Floquet engineering of the Lifshitz phase transition in the Hubbard model

Within the Floquet theory of periodically driven quantum systems, we demonstrate that an off-resonant high-frequency electromagnetic field can induce the Lifshitz phase transition in periodical structures described by the one-dimensional repulsive Hubbard model with the nearest and next-nearest neighbor hopping. The transition changes the topology of electron energy spectrum at the Fermi level, transforming it from the two Fermi-points to the four Fermi-points, what facilitates the emergence of the superconducting fluctuations in the structure. Possible manifestations of the effect and conditions of its experimental observability are discussed

Effect of metformin on transcriptome and adipokinome of adipocytes of local fat deposts of patients with ischemic heart disease

BACKGROUND: The information available to date on the effect of metformin on the transcriptome and secretory capacity of adipocytes in human adipose tissue (AT) is scarce and contradictory. The study analyzed whether metformin in vitro modulates gene expression and secretion of adiponectin and leptin in the AT of subcutaneous (SAT), epicardial (EAT) and perivascular (PVAT) localization of patients with ischemic artery disease (IHD).AIM: To assess the effect of metformin at various concentrations (1 and 10 mmol / L) on the level of expression of adiponectin and leptin genes and their content in adipocytes of subcutaneous, epicardial and perivascular AT of patients with IHD.MATERIALS AND METHODS: The study included 134 patients with IHD and indications for direct myocardial revascularization by coronary artery bypass grafting (CABG). During the operation, biopsies of SAT, EAT and PVAT were obtained for 3–5 gram, which served as a source of adipocytes. Isolated adipocytes were cultured for 24 hours with and without metformin (1 and 10 mmol / L). After a day of incubation, the expression of the ADIPOQ and LEP genes and the level of secretion of adiponectin and leptin in the culture medium of adipocytes were determined. Statistical processing was performed using the GraphPad Prism 6 software package (GraphPad Software, La Jolla, CA, USA) and Statistica software, 6.1 (Dell Software, Inc., Round Rock, TX, USA).RESULTS: The adipocytes of the EAT and PVAT of patients with IHD were characterized by an imbalance in the adipokine system, manifested by a low level of ADIPOQ expression and a high LEP expression in comparison with adipocytes of the SAT. Metformin increased the level of ADIPOQ expression and its secretion by adipocytes regardless of their location, while a low concentration (1 mmol / L) in adipocytes of EAT had a stronger effect compared to 10 mmol / L. Metformin had a multidirectional effect on the level of leptin in adipocytes, which depended on their localization: both in low (1 mmol / L) and high concentrations (10 mmol / L), the drug reduced the level of LEP expression and protein secretion in the culture medium of adipocytes of the SAT. EAT had no significant effect on adipocytes. In PVAT, metformin increased the expression and secretion of leptin regardless of concentration.CONCLUSION: Metformin has a direct effect on adipocytes in SAT, EAT, and PVAT and is able to modulate their activity, which is a promising strategy for maintaining the balance of adipokines in AT, especially epicardial and perivascular localization

ГЕНЕТИЧЕСКИЙ ПРОФИЛЬ И СЕКРЕТОМ АДИПОЦИТОВ ВИСЦЕРАЛЬНОЙ И ПОДКОЖНОЙ ЖИРОВОЙ ТКАНИ У ПАЦИЕНТОВ С СЕРДЕЧНО-СОСУДИСТЫМИ ЗАБОЛЕВАНИЯМИ

Adipose tissue is of interest in the context of its role in pathogenesis of cardiovascular diseases, endothelium and myocardium dysfunction, hypertension, atherosclerosis and a number of other pathological conditions. The accumulation of knowledge about secretory functions of adipocytes is leading to recognition of the role of adipose tissue in regulation of cardiovascular system functioning. Studies of molecular-genetic basis of the functioning of adipose tissue can help to understand etiopathogenetic mechanisms of cardiovascular diseases and develop an effective strategy for their prevention and control. In this review, the information about adipokines and cytokines secreted by various types of adipose tissue (subcutaneous, epicardial, perivascular), as well as changes in the expression of key genes in adipocytes obtained from various fat depots in patients with cardiovascular diseases was analyzed.Жировая ткань представляет особый интерес в контексте ее ключевой роли в патогенезе сердечно-сосудистых заболеваний, дисфункции эндотелия и миокарда, гипертонии, атеросклероза и ряда других патологических состояний. На настоящий момент накоплено достаточное количество сведений о секреторной функции адипоцитов, что ведет к признанию роли жировой ткани в регуляции функционирования сердечно-сосудистой системы. Изучения молекулярно-генетических основ функционирования жировой ткани может помочь более глубокому пониманию этиопатогенетических механизмов сердечно-сосудистых заболеваний и разработать эффективную стратегию их профилактики и контроля. В данном обзоре проанализирована информация об особенностях профиля адипокинов и цитокинов, секретируемых различными типами жировой ткани (подкожная, эпикардиальная, периваскулярная), а также об изменении экспрессии ряда ключевых генов в адипоцитах, полученных из различных жировых депо, у пациентов с сердечно-сосудистыми заболеваниями

On the Wave Stresses in the Rods of Anvil Hammers

With operating anvil hammers, there are rigid impacts of die tools, and as a result, almost instantaneous impact stops of the falling parts of hammer. Such operating conditions lead to the accelerated breakdowns of rods because of significant wave stresses arising in them. Common differential and integral methods to estimate wave stresses are widespread in engineering practice. However, to use them a researcher has to possess certain skills and special software. We consider the method for estimating the wave stresses in the rods of anvil hammers based on Laplace transforms (LT) of wave equation. The article shows a procedure to set up and solve differential wave equations by operator method. These equations describe the wave propagation process of strains and stresses in the rods of anvil hammers with rigid impact and taking into account a damping rod connection with the head of hammer. The method takes into consideration an influence of both piston and rod weights and of mechanical and geometrical characteristics of rod on the stress value in the placement of rod in hammer head. Results analysis shows that a sufficiently efficient method for practical improving the durability of rods is the method of damping impact load on the rod through setting the damping devices in the form either of elastic "pad" of one or another design or of hydraulic shock absorbers in the placement of its connection with the hammer head. In this case there is a change of the wave front, it becomes flatter. It is shown that the stresses in the rod are proportional to the amount of wave stresses because of the own impact of rod and piston, which make a total weight of the system. Effect of piston weight on the stresses value at the rod during impact is directly proportional to the ratio of its weight to the rod weight. The geometric parameters of rod and the speed of the falling parts before the impact also influence on the value of stresses in the rod.The represented method of the stresses analysis can be successfully applied to the selection of optimal weights and the geometric parameters of the piston and rod during rigid impact of falling parts without damping and taking into account the energy dissipation until the stiff stop of falling parts of the anvil hammer.</p

Machine Learning of Analytical Electron Density in Large Molecules Through Message-Passing

Centro de Biotecnología y Genómica de PlantasMachine learning milestones in computational chemistry are overshadowed by their unaccountability and the overwhelming zoo of tools for each specific task. A promising path to tackle these problems is using machine learning to reproduce physical magnitudes as a basis to derive many other properties. By using a model of the electron density consisting of an analytical expansion on a linear set of isotropic and anisotropic functions, we implemented in this work a message-passing neural network able to reproduce electron density in molecules with just a 2.5% absolute error in complex cases. We also adapted our methodology to describe electron density in large biomolecules (proteins) and to obtain atomic charges, interaction energies, and DFT energies. We show that electron density learning is a new promising avenue with a variety of forthcoming applications.This research was funded by the Spanish Ministerio de Ciencia e Innovación, grant number BIO2017-84548R. B.C.-Z. received fund support from a predoctoral Programa Propio Grant, Universidad Politécnica de Madrid and Banco Santander. The authors acknowledge the “Severo Ochoa Program for Centres of Excellence in R&D” from the Agencia Estatal de Investigación of Spain (grant SEV-2016-0672, 2017-2021) for supporting the services provided by the computation cluster of the CBGP. The code of our method as well as instructions to use it are available at GitHub. All of the data sets created to train and test our models as explained in the section are made public at the Zenodo open data repository maintained by CERN.Peer reviewe

Optimal Number of Coarse-Grained Sites in Different Components of Large Biomolecular Complexes

The computational study of large biomolecular complexes (molecular machines, cytoskeletal filaments, etc.) is a formidable challenge facing computational biophysics and biology. To achieve biologically relevant length and time scales, coarse-grained (CG) models of such complexes usually must be built and employed. One of the important early stages in this approach is to determine an optimal number of CG sites in different constituents of a complex. This work presents a systematic approach to this problem. First, a universal scaling law is derived and numerically corroborated for the intensity of the intrasite (intradomain) thermal fluctuations as a function of the number of CG sites. Second, this result is used for derivation of the criterion for the optimal number of CG sites in different parts of a large multibiomolecule complex. In the zeroth-order approximation, this approach validates the empirical rule of taking one CG site per fixed number of atoms or residues in each biomolecule, previously widely used for smaller systems (e.g., individual biomolecules). The first-order corrections to this rule are derived and numerically checked by the case studies of the Escherichia coli ribosome and Arp2/3 actin filament junction. In different ribosomal proteins, the optimal number of amino acids per CG site is shown to differ by a factor of 3.5, and an even wider spread may exist in other large biomolecular complexes. Therefore, the method proposed in this paper is valuable for the optimal construction of CG models of such complexes

Predictive Minisci late stage functionalization with transfer learning

Acknowledgements: Financial support for this work was generously provided by Pfizer and the Royal Society (Newton International Fellowship to E.K.S. and University Research Fellowship to A.A.L.). We wish to thank Rokas Elijošius, William McCorkindale, and Oliver P. King-Smith for their enlightening discussions. We are grateful to Hans Renata and Roger M. Howard for assistance in manuscript preparation. The authors would like to acknowledge several Pfizer colleagues and Spectrix vendor partners who have contributed to this work including Manjinder Lall, Gregory Walker, R. Scott Obach, and Douglas Spracklin for their leadership and execution of the Lead Diversification Platform (LDP), and Danial Morris for LDP product generation, isolations, bioanalytical support, and anyone else who has contributed to the LDP from the date of its inception.Funder: Royal Society; doi: https://doi.org/10.13039/501100000288Funder: Pfizer (Pfizer Inc.); doi: https://doi.org/10.13039/100004319Funder: Spectrix Analytical LLCStructural diversification of lead molecules is a key component of drug discovery to explore chemical space. Late-stage functionalizations (LSFs) are versatile methodologies capable of installing functional handles on richly decorated intermediates to deliver numerous diverse products in a single reaction. Predicting the regioselectivity of LSF is still an open challenge in the field. Numerous efforts from chemoinformatics and machine learning (ML) groups have made strides in this area. However, it is arduous to isolate and characterize the multitude of LSF products generated, limiting available data and hindering pure ML approaches. We report the development of an approach that combines a message passing neural network and 13C NMR-based transfer learning to predict the atom-wise probabilities of functionalization for Minisci and P450-based functionalizations. We validated our model both retrospectively and with a series of prospective experiments, showing that it accurately predicts the outcomes of Minisci-type and P450 transformations and outperforms the well-established Fukui-based reactivity indices and other machine learning reactivity-based algorithms