Time-Varying Networks of Inter-Ictal Discharging Reveal Epileptogenic Zone

The neuronal synchronous discharging may cause an epileptic seizure. Currently, most of the studies conducted to investigate the mechanism of epilepsy are based on EEGs or functional magnetic resonance imaging (fMRI) recorded during the ictal discharging or the resting-state, and few studies have probed into the dynamic patterns during the inter-ictal discharging that are much easier to record in clinical applications. Here, we propose a time-varying network analysis based on adaptive directed transfer function to uncover the dynamic brain network patterns during the inter-ictal discharging. In addition, an algorithm based on the time-varying outflow of information derived from the network analysis is developed to detect the epileptogenic zone. The analysis performed revealed the time-varying network patterns during different stages of inter-ictal discharging; the epileptogenic zone was activated prior to the discharge onset then worked as the source to propagate the activity to other brain regions. Consistence between the epileptogenic zones detected by our proposed approach and the actual epileptogenic zones proved that time-varying network analysis could not only reveal the underlying neural mechanism of epilepsy, but also function as a useful tool in detecting the epileptogenic zone based on the EEGs in the inter-ictal discharging

An Assessment of the Surface Longwave Direct Radiative Effect of Airborne Dust in Zhangye China During the Asian Monsoon Year Field Experiment (2008)

Tiny suspensions of solid particles or liquid droplets, called aerosols, hover in earth's atmosphere and can be found over just about anywhere including oceans, deserts, vegetated areas, and other global regions. Aerosols come in a variety of sizes, shapes, and compositions which depend on such factors as their origin and how long they have been in the atmosphere (i.e., their residence time). Some of the more common types of aerosols include mineral dust and sea salt which get lifted from the desert and ocean surfaces, respectively by mechanical forces such as strong winds. Depending on their size, aerosols will either fall out gravitationally, as in the case of larger particles, or will remain resident in the atmosphere where they can undergo further change through interactions with other aerosols and cloud particles. Not only do aerosols affect air quality where they pose a health risk, they can also perturb the distribution of radiation in the earth-atmosphere system which can inevitably lead to changes in our climate. One aerosol that has been in the forefront of many recent studies, particularly those examining its radiative effects, is mineral dust. The large spatial coverage of desert source regions and the fact that dust can radiatively interact with such a large part of the electromagnetic spectrum due to its range in particle size, makes it an important aerosol to study. Dust can directly scatter and absorb solar and infrared radiation which can subsequently alter the amount of radiation that would otherwise be present in the absence of dust at any level of the atmosphere like the surface. This is known as radiative forcing. At the surface dust can block incoming solar energy, however at infrared wavelengths, dust acts to partially compensate the solar losses. Evaluating the solar radiative effect of dust aerosols is relatively straightforward due in part to the relatively large signal-to-noise ratio in the measurements. At infrared wavelengths, on the other hand, the effect is rather difficult to ascertain since the measured dust signal level is on the same order as the instrumental uncertainties. Although the radiative impact of dust is much smaller in the infrared, it can still have a noticeable influence on the distribution of energy in the Earth-atmosphere system. This is mainly attributed to the strong light-absorptive properties commonly found in many earth minerals

Improving Forest Canopy Height Estimation Using a Semi-Empirical Approach to Overcome TomoSAR Phase Errors

Forest canopy height is an important forest indicator parameter. Synthetic aperture radar tomography (TomoSAR) is an effective method to characterize forest canopy height and describe forest 3D structure; however, the residual phase error of TomoSAR affects the focus of the relative reflectance and can lead to errors in forest canopy height estimation. Therefore, this paper proposes a semi-empirical method to overcome the residual phase effects on forest canopy height estimation. In this study, we used airborne multi-baseline UAVSAR data to estimate forest canopy height via TomoSAR techniques and applied a semi-empirical method to improve forest canopy height estimation without phase calibration to mitigate the effects of phase error. The process is divided into three stages: the first step uses a semi-empirical method to initially determine the optimal relative reflectance loss threshold (K) by excluding the inverse extremes; in the second and third steps, the percentile height was used to gradually reduce the height interval between the upper and lower envelopes to minimize overestimation of extreme values and the lower vegetation. When the root mean square error (RMSE) was minimized, the percentile combinations were determined between the inversion results and a LiDAR dataset of the area. The results show that the canopy height estimation results are not satisfactory when relying solely on the K value to estimate the height difference between the envelope at the top of the forest and the ground; the best result was obtained when K = 0.4, but the corresponding R2 value was only 0.13, and the RMSE was 15.23 m. In our proposed method, the K value is determined as 0.3 by excluding the extreme values of the inversion result in the initial step—the corresponding R2 and RMSE values were 0.59 and 10.73 m, respectively, representing an RMSE decrease of 29.54% relative to the initial K value. After two steps of correction overestimation, the inversion accuracy was significantly improved with an R2 value of 0.65 and an RMSE of 9.69 m, corresponding to an RMSE decrease of 36.38%. Overall, the findings of the study represent an important reference for optimizing future spaceborne TomoSAR forest canopy height estimates

Rate Splitting Multiple Access Assisted Cell-Free Massive MIMO for URLLC Services in 5G and Beyond Networks

With the advent of the fifth-generation (5G) and beyond mobile communications, a plethora of Internet-of-Things (IoTs) applications, such as intelligent factories, smart transportation, and others are rapidly evolving. 5G and beyond networks support three typical application scenarios, i.e., ultra-reliable and low-latency communications (URLLC), enhanced mobile broadband (eMBB) and massive machine type communication (mMTC), each of which renders a distinct set of quality of service in terms of reliability, latency, transmission rate and connectivity. URLLC is seen as a crucial technology for supporting critical applications because of its emphasis on rare and extreme events, as well as its strict demands for low latency and high reliability [1]. For example, in order to effectively support applications like robot control, autonomous vehicles, and virtual reality, it is necessary to have an end-to-end delay threshold of 1 to 10 milliseconds and a block error rate (BLER) between 10−5 and 10−7 [2]. Due to the unique limitations of increased reliability and reduced latency, URLLC traffic often involves very brief transmission blocklengths, making Shannon’s capacity theorem irrelevant [3], [4]. On the other hand, existing cellular systems face difficulties in meeting the stringent quality of service (QoS) criteria needed for URLLC due to structural constraints. Therefore, it is essential to have advanced network architectures and various access technologies in order to achieve URLLC

The Protective Effects of Three Polysaccharides From Abrus cantoniensis Against Cyclophosphamide-Induced Immunosuppression and Oxidative Damage

This study was designed to systematically elucidate the immunomodulatory and antioxidant effects of three polysaccharide fractions (ACP60, ACP80, and ACPt2) from Abrus cantoniensis on cyclophosphamide (CTX)-induced immunosuppressive mice. The experimental mice were divided into 12 groups, then modeled and administrated with different doses of three polysaccharides (50, 150, 300 mg/kg/day) by gavage. The results showed that ACP could markedly recover the CTX-induced decline in immune organ and hemocytes indexes and promote proliferation of splenocytes, earlap swelling rate, secretion of cytokines (TNF-α, IFN-γ, IL-1β, IL-6), and immunoglobulin (Ig-M and Ig-G). Additionally, ACP improved the enzymatic activities of T-SOD and GSH-PX greatly, while the level of MDA was significantly decreased in the liver. In particular, ACPt2 had higher immunomodulatory and antioxidant activities than ACP60 and ACP80. Based on the present findings, ACP could be utilized as an efficacious candidate for immunomodulators and antioxidants, which provide a new application prospect in the food and pharmaceutical industries.</jats:p

Precision-Engineered Construction of Proton-Conducting Metal–Organic Frameworks

Highlights The effects of the size structure and stability of metal–organic frameworks (MOFs) on proton conduction are comprehensively summarized. Advanced strategies for constructing proton conduction MOFs are critically discussed. Challenges and opportunities for the development of novel proton-conducting MOFs are further outlined

A Method for Forest Canopy Height Inversion Based on UAVSAR and Fourier–Legendre Polynomial—Performance in Different Forest Types

Mapping forest canopy height at large regional scales is of great importance for the global carbon cycle. Polarized interferometric synthetic aperture radar is an efficient and irreplaceable remote sensing tool. Developing an efficient and accurate method for forest canopy height estimation is an important issue that needs to be addressed urgently. In this paper, we propose a novel four-stage forest height inversion method based on a Fourier–Legendre polynomial (FLP) with reference to the RVoG three-stage method, using the multi-baseline UAVSAR data from the AfriSAR project as the data source. The third-order FLP is used as the vertical structure function, and a small amount of ground phase and LiDAR canopy height is used as the input to solve and fix the FLP coefficients to replace the exponential function in the RVoG three-stage method. The performance of this method was tested in different forest types (mangrove and inland tropical forests). The results show that: (1) in mangroves with homogeneous forest structure, the accuracy based on the four-stage FLP method is better than that of the RVoG three-stage method. For the four-stage FLP method, R2 is 0.82, RMSE is 6.42 m and BIAS is 0.92 m, while the R2 of the RVoG three-stage method is 0.77, RMSE is 7.33 m, and bias is −3.49 m. In inland tropical forests with complex forest structure, the inversion accuracy based on the four-stage FLP method is lower than that of the RVoG three-stage method. The R2 is 0.50, RMSE is 11.54 m, and BIAS is 6.53 m for the four-stage FLP method; the R2 of the RVoG three-stage method is 0.72, RMSE is 8.68 m, and BIAS is 1.67 m. (2) Compared to the RVoG three-stage method, the efficiency of the four-stage FLP method is improved by about tenfold, with the reduction of model parameters. The inversion time of the FLP method in a mangrove forest is 3 min, and that of the RVoG three-stage method is 33 min. In an inland tropical forest, the inversion time of the FLP method is 2.25 min, and that of the RVoG three-stage method is 21 min. With the application of large regional scale data in the future, the method proposed in this study is more efficient when conditions allow.</jats:p