Deep Neural Network based Anomaly Detection for Real Time Video Surveillance

One of the main concerns across all kinds of domains has always been security. With the crime rates increasing every year the need to control has become crucial. Among the various methods present to monitor crime or any anomalous behavior is through video surveillance. Nowadays security cameras capture incidents in almost all public and private place if desired. Even though we have abundance of data in the form of videos they need to be analyzed manually. This results in long hours of manual labour and even small human discrepancies may have huge consequences negatively. For this purpose, a Convolution Neural Network (CNN) based model is built to detect any form of abnormal activities or anomalies in the video footages. This model converts the input video into frames and detects the anomalous frames. To increase the efficiency of the model, the data is de-noised with Gaussian blur feature. The avenue dataset is used in this work to detect and predict various kinds of anomalies. The performance of the model is measured using classification accuracy and the results are reported

Comparison of coronally advanced versus semilunar coronally repositioned flap in the management of maxillary gingival recessions

Objectives: Maxillary gingival recessions can be managed by both semilunar coronally repositioned flap (SLCRF) and coronally advanced flap (CAF). The objective of this study was to compare SLRCF and CAF in terms of wound healing and periodontal parameters in the presence of magnification. Materials and Methods: Thirty patients with Miller’s class I gingival recession in maxillary anteriors and premolars were assigned to 2 groups including SLCRF and CAF. All procedures were performed using 2.5X magnifying loupes. Wound healing and periodontal clinical parameters were assessed at baseline and at 2nd, 4th, 8th and 12th week. Results: No significant difference was observed in wound healing and mean percentage root coverage in both the groups at 12th week (p > 0.05). However, SLCRF showed a statistically significant reduction in percentage of root coverage (PRC) at 12th week compared to 2nd week (p < 0.05). A significant gain in Clinical attachment level, width of keratinised tissue and a significant reduction in Recession Depth and Probing Depth were seen in both the groups at 12th week. Conclusion: Within the limitation of this study, both techniques resulted in similar wound healing at 12th week with the use of magnification. CAF provided more root coverage compared to SLCRF technique in the maxillary class I gingival recession defects

Isolation and Characterization of Neural Crest-Derived Stem Cells from Dental Pulp of Neonatal Mice

Dental pulp stem cells (DPSCs) are shown to reside within the tooth and play an important role in dentin regeneration. DPSCs were first isolated and characterized from human teeth and most studies have focused on using this adult stem cell for clinical applications. However, mouse DPSCs have not been well characterized and their origin(s) have not yet been elucidated. Herein we examined if murine DPSCs are neural crest derived and determined their in vitro and in vivo capacity. DPSCs from neonatal murine tooth pulp expressed embryonic stem cell and neural crest related genes, but lacked expression of mesodermal genes. Cells isolated from the Wnt1-Cre/R26R-LacZ model, a reporter of neural crest-derived tissues, indicated that DPSCs were Wnt1-marked and therefore of neural crest origin. Clonal DPSCs showed multi-differentiation in neural crest lineage for odontoblasts, chondrocytes, adipocytes, neurons, and smooth muscles. Following in vivo subcutaneous transplantation with hydroxyapatite/tricalcium phosphate, based on tissue/cell morphology and specific antibody staining, the clones differentiated into odontoblast-like cells and produced dentin-like structure. Conversely, bone marrow stromal cells (BMSCs) gave rise to osteoblast-like cells and generated bone-like structure. Interestingly, the capillary distribution in the DPSC transplants showed close proximity to odontoblasts whereas in the BMSC transplants bone condensations were distant to capillaries resembling dentinogenesis in the former vs. osteogenesis in the latter. Thus we demonstrate the existence of neural crest-derived DPSCs with differentiation capacity into cranial mesenchymal tissues and other neural crest-derived tissues. In turn, DPSCs hold promise as a source for regenerating cranial mesenchyme and other neural crest derived tissues

A Weakly-Robust PTAS for Minimum Clique Partition in Unit Disk Graphs

We consider the problem of partitioning the set of vertices of a given unit disk graph (UDG) into a minimum number of cliques. The problem is NP-hard and various constant factor approximations are known, with the current best ratio of 3. Our main result is a {\em weakly robust} polynomial time approximation scheme (PTAS) for UDGs expressed with edge-lengths, it either (i) computes a clique partition or (ii) gives a certificate that the graph is not a UDG; for the case (i) that it computes a clique partition, we show that it is guaranteed to be within (1+\eps) ratio of the optimum if the input is UDG; however if the input is not a UDG it either computes a clique partition as in case (i) with no guarantee on the quality of the clique partition or detects that it is not a UDG. Noting that recognition of UDG's is NP-hard even if we are given edge lengths, our PTAS is a weakly-robust algorithm. Our algorithm can be transformed into an O(\frac{\log^* n}{\eps^{O(1)}}) time distributed PTAS. We consider a weighted version of the clique partition problem on vertex weighted UDGs that generalizes the problem. We note some key distinctions with the unweighted version, where ideas useful in obtaining a PTAS breakdown. Yet, surprisingly, it admits a (2+\eps)-approximation algorithm for the weighted case where the graph is expressed, say, as an adjacency matrix. This improves on the best known 8-approximation for the {\em unweighted} case for UDGs expressed in standard form.Comment: 21 pages, 9 figure

Automatic detection of visual faults on photovoltaic modules using deep ensemble learning network

The present study proposes an ensemble-based deep neural network (DNN) model for autonomous detection of visual faults such as glass breakage, burn marks, snail trail, and discoloration, delamination on various photovoltaic modules (PVM). The proposed technique utilizes an image dataset captured by RGB (Red, Green, Blue) camera mounted on an unmanned aerial vehicle (UAV). In the first step, the images are preprocessed by deriving spatial and frequency domain features, such as discrete wavelet transform (DWT), texture, grey level co-occurrence matrix (GLCM), fast Fourier transform (FFT), and grey level difference method (GLDM). The processed images are inserted as input in the proposed ensemble-based deep neural network (DNN) model in order to detect any visual faults on the photovoltaic modules (PVM). The performance of the proposed model is evaluated through classification accuracy, receiver operating characteristic (ROC) curve, and confusion matrix. The results demonstrate that the proposed ensemble-based deep neural network (DNN) model, along with the random forest classifier, achieved a classification accuracy of 99.68% for detecting visual faults on the PV modules. To verify the performance and robustness of the proposed model, we compare our model’s results to those of various classification approaches described in the literature. The suggested approach is compatible with the commercial unmanned aerial vehicle (UAV) embedded flight management system for fault detection

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Metabolic adaptation drives arsenic trioxide resistance in acute promyelocytic leukemia.

Acquired genetic mutations can confer resistance to arsenic trioxide (ATO) in the treatment of acute promyelocytic leukemia (APL). However, such resistance-conferring mutations are rare and do not explain most disease recurrence seen in the clinic. We have generated stable ATO-resistant promyelocytic cell lines that are also less sensitive to ATRA and the combination of ATO and ATRA compared to the sensitive cell line. Characterization of these in-house generated resistant cell lines showed significant differences in immunophenotype, drug transporter expression, anti-apoptotic protein dependence, and PML-RARA mutation. Gene expression profiling revealed prominent dysregulation of the cellular metabolic pathways in these ATO resistant APL cell lines. Glycolytic inhibition by 2-DG was sufficient and comparable to the standard of care (ATO) in targeting the sensitive APL cell line. 2-DG was also effective in the in vivo transplantable APL mouse model; however, it did not affect the ATO resistant cell lines. In contrast, the resistant cell lines were significantly affected by compounds targeting the mitochondrial respiration when combined with ATO, irrespective of the ATO resistance-conferring genetic mutations or the pattern of their anti-apoptotic protein dependency. Our data demonstrate that the addition of mitocans in combination with ATO can overcome ATO resistance. We further show that this combination has the potential in the treatment of non-M3 AML and relapsed APL. The translation of this approach in the clinic needs to be explored further