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•Artemis protein appears to play an important role in cancer.•It is involved in the non-homologous end joining repair and V(D)J recombination.•Loss of its function causes cellular radiosensitivity.•Its deregulation correlates with poor outcomes and treatment resistance in cancer.•It can be targetable for cancer therapies by non-oncology and novel drugs.

Clinicopathologic profile and outcome of 15 children (15 years or above) with diffuse large B-cell lymphoma treated with MCP-842 protocol are reported. Eleven of 15 presented with advanced (stage-III/IV) disease. Post-2 cycles of chemotherapy, complete metabolic and morphologic response was documented in 10 (66%) and rest 5 (33%) with partial response achieved complete metabolic remission by end of treatment. At a median follow-up of 44 months (range: 16 to 79 mo), the 3-year event-free survival and overall-survival were 77.1%±11.7% and 85.7%±9.4%, respectively. Though majority of our patients had advanced disease, outcome on MCP-842 protocol was satisfactory.

Cross-sections of Myristica malabarica(Lam) seed and mouse brain tissue were imprinted on such ordinary surfaces as printer paper and TLC plates, and successfully imaged by desorption electrospray ionization mass spectrometry (DESI-MS) at 250 μm resolution. Chemical images representing the distribution of the alkaloid malabaricone C in the seed substructures and individual lipids in the substructures of the brain were obtained. Practical implications include analysis of irregular or soft materials, easy recording, transportation and storage of the latent image, and posterior analysis of the samples by different techniques without the requirement of addition of matrices or use of specific types of surfaces.

Vibration Analysis has been extensively used in recent past for gear fault diagnosis. The vibration signals extracted is usually contaminated with noise and may lead to wrong interpretation of results. The denoising of extracted vibration signals helps the fault diagnosis by giving meaningful results. Wavelet Transform (WT) increases signal to noise ratio (SNR), reduces root mean square error (RMSE) and is effective to denoise the gear vibration signals. The extracted signals have to be denoised by selecting a proper denoising scheme in order to prevent the loss of signal information along with noise. An approach has been made in this work to show the effectiveness of Principal Component Analysis (PCA) to denoise gear vibration signal. In this regard three selected wavelet based denoising schemes namely PCA, Empirical Mode Decomposition (EMD), Neighcoeff Coefficient (NC), has been compared with Adaptive Threshold (AT) an extensively used wavelet based denoising scheme for gear vibration signal. The vibration signals acquired from a customized gear test rig were denoised by above mentioned four denoising schemes. The fault identification capability as well as SNR, Kurtosis and RMSE for the four denoising schemes have been compared. Features extracted from the denoised signals have been used to train and test artificial neural network (ANN) models. The performances of the four denoising schemes have been evaluated based on the performance of the ANN models. The best denoising scheme has been identified, based on the classification accuracy results. PCA is effective in all the regards as a best denoising scheme.