Himalayan-Tibetan orogeny has considered as a natural black box in the context of geodynamic evolution and tectonic complexity. The eastward extrusion model of Tibetan crust contradicts with the oblique convergence model in the NE-Himalaya , where the overall convergence rate accommodated in the Himalaya is about 20–25% less than that in the neighbouring central Himalaya and Eastern Himalayan syntaxis (EHS). We propose that instead of partitioning in the backarc, the NE-Himalaya has developed an active sliver along the Assam-Brahmaputra valley in the outer deformation front, in order to accommodate the deficiency in long-term plate convergence between Himalaya and southern Tibet. We argue that the strong eastward extrusion of Tibetan crust along NE-Himalaya is the main driving force for the unusual development of the Assam-Brahmaputra sliver. This new hypothesis can explain active convergence along EHS, low convergence and subdued topography in Bhutan and Arunachal Himalaya, kinematic and space-problem of Indo-Burmese wedge, and finally solves the contradiction between Tibetan extrusion and oblique convergence model of the HimalayanTibetan orogeny. The spectacular topographic features and tectonic history of Himalayan-Tibetan orogen has received global attention in terms of continental collision and related geodynamic processes, seismic hazards, and Asian climatic patterns, among other themes4. Among the geodynamic implications, two popular models have been in focus during the past decades: (a) ductile “glacier-like flow” of the Tibetan lower or upper crust and associated distribution in deformation7; and (b) interference of rigid lithospheric blocks and localised deformation along active faults9.The dynamic topography of the Earth’s surface, particularly those along continental margins, has been linked to mantle processes in recent studies8, and the lateral extrusion model which mainly considers deformation of Tibet8, appear to be mutually contradictory in view of the tectonics and distributed deformation (or slip partitioning) in the NE-Himalaya8. It has been proposed that in the NW-Himalaya, the motion between India and southern Tibet is oblique with respect to the structural trend, which is partitioned between right-lateral strike-slip motion on the Karakorum fault system in the back arc, and slightly oblique motion in the Kashmir Himalaya in the frontal arc, leading to the formation of the NW-Himalayan sliver10. However, in the adjoining Garhwal-Kumaun and Nepal Himalaya, there is no partitioning, and the entire convergence of 18–20 mm/yr occurs in arc-normal manner12.The oblique convergence model which describes the deformation between Himalaya and southern Tibet along the frontal-arc13.In the NE-Himalaya , the motion between India and southern Tibet is again oblique with respect to structural trend of the arc Fig. . In orde33, is mainly responsible for such unusual development of a sliver in the outer deformation front of the NE-Himalaya. Based on the GPS observations, we address the oblique convergence and outer deformation-front strain partitioning process along the Assam-Brahmaputra valley adjacent to NE-Himalaya.This raises question on applicability of the oblique convergence model in the NE-Himalaya. Here we address this issue and propose that instead of partitioning in the back arc, an active sliver in NE Himalaya has developed along the Assam-Brahmaputra valley in the outer deformation front which is accommodating the oblique motion through distributed deformation. The strain partitioning in the outer deformation front of Himalayan region has not been well evaluated in previous studies. Here we take into account the Assam-Brahmaputra valley sliver in the outer deformation front of NE Himalaya, in order to explain the complex geodynamic process. We also propose that the strong eastward flow of Tibetan crust around the EHS, investigated using different methods in different time scale37. The estimate varies from ~13 mm/yr in Kashmir35 to 18–20 mm/yr in the central Himalaya34 to 15–16 mm/yr in Bhutan Himalaya37 and 19–20 mm/yr in the Eastern Himalayan syntaxis38. To accommodate the oblique motion between India and southern Tibet in the NW-Himalaya, the convergence is partitioned into right-lateral strike-slip motion on the Karakorum fault system in the back arc and oblique motion in the Kashmir Himalaya along frontal arc, forming NW-Himalayan sliver10. On the other hand, it has argued that in the NE-Himalaya, the Brahmaputra valley has broken apart from the Indian plate and has rotated in a clockwise direction, resulting in the lowering of relative plate convergence rate across the NE-Himalaya37.The GPS derived convergence rates along the Himalayan arc between the Indian plate and southern Tibet have been reported by several authorsIn view of relative contradiction in oblique convergence and eastward extrusion model of Tibet, we propose here a new hypothesis for the NE Himalaya that is reasonably more compatible in the context of regional tectonics and deformation. We argue that the uncertainties in the GPS site velocities (~3.5 mm/yr) and poor spatial distribution of the data points from the Arunachal and Assam-Brahmaputra valley region appear to be inconsistent for demarcation of micro-block’s boundaries and associated deformation patterns. Also, the differential rotation is difficult to constrain (or distinguish) the variation in slip-rate along the strike of the fault system.39. However, lacking of enough common sites among some of the available velocity fields, we estimated an unique velocity field for Bhutan, Arunachal, Assam, Tibet, Bangladesh, Shillong and Indo-Burmese wedge44, by transforming all available velocity field into the ITRF200845 using the National Geodetic Survey’s horizontal time dependent positioning tool (https://www.ngs.noaa.gov/TOOLS/Htdp/Htdp.shtml). Finally, all the measurements were transformed into two relative reference frames, with India-fixed34 and the Assam block fixed37 respectively for two transects B-B’ and A-A’  absorbs about ~16 mm/yr arc normal convergence, although uncertainties in fault parallel velocity limit further interpretations  act50. It has argued that rapid uplift of Shillong Plateau and westward encroachment of the outer Indo-Burmese wedge are closely related49. Maurin and Rangin49, suggested that during the time range from Late Eocene to Early Oligocene, the Shillong Plateau was significantly uplifted, that facilitated the rapid westward propagation of the outer wedge. Further, it has argued that the systematic westward overprinting from thin-skinned tectonics to thick-skinned tectonics was a manifestation of rapid onset of westward wedge propagation. Recently, using seismic, sedimentological, stratigraphic constrains from the Surma Basin and palaeodrainage analyses of the Brahmaputra river, Najman et al50., upholds the westward wedge propagation hypothesis.Geodynamic evolution of the Indo-Burmese arc associated fold-and-thrust belt structure is significantly influenced by the interaction with the Shillong Plateau and Assam-Brahmaputra valley formation processFurther, existence of Shillong Plateau/Assam-Brahmaputra valley in north of the Indo-Burmese arc, a hyper-oblique to strike-slip plate boundary region may acts as a structural buttress for the outer wedge. Therefore, as a consequence westward wedge propagation and systematic change in fold-and-thrust belt spacing are expected in order to resolve space problem in the region. However, fold-and-thrust belt spacing of the outer wedge does not show any systematic variation with respect to increasing distance further south from the Shillong Plateau Fig. . Moreove52. The space and kinematic problems can be resolved by lengthening the merged faults (zipping) or splitting it (unzipping), named as “Zipper junctions”52. We proposed an opening “Zipper junctions” analogy to explain Shillong Plateau/Assam-Brahmaputra valley sliver and Indo-Burmese wedge interaction process , which forms in the backarc domain are the result of the obliquity of relative plate convergence55  are essential conditions for the outer trench (or deformation front) slip partitioning. Similarly, the outer deformation front of MFT in the NE-Himalaya, the Assam-Brahmaputra valley region, also satisfies the essential condition for the slip partitioning  is rare and ing Fig. .Clark and Royden30 has explored the rheological property , by quantifying geometry of the topographic margins  of the material in the channel can be expressed by integrating the velocity of the material over the channel height (h),Again, it can be related to changes in crustal thickness (c) over time:Further, considering c is the average crustal density (2800 kg/m3), g is the gravitational acceleration, and T(x) is the topographic elevation. By considering topographic wavelengths that are long compared to the flexural wavelength of the crust (according to concept of Airy isostatic equilibrium58), changes in topographic relief can be linearly related to variations in crustal thickness. Therefore, the change in topographic elevations over time as a result of flux of crustal material in the lower crust can be expressed by:m = mantle density = 3300 kg/m3.It has been assumed that the lateral pressure gradient in the channel is a function of topography, where ρhttp://www.cgiar-csi.org). We assumed a spatially uniform lower crustal viscosity in the 15 km lower crustal channel. Flux rate was considered to allow the plateau margin of average 5 km elevation to be produced over 20–25 Ma30. Model topographic profile in the Tarim, Sichuan and Nepal Himalaya fault margins, yields an excellent fit with the topography margins for a higher crustal viscosity of about 1021 to 1022 Pa s, however in the Bhutan and Arunachal Himalaya segments a good fit is obtained with lower viscosity of about 1020 Pa s (Fig. Using the above concept, we generated swath topographic profiles across Himalaya and surrounding margins of Tibet Fig.  using toa s Fig. . Using sa s Fig. . FurtherSupplementary Information


For real time monitoring of the wing state, in this paper, the inverse Finite Element Method (iFEM) is applied, which describes the displacement field of beam according to the Timoshenko theory, to sense the wing frame deformation. In order to maintain the accuracy and stability of frame deformation sensing with iFEM, an optimal placement model of strain sensors based on eigenvalue analysis is constructed. Through the model solution with the Particle Swarm Optimization (PSO) algorithm, two different optimal placement schemes of sensors are obtained. Finally, a simulation is performed on a simple cantilever beam and a static load experiment is conducted on an aluminum alloy wing frame. The results demonstrate that the iFEM is able to accurately sense the deformation of the wing frame, when the two optimal placement schemes of sensors are used. Deformation sensing in real-time is an essential technology for providing feedback to the actuation and control systems of smart structures, especially of next-generation aircrafts. Moreover, when the detailed state of structural deformation is known, other necessary response quantities, such as stress and failure criteria, can also be assessed ,2,3. A rNevertheless, the structural deformation sensing from in situ strain data always represents an inverse problem. The inverse problem is frequently ill-posed, since it does not satisfy conditions of existence, uniqueness and stability ,9. AlthoThe effectiveness of the modal transformation scheme was analyzed and verified by Bogert et al. , in whicIn order to conduct the deformation sensing of beam structures, Ko et al. developed a load-independent scheme to approximate the beam curvature, named Ko’s Displacement Theory . The proThe existing optimal placements of the strain sensors are based on the mode shape analysis of the structure, such as the modal kinetic energy (MKE) method, the effective independence (EI), the modal assurance criteria (MinMAC) and the drive point residue (DPR) ,31,32,3316, the solution algorithm can return results with no accuracy at all; thus, such a matrix is numerically singular and linear systems with this matrix are not solved  when the coefficient matrix is nonsingular; consequently, the bigger condition number in this range does not mean that the coefficient matrix is morbid. Therefore, the condition number is not suitable for the optimal sensor placement for iFEM.When the strain sensors are placed on the ill-suited locations, the coefficient matrix of the equations may be ill-posed since the essence of deformation sensing with iFEM focuses on the solution of a linear system of equations. In ,39,40, tThis paper focuses on the construction of an optimal sensor placement model, based on eigenvalue analysis of the relationship between the sensor placement and the stability of sensing of the beam deformation with iFEM. This is conducted with an aim to maintain the sensing stability of iFEM. The contents of this paper are: firstly, the beam deformation sensing process through iFEM based on Timoshenko beam theory was reviewed. Following, the instability of sensing is discussed; consequently, the optimal placement model of strain sensors based on eigenvalue analysis is constructed. The two optimal results are obtained through the model solution with PSO algorithm. Moreover, a high-fidelity finite element model of the beam is constructed with ANSYS, which generate the discrete strain data to replace the experimental strain measurements captured from strain sensors and which produce the deformation data to replace the experimental deformation. This is executed to verify the robustness of iFEM under the optimal placement schemes. Finally, to examine the iFEM sensing capability for the two optimal placement schemes, deformation sensing test is conducted on an aluminum alloy wing frame model. In the iFEM algorithm, the displacement fields of an isotropic, straight and circular cross section beam element can be described from Timoshenko beam theory ,28,29::The iFEM senses the beam deformation shape by minimizing a weighted least-squares function u(x) is obtained through interpolation from certain shape functions In the finite element framework, the kinematic variables vector N individual element contributions With regard to different loading cases, the orders of shape functions er is c1 . Thus, tn are the axial coordinate of the locations where the section strains are evaluated and the number of locations, respectively. For the end-node loads, Taking into account the effects of axial stretching, bending, twisting and transverse shearing, the element contributions , nmin=3 ,28,29,41The invoking of Equations (3) and (5) results in the theoretical section strains as following:Substituting Equation (9) into Equation (8), Equation (4) yields the following quadratic form:Finally, the minimization of function x-axis. R is the external radius of the beam element. The two key steps in iFEM are: (1) the selection of suitable shape functions for the beam deformation sensing with iFEM; (2) the calculation of section strains from the measured surface strain data. For step (1), literatures ,29,41,42stem see . T and the section strains vector G is the shear modulus. For the thin-walled section, When the section strains vector  = 0.887 . Jx=GIp When the forms of distribution loads 3,xε(xi) ,41. ThroFor the end-node loads, the section strains are expressed as:For the uniformly distributed transverse loads, the section strains are expressed as:When the parameters vector ads, m=8 . When thHowever, through in-depth research, it is found that the transformation matrices tability . i = 1, 2,…,m) are the eigenvalues of the matrix The constant parameter vectors es exist , such asads, m=8 . In viewads, m=8 . Thus, iThe optimal placement model of Equation (24) is a multi-parameter optimization problem, which is solved by using the PSO algorithm with good convergence speed. The optimal result is efficiently obtained in hyperspace. N particles, the status of the i-th particle is described by the current location and the current velocity. i-th particle and i-th particle i-th particle in the k-th iteration, respectively; i-th particle at step k; k. Step 1.N = 50 and the maximum iteration is set to i = 1,2,…,N). Following, the initial local extremum PSO algorithm initializing. The size of the particle swarm is set to Step 2.k = k + 1.Velocity and location update of each particle with Equation (26). The fitness value of each particle Step 3.PSO algorithm termination. The iteration will not break until the iteration number In a population that includes The flow chat of above optimization procedure is shown in In order to assess the optimal placement effectiveness of the sensors proposed in this paper, simulations and model experimentation are performed. In To model the thin-walled beam structure, we made use of the Beam188 elements module with two nodes, based on the Timoshenko beam theory. The Young’s modulus is tors see .For comparison, two normal sensors placements (C1 and C2) are quoted from the literature , while tThree different static loading tests are performed on the beam model. For the first test, the two end-node forces are loaded on the model, −100 N in direction Y and 80 N in direction Z  and the percentage errors (PE) for the free-end displacements along with the rotations of the beam model are presented in The comparisons of The strain measurement system errors are assumed to obey the Gaussian error distributions, which have zero mean value and three-standard deviations equal to 5% of ANSYS simulation strain value, such as The comparisons from L = 666 mm  ,45.(30)In the model test, the experimental strain data are obtained from the strain measurement system composed of FBG strain sensors  and the FBG interrogator . Twenty-four FBG strain sensors  are placed at different locations along the main spar and used to capture the surface strain. Since the locations in placement scheme C3 are contained in scheme C4 refer to , eight FFor the static tests, two different loading cases are considered see E,F:I. the end-node of the main spar is loaded 6 times ;II. the main spar is loaded 5 times with uniform-distributed loads .The details of the above loads, the maximum deformations calculated with iFEM and the maximum deformations captured by NDI are presented in j is the number of the nodes used to describe the beam deformation. In the test, the number of nodes used to describe the beam deformation is 16. The comparisons are shown in The root-mean-square difference (RMSD) is used to assess the sensing accuracy of the entire structure through iFEM .(31) RMv(x) under all loading cases, the errors remain at approximately 5%, when the deformations increase. Although the percentage errors are relatively high, the RMSD is acceptable for the C3 and C4 schemes: (1) under the end-node loads, the maximum RMSD is 0.644 mm for the main deformation The frame is divided into three sections and every section is regarded as an iFEM element, so the entire frame is calculated three times through iFEM. The calculations in the second and third sections of the frame are affected by the computation in the first section. Consequently, accumulative errors occur during the deformation sensing of the entire frame structure. In refer to . The corThe study successfully validates the feasibility of iFEM used for deformation sensing of a wing frame. As aforementioned in the introduction, the locations where the strain sensors are placed affect the accuracy of iFEM for sensing. Therefore, in the paper, an optimal placement model of sensors is proposed to confirm the suitable placement of sensors for the accuracy and stability of iFEM of sensing frame deformation to be maintained. Following experimental test, the results demonstrate that the iFEM is able to precisely sense the deformation of the wing frame for the two optimal placement schemes of C3 and C4. Nevertheless, it is found that certain limits exist for the iFEM application: (1) the cross-section of the beam must be constant along the central axis of the beam and the section must be un-deformed when the loads are applied on the beam; (2) the boundary condition of the sensing equation must be known. Moreover, the test results also demonstrate that the strain measurement system errors (combining location errors of sensors with measurement errors of sensors) affect the accuracy of sensing results. Further works will be focused on the location calibrations of the strain sensors, as well as the relationship between the structural strain and the wavelength shift of the FBG sensor.


This paper proposes a way for drift compensation in electronic noses (e-nose) that often suffers from uncertain and unpredictable sensor drift. Traditional machine learning methods for odor recognition require consistent data distribution, which makes the model trained with previous data less generalized. In the actual application scenario, the data collected previously and the data collected later may have different data distributions due to the sensor drift. If the dataset without sensor drift is treated as a source domain and the dataset with sensor drift as a target domain, a domain correction based on kernel transformation (DCKT) method is proposed to compensate the sensor drift. The proposed method makes the distribution consistency of two domains greatly improved through mapping to a high-dimensional reproducing kernel space and reducing the domain distance. A public benchmark sensor drift dataset is used to verify the effectiveness and efficiency of the proposed DCKT method. The experimental result shows that the proposed method yields the highest average accuracies compared to other considered methods. The electronic nose (e-nose) is an intelligent system consisting of a set of sensors combined with corresponding pattern recognition algorithms to identify gases. When the gas enters the detection chamber, the gas sensor will generate a transient response signal. Then, the pattern recognition algorithm is applied to identify the gas based on the sensor response ,2. The eMany pattern recognition algorithms for classification and regression of e-noses have been proposed ,9,10,11.These algorithms are classical classification algorithms in machine learning, and are often used to identify the gas in e-nose systems. Zhang et al.  proposedMotivated by the idea of transfer learning, we focus on the compensation of sensor drift and try to improve the prediction accuracy from the perspective of data distribution. Usually, we refer to the e-nose data without sensor drift as source domain; the data with sensor drift as target domain. Because the traditional machine learning algorithms require that the data distribution between the source and target data be the same, the models trained by source domain data cannot be used directly on target domain for prediction. This limits the application and development of the e-nose, so the focus of this paper is to suppress drift and improve recognition accuracy from the perspective of domain correction.From the above, the traditional pattern recognition algorithms are not always suitable for the e-nose. The model trained on source domain cannot be used directly on target domain because the distribution of data features between source domain and target domain are different. Therefore, we propose a domain correction based on kernel transformation (DCKT) method to solve this issue, which perform domain correction on source and target domain data. After domain correction, the data distribution difference between source domain and target domain can be significantly reduced and the data properties are preserved. The basic idea of DCKT is illustrated in The rest of this paper is organized as follows. Drift is an unavoidable problem in e-noses. Due to the properties of a gas sensor, the output signal will fluctuate with environmental factors, self-aging, and other causes, which is called sensor drift. The essence of solving the drift problem lies in improving the reliability and stability of the gas sensor. However, this requires breakthroughs in many aspects, such as physics, chemistry, materials, and manufacturing processes, which are time consuming and costly. On the other hand, signal processing methods which are low-cost can be used to suppress the sensor drift. This paper mainly studies the drift problem of E-nose system from the perspective of signal processing.According to the characteristics of the drift, we can divide the sensor drift into long-term drift and short-term drift. For the short-term drift, there is a certain functional relationship between the fluctuation amplitude and environmental factors , and it is generally reversible. Therefore, for short-term drift, the approximate functional relationship can generally be found through mathematical methods such as neural network and data fitting, and then the drift effect caused by environmental factors can be eliminated. Long-term drift refers to the changes in sensor response patterns over time. It is hard to find an exact function to describe the long-term drift of sensors because of the slow random fluctuation on sensor response. Therefore, using the functional relationship to eliminate the drift of the sensor is not a good choice.The performance of the e-nose largely depends on the stability and sensitivity of the sensors. In particular, the performance of the trained discriminant model will deteriorate when the e-nose is subject to drift. Thus, the drift compensation should be taken into consideration. There are multiple works suggest that compensation methods can be defined as five categories . They arThe above methods have reduced the effects of drift in e-nose to a certain extent, but they are all focused on one data domain which leads to a weak generalization of newly collected data. The DCKT methods proposed in this paper is completely different from the previous method. DCKT aims at domain correction of the two domains, which solves the poor generalization of newly collected data.Traditional machine learning usually has two basic assumptions: (1) training samples and test samples satisfy independent identical distribution; (2) training samples should be sufficient. However, it is difficult to satisfy these two basic assumptions in real life sometimes. The sensor drift makes the obtained data do not satisfy these two assumptions, and the models trained by the data obtained in last month are often difficult to apply to predict in the next month. In order to solve this problem, transfer learning that caused widely concern can be considered .The problem that transfer learning solves is: there is little or no label data in the target task. But the source task has sufficient data which are related to the target task but out of date . In other words, transfer learning can apply previously learned knowledge to new learning tasks, which is also the difference from traditional machine learning. From The distribution of data obtained from the e-nose which suffers from sensor drift is different from the distribution of standard data, and it is one of the scenarios that transfer learning solves. Many researches have paid much more effort on drift compensation with the idea of transfer learning. Yan et al.  proposedS” and “T”. The source domain sample set is denoted as D is the dimensionality, and In this paper, the source and target domains are denoted by the subscript “To make the feature distribution difference between the source and target domain data smaller, the source domain and the target domain are mapped to a high-dimensional reproducing kernel Hilbert space through a nonlinear mapping . In thisThe objective function that minimizes the distance between the source and target domains after mapping is as follows:K, which constructed as follows:And then introduce the kernel matrix L is the distribution difference matrix whose elements are:After introducing Equations (2) and (3), the objective function can be written as:The distance between distributions of different domains is minimized in the first term, while the variance in the feature space is maximized in the second term, and P is introduced to transfer the empirical kernel map features to an m-dimensional space. The kernel learning problem becomesm-dimensional identity matrix, H matrix is a central matrix which can be expressed as:To solve this SDP problem which with high computational burden, a dimensionality reduction method for domain adaptation is proposed . A matriP. The purpose of the constraints is to maintain data properties of source domain and target domain.And Equation (5) can be simplified as: m smallest eigenvectors of Similar to the kernel Fisher discriminant analysis , the solFor easy implementation, the proposed DCKT algorithm is summarized in Algorithm 1. Algorithm 1 DCKTInput: Source data m:Procedure:1. Construct the kernel matrix K from L via (3), and centering matric H via (6);2. Solve the eigendecomposition of 3. Build P by m smallest eigenvectors via (8);4. Compute the mapped source domain data 5. Compute the mapped target domain data 6. Train the SVM classifier with Output:The classification results of target data.The proposed DCKT is used to perform domain correction on source and target domains. After domain correcting, the distance between source domain and target domain become closer, and the data properties of domains are preserved. The source domain data This section will present the experimental results to demonstrate the effectiveness of the DCKT algorithm proposed in this paper.In this paper, a verification experiment of the proposed DCKT method is carried out using the sensor drift benchmark dataset of up to three years, from January 2008 to February 2011, collected by the e-nose from Vergara et al.  in UniveIn order to observe and analyze the distribution of these 10 batches of datasets intuitively, the principal component analysis (PCA) is performed on these 10 batches of datasets, and the scatter points are shown in Data with different distributions is called heterogeneous data, and this dataset has a certain difference in the distribution of each batch. Therefore, for each batch of datasets, other batches of datasets are heterogeneous data. The data of the Batch 1 is considered as data before drift , and as a training sample set. However, other batches of datasets have drifted compared to Batch 1 because their distribution has significant difference, and can be considered as target domain. If the traditional classifier trained by the Batch 1 is used to classify the other batches, the recognition performance of the classifier will decline. Because it violates the basic assumptions of machine learning: training samples and test samples should have the same or similar probability distribution .To demonstrate the effectiveness of the proposed DCKT method for distribution alignment and domain correction, the qualitative and quantitative experiments are presented, respectively.i  is treated as the target domain. Batch 1 and Batch i  are paired into Task (i − 1). Therefor, 9 pairwise principal component scatter points after using DCKT are shown in In this paper, the dataset of Batch 1 is treated as the source domain, and the dataset of Batch RC can be defined as follows:RC of target domain data is the proportion of correct samples, the label predicted is same with the original, among the total test samples in one batch. Detailed experimental settings are as follows: i, i = 2, 3, ..., 10.Setting 1: Take Batch 1 as source domain for model training, and test on Batch i as source domain for model training, and test on Batch (i + 1), i = 2, 3, ..., 10.Setting 2: Take Batch The purpose of the DCKT method proposed in this paper is to improve the performance of the classifier. Therefore, the recognition accuracy of six classes on each batch is used as a criterion. The recognition accuracy To fully demonstrate the effectiveness of the DCKT method, this paper compares the proposed DCKT method with principal component analysis (PCA), Fisher linear discriminant analysis (FLDA), SVM-RBF, SVM-comgfk algorithms without any drift suppression of data, direct standardization (DS), and the domain regularization component analysis method (DRCA) by Zhang .In this paper, experiments are conducted on setting 1 and setting 2, respectively. The comparison results of the recognition accuracy for each method under setting 1 are shown in i-th batch of data is used as the source domain to train model, and the (i + 1)-th batch of data is used as the target domain for testing, i = 1, 2, ..., 9, which used Batch (i)⟶Batch (i + 1) to indicates in this paper. The comparison results of the recognition accuracy for each method under Setting 2 are shown in Under Setting 2, the In this paper, a novel domain correction based on the kernel transformation method (DCKT) is proposed for drift compensation in an e-nose. The method is motivated from the different probability distributions of the source domain and the target domain, and the ideas of transfer learning. First, DCKT can be used for domain correction, which solves the problem of different distribution between two domains. Then the source and target domain data after domain correction can be used to train and classify with the same prediction model. The effectiveness of the proposed DCKT method is proved through experiments on the public benchmark sensor drift dataset of an e-nose.A series of studies on sensor drift compensation have been conducted through domain correction. However, the proposed DCKT method is offline drift suppression. Therefore, achieving online drift suppression is an interesting research direction in e-noses.


Currently, left ventricular ejection fraction (LVEF) is the most common endpoint in cardiovascular stem cell therapy research. However, this global measure of cardiac function might not be suitable to detect the regional effects sorted by this therapy and is hampered by high operator variability and loading dependency. Deformation imaging might be more accurate in detecting potential regional functional improvements by cardiac regenerative therapy. The aim of this systematic review is to provide a comprehensive overview of current literature on the value of deformation imaging in cardiac regenerative therapy. A systematic review of current literature available on PubMed, Embase, and Cochrane databases was performed regarding both animal and patient studies in which deformation imaging was used to study cardiac cell therapy. After critical appraisal, outcomes regarding study design, type of cell therapy, procedural characteristics, outcome measure, method for measuring strain, and efficacy on both LVEF and deformation parameters were depicted. A total of 30 studies, 15 preclinical and 15 clinical, were included for analysis. Deformation outcomes improved significantly in 14 out of 15 preclinical studies and in 10 out of 15 clinical studies, whereas LVEF improved in 12 and 4 articles, respectively. Study designs and used deformation outcomes varied significantly among the included papers. Six studies found a positive effect on deformation outcomes without LVEF improvement. Hence, deformation imaging seems at least equal, and perhaps superior, to LVEF measurement in the assessment of cardiac regenerative therapy. However, strategies varied substantially and call for a standardized approach. Reperfusion of ischaemic myocardium by catheter‐based and surgical treatment has proven to be successful in both treatment of acute myocardial infarction (AMI) in order to limit infarct size and chronic ischaemic cardiomyopathy (ICMP) in order to protect the heart from further deterioration , is defined as the magnitude of deformation of a segment of myocardium during a cardiac cycle relative to the original length of this segment and is expressed as a percentage. It can be measured using tissue Doppler echocardiography (TDI), two‐dimensional speckle tracking echocardiography (2DSTE), and MR tagging and tracking. Deformation is measured in either direction in which the myocardium deforms: longitudinal (shortening), circumferential (shortening), and radial  full text available; (b) language other than English/Dutch; (c) study design other than randomized controlled trial, randomized trial, case–control study, or cohort study; (d) ex vivo studies; (e) nonischaemic cardiac disease; (f) the use of surgically placed sheets as a delivery method because this is inherently expected to alter deformation parameters; (g) growth factor therapy and intervention; and (h) assessment of diastolic deformation parameters only.2.3Of the included articles, the following data on study characteristics were collected: study design , population size, setting , and model used . The cell type used  and origin of these cells  were identified, as well as the used delivery method . As for outcome measures, we assessed the deformation imaging modality utilized  and the regional deformation outcomes , global deformation outcomes , strain rate (SR) outcomes , and LVEF measurements. When possible, within‐group changes of the above‐mentioned outcome parameters were derived.Using the Cochrane risk of bias tool  or was not mentioned (n = 1). In addition, two of these papers  and the outcome measure of choice  varied distinctively between the different included studies. Comparing results of different studies or performing meta‐analyses was therefore not feasible.Notwithstanding the heterogeneity in deformation imaging within cardiac regenerative medicine, this article provides an insight into the value of strain analysis within this field of research. The results show that in 13 out of 15 preclinical papers and in nine out of 15 clinical papers deformation imaging results followed the results of LVEF outcome measurement, either both showing a significant effect of cardiac cell therapy on LV function or both showing a nonsignificant effect of cardiac cell therapy on LV function. There are no papers that reported a significant effect of cell therapy on LVEF measurement without deformation outcomes doing so. Importantly, and fitting with our main hypothesis, two out of 15 preclinical papers and seven out of 15 clinical papers showed a significant effect of cell therapy on cardiac deformation, whereas LVEF was not improved significantly in these studies. These results advocate a potential added value of deformation imaging within cardiac regenerative medicine for ischaemic heart disease.Although experience with cardiac stem cell therapy shows a positive effect on cardiac function, underlying mechanisms are not fully elucidated. It is believed to be based on paracrine effects, leading to (a) neovascularization into regions affected by ischaemia (Schuleri et al., Deformation imaging has already proven its value in, for example, hypertrophic cardiomyopathy (Fuster, Van Der Zee, & Miller, The effect of stem cell therapy varies strikingly between preclinical and clinical research: Preclinical papers show a positive effect on deformation parameters more often than clinical studies do. This “translational failure” is a known phenomenon, due to which preclinical results may not always be applicable to the clinical setting. A possible explanation can be found in the risk of bias analysis, showing a higher risk for performance bias, detection bias, and reporting bias in preclinical studies. Also, study models might be more heterogeneous in the patient setting.5This aim of this study was to investigate whether deformation imaging could provide added value over conventional endpoint measurement in stem cell research. Although evidence is not scarce, and data indeed do suggest added value of deformation imaging, articles that were related to in this study were too heterogeneous in their study design for pooled analysis. Therefore, we could not provide a statistical analysis on deformation imaging as a diagnostic tool over conventional functional imaging. In addition, deformation imaging was not compared with other methods of endpoint measurement, such as myocardial perfusion imaging, so the value compared with these remains to be elucidated. Moreover, the relevance of so‐called surrogate endpoints in stem cell research needs to be addressed. We aimed to investigate deformation imaging as a new surrogate endpoint for stem cell research. However, regardless of the probable additional value of deformation imaging over conventional endpoint measurement, we are unsure how this relates to clinical endpoints such as mortality, hospital admissions, quality of life, or recovery from myocardial infarction. This remains to be studied. To conclude, the conventional echocardiography endpoint used in the included papers was two‐dimensional LVEF, of which diagnostic accuracy is inferior compared with three‐dimensional assessment. Therefore, results might not be applicable to the current situation in which three‐dimensional LVEF assessment is common practice.6In order to be able to compare deformation outcomes within cardiac regenerative medicine, the methods of performing this should be standardized. We propose to use the “Definitions for a common standard for 2D speckle tracking echocardiography: consensus document of the EACVI/ASE/Industry Task Force to standardize deformation imaging” (Voigt et al., 7Deformation imaging shows to be a promising new endpoint measure in cardiac stem cell research, which is at least equally, and possibly more, effective than LVEF assessment in the detection of changes in cardiac function and may provide research with a more objective measure of response to therapy. However, current use is diverse and needs standardization in order to compare results between studies.None declared.


A proper balance between the activator and the repressor form of GLI3, a zinc-finger transcription factor downstream of hedgehog signaling, is essential for proper development of various organs during development. Mutations in different domains of the GLI3 gene underlie several congenital diseases including Greig cephalopolysyndactyly syndrome (GCPS) and Pallister-Hall syndrome (PHS).Here, we describe the case of an overlapped phenotype of these syndromes with agenesis of the gallbladder and the pancreas, bearing a c.2155 C > T novel likely pathogenic variant of GLI3 gene by missense point mutation causing p.P719S at the proteolytic cleavage site.Although agenesis of the gallbladder and the pancreas is uncommon in GLI3 morphopathy, a slight difference in the gradient or the balance between activator and repressor in this case may hinder sophisticated spatial and sequential hedgehog signaling that is essential for proper development of gallbladder and pancreas from endodermal buds. GLI3 is a zinc-finger transcription factor that mediate the sonic hedgehog (SHH) signaling acting as both a transcriptional activator and repressor during development , 2. WhenThe 31-year-old woman, gravida-1 para-1, with no apparent risk factors for congenital anomaly, achieved natural pregnancy. Oligohydramnios and intrauterine growth restriction was, however, noted at 14 weeks of gestation. Amniocentesis, carried out at 16 weeks of gestation for chromosomal analyzes, revealed a normal 46, XY male karyotype pattern. At 30 weeks of gestation, echographic examination revealed loss of fetal movement; intrauterine fetal death was confirmed 3 days thereafter, and the fetus was delivered by artificial abortion.Autopsy carried out 2 h after delivery of the 600 g stillborn revealed multiple systemic malformations. Macroscopic autopsy findings are summarized in Table GLI3 comprises several functional domains: repressor domain RD, aa106-aa263), zinc-finger DNA binding domain , proteolytic cleavage site PC, aa703-aa740), CREB binding protein domain CBP, 827-1131), transactivation domain 2  and transactivation domain 1 , [03-aa740,27-1131, 6-aa263, http://genetics.bwh.harvard.edu/pph2) shows that this mutation is predicted to be “PROBABLY DAMAGING” with a score of 0.963 .Interestingly, besides gastrointestinal malformations, agenesis of both the gallbladder and the pancreas was observed. During the early stages of organogenesis, the liver, the gallbladder and the biliary duct system arise as buds in the endodermal epithelium at the distal end of the foregut. A small caudal portion of the liver bud then expands to form the gallbladder [In the current study, we demonstrated a case of GLI3 morphopathy attributed to a novel likely pathogenic variant by missense mutation at the PC domain (p.P719S) of GLI3 . Indeedlbladder . The panlbladder . SHH siglbladder , 22. Suclbladder , cannot In conclusion, this case is an extreme example in which slight difference of genetic alterations in the same gene may result in considerable morphological variations, when sophisticated spatial and sequential expression of the gene is essential and critical for proper development.


The relationship between the rheological stress and the deformation condition was studied, and a processing map at the true strain of 0.5 was proposed. Microstructure evolution during the deformation process and the effects of deformation conditions on microstructures were also investigated, as well as the stability of nanoparticles. Results show that the 14Cr ODS steel possesses positive strain rate sensitivity. The flow stress increases with the decrease of deformation temperature and the increase of strain rate. The recrystallization process is promoted by the increase of deformation temperature and the reduction of strain rate. Nanoparticles possess excellent stability during the deformation process and are coherent with the matrix.Hot deformation tests of 14Cr oxide dispersion strengthened (ODS) steel fabricated by mechanical alloying and hot isostatic pressing (HIP) were performed on a Gleeble-1500D simulator at temperatures ranging from 1050 to 1200 °C with the strain rate range of 0.001 The fitting result is illustrated in The deformation activation energy Q represents the level of difficulty for the deformation and deformation mechanism. When the deformation activation energy is equivalent to the self-diffusion activation energy, the main deformation mechanism is determined to be DRV. The main deformation mechanism is known to be DRX when the activation energy is far higher than that of the self-diffusion ,47. The The following equation can be obtained by substituting Equation (4) into Equation (1).Taking the natural logarithms on both sides of Equation (9) yields the following equation.Obviously, ln A and n are the intercept and slope of the linearly fitting line of the ln Z vs. ln[sinh (ασ)] plot, respectively. The linear regression result is presented in Prasad and Gegel have considered the hot deformation procedure as a closed thermodynamic system, and the dynamic materials model (DMM) has been proposed based on this hypothesis ,49. The G is the power dissipation caused by plastic deformation, and J is the power dissipation caused by microstructure evolution.According to the DMM theory, the power dissipation can be expressed as follows ,50:(12)Pm = 1, J can be expressed as follows:In the linear dissipative process, m < 1, J can be expressed as follows:In the nonlinear dissipative process, η is capable of reflecting the microstructure evolution to some extent. The higher the value of η is, the higher the value of J is. This means that DRV and DRX more easily occur under deformation conditions with higher values of η.The efficiency of power dissipation Ziegler has put forward the instability criterion and expressed it as (15)ξ should be done in order to identify the recrystallization behavior and recrystallization mechanism during the deformation process ,58,62. A−1 at 1050 to 1200 °C is illustrated in 2Ti2O7 in previous work [The distribution of nanoparticles in the samples deformed with the strain rate of 0.01 sous work . These nous work . The siz−1. Same as in The average sizes of nanoparticles in the samples deformed at 1150 and 1200 °C are slightly larger than those in the samples deformed at 1050 and 1100 °C, but the morphologies remain unchanged, resulting from the stable interface between the nanoparticles and matrix. −1 are thus investigated. Results are shown in 2Ti2O7 is parallel to the (200) plane of the matrix. The schematic diagram of the coherent interface between nanoparticle and matrix is depicted in The results indicate that nanoparticles with size less than 10 nm are insensitive to the increase of temperature and strain rate. These nanoparticles play an effective role in pinning the dislocation motion and grain boundary migration, due to the excellent stability . The def−1. The deformation activation energy of the 14Cr ODS steel under these conditions was calculated as 827.526 KJ/mol. The constitutive equation was established and expressed asDeformation of the ODS steel was performed at the temperature range of 1050–1200 °C with the strain rate ranging from 1 to 0.001 sThe processing map at the strain rate of 0.5 is provided as an example, and the deformed microstructures perfectly correspond to the processing map. The processing map provides the theoretical basis for a practical production process.The ODS steel is sensitive to the deformation temperature and strain rate. The fraction of the recrystallized grains increases with the increasing deformation temperature and the reduction of strain rate. Partially and fully dynamic recrystallized microstructures are obtained after deformation under different conditions. The schematic diagrams of these microstructures were established.Large amounts of tangled dislocations and bulged boundaries are found in the partially dynamic recrystallized microstructure. Small amounts of dislocations and bulged boundaries can also be seen in the fully dynamic recrystallized microstructure.−1 were investigated, and interfaces between them and the matrix are coherent. Coherency loss does not occur during deformation.The nanoparticles with size less than 10 nm are stable during the deformation process. Both the size and morphology of the nanoparticles remain unchanged after deformation. Nanoparticles with different size and morphology in the sample deformed at 1200 °C with strain rate of 0.001 s


The reactions of antioxidants with superoxide radical were studied by cyclic voltammetry (CV)—and hydrodynamic voltammetry at a rotating ring-disk electrode (RRDE). In both methods, the superoxide is generated in solution from dissolved oxygen and then measured after being allowed to react with the antioxidant being studied. Both methods detected and measured the radical scavenging but the RRDE was able to give detailed insight into the antioxidant behavior. Three flavonoids, chrysin, quercetin and eriodictyol, were studied, their scavenging activity of superoxide was assessed and the molecular structure of each flavonoid was related to its scavenging capability. From our improved and novel RRDE method, we determine the ability of these 3 antioxidants to react with superoxide radical in a more quantitative manner than the classical CV. Density Functional Theory (DFT) and single crystal X-ray diffraction data provide structural information that assists in clarifying the scavenging molecular mechanism. Hydroxyls associated with the A ring, as found in chrysin, scavenge superoxide in a different manner than those found in the B ring of flavonoids, as those in quercetin and eriodictyol. This radical can cause cellular damage, and is thought to contribute to aging as well as several degenerative diseases , dibenzo-18-crown-6 ether and ethyl benzene were purchased from Sigma-Aldrich . DMSO was kept sealed in the original bottle and removed with a syringe in order to protect the solution from atmospheric moisture. Dibenzo-18-crown-6 ether and (TBAB) were kept dry in a desiccator over silica gel and used as received. Iodine, IThe potentiometric iodine titration was carried out in DMSO using an Orion pH/Ion meter with a platinum sensing electrode and nonaqueous Ag/AgCl reference electrode. The reference electrode was constructed with DMSO containing TBAB electrolyte in contact with a silver wire coated with AgCl. Cyclic voltammagrams were run using a Solartron SI 1287 Potentiostat/galvanostat  controlled through Coreware© software. For the voltammetry study a three electrode configuration consisting of a gold disk (BASi) working electrode, a platinum wire counter electrode and the DMSO-based Ag/AgCl reference electrode was used.Hydrodynamic Voltammetry was done at a rotating ring-disk electrode using the WaveDriver 20 bipotentiostat  with the MSR Electrode Rotator, from Pine Research Durham, NC, USA. The working electrodes, both the rotating ring and disk, were gold  with a coiled platinum wire counter electrode and a reference electrode consisting of a platinum wire immersed in 0.1 M TBAB in dry DMSO in a fritted glass tube. The electrodes were placed in a 5-neck electrochemical cell together with means for either bubbling or blanketing the solution with gas. Voltammograms were collected using Aftermath software provided by Pine Instruments. Careful cleaning of the electrodes was performed to clear potential film formation .X-ray diffraction data were collected on a Bruker APEXII diffractometer  with MoKα radiation (0.71069 Å) at 125 K.2, crown ether, and TBAB were weighed and placed in a cell containing dry DMSO, platinum and reference electrodes. Aliquots of a known solution of iodine in ethylbenzene were added with the cell potential recorded after each addition. From the titration curve the initial concentration of superoxide in the solution could be calculated. The resulting concentration was always substantially less than that expected based on the mass of KO2 used in making the solution; the titration also demonstrated loss of superoxide over time. This result demonstrates the instability of the KO2 in DMSO and also loss of superoxide to residual water in the DMSO.KO2 in solution, the CV was calibrated through a titration of the superoxide with a standardized iodine solution. A 0.2 M iodine solution in ethylbenzene was made from purified iodine crystals. A stock potassium superoxide solution in DMSO was made by dissolving about 0.02 g KO2, 0.1 g dibenzo-18-crown-6 and 0.02 g tetrabutylammonium bromide in 25 mL anhydrous DMSO. Ten mL of the stock superoxide solution was placed in the electrochemical cell with the 3 electrodes and bubbled for 10 min with nitrogen gas. A voltammogram was recorded from −1.10 V to +0.50 V at 50 mV/sec showing the superoxide oxidation peak at −0.30 volts. An aliquot of iodine solution was added to the cell which was then stirred for 2 min, at which point another voltammogram was recorded. The iodine aliquots were continued until the superoxide oxidation peak had disappeared. The peak heights are measured from an extrapolated baseline, and plotted against the amount of iodine added. The oxidation peak disappears when the superoxide is completely removed by reaction with iodine and the titration curve breaks at this point allowing the calculation of the initial superoxide concentration of the stock solution. This titration curve can be recalculated in terms of oxidation peak height due to concentration of superoxide ion, yielding a calibration curve for the cyclic voltammetry (CV), in terms of bulk concentration of superoxide ion.Due to the instability of KO−4 M in anhydrous DMSO and used in trials. For the experiment, a solution of tetrabutylammonium bromide in anhydrous DMSO is bubbled for 10 min with a dry O2/N2 gas mixture to establish the dissolved oxygen level in the electrochemical cell. An initial blank is run on this solution by cycling the cell from a potential of +0.10 volts relative to the reference electrode, moving initially in the negative direction to a potential of −1.5 volts. The reduction peak was observed at −0.6 volts due to the reduction of molecular oxygen (O2) to the superoxide ion, O2−•, on reversal of the sweep, an oxidation peak is observed at −0.3 volts. The height of the oxidation peak is measured and together with the previous calibration gives the concentration of superoxide ion in the vicinity of the electrode surface. Next, an aliquot of one of the antioxidants was added, the solution bubbled with the gas mixture for 2 min and the CV rerecorded. Again the oxidation peak is measured and the change in magnitude from the “blank” is taken as the amount of superoxide removed by the scavenger. From the known addition of flavonoid and the amount of superoxide ion removed, the ratio of moles of superoxide scavenged to moles of flavonoid added may be calculated. Additional aliquots of each flavonoid are added to generate a reaction curve for each flavonoid.Stock solutions of quercetin and chrysin were made to approximately 7.9 × 10−4 M in anhydrous DMSO.The voltaic cell was prepared with 0.1 M TBAB in 50 mL of DMSO. The cell was purged with nitrogen gas (1 atm) for five min and then oxygen gas (1 atm) for ten min. The Au/Au electrode was then rotated at 500 rpm while the disk was swept from 0.1V to −1.5V and the ring was held constant at −0.25 V, the sweep rate was held constant at 25 mV/s. All tests were made with chrysin, quercetin and eriodictyol solutions at approximately 7.9 × 103 was applied to calculate energy, geometry and frequencies implemented in Materials Studio 7.0 (PC platform) [−6 electron/Å3. The convergence criteria applied during geometry optimization were 2.72 10−4 eV for energy and 0.054 eV/Å for force.Calculations were performed using software programs from Biovia, San Diego, CA, USA. Density functional theory code DMollatform) . We empllatform) , and corlatform)  plus Perlatform)  (PBE). A2; data collection and refinement parameters are summarized in A suitable single crystal of eriodictyol grown in a 1:1 ethanol/water solution was chosen for single crystal X-ray diffraction studies. Data were collected at 125 K; details are given in https://www.ccdc.cam.ac.uk.Data have been deposited at CCDC code 1829519 and are available upon request, In this study, we use the forward sweep of cyclic voltammetry (CV) to generate the superoxide radical ion and subsequently, in the reverse sweep, we measure the amount of the superoxide radical ion remaining . Upon adSubsequently, the superoxide radical ion generated at the working electrode can react with reactive species in the solution near the electrode surface. On the “reverse” scan, the remaining superoxide radical ion gives up its electron to the electrode in an anodic reaction. The electrode reaction in both the forward and reverse directions is measured as a current seen as peaks in the CV:The height of each peak is proportional to the quantity of superoxide generated and oxidized and so changes in peak height will inform us about the reactivity of the antioxidant studied.2, using this equation:2 solution was titrated with standardized iodine using the oxidation peak in the CV.As shown in 2 + e− → O2−•), while the reverse reaction occurs at the ring electrode (O2−• → O2 + e−). We focus on the latter reaction (Hydrodynamic voltammetry at a rotating ring-disk electrode (HV-RRDE or just RRDE) was applied to the study of the antioxidant reaction with superoxide radical ion. RRDE uses two independent working electrodes, a central disk electrode with an encircling ring electrode. The rotation of the electrode sets up movement of the solution so that solution is brought up to the disk and then swept across the face of the electrode past the ring. The electrical potential of the disk can be made sufficiently negative that dissolved molecular oxygen is reduced to the superoxide radical ion and subsequently transported to the ring electrode which is held at a sufficiently positive potential that the superoxide radial is oxidized back to molecular oxygen. The method allows the monitoring of radical scavenging by the antioxidant through measurement of collection efficiency, N, which is simply the ratio of the ring to the disk current. This allows a straightforward interpretation of the antioxidant activity. The RRDE thus measures two currents . The proreaction , top. Threaction , top.2 is unstable and cannot be obtained in sufficient purity to make solutions of known concentration. In addition, any water present in the DMSO will react with the superoxide, further decreasing the amount in solution. The concentration of superoxide can be determined by titration with iodine:The reagent KOTitration of potassium superoxide solutions typically showed only 50–70% of expected superoxide concentration and instability over time. The superoxide concentration decreased 5% in 75 min and 24% over 195 min . Because2 in dry DMSO was titrated with aliquots of the iodine solution recording a cyclic voltammogram for the solution after each step in the titration  as superoxide (f–OH + R• → f–O• + RH).For compounds tested , an initThe amount of superoxide radical scavenged is calculated as the difference in oxidation peak height from the initial blank run using the calibration equation to convert the signal from amps to molar concentration of superoxide. A plot of this  shows thPc. However, a pseudo-inversion center is almost certainly present and so the centric 1P2/c space group could be considered with Z = 1. Ultimately, refinement in Pc led to an R-value of about 5% whereas for refinement in 1P2/c, the R-value was about 15%. So we continued our analysis with the acentric space group Pc. Thus, the crystal contains a racemic mixture in the solid state since both enantiomers appear in a disordered arrangement (a disordered solid-solid solution). Since the stereoisomers are comparable in overall shape and the hydrogen-bonding intermolecular interactions are similar, formation of a disordered solid solution is not surprising. The crystal structure and its inverse were refined, and the R-values were similar. There is evident disorder in the chiral carbon atoms of the two eriodictyol molecules over two sites (corresponding to the two enantiomers) at approximately 50:50 occupancy. The atom labeling and a superposition of the two molecules in the asymmetric unit of Pc is shown in Several eriodictyol crystals were investigated because there was uncertainty in space group assignment, possibly due to the presence of a pseudo center of symmetry. This is related to disorder and different conformations in some of the phenyl ring atoms and at the chiral carbon. A variety of refinement situations were attempted and one possibility led to Z = 2 in acentric monoclinic space group There are numerous hydrogen bonds (eight) in the crystal structure leading to a pattern composed of an infinite array of short interactions; it is shown in As seen in the Results section, chrysin and quercetin are effective scavengers of superoxide, and their slope can indicate the proficiency of antioxidant scavenging, The collection efficiency is obtained from the voltammogram, and From this study it is seen that the rotating ring disk electrode technique, RRDE, provides more quantitative results for scavenging antioxidants when compared with standard CV methods. Indeed, we confirm the utility and specificity of such a technique, already applied in studying the superoxide radical in battery investigations using a Au/Au electrode with a platinum reference electrode and a platinum counter electrode .The X-ray structure of eriodictyol provided the atomic coordinates used as a starting point for DFT calculations to study the molecular mechanism responsible for superoxide scavenging. The two molecules in the asymmetric unit have disordered chiral carbon (C2) and show extensive H-bonding throughout the crystal structure.2−•, both molecules were posed at van der Waals separation, 2.60 Å, for the extraction of H• in position 4′ (•(4′) from quercetin, we posed its minimized radical at van der Waals separation from minimized HO2− and the result of the corresponding geometry optimization is depicted in −1. DeltaG for this calculation is −1.5 Kcal/mol and the associated barrier is 0.5 Kcal/mol, which indicates reaction feasibility.Atomic coordinates from the quercetin crystal structure  were useition 4′ . For thiEriodictyol coordinates were taken from the X-ray crystal structure presented here and used to run additional calculations for the equivalent superoxide scavenging ; its Del• in position 7, and geometry minimization was performed as above, and the resulting minimum is shown in 2− to the H7-excluded chrysin radical, the arrangement is identical to the reagent structure shown in • to O2•− radicals, whereas there is involvement of ion transfer for phenols [two molecules of chrysin, even though the flavonoid concentration used in the RRDE measurement is low, by posing a 2nd molecule of chrysin in π-π arrangement to the species depicted in 2O2 shows d(O-O) of 1.447 Å, which is very similar to the related moiety in These calculations show that the quercetin and eriodictyol can scavenge superoxide through eventual formation of semiquinone arrangements of the scavengers, supporting our experimentally found CV data. Chrysin has only 2 hydroxyls in A-ring position 5 and 7, and, due to the intramolecular H-bond established with the neighbor carbonyl, H5 is less reactive that H7, and so the latter was explored for scavenging superoxide. Therefore, an initial state for chrysin, similar to that for quercetin shown in  phenols . Indeed, phenols . This in2O and establishes a chemical bond to the O2 moiety, while the contact in the chrysin-superoxide moiety lengthens. The corresponding product, also geometrically optimized. 2O excluded. The resulting DeltaG is negative, −1.0 Kcal/mol, suggesting reactivity. Therefore, our latter calculations indicate, theoretically, that chrysin reacts differently than quercetin and eriodictyol when scavenging superoxide.Residual protons can also induce ionic interactions and so we posed the conformation shown in 2 generation of superoxide generates instability, after titration of dry DMSO solutions. Through a calibration curve we can ascertain the actual concentration of superoxide ions and then its decrease of concentration due to added antioxidants.In this work we selected 3 representatives of flavonoids, a flavone (chrysin), a flavanol (quercetin) and a flavanone (eriodictyol) with different number of hydroxyl groups to analyze their influence in the scavenging of superoxide radical. Voltammetry is well suited for monitoring redox reactions proceeding through electron transfer. We show the application of CV to produce, in situ, the reactive species, the superoxide radical ion, and then measure the amount of superoxide remaining a short time later. Moreover, the added antioxidant reacts directly with the CV generated superoxide allowing for fewer variables in the aprotic CV cell so that side reactions can be avoided. This method, then, is an attractive alternative to using a biological environment to generate superoxide. In addition, KOAs shown above, the classical CV method , detectsAn improvement is shown in the RRDE CV method, as the presence of an additional electrode (the ring electrode) allows for specific determination of the amount of superoxide remaining in solution after adding a given amount of antioxidant. This permits us to obtain more quantitative results regarding antioxidant scavenging ability, Our DFT results show the superoxide scavenging by quercetin, eriodictyol and chrysin to be energetically and structurally feasible in agreement with the corresponding experimental CV data. However, mechanistic considerations from our DFT study suggest marked differences for scavenging superoxide by these three natural polyphenolic compounds, depending on whether hydroxyl groups are present  or missing (chrysin) in the B-ring.The structure-function relationship for flavonoid protection of lipid peroxidation has been described . The flaunambiguously, of the scavenging reaction at one electrode, and quantitatively, the superoxide consumed by antioxidants.The limitations and side reactions of methods used for generation and analysis of superoxide have been recently reviewed ; however


Prothrombin, or coagulation factor II, is a multidomain zymogen precursor of thrombin that undergoes an allosteric equilibrium between two alternative conformations, open and closed, that react differently with the physiological activator prothrombinase. Specifically, the dominant closed form promotes cleavage at R320 and initiates activation along the meizothrombin pathway, whilst the open form promotes cleavage at R271 and initiates activation along the alternative prethrombin-2 pathway. Here we report how key structural features of prothrombin can be monitored by limited proteolysis with chymotrypsin that attacks W468 in the flexible autolysis loop of the protease domain in the open but not the closed form. Perturbation of prothrombin by selective removal of its constituent Gla domain, kringles and linkers reveals their long-range communication and supports a scenario where stabilization of the open form switches the pathway of activation from meizothrombin to prethrombin-2. We also identify R296 in the A chain of the protease domain as a critical link between the allosteric open-closed equilibrium and exposure of the sites of cleavage at R271 and R320. These findings reveal important new details on the molecular basis of prothrombin function. In the penultimate step of this cascade, the zymogen prothrombin is converted to the active protease thrombin in a reaction catalyzed by the prothrombinase complex composed of the enzyme factor Xa, cofactor Va, Ca2+ and phospholipids. Prothrombin is one of the most abundant proteins circulating in the blood and features a modular assembly that includes the N-terminal Gla domain (residues 1–46), kringle-1 (residues 65–143), kringle-2 (residues 170–248) and a C-terminal protease domain (residues 285–579). Completing the architecture are three linker regions connecting the Gla domain to kringle-1 , the two kringles  and kringle-2 to the protease domain . Prothrombinase cleaves prothrombin at two sites, i.e., R271 in Lnk3 and R320 in the protease domain. Cleavage at R271 sheds the auxiliary domains and generates the inactive intermediate prethrombin-2. The alternative cleavage at R320 separates the A (residues 285–320) and B (residues 321–579) chains in the protease domain, that remain connected through the C293-C439 disulfide bond, and yields the active intermediate meizothrombin correctly folded for catalysis 16. Limited proteolysis by chymotrypsin reveals that the closed form is resistant to proteolysis, as seen for wild-type, but the open form is readily accessible to cleavage at W468 468 Fig. . The res7.The data in Fig. 19, prethrombin-1 as a combination of ΔGla, ΔLnk1 and ΔK120, ΔLnk2 in various deletions21, prethrombin-2 as a combination of ΔGla, ΔLnk1, ΔK1, ΔLnk2, ΔK2 and ΔLnk323) and have architectures consistent with current crystal structures of prothrombin21. Limited proteolysis by chymotrypsin partitions deletions in two groups based on their position along the prothrombin sequence. Removal of the N-terminal Gla domain, Lnk1 or kringle-1 produces derivatives that are resistant to proteolysis, whilst removal of the C-terminal Lnk2, kringle-2 or Lnk3 produces rapid attack at W468 and other sites. Interestingly, the proteolytic profile generated upon removal of Lnk3 is similar to that obtained upon removal of kringle-2, vouching for some structural determinants in Lnk3 that help stabilize the interaction of kringle-2 with the protease domain and communicate long range with the autolysis loop. The sharp partitioning in the proteolytic profiles in Fig. 21. The data in Figs 20 and later confirmed in all structures of prothrombin18. The similarity of proteolytic profiles between removals of Lnk3 and kringle-2 suggests that the two domains are strongly linked and may produce similar structural perturbations. The correlation between deletion of auxiliary components and pathway of activation is less obvious in the case of N-terminal segments  were prepared by extrusion using 100 nm polycarbonate membranes  and their size was confirmed by Dynamic Light Scattering. The vesicles were kept a 4 °C and used within 2 weeks. Human factor Xa, human cofactor Va and dansylarginine-N- amine (DAPA) were purchased from Hematological Technologies (VT). The chromogenic substrate H-D-Phe-Pro-Arg-p-nitroanilide (FPR-pNA) was from Bachem. All other chemicals were purchased from Sigma-Aldrich (MO).Prothrombin wild-type, mutants R296A, Y93A (open), S101C/A470C (closed), truncated forms , meizothrombin, prethrombin-2 and factor Xa were cloned in a pNUT vector, expressed in BHK cells and purified by affinity chromatography, ion exchange chromatography, and size exclusion chromatography as previously described2, pH 7.4) at 37 °C. At different time intervals (0-5-15-30-60-90-120 min), aliquots of proteolysis mixtures were quenched with NuPAGE LDS buffer and analyzed by non-reducing SDS-PAGE (4–12% acrylamide). Chemical identities of proteolytic products were verified by N-terminal sequencing (see Supplementary Table Proteins (0.1 mg/ml) were reacted with chymotrypsin  at 1:50 (w/w) ratio in TBS buffer , aliquots of reactions mixture were quenched with NuPAGE LDS buffer with β-mercaptoethanol as reducing agent and analyzed by SDS-PAGE (4–12% acrylamide). Activation of prothrombin and its truncated forms by prothrombinase  were determined in a discontinuous assay using a chromogenic assay as detailed elsewhere18. Different concentrations of prothrombin  were activated by factor Xa in the presence of PC:PS vesicles and cofactor FVa. After 0.5, 1, 2, 3 and 5 min, aliquots were quenched with 20 mM EDTA and active site generation was quantified in terms of the hydrolysis of FPR-pNA (80 μM), specific for thrombin and meizothrombin. Initial velocities were converted to concentrations using a standard reference curve measured at the time of the experiment. Data were collected on a SpectraMax microplate reader  and analyzed with Origin 2015 .Prothrombin activation was monitored by SDS-PAGE in the presence of 60 μM DAPA in TBS buffer at 37 °C, using prothrombinase composed of factor Xa (0.5 nM), PC:PS vesicles (50 μM) and cofactor Va (30 nM)Supplementary Material


Our study thus decodes that being intrinsically disordered may allow the TMEM106B cytoplasmic domain to dynamically and transiently interact with a variety of distinct partners.TMEM106B was initially identified as a risk factor for FTLD, but recent studies highlighted its general role in neurodegenerative diseases. Very recently TMEM106B has also been characterized to regulate aging phenotypes. TMEM106B is a 274-residue lysosomal protein whose cytoplasmic domain functions in the endosomal/autophagy pathway by dynamically and transiently interacting with diverse categories of proteins but the underlying structural basis remains completely unknown. Here we conducted bioinformatics analysis and biophysical characterization by CD and NMR spectroscopy, and obtained results reveal that the TMEM106B cytoplasmic domain is intrinsically disordered with no well-defined three-dimensional structure. Nevertheless, detailed analysis of various multi-dimensional NMR spectra allowed defining residue-specific conformations and dynamics. Overall, the TMEM106B cytoplasmic domain is lacking of any tight tertiary packing and relatively flexible. However, several segments are populated with dynamic/nascent secondary structures and have relatively restricted backbone motions on ps-ns time scale, as indicated by their positive { Transmembrane protein 106B (TMEM106B) is 274-residue protein whose gene locus was initially identified by a genome-wide association study to be a risk factor for the occurrence of frontotemporal lobar degeneration (FTLD), which is the second most common form of progressive dementia in people under 65 years . RemarkaTMEM106B is a member of the TMEM106 family of proteins of 200–300 amino acids with largely unknown function, which consists of TMEM106A, TMEM106B, and TMEM106C. Immunohistochemical assessments showed that TMEM106B is expressed in neurons as well as in glial and endothelial cells . The expRecently, biochemical characterizations revealed that TMEM106B is a single-pass, type 2 integral membrane glycoprotein predominantly located in the membranes of endosomes and lysosomes with a transmembrane domain predicted to be over residues 96–118 . HoweverSo far no biophysical/structural study has been reported on the cytoplasmic domain of TMEM106B in the free state, or in complex with binding partners. In the present study, we aimed to determine its solution conformation and dynamics by CD and NMR spectroscopy. Our results reveal that despite having relatively random sequence, the TMEM106B cytoplasmic domain is intrinsically disordered without any stable secondary and tertiary structures. Nevertheless, residue-specific NMR probes allowed the identification of several regions of the TMEM106B cytoplasmic domain which are weakly populated with either helical or extended conformations to different extents. Therefore, our present study deciphers that being intrinsically disordered appears to offer a unique capacity for the TMEM106B cytoplasmic domain to interact with diverse binding partners dynamically and transiently, which appears to be required for implementing its physiological functions.The sequence of the TMEM106B cytoplasmic domain over residues 1–93 was extensively analyzed by various bioinformatics programs to assess its hydrophobicity , DisordeThe DNA encoding residues 1–93 of TMEM106B was purchased from GeneScript and subsequently cloned into a modified vector pET28a with a C-terminal His-tag ,19. The All CD experiments were performed on a Jasco J-1500 spectropolarimeter equipped with a thermal controller ,19. Far-All NMR experiments were acquired on an 800 MHz Bruker Avance spectrometer equipped with pulse field gradient units as described previously ,19,21. NIn the present study, to facilitate experimental investigations, the cytoplasmic domain of TMEM106B was first assessed by a variety of bioinformatics tools to gain insights into its amino acid composition, ordered/disordered regions and secondary structure. As shown in The recombinant protein of the TMEM106B cytoplasmic domain is highly soluble in buffer at neutral pH. Its far-UV CD spectrum has the negative signal maximum at ~208 nm and a shoulder negative signal at ~222 nm, as well as a positive signal at ~190 nm , which iWhile far-UV CD spectroscopy reflects secondary structure features of a protein, near-UV CD is very sensitive to the tight tertiary packing . TherefoNMR spectroscopy is the most powerful biophysical tool to pinpoint residual structures existing in the unfolded proteins ,19,26,27To obtain quantitative insights into the populations of different secondary structures, we further analyzed chemical shifts of the TMEM106B cytoplasmic domain by SSP program , and theWe also assessed the backbone rigidity of the TMEM106B cytoplasmic domain by collecting heteronuclear NOEs, which provides a measure to the backbone flexibility on the ps-ns timescale ,22,27–30Indeed, further analysis of HSQC-NOESY spectrum allowed the identification of many sequential and medium-range NOE connectivities (NOEs) characteristic of helical conformations, which include dNN, dαN, dNN, dαN and even dαN NOEs . BrieflyAlthough TMEM106B variants were initially identified as risk factors for FTLD , and othThe TMEM106B cytoplasmic domain functions in the endosomal/autophagy pathway by interacting with various protein partners. Briefly, the TMEM106B cytoplasmic domain has been functionally mapped out to interact with three diverse categories of proteins: endocytic adaptor proteins ; proteinElucidation of the three-dimensional structure of the TMEM106B cytoplasmic domain will not only provide mechanistic insights how the small TMEM106B cytoplasmic domain can interact with such diverse partners, but may also offer the clues for future discovery/design of molecules to interfere in the interactions for therapeutic applications. In the present study, we first assessed the structural properties of the TMEM106B cytoplasmic domain by both bioinformatics analysis and biophysical characterization including CD and NMR HSQC spectroscopy. The results together indicated that the TMEM106B cytoplasmic domain is an intrinsically disordered domain with no well-folded three-dimensional structure.Nevertheless, by analyzing a set of multi-dimensional NMR spectra, we have successfully obtained residue-specific NMR parameters, which thus define conformational and dynamic properties of the TMEM106B cytoplasmic domain. Overall, the TMEM106B cytoplasmic domain is lacking of any tight tertiary packing as evidenced by the lack of any long-range NOE, as well as relatively flexible as judged by its small average value of hNOEs. Nevertheless, many segments are populated with dynamic/nascent secondary structures particularly helical conformations, and have relatively restricted backbone motions as evidenced by positive hNOE values for a large portion of residues. Furthermore, the TMEM106B cytoplasmic domain might have dynamic tertiary packing as we previously detected on the isolated Dengue NS3 protease domain. In this context, the TMEM106B cytoplasmic domain might be regarded to be a molten globule like state which has both secondary structures and dynamic tertiary packing as we previously found on a small protein . Indeed,Our present study thus offers the structural basis for the TMEM106B cytoplasmic domain to dynamically and transiently interact with such diverse protein partners. It has been well-established that one unique advantage for a protein to be intrinsically disordered is the capacity in interacting with a large and diverse set of binding partners dynamically and transiently . One sce