Leveraging VBA in translation

The article is focused on finding ways for the VBA application in translation. Most of the macro code solutions available online date back to the early 2000s. Given the fact, the researchers decided to look into the usefulness of the instrument in today's environment, which has changed significantly over the two decade

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing

Molecular packing, piezo- and pyroelectric properties of tert-butyl N-(tert-butoxycarbonyl)-(S)-prolinamide

The work was financially supported by the Russian Foundation for Basic Research (grant no. 16-33-60122). The equipment of the Ural Centre for Shared Use “Modern Nanotechnologies” UrFU and Centre for Joint Use “Spectroscopy and Analysis of Organic Compounds” was used

Analytical hierarchy process as a tool supporting a decision-making for assessment of the risk of transboundary infectious animal disease introduction to the Russian Federation and previously disease-free territories

The livestock industry is increasingly taking its place in the economy of the Russian Federation. Its export potential is actively growing. Already, up to 10% of agricultural products are exported to foreign markets. The demand for food steadily increases during crises, which in turn increases the role of the veterinary service, whose tasks include protecting the country’s territory from the introduction of infectious diseases of animals from foreign countries; implementation of measures to prevent and eliminate infectious and other diseases in agricultural, domestic, zoo and other animals, fur-bearing animals, birds, fish and bees, as well as the implementation of plans of the regional veterinary service in the field of animal husbandry. The article assesses the validity of the possibilities and use of modern methods of analyzing and predicting the spread of animal morbidity, identifying cause-and-effect relationships and the extent of the spread of particularly dangerous animal diseases. The authors propose to consider the possibility of using the mathematical method of hierarchy analysis as a scientifically sound decisionmaking support tool when assessing the risk of introducing trans-border infectious animal diseases into previously prosperous territories of the Russian Federation. This approach can be used in the process of choosing the most appropriate alternative from several risk assessment options. The Hierarchy Analysis Method (MAI) is a mathematical tool for a qualitative systematic approach to solving decision-making problems. This method was developed by the American scientist Thomas Lewis Saati in 1970, since then it has been actively developing and widely used in practice. The hierarchy analysis method can be used not only to compare objects, but also to solve more complex management and forecasting tasks

Piezoelectric properties of the crystals of ortho-carboranyl (S)-phenylalanine and (S)-valine derivatives

Piezoelectric response of the crystals of carborane-containing pseudo-dipeptides was measured using the piezoresponse force microscopy. Observable piezocoefficients of the crystals reached 76.2 pC/N. Structure and stereo configuration of compounds affected the piezoelectric response of the crystals.The equipment of Ural Center for Shared Use "Modern Nanotechnology" UrFU has been used. The work was financially supported by Russian Foundation for Basic Research (grant no. 16-33-60122)

Chapter 4: The LOTUS regression model

One of the primary motivations of the LOTUS effort is to attempt to reconcile the discrepancies in ozone trend results from the wealth of literature on the subject. Doing so requires investigating the various methodologies employed to derive long-term trends in ozone as well as to examine the large array of possible variables that feed into those methodologies and analyse their impacts on potential trend results. Given the limited amount of time, the LOTUS group focused on the most common methodology of multiple linear regression and performed a number of sensitivity tests with the goal of trying to establish best practices and come to a consensus on a single regression model to use for this study. This chapter discusses the details and results of the sensitivity tests before describing the components of the final single model that was chosen and the reasons for that choice

Crystal structure and growth kinetics of self-assembled microtubes with different chirality

The measurements of microtubes growth kinetics and morphology analysis were done in Ural Federal University (UrFU) and made possible by Russian Science Foundation (Grant No. 18-72-00052). The equipment of the Ural Center for Shared Use “Modern nanotechnology” UrFU was used. Computer simulation was performed under the support of Russian Foundation for Basic Research (Grant No. 19-01-00519 А). X-ray diffraction data were collected under the experiment SC-4587 at the European Synchrotron Radiation Source (ESRF, Grenoble, France). S.K., P.Z., L.M. and A.K. are grateful to FCT project PTDC/CTM-CTM/31679/2017. P.Z. and L.M. are grateful to FCT project PTDC/QEQ-QAN/6373/2014. S.K and A.K were also supported by the joint Portugal-Turkey project (TUBITAK/0006/2014). Part of this work was developed within the scope of the project CICECO-Aveiro Institute of Materials, FCT Ref. UID/CTM/50011/2019, financed by national funds through the FCT/MCTES

Текстурные и КТ-признаки в дифференциальном диагнозе гиперваскулярных нейроэндокринных опухолей поджелудочной железы и метастазов почечно-клеточного рака: диагностическая модель

Objective: to develop a diagnostic model that includes CT and radiomic features for the differential diagnosis of pancreatic neuroendocrine tumors (PNETs) G1 and G2 and pancreatic renal cell carcinoma (RCC) metastases.Material and Methods. 78 patients with 79 hypervascular PNETs and 17 patients with 24 pancreatic RCC metastases who underwent pancreatic resection and histological verification were selected in the study. All the patients underwent preoperative contrast enhanced CT (CECT). We assessed tumor attenuation, composition (cystic/solid), homogeneity (homogeneous/heterogeneous), calcification and presence of the main pancreatic duct (MPD) dilation. We calculated lesion-to-parenchyma contrast (LPC), relative tumor enhancement ratio (RTE) and extracted 52 texture features for arterial phase of CECT. Qualitative and texture features were compared between PNETs and pancreatic RCC metastasis. The selection of predictors for the logistic model was carried out in 2 successive stages: 1) selection of predictors based on one-factor logistic models, the selection criterion was p < 0.2; 2) selection of predictors using L2 regularization (LASSO regression after standardization of independent variables). The selected predictors were included in a logistic regression model without interactions, the coefficients of which were estimated using the maximum likelihood method with a penalty of 0.8.Results. There was no difference in composition, homogeneity (homogeneous/heterogeneous) and presence of the MPD dilation between groups. We did not find calcification in pancreatic RCC metastasis, in contrast to the PNETs (9% contained calcifications). After selection, the LCR, CONVENTIONAL_HUmin, GLCM_Correlation, NGLDM_Coarseness were included in the final diagnostic model, which showed a sensitivity and specificity of 95.8%; 62% in the prediction of pancreatic RCC metastases.Conclusion. The diagnostic model developed on the basis of texture and CT-features has high sensitivity (95.8%) with moderate specificity (62%), which allows it to be used in complex diagnostic cases to determine the patient's treatment tactics.Цель исследования: разработать диагностическую модель, включающую КТ-характеристики и показатели радиомики для дифференциальной диагностики нейроэндокринных опухолей поджелудочной железы (ПНЭО) G1 и G2 и метастазов почечно-клеточного рака (ПКР).Материал и методы. В исследование были отобраны 78 пациентов с 79 гиперваскулярными ПНЭО и 17 пациентов с 24 метастазами ПКР, которым была выполнена резекция поджелудочной железы с гистологической верификацией. Всем пациентам перед операцией была проведена КТ с контрастным усилением. Мы оценивали плотность опухоли, структуру (кистозная/солидная), гомогенность (гомогенная/гетерогенная), кальцификацию и наличие расширения главного протока поджелудочной железы (ГПП). Мы рассчитали отношение плотности опухоли к плотности паренхимы (LPC) и относительный коэффициент контрастирования опухоли (RTE) и вычислили 52 текстурных показателя для артериальной фазы КТ-исследования. Качественные и текстурные характеристики сравнивали между ПНЭО и метастазами ПКР. Отбор предикторов в логистическую модели осуществлялся в 2 последовательных этапа: 1) отбор предикторов на основе однофакторных логистических моделей, критерием отбора служило p < 0.2; 2) отбор предикторов с помощью L2-регуляризации (LASSO-регрессия после стандартизации независимых переменных). Отобранные предикторы включались в логистическую регрессионную модель без взаимодействий, коэффициенты которой рассчитывались с использованием метода максимального праводоподобия со штрафом 0,8.Результаты. Не было различий в структуре, гомогенности (гомогенные/гетерогенные) и наличии дилатации ГПП между группами. Мы не обнаружили кальцификации при метастазах ПКР, в отличие от ПНЭО. После отбора LCR, CONVENTIONAL_HUmin, GLCM_Correlation, NGLDM_Coarseness были включены в окончательную диагностическую модель, которая показала чувствительность и специфичность 95.8%; 62% в прогнозировании метастазов ПКР.Заключение. Разработанная на основании текстурных и КТ-признаков диагностическая модель обладает высокой чувствительностью (95.8%) при умеренной специфичности (62%), что позволяет использовать ее при сложных диагностических случаях для определения тактики лечения пациента

The use of the SAEM algorithm in MONOLIX software for estimation of population pharmacokinetic-pharmacodynamic-viral dynamics parameters of maraviroc in asymptomatic HIV subjects

Using simulated viral load data for a given maraviroc monotherapy study design, the feasibility of different algorithms to perform parameter estimation for a pharmacokinetic-pharmacodynamic-viral dynamics (PKPD-VD) model was assessed. The assessed algorithms are the first-order conditional estimation method with interaction (FOCEI) implemented in NONMEM VI and the SAEM algorithm implemented in MONOLIX version 2.4. Simulated data were also used to test if an effect compartment and/or a lag time could be distinguished to describe an observed delay in onset of viral inhibition using SAEM. The preferred model was then used to describe the observed maraviroc monotherapy plasma concentration and viral load data using SAEM. In this last step, three modelling approaches were compared; (i) sequential PKPD-VD with fixed individual Empirical Bayesian Estimates (EBE) for PK, (ii) sequential PKPD-VD with fixed population PK parameters and including concentrations, and (iii) simultaneous PKPD-VD. Using FOCEI, many convergence problems (56%) were experienced with fitting the sequential PKPD-VD model to the simulated data. For the sequential modelling approach, SAEM (with default settings) took less time to generate population and individual estimates including diagnostics than with FOCEI without diagnostics. For the given maraviroc monotherapy sampling design, it was difficult to separate the viral dynamics system delay from a pharmacokinetic distributional delay or delay due to receptor binding and subsequent cellular signalling. The preferred model included a viral load lag time without inter-individual variability. Parameter estimates from the SAEM analysis of observed data were comparable among the three modelling approaches. For the sequential methods, computation time is approximately 25% less when fixing individual EBE of PK parameters with omission of the concentration data compared with fixed population PK parameters and retention of concentration data in the PD-VD estimation step. Computation times were similar for the sequential method with fixed population PK parameters and the simultaneous PKPD-VD modelling approach. The current analysis demonstrated that the SAEM algorithm in MONOLIX is useful for fitting complex mechanistic models requiring multiple differential equations. The SAEM algorithm allowed simultaneous estimation of PKPD and viral dynamics parameters, as well as investigation of different model sub-components during the model building process. This was not possible with the FOCEI method (NONMEM version VI or below). SAEM provides a more feasible alternative to FOCEI when facing lengthy computation times and convergence problems with complex models