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Addressing the challenge of missing values is a critical step when preparing and analyzing data. This process, known as imputation, helps ensure the dataset is complete, accurate, and reliable. As a result, the possibility of bias and errors in subsequent analysis is significantly reduced. The key contribution of this work is to assess the efficiency of imputation by feature importance employing several base learning algorithms. This study investigates the effectiveness of individual and ensemble machine learning methods as the base learning algorithms, including support vector machines with the linear kernel (SVML), boosted linear regression (BLR), deep boost (DBP), and K-Nearest Neighbor (K-NN), in predicting missingness patterns. The dataset for each category explicitly introduces missingness patterns, including missing not at random (MNAR), at random (MAR), and completely at random (MCAR) at different percentages (15%, 45%, 25%), and Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) are among the commonly used performance matrices employed to gauge the effectiveness of the IBFI framework. The dataset for this study, comprising soil radon and thoron gas concentration time series along with meteorological parameters, the dataset spans a 14-month period. Four earthquake events were recorded during the whole study period. The deep boosting model (DBP) consistently outperforms other base learning models in imputing missing values across various variables within the imputation by feature importance (IBFI) framework. Specifically, DBP achieves an average RMSE value of 573.165 for the Radon variable under MCAR scenarios. For the Thoron variable, DBP demonstrates impressive performance with average MAPE values of 0.7405, 0.7249, and 0.8212 under MCAR, MNAR, and MAR conditions respectively. Additionally, DBP yields competitive results for imputing missing entries in Temperature, Relative Humidity, and Pressure variables. These findings highlight effectiveness of DBP in accurately predicting missing values. This study concludes that the IBFI with deep boosting model executes the imputations quite accurately relative to other base learning models. Moreover, this study recommends using DBP as a base learning algorithm in imputation by feature importance framework for uncovering hidden patterns in time series data like soil radon gas. The replication of the study using heterogeneous datasets would enhance the understanding of the generalization and broader applicability of the imputation by feature importance.

The activation of caspases is central to apoptotic process in living systems. Defects in apoptosis have been implicated with carcinogenesis. Need to develop smart agents capable of inducing apoptosis in tumor cells is obvious. With this motive, diversity oriented synthesis of 1-benzylpyrrolidin-3-ol analogues was envisaged. The multi component Ugi reaction synthesized library of electronically diverse analogues was explored for cytotoxic propensity towards a panel of human cancer cell lines at 10 µM. The lead compounds exhibit a selective cytotoxicity towards HL-60 cells as compared to cell lines derived from solid tumors. Besides, their milder cytotoxic effect on non-cancerous cell lines reaffirm their selective action towards cancer cells only. The lead molecules were tested for their ability to target caspase-3, as a vital protease triggering apoptosis. The lead compounds were observed to induce apoptosis in HL-60 cells around 10 µM concentration. The lead compounds exhibited various non-covalent supra type interactions with caspase-3 key residues around the active site. The binding ability of lead compounds with caspase-3 was studied via molecular docking and molecular dynamic (MD) simulations. MD simulations indicated the stability of compound-caspase-3 complex throughout the 50 ns simulation run. The stability and bio-availability of the lead compounds under physiological conditions was assessed by their interaction with Bovine Serum Albumin (BSA) as model protein. BSA interactions of lead compounds were studied by various bio-physical methods and further substantiated with in silico MD simulations.

Author(s): Sna Rashid [sup.1], Ghulam Mustafa Peerzada [sup.1], Nadeem Bashir Ganaie [sup.1] [sup.2], Shafia Lateef [sup.1] Author Affiliations: (1) grid.412997.0, 0000 0001 2294 5433, Department of Chemistry, University of Kashmir, [...], Ion exchange resins are well known because of their vast applications in the processes like water softening, catalysis, waste water treatment, hydrometallurgy and chromatography. In the present investigation based on the interaction of different ion exchangers with ferroin to be used as catalyst in a nonlinear chemical reaction, quantitative estimation of Fe.sup.2+ was done by loading it on three different ion exchange resins, viz. Dowex Marathon C Na, Amberlite IRC86 and Dowex m4195 at different loading time intervals. The supernatant containing the residual ferroin was separated from ion exchange resins and added directly to the substrate molecule of the chemical oscillator as catalyst and the series of reaction systems were monitored by potentiometry and spectrophotometry and the results obtained were confirmed by advanced cyclic voltammetry as a supportive technique. Thus, the effect of catalyst after its interaction with ion exchange resins at optimal level was monitored. At the same time, it was found that the concentration of ferroin to be involved in the Belousov-Zhabotinsky reaction as a catalyst in the present interactive systems can be estimated up to 10.sup.-5 M and even lower, depending on the experimental conditions.