The microstructural evolution of the Cu–9Ni–6Sn alloy during hot compression was studied by means of an optical microscope and a scanning electron microscope. The constitutive equation of hot compression of the alloy was constructed by peak flow stress, and the corresponding 3D hot processing maps were plotted. The results showed that the peak flow stress decreased with the increase in the compression temperature and the decrease in the strain rate. The hot deformation activation energy was calculated as 243.67 kJ/mol by the Arrhenius equation, and the optimum deformation parameters for the alloy were 740–760 °C and 840–900 °C with a strain rate of 0.001~0.01 s−1. According to Deform-3D finite element simulation results, the distribution of the equivalent strain field in the hot deformation samples was inhomogeneous. The alloy was more sensitive to the deformation rate than to the temperature. The simulation results can provide a guideline for the optimization of the microstructure and hot deformation parameters of the Cu–9Ni–6Sn–0.3Mn–0.2Zn alloy.Cu–9Ni–6Sn alloys have received widespread attention due to their good mechanical properties and resistance to stress relaxation in the electronic and electrical industries. The hot compression deformation behaviors of the Cu–9Ni–6Sn–0.3Mn–0.2Zn alloy were investigated using the Gleeble-3500 thermal simulator at a temperature range of 700–900 °C and a strain rate range of 0.001–1 s Ultra-high-strength elastic copper alloys have been widely used in the electronic and electrical industries . Berylli−1 [−1. It was found that Si and Ti promoted the dynamic recrystallization nucleation of the Cu–15Ni–8Sn alloys, and Ni16Si7Ti6 particles precipitated, inhibiting the growth of recrystallized grains [Due to differences in the melting points and surface tensions of the alloying elements in the Cu–Ni–Sn alloy, segregation of the alloying elements is easy to occur during casting, which directly affects the subsequent processing performance of the materials. The micro-segregation and some of the structural defects of the alloy ingot could be improved by the hot deformation process, and a suitable hot working process should be necessary for the Cu–9Ni–6Sn alloys to obtain good comprehensive performance . The phy−1 . Zhao etd grains . Niu comd grains . Accordid grains . At the d grains . In nume−1 and a temperature range of 700~900 °C. Constitutive equations and thermal processing diagrams of the Cu–9Ni–6Sn alloy were established to fill the gap in the field of high temperature hot deformation of this system alloy. The microstructure evolution of typical regions during the compression process was studied by means of optical microscopy (OM) and scanning electron microscopy (EBSD). The thermal compression process of the alloy was simulated using the Deform-3D finite element software, and the accuracy of the model was verified by comparing the simulation and experimental results.So far, there have been a few reports on the microstructure control and optimization of heat deformation conditions for Cu–9Ni–6Sn alloys by the combination of finite element simulations and hot compression simulation experiments. In this paper, hot compression experiments were performed on the Cu–9Ni–6Sn–0.3Mn–0.2Zn alloy at a strain rate range of 0.001~1 sThe raw materials used in this experiment were obtained by heating them to pure copper, pure nickel, pure tin, pure manganese, and pure zinc in an atmospheric smelting furnace. The composition of the alloy was measured by an inductively coupled plasma optical emission spectrometer (ICP-OES), as shown in −1, 0.01 s−1, 0.1 s−1, and 1 s−1, respectively. The alloys were heated to the target temperatures at a heating rate of 5 °C/s before the compression and then held for 3 min to ensure a uniform temperature distribution. The homogenized alloys were machined into cylindrical specimens with a size of φ6 × 9 mm (a sample size ratio of 1:1.5) according to . The cyl−1 step. The speed of each step was 0.0054 mm·s−1, 0.054 mm·s−1, 0.54 mm·s−1, and 5.4 mm·s−1 .The 3D geometric model of the experiment was drawn by the SolidWorks software. As shown in The rigid visco-plastic finite element method was used in this paper to simulate and analyze the Cu–9Ni–6Sn alloy in the hot compression process, which is applicable to the plastic deformation process of thermally processed and strain rate sensitive materials . The rig(1) Ignore the effect of elastic deformation and bulk force of the material;(2) Material deformation obeys the Levy–Mises flow theory;(3) The material is homogeneous and incompressible, and its volume remains constant throughout the deformation process;(4) The loading condition gives the boundary between the rigid and plastic regions;When plastic deformation of the rigid visco-plastic materials occurs, the plastic mechanics can be expressed by the following basic equations:ijσ is the Cauchy stress component in the workpiece;(1) Balanced equations:(2) Velocity–strain rate relationship equation:(3) Levy–Mises stress–strain rate relationship equation:k is the constant.(4) Mises yield guidelines:(5) Volumetric incompressible conditions:(6) Boundary conditions:The boundary conditions were divided into force surface boundary conditions and velocity boundary conditions, respectively:Q is the heat of the deformation activation energy, R is the ideal gas constant 8.314 J/(mol·K), T is the hot deformation temperature, and A, α, and n are the constants, respectively.(7) Constitutive relationship (the Arrhenius model) :(9)ε˙=AsIn recent years, scholars have built on empirical observations and proposed to describe the dependence of alloy rheological stress on the strain rate, strain, and temperature with respect to stress by building an intrinsic model, e.g., the Johnson–Cook model ,27, the In order to broaden the temperature range for the application of the Arrhenius model, the Zener–Hollomon parameter was used to further modify the model ,32, whic−1 and 1 s−1 at 700 °C continued to increase with the increasing strain. The typical dynamic recovery (DRV) behavior was observed at 750 °C and 0.1 s−1 . As the strain rate increased from 0.001 s−1 to 1 s−1, the peak stress of the samples deformed at 700 °C increased from 82 MPa to 263 MPa.In the initial stage of deformation, a large number of dislocations appeared in the matrix. The entanglement between the dislocations or the interaction between the dislocations and grain boundaries lead to a rapid increase in the strength of the alloy; thus, the flow stress of the alloy increased sharply. After reaching the peak of flow stress, the deformation of the alloy was completed in a short time at the high strain rate. Therefore, the dislocation density and flow stress values in the matrix were also quite high. At the low strain rate, the alloy underwent significant reduction and recrystallization because the hot deformation temperature was higher than the recrystallization temperature .The Arrhenius kinetic equation, which is summarized by Sellars et al., describes the thermal processing properties of a material through the characteristic parameters in the true stress–strain curve. The relationship between these characteristic parameters, such as the peak flow stress, strain rate, and temperature of processing, could be described as a unified constitutive equation  as follo1A, 2A, n1, and b are the constants, and α = b/n1.Under low stress conditions (or Taking logarithms for each side of Equations (1)–(3):n1 are equal to 0.0534 and 3.36, respectively.The linear relationship of Q, defined in [R is the ideal gas constant, N is the average value of the slope obtained by fitting S is the average value of the slope obtained by fitting Under the same temperature during the thermal processing, the hot deformation energy reflects the sensitivity of the deformation resistance to the strain rate, and it also directly represents the ease of deformation. For a given temperature and strain rate, the thermal activation energy fined in  is as foQ) was 243.67 kJmol−1. The relationship between the strain rate and the processing temperature during thermal processing can be determined by temperature-compensated strain rate parameters, which Zener–Hollomon described as follows:The obtained hot deformation energy of the alloy  is averaged with the slope of the linear relationship between n. The calculated results for each parameter of the constitutive equation are shown in Substituting the averaged parameter values into Equation (14), the constitutive equations of the alloy during hot deformation can be obtained as follows:c is the peak stress calculated from Equation (21), and σm is the actual measured peak stress. According to the calculation results, the minimum and maximum relative errors were 0.04% and −10.8%, respectively, and the average relative error was 4.4%. It was demonstrated that the calculated Arrhenius equation has a small error range and can accurately predict the variation of peak stress in the Cu–9Ni–6Sn alloy.As the parameters of the flow stress curve are calculated by several linear fittings, the flow stress curve obtained from the parameters often has some errors compared with the actual measured curve. In order to evaluate the accuracy of the flow stress instantonal equation, the peak stresses under various test conditions were calculated according to the equation and compared with the experimental values. The relative error between the measured and calculated values was calculated using Equation (22), and the results are shown in According to the dynamic material modeling (DMM) theory, the hot deformation constitutive equation of the Cu–9Ni–6Sn alloy can be expressed  as follom is the strain rate sensitivity factor, and K is the constant. According to the dissipative structure theory, the power consumed by the alloy can be expressed [G is the dissipation quantity, and J is the dissipation coefficient. The strain rate sensitivity factor m is defined as follows:The strain rate sensitivity coefficient of the alloy was obtained by taking the partial derivative of Equation (23) as follows:xpressed  as folloWhen the strain rate and the hot deformation temperature are constant, the dissipation coefficients of the alloy can be expressed as follows:m is between 0 and 1, indicating that the dissipation state of the system is nonlinear dissipation. In addition, when the value of m is 0, the system does not dissipate energy. When the value of m is 1, the system dissipation state is ideal dissipation, and the dissipation coefficient of the alloy is at its maximum value (maxJ) at this time, which is as follows:At this time, the system has a steady-state flow stress, and the value of The energy dissipation efficiency factor of the alloy  and (29). The flow instability intervals were obtained from the hot deformation temperature and strain rate, and the flow instability intervals of the alloy were superimposed with an energy dissipation diagram to obtain a thermal processing diagram of the sample under the steady-state flow conditions , as showThe microstructure of the hot compression alloy in the central region of the sample under different compression conditions is shown in −1, 800 °C/0.001 s−1, and 900 °C/0.001 s−1 were selected as the typical unsafe deformation region, the safe deformation region, and the suitable processing region for EBSD analyses, respectively. −1 (the unsafe deformation region) are shown in −1. −1 (the suitable processing region). The grain size of the sample was about 84.41 μm. At a high temperature and low strain rate deformation, the recrystallized grains grew. In order to further investigate the hot processing structure of the Cu–9Ni–6Sn alloy, the samples treated at 700 °C/1 s−1, as shown in −1 had 32% of Q type, 26% of rotated Cube, and 17% of rotated Copper textures, respectively. −1 deformed sample (the unsafe deformation region), while the average local gradient was 0.5 for the 900 °C/0.001 s−1 deformed sample (the suitable processing region). The lowest local gradient was found in the optimum processing region, and the average local gradient was proportional to the geometric dislocation density, suggesting that the geometric dislocation density and the stored energy were low in the optimum processing region.x-axis indicates the effective strain and the y-axis indicates the percentage of different equivalent strains in the cross section. It shows the inhomogeneity of the effective strain distribution under different deformation conditions. The effective strains were symmetrically distributed along the radial centerline and axis of the specimens, showing a distribution phenomenon of dispersion from the center to the periphery. In contrast to −1, 800 °C/0.001 s−1, and 900 °C/0.001 s−1 deformation conditions were used to analyze the equivalent strain change rule of the Cu–9Ni–6Sn alloy during hot compression deformation. According to −1. The distribution of the equivalent strain in the sample was extremely uneven. The maximum value of the equivalent strain was mainly concentrated at the edges of the upper and lower end surfaces and the core of the sample, while the value of the equivalent strain at the center of the end face of the sample was small. The equivalent strain at the waist of the sample varied greatly, and this area was less constrained; it is known as the free deformation area [−1 (−1 (−1 (−1) was the largest throughout the compression.During the hot compression deformation, the internal structure and stress distribution of the alloy can change at any time. In order to explore the internal distribution rule of the samples in the three typical areas and analyze the equivalent strain changes of the alloy in the process of hot compression deformation, the simulation results at 700 °C/1 sion area . During area [−1 a, from 0 [−1  was about 1.2, and the free deformation was about 0.6.In order to further investigate the trend of the equivalent variation with time in the different locations of the two typical regions, the core large deformation region (P1) and the edge free deformation region (P2)  were selIn this work, the hot compression deformation behaviors of the Cu–9Ni–6Sn–0.3Mn–0.2Zn alloy were investigated, and the microstructure evolution and finite element simulation results during hot deformation were discussed. The main conclusions were as follows:(1) The flow stress of the Cu–9Ni–6Sn–0.5Mn–0.2Zn alloy during hot compression experiments is sensitive to the process parameters, with the peak stress decreasing with the increasing deformation temperature and the decreasing strain rate.−1, and the hot deformation constitutive equation was determined as follows:(2) The thermal activation energy of the Cu–9Ni–6Sn–0.5Mn–0.2Zn alloy was 243.67 kJ mol−1 to 0.01 s−1. The optimum hot deformation parameters for the alloys were 740~760 °C with strain rates of 0.01~0.001 s−1 and 840~900 °C with strain rates of 0.001 s−1.(3) Thermal processing diagrams were plotted for the alloys with the rheological instability zone temperatures ranging from 700 °C to 820 °C and the strain rates ranging from 1 s(4) According to the Deform-3D finite element simulation results, the distribution of the equivalent strain field in the hot-pressed specimens were inhomogeneous. The alloy was more sensitive to the processing rate compared to the processing temperature. The simulation results can provide a guideline for optimizing the microstructure and hot deformation parameters of Cu–9Ni–6Sn alloys.


Edge detection of ground objects is a typical task in the field of remote sensing and has advantages in accomplishing many complex ground object extraction tasks. Although recent mainstream edge detection methods based on deep learning have significant effects, these methods have a very high dependence on the quantity and quality of samples. Moreover, using datasets from other domains in detection tasks often leads to degraded network performance due to variations in the ground objects in different regions. If this problem can be solved to allow datasets from other domains to be reused, the number of labeled samples required in the new task domain can be reduced, thereby shortening the task cycle and reducing task costs. In this paper, we propose a weakly supervised domain adaptation method to address the high dependence of edge extraction networks on samples. The domain adaptation is performed on the edge level and the semantic level, which prevents deviations in the semantic features that are caused by the overgeneralization of edge features. Additionally, the effectiveness of our proposed domain adaptation module is verified. Finally, we demonstrate the superior edge extraction performance of our method in the SEGOS edge extraction network in contrast to other edge extraction methods. The extraction of ground objects from high-resolution remote sensing images plays an important role in many fields, such as urban planning and change detection ,2. In adTo reduce the need for the number of samples, domain adaptation methods reuse samples that are not in task domains by reducing the distribution shift between different task domains . In thesCurrently, the mainstream domain adaptation methods are adversarial learning ,13,14,15We used one extractor and two discriminators for adversarial learning. The extractor is used to detect edges and output an edge strength map (it describes the confidence of the predicted edge). Then, the first discriminator inputs the edge strength map to perform domain adaptation on the edge level. The second discriminator inputs the mean map of the ground truth and the edge strength map to perform domain adaptation on the semantics level. In addition, during training, we set different weights for each part, especially the dynamic parameters used inside the extractor. In the early stage of training, overfitting caused by too few samples in the source domain is prevented, and the training weight of the source domain is increased so that the network can fully learn enough edge features. In the later stage of training, the training weights of the source domain are reduced so that the source domain samples are fine-tuned to provide accurate semantic features. As a result, our method achieves its performance with few target domain samples compared with fully supervised edge extraction networks that use sufficient source domain samples.Domain adaptation is applied to edge detection for the first time.A weakly supervised domain adaptation method for high-resolution remote sensing object edge detection is proposed. This method performs domain adaptation at the edge level and the semantic level to reduce the difference between two domains in the edge detection network.The main contributions of this method are as follows:The rest of the paper is organized as follows. This section introduces edge detection and domain adaptation, respectively.Previous edge detection methods use low-level features to determine whether each pixel of an image belongs to a contour. Convolving the image with a local filter detects the pixel with the highest gradient magnitude in its local neighborhood as an edge. To extract discontinuous features that are generated by step edges, linear filters such as Canny  and SobeDue to the success of CNNs, many edge detection methods based on deep learning have been proposed. Liu, Cheng, et al.  combinedThe development of edge detection methods based on deep learning has brought more solutions to the information extraction of high-resolution remote sensing images. Wei et al.  used theDomain adaptation methods reuse the labeled samples in the source domain by aligning the distribution offset between the source domain data and the target domain data, which reduces the high dependence of deep learning methods on the number of labeled samples in the target domain.One of these ideas is adversarial learning ,13,14,15Another idea is self-training ,18,19,20Many methods apply domain adaptation to the high-resolution remote sensing image information extraction field. Song et al.  designedX; Y.Our method uses two datasets in different regions, in which the semantic edges of the same type of ground objects are labeled. The source domain dataset contains a large number of samples; the target domain dataset contains a small number of samples. In our method, Adversarial learning is performed between the edge detection module and the two adaptation modules, each of which is continuously enhanced. If the features generated by the edge detection module can confuse the identification of the adaptation module, the distribution of the features extracted from the source domain image and the target domain image is considered consistent.Each part of the network will be described in detail below. For the edge detection task of high-resolution remote sensing images, connectivity is also one of the important indicators to evaluate the quality of the edge. The edges of objects such as cities and roads are often widely distributed, so they should have a large receptive field size to retain detailed spatial information. Therefore, SEGOS , which iAfter inputting By considering that the distribution of the edge pixels and nonedge pixels is extremely unbalanced, the class-balanced cross-entropy loss function is usually used, and the influence of this unbalanced distribution on the network training process is reduced by introducing the edge scale parameter λ. The loss function formula is as follows:The edge detection network outputs an edge strength map to describe the confidence in predicting object boundaries. However, edge pixels are not independent, and cross-entropy loss does not take into account the continuity of the edges. Therefore, the mean-square error (MSE) loss is added to mitigate this effect:α and β are their weights. The loss function formula is as follows:Therefore, the edge detection loss is the weighted sum of the above loss functions, and The module consists of multiple convolutional layers whose number of channels, kernel, and stride can be adjusted according to the resolution of the remote sensing imagery. Except for the last layer, each convolutional layer is followed by a weakly ReLU , which fHowever, since the edge strength map focuses more on the edge information, the semantic information of the semantic edge is weakened. Therefore, only using the edge adaptation module will lead to more generalization of the transferred edge feature, which will increase confidence in the nontarget object edges and result in no significant changes. Therefore, we should also consider how to better transfer the semantic features.Considering that weakly supervised domain adaptation has a small number of target domain sample labels in contrast to unsupervised domain adaptation, the ground truth contains correct semantic information, so the edge strength map can be combined with the ground truth. First, their mean map is obtained and then input into the network with the same structure as the edge adaptation module; thus, more semantic features can be obtained during the adaptation process. The semantic distribution loss function can be expressed as:P and Y are input into the SA to obtain their respective semantic distribution loss functions To make the edge detection module and the adaptation module perform adversarial learning, Each module has its own weight, and there is also a dynamic parameter δ inside the edge detection module to adjust the role of the samples of the two domains in the network training process. The weight of the source domain samples is increased in the early stage of network training to prevent overfitting caused by too few samples in the target domain. At the end of network training, the weight of the source domain samples is reduced to prevent them from providing too much inaccurate semantic information, and the network is fine-tuned to detect more accurate semantic edges of the target domain.Thus, for ED, the loss function is:For EA, its loss function is:For SA, the loss function is:Based on our goal of adversarial learning, we optimize the following min–max criterion:The goal is to minimize edge detection loss while maximizing the probability that the target domain predictions are regarded as the source domain predictions.In this section, we present the experimental details and the results.The data sets selected in this paper are two sets of multi-category semantic edge datasets, both of which are data sets that were created in our research laboratory. The first set of data sets was produced through the GF2 PMS imagery in Yangyuan County, Zhangjiakou City, Hebei Province. Each image is 1000 pixels × 1000 pixels × 4 channels, for a total of 500 images. Edges of water, crops, fruit trees, forests, buildings, grass, and roads are labeled in the sample. The second set of data sets was produced through the GF2 PMS imagery in Jiashan County, Jiaxing City, Zhejiang Province. Each image is 1000 pixels × 1000 pixels × 4 channels, for a total of 309 images. The edges of the same aforementioned objects are labeled in the samples. A total of 103 samples were selected from each of the two sets of data for testing, and the remaining samples were used for training.As shown in We implemented our method using the PyTorch  deep leaOur method is compared with AdaptsegNet, AdvEnt, and ASS for domain adaptation and with BDCN, DexiNed, and SEGOS for edge detection. The compared methods use optimal parameters for training. For the domain adaptation methods, the loss is the same as the loss function used in our method. The ground truth of the target domain samples is not used in the baselines and unsupervised methods. For the edge detection methods, since they do not use multi-domain samples, they are used for training: only the source domain samples, only the target domain samples, and mixing the two domain samples.The evaluation is performed using the optimal dataset scale (ODS) and optimal image scale (OIS) proposed by HED , which hIn the following experiments, except for the last set of experiments, all samples in the Yangyuan training set were selected as the source domain samples, and 51 samples were selected from the Jiashan training set as the target domain samples to compare the extraction performance of the methods with a small number of target domain samples.The last set of experiments verifies the edge detection of our method by using different numbers of target domain samples in different transfer directions. Our method performs domain adaptations from Yangyuan County to Jiashan County and from Jiashan County to Yangyuan County. Additionally, SEGOS is trained only with samples from the target domain. All samples from the source domain will be used, and 25%, 50%, and 100% of the target domain samples will be used for training.The results of all experiments are predicted on the test set of the target domain.As shown in As seen in As seen in From the experimental results of SEGOS in This study aims to make domain adaptation more effective in edge extraction of high-resolution remote sensing images, thereby obtaining better edge extraction results using a small number of target domain samples. The effectiveness and advancement of this method are verified from multiple perspectives through four sets of experiments.As observed from the results  of the aWe compared our method with other domain adaptation methods in different edge detection networks. The results  show thaThe comparison results  of edge In summary, most of the current domain adaptation methods for semantic segmentation only perform domain adaptation at a single level. Some multi-level domain adaptation methods are also not suitable for semantic edges. The semantic edge contains low-level edge information and high-level semantic information, which easily makes the adaptive process biased and affects the training of edge extraction networks. Therefore, our method performs an adaptation process at both the edge level and the semantic level, using them together to constrain the transfer of semantic edges. At the semantic level, we further introduce ground truth to adapt the entire image so that the network is guided by more accurate semantic information. From the results, it can be seen that our method has an excellent performance in both regions with large domain differences and regions with small domain differences. The overall evaluation results are also higher than the current state-of-the-art methods in all aspects.However, it can be observed from the experimental results that even though adversarial learning is used to improve the edge extraction performance of the network, there are still some pixels lost in the result, resulting in broken lines. If these results are used as pseudo-labels, there is a clear difference from the manually labeled samples. Therefore, the self-training method using pseudo-labels is limited by the difference between the extracted edge and the manually labeled edge and cannot play an effective role. If pseudo-labels similar to human labels can be made by broken-line repairing methods, they will go one step further to provide effective semantic information for the target domain. However, for complex tasks, such as multi-category semantic edge detection, this is still more difficult, so further research into self-training learning is needed.In this paper, we apply the domain adaptation method to edge detection for the first time and reduce the new task domain’s high dependence on the number of samples through weakly supervised domain adaptation. First, by considering the distribution characteristics of ground objects in remote sensing images, we choose SEGOS as the edge detection network. By understanding that the UDA methods lack correct semantic information guidance in the edge detection, we add a small number of target domain samples to make full use of the ground truth of the target domain to provide the correct semantic information. Simultaneously, domain adaptation is performed on the edge level and the semantic level, and dynamic parameters are introduced to adjust the influence weights of the source domain samples on the network training in different training stages. Finally, we conduct sufficient experiments on high-resolution remote sensing data sets that were developed in our research laboratory. The effectiveness of our proposed domain adaptation module is verified. Furthermore, we demonstrate the superior edge extraction performance of our method in the SEGOS edge extraction network in contrast to other edge extraction methods. In addition, if we can further study how to repair the broken lines on the edges, the self-training learning will further improve the edge extraction effect using a small number of target domain samples.


RNA-protein interactions drive key cellular pathways such as protein translation, nuclear organization and genome stability maintenance. The human telomeric protein TRF2 binds to the long noncoding RNA TERRA through independent domains, including its N-terminal B domain. We previously demonstrated that TRF2 B domain binding to TERRA supports invasion of TERRA into telomeric double stranded DNA, leading to the formation of telomeric RNA:DNA hybrids. The other telomeric protein TRF1, which also binds to TERRA, suppresses this TRF2-associated activity by preventing TERRA-B domain interactions. Herein, we show that the binding of both TRF1 and TRF2 to TERRA depends on the ability of the latter to form G-quadruplex structures. Moreover, a cluster of arginines within the B domain is largely responsible for its binding to TERRA. On the other side, a patch of glutamates within the N-terminal A domain of TRF1 mainly accounts for the inhibition of TERRA-B domain complex formation. Finally, mouse TRF2 B domain binds to TERRA, similarly to its human counterpart, while mouse TRF1 A domain lacks the inhibitory activity. Our data shed further light on the complex crosstalk between telomeric proteins and RNAs and suggest a lack of functional conservation in mouse. Telomeres are nucleoprotein structures positioned at the natural ends of linear eukaryotic chromosomes, where they avoid unwanted activation of DNA repair machineries, telomeric DNA degradation and telomeric fusions ,2. MammaSeveral laboratories including ours demonstrated that the two human shelterin proteins TRF1 and TRF2 establish an intricate and functionally relevant crosstalk with TERRA. Recombinant TRF2 binds to TERRA oligonucleotides in vitro through different domains including its N-terminal glycine-arginine-rich (GAR) domain, a 44 amino acid long peptide also dubbed B domain due to its basic nature ,9,10,11.We showed that TRF2 stimulates TERRA invasion of telomeric double-stranded (ds) DNA in vitro, leading to the formation of telR-loops; this activity relies on TRF2 B domain, as a TRF2 variant lacking the B domain (TRF2ΔB) is unable to stimulate TERRA invasion . ConversDespite the importance of the complex interplay between TERRA, TRF proteins and telR-loops, the molecular and structural details of these interactions are still very limited. The UUAGGG tracts of TERRA can form G-quadruplexes (G4s) containing stacked Hoogsteen-bonded G-quartet motifs stabilized by monovalent cations including K+ ,15. RecoRecombinant human TRF1 binds to TERRA in vitro through interactions occurring outside its A domain ,10. IndeTwo isoforms of human and mouse TRF2 B domains of different lengths have been reported , with thTo assess the individual contribution of the positively charged residues towards TERRA binding, we generated a series of mutants, where the 8 Rs clustered between positions 13 and 30 of the short human B domain were substituted with the neutral amino acid alanine (A) . We creaUsing plasmid invasion assays developed to detect the formation of telomeric RNA:DNA hybrids in vitro A, we preNext, we performed plasmid invasion assays using GST-tagged full-length TRF2 variants with B domains carrying the R13-30A or R13-30K substitutions . TRF2R13The ability of TRF2 to promote invasion requires binding to telomeric dsDNA , hence tWe previously reported that fusing the A domain of human TRF1 to the B domain of TRF2 is sufficient to inhibit the binding of the latter to TERRA oligonucleotides . We, theWe then generated two deletion mutants of human AB, one comprising only the first 50 amino acids of the A domain  or by negatively charged aspartates (AP2EDB) . While AThe functional relevance of TERRA interactions with human TRF2 B domain has already been clarified, as these interactions promote the formation of telR-loops . It remaThe interaction of TRF2 B domain with TERRA G4s was reported also by the Lieberman laboratory . In the We have also defined, with unprecedented resolution, the molecular details of how TRF2 B domain binds to TERRA and TRF1 A domain inhibits this binding. As for TRF2 B domain, the 8 Rs clustered within the GAR domain are necessary for binding to TERRA. Replacing them with positively charged Ks (BR13-30K mutant) impairs TERRA binding, although not completely, and the corresponding TRF2 full-length protein (TRF2R13-30K mutant) is still partly able to induce telR-loop formation. This suggests that electrostatic interactions and other types of contacts, likely established through arginine side chains, support TERRA-B domain interactions and consequent telR-loop formation. Consistently, it has been shown that the multivalent guanidinium group of arginines establishes multiple interactions with the backbone phosphates of all RNA bases, thereby stabilizing RNA-protein complexes . In contTERRA-TRF protein interactions, by regulating telR-loop formation and telomere stability, might protect cellular and tissue homeostasis and avoid disease conditions. A survey of cancer-associated mutations found in TRF1 and TRF2 at the cBioPortal for Cancer Genomics ,26 reveaFinally, we show that TERRA-TRF2 B domain interactions are conserved in mouse, while the regulatory function of TRF1 A domain is not. This suggests that mouse TRF2 promotes telR-loop formation, while mouse TRF1 cannot suppress them. If that were the case, factors other than TRF1, RAP1, for example, could be involved in restricting TRF2-stimulated telR-loops. However, conditional deletion of TRF1 in mouse embryonic fibroblasts leads to the appearance of telomere free chromosome ends , a phenoPlasmids pGEX-4T1  containing cDNAs of the proteins of interest N-terminally fused in frame to a Glutathione S-transferase (GST) sequence were used for expression in bacteria cells. Plasmids encoding for TRF proteins, TRF1ΔA, TRF2ΔB, the wild-type A and B domains, as well as the fusion of the two domains, were previously generated in the laboratory . All subg for 15 min at 4 °C) and lysed by sonication in 20 mL of GST pulldown buffer  for proteins with molecular weights (MW) up to 50 kDa or in in Protein lysis buffer  for proteins with MW above 50 kDa. Sonication was performed using a MSE Soniprep 150 apparatus ; samples were subjected to 2 cycles of 3 min “ON”, at maximum power, and 2 min “OFF”, while being kept on ice. Sonicated samples were then centrifuged at 4200× g for 20 min at 4 °C. After centrifugation, lysates were incubated with Glutathione agarose beads  overnight at 4 °C on a tube roller. Beads were then washed three times with 5 mL of GST pulldown buffer and once with 2 mL of GST pulldown buffer low Triton . Bound proteins were eluted in 200 μL of GST elution buffer  for 20 min at 4 °C. Protein concentration and purity were determined using the Bradford protein assay  and BSA reference samples, followed by fractionation in polyacrylamide gels and staining with BlueSafe reagent . Note that all protein concentrations refer to monomers.The pGEX-4T1 plasmids were transformed into competent BL21 cells. Cells were grown in 5 mL LB media overnight at 37 °C and then 250 µL of those cultures were inoculated into 50 mL of fresh LB media. Cells were grown at 37 °C for 2 h, after which protein expression was induced with 50 μM IPTG  for 5 h at 30 °C. Cells were collected by centrifugation  for 20 min on ice followed by 10 min at 25 °C. For dsDNA EMSAs, E. coli tRNAs were excluded from the reaction mix and incubation was carried out for 30 min at 25 °C. After incubation, 4 µL of 6× gel-loading buffer  were added to the reactions, which were then electrophoresed in agarose gels and pre-cooled 0.5× TBE. 2% agarose gels were used for EMSAs with 5-repeat RNA oligonucleotides, 1.5% for 12-repeat RNA oligonucleotides and 1% for dsDNA EMSAs. Gels were run at 70 V for 1 h at 4 °C and then dried and exposed to a phosphoimager screen. Radioactive signals were detected with a Typhoon FLA 9000 or an Amersham Typhoon IP imager . ImageJ was used to quantify signals. The number of independent replicates (n) is indicated in figure legends.For RNA EMSAs, 5-repeats oligonucleotides were synthesized at IDT . Sequences were as follow: TERRA, 5′-(UUAGGG)5-3′, MUT1, 5′-(UUACGG)5-3′, MUT2, 5′-(UUACCG)5-3′, MUT3, 5′-(AUAGGG)5-3′, MUT4, 5′-(AAAGGG)5-3′. Additionally, 12-repeat TERRA oligonucleotide [5′-(UUAGGG)12-3′] was obtained by in vitro transcription of a plasmid containing the respective DNA sequence downstream of a T7 promoter, using the HiScribe T7 High Yield RNA Synthesis Kit , following the manufacturer’s instructions. For dsDNA EMSAs, a ds(TTAGGG) fragment was excised by restriction digestion from the pTel plasmid and contained approximately 1200 bp of telomeric repeats flanked by non-telomeric sequences of 48 (upstream) and 12 bp (downstream). RNA oligonucleotides and dsDNA fragments were 5′-end labeled with T4 polynucleotide kinase  and [γ32P]-ATP and then purified using the Oligo Clean and Concentrator Kit . For RNA EMSAs, recombinant proteins and oligos were incubated in 20 µL of EMSA buffer  containing 50 ng/µL Plasmid invasion assays were performed adapting the protocol previously established in the laboratory . pTel plt-test using GraphPad Prism . The p-values and used tests are indicated in figure legends.For direct comparison of the two groups, we employed a paired or unpaired two-tailed Student’s


Jingmen tick virus (JMTV) is a newly discovered tick-borne virus that can cause disease in humans. This virus has been authenticated as being extremely widespread worldwide and as posing a significant threat to public health and safety.We collected 35 ticks belonging to two tick species from wild boars in Nanping, Fujian Province, China. JMTV-specific genes were amplified by qRT-PCR and nested PCR to confirm the presence of this pathogen.Amblyomma testudinarium. Phylogenetic analysis revealed that JMTV from Fujian Province shared > 90% identity with other isolates derived from China, but was distinct from those reported in France and Cambodia.More than one third of of all ticks collected (11/35) were positive for JMTV. Viral sequences were obtained from three of the JMTV-positive ticks, including the complete genomic sequence from one tick. This was the first time that JMTV was identified in the hard-bodied tick JMTV is characterized by relatively low mutations and has its own local adaptive characteristics in different regions. Our findings provide molecular evidence of the presence of JMTV in an overlooked tick species from an area not unrecognized as being endemic. They also suggest that JMTV occupies a wider geographical distribution than currently believed and is a potential disease vector.The online version contains supplementary material available at 10.1186/s13071-022-05478-2. Ticks are exclusively specialized obligate ectoparasites and are recognized as second only to mosquitoes in terms of transmitting various pathogens to human beings . The maiRhipicephalus microplus from the Jingmen region of Hubei Province, China, in 2014 ) and two H. longicornis ticks (2/9 [22.2%]). However, the virus was not isolated from any positive tick samples.Of the 35 tick specimens collected, positive qRT-PCR results for JMTV were obtained for both A. testudinariu. Pairwise distances analysis indicated that these three sequences shared a 98.4–99.3% nucleotide identity and had > 96% amino acid identity with each other. These results demonstrated that these sequences were extraordinarily closely related and possibly even descended from a single common ancestor. A phylogenetic tree was constructed based on three amino acid sequences of NSP1 obtained in this study, along with corresponding sequences of the Jingmenvirus group retrieved from the GenBank. As shown in Fig. R. microplus from Hubei Province. The results showed that these JMTV sequences obtained from Fujian Province were genetically close to the reported sequences from Hubei Province.In this study, the partial S1 segment encoding NSP1 of JMTV was successfully amplified and sequenced from three of the 11 ticks that tested positive for JMTV by qRT-PCR. The three positive ticks were all Direct next-generation sequencing (NGS) of the JMTV-positive tick sample (WS3) from Fujian Province provided 8.05 G bases data with total read numbers of 30.24 million. The complete nucleotide sequence of the four segments of the JMTV genome in this sample (WS-3) were obtained as: S1, 3115 bp; S2, 2845 bp; S3, 2824 bp; S4, 2795 bp. These sequences have been deposited in the GenBank database with accession numbers OM459837-OM459840.Compared with the information reported on the JMTV genome on the global scale, the JMTV sequences obtained in this study were shown to be similar to those found in Lao PDR, Uganda, Turkey, Kosovo, Brazil, Trinidad and Tobago and other regions of China, with a 76.9%-94.6% nucleotide identity, while relatively low nucleotide identity was found with the strains from France (57.1%-70.4%) and Cambodia (54.1%-70.5%). For a better analysis of these newly identified JMTVs from Fujian Province, phylogenetic trees were constructed using an neighbor-joining approach based on four complete segment sequences Fig. . DespiteFlavivirus NS5, manifested conserved domains for the S-adenosylmethionine (SAM) binding site and nucleic acid substrate binding site of a typical JMTV. Segment 3 encoded a unique ORF corresponding to another nonstructural protein (NSP2) with conserved domains for the ATP binding site, which presented homology with the NS2b-NS3 complex of flaviviruses. In addition, three structural proteins of JMTVs were encoded each in their own way, showing more diversity and unique properties in terms of their genetic organization. For example, glycoprotein was encoded by segment 2 in a monocistronic expression pattern, while segment 4 encoded capsid and membrane proteins in a bicistronic pattern and comprised two ORFs with overlapping reading frames in some strains. The amino acid sequences of JMTV strains from nine different countries were compared with the JMTV_WS3 reported in this study. Our results showed that little difference existed among the motifs in the conserved regions of JMTV nonstructural proteins (NSP1 and NSP2) from all over the world, and that the sequences of important protein binding sites were conservative, with almost no change , Wuhan n the UK ), Guaicon the UK ) and ALSn the UK , 24, 25)n the UK . Fujian In this study, JMTV was identified for the first time in Fujian Province, demonstrating that JMTV may be widely distributed throughout southeastern China and form a stable ecological cycle in the local area. By comparison with a large-scale survey of JMTV in ticks from Turkey , the JMTThe sequence of the complete JMTV genome were obtained by direct NGS from one tick (WS-3) which tested positive for JMTV by qRT-PCR. This sequence was compared with all available JMTV sequences in the GenBank database. The genome topology and functional organization of JMTV_WS3 from Fujian Province were identical to those from other regions, with the highest sequence identities to JMTV strains from Lao PDR and China and the lowest to strains from France and Cambodia. This result is in agreement with the results of phylogenetic analysis on all four genomic fragments. Thus, a spatial segregation of viral subclades was observed, signifying the current local adaptive processes of JMTV. A comprehensive analysis of the amino acid sequences and conserved regions of all available JMTV was also performed. JMTV_WS3 shared significant similarities with other strains from different regions of China. In addition, insignificant heterogeneity were also observed in important protein binding sites of conserved regions, such as the SAM binding site and nucleic acid substrate binding site in segment 1 and ATP binding site in segment 3, among these viruses from different continents. These results demonstrate that JMTV is widely distributed over the world and that is has adapted well to the local natural ecological resources, showing a certain diversity in terms of its genome. However, an obvious homogeneity and low speed of evolution were apparent among the core functional sites of some important proteins.A. testudinarium was the dominant tick species in this study. This tick mainly lives in mountain forests or fields and is widely distributed throughout the coastal areas of southeastern China. Its main host animals are large wild mammals and livestock, which also invade humans. The peak season of its annual activities is from April to October [A. testudinarium, which extended the tick-borne infection spectrum of JMTV and increased the diversity of virus hosts or vectors. Our results shed a new light on our understanding of the survival mode of this virus in nature and its life-cycle characteristics. So far, JMTV has been documented to cause human diseases and viremia in some individuals [Unlike the common tick species collected from the body surface of animals in previous research,  October , 26. Intividuals , 28, butA phylogenetic tree was built by neighbor-joining method based on the partial amino acid sequences of segment S1 amplified in this study and other sequences of Jingmenvirus group from GenBank. The phylogenetic analysis demonstrated that the three strains of JMTV obtained in this study had the closest relationship with isolate SY84 found in Hubei Province  and thatA. testudinarium attached to a wild animal, possibly signifying that JMTV is likely to be able to establish a stable life-cycle in the local wild environment through wild animals and tick vectors. As such, it poses a specific health threat to local residents. In short, research on JMTV is still in the initial stage, and further studies are required to elucidate the pathogenicity of this virus to animals and its epidemiological characteristics in nature.In conclusion, our study provides important molecular evidence on JMTV in a non-endemic region of southeastern China. We report here the first discovery of this virus in Additional file 1. Conventional PCR primers for complete genome sequences of JMTV_WS3


Therefore, Li-S batteries are considered as the great potential for developing future energy storage technology. However, some of problems such as Li dendrites growth, the shuttle effect of sulfides and the electronic insulation feature of sulfur, have brought obstacles to the development of Li-S batteries. The covalent organic frameworks (COFs) are a series of porous materials with different topological structures, which show the versatile characteristics of high specific surface area, permanent pores, ordered porous channels and tunable internal structure. Potentially, their ordered channels and extended conjugated frameworks could facilitate rapid Li-ion diffusion and electron transport for the remarkable rate capability. On the basis of these merits, the COFs become the potential electrode materials to solve the above serious problems of Li-S batteries. In this mini review, we summarize the research progress of COFs utilized as electrode materials in the Li-S batteries, including the cathode, separator and anode materials. Accordingly, the outlook of COFs as electrodes for future development in Li-S batteries is also given.Recently, how to enhance the energy density of rechargeable batteries dramatically is becoming a driving force in the field of energy storage research. Among the current energy storage technologies, the lithium-sulfur (Li-S) batteries are one of the most promising candidates for achieving high-capacity and commercial batteries. The theoretical energy density of Li-S batteries reaches to 2,600 Wh kg Recently, the big problem of energy depletion seriously makes people attach importance to the development of clean and renewable energy to meet the increasing of energy demand . Since 22S2 and Li2S) are also electrically insulating, which is not conducive to for the batteries’ high rate performance  are widely used as electrode materials. COFs are a category of organic crystalline porous materials composed of light components including C, H, N, and O . They arIn recent years, because of the extensive research on COFs-based electrode materials in Li-S batteries, it can be seen that they are potentially great competitors for the high-performance cathode materials. Compared with other materials, COFs have a highly ordered nano-porous structure and a large specific surface area, where it is conducive to the immobilization of sulfur and to limit the loss of soluble polysulfides. In addition, ascribing to the feasible functionalization of COFs, the desirable skeleton structures of COFs with various pore sizes, shapes, and volumes can be flexibly designed at the molecular level.In 2014, Liao used a porous crystalline polymer backbone formed by the polymerization of benzene and triazine rings in a 2D structure (CTF-1) as a sulfur carrier material . To achi−1 after 200 cycles, and the capacity decay rate per cycle was 0.16% . Two years later, A novel microporous COF (Por-COF) based on porphyrin was created by the solvothermal method. The composite material of Por-COF/S with a sulfur content of 55% was prepared in the melt-diffusion process, where it was used as a positive electrode material in Li-S batteries. At a rate of 0.5°C, the specific capacity remained at 633 mAh gas 0.16% . Then, tas 0.16%  under thas 0.16% .−1 at 0.1°C, which was very nearly its predicted specific capacity. After 528 cycles at 0.5°C, it still maintained a high capacity with a capacity decay rate per cycle of 0.077%. These studies have proved that through COFs structure modification, the sulfur loading of active materials can be increased, and ultimately the electrochemical performance of Li-S batteries is improved simultaneously.In 2020, −1 at a low cycling rate, and a high performance above 690 mAh g−1 is maintained even at 1°C or 2°C. This is attributed to the quaternary ammonium cations preventing the diffusion of polysulfide anions into the electrolyte through their strong interactions with polysulfide anions. Therefore, the functional groups enable the electrode to obtain good cycle performance and inhibit the shuttle effect.  The physical sulfur fixation can be carried out by designing porous structures, and the nano-channels formed in COFs can effectively alleviate the dissolution of polysulfides and shuttle effect . 2) By expanding the conjugation system to obtain a high degree of conjugation and appropriate interlayer space, the loading of sulfur in the composite cathode can be increased dramatically (COF-ETTA-ETTCA). 3) The introduction of electron-rich heteroatoms or functional groups can fix polysulfides and reduce the loss of active substances . 4) Through mixing the highly conductive carbon-based materials with COFs or combining COFs with metal organic framework derived carbon/metal composites, the conductivity of cathode materials could be improved obviously .+ flux, thereby improving the lithium loading capacity of the anode material. −1 at 0.1 A g−1 with good rate and cycling performance.Since the anode is metallic lithium in Li-S batteries, the uneven deposition of lithium ions during charging tends to form dendrites, reaches the cathode by penetrating the separator. Eventually, an internal short circuit will lead to a severe safety danger . Therefo+ storage and diffusion. So, how to increase the number of redox active sites of COFs is crucial to meeting the high-capacity requirements of LIBs anodes .In this section, the application of COFs in anode materials could solve the following problems: 1) By finely designing the porous interface layer, the local current density is reduced where the lithium ions can move to the redox active sites uniformly, thereby enhancing the lithium loading capacity of anode materials (PTDCOF). 2) To increase the number of redox active sites in COFs through designing layered porous COFs, it can increase the lithium loading capacity of anode materials (DAAQ-COF) 3) The COFs can also be uniformly encapsulated on other materials  through In the charge-discharge process, the positive electrode would generate a polysulfide intermediate , which would then dissolve in the electrolyte, cross the separator, and diffuse toward the negative electrode. This polysulfide from the positive electrode could react directly with the metal lithium of the negative electrode, which eventually causes the irreversible loss of the effective material in the batteries. Since COFs have the potential to capture polysulfide anions due to their multi-pore structure and designable pore size, some works have proposed strategies to design functional separators to capture polysulfides.+  and excellent rate capability (827.7 mA hg−1 at 2 A g−1) after the first cycle   to modif+ . More soer cycle . With ther cycle  with theer cycle . Then, tst cycle .In this section, the COFs mainly solve two problems in the separators: 1) By introducing functional groups, it repels polysulfide anions and adsorbs molecular LiPS, thereby reducing the negative impact of the shuttle effect . 2) Another strategy is to enhance the electron mobility for efficient electrocatalysis while trapping LiPS efficiently, providing sufficient lithiophilic sites for the strong chemisorption and catalysis of polysulfides (TAPP-ETTB COF).−5 S cm−1 and a high mobility number of 0.91 at room temperature  The poor electrical conductivity of COFs materials makes it difficult to balance its cycle life and electrical conductivity, which is one of the main reasons that restrict its practical application regarding the technology of energy storage. 2) Due to the low volumetric energy density of COFs materials, the high specific capacity properties of cathodes composing of COFs materials have not been fully exploited. 3) Compared with the application of COFs around the design and development of cathode materials in Li-S batteries, their applications as separators, anode materials and solid electrolytes also show good prospects, which should be attracted much interests in further studies.


Sorghum and millet are rich in nutrients and bioactive compounds. They can enhance nutrition and health. This study was aimed to develop sorghum–millet gluten‐free cookies from sorghum and millet composite flour. Cookies were made using 100% wheat WF100%, sorghum SF100% ,and millet MF100% flours and composite flours of 25% sorghum–75% millet (SF25%–MF75%), 50% sorghum−50% millet (SF50%–MF50%), and 75% sorghum–25% millet (SF75%–MF25%). The physicochemical, nutritional, bioactive, and sensory quality attributes of the developed cookies were evaluated.p ≤ .05). The cookies made using the composite flour of SF25%–MF75% appeared to have the uppermost values of moisture, protein, fat, total phenolic content (TPC), and antioxidant activity. They have the highest contents (p ≤ .05) of potassium, sodium, magnesium, and calcium. The scores of all sensory attributes of all cookie types were higher than the cutoff score (5). Overall, the incorporation of sorghum and millet flour in cookies formulation enhanced the physicochemical, nutritional, and bioactive properties without major effect on the sensory attributes of the product, which make it excellent nutritive and healthy sorghum–millet gluten‐free cookies.The results of flours displayed that the highest values of peak viscosity and final viscosity were observed in SF100%, breakdown and pasting temperature were observed in WF100%, setback viscosity in composite flour of SF25%–MF75%, and hardness and chewiness in WF 100%. Regarding cookies analysis results, the highest values of hardness were shown in WF100% and SF100% cookies, and the highest water activity was observed in the cookies of (SF25%–MF75% and SF50%–MF50%), and the (SF75%–MF25%) cookies had the best diameter and thickness. The lowest water‐holding capacity (WHC) and oil‐holding capacity (OHC) were found in (WF100%) cookies, while they were high in the cookies of SF and MF flours and their combinations ( Sorghum and millet flour in cookies formulation enhanced the physicochemical, nutritional, and bioactive properties without major effect on the sensory attributes of the product. They were of analytical grade and were obtained from Sigma (Sigma‐Aldrich).The chemicals used such as  and oil‐holding capacity (OHC) for all flour samples were assessed following the method described by Berton et al.  with somWhere: W0 represents the initial weight (g) of samples and W1 represents the final weight (g) of samples. The results are expressed as the mean values of three replicates.2.3The wheat flour and the blends of composite (sorghum and millet) flour with 2% guar gum were prepared. The measurement of pasting properties for wheat and composite flours was done by using the Rapid Visco Analyzer (RVA) (Newport Scientific). The moisture content of the samples was adjusted to 14%, and then 3.5 g of each sample was weighed into aluminum canisters, and then distilled water was added to adjust the weight to 28 g. The sample was mixed, and the obtained slurry was poured into an aluminum pan, loaded into the RVA chamber, and held for 50 s at 50°C. After that, the temperature was raised to 95°C at 13.15°C/min in 3.42 min and then kept for 3.30 min at this temperature. The samples were subjected to cooling to 50°C at a rate of 12.93°C/min in 3.48 min and then held for 2 min at this temperature. The samples were measured in triplicate, and the data were processed by using Thermocline window software. Peak viscosity (PV), final viscosity (FV), peak time (Pt), peak temperature (PT), setback (SB), and breakdown (BD) were the main parameters measured.2.4After the RVA tests, the gel was subjected to overnight storage at 25°C in a beaker (25 ml volume) with the height and internal diameter of 35 mm and 30 mm, respectively. A Brookfield CT3 Texture Analyzer  was used for texture analysis. In the analyzer, the gel was compressed at a speed of 0.5 mm/s using two penetration cycles to a distance of 10 mm using 12.7 mm (width) × 35 mm (length) cylindrical probe. Thereafter, the texture parameters of the gels such as hardness, cohesiveness, adhesiveness, and springiness were measured. The harness and cohesiveness results were used to calculate the gumminess as reported previously  was applied for the determination of water activity of cookies water activity (aw) at 25°C , a* (redness), and b* (yellowness) values in triplicate using a color grader  with a D65 light source.2.6.4A TA‐XT plus texture analyzer (Stable Micro Systems) was used to measure the texture profile of the cookie samples as described previously , 1.7% of H2O2 (30%) was added and mixed well. The digestion proceeded with heating in a microwave, a temperature ramp‐up to 170°C for 20 min, and then it was sustained at the same temperature for 15 min. The power was set in the range of 290–1800 W, and at the end of the program, the samples were cooled to room temperature, and the solution was transferred to 25.0 ml conical tubes and filled with Milli‐Q water. The mineral contents were measured using a PerkinElmer Optima 7300DV ICP‐OES   under the following conditions: measuring power 1300 W; integration time of signal 1 s; plasma gas flow 15 L min‐1; auxiliary gas flow 1.5 L min‐1; nebulization gas flow 0.70 L min‐1; and pumping rate of the sample 0.70 ml min‐1.2.8Initially, 1 g of cookies was extracted in ethanol (25 ml) in a shaker for 24 h and then centrifuged for 15 min at 10,000 × g. The mixture was filtered by using filter paper (Whatman #41), and the transparent supernatant was separated. The volume of the collected supernatant was readjusted to 25 ml and kept at 4°C for the estimation of phenolic and radical scavenging activity. The cookie extract was estimated for total phenolic content following the method described by Wu et al.  with min2.9The panel was composed of 20 semitrained panelists of the male students and staff of the Department of Food Science and Nutrition, King Saud University. The cookie samples were coded using a three‐digit number and randomly presented to the panelists. The sensory attributes  were assessed using a 9‐point hedonic scale   and Duncan’s Multiple Range (DMR) test at sig ≤0.05 was used to compare means using SPSS .33.1p ≤ .05) differences in the pasting properties and texture of wheat flour (WF), sorghum flour (SF), millet flour (MF), and composite flour of different levels of sorghum and millet flours (Table p ≤ .05). Increasing millet flour in the blend resulted in a reduction of peak and final viscosities, which are likely due to the differences in the nature of starch between millet and sorghum flours and diluting effects of millet starch on the sorghum starch in the blend. Similarly, Hussain et al. (p ≤ .05). It is because of the presence of gelatin in wheat flour and its absence in sorghum and millet flours. The highest setback viscosity value was observed in composite flour of SF25%–MF75%, and the least was seen in SF50%–MF50% composite flour (p ≤ .05), indicating that the inclusion of MF in the composite flour increases the setback viscosity because of amylose retrogradation tendency in this flour mixture . The highest values of hardness were found in control (100% WF), and the last ones were seen in MF100% and composite flour with a high level of MF (SF25%–MF75%), suggesting the negative influence of MF on the hardness of the dough. However, it increased the cohesiveness when composted with sorghum at the aforementioned ratio (SF25%–MF75%). The chewiness value was the highest in control (100% WF), followed by sorghum flour (SF100%), and the least in the composite of SF50%–MF50%. The reduction of hardness following the addition of MF in this study could be attributed to the low starch, amylose, and amylopectin content of millet flour, compared to wheat and sorghum flour. The gel hardness is referred to as the retrogradation of the starch in the flour  in the physical, textural, functional, and color properties of cookies prepared using WF, SF, MF, and composite flour of different levels of sorghum and millet flours were observed (Table p ≤ .05), these results’ findings suggesting that the incorporation of high level of millet in the composite flour reduces the hardness of the product. The fracturability was low in cookies made using MF100%. It was high in other samples, whereas it was insignificant among them. Water activity was high in cookies made using composite flour (SF25%–MF75% and SF50%–MF50%) and low in cookies made using SF100% and composite flour of SF75%–MF25% (p ≤ .05). The concomitant reduction of the hardness and increment of the fracturability following the incorporation of MF in the cookies that developed in this work are likely due to the dilution of gluten and the existence of higher fiber in the blends with higher levels of MF. In agreement with our findings, numerous reports have shown that the addition of different gluten‐free flours to cookie formulations concomitantly increased the fracturability and reduced the hardness of the cookies  and least values in cookies made using WF100% Table . The sprp ≤ .05). The values of these functional properties were insignificantly high in cookies made using high levels of SF (SF75%–MF25%) in the formulations suggesting the influence of SF on these properties. High WHC and OHC in cookies made using SF or MF and their combination could be attributed to the use of whole flour of sorghum and millet, which contain the bran that is rich in fiber and protein, and which are known to bind more water and oil in their structure  and oil‐holding capacity (OHC) were low in the cookies made using control flour (WF100%), and they were high in cookies made using SF and MF flour separately or in combinations (p ≤ .05) differences (Table p ≤ .05). The highest value of redness (a*) was found in the cookies made from sorghum (SF100%) and millet (MF100%) flours, whereas the least values were found in cookies made from the composite flour of SF75%–MF25% (p ≤ .05). The yellowness (b*) values were high in cookies made using wheat flour, followed by those of sorghum and millet flours, while the least values were seen in cookies made using SF75%–MF25% composite flour (p ≤ .05). The reduction of lightness and yellowness and increase in the redness of cookies made from SF and MF and their combinations are likely due to the whole flours of MF and SF used, which contain the constituents of the bran with darker colors such as fibers, phenolics, and flavonoids . The ash content was high in cookies made using wheat flour (WF100%), and the lowest values were seen in those cookies made using MF100% and composite flours (SF75%–MF25% and SF25%–MF75%). The carbohydrate content was high in cookies of composite flour (SF75%–MF25%), and it was low in those made using (SF25%–MF75%). The variations in the chemical composition of cookies made with different flours could be attributed to the differences in the chemical composition of the raw materials. Similarly, previous studies showed differences in the chemical composition of the cookies made using different flours  enhanced these properties (Table p ≤ .05). This could be attributed to the richness of sorghum and millet with bioactive compounds as well as compound forms during Millard reaction with such melanoidins that possess antioxidant activity. In agreement with our findings, several studies indicated that the incorporation of gluten‐free flours in cookies formulations significantly enhanced the total phenolic contents and the antioxidant activity of the cookies  of cookies indicate that the incorporation of sorghum and millet in the formulations significantly (es Table . The higp ≤ .05) values of potassium, sodium, magnesium, and calcium were found in cookies made using the composite flour of SF75%–MF25% and the least values of these elements were seen in cookies made using wheat flour (WF100%). The iron content was the highest in cookies made using wheat flour (WF100%), and the least value was found in cookies made using sorghum flour (SF100%) (p ≤ .05). The cookies made using SF100% showed the highest values of phosphorus, whereas those made using MF100% showed the least values (p ≤ .05). High levels of minerals in cookies made using composite flour of sorghum and millet are attributed to the use of whole flours of these grains, which contain the bran that is known for its richness of nutritional elements. A previous study has shown that magnesium, phosphorus, and potassium were higher in cookies made using sorghum flour than those made using wheat flour (Rao et al., Mineral analysis also showed significant variations among cookie samples Table . The hig3.4The sensory attributes of cookies made using WF, SF, MF, and composite flour of different levels of sorghum and millet flours are depicted in Figure 4This study concludes that cookies can be developed using millet and sorghum flour blends with acceptable physical and nutritional quality. The incorporation of sorghum and millet flour in cookies formulation improved the physicochemical, nutritional, and bioactive properties of the cookies without a major effect on the sensory quality. The developed gluten‐free cookies can be good foods for people suffering from gluten intolerance and those with low income. Based on this study, it is highly recommended that further research should be done to explore gluten‐free cookies from sorghum and millet in nutritional properties and clinical studies are recommended.No potential conflict of interest was reported on behalf of all authors.


Pseudomonas aeruginosa virulence factor CbpD both supports and challenges the current model of how lytic polysaccharide monooxygenases bind chitin and raises interesting possibilities about how type 2 secretion-system substrates may interact with the secretion machinery. This structure also demonstrates the utility of new, AI-powered, protein structure-prediction algorithms in making challenging structural targets tractable.The 3 Å resolution crystal structure of the  Pseudomonas aeruginosa secretes diverse proteins via its type 2 secretion system, including a 39 kDa chitin-binding protein, CbpD. CbpD has recently been shown to be a lytic polysaccharide monooxygenase active on chitin and to contribute substantially to virulence. To date, no structure of this virulence factor has been reported. Its first two domains are homologous to those found in the crystal structure of Vibrio cholerae GbpA, while the third domain is homologous to the NMR structure of the CBM73 domain of Cellvibrio japonicusCjLPMO10A. Here, the 3.0 Å resolution crystal structure of CbpD solved by molecular replacement is reported, which required ab initio models of each CbpD domain generated by the artificial intelligence deep-learning structure-prediction algorithm RoseTTAFold. The structure of CbpD confirms some previously reported substrate-specificity motifs among LPMOAA10s, while challenging the predictive power of others. Additionally, the structure of CbpD shows that post-translational modifications occur on the chitin-binding surface. Moreover, the structure raises interesting possibilities about how type 2 secretion-system substrates may interact with the secretion machinery and demonstrates the utility of new artificial intelligence protein structure-prediction algorithms in making challenging structural targets tractable. Protein expression was induced with 1 mM isopropyl β-d-1-thiogalactopyranoside (IPTG) and proceeded overnight at 30°C. Cells were collected using low-speed centrifugation (6000g) and the pellet was resuspended in buffer A  and frozen at −80°C. After 2 h, the cells were thawed in a 37°C water bath for 20–30 min to induce lysis. DNAse, MgSO4 and lysozyme were added to final concentrations of 20 µg ml−1, 20 mM and 0.5 µg ml−1, respectively. The mixture was stirred at 4°C for 1 h to complete lysis. The clarified lysate obtained by centrifugation at 20 000g was loaded onto a 5 ml FFcrude prepacked nickel column  pre-equilibrated with buffer A. The column was washed with 10% buffer B , and CbpD was eluted using a gradient from 10 to 50% buffer B over 20 min. The elution fraction containing CbpD was concentrated to 5 ml using a 10K Amicon concentrator and loaded onto a Superdex S200 26/60 column equilibrated with 20 mM Tris, 100 mM NaCl pH 8. Fractions were analyzed by SDS–PAGE, which showed high purity of the purified protein (>95%), implying that no residual CbpD uncleaved by signal peptidase remained . Fractions containing CbpD were pooled, resulting in a yield of 2 mg total purified protein per litre of culture. For crystallization trials, the protein was concentrated to 8 mg ml−1.CbpD from M diammonium tartrate in 20%(w/v) PEG 3350 (condition H2 of the Qiagen PEGs Suite sparse-matrix screen). A crystal was harvested, mounted and flash-cooled in liquid nitrogen without additional cryoprotection.Crystals were grown by the hanging-drop method against a reservoir solution consisting of 0.2 m2.2.XSCALE , which was held fixed in a subsequent round of MR using the RoseTTAFold GbpA2 output as the search model. This generated a successful MR solution  containing the AA10 and GbpA2 domains of CbpD. An ensemble of CBM73 domains, including models generated by RoseTTAFold, Raptor-X  by setting the B factor for all atoms to 100 Å2, setting the Gaussian resolution to 8.0, generating a Gaussian map for each CBM73 domain  and finally generating an isosurface for each CBM73 domain .Due to the lack of traceable electron density for the CBM73 domain, we evaluated the possible packing of this domain in the CbpD crystal lattice using the ChimeraX  into a density map using the molmap command. CbpD models were then manually placed into the density map and their positions were refined in the density map using the ‘Fit in Map’ tool in ChimeraX.To assess the fit of structural models to experimental small-angle scattering envelopes, a 15 Å volume map was generated in PyMOL. Structure-based topology was depicted through the use of Pro-origami . Residues 297–374 (the remainder of the linker and the CBM73 domain) were not visible in the electron-density map. We compared the AI-predicted structure with the final CbpD structure and found an all-atom r.m.s.d. of 11.3 Å (2215 atoms). We then compared each domain and found all-atom r.m.s.d.s of 1.5 Å (1370 atoms) and 1.6 Å (733 atoms) for the AA10 and GbpA2 domains, respectively , compared with 8.9 Å (874 atoms) and 4.6 Å (569 atoms) for the refined CbpD domains and the representative domains in GbpA. In hindsight, it is noteworthy that a fern insecticidal LPMOAA10  has a very low r.m.s.d. of 1.5 Å over 1252 atoms when compared with the CbpD AA10 domain, and is the most structurally similar to CbpD of those LPMOAA10s for which structures are now available . This structure was not yet available when our X-ray data were collected, but would have proved useful in phase determination by molecular replacement. MR with PDB entry 6if7 and the RoseTTAFold GbpA2 domain generated a successful MR solution containing the AA10 and GbpA2 domains of CbpD that had better statistics  than MR with the RoseTTAFold domain predictions alone.Models generated from newly introduced protein structure-prediction AI algorithms enabled us to solve the structure of CbpD using molecular replacement, which had not been successful using the representative domain structures shown in Fig. 1ly Fig. 2c, compa3.1.1 and β2 . This L2 region forms the majority of the substrate-binding surface and displays a majority of the sequence variation in LPMOAA10s , one linking two helices in the L2 domain (Cys14 and Cys27) and one linking the L2 domain to the β-sandwich through β7 (Cys64 and Cys179).The AA10 domain contains a typical LPMOAA10 fold, with a central seven-stranded β-sandwich comprising two antiparallel β-sheets connected by loop regions and an L2 subdomain between β3.1.1.a). Although the active site did not appear to contain a coordinated metal, a copper could be modeled into the active site with the electron density contoured to 0.7σ . Missing or partially occupied active-site copper is common in LPMOAA10 structures . These include a phenylalanine . By interacting with Asn43 in the L2 loop, this serine secondly serves as one of the two amino acids in the ‘Gln–Thr pair’ previously identified as conserved among chitin-binding LPMOAA10s . In other words, the ‘Gln–Thr pair’ in CbpD becomes an Asn–Ser pair. It provides a polar staple from the L2 subdomain to the β-sandwich domain. In cellulose-active AA10s this pair is replaced by two large, hydrophobic residues, generally Phe–Trp  that appears to gate a cosubstrate tunnel from the bulk solvent to the copper active site when an LPMOAA10 is bound to crystalline chitin  could access the active site. All LPMOs have the Glu (sometimes Gln) which is required for enzymatic activity , a feature that is also present in CjLPMO10A . The maintenance of the polar interaction between the side chains and the proximity of Glu174 to the active site imply that Glu174/Asn46 may also gate the access of cosubstrates or electron donors to the active site when CbpD is bound to crystalline chitin. We also note that this amino-acid pair also serves to stabilize the L2/ β-sandwich interface. In these three chitin-recognition motifs, side-chain properties are conserved.In addition to the histidine brace and the characteristic LPMOAA10 residues described above, the CbpD active site contains three highly conserved features of chitin-specific LPMOAA10s. Firstly, in CbpD the reportedly invariant TAet al., 2015c). This pattern of three aromatic residues has been identified in the strictly cellulose-active ScLPMO10C . In chitin-active CbpD, however, this position is Arg169 . It has been postulated that the short aliphatic residue in this position creates a cavity on the substrate-binding surface of chitin-active LPMOAA10s that could accommodate a dioxygen cosubstrate for catalysis . This is also the case for chitin-active CjLPMO10A . The Arg here as opposed to Ala or another short aliphatic residue implies that a surface cavity may not be required for catalytic activity or substrate discrimination.CbpD, however, deviates from or lacks two features previously postulated to be conserved among chitin-active LPMOAA10s. CbpD lacks the cluster of three tryptophans previously identified as being conserved among bacterial chitin-active LPMOAA10s . While no studies have directly examined the role that residues on this surface play in mediating chitin binding by CbpD, the structure of CbpD enables the mapping of homologous chitin-interacting residues from other characterized LPMOAA10s to CbpD. Residues that interact with chitin in chitin-specific LPMOAA10s have been identified through mutagenesis . These residues form a surface that extends from the conserved single aromatic residue (Tyr40) found on the substrate-binding surface of chitin-active LPMOAA10s . The cellulose-specific LPMOAA10s examined had a motif containing more than 70% hydrophobic residues. In CbpD, this L2 motif is YDWNGVN, beginning with the conserved Tyr40. This sequence differs from the consensus chitin-specific sequence, and the side-chain properties fall between the properties of the chitin- and cellulose-specific motifs . This motif in CbpD still forms a flat surface that is an appropriate area for substrate binding , and the side-chain characteristics of position 2 in the consensus sequence are conserved. This residue may be particularly important for chitin recognition and enzymatic activity because mutating this residue significantly reduces chitin binding . The divergence of most L2 motif residues in CbpD from previous trends raises questions about the extent to which the entire motif in LPMOAA10s determines substrate specificity. Interestingly, the chitin-active insecticidal putative LPMOAA10 produced by the fern Tectaria macrodonta (Tma12) has the sequence YEWNEVN that closely matches the outlier CbpD sequence here, providing further evidence that the Y(W)EPQSVE motif is not the determinant of chitin specificity.Interestingly, as for the sequence motifs examined above, mapping the features of chitin-specific LPMOAA10 substrate surfaces to CbpD revealed deviations in CbpD from previously identified trends. A motif on the L2 domain, beginning with the conserved substrate-binding Tyr and running down the center of the substrate-binding surface, purportedly exhibits differing polar characteristics in chitin- and cellulose-specific LPMOAA10s , with an unusual bend that allows β11 to pair with β14 while its immediate extension β12 interacts with the short β13 to form a hairpin above the helix . The orientation of the GbpA2 domain relative to the AA10 domain is shifted in the structure of CbpD by 1 Å and 135.6° compared with the structure of GbpA . This shift means the CbpD crystal structure is closer to the elongated conformation observed for both GbpA and CbpD by solution scattering . In contrast, GbpA adopts a U-shaped conformation in its crystal, forming an extensive interface in which each subunit contributes 3120 Å2 to the dimerization interface, as calculated with PISA .The second domain of CbpD is, as expected, similar to the GbpA2 domain structure in GbpA , leaving nine residues of the linker between the GbpA2 and CBM73 domains, as well as the CBM73 domain itself, unmodeled. Despite the absence of strong density to place the CBM73 domain, it is unlikely that the residues were missing from the protein crystal. Given that the His10 affinity tag was attached to the C-terminus of CbpD, it is clear that full-length CbpD was purified via metal-affinity chromatography . Crystal solvent-content analysis with phenix.xtriage , although in a more compact configuration than the fully elongated configuration deduced from SAXS data . These AI-generated models could be particularly beneficial for crystal targets that are difficult to express and purify in large quantities, produce few high-quality diffracting crystals or lack closely homologous structures that otherwise could serve as the basis for MR. The close agreement between AI-generated ab initio models and the crystal structures of each domain of CbpD, specifically the AA10 catalytic domain, also indicates that these AI results may provide high-quality structural models for the thousands of LPMOs that have been identified but lack experimental structures to develop more robust hypotheses about the characteristics of LPMOs that affect catalysis and substrate selectivity.The structure of CbpD reported here is the first structurally determined LPMO from XH and the substrate tunnel gating pair) that demonstrate conservation of the structural properties of chitin-active LPMOAA10s despite deviations in motif sequences . The similarity of Tyr40, Asp41, Asn43 and Ser101 in the Gln–Thr pair and TAXH motifs to the chitinolytic sequence identified by Jensen et al. . In the proximity of the active site, the presence of Arg169 and the absence of a pocket on the substrate surface is noteworthy because previously an Arg in this position was associated with only cellulose activity in LPMOAA10s , and all three are structurally similar to the cellulose-active LPMOAA10 ScLPMO10C based on a DALI server ‘all against all’ comparison . This raises the possibility that there are distinct yet convergent features of LPMOAA10s that have evolved to confer chitin-specific activity and indicates that these features may not all have been identified. Interestingly, CbpD may have a shallow pocket just to the side of the active site that may be able to accommodate an N-acetyl group , although it is between the putative channel gating resides (Glu174/Asn46) and the copper active site and may be part of the substrate tunnel when CbpD is bound to chitin. The structures of CbpD, Tma12 and CjLPMP10A now provide three examples of LPMOAA10s that are strictly active on chitin, yet lack the pocket previously thought to facilitate either substrate hydrolysis by accommodating the oxygen cosubstrate or substrate binding by accommodating the N-acetyl group present in chitin, and should inform future investigations into the deterministic features that confer substrate specificity in LPMOAA10s.Other features, however, deviate substantially from previously characterized LPMOAA10s Fig. 3c. In thup Fig. 4c, althoet al., 2015SmLPMO10C  catalytic mechanism based on Oet al., 2016et al., 2018P. aeruginosa EPQSVE on the substrate-binding surface of CbpD further challenges previously strong conclusions about the deterministic features of LPMOAA10s that confer substrate selectivity. It is unclear whether the L2 motif is more tolerant of sequence variation than first supposed . The presence of the same L2 motif sequence from CbpD in the structurally similar yet evolutionarily distant fern LPMOAA10 Tma12 demonstrates that the structural features of LPMOAA10s that enable chitin binding and degradation have yet to be fully understood and require further investigation among structurally diverse members of the AA10 family.Many previous structural studies  activation and may alter the copper redox potential (Askarian et al., 2021O-glycosylation (Tamburrini et al., 2021et al., 2019N-glycosylation at Asn158, structurally equivalent to Ser136 in CbpD, which has been identified as phosphorylated when CbpD is expressed and secreted by P. aeruginosa (Ouidir et al., 2014The presence of a few discrepancies in previously identified substrate-specificity motifs coupled with its role in virulence mean that CbpD should serve as a basis for more extensive modeling of the interaction of LPMOAA10s with chitin. LPMOAA10–chitin modeling to date has only been performed with single-domain LPMOAA10s (Bissaro et al., 2013et al., 2014et al., 2021P. aeruginosa T2SS (Cianciotto & White, 2017et al., 2011et al., 2018et al., 2017et al., 202115 edge strand of CbpD may serve as a handle for interaction with secretion-machinery proteins along the pathway through the cell envelope. The CbpD structure may thus provide an experimental entrée into a better understanding of how T2SS substrates are recognized.The β-strand interaction observed between the GbpA2 domains of crystallographic symmetry mates raises another question about the possible biological function of structural features of CbpD. Edge β-strands are well known to mediate protein–protein interactions, which in some cases are dynamic and interchangeable (Richardson & Richardson, 20025.In addition to demonstrating the utility of new structural biology tools, the structure of CbpD provides evidence to both support and challenge aspects of the current model of how LPMOAA10s bind their substrates, specifically those that are preferential for chitin. The absence of the CBM73 domain in the structure also raises questions about the inherent stability of smaller carbohydrate-binding domains, and a crystal structure of a CBM73 domain remains to be solved. One possibility is to perform high-resolution, single-particle electron microscopy of CbpD bound to chitin fibrils. This could provide the structure of full-length CbpD and shed light on the conformation of CbpD when bound to crystalline chitin. A better understanding of the structure, conformations and dynamics of multimodular LPMOs could lead to advances in understanding the role of these enzymes in biomass degradation and virulence.CbpD, 7sqxPDB reference: 10.1107/S2059798322007033/jc5051sup1.pdfSupplementary Figures. DOI:


Mechanistically, it is very difficult to establish if this π-π interaction proceeds before or after the most common mode of scavenging superoxide, e.g., abstraction of an aromatic polyphenol H(hydroxyl), which then is used to form H2O2 (the other molecule produced by SOD action). At the end of this cycle of superoxide scavenging, 4-methyl-7,8-di-hydroxy-coumarin and the flavonoid galangin reform themselves. An alternative mechanistic pathway by galangin forms the η-(H2O2)-galangin-η-O2 complex that includes additional H2O2 and O2 molecules. Another mode of action is seen with the chalcone butein, in which the polyphenol system incorporates a molecule of O2, e.g., a η-O2-butein complex is formed, ready for additional scavenging. Of the several families of polyphenols analyzed in this review, only butein was able to circumvent an initial π-π interaction, directing the superoxide towards H(hydroxyl) in position 4, e.g., acting as a typical polyphenol scavenger of superoxide. This fact did not impede an additional superoxide to later react with the aromatic ring in π-π fashion. It is concluded that by mimicking SOD enzyme action, the low concentration of polyphenols in biological fluids is not a limiting factor for effective scavenging of superoxide.Polyphenols are valuable natural antioxidants present in our diet that likely mitigate aging effects, neurodegenerative conditions, and other diseases. However, because of their poor absorption in the gut and consequent low concentration in biological fluids (µM range), reservations about polyphenol antioxidant efficiency have been raised. In this review, it is shown that after scavenging superoxide radicals, coumarin, chalcone, and flavonoid polyphenols can reform themselves, becoming ready for additional cycles of scavenging, similar to the catalytic cycle in superoxide dismutase (SOD) action. The π-π interaction between one polyphenol ring and superoxide is associated with oxidation of the latter due to transfer of its unpaired electron to a polyphenolic aromatic ring, and consequent formation of a molecule of O It is involved in oxygen consumption for ATP formation and storage in the mitochondria. About 0.2–2.0% of the molecular oxygen consumed by mitochondria is reduced to superoxide anions, which are converted to other ROS, such as H2O2 and hydroxyl radical  and the second proton regenerating galangin [involving compound (f)]; the products are H2O2 [involving compound (c)] plus O2 involving compound (e).As described earlier with 4-methyl-7,8-di-hydroxy coumarin, correct2O2 results from the superoxide release of its unpaired electron to the galangin ring [steps (d) → (e)], and it is still linked to the ring. 2O2 [steps (f)–(g)], plus a molecule of oxygen separated 3.106 Å, 2 and one of H2O2, π-π bound to a galangin pyrone ring, e.g., η-(H2O2)-galangin-η-O2, 2O2, 2  and 2O2 formation), whereas O2 remains trapped within the reformed butein  Equation (5). The involved molecules are seen in d butein F. We envIn this section, we study only the initial superoxide π-π interaction on other polyphenols. That is, this action precludes the most common superoxide σ attack on an H(hydroxyl). Most of the studied compounds described in this work have also been studied using the Rotating Ring Disk Electrode method. This was developed in our lab  and resuReaction 1: Reduction of molecular oxygen at disk electrodeReaction 2: Oxidation of superoxide radicals at the ring electrodeThus, the rate at which increasing concentrations of antioxidants scavenge the generated superoxide radicals during the electrolytic reaction is determined by obtaining the percent value of the ring current/disk current at each concentration. These percent values are denoted as the collection efficiency of each antioxidant at different concentrations. Collective efficiency values are plotted against the total concentrations of antioxidants to produce a graph illustrating the effect of increasing concentrations of antioxidants on the scavenging of superoxide radicals in the electrolytic solution. It is observed that for low concentration of antioxidant (μM range), there is a linear trend. Ultimately, the slope of the curves serves as a quantitative measure of the antioxidant activity of superoxide scavengers; the steeper the slope, the stronger the scavenger. 2 in the polyphenol system, e.g., a η-O2-butein complex is formed; galangin alternative mechanism (2O2 ligand and forms the η-(H2O2)-galangin-η-O2 complex.Polyphenols are valuable natural products present in our diet that may contribute to decrease aging effects and some diseases. However, because of their poor absorption in the gut and consequent low concentration in biological fluids, reservations about antioxidant polyphenol efficiency have been raised. In this study, we focus our attention on the potential reformation of polyphenols after scavenging superoxide, and we find that polyphenol mimicking SOD enzyme activity is feasible for a number of polyphenolic compounds. Thus, after scavenging superoxide, 4-methyl-7,8-di-hydroxy-coumarin and galangin  exactly echanism  includes2 release as part of the SOD activity here proposed. However, it is very difficult to establish if this π-π reactionWe demonstrate using DFT methods that the π-π interaction between superoxide and one polyphenol ring is associated with oxidation of the superoxide radical, due to transfer of its unpaired electron to an aromatic ring of the polyphenol. This is also responsible for O2 except for butein. Circumventing the π-π interaction resulted in butein behaving as a typical σ scavenger of superoxide, e.g., sequestering a H(hydroxyl). It is suggested that these polyphenols are able to mimic SOD enzyme action in the disproportionation reaction of O2•− [Equation (5)] and thus refute the claim that low polyphenol concentration in biological fluids acts as a limiting factor in their effective scavenging of superoxide.In addition, the initial direct π-π interaction between superoxide and one polyphenol aromatic ring was also studied for a variety of polyphenols. In this specific initial π-π interaction, all polyphenols studied are shown through DFT methods to have minimum energy radical complexes of the type polyphenol-η-O