The otherwise considered being one of the smallest and weakest teeth in the arch, mandibular second premolars have been found to almost double its size in macrodontia. Such anomalies of morphological alterations have caused the teeth to be more weak and prone to diseases, as accredited to the increased surface area and particular morphology. Bilateral mandibular second premolar macrodontia is an extremely rare dental anomaly with only 5 cases reported to date, among which the first case was reported in 1967 by Primack. This article focuses on a rare case report of bilateral macrodontia of mandibular second premolar in an 18year old male.

In video surveillance, automatic detection of the anomalies is the active research area in computer technology. Even though various video anomaly detection methods are introduced, detecting anomalous events, such as illegal actions and crimes, is a major challenging issue in video surveillance. Thus, an effective automatic video anomaly detection strategy based on the deep convolutional neural network (deep CNN) is developed in this research. Initially, the input video surveillance is passed into the spatiotemporal feature descriptor, named Histograms of Optical Flow Orientation and Magnitude. The features obtained from the descriptor provide the optical flow details with the aspect of normal patterns from the scene. These patterns are further subjected to the deep CNN, which is trained using the proposed dragonfly-rider optimization algorithm (DragROA) to assure the classification either as an anomalous activity or normal. The proposed DragROA is the combination of the standard dragonfly optimization algorithm and the standard rider optimization algorithm. The implementation of the proposed DragROA-based deep CNN is carried out using two datasets, namely anomaly detection dataset and UMN dataset; the performance is analyzed using the metrics, namely accuracy, sensitivity and specificity. From the analysis, it is depicted that the proposed method obtains the maximum accuracy, sensitivity and specificity of 0.9922, 0.9809 and 1, respectively, for the UCSD dataset.

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Early diagnosis of diabetes is clinically important in reducing health complications worldwide. In this respect HbA1c has become an accurate biomarker for the diagnosis of Diabetes Mellitus (DM) and its complication [1]. In the present study HbA1c measured in subject of age <20,21-30,31-40 yrs and the level found to show high risk for DM in youngsters. Hence counselling at least once a month is warranted. To be most effective to reduce or prevent the prevalence in youngsters the importance of controlling HbA1c and keeping it at low level can be achieved by including in the curriculum right from school ageing. It will reduce the financial burden on state and central government authorities. Key words: HbA1c, Diabetes Mellitus 2.