Alzheimer Disease Detection Techniques and Methods: A Review

Brain pathological changes linked with Alzheimer's disease (AD) can be measured with Neuroimaging. In the past few years, these measures are rapidly integrated into the signatures of Alzheimer disease (AD) with the help of classification frameworks which are offering tools for diagnosis and prognosis. Here is the review study of Alzheimer's disease based on Neuroimaging and cognitive impairment classification. This work is a systematic review for the published work in the field of AD especially the computer-aided diagnosis. The imaging modalities include 1) Magnetic resonance imaging (MRI) 2) Functional MRI (fMRI) 3) Diffusion tensor imaging 4) Positron emission tomography (PET) and 5) amyloid-PET. The study revealed that the classification criterion based on the features shows promising results to diagnose the disease and helps in clinical progression. The most widely used machine learning classifiers for AD diagnosis include Support Vector Machine, Bayesian Classifiers, Linear Discriminant Analysis, and K-Nearest Neighbor along with Deep learning. The study revealed that the deep learning techniques and support vector machine give higher accuracies in the identification of Alzheimer’s disease. The possible challenges along with future directions are also discussed in the paper

Introduction of channel catfish Ictalurus punctatus (Rafinesque) in Pakistan and its performance during acclimatization and pond culture.

Abstract.-Two thousand fingerlings of channel catfish Ictalurus punctatus were imported from Thailand in a bid to introduce this fish in Pakistan. Five percent mortality occurred during transportation. Experiments were designed to observe catfish acclimatization in tanks or raceways for which five hundred fingerlings (average weight 10.86±1.20g) were placed in five tanks of 2000-liter water capacity and another 500 fingerlings (average weight, 10.56±0.68g) were kept in five raceways of 5000-liter water capacity each. The fish were fed on imported diet for a period of 75 days. Mean weight gain of 27.22±1.75 g and 31.5±1.04 g and surv~7.5% and 95.9% were recorded in tanks and raceways, respectively. For studying growth of fish two stocking densities (3,000 and 3,500/ha) were maintained in ponds (0.04 ha) from December 2003 -November 2004. The weight gain was significantly higher in low stocking density (1,263.3 ± 60.9 g) compared with high stocking density (1,184.9±57.1 g). Fish production and survival between two stocking densities was not different (P>0.05)

Planar SIW leaky wave antenna with electronically reconfigurable E-and H-plane scanning

This paper reports on a novel technique of switching radiation characteristics electronically between E-and H-planes of planar Substrate Integrated Waveguide Leaky Wave Antennas (SIW-LWAs). The leaky wave mode is achieved through increasing the pitch of bounding metallic via posts on one side of SIW transmission section. The radiation switching is achieved by extending the top and bottom metallic planes to a distance of 1 mm along the leakage side. The extended section acts as a parallel plate section which is conveniently connected or disconnected from the leaking side of SIW through PIN diodes. The ‘ON’ state of PIN diodes extends the metal guides and results in the H-plane leakage whereas ‘OFF’ state of PIN diodes truncates the extended metal earlier and alter the leakage line boundary condition towards E-plane. The whole concept is validated by series of simulations followed by the realization and testing of the SIW-LWA. The measured radiation pattern scans about 54° in the E-plane between 10.0 GHz to 11.7 GHz, and 58° in the H-plane from 9 GHz to 10.6 GHz. The proposed topology is a suitable candidate for remote sensing and airborne applications

2-{4-[Acetyl(ethyl)amino]benzene-sulfonamido}benzoic acid

In the title compound, C17H18N2O5S, the dihedral angle between the aromatic rings is 68.59 (10)degrees and the C-S-N-C torsion angle is -81.84 (18)degrees. The molecular conformation is stabilized by an intramolecular N-H center dot center dot center dot O hydrogen bond, generating an S(6) ring. In the crystal, molecules are linked by C-H center dot center dot center dot O and O-H center dot center dot center dot O hydrogen bonds into a three-dimensional network

A robust regression-based stock exchange forecasting and determination of correlation between stock markets

Knowledge-based decision support systems for financial management are an important part of investment plans. Investors are avoiding investing in traditional investment areas such as banks due to low return on investment. The stock exchange is one of the major areas for investment presently. Various non-linear and complex factors affect the stock exchange. A robust stock exchange forecasting system remains an important need. From this line of research, we evaluate the performance of a regression-based model to check the robustness over large datasets. We also evaluate the effect of top stock exchange markets on each other. We evaluate our proposed model on the top 4 stock exchanges-New York, London, NASDAQ and Karachi stock exchange. We also evaluate our model on the top 3 companies-Apple, Microsoft, and Google. A huge (Big Data) historical data is gathered from Yahoo finance consisting of 20 years. Such huge data creates a Big Data problem. The performance of our system is evaluated on a 1-step, 6-step, and 12-step forecast. The experiments show that the proposed system produces excellent results. The results are presented in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE)

Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks

Flying ad-hoc networks (FANETs) are a very vibrant research area nowadays. They have many military and civil applications. Limited battery energy and the high mobility of micro unmanned aerial vehicles (UAVs) represent their two main problems, i.e., short flight time and inefficient routing. In this paper, we try to address both of these problems by means of efficient clustering. First, we adjust the transmission power of the UAVs by anticipating their operational requirements. Optimal transmission range will have minimum packet loss ratio (PLR) and better link quality, which ultimately save the energy consumed during communication. Second, we use a variant of the K-Means Density clustering algorithm for selection of cluster heads. Optimal cluster heads enhance the cluster lifetime and reduce the routing overhead. The proposed model outperforms the state of the art artificial intelligence techniques such as Ant Colony Optimization-based clustering algorithm and Grey Wolf Optimization-based clustering algorithm. The performance of the proposed algorithm is evaluated in term of number of clusters, cluster building time, cluster lifetime and energy consumption

Transfer Learning Assisted Classification and Detection of Alzheimer’s Disease Stages Using 3D MRI Scans

Alzheimer’s disease effects human brain cells and results in dementia. The gradual deterioration of the brain cells results in disability of performing daily routine tasks. The treatment for this disease is still not mature enough. However, its early diagnosis may allow restraining the spread of disease. For early detection of Alzheimer’s through brain Magnetic Resonance Imaging (MRI), an automated detection and classification system needs to be developed that can detect and classify the subject having dementia. These systems also need not only to classify dementia patients but to also identify the four progressing stages of dementia. The proposed system works on an efficient technique of utilizing transfer learning to classify the images by fine-tuning a pre-trained convolutional network, AlexNet. The architecture is trained and tested over the pre-processed segmented (Grey Matter, White Matter, and Cerebral Spinal Fluid) and un-segmented images for both binary and multi-class classification. The performance of the proposed system is evaluated over Open Access Series of Imaging Studies (OASIS) dataset. The algorithm showed promising results by giving the best overall accuracy of 92.85% for multi-class classification of un-segmented images

2-Phenitidine derivatives as suitable inhibitors of butyrylcholinesterase

This manuscript reports the synthesis of a series of N-substituted derivatives of 2-phenitidine. First, the reaction of 2-phenitidine (1) with benzene sulfonyl chloride (2) yielded N-(2-ethoxyphenyl) benzenesulfonamide (3), which further on treatment with sodium hydride and alkyl halides (4a-g) furnished into new sulfonamides (5a-g). Second, the phenitidine reacted with benzoyl chloride (6) and acetyl chloride (8) to yield the reported N-benzoyl phenitidine (7) and N-acetyl phenitidine (9), respectively. These derivatives were characterized by infrared spectroscopy, ¹H-NMR, and EI-MS, and then screened against acetylcholinesterase, butylcholinesterase, and lipoxygenase enzyme, and were found to be potent inhibitors of butyrylcholinesterase alone.<br>Este trabalho apresenta a síntese de uma série de derivados da 2-fenetidina N-substituídos. Primeiro, a reação da 2-fenetidina (1) com cloreto de benzenossulfonila (2) conduziu à N-(2-etoxifenil)benzenossulfonamida (3) que, após tratamento com hidreto de sódio e haletos de alquila (4a-g), originou novas sulfonamidas (5a-g). Em segundo lugar, a reação da fenetidina com cloreto de benzoíla (6) e cloreto de acetila (8) conduziu, respectivamente, à N-benzoilfenetidina (7) e N-acetilfenetidina (9). A caracterização destes derivados fez-se por IV, ¹H-RMN e EM-IE. Procedeu-se à avaliação da atividade inibidora destes compostos em relação às enzimas acetilcolinesterase, butirilcolinesterase e lipoxigenase. No entanto, apenas revelaram atividade inibidora da butirilcolinesterase