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Parkinson's disease (PD) is a neurological disease that produces uncontrollable movements and a variety of other symptoms. It can be difficult to make an accurate PD diagnosis since the signs and symptoms, especially early on, might be mistaken for other medical diseases or physiological changes associated with normal aging. This research proposed novel technique in predicting PD based on dopamine transporter scan (DaTscan) images of brain using deep learning techniques. Here the aim is to collect the historical data and live DaTscan image of patients with symptoms of PD and predict disease. Initially input data have been pre-processed for image resize, noise removal and smoothening. Then the processed image has been selected based on their features using kernel-based deep convolution neural network (KDCNN). The selected deep features have been classified using reinforcement Q-learning-based neural networks (RQLNNs) to predict the presence of PD. Here experimental results show feature-selected and classified output of DaTscan brain image using the proposed model. For MRI image dataset, the proposed technique obtained accuracy of 97.5%, precision of 93%, recall of 82% and F-1 score of 87%. The proposed technique obtained accuracy of 98%, precision of 93%, recall of 80% and F-1 score of 88% for DaTscan dataset. [ABSTRACT FROM AUTHOR]

Plant species identification is essential for healthy survival as well as the preservation and protection of biodiversity. Manual identification is time-consuming, hence to address this issue deep learning algorithms for automated plant species identification have been developed. A Novel Architecture comprising of EfficientB4Net, Convolutional Block Attention Module (CBAM) and Residual Block Decoder is proposed to act as Autoencoder for identification and retrieval of twenty distinct groups of medicinal plants, widely available in southern India. The EfficientB4 encoder compresses and encodes the input features along with channel and spatial features to the Residual Block Decoder for efficient learning. Residual Block Decoders work to reconstruct the data from the encoded form to be as close to the original input as possible, by eliminating noise. The information-rich encoded features and the global features from the CBAM are transferred to the fully connected layer and stored in the database for retrieval of the plants. When a query image is received, the encoded feature of the query image and the database images are compared using similarity measurement, and the related images are retrieved. From the retrieved images, the query image is identified and the experimental results clearly show that the proposed method has achieved 95% accuracy when compared with other methods. [ABSTRACT FROM AUTHOR]

In the present study, self-forming dynamic membrane (SFDM) on rigid high-density polyethylene (HDPE) mesh with a large pore size of 2 mm is reported for the first time. The system was investigated for utilisation in simulated wastewater treatment by integrating the mesh with an aerobic bioreactor. The SFDM was analysed using Fourier transform infra-red (FT-IR) spectroscopy and visualised by scanning electron microscopy (SEM). The effect of the operating parameter on the change in composition of SFDM was also investigated. The system was used as a single unit for treatment of wastewater and showed stability over long-term treatment. The system could achieve a chemical oxygen demand (COD) reduction of 82.16 ± 6.47% at an influent COD concentration of 613.93 ± 72.13 mg/l and ammonia removal efficiency of 97.21 ± 0.62% at an influent ammonia concentration of 55.54 ± 2.23 mg/l. The reactor generated high-quality effluent and the turbidity recorded was less than 2NTU. In addition, the operational parameters, namely hydraulic retention time and organic loading rate, were optimised. [ABSTRACT FROM AUTHOR]

Background: Human dentition starts to erupt from age 6 months to 2 years with appearance of temporary teeth followed by appearance of permanent set of teeth from 5 to 25 years. The assessment of age in school going children aged 12-14 years by clinical eruption of 2nd molar teeth is very important in medico-legal work and comes up frequently in connection with the questions of criminal responsibility, rape, kidnapping, child labor, etc. Materials & method: The subject for the present study were selected by random sampling and consists of 50 boys and 50 girls, studying in 7th to 10th standard in various higher secondary schools of Chennai city and were broadly classified in the 2 groups of equal strength and equal male to female ratio. The oral cavity was examined with the help of a torch light, mouth mirror, probe and a tongue depressor. For the conduction of the study, a proforma was devised to collect all relevant information like age, sex, socio-economic status, diet, habits, etc. from the subject. Dental charting was done as per the Federation Dentaire Internationale system (F.D.I). The subjects were also classified after assessing their socio-economic status as per the Modified B.G. Prasad classification for the year 2008. Results: In age group 12-13 years, more than 40% of the children had more or the other 2nd molar teeth not erupted whereas their countersex had 2nd molar in stage 2 or 3. In age group 13-14 years the contrast in eruption stages between male and female was blurred. The dental eruption is quite earlier in females than males and the 2nd molars of the lower jaw appear earlier than the upper jaw. However there is no significant difference in eruption of 2nd molars of the right and left side in the same jaw. [ABSTRACT FROM AUTHOR]

The styloid process (SP) on the temporal bone is a highly variable formation. The normal length of the SP ranges from 20 to 30 mm. In spite of its being normally distributed in the population, SPs could be divided into two groups - short SPs with >20 mm and long SPs with <20 mm in length. The SP is often denoted as elongated when it is longer than 30 mm or 33 mm. These dimensions, based on early reports, do not respect the natural variation of the SP. The aim of this study is to investigate the natural variation of the length of the SP. [ABSTRACT FROM AUTHOR]

A novel magnetic biocomposite was synthesized using metal chlorides and aquatic macrophytes by co-precipitation method. The resulting product, magnetic biocomposite was characterized by Fourier transform infrared spectra (FTIR), X-ray diffraction (XRD), Energy-dispersive X-ray spectroscopy (EDX) and Scanning electron microscope (SEM). The adsorption performance of the magnetic biocomposite was tested with removal of Metanil Yellow dye from aqueous solution. The effect of influencing parameters such as initial dye concentration, solution pH and agitation were investigated. The equilibrium isotherm was well described by the Langmuir model with the with maximum adsorption capacity of 90.91mg/g. Adsorption kinetics experiments were carried out and the data were well fitted by a pseudo-second-order equation. The results revealed that the magnetic biocomposite could efficiently adsorb the azo dyes from aqueous solution, and the spent adsorbents could be recovered completely by magnetic separation process. Therefore, the prepared magnetic biocomposite could thus be used as promising adsorbent for the removal of azo dyes from polluted water., (Copyright © 2015 Elsevier Inc. All rights reserved.)