,researchers have identified diverse pathological characteristics and molecular pathways associated with its progression.Advances in scientific research have increasingly highlighted the crucial role of non-coding RNAs in the progression of Alzheimer's disease.These non-coding RNAs regulate several biological processes critical to the advancement of the disease,offering promising potential as therapeutic targets and diagnostic biomarkers.Therefore,this review aims to investigate the underlying mechanisms of Alzheimer's disease onset,with a particular focus on microRNAs,long non-coding RNAs,and circular RNAs associated with the disease.The review elucidates the potential pathogenic processes of Alzheimer's disease and provides a detailed description of the synthesis mechanisms of the three aforementioned non-coding RNAs.It comprehensively summarizes the various non-coding RNAs that have been identified to play key regulatory roles in Alzheimer's disease,as well as how these noncoding RNAs influence the disease's progression by regulating gene expression and protein functions.For example,miR-9 targets the UBE4B gene,promoting autophagy-mediated degradation of Tau protein,thereby reducing Tau accumulation and delaying Alzheimer's disease progression.Conversely,the long non-coding RNA BACE1-AS stabilizes BACE1 mRNA,promoting the generation of amyloid-βand accelerating Alzheimer's disease development.Additionally,circular RNAs play significant roles in regulating neuroinflammatory responses.By integrating insights from these regulatory mechanisms,there is potential to discover new therapeutic targets and potential biomarkers for early detection and management of Alzheimer's disease.This review aims to enhance the understanding of the relationship between Alzheimer's disease and non-coding RNAs,potentially paving the way for early detection and novel treatment strategies.
基金supported by the Judith Jane Mason and Harold Stannett Williams Memorial Foundation National Medical Program(#Mason2210)to JX。
文摘Myalgic encephalomyelitis/chronic fatigue syndrome-an insidious disease:The recent COVID-19 pandemic has brought substantial attention to the overlapping symptoms between long COVID and myalgic encephalomyelitis/chronic fatigue syndrome(ME/CFS),a chronic and poorly understood neurological disorder(Shankar et al.,2024).
基金supported by the National Research Foundation of Korea(NRF),funded by the Ministry of Science and ICT(MSIT),Republic of Korea(grant numbers:RS-2022-NR070489 and RS-2023-00210847)the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI),funded by the Ministry of Health and Welfare,Republic of Korea(grant number HR21C1003).
文摘Hepatocellular carcinoma(HCC)remains one of the most prevalent and lethal malignancies worldwide.Long non-coding RNAs(lncRNAs)have emerged as crucial regulators of gene expression and cancer progression,yet the functional diversity of RP11-derived lncRNAs—originally mapped to bacterial artificial chromosome(BAC)clones from the Roswell Park Cancer Institute—has only recently begun to be appreciated.This mini-review aims to systematically synthesize current findings on RP11-derived lncRNAs in HCC,outlining their genomic origins,molecular mechanisms,and biological significance.We highlight their roles in metabolic reprogramming,microRNA network modulation,and tumor progression,as well as their diagnostic and prognostic value in tissue and serum-based analyses.Finally,we discuss therapeutic opportunities and propose future directions to translate RP11-derived lncRNAs into clinically actionable biomarkers and targets for precision liver cancer therapy.
基金supported by the China Postdoctoral Science Foundation,No.2022M712689the Natural Science Foundation of the Jiangsu Higher Education Institutions of China,No.22KJB1800029+1 种基金The University Student Innovation Project of Yangzhou University,No.XCX20240856The Jiangsu Provincial Science and Technology Talent Project,No.FZ20240964(all to TX).
文摘Ischemic stroke is a serious medical event that cannot be predicted in advance and can have longlasting effects on patients,families,and communities.A deeper understanding of the changes in gene expression and the fundamental molecular mechanisms involved could help address this critical issue.In recent years,research into regulatory long non-coding(lnc)RNAs,a diverse group of RNA molecules with regulatory functions,has emerged as a promising direction in the study of cerebral infarction.This review paper aims to provide a comprehensive exploration of the roles of regulatory lncRNAs in cerebral infarction,as well as potential strategies for their application in clinical settings.LncRNAs have the potential to act as“sponges”that attract specific microRNAs,thereby regulating the expression of microRNA target genes.These interactions influence various aspects of ischemic stroke,including reperfusion-induced damage,cell death,immune responses,autophagy,angiogenesis,and the generation of reactive oxygen species.We highlight several regulatory lncRNAs that have been utilized in animal model treatments,including lncRNA NKILA,lncRNA Meg8,and lncRNA H19.Additionally,we discuss lncRNAs that have been used as biomarkers for the diagnosis and prognosis of cerebral infarction,such as lncRNA FOXO3,lncRNA XIST,and lncRNA RMST.The lncRNAs hold potential for genetic-level treatments in patients.However,numerous challenges,including inefficiency,low targeting accuracy,and side effects observed in preliminary studies,indicate the need for thorough investigation.The application of lncRNAs in ischemic stroke presents challenges that require careful and extensive validation.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R195)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘With the increasing growth of online news,fake electronic news detection has become one of the most important paradigms of modern research.Traditional electronic news detection techniques are generally based on contextual understanding,sequential dependencies,and/or data imbalance.This makes distinction between genuine and fabricated news a challenging task.To address this problem,we propose a novel hybrid architecture,T5-SA-LSTM,which synergistically integrates the T5 Transformer for semantically rich contextual embedding with the Self-Attentionenhanced(SA)Long Short-Term Memory(LSTM).The LSTM is trained using the Adam optimizer,which provides faster and more stable convergence compared to the Stochastic Gradient Descend(SGD)and Root Mean Square Propagation(RMSProp).The WELFake and FakeNewsPrediction datasets are used,which consist of labeled news articles having fake and real news samples.Tokenization and Synthetic Minority Over-sampling Technique(SMOTE)methods are used for data preprocessing to ensure linguistic normalization and class imbalance.The incorporation of the Self-Attention(SA)mechanism enables the model to highlight critical words and phrases,thereby enhancing predictive accuracy.The proposed model is evaluated using accuracy,precision,recall(sensitivity),and F1-score as performance metrics.The model achieved 99%accuracy on the WELFake dataset and 96.5%accuracy on the FakeNewsPrediction dataset.It outperformed the competitive schemes such as T5-SA-LSTM(RMSProp),T5-SA-LSTM(SGD)and some other models.
基金supported by the Qingdao Medical Health Research Project,No.2023-WJZD212(to XX)。
文摘Recent evidence suggests that ferroptosis plays a crucial role in the occurrence and development of white matter lesions.However,the mechanisms and regulatory pathways involved in ferroptosis within white matter lesions remain unclear.Long non-coding RNAs(lnc RNAs)have been shown to influence the occurrence and development of these lesions.We previously identified lnc_011797 as a biomarker of white matter lesions by high-throughput sequencing.To investigate the mechanism by which lnc_011797 regulates white matter lesions,we established subjected human umbilical vein endothelial cells to oxygenglucose deprivation to simulate conditions associated with white matter lesions.The cells were transfected with lnc_011797 overexpression or knockdown lentiviruses.Our findings indicate that lnc_011797 promoted ferroptosis in these cells,leading to the formation of white matter lesions.Furthermore,lnc_011797 functioned as a competitive endogenous RNA(ce RNA)for mi R-193b-3p,thereby regulating the expression of WNK1 and its downstream ferroptosis-related proteins.To validate the role of lnc_011797 in vivo,we established a mouse model of white matter lesions through bilateral common carotid artery stenosis.The results from this model confirmed that lnc_011797 regulates ferroptosis via WNK1 and promotes the development of white matter lesions.These findings clarify the mechanism by which lnc RNAs regulate white matter lesions,providing a new target for the diagnosis and treatment of white matter lesions.
基金This work was funded by the Austrian COMET Program(project InTribology,No.872176)via the Austrian Research Promotion Agency(FFG)and the Provinces of Niederosterreich and Vorarlberg,and has been carried out within the Austrian Excellence Centre of Tribology(AC2T research GmbH).
文摘The existing knowledge regarding the interfacial forces,lubrication,and wear of bearings in real-world operation has significantly improved their designs over time,allowing for prolonged service life.As a result,self-lubricating bearings have become a viable alternative to traditional bearing designs in industrial machines.However,wear mechanisms are still inevitable and occur progressively in self-lubricating bearings,as characterized by the loss of the lubrication film and seizure.Therefore,monitoring the stages of the wear states in these components will help to impart the necessary countermeasures to reduce the machine maintenance downtime.This article proposes a methodology for using a long short-term memory(LSTM)-based encoder-decoder architecture on interfacial force signatures to detect abnormal regimes,aiming to provide early predictions of failure in self-lubricating sliding contacts even before they occur.Reciprocating sliding experiments were performed using a self-lubricating bronze bushing and steel shaft journal in a custom-built transversally oscillating tribometer setup.The force signatures corresponding to each cycle of the reciprocating sliding motion in the normal regime were used as inputs to train the encoder-decoder architecture,so as to reconstruct any new signal of the normal regime with the minimum error.With this semi-supervised training exercise,the force signatures corresponding to the abnormal regime could be differentiated from the normal regime,as their reconstruction errors would be very high.During the validation procedure for the proposed LSTM-based encoder-decoder model,the model predicted the force signals corresponding to the normal and abnormal regimes with an accuracy of 97%.In addition,a visualization of the reconstruction error across the entire force signature showed noticeable patterns in the reconstruction error when temporally decoded before the actual critical failure point,making it possible to be used for early predictions of failure.
基金supported by the National Natural Science Foundation of China,Nos.82301486(to SL)and 82071325(to FY)Medjaden Academy&Research Foundation for Young Scientists,No.MJR202310040(to SL)+2 种基金Nanjing Medical University Science and Technique Development,No.NMUB20220060(to SL)Medical Scientific Research Project of Jiangsu Commission of Health,No.ZDA2020019(to JZ)Health China Buchang Zhiyuan Public Welfare Project for Heart and Brain Health,No.HIGHER202102(to QD).
文摘Acute ischemic stroke is a clinical emergency and a condition with high morbidity,mortality,and disability.Accurate predictive,diagnostic,and prognostic biomarkers and effective therapeutic targets for acute ischemic stroke remain undetermined.With innovations in high-throughput gene sequencing analysis,many aberrantly expressed non-coding RNAs(ncRNAs)in the brain and peripheral blood after acute ischemic stroke have been found in clinical samples and experimental models.Differentially expressed ncRNAs in the post-stroke brain were demonstrated to play vital roles in pathological processes,leading to neuroprotection or deterioration,thus ncRNAs can serve as therapeutic targets in acute ischemic stroke.Moreover,distinctly expressed ncRNAs in the peripheral blood can be used as biomarkers for acute ischemic stroke prediction,diagnosis,and prognosis.In particular,ncRNAs in peripheral immune cells were recently shown to be involved in the peripheral and brain immune response after acute ischemic stroke.In this review,we consolidate the latest progress of research into the roles of ncRNAs(microRNAs,long ncRNAs,and circular RNAs)in the pathological processes of acute ischemic stroke–induced brain damage,as well as the potential of these ncRNAs to act as biomarkers for acute ischemic stroke prediction,diagnosis,and prognosis.Findings from this review will provide novel ideas for the clinical application of ncRNAs in acute ischemic stroke.
基金supported by the National Key Research and Development Program of China(No.2021YFA0715900).
文摘Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowadays,the groundwater vulnerability assessment(GVA)has become an essential task to identify the current status and development trend of groundwater quality.In this study,the Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism.The study firstly builds the CNN-LSTM modelwith self-attention(SA)mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine(SVM),Random Forest(RF),and Extreme Gradient Boosting(XGBoost).The results indicate that the CNNLSTM model outperforms thesemodels,demonstrating its significance in groundwater vulnerability assessment.It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years.This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities.Moreover,the overall groundwater vulnerability risk in the entire region has increased,evident fromboth the notably high value and standard deviation.This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities.The model can be optimized for diverse applications across regional environmental assessment,pollution prediction,and risk statistics.This study holds particular significance for ecological protection and groundwater resource management.
基金support of the National Natural Science Foundation of China(No.22361011)Guizhou Provincial Science and Technology Projects(No.ZK[2023]General 040)the Guizhou Provincial Key Laboratory Platform Project(No.ZSYS[2025]008)。
文摘This investigation focuses on the utilization of cucurbit[6]uril(Q[6])as the host compound for the development of long-lasting afterglow materials.By strategically manipulating the outer surface interactions of Q[6],classical aggregation-caused quenching(ACQ)compounds such as fluorescein sodium(FluNa)and calcein sodium(CalNa)were transformed into afterglow materials with varying colors and durations upon exposure to ultraviolet light.This transformation was facilitated through a host-vip doping method combined with coordination with metal ions.Even at a reduced doping concentration of 5×10^(-5)wt%,the materials exhibit remarkable afterglow properties,lasting up to 2 s,with a phosphorescence lifetime of up to 150 ms.Moreover,by adjusting the concentration of the vip compound,the persistent luminescence color of the materials could be easily transitioned from orange to yellow and subsequently to green.These findings suggest that the developed afterglow materials hold significant potential for multilevel anti-counterfeiting and information encryption applications when exposed to ultraviolet light.The supramolecular assembly strategy,which relies on the outer surface interactions of cucurbit[n]uril,offers a simpler and more efficient approach to crafting multi-color luminescent materials.Additionally,this method opens avenues for enhancing the application potential of aggregation-caused quenching(ACQ)compounds in various technological domains.
基金founded by the Open Project Program of Anhui Province Key Laboratory of Metallurgical Engineering and Resources Recycling(Anhui University of Technology)(No.SKF21-06)Research Fund for Young Teachers of Anhui University of Technology in 2020(No.QZ202001).
文摘Real-time prediction and precise control of sinter quality are pivotal for energy saving,cost reduction,quality improvement and efficiency enhancement in the ironmaking process.To advance,the accuracy and comprehensiveness of sinter quality prediction,an intelligent flare monitoring system for sintering machine tails that combines hybrid neural networks integrating convolutional neural network with long short-term memory(CNN-LSTM)networks was proposed.The system utilized a high-temperature thermal imager for image acquisition at the sintering machine tail and employed a zone-triggered method to accurately capture dynamic feature images under challenging conditions of high-temperature,high dust,and occlusion.The feature images were then segmented through a triple-iteration multi-thresholding approach based on the maximum between-class variance method to minimize detail loss during the segmentation process.Leveraging the advantages of CNN and LSTM networks in capturing temporal and spatial information,a comprehensive model for sinter quality prediction was constructed,with inputs including the proportion of combustion layer,porosity rate,temperature distribution,and image features obtained from the convolutional neural network,and outputs comprising quality indicators such as underburning index,uniformity index,and FeO content of the sinter.The accuracy is notably increased,achieving a 95.8%hit rate within an error margin of±1.0.After the system is applied,the average qualified rate of FeO content increases from 87.24%to 89.99%,representing an improvement of 2.75%.The average monthly solid fuel consumption is reduced from 49.75 to 46.44 kg/t,leading to a 6.65%reduction and underscoring significant energy saving and cost reduction effects.
基金Supported by National Natural Science Foundation of China,No.82405223Yunling Scholars Program,No.XDYC-YLXZ-2022-0027.
文摘Hepatocellular carcinoma(HCC)is a highly lethal malignancy with limited treatment options,particularly for patients with advanced stages of the disease.Sorafenib,the standard first-line therapy,faces significant challenges due to the development of drug resistance.Yu et al explored the mechanisms by which lncRNA KIF9-AS1 regulates the stemness and sorafenib resistance in HCC using a combination of cell culture,transfection,RNA immunoprecipitation,co-immunoprecipitation,and xenograft tumor models.They demonstrate that N6-methyladenosine-modified long non-coding RNA KIF9-AS1 acts as an oncogene in HCC.This modification involves methyltransferase-like 3 and insulin-like growth factor 2 mRNA-binding protein 1,which play critical roles in regulating KIF9-AS1.Furthermore,KIF9-AS1 stabilizes and upregulates short stature homeobox 2 by promoting its deubiquitination through ubiquitin-specific peptidase 1,thereby enhancing stemness and contributing to sorafenib resistance in HCC cells.These findings provide a theoretical basis for KIF9-AS1 as a diagnostic marker and therapeutic target for HCC,highlighting the need for further investigation into its clinical application potential.
文摘The Internet of Things(IoT)has orchestrated various domains in numerous applications,contributing significantly to the growth of the smart world,even in regions with low literacy rates,boosting socio-economic development.This study provides valuable insights into optimizing wireless communication,paving the way for a more connected and productive future in the mining industry.The IoT revolution is advancing across industries,but harsh geometric environments,including open-pit mines,pose unique challenges for reliable communication.The advent of IoT in the mining industry has significantly improved communication for critical operations through the use of Radio Frequency(RF)protocols such as Bluetooth,Wi-Fi,GSM/GPRS,Narrow Band(NB)-IoT,SigFox,ZigBee,and Long Range Wireless Area Network(LoRaWAN).This study addresses the optimization of network implementations by comparing two leading free-spreading IoT-based RF protocols such as ZigBee and LoRaWAN.Intensive field tests are conducted in various opencast mines to investigate coverage potential and signal attenuation.ZigBee is tested in the Tadicherla open-cast coal mine in India.Similarly,LoRaWAN field tests are conducted at one of the associated cement companies(ACC)in the limestone mine in Bargarh,India,covering both Indoor-toOutdoor(I2O)and Outdoor-to-Outdoor(O2O)environments.A robust framework of path-loss models,referred to as Free space,Egli,Okumura-Hata,Cost231-Hata and Ericsson models,combined with key performance metrics,is employed to evaluate the patterns of signal attenuation.Extensive field testing and careful data analysis revealed that the Egli model is the most consistent path-loss model for the ZigBee protocol in an I2O environment,with a coefficient of determination(R^(2))of 0.907,balanced error metrics such as Normalized Root Mean Square Error(NRMSE)of 0.030,Mean Square Error(MSE)of 4.950,Mean Absolute Percentage Error(MAPE)of 0.249 and Scatter Index(SI)of 2.723.In the O2O scenario,the Ericsson model showed superior performance,with the highest R^(2)value of 0.959,supported by strong correlation metrics:NRMSE of 0.026,MSE of 8.685,MAPE of 0.685,Mean Absolute Deviation(MAD)of 20.839 and SI of 2.194.For the LoRaWAN protocol,the Cost-231 model achieved the highest R^(2)value of 0.921 in the I2O scenario,complemented by the lowest metrics:NRMSE of 0.018,MSE of 1.324,MAPE of 0.217,MAD of 9.218 and SI of 1.238.In the O2O environment,the Okumura-Hata model achieved the highest R^(2)value of 0.978,indicating a strong fit with metrics NRMSE of 0.047,MSE of 27.807,MAPE of 27.494,MAD of 37.287 and SI of 3.927.This advancement in reliable communication networks promises to transform the opencast landscape into networked signal attenuation.These results support decision-making for mining needs and ensure reliable communications even in the face of formidable obstacles.
基金National Natural Science Foundation of China(71690233,71971213,71901214)。
文摘Architecture framework has become an effective method recently to describe the system of systems(SoS)architecture,such as the United States(US)Department of Defense Architecture Framework Version 2.0(DoDAF2.0).As a viewpoint in DoDAF2.0,the operational viewpoint(OV)describes operational activities,nodes,and resource flows.The OV models are important for SoS architecture development.However,as the SoS complexity increases,constructing OV models with traditional methods exposes shortcomings,such as inefficient data collection and low modeling standards.Therefore,we propose an intelligent modeling method for five OV models,including operational resource flow OV-2,organizational relationships OV-4,operational activity hierarchy OV-5a,operational activities model OV-5b,and operational activity sequences OV-6c.The main idea of the method is to extract OV architecture data from text and generate interoperable OV models.First,we construct the OV meta model based on the DoDAF2.0 meta model(DM2).Second,OV architecture named entities is recognized from text based on the bidirectional long short-term memory and conditional random field(BiLSTM-CRF)model.And OV architecture relationships are collected with relationship extraction rules.Finally,we define the generation rules for OV models and develop an OV modeling tool.We use unmanned surface vehicles(USV)swarm target defense SoS architecture as a case to verify the feasibility and effectiveness of the intelligent modeling method.
文摘Metalens technology has been applied extensively in miniaturized and integrated infrared imaging systems.However,due to the high phase dispersion of unit structures,metalens often exhibits chromatic aberration,making broadband achromatic infrared imaging challenging to achieve.In this paper,six different unit structures based on chalcogenide glass are constructed,and their phase-dispersion parameters are analyzed to establish a database.On this basis,using chromatic aberration compensation and parameterized adjoint topology optimization,a broadband achromatic metalens with a numerical aperture of 0.5 is designed by arranging these six unit structures in the far-infrared band.Simulation results show that the metalens achieves near diffraction-limited focusing within the operating wavelength range of 9−11μm,demonstrating the good performance of achromatic aberration with flat focusing efficiency of 54%−58%across all wavelengths.
文摘This paper mainly studies the well-posedness of steady incompressible impinging jet flow problem through a 3D axisymmetric finitely long nozzle.This problem originates from the physical phenomena encountered in practical engineering fields,such as in short take-off and vertical landing(STOVL)aircraft.Nowadays many intricate phenomena associated with impinging jet flows remain inadequately elucidated,which limits the ability to optimize aircraft design.Given a boundary condition in the inlet,the impinging jet problem is transformed into a Bernoulli-type free boundary problem according to the stream function.Then the variational method is used to study the corresponding variational problem with one parameter,thereby the wellposedness is established.The main conclusion is as follows.For a 3D axisymmetric finitely long nozzle and an infinitely long vertical wall,given an axial velocity in the inlet of nozzle,there exists a unique smooth incom‑pressible impinging jet flow such that the free boundary initiates smoothly at the endpoint of the nozzle and extends to infinity along the vertical wall at far fields.The key point is to investigate the regularity of the corner where the nozzle and the vertical axis intersect.
