Synthesis and Characterization of ZnO/CuO Nanocomposites as an Effective Photocatalyst and Gas Sensor for Environmental Remediation

Journal of Inorganic and Organometallic Polymers and Materials - Current study delineates the synthesis and environmental applications of ZnO/CuO nanocomposite in photocatalysis and gas sensing....     

Deep learning and machine learning-based early survival predictions of glioblastoma patients using pre-operative three-dimensional brain magnetic resonance imaging modalities

To propose and implement an automated machine learning (ML) based methodology to predict the overall survival of glioblastoma multiforme (GBM) patients. In the proposed methodology, we used deep learning (DL) based 3D U-shaped Convolutional Neural Network inspired encoder-decoder architecture to segment the brain tumor. Further, feature extraction was performed on these segmented and raw magnetic resonance imaging (MRI) scans using a pre-trained 2D residual neural network. The dimension-reduced principal components were integrated with clinical data and the handcrafted features of tumor subregions to compare the performance of regression-based automated ML techniques. Through the proposed methodology, we achieved the mean squared error (MSE) of 87 067.328, median squared error of 30 915.66, and a SpearmanR correlation of 0.326 for survival prediction (SP) with the validation set of Multimodal Brain Tumor Segmentation 2020 dataset. These results made the MSE far better than the existing automated techniques for the same patients. Automated SP of GBM patients is a crucial topic with its relevance in clinical use. The results proved that DL-based feature extraction using 2D pre-trained networks is better than many heavily trained 3D and 2D prediction models from scratch. The ensembled approach has produced better results than single models. The most crucial feature affecting GBM patients' survival is the patient's age, as per the feature importance plots presented in this work. The most critical MRI modality for SP of GBM patients is the T2 fluid attenuated inversion recovery, as evident from the feature importance plots.     

Nucleoside-Driven Specificity of DNA Methyltransferase**

We have studied the adenosine binding specificities of two bacterial DNA methyltransferases, Taq methyltransferase (M.TaqI), and HhaI methyltransferase (M.HhaI). While they have similar cofactor binding pocket interactions, experimental data showed different specificity for novel S-nucleobase-l -methionine cofactors (SNMs; N=guanosyl, cytidyl, uridyl). Protein dynamics corroborate the experimental data on the cofactor specificities. For M.TaqI the specificity for S-adenosyl-l -methionine (SAM) is governed by the tight binding on the nucleoside part of the cofactor, while for M.HhaI the degree of freedom of the nucleoside chain allows the acceptance of other bases. The experimental data prove catalytically productive methylation by the M.HhaI binding pocket for all the SNMs. Our results suggest a new route for successful design of unnatural SNM analogues for methyltransferases as a tool for cofactor engineering.     

An improved sparsity-aware normalized least-mean-square scheme for underwater communication

Underwater communication (UWC) is widely used in coastal surveillance and early warning systems. Precise channel estimation is vital for efficient and reliable UWC. The sparse direct-adaptive filtering algorithms have become popular in UWC. Herein, we present an improved adaptive convex-combination method for the identification of sparse structures using a reweighted normalized least-mean-square (RNLMS) algorithm. Moreover, to make RNLMS algorithm independent of the reweighted $l_1$-norm parameter, a modified sparsity-aware adaptive zero-attracting RNLMS (AZA-RNLMS) algorithm is introduced to ensure accurate modeling. In addition, we present a quantitative analysis of this algorithm to evaluate the convergence speed and accuracy. Furthermore, we derive an excess mean-square-error expression that proves that the AZA-RNLMS algorithm performs better for the harsh underwater channel. The measured data from the experimental channel of SPACE08 is used for simulation, and results are presented to verify the performance of the proposed algorithm. The simulation results confirm that the proposed algorithm for underwater channel estimation performs better than the earlier schemes.