Glucocorticoid receptor signaling in the brain and its involvement in cognitive function

The hypothalamic-pituitary-adrenal axis regulates the secretion of glucoco rticoids in response to environmental challenges.In the brain,a nuclear receptor transcription fa ctor,the glucocorticoid recepto r,is an important component of the hypothalamicpituitary-a d renal axis's negative feedback loop and plays a key role in regulating cognitive equilibrium and neuroplasticity.The glucoco rticoid receptor influences cognitive processes,including glutamate neurotransmission,calcium signaling,and the activation of brain-derived neurotrophic factor-mediated pathways,through a combination of genomic and non-genomic mechanisms.Protein interactions within the central nervous system can alter the expression and activity of the glucocorticoid receptor,there by affecting the hypothalamic-pituitary-a d renal axis and stress-related cognitive functions.An appropriate level of glucocorticoid receptor expression can improve cognitive function,while excessive glucocorticoid receptors or long-term exposure to glucoco rticoids may lead to cognitive impairment.Patients with cognitive impairment-associated diseases,such as Alzheimer's disease,aging,depression,Parkinson's disease,Huntington's disease,stroke,and addiction,often present with dysregulation of the hypothalamic-pituitary-adrenal axis and glucocorticoid receptor expression.This review provides a comprehensive overview of the functions of the glucoco rticoid receptor in the hypothalamic-pituitary-a d renal axis and cognitive activities.It emphasizes that appropriate glucocorticoid receptor signaling fa cilitates learning and memory,while its dysregulation can lead to cognitive impairment.This provides clues about how glucocorticoid receptor signaling can be targeted to ove rcome cognitive disability-related disorders.

Regulator of G protein signaling 6 mediates exercise-induced recovery of hippocampal neurogenesis,learning,and memory in a mouse model of Alzheimer’s disease

Hippocampal neuronal loss causes cognitive dysfunction in Alzheimer’s disease.Adult hippocampal neurogenesis is reduced in patients with Alzheimer’s disease.Exercise stimulates adult hippocampal neurogenesis in rodents and improves memory and slows cognitive decline in patients with Alzheimer’s disease.However,the molecular pathways for exercise-induced adult hippocampal neurogenesis and improved cognition in Alzheimer’s disease are poorly understood.Recently,regulator of G protein signaling 6(RGS6)was identified as the mediator of voluntary running-induced adult hippocampal neurogenesis in mice.Here,we generated novel RGS6fl/fl;APP_(SWE) mice and used retroviral approaches to examine the impact of RGS6 deletion from dentate gyrus neuronal progenitor cells on voluntary running-induced adult hippocampal neurogenesis and cognition in an amyloid-based Alzheimer’s disease mouse model.We found that voluntary running in APP_(SWE) mice restored their hippocampal cognitive impairments to that of control mice.This cognitive rescue was abolished by RGS6 deletion in dentate gyrus neuronal progenitor cells,which also abolished running-mediated increases in adult hippocampal neurogenesis.Adult hippocampal neurogenesis was reduced in sedentary APP_(SWE) mice versus control mice,with basal adult hippocampal neurogenesis reduced by RGS6 deletion in dentate gyrus neural precursor cells.RGS6 was expressed in neurons within the dentate gyrus of patients with Alzheimer’s disease with significant loss of these RGS6-expressing neurons.Thus,RGS6 mediated voluntary running-induced rescue of impaired cognition and adult hippocampal neurogenesis in APP_(SWE) mice,identifying RGS6 in dentate gyrus neural precursor cells as a possible therapeutic target in Alzheimer’s disease.

Netrin-1 signaling pathway mechanisms in neurodegenerative diseases

Netrin-1 and its receptors play crucial roles in inducing axonal growth and neuronal migration during neuronal development.Their profound impacts then extend into adulthood to encompass the maintenance of neuronal survival and synaptic function.Increasing amounts of evidence highlight several key points:(1)Diminished Netrin-1 levels exacerbate pathological progression in animal models of Alzheimer’s disease and Parkinson’s disease,and potentially,similar alterations occur in humans.(2)Genetic mutations of Netrin-1 receptors increase an individuals’susceptibility to neurodegenerative disorders.(3)Therapeutic approaches targeting Netrin-1 and its receptors offer the benefits of enhancing memory and motor function.(4)Netrin-1 and its receptors show genetic and epigenetic alterations in a variety of cancers.These findings provide compelling evidence that Netrin-1 and its receptors are crucial targets in neurodegenerative diseases.Through a comprehensive review of Netrin-1 signaling pathways,our objective is to uncover potential therapeutic avenues for neurodegenerative disorders.

Prolonged intermittent theta burst stimulation restores the balance between A_(2A)R-and A_(1)R-mediated adenosine signaling in the 6-hydroxidopamine model of Parkinson's disease

An imbalance in adenosine-mediated signaling,particularly the increased A_(2A)R-mediated signaling,plays a role in the pathogenesis of Parkinson's disease.Existing therapeutic approaches fail to alter disease progression,demonstrating the need for novel approaches in PD.Repetitive transcranial magnetic stimulation is a non-invasive approach that has been shown to improve motor and non-motor symptoms of Parkinson's disease.However,the underlying mechanisms of the beneficial effects of repetitive transcranial magnetic stimulation remain unknown.The purpose of this study is to investigate the extent to which the beneficial effects of prolonged intermittent theta burst stimulation in the 6-hydroxydopamine model of experimental parkinsonism are based on modulation of adenosine-mediated signaling.Animals with unilateral 6-hydroxydopamine lesions underwent intermittent theta burst stimulation for 3 weeks and were tested for motor skills using the Rotarod test.Immunoblot,quantitative reverse transcription polymerase chain reaction,immunohistochemistry,and biochemical analysis of components of adenosine-mediated signaling were performed on the synaptosomal fraction of the lesioned caudate putamen.Prolonged intermittent theta burst stimulation improved motor symptoms in 6-hydroxydopamine-lesioned animals.A 6-hydroxydopamine lesion resulted in progressive loss of dopaminergic neurons in the caudate putamen.Treatment with intermittent theta burst stimulation began 7 days after the lesion,coinciding with the onset of motor symptoms.After treatment with prolonged intermittent theta burst stimulation,complete motor recovery was observed.This improvement was accompanied by downregulation of the e N/CD73-A_(2A)R pathway and a return to physiological levels of A_(1)R-adenosine deaminase 1 after 3 weeks of intermittent theta burst stimulation.Our results demonstrated that 6-hydroxydopamine-induced degeneration reduced the expression of A_(1)R and elevated the expression of A_(2A)R.Intermittent theta burst stimulation reversed these effects by restoring the abundances of A_(1)R and A_(2A)R to control levels.The shift in ARs expression likely restored the balance between dopamine-adenosine signaling,ultimately leading to the recovery of motor control.

Signal de-noising method based on wavelet decomposition

A noise reduction method for infrared detector output signal is studied during dynamic calibration of thermocou- pie. Firstly, the deficiency of the classical filter method is analyzed and the application of the wavelet analysis is introduced for signal de-noising during the dynamic testing. Secondly, the theoretical basis of wavelet analysis, the choice of wavelet base and the determination of decomposed series and threshold are analyzed. Finally, the de-noising experiment for infrared detector signal is carried out on the Matlab platform. The results indicate the proposed wavelet de-noising method is effective to remove fixed frequency and high-frequency noise; furthermore, good synchronization is achieved between the de-noised signal and the useful signal components in the original signal, which is of great significance to thermocouple modeling analys- is.

Research on fiber optic gyro signal de-noising based on wavelet packet soft-threshold

Gyro＇s drift is not only the main drift error which influences gyro＇s precision but also the primary factor that affects gyro＇s reliability. Reducing zero drift and random drift is a key problem to the output of a gyro signal. A three-layer de-nosing threshold algorithm is proposed based on the wavelet decomposition to dispose the signal which is collected from a running fiber optic gyro （FOG）. The coefficients are obtained from the three-layer wavelet packet decomposition. By setting the high frequency part which is greater than wavelet packet threshold as zero, then reconstructing the nodes which have been filtered out noise and interruption, the soft threshold function is constructed by the coefficients of the third nodes. Compared wavelet packet de-noise with forced de-noising method, the proposed method is more effective. Simulation results show that the random drift compensation is enhanced by 13.1%, and reduces zero drift by 0.052 6°/h.

Moving horizon based wavelet de-noising method of dual-observed geomagnetic signal for nonlinear high spin projectile roll positioning

Phase-frequency characte ristics of approximate sinusoidal geomagnetic signals can be used fo r projectile roll positioning and other high-precision trajectory correction applications.The sinusoidal geomagnetic signal deforms in the exposed and magnetically contaminated environment.In order to preciously recognize the roll information and effectively separate the noise component from the original geomagnetic sequence,based on the error source analysis,we propose a moving horizon based wavelet de-noising method for the dual-observed geomagnetic signal filtering where the captured rough roll frequency value provides reasonable wavelet decomposition and reconstruction level selection basis for sampled sequence;a moving horizon window guarantees real-time performance and non-cumulative calculation amount.The complete geomagnetic data in full ballistic range and three intercepted paragraphs are used for performance assessment.The positioning performance of the moving horizon wavelet de-noising method is compared with the band-pass filter.The results show that both noise reduction techniques improve the positioning accuracy while the wavelet de-noising method is always better than the band-pass filter.These results suggest that the proposed moving horizon based wavelet de-noising method of the dual-observed geomagnetic signal is more applicable for various launch conditions with better positioning performance.

Geotechnical engineering blasting:a new modal aliasing cancellation methodology of vibration signal de-noising

In the present study of peak particle velocity(PPV)and frequency,an improved algorithm(principal empirical mode decomposition,PEMD)based on principal component analysis(PCA)and empirical mode decomposition(EMD)is proposed,with the goal of addressing poor filtering de-noising effects caused by the occurrences of modal aliasing phenomena in EMD blasting vibration signal decomposition processes.Test results showed that frequency of intrinsic mode function(IMF)components decomposed by PEMD gradually decreases and that the main frequency is unique,which eliminates the phenomenon of modal aliasing.In the simulation experiment,the signal-to-noise(SNR)and root mean square errors(RMSE)ratio of the signal de-noised by PEMD are the largest when compared to EMD and ensemble empirical mode decomposition(EEMD).The main frequency of the de-noising signal through PEMD is 75 Hz,which is closest to the frequency of the noiseless simulation signal.In geotechnical engineering blasting experiments,compared to EMD and EEMD,the signal de-noised by PEMD has the lowest level of distortion,and the frequency band is distributed in a range of 0-64 Hz,which is closest to the frequency band of the blasting vibration signal.In addition,the proportion of noise energy was the lowest,at 1.8%.

Fault Diagnosis of Motor in Frequency Domain Signal by Stacked De-noising Auto-encoder

With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big data.Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities,this also brings influence to the mechanical fault diagnosis field.Therefore,according to the characteristics of motor vibration signals(nonstationary and difficult to deal with)and mechanical‘big data’,combined with deep learning,a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed.The frequency domain signals obtained by the Fourier transform are used as input to the network.This method can extract features adaptively and unsupervised,and get rid of the dependence of traditional machine learning methods on human extraction features.A supervised fine tuning of the model is then carried out by backpropagation.The Asynchronous motor in Drivetrain Dynamics Simulator system was taken as the research object,the effectiveness of the proposed method was verified by a large number of data,and research on visualization of network output,the results shown that the SDAE method is more efficient and more intelligent.

Application of RLS adaptive filteringin signal de-noising

In view of the problem that noises are prone to be mixed in the signals,an adaptive signal de-noising system based on reursive least squares （RLS） algorithm is introduced.The principle of adaptive filtering and the process flow of RLS algorithm are described.Through example simulation,simulation figures of the adaptive de-noising system are obtained.By analysis and comparison,it can be proved that RLS adaptive filtering is capable of eliminating the noises and obtaining useful signals in a relatively good manner.Therefore,the validity of this method and the rationality of this system are demonstrated.

Technology of signal de-noising and singularity elimination based on wavelet transform

Based on wavelet transform theory,a method for signal de-noising and singularity detection and elimination is proposed,which can reduce the noises and express local singularity.Each singularity can also be detected and located through the local modulus maxima of wavelet transform.Simulation experiments are conducted with MATLAB software.The experimental results demonstrate that the method proposed in this paper is effective and feasible.

Implementation of GPR Signals De-Noising Based on DSP

An important issue of ground-penetrating radar （GPR） signals analysis is de-noising thai is the guarantee of acquiring good detecting effect. The paper illustrates a successful application of digital single processor （DSP） based on wavelet shrinkage algorithm. In order to realize real-time GPP, signals analysis, some key issues are discussed such as the realization of fast wavelet transformation, the selection of CPU chip and the optimization of data movement. Experimenial results show that the DSP based application not only basically meets the real-time requirement of GPP, signals analysis, but also assures the quality of the GPR signals analysis.

Automatic de-noising and recognition algorithm for drilling fluid pulse signal

Wavelet forced de-noising algorithm is suitable for denoising of unsteady drilling fluid pulse signal, including baseline drift rectification and two-stage de-noising processing of frame synchronization signal and instruction signal. Two-stage de-noising processing can reduce the impact of baseline drift and determine automatic peak detection threshold range for signal recognition by distinguishing different features of frame synchronization pulse and instruction pulse. Rising and falling edge relative protruding threshold is defined for peak detection in signal recognition, which can make full use of the degree of the signal peak change and detect peaks flexibly with rising and falling edge relative protruding threshold combination. A synchronous decoding method was designed to reduce position uncertainty of the frame synchronization pulse and eliminate the accumulative error of time base drift, which determines the first instruction pulse position according to position of the frame synchronization pulse and decodes subsequent instruction pulse by taking current instruction pulse as new bit synchronization pulse. Special tool software was developed to tune algorithm parameters, which has a decoding success rate of about 95% for the universal coded signals. For the special coded signals with check byte, decoding success rate using the automatic threshold adjustment algorithm is as high as 99%.

A New Matlab De-noising Algorithm for Signal Extraction

The goal of a de-noising algorithm is to reconstruct a signal from its noise-corrupted observations. Perfect reconstruction is seldom possible and performance is measured under a given fidelity criterion. In a recent work, the authors addressed a new Matlab algorithm for de-noising. A key method of the algorithm is selecting an optimal basis from a library of wavelet bases for ideal de-noising. The algorithm with an optimal basis from a library of wavelet bases for de-noising was created through making use of Matlab＇s Wavelet Toolbox. The experimental results show that the new algorithm is efficient in signal de-nosing.

A De-Noising Method for Track State Detection Signal Based on EMD

In the track irregularity detection, the acceleration signals of the inertial measurement unit (IMU) output which with low frequency components and noise, this paper studied a de-noising algorithm. Based on the criterion of consecutive mean square error, a de-noising method for IMU acceleration signals based on empirical mode decomposition (EMD) was proposed. This method can divide the intrinsic mode functions (IMFs) derived from EMD into signal dominant modes and noise dominant modes, then the modes reflecting the important structures of a signal were combined together to form partially reconstructed de-noised signal. Simulations were conducted for simulated signals and a real IMU acceleration signals using this method. Experimental results indicate that this method can efficiently and adaptively remove noise, and this method can better meet the precision requirement.

Timosaponin AⅢ induces drug-metabolizing enzymes by activating constitutive androstane receptor (CAR) via dephosphorylation of the EGFR signaling pathway

The current study aimed to assess the effect of timosaponin AⅢ(T-AⅢ)on drug-metabolizing enzymes during anticancer therapy.The in vivo experiments were conducted on nude and ICR mice.Following a 24-day administration of T-AⅢ,the nude mice exhibited an induction of CYP2B10,MDR1,and CYP3A11 expression in the liver tissues.In the ICR mice,the expression levels of CYP2B10 and MDR1 increased after a three-day T-AⅢ administration.The in vitro assessments with HepG2 cells revealed that T-AⅢ induced the expression of CYP2B6,MDR1,and CYP3A4,along with constitutive androstane receptor(CAR)activation.Treatment with CAR siRNA reversed the T-AⅢ-induced increases in CYP2B6 and CYP3A4 expression.Furthermore,other CAR target genes also showed a significant increase in the expression.The up-regulation of murine CAR was observed in the liver tissues of both nude and ICR mice.Subsequent findings demonstrated that T-AⅢ activated CAR by inhibiting ERK1/2 phosphorylation,with this effect being partially reversed by the ERK activator t-BHQ.Inhibition of the ERK1/2 signaling pathway was also observed in vivo.Additionally,T-AⅢ inhibited the phosphorylation of EGFR at Tyr1173 and Tyr845,and suppressed EGF-induced phosphorylation of EGFR,ERK,and CAR.In the nude mice,T-AⅢ also inhibited EGFR phosphorylation.These results collectively indicate that T-AⅢ is a novel CAR activator through inhibition of the EGFR pathway.

Digital signal acquisition system for complex nuclear reaction experiments

A digital data-acquisition system based on XIA LLC products was used in a complex nuclear reaction experiment using radioactive ion beams.A flexible trigger system based on a field-programmable gate array(FPGA)parametrization was developed to adapt to different experimental sizes.A user-friendly interface was implemented,which allows converting script language expressions into FPGA internal control parameters.The proposed digital system can be combined with a conventional analog data acquisition system to provide more flexibility.The performance of the combined system was veri-fied using experimental data.

A De-Noising Method for Track State Detection Signal Based on the Statistical Characteristic of Noise

Based on the statistical characteristics analysis of random noise power and autocorrelation function, this paper proposes a de-noising method for track state detection signal by using Empirical Mode Decomposition (EMD). This method is used to noise reduction refactoring for the first Intrinsic Mode Function (IMF) component in accordance with the “random sort-accumulation-average-refactoring' order. Signal autocorrelation function characteristics are used to determine the cut-off point of the dominant mode. This method was applied to test signals and the actual inertial unit signals;the experimental results show that the method can effectively remove the noise and better meet the precision requirement.

PHD17 acts as a target of miR1320 to negatively control cold tolerance via JA-activated signaling in rice

Plant Homeo Domain(PHD)proteins are involved in diverse biological processes during plant growth.However,the regulation of PHD genes on rice cold stress response remains largely unknown.Here,we reported that PHD17 negatively regulated cold tolerance in rice seedlings as a cleavage target of miR1320.PHD17 expression was greatly induced by cold stress,and was down-regulated by miR1320 overexpression and up-regulated by miR1320 knockdown.Through 5'RACE and dual luciferase assays,we found that miR1320 targeted and cleaved the 3'UTR region of PHD17.PHD17 was a nuclearlocalized protein and acted as a transcriptional activator in yeast.PHD17 overexpression reduced cold tolerance of rice seedlings,while knockout of PHD17 increased cold tolerance,partially via the CBF cold signaling.By combining transcriptomic and physiological analyses,we demonstrated that PHD17 modulated ROS homeostasis and flavonoid accumulation under cold stress.K-means clustering analysis revealed that differentially expressed genes in PHD17 transgenic lines were significantly enriched in the jasmonic acid(JA)biosynthesis pathway,and expression of JA biosynthesis and signaling genes was verified to be affected by PHD17.Cold stress tests applied with MeJA or IBU(JA synthesis inhibitor)further suggested the involvement of PHD17 in JA-mediated cold signaling.Taken together,our results suggest that PHD17 acts downstream of miR1320 and negatively regulates cold tolerance of rice seedlings through JA-mediated signaling pathway.

Calmodulins and calmodulin-like proteins-mediated plant organellar calcium signaling networks under abiotic stress

Plant calmodulins(CaMs)and calmodulin-like proteins(CMLs)mediate Ca~(2+)signaling in response to abiotic stresses.Manipulation of this signaling in crops could increase stress tolerance.We review methods for detecting Ca~(2+)signals,regulatory roles of Ca Ms and CMLs,binding targets,and Ca~(2+)networks under abiotic stress in organelles.