Structural, static, and dynamic functional MRI predictors for conversion from mild cognitive impairment to Alzheimer's disease: Inter-cohort validation of Shanghai Memory Study and ADNI.

Mild cognitive impairment (MCI) is a critical prodromal stage of Alzheimer's disease (AD), and the mechanism underlying the conversion is not fully explored. Construction and inter-cohort validation of imaging biomarkers for predicting MCI conversion is of great challenge at present, due to lack of longitudinal cohorts and poor reproducibility of various study-specific imaging indices. We proposed a novel framework for inter-cohort MCI conversion prediction, involving comparison of structural, static, and dynamic functional brain features from structural magnetic resonance imaging (sMRI) and resting-state functional MRI (fMRI) between MCI converters (MCI_C) and non-converters (MCI_NC), and support vector machine for construction of prediction models. A total of 218 MCI patients with 3-year follow-up outcome were selected from two independent cohorts: Shanghai Memory Study cohort for internal cross-validation, and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort for external validation. In comparison with MCI_NC, MCI_C were mainly characterized by atrophy, regional hyperactivity and inter-network hypo-connectivity, and dynamic alterations characterized by regional and connectional instability, involving medial temporal lobe (MTL), posterior parietal cortex (PPC), and occipital cortex. All imaging-based prediction models achieved an area under the curve (AUC) > 0.7 in both cohorts, with the multi-modality MRI models as the best with excellent performances of AUC > 0.85. Notably, the combination of static and dynamic fMRI resulted in overall better performance as relative to static or dynamic fMRI solely, supporting the contribution of dynamic features. This inter-cohort validation study provides a new insight into the mechanisms of MCI conversion involving brain dynamics, and paves a way for clinical use of structural and functional MRI biomarkers in future.

Epilepsy and driving: A preliminary survey of people with epilepsy at an epilepsy clinic in China.

BACKGROUND: Driving is an important part of the daily life for most adults, and restrictions on driving can significantly affect the quality of life for people with epilepsy. This study aimed to investigate the current driving status of patients at an epilepsy clinic in China. METHOD: Study participants were administered a survey by a questionnaire including the demographic and clinical characteristics of seizure, driving-related questions and attitudes to driving. RESULTS: A total of 101 patients responded the survey. Among 33(32.7%) who hold the driving license, 20 (60.6%) still drive, 3 had seizures while driving, and the rate of traffic accidents was 0. There was no significant difference in seizure frequency and type of medication between patients with and without the driving license, but compliance with medication was significantly better for those who held the driving license. CONCLUSIONS: One-third of people with epilepsy hold the driving license and good drug compliance is a favorable factor for driving. Standardizing different levels of restriction on driving for people with epilepsy is urgently needed.

Disturbance Observation and Suppression in an Airborne Electro-Optical Stabilized Platform Based on a Generalized High-Order Extended State Observer.

Active disturbance rejection control (ADRC) is widely used in airborne optoelectronic stabilization platforms due to its minimal reliance on the mathematical model of the controlled object. The extended state observer (ESO) is the core of ADRC, which treats internal parameter variations and external disturbances as total disturbances, observes the disturbances as extended states, and then compensates them into the control loop to eliminate their effects. However, the ESO can only achieve a precise estimation of constant or slowly varying disturbances. When the disturbance is periodically changing, satisfactory results cannot be obtained. In this paper, a generalized high-order extended state observer (GHOESO) is proposed to achieve the precise estimation of known frequency sinusoidal disturbance signals and improve disturbance suppression levels. Through numerical simulations, a traditional ESO and GHOESO are compared in terms of disturbance observation capability and disturbance suppression ability for single and compound disturbances based on our prior knowledge of disturbance frequency. The effectiveness of the proposed GHOESO method is verified. Finally, the algorithm is applied to an airborne optoelectronic stabilization platform for a 1°/1 Hz swing experiment on a space hexapod swing table. The experimental results demonstrate the superiority of the GHOESO proposed in this paper.

Mouse Abdominal Aortic Aneurysm Model Induced by Periarterial Incubation of Papain.

For decades, numerous experimental animal models have been developed to examine the pathophysiologic mechanisms and potential treatments for abdominal aortic aneurysms (AAAs) in diverse species with varying chemical or surgical approaches. This study aimed to create an AAA mouse model by the periarterial incubation with papain, which can mimic human AAA with advantages such as simplicity, convenience, and high efficiency. Eighty C57BL/6J male mice were randomly assigned to 1 of the 4 groups: papain (1.0 or 2.0 mg), porcine pancreatic elastase, and phosphate-buffered solution. The aortic segment was wrapped for 20 minutes, and the diameter was measured using ultrasound preoperatively and postoperative days 7 and 14. Then, the mice were killed for histomorphometric and immunohistochemical analyses. According to ultrasound measurements and histomorphometric analyses, on postoperative day 7, 65% of mice in the 1.0-mg papain group and 60% of mice in the 2.0-mg papain group developed AAA. In both papain groups, 100% of mice developed AAA, and 65% of mice in the porcine pancreatic elastase group developed AAA on postoperative day 14. Furthermore, hematoxylin/eosin, elastin van Gieson, and Masson staining of tissues from the papain group revealed thickened media and intimal hyperplasia, collagen sediments, and elastin destruction, indicating that AAA histochemical alteration was similar to that of humans. In addition, the immunohistochemical analysis was conducted to detect infiltrated inflammatory cells, such as macrophages and leukocytes, in the aortic wall and hyperplasic adventitia. The expression of matrix metalloproteinase 2 and 9 was significantly upregulated in papain and human AAA tissues. Periarterial incubation with 1.0 mg of papain for 20 minutes can successfully create an experimental AAA model in mice for 14 days, which can be used to explore the mechanism and treatment of human AAA.

High-Precision Control of Aviation Photoelectric-Stabilized Platform Using Extended State Observer-Based Kalman Filter.

The accuracy of the line-of-sight of aviation photoelectric optoelectronic stabilization platforms is limited by two factors: external disturbance and sensor noise. An extended state observer (ESO) can effectively improve their anti-interference ability. However, due to the serious problem of gyroscope noise, further improvement of an ESO's disturbance suppression effect is limited. This article proposes a control structure that combines a Kalman filter (KF) and ESO, effectively improving upon the interference suppression ability of a traditional ESO under the influence of noise. Firstly, an ESO was used to observe the lumped disturbance of the system, and then, the observed disturbance was compensated for in the control loop. Secondly, based on the compensation servo control system, the state equation of the system was reconstructed using a Kalman filter. Finally, the reconstructed filtered state variables were iterated onto the universal state observer, achieving the observation of disturbances while filtering out sensor noise. Under the conditions of a laboratory flight simulation turntable, the line-of-sight stability accuracy level was improved under disturbance excitation. It can be seen that the combination of a Kalman filter and extended disturbance observer proposed in this project improves the ESO's anti-interference ability under the influence of noise.

Estrogen-induced downregulation of TASK-1 expression through estrogen receptor ß in N2A cells.

BACKGROUND: Our previous data revealed that reduction of TASK-1 expression, as a consequence of exposure to 17ß-estradiol, could participate in neuroprotective effects in N2A cells. However, it is unclear which estrogen receptor underlies these effects of 17ß-estradiol. METHODS AND RESULTS: In this study, the knockdown experiments are carried out to clarify the estrogen receptor responsible for effects of estrogen on TASK-1 channels. Subsequently, data from QPCR measurements reveal that estrogen receptor ß (ERß), but not estrogen receptor α, serves as a binding target for 17ß-estradiol after a 48-h treatment. CONCLUSIONS: The current result suggests the implication of the ERß-dependent manner in the pro-proliferative action of estrogen via TASK-1 channels.

Early Renal-Replacement Therapy May Reduce the All-Cause Mortality of Severe COVID-19: An Observational Cohort Study.

INTRODUCTION: The efficacy of renal-replacement treatment (RRT) remains to be validated in COVID-19. In this retrospective cohort study, we aimed to assess the efficacy of early initiation of RRT in intensive care unit (ICU) adults with severe COVID-19. METHODS: Fifty-eight adult patients in ICU with critically ill or severe COVID-19 with a tendency of critical illness were recruited from February 9, 2020, to March 30, 2020. Early RRT were determined by the ICU medical team based on boom in cytokines levels, increased organs injury/failure, and rapid aggravation of condition. All participants were followed up from the first day of ICU admission to March 30, 2020. The primary outcome was all-cause mortality in ICU. RESULTS: The mean age of the cohort was 68.4 ± 14.6 years, with 81.0% having at least one comorbidity before hospitalization. Twenty patients (34.5%) initiated early RRT after 24.1 ± 10.4 days from the onset and 6.4 ± 3.6 days from ICU admission. Thirty-four of 58 participants (58.6%) died during ICU follow-up. Univariate and multivariate Cox proportional-hazards model showed that early RRT was associated with a lower risk of all-cause mortality in ICU with an adjusted HR of 0.280 (95% CI: 0.106-0.738, p = 0.010). Sudden unexpected death (SUD) was remarkably reduced in the early RRT group, compared with the control group (0.2 vs. 2.9 per 100 person-day, p = 0.02). CONCLUSION: Early RRT can reduce the all-cause in-hospital mortality, especially SUD in patients with severe COVID-19, but not improve multi-organ impairment or increase the risk of AKI. Early initiation of RRT merits an optional strategy in critically ill patients with COVID-19 (ChiCTR2000030773).

Alzheimer's disease therapy based on acetylcholinesterase inhibitor/blocker effects on voltage-gated potassium channels.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder with progressive loss of memory and other cognitive functions. The pathogenesis of this disease is complex and multifactorial, and remains obscure until now. To enhance the declined level of acetylcholine (ACh) resulting from loss of cholinergic neurons, acetylcholinesterase (AChE) inhibitors are developed and successfully approved for AD treatment in the clinic, with a limited therapeutic effectiveness. At present, it is generally accepted that multi-target strategy is potently useful for designing novel drugs for AD. Accumulated evidence reveals that Kv channels, which are broadly expressed in brain and possess crucial functions in modulating the neuronal activity, are inhibited by several acetylcholinesterase (AChE) inhibitors, such as tacrine, bis(7)-tacrine, donepezil and galantamine. Inhibition of Kv channels by these AChE inhibitors can generate neuroprotective effects by either mitigating Aß toxicity and neuronal apoptosis, or facilitating cell proliferation. These inhibitory effects provide additional explanations for clinical beneficial effectiveness of AChE inhibitors, meaning that Kv channel is a promising candidate target for novel drugs for AD therapy.

Stability preserving data-driven models with latent dynamics.

In this paper, we introduce a data-driven modeling approach for dynamics problems with latent variables. The state-space of the proposed model includes artificial latent variables, in addition to observed variables that can be fitted to a given data set. We present a model framework where the stability of the coupled dynamics can be easily enforced. The model is implemented by recurrent cells and trained using backpropagation through time. Numerical examples using benchmark tests from order reduction problems demonstrate the stability of the model and the efficiency of the recurrent cell implementation. As applications, two fluid-structure interaction problems are considered to illustrate the accuracy and predictive capability of the model.

Resonance Suppression of Servo System Based on State Equalizer Method.

Aiming at the problem of mechanical resonance faced by the servo control system of the aero-optical stabilization platform, based on the proportional integral and disturbance observer combination (PI+DOB) control algorithm, a state equalizer speed closed loop is proposed. Compared with the traditional PI+DOB control algorithm, this new control structure can suppress the resonance peak and the anti-resonance peak at the same time. The experimental results show that compared with the PI+DOB control algorithm, after adding the state equalizer speed closed-loop to compensate for the model, the closed-loop bandwidth is increased by 42%. The anti-disturbance capability of the control system has been significantly improved, and it has good robustness under vibration conditions. To sum up, adding the state equalizer speed closed loop on the basis of PI+DOB has an obvious effect on the suppression of mechanical resonance and the performance improvement of the control system.

Iterative Learning-Based Negative Effect Compensation Control of Disturbance to Improve the Disturbance Isolation of System.

At present, the cogging torque of permanent magnet synchronous motors (PMSM) seriously limits the Los pointing accuracy of aviation photoelectric stabilization platforms based on PMSM, which also restricts the requirements of ultra-long-distance and high-precision aviation reconnaissance and detection. For this problem, an off-line iterative learning control (ILC) was designed, and on this basis, a control method of negative effect compensation of disturbance (NECOD) is proposed. Firstly, the "dominant disturbance torque" in the system, that is, the cogging torque with the characteristics of position periodicity, was suppressed by off-line ILC according to different positions. Then, for the "residual disturbance" after compensation, NECOD was used to suppress it. In the constant speed scanning experiment of the aviation photoelectric stabilization platform, the method of combining the off-line iterative learning controller and the negative effect compensation of disturbance (NECOD + ILC) proposed in this paper significantly improved the Los control accuracy of the platform when compared with the classical active disturbance rejection control (ADRC) and ADRC + ILC methods, and the Los pointing error of the constant speed scanning process had only increased by less than 5% when the system had ±15% parameter perturbation. In addition, NECOD + ILC has fewer parameters and is easy to adjust, which is conducive to engineering application and promotion.

Petrov-Galerkin methods for the construction of non-Markovian dynamics preserving nonlocal statistics.

A common observation in coarse-graining a molecular system is the non-Markovian behavior, primarily due to the lack of scale separations. This is reflected in the strong memory effect and the non-white noise spectrum, which must be incorporated into a coarse-grained description to correctly predict dynamic properties. To construct a stochastic model that gives rise to the correct non-Markovian dynamics, we propose a Galerkin projection approach, which transforms the exhausting effort of finding an appropriate model to choosing appropriate subspaces in terms of the derivatives of the coarse-grained variables and, at the same time, provides an accurate approximation to the generalized Langevin equation. We introduce the notion of fractional statistics that embodies nonlocal properties. More importantly, we show how to pick subspaces in the Galerkin projection so that those statistics are automatically matched.

A projection-based reduced-order method for electron transport problems with long-range interactions.

Long-range interactions play a central role in electron transport. At the same time, they present a challenge for direct computer simulations since sufficiently large portions of the bath have to be included in the computation to accurately compute the Coulomb potential. This article presents a reduced-order approach by deriving an open quantum model for the reduced density matrix. To treat the transient dynamics, the problem is placed in a reduced-order framework. The dynamics described by the Liouville-von Neumann equation is projected to subspaces using a Petrov-Galerkin projection. In order to recover the global electron density profile as a vehicle to compute the Coulomb potential, we propose a domain decomposition approach, where the computational domain also includes segments of the bath that are selected using logarithmic grids. This approach leads to a multi-component self-energy that enters the effective Hamiltonian. We demonstrate the accuracy of the reduced model using a molecular junction built from lithium chains.

Tacrine modulates Kv2.1 channel gene expression and cell proliferation.

Purpose/Aim: Besides as a cholinesterase (ChE) inhibitor, tacrine is able to act on multiple targets such as nicotinic receptors (nAChRs) and voltage-gated K+ (Kv) channels. Kv2.1, a Kv channel subunit underlying delayed rectifier currents with slow kinetics of inactivation, is highly expressed in the mammalian brain, especially in the hippocampus. Nevertheless, limited data are available concerning the relationship between tacrine and Kv2.1 channels. In the present study, we explore the possible effects of tacrine on Kv2.1 channels in heterologous expression systems and N2A cells.Materials and methods: The change of expression and currents of Kv2.1 after treatment with tacrine was detected by PCR and whole-cell recordings, respectively. WST-8 experiments were performed to reveal the effects of tacrine on cell proliferation.Results: Incubation with tacrine induced a significant reduction of the mRNA level of Kv2.1 channels in HEK293 cells. The decline of corresponding currents carried by Kv2.1 was also observed. Moreover, the proliferation rates of HEK293 cells with Kv2.1 channel were substantially enhanced after treatment with this chemical for 24 h. Similar results were also detected after exposure to tacrine in N2A cells with native expression of Kv2.1 channels.Conclusion: These lines of evidence indicate that application of tacrine downregulates the expression of Kv2.1 channels and increase cell proliferation. The effect of tacrine on Kv2.1 channels may provide an alternative explanation for its neuroprotective action.

High Precision Low-Speed Control for Permanent Magnet Synchronous Motor.

Due to the process defects and imperfection of drivers, permanent magnet synchronous motors (PMSM) are problematic to control. There is still a lack of effective high-performance control methods for inertial stabilized platforms based on PMSM currently. At present, the most frequently used method is sliding mode control (SMC), but traditional sliding mode control cannot overcome the contradiction between high performance and system chattering. In order to solve this problem and improve the system reliability and pointing accuracy, a new approach law for the sliding mode controller is proposed in this paper. In view of the large periodic torque ripple in PMSM, an iterative learning controller (ILC) is introduced to compensate for the disturbance. Based on these, aimed at suppressing all kinds of real-time disturbances in the working environment of the system, the extended state observer (ESO) is brought into the servo system to observe the lumped disturbance of the system, and the total disturbance observed is compensated into the sliding mode controller, so as to better suppress the system chattering and enhance the system's ability of resisting external disturbance. Experiments are carried out on an inertial stabilization platform based on DSP + CPLD. The final experiments verify that the SMC with the new approach, combined with ILC and ESO, is of outstanding performance when compared with the traditional proportional integral (PI) + disturbance observer (DOB) control scheme.

Network pharmacology and molecular docking analysis on molecular targets and mechanisms of Huashi Baidu formula in the treatment of COVID-19.

PURPOSE: Huashi Baidu formula (HSBDF) was developed to treat the patients with severe COVID-19 in China. The purpose of this study was to explore its active compounds and demonstrate its mechanisms against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) through network pharmacology and molecular docking. METHODS: All the components of HSBDF were retrieved from the pharmacology database of TCM system. The genes corresponding to the targets were retrieved using UniProt and GeneCards database. The herb-compound-target network was constructed by Cytoscape. The target protein-protein interaction network was built using STRING database. The core targets of HSBDF were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The main active compounds of HSBDF were docked with SARS-CoV-2 and angiotensin converting enzyme II (ACE2). RESULTS: Compound-target network mainly contained 178 compounds and 272 corresponding targets. Key targets contained MAPK3, MAPK8, TP53, CASP3, IL6, TNF, MAPK1, CCL2, PTGS2, etc. There were 522 GO items in GO enrichment analysis (p < .05) and 168 signaling pathways (p < .05) in KEGG, mainly including TNF signaling pathway, PI3K-Akt signaling pathway, NOD-like receptor signaling pathway, MAPK signaling pathway, and HIF-1 signaling pathway. The results of molecular docking showed that baicalein and quercetin were the top two compounds of HSBDF, which had high affinity with ACE2. CONCLUSION: Baicalein and quercetin in HSBDF may regulate multiple signaling pathways through ACE2, which might play a therapeutic role on COVID-19.

Absorbing boundary conditions for time-dependent Schrödinger equations: A density-matrix formulation.

This paper presents some absorbing boundary conditions for simulations based on the time-dependent Schrödinger equations. The boundary conditions are expressed in terms of the elements of the density-matrix, and it is derived from the full model over a much larger domain. To make the implementation much more efficient, several approximations for the convolution integral will be constructed with guaranteed stability. These approximations lead to modified density-matrix equations at the boundary. The effectiveness is examined via numerical tests.

Alisol A 24-Acetate Isolated from the Alismatis Rhizoma Improves Hepatic Lipid Deposition in Hyperlipidemic Mice by ABCA1/ABCG1 Pathway.

The Alisol A 24-acetate is an effective component of the Alismatis Rhizoma (AR) extract, which is often used in the treatment of hyperlipidemia. This study explored the effect and mechanism of the Alisol A 24-acetate from AR on lipid deposition in the liver of hyperlipidemic mice. After establishing hyperlipidemic mouse model (Model) by oral high-fat diet (HFD), the animals were treated with Alisol A 24-acetate for 4 weeks. The changes of blood lipid in mice were detected by ELISA. Hematoxylin and eosin (H&E) staining was used to evaluate the degree of liver lipid deposition in hyperlipidemic mice. Quantitative reverse transcriptase PCR (RT-qPCR) was used to detect the expression of ABCG1 and ABCA1 mRNA in the liver. The expression of ABCG1 and ABCA1 protein was detected by Western blotting (WB). After 4 weeks of high-fat diet, the levels of TC, TG, and LDL-C in the mouse were increased, and the HDL-C level was decreased. After treatment with Alisol A 24-acetate, the levels of TC, TG, and LDL-C in the blood of hyperlipidemic mouse were significantly reduced, and the level of HDL-C was increased. The results of H&E staining showed that the lipid deposition in the liver of hyperlipidemic mouse was improved after treatment with Alisol A 24-acetate. RT-qPCR and WB analysis documented that ABCG1 and ABCA1 mRNA and protein expression in hyperlipidemic mice were promoted after the Alisol A 24-acetate treatment. In conclusion, Alisol A 24-acetate effectively alleviates the liver lipid deposition in hyperlipidemic mice, and this effect is achieved mostly by promoting the expression of ABCG1 and ABCA1 at the mRNA and protein levels.

Data-driven parameterization of the generalized Langevin equation.

We present a data-driven approach to determine the memory kernel and random noise in generalized Langevin equations. To facilitate practical implementations, we parameterize the kernel function in the Laplace domain by a rational function, with coefficients directly linked to the equilibrium statistics of the coarse-grain variables. We show that such an approximation can be constructed to arbitrarily high order and the resulting generalized Langevin dynamics can be embedded in an extended stochastic model without explicit memory. We demonstrate how to introduce the stochastic noise so that the second fluctuation-dissipation theorem is exactly satisfied. Results from several numerical tests are presented to demonstrate the effectiveness of the proposed method.

A parameter estimation method using linear response statistics: Numerical scheme.

This paper presents a numerical method to implement the parameter estimation method using response statistics that was recently formulated by the authors. The proposed approach formulates the parameter estimation problem of Itô drift diffusions as a nonlinear least-squares problem. To avoid solving the model repeatedly when using an iterative scheme in solving the resulting least-squares problems, a polynomial surrogate model is employed on appropriate response statistics with smooth dependence on the parameters. The existence of minimizers of the approximate polynomial least-squares problems that converge to the solution of the true least square problem is established under appropriate regularity assumption of the essential statistics as functions of parameters. Numerical implementation of the proposed method is conducted on two prototypical examples that belong to classes of models with a wide range of applications, including the Langevin dynamics and the stochastically forced gradient flows. Several important practical issues, such as the selection of the appropriate response operator to ensure the identifiability of the parameters and the reduction of the parameter space, are discussed. From the numerical experiments, it is found that the proposed approach is superior compared to the conventional approach that uses equilibrium statistics to determine the parameters.