Affine Parameter Research Articles

Multimodal MR image registration using weakly supervised constrained affine network

Multimodal image registration is an important technique for many clinical applications. However, it is particularly challenging to obtain good spatial alignment. This paper introduces a novel architecture named the constrained affine network, which combines deformable image registration with affine transformation for multimodal MR image registration. A weakly supervised manner is adapted to train the network and anatomical labels are used in training. The network directly learns to predict a displacement vector field (DVF) between pairs of input images. Different from the existing deformable image registration methods based on the convolutional neural network (CNN), the method proposes a global constrained affine module, which can predict an affine transformation by pre-computing the range of affine parameters, and the model can be combined with a deformable registration network. We evaluated the proposed method on 3D multimodal medical images. Experimental results indicate that the proposed method has better performance.

Structural dynamics of SARS-CoV-2 variants: A health monitoring strategy for anticipating Covid-19 outbreaks

Objectivesthe Covid-19 pandemic has been marked by sudden outbreaks of SARS-CoV-2 variants harboring mutations in both the N-terminal (NTD) and receptor binding (RBD) domains of the spike protein. The goal of this study was to predict the transmissibility of SARS-CoV-2 variants from genomic sequence data. Methodswe used a target-based molecular modeling strategy combined with surface potential analysis of the NTD and RBD. Resultswe observed that both domains act synergistically to ensure optimal virus adhesion, which explains why most variants exhibit concomitant mutations in the RBD and in the NTD. Some mutation patterns affect the affinity of the spike protein for ACE-2. However, other patterns increase the electropositive surface of the spike, with determinant effects on the kinetics of virus adhesion to lipid raft gangliosides. Based on this new view of the structural dynamics of SARS-CoV-2 variants, we defined an index of transmissibility (T-index) calculated from kinetic and affinity parameters of coronavirus binding to host cells. The T-index is characteristic of each variant and predictive of its dissemination in animal and human populations. Conclusionsthe T-index can be used as a health monitoring strategy to anticipate future Covid-19 outbreaks due to the emergence of variants of concern.

Extraction and Characterization of Polyphenol Oxidase from Plant Materials: A Review

Polyphenol oxidase (PPO) is a copper-containing enzyme that can be used for different applications including wastewater treatment and biosensing. Given these wide arrays of application, the reaction and biochemical characteristics of the enzyme must be known to further determine its other applications and to control the essential factors during processing. The purpose of this research was to review the different factors that influence the effective extraction and characterization of PPO from plant materials. The pH of the extraction mixture, extraction temperature, type of buffer, mass to solvent ratio, extraction time, and additives are the factors that influence the effective extraction of PPO from plant materials. Since PPOs taken from different plant sources have varied protein structures, these factors have different effects during extraction. The isolated PPO from the extraction process can be characterized based on its activity as a function of pH, temperature, and type of substrate, and on the values of its kinetic parameters (Km and Vmax). PPO isolated from different plant sources shows varied optimum pH, optimum temperature, substrate affinity, and kinetic parameter values.

Accurate quantitative determination of affinity and binding kinetics for tight binding inhibition of xanthine oxidase

The accurate quantitative determination of affinity and binding kinetics (BK) for tight binding inhibition is extraordinary important from both the continuous optimization of compounds, particularly in developing structure–activity relationships (SAR), and the prediction of in vivo target occupancy (TO). Due to the unique properties for tight binding inhibition that the inhibitors are characterized by the ultrahigh-affinity, relatively fast association to the target enzyme combined with extremely slow dissociation of the inhibitor-enzyme binary complex, the classical steady state equilibrium methods are no longer valid. Here, we made several recommendations of how to design the optimal experiments and apply special mathematical calculation approaches to quantitatively evaluate the accurate affinity and BK as the examples of two tight binding inhibitors against the xanthine oxidase (XO), as well as compared the differences in the results calculated from the different data analytical methods and analyzed the influence of these differences on the XO engagement in human. Analysis of the results displayed that the accurate apparent dissociation constant (Ki*,app) was 0.2 ± 0.06 nM for topiroxotstat and was 0.45 ± 0.2 nM for febuxostat; that on-rate (kon) was (4.3 ± 1.1) × 106 M−1s−1 for topiroxotstat and was(133.3 ± 3.5) × 106 M−1s−1 for febuxostat, and off-rate (koff) was (1.0±0.2) × 10−5 s−1 for topiroxotstat and was ≤ 0.16 × 10−5 s−1for febuxostat. Moreover, there were significant differences in the Ki*,app and koff values estimated using the appropriate specialized methods for tight binding inhibition versus classical steady state equilibrium methods, with the substantial differences of 14-fold and 32-fold reduction for topiroxostat, respectively, and of 9.6-fold and ≥ 213-fold reduction for febuxostat, while the kon values remain the moderate differences for the two inhibitors. The obvious greater AUC of XO engagement time courses and longer durations of above 70% engagement by the appropriate specialized methods for tight binding inhibition were observed that the results display the differences of 70.1% and 88%, respectively for topiroxostat and of 38.1% and 35.0%, respectively for febuxostat in human liver cell than by classical steady state equilibrium methods. Again, our studies provide several valuable recommendations of the optimal experiment protocols and appropriate analytical approaches for accurately quantitatively assessing the affinity and BK parameters as well as demonstrate the ability of our recommended methods to generate reliable data for tight binding inhibitors against XO.

Integration of affinity and abundance parameters for the identification of novel CD8+ T cell specificities in the CT26 tumor model

Abstract The CT26 model is one of the most widely tested systems for cancer immunotherapy. Over 70% of the CD8+ CTL responses in CT26 tumors are directed against two peptides: SPSYVYHQF (AH1) presented by H-2Ld, or GGPESFYCASW (GSW11) presented by H-2Dd. Both peptides derive from the ecotropic murine leukaemia virus gp70 envelope glycoprotein, which is the highest expressed gene in CT26, and have relative low affinity values for their respective MHC-I. However, there is a lack of knowledge about other CTL specificities in CT26, the relationship between abundance and affinity of such peptides, and their roles in tumor rejection. We identified 96 potential epitopes restricted by MHC-I molecules in BALB/c mice based on previously published immuno-transcriptomic data. We used a mathematical algorithm integrating the peptides relative affinity to MHC-I measured by NetMHC4.0, and their abundance in the proteome, which resulted in a prediction of the likelihood of peptide presentation. Screening was performed in splenocytes from Treg-depleted CT26-challenged mice via IFNγ intracellular cytokine staining and T cell proliferation assays. Our approach led to the identification of a novel CTL specificity restricted by H-2Kd (Kd34), which shows moderate IFNγ responses in splenocytes from mice responding to the therapy. Moreover, using Kd34-specific dextramers, we were able to detect a small population of tumor-infiltrating lymphocytes (TILs) specific for this peptide, with similar phenotypic traits to AH1- and GSW1-specific TILs. The identification of such novel CD8+ T cell specificities will improve the understanding about the relationship between immunodominance and epitope abundance/affinity to better tailor vaccine strategies in cancer.

Adapting T Cell Receptor Ligand Discrimination Capability via LAT.

Self- and non-self ligand discrimination is a core principle underlying T cell-mediated immunity. Mature αβ T cells can respond to a foreign peptide ligand presented by major histocompatibility complex molecules (pMHCs) on antigen presenting cells, on a background of continuously sensed self–pMHCs. How αβ T cells can properly balance high sensitivity and high specificity to foreign pMHCs, while surrounded by a sea of self-peptide ligands is not well understood. Such discrimination cannot be explained solely by the affinity parameters of T cell antigen receptor (TCR) and pMHC interaction. In this review, we will discuss how T cell ligand discrimination may be molecularly defined by events downstream of the TCR–pMHC interaction. We will discuss new evidence in support of the kinetic proofreading model of TCR ligand discrimination, and in particular how the kinetics of specific phosphorylation sites within the adaptor protein linker for activation of T cells (LAT) determine the outcome of TCR signaling. In addition, we will discuss emerging data regarding how some kinases, including ZAP-70 and LCK, may possess scaffolding functions to more efficiently direct their kinase activities.

A CNN-Based High-Accuracy Registration for Remote Sensing Images

In this paper, a convolutional neural network-based registration framework is proposed for remote sensing to improve the registration accuracy between two remote-sensed images acquired from different times and viewpoints. The proposed framework consists of four stages. In the first stage, key-points are extracted from two input images—a reference and a sensed image. Then, a patch is constructed at each key-point. The second stage consists of three processes for patch matching—candidate patch pair list generation, one-to-one matched label selection, and geometric distortion compensation. One-to-one matched patch pairs between two images are found, and the exact matching is found by compensating for geometric distortions in the matched patch pairs. A global geometric affine parameter set is computed using the random sample consensus algorithm (RANSAC) algorithm in the third stage. Finally, a registered image is generated after warping the input sensed image using the affine parameter set. The proposed high-accuracy registration framework is evaluated using the KOMPSAT-3 dataset by comparing the conventional frameworks based on machine learning and deep-learning-based frameworks. The proposed framework obtains the least root mean square error value of 34.922 based on all control points and achieves a 68.4% increase in the matching accuracy compared with the conventional registration framework.

Related parameters of affinity and stability prediction of HLA-A*2402 restricted antigen peptides based on molecular docking.

BackgroundMajor histocompatibility complex class I (MHC-I) plays an important role in cell immune response, and stable interaction between polypeptides and MHC-I ensures efficient presentation of polypeptide-MHC-I (pMHC-I) molecular complexes to T cells. The aim of this study was to explore ways to improve the affinity and stability of the p-Human Leukocyte Antigen (HLA)-A*2402 complex.MethodsThe peptide sequences of the restricted antigen peptides for HLA-A*2402 and the results of the in vitro competitive binding test were retrieved from the literature. The affinity values were predicted using NetMHCpan v4.1 server, and the stability values were predicted using the NetMHCstab v1.0 server. Auto Vina was used to dock peptides to HLA-A*2402 protein in a flexible docking manner, while Flexpepdock was employed to optimize the docking morphology. Maestro was used to analyze the intermolecular forces and the binding affinity of the complex, while MM-GBSA was used to calculate the binding free energy values.ResultsThe intermolecular interactions that maintained the affinity and stability of peptide-HLA-A*2402 complex relied mainly on HB, followed by pi stack. The binding affinity values of molecular docking were associated with the predicted values of affinity and stability, the binding affinity and the binding free energy, as well as the intermolecular force pi-stack. The pi stack had a significant negative correlation with binding affinity and binding free energy. The replacement of the residues of the polypeptides that did not form pi-stack interactions with HLA-A*2402 improved the affinity and/or stability compared to before replacement.ConclusionsThe generation and increase in the number of pi-stacks between peptides and HLA-A*2402 molecules may help improve the affinity and stability of p-HLA-A*2402 complexes. The prediction of intermolecular forces and binding affinity of peptide-HLA by means of molecular docking is a supplement to the current commonly used prediction databases.

Deep Learning-Based Incorporation of Planar Constraints for Robust Stereo Depth Estimation in Autonomous Vehicle Applications

In autonomous vehicles, depth information for the environment surrounding the vehicle is commonly extracted using time-of-flight (ToF) sensors such as LiDARs and RADARs. Those sensors have some limitations that may potentially degrade the quality and utility of the depth information to a substantial extent. An alternative solution is depth estimation from stereo pairs. However, stereo matching and depth estimation often fails at ill-posed regions including areas with repetitive patterns or textureless surfaces which are commonly found on planar surfaces. This paper focuses on designing an efficient framework for stereo depth estimation, using deep learning technique, that is robust against the mentioned ill-posed regions. With the observation that disparities of all pixels belonging to planar areas (scene plane) viewed by two rectified stereo images can be described using affine transformations, our proposed method predicts pixel-wise affine transformation parameters based on the depth information encoded in the aggregated cost volume. We also introduce a propagation term which enforces all pixels belonging to the same scene plane to be transformed using the same parameters. Disparity can then be computed by multiplying the predicted affine parameters with the corresponding pixel locations. The proposed method was evaluated on several benchmark datasets. We are able to obtain competitive results and at the same time reducing the processing time of common convolution neural network (CNN) in stereo matching by 50%. Analysis of the findings shows that our method can produce reliable results at the ill-posed regions which are challenging to the current state-of-the-arts methods.

Two-step homogeneous geodesics in pseudo-Riemannian manifolds

Given a homogeneous pseudo-Riemannian space $$(G/H,\langle \ , \ \rangle),$$ a geodesic $$\gamma :I\rightarrow G/H$$ is said to be two-step homogeneous if it admits a parametrization $$t=\phi (s)$$ (s affine parameter) and vectors X, Y in the Lie algebra $${\mathfrak{g}}$$ , such that $$\gamma (t)=\exp (tX)\exp (tY)\cdot o$$ , for all $$t\in \phi (I)$$ . As such, two-step homogeneous geodesics are a natural generalization of homogeneous geodesics (i.e., geodesics which are orbits of a one-parameter group of isometries). We obtain characterizations of two-step homogeneous geodesics, both for reductive homogeneous spaces and in the general case, and undertake the study of two-step g.o. spaces, that is, homogeneous pseudo-Riemannian manifolds all of whose geodesics are two-step homogeneous. We also completely determine the left-invariant metrics $$\langle \ ,\ \rangle$$ on the unimodular Lie group $$SL(2,{{\mathbb{R}}})$$ such that $$\big (SL(2,{{\mathbb{R}}}),\langle \ ,\ \rangle \big )$$ is a two-step g.o. space.

Quasi-geodesics in relativistic gravity

A four-force parallel to the trajectory of a massive particle can always be eliminated by going to an affine parametrization, but the affine parameter is different from the proper time. The main application is to cosmology, in which elements of the cosmic fluid are subject to a pressure gradient parallel to their four-velocities. Natural implementations of parallel four-forces occur when the particle mass changes, in scalar–tensor cosmology, and in cosmic antifriction due to particle production.

Supression of the Flexural Vibration of the Railway Car Body Floor

In this paper, the railway vehicle is modeled by a seventeenth-degrees-of freedom full vehicle model to investigate the elastic vibrations of the railway vehicles’s car body subjected to the excitations from vertical track irregularities. These irregularities are represented by the power spectral density functions which are authenticated for the stochastic real track data collected from the Konya-Polatli railway line in Turkey. The car body floor is modelled as a structure consisting of a plate and its connecting components by introducing artificial springs at the joint points. The model can express the complicated elastic vibrations with fewer degrees of freedom and at a lower computational cost than the detailed finite element model. To suppress the vertical vibration of the flexible-bodied railway vehicle secondary suspension control design based on affine parameter dependent model has been built with a single objective function and the solutions are obtained via H ∞ optimization. The numerical results show that the complicated mode shapes can successfully be expressed analytically and the simulation studies illustrate that the active system is effective in reducing both the flexible and rigid modes of the car body and improving the ride comfort.

Deep-learning based multi-modal retinal image registration for the longitudinal analysis of patients with age-related macular degeneration.

This work reports a deep-learning based registration algorithm that aligns multi-modal retinal images collected from longitudinal clinical studies to achieve accuracy and robustness required for analysis of structural changes in large-scale clinical data. Deep-learning networks that mirror the architecture of conventional feature-point-based registration were evaluated with different networks that solved for registration affine parameters, image patch displacements, and patch displacements within the region of overlap. The ground truth images for deep learning-based approaches were derived from successful conventional feature-based registration. Cross-sectional and longitudinal affine registrations were performed across color fundus photography (CFP), fundus autofluorescence (FAF), and infrared reflectance (IR) image modalities. For mono-modality longitudinal registration, the conventional feature-based registration method achieved mean errors in the range of 39-53 µm (depending on the modality) whereas the deep learning method with region overlap prediction exhibited mean errors in the range 54-59 µm. For cross-sectional multi-modality registration, the conventional method exhibited gross failures with large errors in more than 50% of the cases while the proposed deep-learning method achieved robust performance with no gross failures and mean errors in the range 66-69 µm. Thus, the deep learning-based method achieved superior overall performance across all modalities. The accuracy and robustness reported in this work provide important advances that will facilitate clinical research and enable a detailed study of the progression of retinal diseases such as age-related macular degeneration.

Affine transformation of virtual 3D object using 2D localization of fingertips

BackgroundInteractions with virtual 3D objects in the virtual reality (VR) environment using the gesture of fingers captured in a wearable 2D camera have emerging applications in real-life. MethodThis paper presents an approach of a two-stage convolutional neural network, one for the detection of hand and another for the fingertips. One purpose of VR environments is to transform a virtual 3D object with affine parameters by using the gesture of thumb and index fingers. ResultsTo evaluate the performance of the proposed system, one existing, and another developed egocentric fingertip databases are employed so that learning involves large variations that are common in real-life. Experimental results show that the proposed fingertip detection system outperforms the existing systems in terms of the precision of detection. ConclusionThe interaction performance of the proposed system in the VR environment is higher than that of the existing systems in terms of estimation error and correlation between the ground truth and estimated affine parameters.

Binding properties to nicotinic acetylcholine receptors can explain differential toxicity of neonicotinoid insecticides in Chironomidae

Neonicotinoids are neuroactive insecticides commonly detected in freshwater ecosystems. Recent studies have indicated that these compounds are markedly toxic to Chironomidae, a widespread family of ecologically important aquatic insects. However, despite their sensitivity, the pharmacological mechanisms driving neonicotinoid toxicity have yet to be characterized in these insect species. Here, we used a combination of saturation and competition binding studies to characterize neonicotinoid binding properties to nicotinic acetylcholine receptors (nAChR) in two different Chironomidae (Chironomus riparius and Chironomus dilutus) at two different life stages (larval and adult). Using radiolabeled imidacloprid ([3H]-IMI), we characterized and compared receptor density (Bmax), imidacloprid binding affinity (KD), and receptor binding affinity (Ki) to three different neonicotinoid competitors (imidacloprid, clothianidin, and thiamethoxam). We then compared receptor density and binding affinity parameters derived for Chironomidae to data previously generated for other dipterans and agricultural pests. We found that there were limited differences in neonicotinoid binding between C. riparius and C. dilutus, with both organisms demonstrating high affinities for imidacloprid (KD = 0.22−0.87 nM) and high receptor densities (Bmax = 0.92–6.53 pmol/mg). However, there were significant differences between life-stages, with larvae expressing higher densities of nicotinic acetylcholine receptors and higher imidacloprid affinities than adults. Moreover, there were compound-specific differences in receptor affinity, with larval stages displaying relative affinities (Ki) that generally correlated with acute neonicotinoid toxicity (e.g. clothianidin ≥ imidacloprid >>> thiamethoxam). Finally, compared to other dipterans and agricultural pests, Chironomidae display very high densities of high affinity nicotinic acetylcholine receptors, which likely contribute to their sensitivity. Results indicated that receptor-level differences in neonicotinoid binding may be responsible for ecotoxicological differences amongst distinct insect species, and they likely play a role in life stage-, and compound-level toxicity differences previously observed for Chironomidae. Overall, this study highlights the value of understanding the toxicological mechanisms of action of neonicotinoids in sensitive, non-target aquatic insects, to better predict adverse effects associated with unintentional neonicotinoid exposure.

In Silico Investigation of Electronic Structure, Binding Patterns and Molecular Docking of Nevirapine: An anti-HIV Type-1 Drug

Nevirapine is used in the treatment of Human Immunodeficiency Virus Type 1 (HIV-1) infection. The structure of nevirapine belongs to the dipyridodiazepinone class of chemical. In the present work, molecular geometries of nevirapine drug, AU and GC base pairs have been optimized by DFT method with B3LYP/6-31G(d,p) basis set. The MEP and HOMO-LUMO surfaces have been scanned at 0.001 electron/Bohr3 isodensity surface. Further, HOMO-LUMO energies have been used to calculate ionization potential, electron affinity, electronegativity, global hardness and softness parameters of the drug molecule as well as AU and GC base pairs. IR and Raman spectra of the drug molecule have been analyzed in the light of standard data. Results obtained from NBO analysis have been analyzed in terms of the hybridization of atoms and electronic structure of drug molecule. Binding patterns of the drug molecule with nucleic acid base pairs (AU and GC) have been examined using second order perturbation theory valid at intermediate range. Efforts have been made to elucidate the chemical and biological properties of the drug. The inhibition activity of the drug molecule against HIV-1 reverse transcriptase (Y2NG) in complex with inhibitor GSK560 has been examined with the help of molecular docking. It has been observed that nevirapine prefers to bind in the guanine-cytosine rich region of nucleic acid helices. Further, there exists gross similarity between calculated vibrational assignment and the standard data as reported in the literature.

Sampling-free model reduction of systems with low-rank parameterization

We consider the reduction of parametric families of linear dynamical systems having an affine parameter dependence that allow for low-rank variation in the state matrix. Usual approaches for parametric model reduction typically involve exploring the parameter space to identify representative parameter values and the associated models become the principal focus of model reduction methodology. These models are then combined in various ways in order to interpolate the response. The initial exploration of the parameter space can be a forbiddingly expensive task. A different approach is proposed here that requires neither parameter sampling nor parameter space exploration. Instead, we represent the system response function as a composition of four subsystem response functions that are non-parametric with a purely parameter-dependent function. One may apply any one of a number of standard (non-parametric) model reduction strategies to reduce the subsystems independently, and then conjoin these reduced models with the underlying parameterization to obtain the overall parameterized response. Our approach has elements in common with the parameter mapping approach of Baur et al. (PAMM 14(1), 19–22 2014) but offers greater flexibility and potentially greater control over accuracy. In particular, a data-driven variation of our approach is described that exercises this flexibility through the use of limited frequency-sampling of the underlying non-parametric models. The parametric structure of our system representation allows for a priori guarantees of system stability in the resulting reduced models across the full range of parameter values. Incorporation of system theoretic error bounds allows us to determine appropriate approximation orders for the non-parametric systems sufficient to yield uniformly high accuracy across the parameter range. We illustrate our approach on a class of structural damping optimization problems and on a benchmark model of thermal conduction in a semiconductor chip. The parametric structure of our reduced system representation lends itself very well to the development of optimization strategies making use of efficient cost function surrogates. We discuss this in some detail for damping parameter and location optimization for vibrating structures.

Observer design via interconnections of second‐order mixed sliding‐mode/linear differentiators

SummaryHigh‐gain observers and sliding mode observers are two of the most common techniques to design observers (or differentiators) for lower triangular nonlinear dynamics. While sliding mode observers can handle globally bounded nonlinearities, high‐gain linear techniques can deal with globally Lipschitz nonlinearities. To gain in generality and avoid the usual assumption that the plant's solutions are bounded with known bound, we propose here to mix both designs in the more general case where the nonlinearities satisfy a global incremental affine bound. We inspire from the recently developed low‐power high‐gain observer technique, which relies on the interconnection of several second‐order high‐gain observers. Adding sliding‐mode correction terms into this low‐power structure enables to guarantee global convergence of the estimation error in finite‐time with gains depending only on the parameters of the incremental affine bound of the nonlinearities. The estimation error is also proved to be uniformly stable along solutions starting from any compact sets of initial conditions.

Approximate Lie Symmetry Conditions of Autoparallels and Geodesics

This paper is devoted to the study of approximate Lie point symmetries of general autoparallel systems. The significance of such systems is that they characterize the equations of motion of a Riemannian space under an affine parametrization. In particular, we formulate the first-order symmetry determining equations based on geometric requirements and stipulate that the underlying Riemannian space be approximate in nature. Lastly, we exemplify the results by application to some approximate wave-like manifolds.

Should Virus Capsids Assemble Perfectly? Theory and Observation of Defects

Although published structural models of viral capsids generally exhibit a high degree of regularity or symmetry, structural defects might be expected because of the fluctuating environment in which capsids assemble and the requirement of some capsids for disassembly before genome delivery. Defective structures are observed in computer simulations, and are evident in single-particle cryoelectron microscopy studies. Here, we quantify the conditions under which defects might be expected, using a statistical mechanics model allowing for ideal, defective, and vacant sites. The model displays a threshold in affinity parameters below which there is an appreciable population of defective capsids. Even when defective sites are not allowed, there is generally some population of vacancies. Analysis of single particles in cryoelectron microscopy micrographs yields a confirmatory ≳15% of defective particles. Our findings suggest structural heterogeneity in virus capsids may be under-appreciated, and also points to a nontraditional strategy for assembly inhibition.