Using inverse Laplace transform in positronium lifetime imaging

Positronium (Ps) lifetime imaging is gaining attention to bring out additional biomedical information from positron emission tomography (PET). The lifetime of Ps in vivo can change depending on the physical and chemical environments related to some diseases. Due to the limited sensitivity, Ps lifetime imaging may require merging some voxels for statistical accuracy. This paper presents a method for separating the lifetime components in the voxel to avoid information loss due to averaging. The mathematics for this separation is the inverse Laplace transform (ILT), and the authors examined an iterative numerical ILT algorithm using Tikhonov regularization, namely CONTIN, to discriminate a small lifetime difference due to oxygen saturation. The separability makes it possible to merge voxels without missing critical information on whether they contain abnormally long or short lifetime components. The authors conclude that ILT can compensate for the weaknesses of Ps lifetime imaging and extract the maximum amount of information.

Computational study of the furin cleavage domain of SARS-CoV-2: delta binds strongest of extant variants

We demonstrate that AlphaFold and AlphaFold Multimer, implemented within the ColabFold suite, can accurately predict the structures of the furin enzyme with known six residue inhibitory peptides. Noting the similarity of the peptide inhibitors to polybasic furin cleavage domain insertion region of the SARS-CoV-2, which begins at P681, we implement this approach to study the wild type furin cleavage domain for the virus and several mutants. We introduce mutations in silico for alpha, omicron, and delta variants, for several sequences which have been rarely observed, for sequences which have not yet been observed, for other coronaviruses (NL63, OC43, HUK1a, HUK1b, MERS, and 229E), and for the H5N1 flu. We show that interfacial hydrogen bonds between the furin cleavage domain and furin are a good measure of binding strength that correlate well with endpoint binding free energy estimates, and conclude that among all candidate viral sequences studied, delta is near the very top binding strength within statistical accuracy. However, the binding strength of several rare sequences match delta within statistical accuracy. We find that the furin S1 pocket is optimized for binding arginine as opposed to lysine. This residue, typically at sequence position five, contains the most hydrogen bonds to the furin, and hydrogen bond count for just this residue shows a strong positive correlation with the overall hydrogen bond count . We demonstrate that the root mean square backbone C-alpha fluctuation of the first residue in the furin cleavage domain has a strong negative correlation with the interfacial hydrogen bond count. We show by considering the variation with the number of basic residues that the maximum mean number of interfacial hydrogen bonds expected is 15.7 at 4 basic residues.

Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework

Global and national surveillance of SARS-CoV-2 epidemiology is mostly based on targeted schemes focused on testing individuals with symptoms. These tested groups are often unrepresentative of the wider population and exhibit test positivity rates that are biased upwards compared with the true population prevalence. Such data are routinely used to infer infection prevalence and the effective reproduction number, Rt, which affects public health policy. Here, we describe a causal framework that provides debiased fine-scale spatiotemporal estimates by combining targeted test counts with data from a randomized surveillance study in the United Kingdom called REACT. Our probabilistic model includes a bias parameter that captures the increased probability of an infected individual being tested, relative to a non-infected individual, and transforms observed test counts to debiased estimates of the true underlying local prevalence and Rt. We validated our approach on held-out REACT data over a 7-month period. Furthermore, our local estimates of Rt are indicative of 1-week- and 2-week-ahead changes in SARS-CoV-2-positive case numbers. We also observed increases in estimated local prevalence and Rt that reflect the spread of the Alpha and Delta variants. Our results illustrate how randomized surveys can augment targeted testing to improve statistical accuracy in monitoring the spread of emerging and ongoing infectious disease.

Leaf Area Calculation Models for Vines Based on Foliar Descriptors

In the case of foliar area studies on vines, with a large number of determinations, a simple, fast, sufficiently accurate and low-cost method is very useful. The typology of leaves on the vine is complex, characterized by several descriptive parameters: median rib; secondary venations of the first and second order; angles between the median rib and the secondary venations; sinuses; length and width of the leaf. The present study aimed to evaluate models for calculating the leaf area based on descriptive parameters and KA (KA as the surface constant used to calculate the leaf area) for six vine cultivars, ‘Cabernet Sauvignon’ (CS), ‘Muscat Iantarnîi’ (MI), ‘Muscat Ottonel’ (MO), ‘Chasselas’ (Ch), ‘Victoria’ (Vi) and ‘Muscat Hamburg’ (MH). The determined KA surface constants had subunit values (0.91 to 0.97), except for the cultivars ‘Muscat Iantarnîi’ and ‘Muscat Ottonel’ where the surface constant KA2 (in relation to the second-order secondary venations) had supraunitary values (1.07 and 1.08, respectively). The determination of the leaf area was possible under different conditions of statistical accuracy (R2 = 0.477, p = 0.0119, up to R2 = 0.988, p < 0.001) in relation to the variety and parametric descriptors considered. The models obtained from the regression analysis facilitated a more reliable prediction of the leaf area based on the elements on the left side of the leaf, in relation to the median rib, compared to those on the right. The accuracy of the results was checked on the basis of minimum error (ME) and confirmed by parameters R2, p and RMSE.

Forecasting Economic Indicators of Transport Activity

The article discusses the methodological and practical aspects of forecasting the economic indicators of the transport sector at the level of a transport company and the type of economic activity. The development of forecasting methodology at the present time is analyzed. The necessity, features and main directions of development of the forecasting methodology for the type of economic activity are revealed. The methodological basis for forecasting the development of the transport sector is investigated and characterized. A method for forecasting transportation and storage as a type of economic activity under conditions of uncertainty is proposed and tested. Based on the results of the correlation analysis, subsets of predicted indicators and factors were formed that were optimal for constructing the corresponding linear regression models. Predictive regression models have been developed, their significance and statistical accuracy have been confirmed.

A Parallel Dynamic Asynchronous Framework for Uncertainty Quantification by Hierarchical Monte Carlo Algorithms

The necessity of dealing with uncertainties is growing in many different fields of science and engineering. Due to the constant development of computational capabilities, current solvers must satisfy both statistical accuracy and computational efficiency. The aim of this work is to introduce an asynchronous framework for Monte Carlo and Multilevel Monte Carlo methods to achieve such a result. The proposed approach presents the same reliability of state of the art techniques, and aims at improving the computational efficiency by adding a new level of parallelism with respect to existing algorithms: between batches, where each batch owns its hierarchy and is independent from the others. Two different numerical problems are considered and solved in a supercomputer to show the behavior of the proposed approach.

Computing Optimization of a Parallel Structure-Based Monolithic Gripper for Manipulation Using Weight Method-Based Grey Relational Analysis

This study proposes an integration of the weight method and grey relational analysis to optimize a monolithic gripper. This gripper is desired for use in the assembling industry of cylindrical parts with diameters from 500 µm to 800 µm. The weight factor for each response is calculated accurately. Response surface methodology and Taguchi method are utilized to build an experiment matrix, and grey relational analysis is utilized to predict optimal results. The results found that the predicted displacement value is 0.5699 µm, and the predicted frequency value is 780.9 Hz. Compared to the initial design, the quality of responses is improved by 7.53% for the natural frequency and 35.29% for the output displacement. The error between the predicted result and the verified result is 1.15% for the natural frequency and 16.62% for the output displacement, respectively. It implies that the proposed method has a statistical accuracy.

Bandwidth Selection for Kernel Generalized Regression Neural Networks in Identification of Hammerstein Systems

This paper addresses the issue of data-driven smoothing parameter (bandwidth) selection in the context of nonparametric system identification of dynamic systems. In particular, we examine the identification problem of the block-oriented Hammerstein cascade system. A class of kernel-type Generalized Regression Neural Networks (GRNN) is employed as the identification algorithm. The statistical accuracy of the kernel GRNN estimate is critically influenced by the choice of the bandwidth. Given the need of data-driven bandwidth specification we propose several automatic selection methods that are compared by means of simulation studies. Our experiments reveal that the method referred to as the partitioned cross-validation algorithm can be recommended as the practical procedure for the bandwidth choice for the kernel GRNN estimate in terms of its statistical accuracy and implementation aspects.

Consulting Support of Technologization of Professional Practice of a Teacher

The article presents the results of experimental work on consulting support of professional practice of a teacher, which introduces local innovations. These innovations are initiated by teachers who need advice on developing a project to implement them in their professional activities. Innovative changes developed by the teacher infringe the order in the implementation of existing professional practice, which leads to the need for its technologization. The presented circumstances actualize consulting support as a form of teacher training directly at the workplace. The purpose of the research stated in the publication is to identify the impact of consulting support on the formation of competencies of consulted ones on the implementation of local innovations developed by them in their own professional practice. Consulting support of technologization by teachers-clients of their own professional practice, after the introduction of local innovative changes, was implemented in a mixed type, combining offline and online formats. The authors of the publication propose to evaluate the success of participants in consulting support for the products developed in its process: the project of technologization of professional practice and the program for its implementation. The obtained data were statistically processed using the parametric method of variance analysis. Statistical processing of the data obtained confirmed the effectiveness of this approach. Professional growth and motivation of educational institution specialists are regulated with statistical accuracy by consulting, which acts as an educational management practice. The consulting on the development of local innovations should be carried out continuously to support the teacher's strategically significant needs, and not just their fragmentary situational requests.