Affine Model Research Articles

Quantitative prediction model for affinity of drug-target interactions based on molecular vibrations and overall system of ligand-receptor

Herein, surface-enhanced Raman scattering (SERS) and ultraviolet visible absorption spectrum in combination with fluorescence spectroscopy were jointly explored to investigate the binding interaction between Enrofloxacin (EFLX) with Bovine serum albumin (BSA) at a physiological condition. A facile SERS substrate based on the self-assembly of colloid silver on the modified TiN-Ag surface was obtained, an advanced SERS analysis method basing on this as-prepared substrate was established for sensitive and rapid detection of enrofloxacin, the most enhanced mode was that with certain motions perpendicular to the metal surface. In addition, the fluorescence and ultraviolet spectrum were allowed to found that enrofloxacin may bind to BSA site I via the carboxyl group with strong binding power.

Fast Training of Neural Affine Models for Model Predictive Control: An Explicit Solution

This work describes a nonlinear affine model developed especially for prediction in Model Predictive Control (MPC). A multi-model structure is used in which independent sub-models compute predictions for the consecutive sampling instants. All sub-models are affine with respect to the future values of the manipulated variable that are calculated in MPC. Model coefficients are time-varying; they are determined online by a neural network of the Radial Basis Function type. It is proved that the described model configuration makes it possible to formulate the training task as a least squares problem that has an explicit solution (the global optimum). A chemical reactor benchmark is considered to show advantages of the discussed modeling approach and the resulting MPC scheme. As a result of model affinity, MPC requires solving online simple quadratic optimization tasks.

Model-Based Reinforcement Learning via Stochastic Hybrid Models

Optimal control of general nonlinear systems is a central challenge in automation. Enabled by powerful function approximators, data-driven approaches to control have recently successfully tackled challenging applications. However, such methods often obscure the structure of dynamics and control behind black-box over-parameterized representations, thus limiting our ability to understand closed-loop behavior. This article adopts a hybrid-system view of nonlinear modeling and control that lends an explicit hierarchical structure to the problem and breaks down complex dynamics into simpler localized units. We consider a sequence modeling paradigm that captures the temporal structure of the data and derive an expectation-maximization (EM) algorithm that automatically decomposes nonlinear dynamics into stochastic piecewise affine models with nonlinear transition boundaries. Furthermore, we show that these time-series models naturally admit a closed-loop extension that we use to extract local polynomial feedback controllers from nonlinear experts via behavioral cloning. Finally, we introduce a novel hybrid relative entropy policy search (Hb-REPS) technique that incorporates the hierarchical nature of hybrid models and optimizes a set of time-invariant piecewise feedback controllers derived from a piecewise polynomial approximation of a global state-value function.

A Switching Predictive Control Method of Three Phase VSCs Based on Switching Affine Model

The working process of three-phase voltage source converters (VSCs) can be treated as the switching between the different subsystems, which is a typical switched system. The switching control strategies based on the switching model achieve satisfactory control performance of three-phase VSCs with very simple control structure and less control parameters. However, two drawbacks restrict the flexible applications of the present switching control strategies, i.e. the unfixed switching frequency problem and the unstable subsystem switching problem caused by the system delay. In this paper, a current switching predictive control (CSPC) method based on the current switching affine error model is proposed to the three-phase VSCs. The proposed method, by remaining the advantages of traditional switching control methods, realizes the fixed switching frequency of three-phase VSCs in the framework of switched system and achieves desired performance under system delay conditions. The simulation and experiment results verify the effectiveness of the proposed methodology.

Paleoproterozoic crustal evolution of the northern Borborema Province, NE Brazil: Insights from high-grade metamorphic rocks of the Canindé do Ceará Complex

Archean to Paleoproterozoic crustal segments are widely recognized as basement inliers within the Neoproterozoic orogenic belts of the Pan-African/Brasiliano orogeny. For example, the Canindé do Ceará Complex, in the northern Borborema Province, NE Brazil, comprises a large domain of Paleoproterozoic high-grade metamorphic rocks with a protracted tectonic history. This complex is dominantly represented by sillimanite- and graphite-bearing metasedimentary rocks with associated tonalitic to granodioritic orthogneisses. All these rocks are highly deformed and variably migmatized. Minor granitic intrusions and lenses of garnet amphibolites are also present. In this work, in order to discuss the tectonic evolution of the area, we present new U-Pb LA-ICPMS zircon dating, Sm-Nd isotopes, and whole-rock geochemistry for selected samples of the Canindé do Ceará Complex. The amphibolites show tholeiitic affinity and Sm-Nd TDM model ages of 2.22–2.52 Ga with positive εNd(t), attesting to an oceanic signature. The tonalitic/granodioritic orthogneisses show U-Pb ages of 2.19 to 2.12 Ga, Sm-Nd TDM model ages of 2.16 to 2.34 Ga, which probably reflect arc-related environments. The collisional event is marked by granitic intrusions (aplites) and diatexites, which display U-Pb ages ranging from 2.08 to 2.03 Ga. This event is also recorded in zircon recrystallized from tonalitic/granodioritic rocks, which show U-Pb age at 2.04 Ga. Therefore, from our results and literature information, the Rhyacian/Orosirian basement rocks of the Canindé do Ceará Complex show a protracted evolutionary history, ranging from 2.2 to 2.1 Ga arc-related environments, to 2.1–2.0 Ga continental collision and late Neoproterozoic metamorphic overprint.

3CLpro inhibitors: DEL-based molecular generation.

Molecular generation (MG) via machine learning (ML) has speeded drug structural optimization, especially for targets with a large amount of reported bioactivity data. However, molecular generation for structural optimization is often powerless for new targets. DNA-encoded library (DEL) can generate systematic, target-specific activity data, including novel targets with few or unknown activity data. Therefore, this study aims to overcome the limitation of molecular generation in the structural optimization for the new target. Firstly, we generated molecules using the structure-affinity data (2.96 million samples) for 3C-like protease (3CLpro) from our own-built DEL platform to get rid of using public databases (e.g., CHEMBL and ZINC). Subsequently, to analyze the effect of transfer learning on the positive rate of the molecule generation model, molecular docking and affinity model based on DEL data were applied to explore the enhanced impact of transfer learning on molecule generation. In addition, the generated molecules are subjected to multiple filtering, including physicochemical properties, drug-like properties, and pharmacophore evaluation, molecular docking to determine the molecules for further study and verified by molecular dynamics simulation.

A human-centered designed outreach strategy for a youth contraception navigator program.

To identify key elements of an outreach strategy for a youth contraception navigator program designed to help young people overcome barriers to contraception access. A human-centered design approach was used to engage adolescents aged 15-17 in co-design sessions. Human-centered design techniques, such as affinity diagramming and model building were used to inform key elements of the communication model and the final outreach strategy messages. Messages focused on the individual, normalizing talking about birth control, acknowledging the challenges to obtaining birth control, explaining how the navigator program works resonate with young people. Having images of diverse participants, offering information about birth control, and showing images of reputable sources will enhance trust. A name (IN Control) and key elements of an outreach strategy were determined for the navigator program. It is important to work with key stakeholders and co-design the optimal strategy and messages to assure that the intended audience is reached, and the desired behavior change is achieved. Human-centered design techniques can be used to provide insight into programmatic outreach strategies for a contraception navigator program to increase their impact and ultimate success.

Hybrid model predictive control of a nonlinear three-tank system based on the proposed compact form of piecewise affine model

Hybrid model predictive control of a nonlinear three-tank system based on the proposed compact form of piecewise affine model

The Impact of Polymerization Chemistry on the Mechanical Properties of Poly(dimethylsiloxane) Bottlebrush Elastomers.

We compare the low-strain mechanical properties of bottlebrush elastomers (BBEs) synthesized using ring-opening metathesis and free radical polymerization. Through comparison of experimentally measured elastic moduli and those predicted by an ideal, affine model, we evaluate the efficiency of our networks in forming stress-supporting strands. This comparison allowed us to develop a structural efficiency ratio that facilitates the prediction of mechanical properties relative to polymerization chemistry (e.g., softer BBEs when polymerizing under dilute conditions). This work highlights the impact that polymerization chemistry has on the structural efficiency ratio and the resultant mechanical properties of BBEs with identical side chains, providing another "knob" by which to control polymer network properties.

A Tangent Approximation Coefficient Extraction Method for Instantaneous Sample Indexed Magnitude-Selective Affine Behavioral Model

The coefficients of digital predistortion (DPD) models are often extracted by the least square (LS) method, which has high computational complexity because of the operations of the large matrix. In this letter, a low complexity coefficient extraction method for instantaneous sample indexed magnitude-selective affine (I-MSA) model is proposed. The proposed method uses the tangent of the midpoint of the piecewise function to fit the curve. Therefore, the LS coefficient extraction only needs a small amount of data near the midpoint, rather than using all the data in the segmented region. Simulation and experimental results have confirmed that compared with the original LS method, the proposed method has similar modeling accuracy and linearization performance, but the computational complexity of coefficient extraction is greatly reduced.

Root number of the Jacobian of y 2 =x p +a

Let $C/\mathbb{Q}$ be a hyperelliptic curve with an affine model of the form $y^2=x^p+a$. We explicitly determine the root number of the Jacobian of $C$, with particular focus on the local root number at $p$ where $C$ has wild ramification.

A patient- and parent-centered approach to urinary and fecal incontinence in children and adolescents with spina bifida: understanding experiences in the context of other competing care issues

Despite significant and known challenges to urinary and fecal incontinence (UI and FI, respectively) among children with spina bifida (SB) and their families, few studies have identified patient-centered measures and approaches to assessing them. This study represents the first stage of a larger study to develop a patient-centered goal-setting tool to guide incontinence management (Figure). Our aim was to understand patient experiences of UI and FI, and what goals should be included in the tool. We used a qualitative research approach integrated with human-centered design methods. We recruited, in clinic and online, children with SB (8-17 years old) and parents (>=18 years old) of children with SB (8-17 years old). Online activities were analyzed by four experienced design researchers using affinity diagramming, group analysis and modeling activities (mind maps, challenge maps, experience maps). Recruitment and thematic qualitative analysis continued until saturation was reached. Seventeen children with SB participated (9 female, 12 shunted, 13 using bladder catheterizations, 6 using Malone antegrade continence enema, median age: 15 years old). Fifteen parents participated (13 mothers/2 fathers, median age: 42 years old), including six mother-child dyads. Five major themes each were identified for UI and FI experiences: (1) negative emotional impact of incontinence, (2) unpredictability of incontinence, (3) challenging/unpleasant incontinence management, (4) inconvenient/unreliable incontinence management, (5) UI management having unpleasant complications and FI putting much responsibility on parents. We identified six UI goal domains and five FI goal domains. Four overlapping domains included: accidents, independence, interruptions at school and social/friends. Unique domains were: urinary tract infections (UI), catheterizations (UI) and enemas (FI). Findings of our study improve our understanding of children's and parents' experiences associated with incontinence in SB and potential continence goals. Children with SB experience incontinence as distressing and unpredictable. Incontinence management can be challenging and unreliable. Patient-centered continence goals cluster in domains, indicating how incontinence intermeshes with other life areas and offering potential approaches to structure continence goals in a goals-selection tool.

Hyperelastic continuum models for isotropic athermal fibrous networks.

Many biological materials contain fibrous protein networks as their main structural components. Understanding the mechanical properties of such networks is important for creating biomimicking materials for cell and tissue engineering, and for developing novel tools for detecting and diagnosing disease. In this work, we develop continuum models for isotropic, athermal fibrous networks by combining a single-fibre model that describes the axial response of individual fibres, with network models that assemble individual fibre properties into overall network behaviour. In particular, we consider four different network models, including the affine, three-chain, eight-chain, and micro-sphere models, which employ different assumptions about network structure and kinematics. We systematically investigate the ability of these models to describe the mechanical response of athermal collagen and fibrin networks by comparing model predictions with experimental data. We test how each model captures network behaviour under three different loading conditions: uniaxial tension, simple shear, and combined tension and shear. We find that the affine and three-chain models can accurately describe both the axial and shear behaviour, whereas the eight-chain and micro-sphere models fail to capture the shear response, leading to unphysical zero shear moduli at infinitesimal strains. Our study is the first to systematically investigate the applicability of popular network models for describing the macroscopic behaviour of athermal fibrous networks, offering insights for selecting efficient models that can be used for large-scale, finite-element simulations of athermal networks.

Optimal design of a driver assistance controller based on surrounding vehicle’s social behavior game model

Driver assistance control in the cut-in scenarios is challenging, since the controller needs to ensure driving safety and avoid unnecessary intervention, while considering the interaction with surrounding vehicles. This paper proposes an optimal driver assistance controller considering the social behaviors of the surrounding vehicles to assist the drivers in the cut-in scenarios. To model the social behavior of the surrounding vehicle, we first formulate the interaction between the semi-autonomous vehicle and the surrounding vehicle as a misinformation game, which is achieved by assuming the surrounding vehicle is interacting with a hypothetical vehicle under the framework of non-cooperative game. Then, adaptive dynamic programming theory is utilized to find the Nash equilibrium, which is represented by deep neural networks and solved iteratively. Based on the established social behavior model and the nonlinear driver-vehicle dynamic model, an affine input nonlinear system model is obtained for the design of driver assistance controller, and the optimal assistance control strategy is also derived under the structure of adaptive dynamic programming. Several numerical simulations and driver-in-the-loop simulator experiments are conducted for validation. Results show that the proposed strategy can assist the driver in the cut-in scenario while addressing different social interactions with the surrounding vehicles. Importantly, by taking into account the surrounding vehicle’s social behavior through our established social behavior model, our proposed strategy significantly outperforms the no-interaction strategy both in terms of driving safety and intervention degree, validating its effectiveness and superiority.

Hybrid Model Linearization Predictive Control for DC Bus Stabilization in Spacecraft Combined Power System

In spacecraft, facing different loading conditions of subsystems, especially of permanent magnet synchronous motor (PMSM) system, the bus voltage has large fluctuation, which will seriously affect the power quality of the spacecraft combined power system. Therefore, it is necessary to introduce a bidirectional DC–DC converter to suppress the fluctuation of the bus voltage. Aiming at the need of controlling the bidirectional DC–DC converter, a hybrid model linearization predictive control (HMLPC) algorithm is proposed. According to the strong nonlinear hybrid characteristics of the bidirectional DC–DC converter, an optimal linear approximation model at the sampling point is established using the Jacobian matrix, based on which a piecewise affine (PWA) model is established for Buck and Boost mode, and then, a hybrid linearization predictive model is constructed. Ultimately, by introducing a two-period PWA and multistep prediction strategy, proposing a prediction error, and solving the constrained finite time optimization control (CFTOC) problem online, an optimal control law is derived. In addition, a stability analysis is provided for the proposed control algorithm. In this article, a simulative validation and a comparative analysis are presented; moreover, a combined power test platform is built to validate the proposed control algorithm. Both simulation and experiment results show that the HMLPC algorithm can effectively suppress the fluctuation of bus voltage and enhance the anti-interference ability of the system.

Novel activity of human BChE: Lipid hydrolysis

Acetylcholinesterase and butyrylcholinesterase (BChE) typically hydrolyze the neurotransmitter acetylcholine. The multifunctional enzyme BChE is associated with lipid metabolism through an undefined mechanism. Based on lipid-related studies and by comparing the structural similarities between lipases and BChE we postulated that the association of BChE with lipid metabolism could occur through hydrolytic activity. Utilizing purified BChE enzymes from different sources and several lipases as controls, the ability of BChE to hydrolyze 4-methylumbelliferyl (4-mu) palmitate is investigated. Using lectin affinity, inhibition kinetics, and molecular modeling, we demonstrated that purified BChE hydrolyzed 4-mu palmitate at pH 8 as effectively as wheat germ lipase. The affinity Km value of the enzymes for 4-mu palmitate as substrate is found as 10.4 μM, 34.2 μM, 129.8 μM, and 186 μM for wheat germ lipase, purified BChE, pancreatic lipase, and commercial BChE, respectively. Analysis of the inhibitory effect of 4-mu palmitate on BChE using butyrylthiocholine as substrate revealed competitive inhibition with Ki and IC50 values of 448 μM and 987.2 μM, respectively. The binding affinity and interactions of 4-mu palmitate with BChE and pancreatic lipase were predicted by molecular docking. These results suggest that BChE possesses lipolytic activity. The possibility that BChE hydrolyzes not only 4-mu palmitate but also other types of lipids will lead to a new approach to those disease states associated with increased BChE activity/expression.

Modelling USA Age-Cohort Mortality: A Comparison of Multi-Factor Affine Mortality Models

Affine mortality models are well suited for theoretical and practical application in pricing and risk management of mortality risk. They produce consistent, closed-form stochastic survival curves allowing for the efficient valuation of mortality-linked claims. We model USA age-cohort mortality data using five multi-factor affine mortality models. We focus on three-factor models and compare four Gaussian models along with a model based on the Cox–Ingersoll–Ross (CIR) process, allowing for Gamma-distributed mortality rates. We compare and assess the Gaussian Arbitrage-Free Nelson–Siegel (AFNS) mortality model, which incorporates level, slope and curvature factors, and the canonical Gaussian factor model, both with and without correlations in the factor dynamics. We show that for USA mortality data, the probability of negative mortality rates in the Gaussian models is small. Models are estimated using discrete time versions of the models with age-cohort data capturing variability in cohort mortality curves. Poisson variation in mortality data is included in the model estimation using the Kalman filter through the measurement equation. We consider models incorporating factor dependence to capture the effects of age-dependence in the mortality curves. The analysis demonstrates that the Gaussian independent-factor AFNS model performs well compared to the other affine models in explaining and forecasting USA age-cohort mortality data.

EISA-Score: Element Interactive Surface Area Score for Protein-Ligand Binding Affinity Prediction.

Molecular surface representations have been advertised as a great tool to study protein structure and functions, including protein-ligand binding affinity modeling. However, the conventional surface-area-based methods fail to deliver a competitive performance on the energy scoring tasks. The main reason is the lack of crucial physical and chemical interactions encoded in the molecular surface generations. We present novel molecular surface representations embedded in different scales of the element interactive manifolds featuring the dramatically dimensional reduction and accurately physical and biological properties encoders. Those low-dimensional surface-based descriptors are ready to be paired with any advanced machine learning algorithms to explore the essential structure-activity relationships that give rise to the element interactive surface area-based scoring functions (EISA-score). The newly developed EISA-score has outperformed many state-of-the-art models, including various well-established surface-related representations, in standard PDBbind benchmarks.

Multiassay Profiling of a Focused Small Molecule Library Reveals Predictive Bidirectional Modulation of the lncRNA MALAT1 Triplex Stability In Vitro.

The rapidly accelerating characterization of RNA tertiary structures has revealed their pervasiveness and active roles in human diseases. Small molecule-mediated modulation of RNA tertiary structures constitutes an attractive avenue for the development of tools for therapeutically targeting and/or uncovering the pathways associated with these RNA motifs. This potential has been highlighted by targeting of the triple helix present at the 3'-end of the noncoding RNA MALAT1, a transcript implicated in several human diseases. This triplex has been reported to decrease the susceptibility of the transcript to degradation and promote its cellular accumulation. While small molecules have been shown to bind to and impact the stability of the MALAT1 triple helix, the small molecule properties that lead to these structural modulations are not well understood. We designed a library utilizing the diminazene scaffold, which is underexplored but precedented for nucleic acid binding, to target the MALAT1 triple helix. We employed multiple assays to holistically assess what parameters, if any, could predict the small molecule affinity and effect on triplex stability. We designed and/or optimized competition, calorimetry, and thermal shift assays as well as an enzymatic degradation assay, the latter of which led to the discovery of bidirectional modulators of triple helix stability within the scaffold-centric library. Determination of quantitative structure-activity relationships afforded predictive models for both affinity- and stability-based assays. This work establishes a suite of powerful orthogonal biophysical tools for the evaluation of small molecule:RNA triplex interactions that generate predictive models and will allow small molecule interrogation of the growing body of disease-associated RNA triple helices.

Monte Carlo technique to study the adsorption affinity of azo dyes by applying new statistical criteria of the predictive potential

ABSTRACT Azo dyes are broadly used in different industries through their chemical stability and ease of synthesis. However, these dyes are usually identified as critical environmental pollutants. Hence, a mathematical model for the adsorption affinity of azo dyes can be applied for solving tasks of medicine and ecology. Quantitative structure-property relationships for the adsorption affinity of azo dyes to a substrate (DAF, kJ/mol) were established using the Monte Carlo method by generating optimal SMILES-based descriptors. The index of ideality of correlation (IIC) and the correlation intensity index (CII) improved the model’s predictive potential, especially when they were used simultaneously. The statistical quality of the best model on the validation set was characterized by n = 18, r 2 = 0.9468, and RMSE = 1.26 kJ/mol.