Reversible signal abnormalities in the hippocampus and neocortex after prolonged seizures

PURPOSE: To investigate the phenomenon of reversible increased signal intensity of medial temporal lobe structures and cerebral neocortex seen on MR images of six patients with recent prolonged seizure activity. METHODS: After excluding patients with known causes of reversible signal abnormalities (such as hypertensive encephalopathy), we retrospectively reviewed the clinical findings and MR studies of six patients whose MR studies showed reversible signal abnormalities. MR pulse sequences included T2-weighted spin-echo coronal views or conventional short-tau inversion-recovery coronal images of the temporal lobes. RESULTS: All six MR studies showed increased signal intensity within the medial temporal lobe, including the hippocampus in five studies. All follow-up MR examinations showed partial or complete resolution of the hyperintensity within the medial temporal lobe and the neocortex. In one patient, results of a brain biopsy revealed severe cerebral cortical gliosis. Temporal lobectomy performed 4 years later showed moderate cortical gliosis and nonspecific hippocampal cell loss and gliosis. CONCLUSION: Significant hyperintensity within the temporal lobe is demonstrable on MR images after prolonged seizure activity, suggestive of seizure-induced edema or gliosis. Damage to medial temporal lobe structures by prolonged seizure activity indicates a possible mechanism of epileptogenic disorders.     

A new bath for the electrodeposition of aluminium. II. Kinetics and mechanism of the deposition and dissolution processes

The electrodeposition of aluminium was carried out successfully in AlCl₃-LiAlH₄/tetrahydrofuran-toluene solutions. The kinetic parameters controlling the cathodic deposition and anodic dissolution of aluminium were derived. The effect of the total concentration of aluminium and the molar ratio of AlCl₃ to LiAlH₄ on the current density-potential behaviour of the rotating disc aluminium electrode was also studied. A detailed mechanism for the electrode reactions was suggested.     

True Three-Dimensional Reconstruction (TTR) Application of Algorithm toward Full Utilization of Oblique Rays

The true three-dimensional reconstruction (TTR) algorithm previously proposed by the authors is extended to an algorithm with which full utilization of all the oblique rays is possible. Through this extended TTR (ETTR) algorithm, it is now possible not only to reconstruct an image of a larger object but also possible to obtain images which have substantially better signal-to-noise ratio. The basic TTR algorithm, as well as its extended version, will be discussed together with computer simulation results. In the appendixes, a new two-dimensional Fourier domain weighting function necessary for the implementation of the TTR and ETTR algorithms as well as the generality of the proposed TTR algorithm are discussed.     

A New Support Film for Cryo Electron Microscopy Protein Structure Analysis Based on Covalently Functionalized Graphene

Protein adsorption at the air–water interface is a serious problem in cryogenic electron microscopy (cryoEM) as it restricts particle orientations in the vitrified ice-film and promotes protein denaturation. To address this issue, the preparation of a graphene-based modified support film for coverage of conventional holey carbon transmission electron microscopy (TEM) grids is presented. The chemical modification of graphene sheets enables the universal covalent anchoring of unmodified proteins via inherent surface-exposed lysine or cysteine residues in a one-step reaction. Langmuir–Blodgett (LB) trough approach is applied for deposition of functionalized graphene sheets onto commercially available holey carbon TEM grids. The application of the modified TEM grids in single particle analysis (SPA) shows high protein binding to the surface of the graphene-based support film. Suitability for high resolution structure determination is confirmed by SPA of apoferritin. Prevention of protein denaturation at the air–water interface and improvement of particle orientations is shown using human 20S proteasome, demonstrating the potential of the support film for structural biology.     

An Optimized Algorithm for CR-MIMO Wireless Networks

With the rapid development of wireless communication technology, the spectrum resources are increasingly strained which needs optimal solutions. Cognitive radio (CR) is one of the key technologies to solve this problem. Spectrum sensing not only includes the precise detection of the communication signal of the primary user (PU), but also the precise identification of its modulation type, which can then determine the a priori information such as the PU’ service category, so as to use this information to make the cognitive user (CU) aware to discover and use the idle spectrum more effectively, and improve the spectrum utilization. Spectrum sensing is the primary feature and core part of CR. Classical sensing algorithms includes energy detection, cyclostationary feature detection, matched filter detection, and so on. The energy detection algorithm has a simple structure and does not require prior knowledge of the PU transmitter signal, but it is easily affected by noise and the threshold is not easy to determine. The combination of multiple-input multiple-output (MIMO) with CR improves the spectral efficiency and multi-path fading utilization. To best utilize the PU spectrum while minimizing the overall transmit power, an iterative technique based on semidefinite programming (SDP) and minimum mean squared error (MMSE) is proposed. Also, this article proposed a new method for max-min fairness beamforming. When compared to existing algorithms, the simulation results show that the proposed algorithms perform better in terms of total transmitted power and signal-to-interference plus noise ratio (SINR). Furthermore, the proposed algorithm effectively improved the system performance in terms of number of iterations, interference temperature threshold and balance SINR level which makes it superior over the conventional schemes.     

Synthesis,characterization, and voltammetric hydrogen peroxide sensing on novel monometallic (Ag,Co/MWCNT) and bimetallic (AgCo/MWCNT) alloy nanoparticles

Multiwall carbon nanotube supported (MWCNT) Ag, Co, and Ag-Co alloy nanocatalysts were synthesized at varying metal loadings by borohydride reduction methods without stabilizers to obtain enhanced hydrogen peroxide sensitivity. The resulting materials were characterized employing Scanning electron microscopy (SEM), Energy dispersive X-ray spectroscopy (EDX), and X-ray diffraction (XRD). For electrochemical measurements carried out cyclic voltammetry (CV) and differential pulse voltammetry (DPV), glassy carbon electrode (GCE) was modified with Ag/MWCNT, Co/MWCNT, and Ag-Co/MWCNT alloy nanoparticles. Ag-Co/MWCNT/GCE exhibited the highest performance toward electrochemical oxidation of H₂O₂ in 0.1 M phosphate buffered solution (PBS). Furthermore, the sensitivity and the limit of detection values for Ag-Co/MWCNT/GCE were obtained as 57.14 µA cm^?2 mM^?1and 0.74 µM, respectively. However, the sensitivity values for Ag/MWCNT/GCE, and Co/MWCNT/GCE are 41.66 and 13.88 µA cm^?2 mM^?1, respectively. The LOD values were predicted as 1.84 µM for Ag/MWCNT/GCE and 3.3 µM for Co/MWCNT/GCE.

In addition, the interference experiment indicated that the Ag-Co/MWCNT alloy nanoparticles have good selectivity toward H₂O₂.     

Intelligent DoS Attack Detection with Congestion Control Technique for VANETs

Vehicular Ad hoc Network (VANET) has become an integral part of Intelligent Transportation Systems (ITS) in today's life. VANET is a network that can be heavily scaled up with a number of vehicles and road side units that keep fluctuating in real world. VANET is susceptible to security issues, particularly DoS attacks, owing to maximum unpredictability in location. So, effective identification and the classification of attacks have become the major requirements for secure data transmission in VANET. At the same time, congestion control is also one of the key research problems in VANET which aims at minimizing the time expended on roads and calculating travel time as well as waiting time at intersections, for a traveler. With this motivation, the current research paper presents an intelligent DoS attack detection with Congestion Control (IDoS-CC) technique for VANET. The presented IDoS-CC technique involves two-stage processes namely, Teaching and Learning Based Optimization (TLBO)-based Congestion Control (TLBO-CC) and Gated Recurrent Unit (GRU)-based DoS detection (GRU-DoSD). The goal of IDoS-CC technique is to reduce the level of congestion and detect the attacks that exist in the network. TLBO algorithm is also involved in IDoS-CC technique for optimization of the routes taken by vehicles via traffic signals and to minimize the congestion on a particular route instantaneously so as to assure minimal fuel utilization. TLBO is applied to avoid congestion on roadways. Besides, GRU-DoSD model is employed as a classification model to effectively discriminate the compromised and genuine vehicles in the network. The outcomes from a series of simulation analyses highlight the supremacy of the proposed IDoS-CC technique as it reduced the congestion and successfully identified the DoS attacks in network.     

Compact Bat Algorithm with Deep Learning Model for Biomedical EEG EyeState Classification

Electroencephalography (EEG) eye state classification becomes an essential tool to identify the cognitive state of humans. It can be used in several fields such as motor imagery recognition, drug effect detection, emotion categorization, seizure detection, etc. With the latest advances in deep learning (DL) models, it is possible to design an accurate and prompt EEG EyeState classification problem. In this view, this study presents a novel compact bat algorithm with deep learning model for biomedical EEG EyeState classification (CBADL-BEESC) model. The major intention of the CBADL-BEESC technique aims to categorize the presence of EEG EyeState. The CBADL-BEESC model performs feature extraction using the ALexNet model which helps to produce useful feature vectors. In addition, extreme learning machine autoencoder (ELM-AE) model is applied to classify the EEG signals and the parameter tuning of the ELM-AE model is performed using CBA. The experimental result analysis of the CBADL-BEESC model is carried out on benchmark results and the comparative outcome reported the supremacy of the CBADL-BEESC model over the recent methods.     

Optimal Water Allocation from Subsurface Dams: A Risk-Based Optimization Approach

Subsurface dams, strongly advocated in the 1992 United Nations Agenda-21, have been widely studied to increase groundwater storage capacity. However, an optimal allocation of augmented water with the construction of the subsurface dams to compensate for the water shortage during dry periods has not so far been investigated. This study, therefore, presents a risk-based simulation–optimization framework to determine optimal water allocation with subsurface dams, which minimizes the risk of water shortage in different climatic conditions. The developed framework was evaluated in Al-Aswad falaj, an ancient water supply system in which a gently sloping underground channel was dug to convey water from an aquifer via the gravity force to the surface for irrigation of downstream agricultural zones. The groundwater dynamics were modeled using MODFLOW UnStructured-Grid. The data of boreholes were used to generate a three-dimensional stratigraphic model, which was used to define materials and elevations of five-layer grid cells. The validated groundwater model was employed to assess the effects of the subsurface dam on the discharge of the falaj. A Conditional Value-at-Risk optimization model was also developed to minimize the risk of water shortage for the augmented discharge on downstream agricultural zones. Results show that discharge of the falaj is significantly augmented with a long-term average increase of 46.51%. Moreover, it was found that the developed framework decreases the water shortage percentage in 5% of the worst cases from 87%, 75%, and 32% to 53%, 32%, and 0% under the current and augmented discharge in dry, normal, and wet periods, respectively.