Dual roles of the amygdala-hippocampus circuit in the regulation of rapid eye movement sleep and depression symptoms by repetitive transcranial magnetic stimulation in patients with insomnia

To the editor:It is commonly reported that people with insomnia often experience comorbid emotional disorders,such as mood and anxiety disorders.12 A study found that fragmented rapid eye movement(REM)sleep in individuals with insomnia is associated with higher Beck Depression Inventory(BDI)scores.3 REM sleep architecture disruption is a typical symptom of insomnia.

Comparisons of transcranial alternating current stimulation and repetitive transcranial magnetic stimulation treatment therapy for insomnia:a pilot study

To the editor:Insomnia disorder has a serious and widespread detrimental effect on humans with comorbidity with other mental or physical health problems.In recent years,noninvasive brain stimulation(NIBS)techniques,especially transcranial magnetic stimulation(TMS)and transcranial electrical stimulation,have been increasingly used for the treatment of brain diseases,including insomnia disorder.

Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma

Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (ve), volume fraction of plasma space (vp), volume transfer constant (Ktrans), and reverse reflux rate constant (kep) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model’s ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%-52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed promising results in delivering more accurate prognostic predictions and provided more clinical benefits in quantifying and monitoring phenotypic changes associated with NPC prognosis.

Apogossypolone, a novel inhibitor of anfiapoptotic Bcl-2 family proteins, induces autophagy of PC-3 and LNCaP prostate cancer cells in vitro

Limited treatment options are available for aggressive prostate cancer. Gossypol has been reported to have a potent anticancer activity in many types of cancer. It can increase the sensitivity of cancer cells to alkylating agents, diminish multidrug resistance and decrease metastasis. Whether or not it can induce autophagy in cancer cells has not yet been determined. Here we investigated the antiproliferative activity of apogossypolone （ApoG2） and （-）-gossypol on the human prostate cancer cell line PC3 and LNCaP in vitro. Exposure of PC-3 and LNCaP cells to ApoG2 resulted in several specific features characteristic of autophagy, including the appearance of membranous vacuoles in the cytoplasm and formation of acidic vesicular organelles. Expression of autophagy-associated LC3-Ⅱ and beclin-1 increased in both cell lines after treatment. Inhibition of autophagy with 3-methyladenine promoted apoptosis of both cell types. Taken together, these data demonstrated that induction of autophagy could represent a defense mechanism against apoptosis in human prostate cancer cells.

Extraction of auroral oval boundaries from UVI images: A new FLICM clustering-based method and its evaluation

Based on the fuzzy local information c-means （FLICM） clustering algorithm, a new method is developed for extracting the equatorward and poleward boundaries of the auroral oval from images acquired by the Ultraviolet Imager （UVI） aboard the POLAR satellite. First, the method iteratively segments the UVI image with the FLICM clustering algorithm, according to an integrity criterion for the segmented auroral oval. Then, possible gaps in the extracted auroral oval are filled, based on prior knowledge of its shape. To evaluate the method objectively, the extracted boundaries are compared with the precipitating electron boundaries determined from DMSP satellite precipitation particle data. The evaluation results demonstrate that the proposed method generates more accurate auroral boundaries than traditional methods.

Security Analysis and Improvement of TNC IF-T Protocol Binding to TLS

The TNC IF-T Protocol Binding to TLS(TIPBT) is specified by Trusted Computing Group(TCG) for TNC assessment exchanges.However,the TIPBT cannot be analysed by current Strand Space Model(SSM) because of the different requirements from the traditional security protocols.In order to solve this problem,first,we give an extension of the SSM and point out the TIPBT cannot prevent Man-in-the-Middle(MITM) attacks in some cases based on the extended SSM.Then,we improve the TIPBT and show that the improved TIPBT can resist MITM attacks in the extended SSM.

Research and Implementation of a License Plate Recognition Algorithm Based on Hierarchical Classification

This paper proposed an improved method for license plate recognition based on hierarchical classification. First, the method of feature extraction and dimension reduction is presented by finding the optimal wavelet packet basis in the process of wavelet packet decomposition and K-L transform. Then the recognition algorithm is introduced based on feature extraction and hierarchical classification. Finally, the principles and procedures of using support vector machines, Harris corner detection algorithm and digital character classification are explained in detail. Simulation results indicate that the presented recognition algorithm performs well with higher speed and efficiency in recognition.

Early diagnosis and bioimaging of lung adenocarcinoma cells/organs based on spectroscopy machine learning

Early diagnosis and fast detection with a high accuracy rate of lung cancer are important to improve the treatment effect.In this research,an early fast diagnosis and in vivo imaging method for lung adenocarcinoma are proposed by collecting the spectral data from normal and patients'cells/tissues,such as Fourier infrared spectroscopy(FTIR),UV-vis absorbance,and fluorescence spectra using anthocyanin.The FTIR spectra of human normal lung epithelial cells(BEAS-2B cells)and human lung adenocarcinoma cells(A549 cells)were collected.After the data is cleaned,a feature selection algorithm is used to select important wavelengths,and then,the classification models of support vector machine(SVM)and the grid search method are used to select the optimal model parameters(accuracy:96.89%on the training set and 88.57%on the test set).The optimal model is used to classify all samples,and the accuracy is 94.37%.Moreover,the anthocyanin was prepared and used for the intracellular absorbance and fluorescence,and the optimized algorithm was used for classification(accuracy:91.38%on the training set and 80.77%on the test set).Most importantly,the in vivo cancer imaging can be performed using anthocyanin.The results show that there are differences between lung ade-nocarcinoma and normal lung tissues at the molecular level,reflecting the accuracy,intui-tiveness,and feasibility of this algorithm-assistant anthocyanin imaging in lung cancer diagnosis,thus showing the potential to become an accurate and effective technical means for basic research and clinical diagnosis.

ENERGY FEATURE EXTRACTION AND SVM CLASSIFICATION OFMOTORIMAGERY-INDUCED ELECTROENCEPHALOGRAMS

The precise classification for the electroencephalogram(EEG)in different mental tasks in the research on brain
computer interface(BCI)is the key for the design and clinical application of the system.In this paper,a new combination classification algorithm is presented and tested using the EEG data of right and left motor imagery experiments.First,to eliminate the low frequency noise in the original EEGs,the signals were decomposed by empirical mode decomposition(EMD)and then the optimal kernel parameters for support vector machine(SVM)were determined,the energy features of thefirst three intrinsic mode functions(IMFs)of every signal were extracted and used as input vectors of the employed SVM.The output of the SVM will be classification result for different mental task EEG signals.The study shows that mean identification rate of the proposed algorithm is 95%,which is much better than the present traditional algorithms.

AMPLITUDE OF LOW FREQUENCY FLUCTUATION AT DIFFERENT FREQUENCY BANDS IN EARLY AMNESTIC MILD COGNITIVE IMPAIRMENT: RESULTS FROM ADNI

We used resting-state functional magnetic resonance imaging(fMRI)to determine whether there are any abnormalities in different frequency bands between amplitude of low-frequency fluctuations(ALFF)and fractional ALFF(fALFF)and between 10 early amnestic mild cognitive impairment(EMCI)patients and eight normal controls participating in the Alzheimer’s Disease Neuroimaging Initiative(ADNI).We showed widespread difference in ALFF/fALFF between two frequency bands(slow-4:0.027-0.073 Hz,slow-5:0.01-0.027 Hz)in many brain areas including posterior cingulate cortex(PCC),medial prefrontal cortex(MPFC),suprasellar cistern(SC)and ambient cistern(AC).Compared to the normal controls,the EMCI patients showed increased ALFF values in PCu,cerebellum,occipital lobe and cerebellum posterior lobe in frequency band slow-4.While in frequency band slow-5,the EMCI patients showed decreased ALFF values in temporal lobe,left cerebrum and middle temporal gyrus5.Moreover,the EMCI patients showed increased fALFF values in frontal lobe and inferior frontal gyrus in band slow-5.While in frequency band slow-4,the EMCI patients showed decreased fALFF values in limbic lobe,cingulate gyrus and corpus callosum.These results demonstrated that EMCI patients had widespread abnormalities of amplitude of LFF in different frequency bands.

从信号到知识——基于人工智能的医学影像裸数据诊断价值初探

Encouraging and astonishing developments have recently been achieved in image-based diagnostic technology.Modern medical care and imaging technology are becoming increasingly inseparable.However,the current diagnosis pattern of signal to image to knowledge inevitably leads to information distortion and noise introduction in the procedure of image reconstruction(from signal to image).Artificial intelligence(AI)technologies that can mine knowledge from vast amounts of data offer opportunities to disrupt established workflows.In this prospective study,for the first time,we develop an AI-based signal-toknowledge diagnostic scheme for lung nodule classification directly from the computed tomography(CT)raw data(the signal).We find that the raw data achieves almost comparable performance with CT,indicating that it is possible to diagnose diseases without reconstructing images.Moreover,the incorporation of raw data through three common convolutional network structures greatly improves the performance of the CT models in all cohorts(with a gain ranging from 0.01 to 0.12),demonstrating that raw data contains diagnostic information that CT does not possess.Our results break new ground and demonstrate the potential for direct signal-to-knowledge domain analysis.

Student Academic Performance Predictive Model Based on Dual-stream Deep Network

Blended teaching is one of the essential teaching methods with the development of information technology.Constructing a learning effect evaluation model is helpful to improve students’academic performance and helps teachers to better implement course teaching.However,a lack of evaluation models for the fusion of temporal and non-temporal behavioral data leads to an unsatisfactory evaluation effect.To meet the demand for predicting students’academic performance through learning behavior data,this study proposes a learning effect evaluation method that integrates expert perspective indicators to predict academic performance by constructing a dual-stream network that combines temporal behavior data and non-temporal behavior data in the learning process.In this paper,firstly,the Delphi method is used to analyze and process the course learning behavior data of students and establish an effective evaluation index system of learning behavior with universality;secondly,the Mann-Whitney U-test and the complex correlation analysis are used to analyze further and validate the evaluation indexes;and lastly,a dual-stream information fusion model,which combines temporal and non-temporal features,is established.The learning effect evaluation model is built,and the results of the mean absolute error(MAE)and root mean square error(RMSE)indexes are 4.16 and 5.29,respectively.This study indicates that combining expert perspectives for evaluation index selection and further fusing temporal and non-temporal behavioral features that for learning effect evaluation and prediction is rationality,accuracy,and effectiveness,which provides a powerful help for the practical application of learning effect evaluation and prediction.

Changes of resting cerebral activities in subacute ischemic stroke patients

This study aimed to detect the difference in resting cerebral activities between ischemic stroke pa- tients and healthy participants, define the abnormal site, and provide new evidence for pathological mechanisms, clinical diagnosis, prognosis prediction and efficacy evaluation of ischemic stroke. At present, the majority of functional magnetic resonance imaging studies focus on the motor dysfunc- tion and the acute stage of ischemic stroke. This study recruited 15 right-handed ischemic stroke patients at subacute stage （15 days to 11.5 weeks） and 15 age-matched healthy participants. A rest- ing-state functional magnetic resonance imaging scan was performed on each subject to detect cerebral activity. Regional homogeneity analysis was used to investigate the difference in cerebral activities between ischemic stroke patients and healthy participants. The results showed that the ischemic stroke patients had lower regional homogeneity in anterior cingulate and left cerebrum and higher regional homogeneity in cerebellum, left precuneus and left frontal lobe, compared with healthy participants. The experimental findings demonstrate that the areas in which regional homogeneity was different between ischemic stroke patients and healthy participants are in the cerebellum, left precuneus, left triangle inferior frontal gyrus, left inferior temporal gyrus and anterior cingulate. These locations, related to the motor, sensory and emotion areas, are likely po- tential targets for the neural regeneration of subacute ischemic stroke patients.

A central analgesic mechanism of acupuncture for migraine An ongoing functional MRI study

Shaoyang acupoints are the most frequently used in migraine treatment. However, the central anal- gesic mechanism remains poorly understood. Studies have demonstrated that single stimulus of the verum acupuncture in healthy subjects can induce significant connectivity or activity changes in pain- related central networks compared with sham acupuncture. However, these findings are not indicative of the central analgesic mechanism of acupuncture at Shaoyang acupoints. Thus, we recruited 100 migraine sufferers and randomly assigned them into five groups： Shaoyang uncommon acupoint, Shaoyang common acupoint, Yangming uncommon acupoint, non-acupoint control, and blank control groups. Subjects were subjected to evaluation of curative effects and functional MRI prior to and after 10 and 20 acupuncture treatments. All subjects were diagnosed by physicians and enrolled following clinical physical examination. Subjects were observed during 1-4 weeks after inclusion. At the fifth week, the first clinical evaluation and resting functional MRI were conducted. The Shaoyang uncom- mon acupoint, Shaoyang common acupoint, Yangming uncommon acupoint, and non-acupoint control grousp then were treated with acupuncture, five times per week, 20 times in total over 4 weeks. The second and third clinical evaluations and resting functional MRI screenings were conducted following 10 and 20 acupuncture treatments. The blank control group was observed during the 5 to 8 week pe- riod, followed by clinical evaluation and resting functional MRI. The aim of this study was to examine changes in brain functional activity and central networks in subjects with migraine undergoing acu- puncture at Shaoyang uncommon acupoints. This study provides a further explanation of the central analgesic mechanism by which acupuncture at Shaoyang acupoints treats migraine,

Simulation of stimulated Raman scattering signal generation in scattering tissues excited by Bessel beams

Stimulated Raman scattering(SRS)microscopy has the ability of noninvasive imaging of specific chemical bonds and been increasingly used in biomedicine in recent years.Two pulsed Gaussian beams are used in traditional SRS microscopes,providing with high lateral and axial spatial resolution.Because of the tight focus of the Gaussian beam,such an SRS microscopy is difficult to be used for imaging deep targets in scattering tissues.The SRS microscopy based on Bessel beams can solve the imaging problem to a certain extent.Here,we establish a theoretical model to calculate the SRS signal excited by two Bessel beams by integrating the SRS signal generation theory with the fractal propagation method.The fractal model of refractive index turbulence is employed to generate the scattering tissues where the light transport is modeled by the beam propagation method.We model the scattering tissues containing chemicals,calculate the SRS signals stimulated by two Bessel beams,discuss the influence of the fractal model parameters on signal generation,and compare them with those generated by the Gaussian beams.The results show that,even though the modeling parameters have great influence on SRS signal generation,the Bessel beams-based SRS can generate signals in deeper scattering tissues.

Multi-Omics and Its Clinical Application in Hepatocellular Carcinoma:Current Progress and Future Opportunities

Hepatocellular carcinoma(HCC)is the sixth most common malignancy and the fourth leading cause of cancer related death worldwide.China covers over half of cases,leading HCC to be a vital threaten to public health.Despite advances in diagnosis and treatments,high recurrence rate remains a major obstacle in HCC management.Multi-omics currently facilitates surveillance,precise diagnosis,and personalized treatment decision making in clinical setting.Non-invasive radiomics utilizes preoperative radiological imaging to reflect subtle pixel-level pattern changes that correlate to specific clinical outcomes.Radiomics has been widely used in histopathological diagnosis prediction,treatment response evaluation,and prognosis prediction.High-throughput sequencing and gene expression profiling enabled genomics and proteomics to identify distinct transcriptomic subclasses and recurrent genetic alterations in HCC,which would reveal the complex multistep process of the pathophysiology.The accumulation of big medical data and the development of artificial intelligence techniques are providing new insights for our better understanding of the mechanism of HCC via multi-omics,and show potential to convert surgical/intervention treatment into an antitumorigenic one,which would greatly advance precision medicine in HCC management.

Auroral event detection using spatiotemporal statistics of local motion vectors

The analysis and exploration of auroral dynamics are very significant for studying auroral mechanisms. This paper proposes a method based on auroral dynamic processes for detecting auroral events automatically. We first obtained the motion fields using the multiscale fluid flow estimator. Then, the auroral video frame sequence was represented by the spatiotemporal statistics of local motion vectors. Finally, automatic auroral event detection was achieved. The experimental results show that our methods could detect the required auroral events effectively and accurately, and that the detections were independent on any specific auroral event. The proposed method makes it feasible to statistically analyze a large number of continuous observations based on the auroral dynamic process.

CT-based radiomics to predict development of macrovascular invasion in hepatocellular carcinoma:A multicenter study

Background:Macrovascular invasion(MaVI)occurs in nearly half of hepatocellular carcinoma(HCC)patients at diagnosis or during follow-up,which causes severe disease deterioration,and limits the possibility of surgical approaches.This study aimed to investigate whether computed tomography(CT)-based radiomics analysis could help predict development of MaVI in HCC.Methods:A cohort of 226 patients diagnosed with HCC was enrolled from 5 hospitals with complete MaVI and prognosis follow-ups.CT-based radiomics signature was built via multi-strategy machine learning methods.Afterwards,MaVI-related clinical factors and radiomics signature were integrated to construct the final prediction model(CRIM,clinical-radiomics integrated model)via random forest modeling.Cox-regression analysis was used to select independent risk factors to predict the time of MaVI development.Kaplan-Meier analysis was conducted to stratify patients according to the time of MaVI development,progression-free survival(PFS),and overall survival(OS)based on the selected risk factors.Results:The radiomics signature showed significant improvement for MaVI prediction compared with conventional clinical/radiological predictors(P<0.001).CRIM could predict MaVI with satisfactory areas under the curve(AUC)of 0.986 and 0.979 in the training(n=154)and external validation(n=72)datasets,respectively.CRIM presented with excellent generalization with AUC of 0.956,1.000,and 1.000 in each external cohort that accepted disparate CT scanning protocol/manufactory.Peel9_fos_InterquartileRange[hazard ratio(HR)=1.98;P<0.001]was selected as the independent risk factor.The cox-regression model successfully stratified patients into the high-risk and low-risk groups regarding the time of MaVI development(P<0.001),PFS(P<0.001)and OS(P=0.002).Conclusions:The CT-based quantitative radiomics analysis could enable high accuracy prediction of subsequent MaVI development in HCC with prognostic implications.

Comparative evaluations of the Monte Carlo-based light propagation simulation packages for optical imaging

Monte Carlo simulation of light propagation in turbid medium has been studied for years.A number of software packages have been developed to handle with such issue.However,it is hard to compare these simulation packages,especially for tissues with complex heterogeneous structures.Here,we first designed a group of mesh datasets generated by Iso2Mesh software,and used them to cross-validate the accuracy and to evaluate the performance of four Monte Carlo-based simulation packages,including Monte Carlo model of steady-state light transport in multi-layered tissues(MCML),tetrahedron-based inhomogeneous Monte Carlo optical simulator(TIMOS),Molecular Optical Simulation Environment(MOSE),and Mesh-based Monte Carlo(MMC).The performance of each package was evaluated based on the designed mesh datasets.The merits and demerits of each package were also discussed.Comparative results showed that the TIMOS package provided the best performance,which proved to be a reliable,efficient,and stable MC simulation package for users.

Investigation of passivation effects in AlGaN/GaN metal-insulator-semiconductor high electron-mobility transistor by gate-drain conductance dispersion study

This paper studies the drain current collapse of A1GaN/GaN metal insulator-semiconductor high electron-mobility transistors （MIS-HEMTs） with NbA10 dielectric by applying dual-pulsed stress to the gate and drain of the device. For NbA10 MIS-HEMT, smaller current collapse is found thorough study of the gate-drain conductance dispersion especially when the gate static voltage is -8 V. Through a it is found that the growth of NbA10 can reduce the trap density of the AlGaN surface. Therefore, fewer traps can be filled by gate electrons, and hence the depletion effect in the channel is suppressed effectively. It is proved that the NbAIO gate dielectric can not only decrease gate leakage current but also passivate the A1GaN surface effectively, and weaken the current collapse effect accordingly.