Estimating probability density functions using a combined maximum entropy moments and Bayesian method. Theory and numerical examples

© 2019 BIPM & IOP Publishing Ltd. Estimating the probability density function (pdf) from a limited sample of data is a challenging data analysis problem. Furthermore, determining which pdf best describes the available data involves an extra layer of complexity to the analysis, which if ignored, can have considerable consequences. We propose a combined maximum entropy (MaxEnt) moments and Bayesian model selection method to address this problem. The MaxEnt moments component is used to formulate a set of possible pdf models, each constrained by a different set of moments and parameterised by a set of Lagrangian multipliers. The Bayesian model selection component makes an inference about the most probable model, from the set of MaxEnt moment models. The structure of the prior pdf for the Lagrangian multipliers is determined from an expansion of the free energy functional for each MaxEnt model, and corresponding hyperparameters are calculated empirically. Numerical experiments were used to test the proposed method on samples taken from Gaussian and (more complex) non-Gaussian distributions, over a range of sample sizes. The results clearly demonstrate that the method can discriminate between simple and complex MaxEnt models for sample sizes approximately greater than 60. Our results demonstrate that MaxEnt and Bayesian methods are complementary. More critically, Bayesian inference is necessary when a set of competing MaxEnt models can be derived for a single dataset from a range of assumptions

Modelling credit spreads with time volatility, skewness, and kurtosis

This paper seeks to identify the macroeconomic and financial factors that drive credit spreads on bond indices in the US credit market. To overcome the idiosyncratic nature of credit spread data reflected in time varying volatility, skewness and thick tails, it proposes asymmetric GARCH models with alternative probability density functions. The results show that credit spread changes are mainly explained by the interest rate and interest rate volatility, the slope of the yield curve, stock market returns and volatility, the state of liquidity in the corporate bond market and, a heretofore overlooked variable, the foreign exchange rate. They also confirm that the asymmetric GARCH models and Student-t distributions are systematically superior to the conventional GARCH model and the normal distribution in in-sample and out-of-sample testing

Daily photoprotection to prevent photoaging

Background: Extrinsic skin aging or photoaging was previously thought to be almost exclusively due to solar ultraviolet (UV) radiation. However, recent literature has described other contributing factors and clarification is thus required as to what extent and what type of daily photoprotection is needed to mitigate extrinsic skin aging. Methods: We reviewed the existing scientific evidence on daily photoprotection, and specific requirements at the product level, to prevent extrinsic skin aging. We critically reviewed the existing evidence on potential ecological and toxicological risks which might be associated with daily photoprotection. Results: Evidence shows that broad protection against the entire solar range of UVB, UVA, UVA1, visible light, and short infrared (IRA) is required to prevent extrinsic aging. Other exposome factors, such as air pollution and smoking, also contribute to skin aging. Daily broad-spectrum sunscreen photoprotection should thus contain antioxidant ingredients for additional benefits against UV, IRA, and pollution-induced oxidative stress as well as anti-aging active ingredients to provide clinical benefits against skin aging signs, such as wrinkles and dark spots. Broad-spectrum sunscreen containing pigments, such as iron oxide, may be required for melasma prevention. There is no conclusive clinical evidence that daily sunscreen use is unsafe or that it compromises vitamin D synthesis. Conclusion: Daily use of broad-spectrum sunscreen containing antioxidant and anti-aging active ingredients can effectively reduce extrinsic aging.</p

Re-examining HSPC1 inhibitors

© 2017, The Author(s). HSPC1 is a critical protein in cancer development and progression, including colorectal cancer (CRC). However, clinical trial data reporting the effectiveness of HSPC1 inhibitors on several cancer types has not been as successful as predicted. Furthermore, some N-terminal inhibitors appear to be much more successful than others despite similar underlying mechanisms. This study involved the application of three N-terminal HSPC1 inhibitors, 17-DMAG, NVP-AUY922 and NVP-HSP990 on CRC cells. The effects on client protein levels over time were examined. HSPC1 inhibitors were also applied in combination with chemotherapeutic agents commonly used in CRC treatment (5-fluorouracil, oxaliplatin and irinotecan). As HSPA1A and HSPB1 have anti-apoptotic activity, gene-silencing techniques were employed to investigate the significance of these proteins in HSPC1 inhibitor and chemotherapeutic agent resistance. When comparing the action of the three HSPC1 inhibitors, there are distinct differences in the time course of important client protein degradation events. The differences between HSPC1 inhibitors were also reflected in combination treatment—17-DMAG was more effective compared with NVP-AUY922 in potentiating the cytotoxic effects of 5-fluorouracil, oxaliplatin and irinotecan. This study concludes that there are distinct differences between N-terminal HSPC1 inhibitors, despite their common mode of action. Although treatment with each of the inhibitors results in significant induction of the anti-apoptotic proteins HSPA1A and HSPB1, sensitivity to HSPC1 inhibitors is not improved by gene silencing of HSPA1A or HSPB1. HSPC1 inhibitors potentiate the cytotoxic effects of chemotherapeutic agents in CRC, and this approach is readily available to enter clinical trials. From a translational point of view, there may be great variability in sensitivity to the inhibitors between individual patients

Tutorial: Multivariate Classification for Vibrational Spectroscopy in Biological Samples

Vibrational spectroscopy techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, have been successful methods for studying the interaction of light with biological materials and facilitating novel cell biology analysis. Spectrochemical analysis is very attractive in disease screening and diagnosis, microbiological studies and forensic and environmental investigations because of its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, there is now an urgent need for multivariate classification protocols allowing one to analyze biologically derived spectrochemical data to obtain accurate and reliable results. Multivariate classification comprises discriminant analysis and class-modeling techniques where multiple spectral variables are analyzed in conjunction to distinguish and assign unknown samples to pre-defined groups. The requirement for such protocols is demonstrated by the fact that applications of deep-learning algorithms of complex datasets are being increasingly recognized as critical for extracting important information and visualizing it in a readily interpretable form. Hereby, we have provided a tutorial for multivariate classification analysis of vibrational spectroscopy data (FTIR, Raman and near-IR) highlighting a series of critical steps, such as preprocessing, data selection, feature extraction, classification and model validation. This is an essential aspect toward the construction of a practical spectrochemical analysis model for biological analysis in real-world applications, where fast, accurate and reliable classification models are fundamental