Prediction of probability distributions of molecular properties: towards more efficient virtual screening and better understanding of compound representations.

Various in silico approaches to predict activity and properties of chemical compounds constitute nowadays the basis of computer-aided drug design. While there is a general focus on the predictions of values, mathematically more appropriate is the prognosis of probability distributions, which offers additional possibilities, such as the evaluation of uncertainty, higher moments, and quantiles. In this study, we applied the Hierarchical Correlation Reconstruction approach to assess several ADMET properties of chemical compounds. It uses multiple linear regression to independently assess multiple moments, which are then finally combined into predicted probability distribution. The method enables inexpensive selection of compounds with properties nearly certain to fall into the particular range during virtual screening and automatic rejection of predictions characterized by high rate of uncertainty; however, unlike to the currently used virtual screening methods, it focuses on the prediction of the property distribution, not its actual value. Moreover, the presented protocol enables detection of structural features, which should be carefully considered when optimizing compounds towards particular property, as well as it provides deeper understanding of the examined compound representations.

Multi-feature evaluation of financial contagion.

Financial contagion refers to the spread of market turmoils, for example from one country or index to another country or another index. It is standardly assessed by modelling the evolution of the correlation matrix, for example of returns, usually after removing univariate dynamics with the GARCH model. However, significant events like crises visible in one financial market are usually reflected in other financial markets/countries simultaneously in several dimensions, i.e., in general, entire distributions of returns in other markets are affected. These distributions are determined/described by their expected value, variance, skewness, kurtosis and other statistics that determine the shape of the distribution function of returns, which can be based on higher (mixed) moments. These descriptive statistics are not constant over time, and, moreover, they can interreact within the given market and among the markets over time. In this article we propose, and use for the daily values of five indexes (CAC40, DAX30, DJIA, FTSE250 and WIG20) over the time period 2006-2017, a new, simple and computationally inexpensive methodology to automatically extend contagion evaluation from the evolution of the correlation matrix to the evolution of multiple higher mixed moments as well. Specifically, the joint distribution of normalized variables for each pair of indexes is modeled as a polynomial with time evolving coefficients estimated using an exponential moving average. As we can obtain any arbitrary number of evolving mixed moments this way, its dimensionality reduction using PCA (principal component analysis) is also discussed, obtaining a lower number of dominating and relatively independent features, which can each be interpreted through a polynomial that describes the corresponding perturbation of joint distribution. We obtain features that describe the interrelations among stock markets in several dimensions and that provide information about the current stage of crisis and the strength of the contagion process.

Impact of the healthcare potential in the European countries on infections and mortality caused by Covid-19.

The COVID-19 outbreak posed several challenges to healthcare professionals in treating patients: limiting the spread of infection; develop appropriate short-term strategies; and formulating long-term plans. Each of the affected countries had specific capacities before the outbreak began. This potential may have helped reduce the spread of the disease and should help reduce its impact. This paper uses the VMCM method to determine the capacity of health services in European countries. It allowed for a ranking of countries in terms of this potential. This allows the capacity of the health service to be related to the number of infected and deceased people. The purpose of this article is to examine the relationship of infections and mortality on COVID-19 to health care capacity.

Determination of the Inaccuracies of Calculated EEG Indices.

The data obtained as a result of an EEG measurement are burdened with inaccuracies related to the measurement process itself and the need to remove recorded disturbances. The article presents an example of how to calculate the Approach-Withdraw Index (EEG-AW) and Memorization Index (MI) indices in such a way that their inaccuracy resulting from the removal of artifacts can be periodically calculated. This inaccuracy is expressed in terms of standard deviation. This allows you to determine the reliability of the obtained conclusions in the context of examining elements in a 2D computer game created in the Unity engine.

Fundamental Bounds for Sequence Reconstruction from Nanopore Sequencers.

Nanopore sequencers are emerging as promising new platforms for high-throughput sequencing. As with other technologies, sequencer errors pose a major challenge for their effective use. In this paper, we present a novel information theoretic analysis of the impact of insertion-deletion (indel) errors in nanopore sequencers. In particular, we consider the following problems: (i) for given indel error characteristics and rate, what is the probability of accurate reconstruction as a function of sequence length; (ii) using replicated extrusion (the process of passing a DNA strand through the nanopore), what is the number of replicas needed to accurately reconstruct the true sequence with high probability? Our results provide a number of important insights: (i) the probability of accurate reconstruction of a sequence from a single sample in the presence of indel errors tends quickly (i.e., exponentially) to zero as the length of the sequence increases; and (ii) replicated extrusion is an effective technique for accurate reconstruction. We show that for typical distributions of indel errors, the required number of replicas is a slow function (polylogarithmic) of sequence length - implying that through replicated extrusion, we can sequence large reads using nanopore sequencers. Moreover, we show that in certain cases, the required number of replicas can be related to information-theoretic parameters of the indel error distributions.