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Cancer remains the second most common cause of death after cardiovascular diseases, accounting for nearly 10 million deaths in 2020. Although the incidence of cancer increases considerably with age, the cancer burden can also be reduced and have a high chance of cure through early detection, appropriate treatment, and care of patients. The development of high-throughput analytical approaches, like matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), contributes to identifying a pool of proteins/peptides as putative biomarkers for the early detection, diagnosis, and tumor progression. The purpose of the current review is to present an updated outline of recent proteome/peptidome research to establish putative cancer biomarkers using MALDI-TOF MS and highlight the applicability of statistical analysis in the oncology field. The pros and cons of MALDI-TOF MS application on cancer diagnostics and monitoring will be discussed, as well as compared with tandem mass spectrometry (MS/MS)-based proteomics (e.g., liquid chromatography–tandem mass spectrometry). In addition, pre-analytical (e.g., sample quality control) and analytical (e.g., sample pre-treatment, instrumental analytical conditions) properties that influence the robustness of MALDI-TOF MS data will be also discussed. [ABSTRACT FROM AUTHOR]

Any wireless communication system's performance depends on channel parameters' accuracy. The classical Rayleigh and Nakagami-m research subjects remain vital even in the most modern millimeter-wave (mm-wave) applications. This research aims to create a generalized cumulative distribution function for describing random changes in wireless channels. It is vital to have a suitable channel representation model to represent varied fifth-generation applications to ease network implementation. This study provides mm-wave measurement data at 28 GHz carrier frequencies in line of sight and non-line of sight propagation. Lognormal, Nakagami, Gaussian, Weibull, and Rayleigh distributions outperform the proposed model's universal exponential density function. The experimental data verified the introduced method. [ABSTRACT FROM AUTHOR]

Highlights: [•] We present a review on the statistical tools used to deal with multiple comparisons. [•] We discuss the fundamental theoretical concepts and ideas. [•] We present application to metabolomics and proteomics. [ABSTRACT FROM AUTHOR]

In this study, the authors introduce a new weighted Student's t‐mixture model (WSMM) for image segmentation. Gaussian distribution and Student's t‐distribution are the two commonly used probabilities in the finite mixture model (FMM). The Student's t‐mixture model has come to be regarded as an alternative to Gaussian mixture models, as it is heavily tailed and more robust for outliers. Moreover, the pixels are considered independent of each other in the FMM. Although some existing methods incorporate the spatial relationship between neighbouring pixels, they do not consider the relationship between spatial information and clustering information, thus those reported methods remain sensitive to noise. The advantages of the authors method are as follows: first, the authors introduce WSMM to incorporate the local spatial information, pixel intensity value and clustering information in an image. Second, the authors model is simple, easy to implement and has a good balance between noise insensitiveness and image detail preservation. Third, they adopt the gradient method and expectation maximisation algorithm, which allow for simultaneous estimation of optimal parameters. Finally, the most useful statistical tool for image segmentation, the well‐known hidden Markov random field model, is a special case of their model. Thus, their method is general enough for model‐based techniques construction. Experimental results on synthetic and real images demonstrate the improved robustness and effectiveness of their approach. [ABSTRACT FROM AUTHOR]

Abstract: Determination of the geographical origin of food and beverages has been a growing issue over the past decade for all countries around the world, mostly because of the concern of consumers about the authenticity of the food that they eat. An increasing number of research articles in the past five years have investigated the elemental composition and the isotope ratios as indicators to determine the origin of food and beverages. The indicators commonly used are stable-isotope ratios of hydrogen, oxygen, nitrogen, carbon and sulfur, combined with isotope ratios of strontium and lead, and elemental concentrations. This article reviews the application of multi-element and multi-isotope methods and the statistical tools used to determine the geographical origin of food and beverages in the European Union from 2008 to early 2012. We review 48 papers, divided between different food commodities. [Copyright &y& Elsevier]