A REVIEW ON MATRIX ASSISTED LASER DESORPTION/INOZATION MASS SPECTROSCOPY

ABSTRACTMatrix assisted laser desorption ionization mass spectroscopy (MALDI-MS) is the most important technique of MS to analyze polymer systems. It isa special case of MS using specific sample preparation methods and low fluence laser desorption to create the analyte ions. This technique is basedupon an ultraviolet absorbing matrix. The matrix and the polymer are mixed at a molecular level in an appropriate solvent. The solvent helps preventaggregation of the polymer. The sample matrix mixture is placed on the sample probe tip, under vacuum conditions; the solvent is removed, leaving cocrystallizedpolymer molecules homogenously dispersed within matrix molecules. When the pulsed laser beam is tuned to the appropriate frequency,the energy is transferred to the matrix which is partially vaporized, carrying intact polymer into the vapor phase and charging the polymer chains inthe linear time of flight (TOF) analyzer. This review includes the detailed information of MALDI-MS, MALDI-TOF.Keywords: Matrix assisted laser desorption ionization mass spectroscopy, Principle, Sample preparation techniques, Matrix assisted laser desorptionionization - time of flight, Matrix assisted laser desorption ionization - mass spectrometric imaging, Applications

An exploration of fractal-based prognostic model and comparative analysis for second wave of COVID-19 diffusion

The coronavirus disease 2019 (COVID-19) pandemic has fatalized 216 countries across the world and has claimed the lives of millions of people globally. Researches are being carried out worldwide by scientists to understand the nature of this catastrophic virus and find a potential vaccine for it. The most possible efforts have been taken to present this paper as a form of contribution to the understanding of this lethal virus in the first and second wave. This paper presents a unique technique for the methodical comparison of disastrous virus dissemination in two waves amid five most infested countries and the death rate of the virus in order to attain a clear view on the behaviour of the spread of the disease. For this study, the data set of the number of deaths per day and the number of infected cases per day of the most affected countries, the USA, Brazil, Russia, India, and the UK, have been considered in the first and second waves. The correlation fractal dimension has been estimated for the prescribed data sets of COVID-19, and the rate of death has been compared based on the correlation fractal dimension estimate curve. The statistical tool, analysis of variance, has also been used to support the performance of the proposed method. Further, the prediction of the daily death rate has been demonstrated through the autoregressive moving average model. In addition, this study also emphasis a feasible reconstruction of the death rate based on the fractal interpolation function. Subsequently, the normal probability plot is portrayed for the original data and the predicted data, derived through the fractal interpolation function to estimate the accuracy of the prediction. Finally, this paper neatly summarized with the comparison and prediction of epidemic curve of the first and second waves of COVID-19 pandemic to visualize the transmission rate in the both times

Regression based predictor for p53 transactivation

<p>Abstract</p> <p>Background</p> <p>The p53 protein is a master regulator that controls the transcription of many genes in various pathways in response to a variety of stress signals. The extent of this regulation depends in part on the binding affinity of p53 to its response elements (REs). Traditional profile scores for p53 based on position weight matrices (PWM) are only a weak indicator of binding affinity because the level of binding also depends on various other factors such as interaction between the nucleotides and, in case of p53-REs, the extent of the spacer between the dimers.</p> <p>Results</p> <p>In the current study we introduce a novel <it>in-silico </it>predictor for p53-RE transactivation capability based on a combination of multidimensional scaling and multinomial logistic regression. Experimentally validated known p53-REs along with their transactivation capabilities are used for training. Through cross-validation studies we show that our method outperforms other existing methods. To demonstrate the utility of this method we (a) rank putative p53-REs of target genes and target microRNAs based on the predicted transactivation capability and (b) study the implication of polymorphisms overlapping p53-RE on its transactivation capability.</p> <p>Conclusion</p> <p>Taking into account both nucleotide interactions and the spacer length of p53-RE, we have created a novel <it>in-silico </it>regression-based transactivation capability predictor for p53-REs and used it to analyze validated and novel p53-REs and to predict the impact of SNPs overlapping these elements.</p

Fractal functions, dimensions and signal analysis

This book introduces the fractal interpolation functions (FIFs) in approximation theory to the readers and the concerned researchers in advanced level. FIFs can be used to precisely reconstruct the naturally occurring functions when compared with the classical interpolants. The book focuses on the construction of fractals in metric space through various iterated function systems. It begins by providing the Mathematical background behind the fractal interpolation functions with its graphical representations and then introduces the fractional integral and fractional derivative on fractal functions in various scenarios. Further, the existence of the fractal interpolation function with the countable iterated function system is demonstrated by taking suitable monotone and bounded sequences. It also covers the dimension of fractal functions and investigates the relationship between the fractal dimension and the fractional order of fractal interpolation functions. Moreover, this book explores the idea of fractal interpolation in the reconstruction scheme of illustrative waveforms and discusses the problems of identification of the characterizing parameters. In the application section, this research compendium addresses the signal processing and its Mathematical methodologies. A wavelet-based denoising method for the recovery of electroencephalogram (EEG) signals contaminated by nonstationary noises is presented, and the author investigates the recognition of healthy, epileptic EEG and cardiac ECG signals using multifractal measures. This book is intended for professionals in the field of Mathematics, Physics and Computer Science, helping them broaden their understanding of fractal functions and dimensions, while also providing the illustrative experimental applications for researchers in biomedicine and neuroscience