Astrocytic endothelin-1 overexpression impairs learning and memory ability in ischemic stroke via altered hippocampal neurogenesis and lipid metabolism

Vascular etiology is the second most prevalent cause of cognitive impairment globally.Endothelin-1,which is produced and secreted by endothelial cells and astrocytes,is implicated in the pathogenesis of stroke.However,the way in which changes in astrocytic endothelin-1 lead to poststroke cognitive deficits following transient middle cerebral artery occlusion is not well understood.Here,using mice in which astrocytic endothelin-1 was overexpressed,we found that the selective overexpression of endothelin-1 by astrocytic cells led to ischemic stroke-related dementia(1 hour of ischemia;7 days,28 days,or 3 months of reperfusion).We also revealed that astrocytic endothelin-1 overexpression contributed to the role of neural stem cell proliferation but impaired neurogenesis in the dentate gyrus of the hippocampus after middle cerebral artery occlusion.Comprehensive proteome profiles and western blot analysis confirmed that levels of glial fibrillary acidic protein and peroxiredoxin 6,which were differentially expressed in the brain,were significantly increased in mice with astrocytic endothelin-1 overexpression in comparison with wild-type mice 28 days after ischemic stroke.Moreover,the levels of the enriched differentially expressed proteins were closely related to lipid metabolism,as indicated by Kyoto Encyclopedia of Genes and Genomes pathway analysis.Liquid chromatography-mass spectrometry nontargeted metabolite profiling of brain tissues showed that astrocytic endothelin-1 overexpression altered lipid metabolism products such as glycerol phosphatidylcholine,sphingomyelin,and phosphatidic acid.Overall,this study demonstrates that astrocytic endothelin-1 overexpression can impair hippocampal neurogenesis and that it is correlated with lipid metabolism in poststroke cognitive dysfunction.

Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature

The martensitic transformation temperature is the basis for the application of shape memory alloys(SMAs),and the ability to quickly and accurately predict the transformation temperature of SMAs has very important practical significance.In this work,machine learning(ML)methods were utilized to accelerate the search for shape memory alloys with targeted properties(phase transition temperature).A group of component data was selected to design shape memory alloys using reverse design method from numerous unexplored data.Component modeling and feature modeling were used to predict the phase transition temperature of the shape memory alloys.The experimental results of the shape memory alloys were obtained to verify the effectiveness of the support vector regression(SVR)model.The results show that the machine learning model can obtain target materials more efficiently and pertinently,and realize the accurate and rapid design of shape memory alloys with specific target phase transition temperature.On this basis,the relationship between phase transition temperature and material descriptors is analyzed,and it is proved that the key factors affecting the phase transition temperature of shape memory alloys are based on the strength of the bond energy between atoms.This work provides new ideas for the controllable design and performance optimization of Cu-based shape memory alloys.

Promotion of structural plasticity in area V2 of visual cortex prevents against object recognition memory deficits in aging and Alzheimer's disease rodents

Memory deficit,which is often associated with aging and many psychiatric,neurological,and neurodegenerative diseases,has been a challenging issue for treatment.Up till now,all potential drug candidates have failed to produce satisfa ctory effects.Therefore,in the search for a solution,we found that a treatment with the gene corresponding to the RGS14414protein in visual area V2,a brain area connected with brain circuits of the ventral stream and the medial temporal lobe,which is crucial for object recognition memory(ORM),can induce enhancement of ORM.In this study,we demonstrated that the same treatment with RGS14414in visual area V2,which is relatively unaffected in neurodegenerative diseases such as Alzheimer s disease,produced longlasting enhancement of ORM in young animals and prevent ORM deficits in rodent models of aging and Alzheimer’s disease.Furthermore,we found that the prevention of memory deficits was mediated through the upregulation of neuronal arbo rization and spine density,as well as an increase in brain-derived neurotrophic factor(BDNF).A knockdown of BDNF gene in RGS14414-treated aging rats and Alzheimer s disease model mice caused complete loss in the upregulation of neuronal structural plasticity and in the prevention of ORM deficits.These findings suggest that BDNF-mediated neuronal structural plasticity in area V2 is crucial in the prevention of memory deficits in RGS14414-treated rodent models of aging and Alzheimer’s disease.Therefore,our findings of RGS14414gene-mediated activation of neuronal circuits in visual area V2 have therapeutic relevance in the treatment of memory deficits.

The complex roles of m^(6)A modifications in neural stem cell proliferation, differentiation, and self-renewal and implications for memory and neurodegenerative diseases

N6-methyladenosine(m^(6)A), the most prevalent and conserved RNA modification in eukaryotic cells, profoundly influences virtually all aspects of mRNA metabolism. mRNA plays crucial roles in neural stem cell genesis and neural regeneration, where it is highly concentrated and actively involved in these processes. Changes in m^(6)A modification levels and the expression levels of related enzymatic proteins can lead to neurological dysfunction and contribute to the development of neurological diseases. Furthermore, the proliferation and differentiation of neural stem cells, as well as nerve regeneration, are intimately linked to memory function and neurodegenerative diseases. This paper presents a comprehensive review of the roles of m^(6)A in neural stem cell proliferation, differentiation, and self-renewal, as well as its implications in memory and neurodegenerative diseases. m^(6)A has demonstrated divergent effects on the proliferation and differentiation of neural stem cells. These observed contradictions may arise from the time-specific nature of m^(6)A and its differential impact on neural stem cells across various stages of development. Similarly, the diverse effects of m^(6)A on distinct types of memory could be attributed to the involvement of specific brain regions in memory formation and recall. Inconsistencies in m^(6)A levels across different models of neurodegenerative disease, particularly Alzheimer's disease and Parkinson's disease, suggest that these disparities are linked to variations in the affected brain regions. Notably, the opposing changes in m^(6)A levels observed in Parkinson's disease models exposed to manganese compared to normal Parkinson's disease models further underscore the complexity of m^(6)A's role in neurodegenerative processes. The roles of m^(6)A in neural stem cell proliferation, differentiation, and self-renewal, and its implications in memory and neurodegenerative diseases, appear contradictory. These inconsistencies may be attributed to the timespecific nature of m^(6)A and its varying effects on distinct brain regions and in different environments.

Exercise preconditioning alleviates ischemia-induced memory deficits by increasing circulating adiponectin

Cerebral ischemia is a major health risk that requires preventive approaches in addition to drug therapy.Physical exercise enhances neurogenesis and synaptogenesis,and has been widely used for functional rehabilitation after stroke.In this study,we determined whether exercise training before disease onset can alleviate the severity of cerebral ischemia.We also examined the role of exercise-induced circulating factors in these effects.Adult mice were subjected to 14 days of treadmill exercise training before surgery for middle cerebral artery occlusion.We found that this exercise pre-conditioning strategy effectively attenuated brain infarct area,inhibited gliogenesis,protected synaptic proteins,and improved novel object and spatial memory function.Further analysis showed that circulating adiponectin plays a critical role in these preventive effects of exercise.Agonist activation of adiponectin receptors by Adipo Ron mimicked the effects of exercise,while inhibiting receptor activation abolished the exercise effects.In summary,our results suggest a crucial role of circulating adiponectin in the effects of exercise pre-conditioning in protecting against cerebral ischemia and supporting the health benefits of exercise.

The study of lithographic variation in resistive random access memory

Reducing the process variation is a significant concern for resistive random access memory(RRAM).Due to its ultrahigh integration density,RRAM arrays are prone to lithographic variation during the lithography process,introducing electrical variation among different RRAM devices.In this work,an optical physical verification methodology for the RRAM array is developed,and the effects of different layout parameters on important electrical characteristics are systematically investigated.The results indicate that the RRAM devices can be categorized into three clusters according to their locations and lithography environments.The read resistance is more sensitive to the locations in the array(~30%)than SET/RESET voltage(<10%).The increase in the RRAM device length and the application of the optical proximity correction technique can help to reduce the variation to less than 10%,whereas it reduces RRAM read resistance by 4×,resulting in a higher power and area consumption.As such,we provide design guidelines to minimize the electrical variation of RRAM arrays due to the lithography process.

Advances of embedded resistive random access memory in industrial manufacturing and its potential applications

Embedded memory,which heavily relies on the manufacturing process,has been widely adopted in various industrial applications.As the field of embedded memory continues to evolve,innovative strategies are emerging to enhance performance.Among them,resistive random access memory(RRAM)has gained significant attention due to its numerousadvantages over traditional memory devices,including high speed(<1 ns),high density(4 F^(2)·n^(-1)),high scalability(~nm),and low power consumption(~pJ).This review focuses on the recent progress of embedded RRAM in industrial manufacturing and its potentialapplications.It provides a brief introduction to the concepts and advantages of RRAM,discusses the key factors that impact its industrial manufacturing,and presents the commercial progress driven by cutting-edge nanotechnology,which has been pursued by manysemiconductor giants.Additionally,it highlights the adoption of embedded RRAM in emerging applications within the realm of the Internet of Things and future intelligent computing,with a particular emphasis on its role in neuromorphic computing.Finally,the review discusses thecurrent challenges and provides insights into the prospects of embedded RRAM in the era of big data and artificial intelligence.

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.

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.

Electromagnetic Performance Analysis of Variable Flux Memory Machines with Series-magnetic-circuit and Different Rotor Topologies

In this paper,the electromagnetic performance of variable flux memory(VFM)machines with series-magnetic-circuit is investigated and compared for different rotor topologies.Based on a V-type VFM machine,five topologies with different interior permanent magnet(IPM)arrangements are evolved and optimized under same constrains.Based on two-dimensional(2-D)finite element(FE)method,their electromagnetic performance at magnetization and demagnetization states is evaluated.It reveals that the iron bridge and rotor lamination region between constant PM(CPM)and variable PM(VPM)play an important role in torque density and flux regulation(FR)capabilities.Besides,the global efficiency can be improved in VFM machines by adjusting magnetization state(MS)under different operating conditions.

Regulation role of miR-204 on SIRT1/VEGF in metabolic memory induced by high glucose in human retinal pigment epithelial cells

AIM:To examine the regulatory role of microRNA-204(miR-204)on silent information regulator 1(SIRT1)and vascular endothelial growth factor(VEGF)under highglucose-induced metabolic memory in human retinal pigment epithelial(hRPE)cells.METHODS:Cells were cultured with either normal(5 mmol/L)or high D-glucose(25 mmol/L)concentrations for 8d to establish control and high-glucose groups,respectively.To induce metabolic memory,cells were cultured with 25 mmol/L D-glucose for 4d followed by culture with 5 mmol/L D-glucose for 4d.In addition,exposed in 25 mmol/L D-glucose for 4d and then transfected with 100 nmol/L miR-204 control,miR-204 inhibitor or miR-204 mimic in 5 mmol/L D-glucose for 4d.Quantitative reverse transcription-polymerase chain reaction(RT-qPCR)was used to detect miR-204 mRNA levels.SIRT1 and VEGF protein levels were assessed by immunohistochemical and Western blot.Flow cytometry was used to investigate apoptosis rate.RESULTS:It was found that high glucose promoted miR-204 and VEGF expression,and inhibited SIRT1 activity,even after the return to normal glucose culture conditions.Upregulation of miR-204 promoted apoptosis inhibiting SIRT1 and increasing VEGF expression.However,downregulation of miR-204 produced the opposite effects.CONCLUSION:The study identifies that miR-204 is the upstream target of SIRT1and VEGF,and that miR-204 can protect hRPE cells from the damage caused by metabolic memory through increasing SIRT1 and inhibiting VEGF expression.

Novel Adaptive Memory Event-Triggered-Based Fuzzy Robust Control for Nonlinear Networked Systems via the Differential Evolution Algorithm

This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.

Design and implementation of dual-mode configurable memory architecture for CNN accelerator

With the rapid development of deep learning algorithms,the computational complexity and functional diversity are increasing rapidly.However,the gap between high computational density and insufficient memory bandwidth under the traditional von Neumann architecture is getting worse.Analyzing the algorithmic characteristics of convolutional neural network(CNN),it is found that the access characteristics of convolution(CONV)and fully connected(FC)operations are very different.Based on this feature,a dual-mode reronfigurable distributed memory architecture for CNN accelerator is designed.It can be configured in Bank mode or first input first output(FIFO)mode to accommodate the access needs of different operations.At the same time,a programmable memory control unit is designed,which can effectively control the dual-mode configurable distributed memory architecture by using customized special accessing instructions and reduce the data accessing delay.The proposed architecture is verified and tested by parallel implementation of some CNN algorithms.The experimental results show that the peak bandwidth can reach 13.44 GB·s^(-1)at an operating frequency of 120 MHz.This work can achieve 1.40,1.12,2.80 and 4.70 times the peak bandwidth compared with the existing work.

Relationship between physical activity and specific working memory indicators of depressive symptoms in university students

BACKGROUND The detection rate of depression among university students has been increasing in recent years,becoming one of the main psychological diseases that endangers their physical and mental health.According to statistics,self-harm and suicide,for which there is no effective intervention,are the second leading causes of death.AIM To explore the relationship between different elements and levels of physical activity and college students’depression-symptom-specific working memory indicators.METHODS Of 143 college students were analyzed using the Beck Depression Self-Rating Scale,the Physical Activity Rating Scale,and the Working Memory Task.RESULTS There was a significant difference between college students with depressive symptoms and healthy college students in completing verbal and spatial working memory(SWM)tasks correctly(all P<0.01).Physical Activity Scale-3 scores were significantly and positively correlated with the correct rate of the verbal working memory task(r=0.166)and the correct rate of the SWM task(r=0.210)(all P<0.05).There were significant differences in the correct rates of verbal and SWM tasks according to different exercise intensities(all P<0.05)and different exercise durations(all P<0.05),and no significant differences in the correct rates of verbal and SWM tasks by exercise frequency(all P>0.05).CONCLUSION An increase in physical exercise among college students,particularly medium-and high-intensity exercise and exercise of 30 min or more,can improve the correct rate of completing working memory tasks.

Text Difficulty,Working Memory Capacity and Mind Wandering During Chinese EFL Learners’Reading

This experimental study investigated how text difficulty and different working memory capacity(WMC)affected Chinese EFL learners’reading comprehension and their tendency to engage in task-unrelated thoughts,that is,mind wandering(MW),in the course of reading.Sixty first-year university non-English majors participated in the study.A two-factor mixed experimental design of 2(text difficulty:difficult and simple)×2(WMC:high/large and low/small)was employed.Results revealed that 1)the main and interaction effects of WMC and text difficulty on voluntary MW were significant,whereas those on involuntary MW were not;2)while reading the easy texts,the involuntary MW of high-WMC individuals was less frequent than that of low-WMC ones,whereas while reading the difficult ones,the direct relationship between WMC and involuntary MW was not found;and that 3)high-WMC individuals had a lower overall rate of MW and better reading performance than low-WMC individuals did,but with increasing text difficulty,their rates of overall MW and voluntary MW were getting higher and higher,and the reading performance was getting lower and lower.These results lend support to WM theory and have pedagogical implications for the instruction of L2 reading.

Between the City and Images:An Analysis of Mainstream Media’s Paths of Constructing the Cultural Memory of a City:Taking Chengdu Radio and Television’s“Hi Chengdu”as an Example

Mainstream media play a crucial role in constructing the cultural memory of a city.This study used 319 short videos released by“Hi Chengdu,”a new media product of Chengdu Radio and Television,as samples.Based on the grounded theory,a research framework encompassing“content,technology,and discourse”was established to explore the paths through which mainstream media construct the cultural memory.Regarding content,this paper emphasized temporal and spatial contexts and urban spaces,delving deep into the themes of the cultural memory and vehicles for it.In terms of technology,this paper discussed the practice of leveraging audio/visual-mode discourse to stitch together the impressions of a city and evoke emotional resonance to create a“flow”of memory.As for discourse,this paper looked at the performance of a communication ritual to frame concepts and shape urban identity.It is essential to break free from conventional thinking and leverage local culture as the primary driving force to further boost a city’s productivity,in order to excel in cultural communication.

The Impact of Opioid Drugs on Memory and Other Cognitive Functions: A Review

Background and Purpose: Opioids, used for centuries to alleviate pain, have become a double-edged sword. While effective, they come with a host of adverse effects, including memory and cognition impairment. This review delves into the impact of opioid drugs on cognitive functions, explores underlying mechanisms, and investigates their prevalence in both medical care and illicit drug use. The ultimate goal is to find ways to mitigate their potential harm and address the ongoing opioid crisis. Methods: We sourced data from PubMed and Google Scholar, employing search combinations like “opioids,” “memory,” “cognition,” “amnesia,” “cognitive function,” “executive function,” and “inhibition.” Our focus was on English-language articles spanning from the inception of these databases up to the present. Results: The literature consistently reveals that opioid use, particularly at high doses, adversely affects memory and other cognitive functions. Longer deliberation times, impaired decision-making, impulsivity, and behavioral disorders are common consequences. Chronic high-dose opioid use is associated with conditions such as amnesiac syndrome (OAS), post-operative cognitive dysfunction (POCD), neonatal abstinence syndrome (NAS), depression, anxiety, sedation, and addiction. Alarming trends show increased opioid use over recent decades, amplifying the risk of these outcomes. Conclusion: Opioids cast a shadow over memory and cognitive function. These effects range from amnesiac effects, lessened cognitive function, depression, and more. Contributing factors include over-prescription, misuse, misinformation, and prohibition policies. Focusing on correct informational campaigns, removing punitive policies, and focusing on harm reduction strategies have been shown to lessen the abuse and use of opioids and thus helping to mitigate the adverse effects of these drugs. Further research into the impacts of opioids on cognitive abilities is also needed as they are well demonstrated in the literature, but the mechanism is not often completely understood.

The research progress of epigenetics and metabolic memory in diabetic kidney disease

Diabetic kidney disease(DKD)is a clinical syndrome that is one of the major causes of end-stage renal disease(ESRD).The pathogenesis of DKD is complex and multifaceted,with most studies indicating its association with genetics,advanced glycosylation end-product deposition,polyol pathway and protein C activation,lipid metabolism abnormalities,microcirculatory dysfunction,oxidative stress,inflammatory factors,and the kallikrein-kinin system.Epigenetics is the science studying gene expression regulation without changes in the DNA sequence.In recent years,increasing evidence has shown that epigenetic mechanisms play a crucial role in the initiation and progression of DKD.For instance,epigenetic modifications such as DNA methylation,histone modifications,and non-coding RNAs can influence the expression of DKD-related genes,thereby regulating the development and progression of DKD.On the other hand,metabolic memory is an important concept in DKD research.Metabolic memory refers to the phenomenon where cells maintain a certain metabolic state even after the disappearance of metabolic stress factors.This state can influence cell function and fate.In DKD,metabolic stress factors such as hyperglycemia can lead to metabolic memory in renal cells,affecting their function and fate,ultimately leading to the development and progression of DKD.Therefore,to further explore the pathogenesis of DKD,research on epigenetics should be strengthened,aiming to provide new ideas and methods for the prevention and treatment of DKD.

Positive Effect of Transcranial Direct Current Stimulation on Visual Verbal Working Memory in Patients with Attention-Deficit/Hyperactivity Disorder

Background: Working memory is an executive function that plays an important role in many aspects of daily life, and its impairment in patients with attention-deficit/hyperactivity disorder (ADHD) affects quality of life. The dorsolateral prefrontal cortex (DLPFC) has been a good target site for transcranial direct current stimulation (tDCS) due to its intense involvement in working memory. In our 2018 study, tDCS improved visual-verbal working memory in healthy subjects. Objective: This study examines the effects of tDCS on ADHD patients, particularly on verbal working memory. Methods: We conducted an experiment involving verbal working memory of two modalities, visual and auditory, and a sustained attention task that could affect working memory in 9 ADHD patients. Active or sham tDCS was applied to the left DLPFC in a single-blind crossover design. Results: tDCS significantly improved the accuracy of visual-verbal working memory. In contrast, tDCS did not affect auditory-verbal working memory and sustained attention. Conclusion: tDCS to the left DLPFC improved visual-verbal working memory in ADHD patients, with important implications for potential ADHD treatments.

Slope stability prediction based on a long short-term memory neural network:comparisons with convolutional neural networks,support vector machines and random forest models

The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning models have some problems,such as poor nonlinear performance,local optimum and incomplete factors feature extraction.These issues can affect the accuracy of slope stability prediction.Therefore,a deep learning algorithm called Long short-term memory(LSTM)has been innovatively proposed to predict slope stability.Taking the Ganzhou City in China as the study area,the landslide inventory and their characteristics of geotechnical parameters,slope height and slope angle are analyzed.Based on these characteristics,typical soil slopes are constructed using the Geo-Studio software.Five control factors affecting slope stability,including slope height,slope angle,internal friction angle,cohesion and volumetric weight,are selected to form different slope and construct model input variables.Then,the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors.Each slope stability coefficient and its corresponding control factors is a slope sample.As a result,a total of 2160 training samples and 450 testing samples are constructed.These sample sets are imported into LSTM for modelling and compared with the support vector machine(SVM),random forest(RF)and convo-lutional neural network(CNN).The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features.Furthermore,LSTM has a better prediction performance for slope stability compared to SVM,RF and CNN models.