High-frequency repetitive transcranial magnetic stimulation promotes neural stem cell proliferation after ischemic stroke

Prolife ration of neural stem cells is crucial for promoting neuronal regeneration and repairing cerebral infarction damage.Transcranial magnetic stimulation(TMS)has recently emerged as a tool for inducing endogenous neural stem cell regeneration,but its underlying mechanisms remain unclea r In this study,we found that repetitive TMS effectively promotes the proliferation of oxygen-glucose deprived neural stem cells.Additionally,repetitive TMS reduced the volume of cerebral infa rction in a rat model of ischemic stro ke caused by middle cerebral artery occlusion,im p roved rat cognitive function,and promoted the proliferation of neural stem cells in the ischemic penumbra.RNA-sequencing found that repetitive TMS activated the Wnt signaling pathway in the ischemic penumbra of rats with cerebral ischemia.Furthermore,PCR analysis revealed that repetitive TMS promoted AKT phosphorylation,leading to an increase in mRNA levels of cell cycle-related proteins such as Cdk2 and Cdk4.This effect was also associated with activation of the glycogen synthase kinase 3β/β-catenin signaling pathway,which ultimately promotes the prolife ration of neural stem cells.Subsequently,we validated the effect of repetitive TMS on AKT phosphorylation.We found that repetitive TMS promoted Ca2+influx into neural stem cells by activating the P2 calcium channel/calmodulin pathway,thereby promoting AKT phosphorylation and activating the glycogen synthase kinase 3β/β-catenin pathway.These findings indicate that repetitive TMS can promote the proliferation of endogenous neural stem cells through a Ca2+influx-dependent phosphorylated AKT/glycogen synthase kinase 3β/β-catenin signaling pathway.This study has produced pioneering res ults on the intrinsic mechanism of repetitive TMS to promote neural function recove ry after ischemic stro ke.These results provide a stro ng scientific foundation for the clinical application of repetitive TMS.Moreover,repetitive TMS treatment may not only be an efficient and potential approach to support neurogenesis for further therapeutic applications,but also provide an effective platform for the expansion of neural stem cells.

Kinematic deformation and intensity assessment of the 2021 Maduo M_(S)7.4 earthquake in Qinghai revealed by high-frequency GNSS

Rapid acquisition of the kinematic deformation field and seismic intensity distribution of large earthquakes is crucial for postseismic emergency rescue,disaster assessment,and future seismic risk research.The advancement of GNSS observation and data processing makes it play an important role in this field,especially the high-frequency GNSS.We used the differential positioning method to calculate the 1 HZ GNSS data from 98 sites within 1000 km of the M_(S)7.4 Maduo earthquake epicenter.The kinematic deformation field and the distribution of the seismic intensity by using the peak ground velocity derived from displacement waveforms were obtained.The results show that:1)Horizontal coseismic response deformation levels ranging from 25 mm to 301 mm can be observed within a 1000 km radius from the epicenter.Coseismic response deformation on the east and west sides shows bilateral asymmetry,which markedly differs from the symmetry presented by surface rupture.2)The seismic intensity obtained through high-frequency GNSS and field investigations exhibits good consistency of the scope and orientation in the high seismic intensity area,although the former is generally slightly smaller than the latter.3)There may exist obstacles on the eastern side of the seismogenic fault.The Maduo earthquake induced a certain tectonic stress loading effect on the western Kunlun Pass-Jiangcuo fault(KPJF)and Maqin-Maqu segment,resulting in higher seismic risk in the future.

2D DOA Estimation of Coherent Signals with a Separated Linear Acoustic Vector-Sensor Array

In this paper, a two-dimensional(2D) DOA estimation algorithm of coherent signals with a separated linear acoustic vector-sensor(AVS) array consisting of two sparse AVS arrays is proposed. Firstly,the partitioned spatial smoothing(PSS) technique is used to construct a block covariance matrix, so as to decorrelate the coherency of signals. Then a signal subspace can be obtained by singular value decomposition(SVD) of the covariance matrix. Using the signal subspace, two extended signal subspaces are constructed to compensate aperture loss caused by PSS.The elevation angles can be estimated by estimation of signal parameter via rotational invariance techniques(ESPRIT) algorithm. At last, the estimated elevation angles can be used to estimate automatically paired azimuth angles. Compared with some other ESPRIT algorithms, the proposed algorithm shows higher estimation accuracy, which can be proved through the simulation results.

Dynamic Response of Foundations during Startup of High-Frequency Tunnel Equipment

The specialized equipment utilized in long-line tunnel engineering is evolving towards large-scale,multifunctional,and complex orientations.The vibration caused by the high-frequency units during regular operation is supported by the foundation of the units,and the magnitude of vibration and the operating frequency fluctuate in different engineering contexts,leading to variations in the dynamic response of the foundation.The high-frequency units yield significantly diverse outcomes under different startup conditions and times,resulting in failure to meet operational requirements,influencing the normal function of the tunnel,and causing harm to the foundation structure,personnel,and property in severe cases.This article formulates a finite element numerical computation model for solid elements using three-dimensional elastic body theory and integrates field measurements to substantiate and ascertain the crucial parameter configurations of the finite element model.By proposing a comprehensive startup timing function for high-frequency dynamic machines under different startup conditions,simulating the frequency andmagnitude variations during the startup process,and suggesting functions for changes in frequency and magnitude,a simulated startup schedule function for high-frequency machines is created through coupling.Taking into account the selection of the transient dynamic analysis step length,the dynamic response results for the lower dynamic foundation during its fundamental frequency crossing process are obtained.The validation checks if the structural magnitude surpasses the safety threshold during the critical phase of unit startup traversing the structural resonance region.The design recommendations for high-frequency units’dynamic foundations are provided,taking into account the startup process of the machine and ensuring the safe operation of the tunnel.

Review of the Analysis and Suppression for High-frequency Oscillations of the Grid-connected Wind Power Generation System

High-frequency oscillation(HFO)of gridconnected wind power generation systems(WPGS)is one of the most critical issues in recent years that threaten the safe access of WPGS to the grid.Ensuring the WPGS can damp HFO is becoming more and more vital for the development of wind power.The HFO phenomenon of wind turbines under different scenarios usually has different mechanisms.Hence,engineers need to acquire the working mechanisms of the different HFO damping technologies and select the appropriate one to ensure the effective implementation of oscillation damping in practical engineering.This paper introduces the general assumptions of WPGS when analyzing HFO,systematically summarizes the reasons for the occurrence of HFO in different scenarios,deeply analyses the key points and difficulties of HFO damping under different scenarios,and then compares the technical performances of various types of HFO suppression methods to provide adequate references for engineers in the application of technology.Finally,this paper discusses possible future research difficulties in the problem of HFO,as well as the possible future trends in the demand for HFO damping.

Efficient activation of the Co/SBA-15catalyst by high-frequency AC-DBD plasma thermal effects for toluene removal

Dielectric barrier discharge(DBD)plasma excited by a high-frequency alternating-current(AC)power supply is widely employed for the degradation of volatile organic compounds(VOCs).However,the thermal effect generated during the discharge process leads to energy waste and low energy utilization efficiency.In this work,an innovative DBD thermally-conducted catalysis(DBD-TCC)system,integrating high-frequency AC-DBD plasma and its generated thermal effects to activate the Co/SBA-15 catalyst,was employed for toluene removal.Specifically,Co/SBA-15 catalysts are closely positioned to the ground electrode of the plasma zone and can be heated and activated by the thermal effect when the voltage exceeds 10 k V.At12.4 k V,the temperature in the catalyst zone reached 261℃ in the DBD-TCC system,resulting in an increase in toluene degradation efficiency of 17%,CO_(2)selectivity of 21.2%,and energy efficiency of 27%,respectively,compared to the DBD system alone.In contrast,the DBD thermally-unconducted catalysis(DBD-TUC)system fails to enhance toluene degradation due to insufficient heat absorption and catalytic activation,highlighting the crucial role of AC-DBD generated heat in the activation of the catalyst.Furthermore,the degradation pathway and mechanism of toluene in the DBD-TCC system were hypothesized.This work is expected to provide an energy-efficient approach for high-frequency AC-DBD plasma removal of VOCs.

Application of the CatBoost Model for Stirred Reactor State Monitoring Based on Vibration Signals

Stirred reactors are key equipment in production,and unpredictable failures will result in significant economic losses and safety issues.Therefore,it is necessary to monitor its health state.To achieve this goal,in this study,five states of the stirred reactor were firstly preset:normal,shaft bending,blade eccentricity,bearing wear,and bolt looseness.Vibration signals along x,y and z axes were collected and analyzed in both the time domain and frequency domain.Secondly,93 statistical features were extracted and evaluated by ReliefF,Maximal Information Coefficient(MIC)and XGBoost.The above evaluation results were then fused by D-S evidence theory to extract the final 16 features that are most relevant to the state of the stirred reactor.Finally,the CatBoost algorithm was introduced to establish the stirred reactor health monitoring model.The validation results showed that the model achieves 100%accuracy in detecting the fault/normal state of the stirred reactor and 98%accuracy in diagnosing the type of fault.

N-th root slant stack for enhancing weak seismic signals

Seismic imaging of complicated underground structures with severe surface undulation(i.e.,double complex areas)is challenging owing to the difficulty of collecting the very weak reflected signal.Enhancing the weak signal is difficult even with state-of-the-art multi-domain and multidimensional prestack denoising techniques.This paper presents a time–space dip analysis of offset vector tile(OVT)domain data based on theτ-p transform.The proposed N-th root slant stack method enhances the signal in a three-dimensionalτ-p domain by establishing a zero-offset time-dip seismic attribute trace and calculating the coherence values of a given data sub-volume(i.e.,inline,crossline,time),which are then used to recalculate the data.After sorting,the new data provide a solid foundation for obtaining the optimal N value of the N-th root slant stack,which is used to enhance a weak signal.The proposed method was applied to denoising low signal-to-noise ratio(SNR)data from Western China.The optimal N value was determined for improving the SNR in deep strata,and the weak seismic signal was enhanced.The results showed that the proposed method effectively suppressed noise in low-SNR data.

Effects of unilateral superimposed high-frequency jet ventilation on porcine hemodynamics and gas exchange during one-lung flooding

BACKGROUND Superimposed high-frequency jet ventilation(SHFJV)is suitable for respiratory motion reduction and essential for effective lung tumor ablation.Fluid filling of the target lung wing one-lung flooding(OLF)is necessary for therapeutic ultrasound applications.However,whether unilateral SHFJV allows adequate hemodynamics and gas exchange is unclear.AIM To compared SHFJV with pressure-controlled ventilation(PCV)during OLF by assessing hemodynamics and gas exchange in different animal positions.METHODS SHFJV or PCV was used alternatingly to ventilate the non-flooded lungs of the 12 anesthetized pigs during OLF.The animal positions were changed from left lateral position to supine position(SP)to right lateral position(RLP)every 30 min.In each position,ventilation was maintained for 15 min in both modalities.Hemodynamic variables and arterial blood gas levels were repeatedly measured.RESULTS Unilateral SHFJV led to lower carbon dioxide removal than PCV without abnormally elevated carbon dioxide levels.SHFJV slightly decreased oxygenation in SP and RLP compared with PCV;the lowest values of PaO_(2) and PaO_(2)/FiO_(2) ratio were found in SP[13.0;interquartile range(IQR):12.6-5.6 and 32.5(IQR:31.5-38.9)kPa].Conversely,during SHFJV,the shunt fraction was higher in all animal positions(highest in the RLP:0.30).CONCLUSION In porcine model,unilateral SHFJV may provide adequate ventilation in different animal positions during OLF.Lower oxygenation and CO_(2) removal rates compared to PCV did not lead to hypoxia or hypercapnia.SHFJV can be safely used for lung tumor ablation to minimize ventilation-induced lung motion.

Extracting useful high-frequency information from wide-field electromagnetic data using time-domain signal reconstruction

The wide-field electromagnetic method is widely used in hydrocarbon exploration,mineral deposit detection,and geological disaster prediction.However,apparent resistivity and normalized field amplitude exceeding 2048 Hz often exhibit upward warping in data,making geophysical inversion and interpretation challenging.The cumulative error of the crystal oscillator in signal transmission and acquisition contributes to an upturned apparent resistivity curve.To address this,a high-frequency information extraction method is proposed based on time-domain signal reconstruction,which helps to record a complete current data sequence;moreover,it helps estimate the crystal oscillator error for the transmitted signal.Considering the recorded error,a received signal was corrected using a set of reconstruction algorithms.After processing,the high-frequency component of the wide-field electromagnetic data was not upturned,while accurate high-frequency information was extracted from the signal.Therefore,the proposed method helped effectively extract high-frequency components of all wide-field electromagnetic data.

THE STABLE RECONSTRUCTION OF STRONGLY-DECAYING BLOCK SPARSE SIGNALS

In this paper,we reconstruct strongly-decaying block sparse signals by the block generalized orthogonal matching pursuit(BgOMP)algorithm in the l2-bounded noise case.Under some restraints on the minimum magnitude of the nonzero elements of the strongly-decaying block sparse signal,if the sensing matrix satisfies the the block restricted isometry property(block-RIP),then arbitrary strongly-decaying block sparse signals can be accurately and steadily reconstructed by the BgOMP algorithm in iterations.Furthermore,we conjecture that this condition is sharp.

Identification of Early Warning Signals of Infectious Diseases in Hospitals by Integrating Clinical Treatment and Disease Prevention

The global incidence of infectious diseases has increased in recent years,posing a significant threat to human health.Hospitals typically serve as frontline institutions for detecting infectious diseases.However,accurately identifying warning signals of infectious diseases in a timely manner,especially emerging infectious diseases,can be challenging.Consequently,there is a pressing need to integrate treatment and disease prevention data to conduct comprehensive analyses aimed at preventing and controlling infectious diseases within hospitals.This paper examines the role of medical data in the early identification of infectious diseases,explores early warning technologies for infectious disease recognition,and assesses monitoring and early warning mechanisms for infectious diseases.We propose that hospitals adopt novel multidimensional early warning technologies to mine and analyze medical data from various systems,in compliance with national strategies to integrate clinical treatment and disease prevention.Furthermore,hospitals should establish institution-specific,clinical-based early warning models for infectious diseases to actively monitor early signals and enhance preparedness for infectious disease prevention and control.

An improved initial rotor position estimation method using high-frequency pulsating voltage injection for PMSM

High frequency pulsating voltage injection method is a good candidate for detecting the initial rotor position of permanent magnet synchronous motor.However,traditional methods require a large number of filters,which leads to the deterioration of system stability and dynamic performance.In order to solve these problems,a new signal demodulation method is proposed in this paper.The proposed new method can directly obtain the amplitude of high-frequency current,thus eliminating the use of filters,improving system stability and dynamic performance and saving the work of adjusting filter parameters.In addition,a new magnetic polarity detection method is proposed,which is robust to current measurement noise.Finally,experiments verify the effectiveness of the method.

Therapeutic efficacy of methylprednisolone sodium succinate via diverse administration routes for mid-to high-frequency sudden sensorineural hearing loss

BACKGROUND Sudden sensorineural hearing loss(SSNHL),characterized by a rapid and unexplained loss of hearing,particularly at moderate to high frequencies,presents a significant clinical challenge.The therapeutic use of methylprednisolone sodium succinate(MPSS)via different administration routes,in combination with conventional medications,remains a topic of interest.AIM To compare the therapeutic efficacy of MPSS administered via different routes in combination with conventional drugs for the treatment of mid-to high-frequency SSNHL.METHODS The medical records of 109 patients with mid-to high-frequency SSNHL were analyzed.The patients were divided into three groups based on the route of administration:Group A[intratympanic(IT)injection of MPSS combined with mecobalamin and Ginkgo biloba leaf extract injection],Group B(intravenous injection of MPSS combined with mecobalamin and Ginkgo biloba leaf extract injection),and Group C(single IT injection of MPSS).The intervention effects were compared and analyzed.RESULTS The posttreatment auditory thresholds in Group A(21.23±3.34)were significantly lower than those in Groups B(28.52±3.36)and C(30.23±4.21;P<0.05).Group A also exhibited a significantly greater speech recognition rate(92.23±5.34)than Groups B and C.The disappearance time of tinnitus,time to hearing recovery,and disappearance time of vertigo in Group A were significantly shorter than those in Groups B and C(P<0.05).The total effective rate in Group A(97.56%)was significantly greater than that in Groups B and C(77.14%and 78.79%,χ^(2)=7.898,P=0.019).Moreover,the incidence of adverse reactions in Groups A and C was significantly lower than that in Group B(4.88%,3.03%vs 2.57%,χ^(2)=11.443,P=0.003),and the recurrence rate in Group A was significantly lower than that in Groups B and C(2.44%vs 20.00%vs 21.21%,χ^(2)=7.120,P=0.028).CONCLUSION IT injection of MPSS combined with conventional treatment demonstrates superior efficacy and safety compared to systemic administration via intravenous infusion and a single IT injection of MPSS.This approach effectively improves patients'hearing and reduces the risk of disease recurrence.

Adversarial attacks and defenses for digital communication signals identification

As modern communication technology advances apace,the digital communication signals identification plays an important role in cognitive radio networks,the communication monitoring and management systems.AI has become a promising solution to this problem due to its powerful modeling capability,which has become a consensus in academia and industry.However,because of the data-dependence and inexplicability of AI models and the openness of electromagnetic space,the physical layer digital communication signals identification model is threatened by adversarial attacks.Adversarial examples pose a common threat to AI models,where well-designed and slight perturbations added to input data can cause wrong results.Therefore,the security of AI models for the digital communication signals identification is the premise of its efficient and credible applications.In this paper,we first launch adversarial attacks on the end-to-end AI model for automatic modulation classifi-cation,and then we explain and present three defense mechanisms based on the adversarial principle.Next we present more detailed adversarial indicators to evaluate attack and defense behavior.Finally,a demonstration verification system is developed to show that the adversarial attack is a real threat to the digital communication signals identification model,which should be paid more attention in future research.

Subtraction of liposome signals in cryo-EM structural determination of protein-liposome complexes

Reconstituting membrane proteins in liposomes and determining their structure is a common method for determining membrane protein structures using single-particle cryo-electron microscopy(cryo-EM).However,the strong signal of liposomes under cryo-EM imaging conditions often interferes with the structural determination of the embedded membrane proteins.Here,we propose a liposome signal subtraction method based on single-particle two-dimensional(2D)classification average images,aimed at enhancing the reconstruction resolution of membrane proteins.We analyzed the signal distribution characteristics of liposomes and proteins within the 2D classification average images of protein–liposome complexes in the frequency domain.Based on this analysis,we designed a method to subtract the liposome signals from the original particle images.After the subtraction,the accuracy of single-particle three-dimensional(3D)alignment was improved,enhancing the resolution of the final 3D reconstruction.We demonstrated this method using a PIEZO1-proteoliposome dataset by improving the resolution of the PIEZO1 protein.

Automatic modulation recognition of radiation source signals based on two-dimensional data matrix and improved residual neural network

Automatic modulation recognition(AMR)of radiation source signals is a research focus in the field of cognitive radio.However,the AMR of radiation source signals at low SNRs still faces a great challenge.Therefore,the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper.First,the time series of the radiation source signals are reconstructed into two-dimensional data matrix,which greatly simplifies the signal preprocessing process.Second,the depthwise convolution and large-size convolutional kernels based residual neural network(DLRNet)is proposed to improve the feature extraction capability of the AMR model.Finally,the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type.Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method.The recognition accuracy of the proposed method maintains a high level greater than 90% even at -14 dB SNR.

Impact of correlated private signals on continuous-time insider trading

A model of continuous-time insider trading in which a risk-neutral in-sider possesses two imperfect correlated signals of a risky asset is studied.By conditional expectation theory and filtering theory,we first establish three lemmas:normal corre-lation,equivalent pricing and equivalent profit,which can guarantee to turn our model into a model with insider knowing full information.Then we investigate the impact of the two correlated signals on the market equilibrium consisting of optimal insider trading strategy and semi-strong pricing rule.It shows that in the equilibrium,(1)the market depth is constant over time;(2)if the two noisy signals are not linerly correlated,then all private information of the insider is incorporated into prices in the end while the whole information on the asset value can not incorporated into prices in the end;(3)if the two noisy signals are linear correlated such that the insider can infer the whole information of the asset value,then our model turns into a model with insider knowing full information;(4)if the two noisy signals are the same then the total ex ant profit of the insider is increasing with the noise decreasing,while down to O as the noise going up to infinity;(5)if the two noisy signals are not linear correlated then with one noisy signal fixed,the total ex ante profit of the insider is single-peaked with a unique minimum with respect to the other noisy signal value,and furthermore as the noisy value going to O it gets its maximum,the profit in the case that the real value is observed.

HQNN-SFOP:Hybrid Quantum Neural Networks with Signal Feature Overlay Projection for Drone Detection Using Radar Return Signals-A Simulation

With the wide application of drone technology,there is an increasing demand for the detection of radar return signals from drones.Existing detection methods mainly rely on time-frequency domain feature extraction and classical machine learning algorithms for image recognition.This method suffers from the problem of large dimensionality of image features,which leads to large input data size and noise affecting learning.Therefore,this paper proposes to extract signal time-domain statistical features for radar return signals from drones and reduce the feature dimension from 512×4 to 16 dimensions.However,the downscaled feature data makes the accuracy of traditional machine learning algorithms decrease,so we propose a new hybrid quantum neural network with signal feature overlay projection(HQNN-SFOP),which reduces the dimensionality of the signal by extracting the statistical features in the time domain of the signal,introduces the signal feature overlay projection to enhance the expression ability of quantum computation on the signal features,and introduces the quantum circuits to improve the neural network’s ability to obtain the inline relationship of features,thus improving the accuracy and migration generalization ability of drone detection.In order to validate the effectiveness of the proposed method,we experimented with the method using the MM model that combines the real parameters of five commercial drones and random drones parameters to generate data to simulate a realistic environment.The results show that the method based on statistical features in the time domain of the signal is able to extract features at smaller scales and obtain higher accuracy on a dataset with an SNR of 10 dB.On the time-domain feature data set,HQNNSFOP obtains the highest accuracy compared to other conventional methods.In addition,HQNN-SFOP has good migration generalization ability on five commercial drones and random drones data at different SNR conditions.Our method verifies the feasibility and effectiveness of signal detection methods based on quantum computation and experimentally demonstrates that the advantages of quantum computation for information processing are still valid in the field of signal processing,it provides a highly efficient method for the drone detection using radar return signals.

Regulation of specific abnormal calcium signals in the hippocampal CA1 and primary cortex M1 alleviates the progression of temporal lobe epilepsy

Temporal lobe epilepsy is a multifactorial neurological dysfunction syndrome that is refractory,resistant to antiepileptic drugs,and has a high recurrence rate.The pathogenesis of temporal lobe epilepsy is complex and is not fully understood.Intracellular calcium dynamics have been implicated in temporal lobe epilepsy.However,the effect of fluctuating calcium activity in CA1 pyramidal neurons on temporal lobe epilepsy is unknown,and no longitudinal studies have investigated calcium activity in pyramidal neurons in the hippocampal CA1 and primary motor cortex M1 of freely moving mice.In this study,we used a multichannel fiber photometry system to continuously record calcium signals in CA1 and M1 during the temporal lobe epilepsy process.We found that calcium signals varied according to the grade of temporal lobe epilepsy episodes.In particular,cortical spreading depression,which has recently been frequently used to represent the continuously and substantially increased calcium signals,was found to correspond to complex and severe behavioral characteristics of temporal lobe epilepsy ranging from gradeⅡto gradeⅤ.However,vigorous calcium oscillations and highly synchronized calcium signals in CA1 and M1 were strongly related to convulsive motor seizures.Chemogenetic inhibition of pyramidal neurons in CA1 significantly attenuated the amplitudes of the calcium signals corresponding to gradeⅠepisodes.In addition,the latency of cortical spreading depression was prolonged,and the above-mentioned abnormal calcium signals in CA1 and M1 were also significantly reduced.Intriguingly,it was possible to rescue the altered intracellular calcium dynamics.Via simultaneous analysis of calcium signals and epileptic behaviors,we found that the progression of temporal lobe epilepsy was alleviated when specific calcium signals were reduced,and that the end-point behaviors of temporal lobe epilepsy were improved.Our results indicate that the calcium dynamic between CA1 and M1 may reflect specific epileptic behaviors corresponding to different grades.Furthermore,the selective regulation of abnormal calcium signals in CA1 pyramidal neurons appears to effectively alleviate temporal lobe epilepsy,thereby providing a potential molecular mechanism for a new temporal lobe epilepsy diagnosis and treatment strategy.