Selectfluor Mediated Direct C-H Fluorination of 3-Heteroaryl-Oxindoles.

An efficient transition-metal-free fluorination synthesis of N-H-free 3-heteroaryl-oxindoles with Selectfluor was depicted. Under mild reaction conditions, a series of 3-heteroaryl-fluorooxindoles were produced in yield of 62-88% using Selectfluor as a fluorine source.

K2S2O8-mediated direct C-H heteroarylation/hydroxylation of indolin-2-ones with quinoxalin-2(1H)-ones.

Herein, a K2S2O8-mediated direct heteroarylation and hydroxylation reaction between quinoxalin-2(1H)-ones with a C(sp2)-H bond and indolin-2-ones with a C(sp3)-H bond via an oxidative cross-coupling reaction has been reported. We have successfully established a feasible and concise reaction system that represents the first example of free-radical-promoted heteroarylation and hydroxylation reaction on the C-3 position of oxindole. A series of 3-substituted 3-hydroxyoxindoles are obtained in 0-83% yield using this methodology.

Apolipoprotein E E3/E4 genotype is associated with an increased risk of type 2 diabetes mellitus complicated with coronary artery disease.

OBJECTIVE: Dyslipidemia is a co-existing problem in patients with diabetes mellitus (DM) and coronary artery disease (CAD), and apolipoprotein E (APOE) plays an important role in lipid metabolism. However, the relationship between the APOE gene polymorphisms and the risk of developing CAD in type 2 DM (T2DM) patients remains controversial. The aim of this study was to assess this relationship and provide a reference for further risk assessment of CAD in T2DM patients. METHODS: The study included 378 patients with T2DM complicated with CAD (T2DM + CAD) and 431 patients with T2DM alone in the case group, and 351 individuals without DM and CAD were set as controls. The APOE rs429358 and rs7412 polymorphisms were genotyped by polymerase chain reaction (PCR) - microarray. Differences in APOE genotypes and alleles between patients and controls were compared. Multiple logistic regression analysis was performed after adjusting for age, gender, body mass index (BMI), history of smoking, and history of drinking to access the relationship between APOE genotypes and T2DM + CAD risk. RESULTS: The frequencies of the APOE ɛ3/ɛ4 genotype and ε4 allele were higher in the T2DM + CAD patients, and the frequencies of the APOE ɛ3/ɛ3 genotype and ε3 allele were lower than those in the controls (all p < 0.05). The T2DM + CAD patients with ɛ4 allele had higher level in low-density lipoprotein cholesterol (LDL-C) than those in patients with ɛ2 and ɛ3 allele (p < 0.05). The results of logistic regression analysis showed that age ≥ 60 years old, and BMI ≥ 24.0 kg/m2 were independent risk factors for T2DM and T2DM + CAD, and APOE ɛ3/ɛ4 genotype (adjusted odds ratio (OR) = 1.93, 95% confidence interval (CI) = 1.18-3.14, p = 0.008) and ɛ4 allele (adjusted OR = 1.97, 95% CI = 1.23-3.17) were independent risk factors for T2DM + CAD. However, the APOE genotypes and alleles were not found to have relationship with the risk of T2DM. CONCLUSIONS: APOE ε3/ε4 genotype and ε4 allele were independent risk factors for T2DM complicated with CAD, but not for T2DM.

A Localization and Tracking Approach in NLOS Environment Based on Distance and Angle Probability Model.

In this paper, an optimization algorithm is presented based on a distance and angle probability model for indoor non-line-of-sight (NLOS) environments. By utilizing the sampling information, a distance and angle probability model is proposed so as to identify the NLOS propagation. Based on the established model, the maximum likelihood estimation (MLE) method is employed to reduce the error of distance in the NLOS propagation. In order to reduce the computational complexity, a modified Monte Carlo method is applied to search the optimal position of the target. Moreover, the extended Kalman filtering (EKF) algorithm is introduced to achieve localization. The simulation and experimental results show the effectiveness of the proposed algorithm in the improvement of localization accuracy.

Adjunct Methods for Alzheimer's Disease Detection: A Review of Auditory Evoked Potentials.

The auditory afferent pathway as a clinical marker of Alzheimer's disease (AD) has sparked interest in investigating the relationship between age-related hearing loss (ARHL) and AD. Given the earlier onset of ARHL compared to cognitive impairment caused by AD, there is a growing emphasis on early diagnosis and intervention to postpone or prevent the progression from ARHL to AD. In this context, auditory evoked potentials (AEPs) have emerged as a widely used objective auditory electrophysiological technique for both the clinical diagnosis and animal experimentation in ARHL due to their non-invasive and repeatable nature. This review focuses on the application of AEPs in AD detection and the auditory nerve system corresponding to different latencies of AEPs. Our objective was to establish AEPs as a systematic and non-invasive adjunct method for enhancing the diagnostic accuracy of AD. The success of AEPs in the early detection and prediction of AD in research settings underscores the need for further clinical application and study.

Feasibility of rib fracture detection in low-dose computed tomography images with a large, multicenter datasets-based model.

Purpose: To evaluate the feasibility of rib fracture detection in low-dose computed tomography (CT) images with a RetinaNet-based approach and to evaluate the potential of lowdose CT for rib fracture detection compared with regular-dose CT images. Materials and methods: The RetinaNet-based deep learning model was trained using 7300 scans with 50,410 rib fractures that were used as internal training datasets from four multicenter. The external test datasets consisted of both regular-dose and low-dose chest-abdomen CT images of rib fractures; the MICCAI 2020 RibFrac Challenge Dataset was used as the public dataset. Radiologists' interpretations were used as reference standards. The performance of the model in rib fracture detection was compared with the radiologists' interpretation. Results: In total, 728 traumatic rib fractures of 100 patients [60 men (60 %); mean age, 53.45 ± 11.19 (standard deviation (SD)); range, 18-77 years] were assessed in the external datasets. In these patients, the regular-dose group had a mean CT dose index volume (CTDIvol) of 7.18 mGy (SD: 2.22) and a mean dose length product (DLP) of 305.38 mGy cm (SD: 95.31); the low-dose group had a mean CTDIvol of 2.79 mGy (SD: 1.11) and a mean DLP of 131.52 mGy cm (SD: 55.58). The sensitivity of the RetinaNet-based model and that of the radiologists was 0.859 and 0.721 in the low-dose CT images and 0.886 and 0.794 in the regular-dose CT images, respectively. Conclusions: These findings indicate that the RetinaNet-based model can detect rib fractures in low-dose CT images with a robust performance, indicating its feasibility in assisting radiologists with rib fracture diagnosis.

Lactobacillus plantarum AR495 improves colonic transport hyperactivity in irritable bowel syndrome through tryptophan metabolism.

Lactobacillus plantarum AR495 is a widely used probiotic for the treatment of various digestive diseases, including irritable bowel syndrome (IBS). However, the specific mechanisms of L. plantarum AR495 in alleviating IBS remain unclear. Abnormal intestinal tryptophan metabolism can cause disordered immune responses, gastrointestinal peristalsis, digestion and sensation, which is closely related to IBS pathogenesis. The aim of this study is to explore the effects and mechanisms of L. plantarum AR495 in regulating tryptophan metabolism. Primarily, tryptophan and its related metabolites in patients with IBS and healthy people were analyzed, and an IBS rat model of acetic acid enema plus restraint stress was established to explore the alleviation pathway of L. plantarum AR495 in tryptophan metabolism. It was found that the 5-HT pathway was significantly changed, and the 5-HTP and 5-HT metabolites were significantly increased in the feces of patients with IBS, which were consistent with the results obtained for the IBS rat model. Maladjusted 5-HT could increase intestinal peristalsis and lead to an increase in the fecal water content and shapeless stool in rats. On the contrary, these two metabolites could be restored to normal levels via intragastric administration of L. plantarum AR495. Further study of the metabolic pathway showed that L. plantarum AR495 could effectively reduce the abundance of 5-HT by inhibiting the expression of enterochromaffin cells rather than promoting its decomposition. In addition, the results showed that L. plantarum AR495 did not affect the expression of SERT. To sum up, L. plantarum AR495 could restore the normal levels of 5-HT by inhibiting the abnormal proliferation of enterochromaffin cells and the excessive activation of TPH1 to inhibit the intestinal peristalsis in IBS. These findings provide insights for the use of probiotics in the treatment of IBS and other diarrheal diseases.

Neural-Network-Based Set-Membership Fault Estimation for 2-D Systems Under Encoding-Decoding Mechanism.

In this article, the simultaneous state and fault estimation problem is investigated for a class of nonlinear 2-D shift-varying systems, where the sensors and the estimator are connected via a communication network of limited bandwidth. With the purpose of relieving the communication burden and enhancing the transmission security, a new encoding-decoding mechanism is put forward so as to encode the transmitted data with a finite number of bits. The aim of the addressed problem is to develop a neural-network (NN)-based set-membership estimator for jointly estimating the system states and the faults, where the estimation errors are guaranteed to reside within an optimized ellipsoidal set. With the aid of the mathematical induction technique and certain convex optimization approaches, sufficient conditions are derived for the existence of the desired set-membership estimator, and the estimator gains and the NN tuning scalars are then presented in terms of the solutions to a set of optimization problems subject to ellipsoidal constraints. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed estimator design method.

Distributed Set-Membership Fusion Filtering for Nonlinear 2-D Systems Over Sensor Networks: An Encoding-Decoding Scheme.

In this article, the distributed set-membership fusion filtering problem is investigated for a class of nonlinear 2-D shift-varying systems subject to unknown-but-bounded noises over sensor networks. The sensors are communicated with their neighbors according to a given topology through wireless networks of limited bandwidth. With the purpose of relieving the communication burden as well as enhancing the transmission security, a logarithmic-type encoding-decoding mechanism is introduced for each sensor node so as to encode the transmitted data with a finite number of bits. A distributed set-membership filter is designed to determine the local ellipsoidal set that contains the system state by only utilizing the data from the local sensor node and its neighbors, where the proposed filter scheme is truly distributed with desirable scalability. Then, a new ellipsoid-based fusion rule is developed for the designed set-membership filters in order to form the fused ellipsoidal set that has a globally smaller volume than all local ellipsoidal sets. With the aid of the mathematical induction technique, the set theory, and the convex optimization approach, sufficient conditions are derived for the existence of the desired distributed set-membership filters and the fusion weights. Then, the filter parameters and the fusion weights are acquired by solving a set of constrained optimization problems. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed fusion filtering algorithm.

Proportional-Integral Observer Design for Multirate-Networked Systems Under Constrained Bit Rate: An Encoding-Decoding Mechanism.

In this article, the proportional-integral observer design problem is studied for a class of multirate networked systems subject to constrained bit rate. The sensor sampling period is allowed to be different from the system updating period and, to facilitate the observer design, the underlying multirate system is cast into a general single-rate one by resorting to the lifting technique. In order to curb the communication burden and promote the data security, the encoding-decoding procedure is implemented on the sensor-to-observer channel to convert the measurement signals into binary codewords. A sufficient condition is first proposed to reveal the fundamental relationship between the bit-rate constraints and the decoding accuracy, and then the exponentially ultimate boundedness of the error dynamics is assessed with the aid of the Lyapunov method. Subsequently, the desired observer gains are determined by solving two optimization problems with the aim to achieve two distinct performance indices, namely, the smallest ultimate bound and the fastest decay rate. Finally, the validity of the developed observer design approach is thoroughly demonstrated via the simulation examples.

Adaptive Set-Membership State Estimation for Nonlinear Systems Under Bit Rate Allocation Mechanism: A Neural-Network-Based Approach.

In this article, the adaptive neural-network-based (NN-based) set-membership state estimation problem is studied for a class of nonlinear systems subject to bit rate constraints and unknown-but-bounded noises. The measurement output signals are transmitted from sensors to a remote estimator via a bit rate constrained communication channel. To relieve the communication burden and ameliorate the state estimation accuracy, a bit rate allocation mechanism is put forward for the sensor nodes by solving a constrained optimization problem. Subsequently, through the NN learning method, an NN-based set-membership estimator is designed to determine an ellipsoidal set that contains the system state, where the proposed estimator relies upon a prediction-correction structure. With the help of the mathematical induction technique and the set theory, sufficient conditions are obtained to ensure the existence of both the adaptive tuning parameters and the set-membership estimators, and then, the corresponding parameters and estimator gains are calculated by solving a set of optimization problems. In addition, the monotonicity of the upper bound on the squared estimation error with respect to the bit rate and the convergence of the NN weight are analyzed, respectively. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed state estimation algorithm.

Multifeature Fusion Classification Method for Adaptive Endoscopic Ultrasonography Tumor Image.

Endoscopic ultrasonography (EUS) has been found to be of great advantage in the diagnosis of digestive tract submucosal tumors. However, EUS-based diagnosis is limited by variability in subjective interpretation on the part of doctors. Tumor classification of ultrasound images with the computer-aided diagnosis system can significantly improve the diagnostic efficiency and accuracy of doctors. In this study, we proposed a multifeature fusion classification method for adaptive EUS tumor images. First, for different ultrasound tumor images, we selected the region of interest based on prior information to facilitate the estimation in the subsequent works. Second, we proposed a method based on image gray histogram feature extraction with principal component analysis dimensionality reduction, which learns the gray distribution of different tumor images effectively. Third, we fused the reduced grayscale features with the improved local binary pattern features and gray-level co-occurrence matrix features, and then used the multiclassification support vector machine. Finally, in the experiment, we selected the 431 ultrasound images of 109 patients in the hospital and compared the experimental effects of different features and different classifiers. The results revealed that the proposed method performed best, with the highest accuracy of 96.18% and an area under the curve of 99%. It is evident that the method proposed in this study can efficiently contribute to the classification of EUS tumor images.

Micro SleepNet: efficient deep learning model for mobile terminal real-time sleep staging.

The real-time sleep staging algorithm that can perform inference on mobile devices without burden is a prerequisite for closed-loop sleep modulation. However, current deep learning sleep staging models have poor real-time efficiency and redundant parameters. We propose a lightweight and high-performance sleep staging model named Micro SleepNet, which takes a 30-s electroencephalography (EEG) epoch as input, without relying on contextual signals. The model features a one-dimensional group convolution with a kernel size of 1 × 3 and an Efficient Channel and Spatial Attention (ECSA) module for feature extraction and adaptive recalibration. Moreover, the model efficiently performs feature fusion using dilated convolution module and replaces the conventional fully connected layer with Global Average Pooling (GAP). These design choices significantly reduce the total number of model parameters to 48,226, with only approximately 48.95 Million Floating-point Operations per Second (MFLOPs) computation. The proposed model is conducted subject-independent cross-validation on three publicly available datasets, achieving an overall accuracy of up to 83.3%, and the Cohen Kappa is 0.77. Additionally, we introduce Class Activation Mapping (CAM) to visualize the model's attention to EEG waveforms, which demonstrate the model's ability to accurately capture feature waveforms of EEG at different sleep stages. This provides a strong interpretability foundation for practical applications. Furthermore, the Micro SleepNet model occupies approximately 100 KB of memory on the Android smartphone and takes only 2.8 ms to infer one EEG epoch, meeting the real-time requirements of sleep staging tasks on mobile devices. Consequently, our proposed model has the potential to serve as a foundation for accurate closed-loop sleep modulation.

Fault tolerant consensus control of multi-agent systems under dynamic event-triggered mechanisms.

In this paper, the fault-tolerant consensus control (FTCC) problem is studied for multi-agent systems (MASs) with the dynamic event-triggered mechanism (DETM). To ease the communication burden, DETM governed by an additional internal dynamical variable is introduced. A novel fault-tolerant controller is presented to mitigate system performance degradation caused by failures, where a simple fault compensator is constructed through a protocol-based observer. Then, some sufficient conditions are established to examine the bounded consensus while optimizing the predetermined quadratic cost criterion. In addition, the explicit expression of the desired controller is also parameterized through orthogonal decomposition. Finally, two simulation results are made for the sake of verifying the effectiveness of the developed FTCC scheme, the fault compensation test as well as the quadratic cost one.

H∞ Pinning Control of Complex Dynamical Networks Under Dynamic Quantization Effects: A Coupled Backward Riccati Equation Approach.

In this article, a pinning control strategy is developed for the finite-horizon H∞ synchronization problem for a kind of discrete time-varying nonlinear complex dynamical network in a digital communication circumstance. For the sake of complying with the digitized data exchange, a feedback-type dynamic quantizer is introduced to reflect the transformation from the raw signals into the discrete-valued ones. Then, a quantized pinning control scheme takes place on a small fraction of the network nodes with the hope of cutting down the control expenses while achieving the expected global synchronization objective. Subsequently, by resorting to the completing-the-square technique, a sufficient condition is established to ensure the finite-horizon H∞ index of the synchronization error dynamics against both quantization errors and external noises. Moreover, a controller design algorithm is put forward via an auxiliary H2 -type criterion, and the desired controller gains are acquired in terms of two coupled backward Riccati equations. Finally, the validity of the presented results is verified via a simulation example.

Partial-Neurons-Based Proportional-Integral Observer Design for Artificial Neural Networks: A Multiple Description Encoding Scheme.

This article is concerned with a new partial-neurons-based proportional-integral observer (PIO) design problem for a class of artificial neural networks (ANNs) subject to bounded disturbances. For the purpose of improving the reliability of the data transmission, the multiple description encoding mechanisms are exploited to encode the measurement data into two identically important descriptions, and the encoded data are then transmitted to the decoders via two individual communication channels susceptible to packet dropouts, where Bernoulli-distributed stochastic variables are utilized to characterize the random occurrence of the packet dropouts. An explicit relationship is discovered that quantifies the influences of the packet dropouts on the decoding accuracy, and a sufficient condition is provided to assess the boundedness of the estimation error dynamics. Furthermore, the desired PIO parameters are calculated by solving two optimization problems based on two metrics (i.e., the smallest ultimate bound and the fastest decay rate) characterizing the estimation performance. Finally, the applicability and advantage of the proposed PIO design strategy are verified by means of an illustrative example.

Transcranial Direct Current Stimulation Alleviates Neurovascular Unit Dysfunction in Mice With Preclinical Alzheimer's Disease.

Neurons, glial cells and blood vessels are collectively referred to as the neurovascular unit (NVU). In the Alzheimer's disease (AD) brain, the main components of the NVU undergo pathological changes. Transcranial direct current stimulation (tDCS) can protect neurons, induce changes in glial cells, regulate cerebral blood flow, and exert long-term neuroprotection. However, the mechanism by which tDCS improves NVU function is unclear. In this study, we explored the effect of tDCS on the NVU in mice with preclinical AD and the related mechanisms. 10 sessions of tDCS were given to six-month-old male APP/PS1 mice in the preclinical stage. The model group, sham stimulation group, and control group were made up of APP/PS1 mice and C57 mice of the same age. All mice were histologically evaluated two months after receiving tDCS. Protein content was measured using Western blotting and an enzyme-linked immunosorbent assay (ELISA). The link between glial cells and blood vessels was studied using immunofluorescence staining and lectin staining. The results showed that tDCS affected the metabolism of Aß; the levels of Aß, amyloid precursor protein (APP) and BACE1 were significantly reduced, and the levels of ADAM10 were significantly increased in the frontal cortex and hippocampus in the stimulation group. In the stimulation group, tDCS reduced the protein levels of Iba1 and GFAP and increased the protein levels of NeuN, LRP1 and PDGRFß. This suggests that tDCS can improve NVU function in APP/PS1 mice in the preclinical stage. Increased blood vessel density and blood vessel length, decreased IgG extravasation, and increased the protein levels of occludin and coverage of astrocyte foot processes with blood vessels suggested that tDCS had a protective effect on the blood-brain barrier. Furthermore, the increased numbers of Vimentin, S100 expression and blood vessels (lectin-positive) around Aß indicated that the effect of tDCS was mediated by astrocytes and blood vessels. There was no significant difference in these parameters between the model group and the sham stimulation group. In conclusion, our results show that tDCS can improve NVU function in APP/PS1 mice in the preclinical stage, providing further support for the use of tDCS as a treatment for AD.

[Repetition of teaching contents between genetics and relative courses in agricultural and forestry colleges and approaches to solving the problem].

Genetics is one of the main courses in agricultural and forestry colleges. However, there is large repetition of teaching contents and joining problems between genetics and the relative courses. The negative effects of above problems are discussed in this paper. In order to relieve the conflict between the increase of genetics contents and the decrease of teaching hours in genetics teaching of undergraduates and provide reference for future textbook compilation, some approaches on solving repetition of teaching content and suggestions on joining problems are put forward.

Nonfragile H∞ State Estimation for Recurrent Neural Networks With Time-Varying Delays: On Proportional-Integral Observer Design.

In this article, a novel proportional-integral observer (PIO) design approach is proposed for the nonfragile H∞ state estimation problem for a class of discrete-time recurrent neural networks with time-varying delays. The developed PIO is equipped with more design freedom leading to better steady-state accuracy compared with the conventional Luenberger observer. The phenomena of randomly occurring gain variations, which are characterized by the Bernoulli distributed random variables with certain probabilities, are taken into consideration in the implementation of the addressed PIO. Attention is focused on the design of a nonfragile PIO such that the error dynamics of the state estimation is exponentially stable in a mean-square sense, and the prescribed H∞ performance index is also achieved. Sufficient conditions for the existence of the desired PIO are established by virtue of the Lyapunov-Krasovskii functional approach and the matrix inequality technique. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed PIO design scheme.

Finite-Horizon H∞ Bipartite Consensus Control of Cooperation-Competition Multiagent Systems With Round-Robin Protocols.

This article focuses on the finite-horizon H∞ bipartite consensus control problem for a class of discrete time-varying cooperation-competition multiagent systems (DTV-CCMASs) with the round-robin (RR) protocol. The cooperation-competition relationship among agents is characterized by a signed graph, whose edges are with positive or negative connection weights. Specifically, a positive weight corresponds to an allied relationship between two agents and a negative one means an adversary relationship. The data exchange between each agent and its neighbors is orchestrated by an RR protocol, where only one neighboring agent is authorized to transmit the data packet at each time instant, and therefore, the data collision is prevented. This article aims to design a bipartite consensus controller for DTV-CCMASs with the RR protocol such that the predetermined H∞ bipartite consensus is satisfied over a given finite horizon. A sufficient condition is first established to guarantee the desired H∞ bipartite consensus by resorting to the completing square method. With the help of an auxiliary cost combined with the Moore-Penrose pseudoinverse method, a design scheme of the bipartite consensus controller is obtained by solving two coupled backward recursive Riccati difference equations (BRRDEs). Finally, a simulation example is given to verify the effectiveness of the proposed scheme of the bipartite consensus controller.