Industrial applications of pulses in various food products depend on pulse flour techno-functionality. To manipulate the techno-functional properties of yellow pea flour, the effects of flour particle size , extrusion temperature profile  and air injection pressure  during extrusion cooking were investigated. Extrusion cooking caused the denaturation of proteins and gelatinization of starch in the flour, which induced changes in the techno-functionality of the extruded flour . In general, the large particle size flour required less energy input to be extruded and had higher emulsion stability and trough and final viscosities compared to the small particle size flour. Overall, among all of the treatments studied, extrudates produced with air injection at 140 and 160 °C had higher emulsion capacity and emulsion stability, making them relatively better suited food ingredients for emulsified foods . The results indicated air injection’s potential as a novel extrusion technique combined with modification of flour particle size distribution and extrusion processing conditions to effectively manipulate product techno-functionality and broaden the applications of pulse flours in the food industry. Techno-functional properties are the characteristics of a food ingredient that are important for its processing and technological applications. These properties are essential for determining the suitability of a food ingredient for specific food systems ,2,3,4. TExtrusion has shown great potential to fine-tune ingredient techno-functionality due to its capability for precise control of the changes in food polymers  as a function of temperature, pressure and shear during the process. For instance, it has been reported that extrusion cooking of pea and kidney bean flours  and yell2, CO2 and air) assisted extrusion cooking [Maintaining the renowned nutritional and environmental benefits of pulses warrants the investigation of novel technologies to further develop techno-functionally superior ingredients for use by the food industry. One such technology is blowing agent  was obtained from AGT Food and Ingredients . The AC Agassiz variety of peas used in this study was grown in Southern Manitoba during the crop year of 2017. Yellow pea  with mesh sizes of (i) 0.75 mm to obtain flour with relatively smaller particle sizes , and (ii) 2.0 mm to obtain flour with relatively larger particle sizes (referred to as "large" throughout this study) was used. The particle size distribution of the small and large flours was measured using a particle size analyzer . Particle size distribution of the raw flours was defined by d(0.1), d(0.5) and d(0.9), representing the size in microns below which 10%, 50% and 90% of the particles resided by volume, respectively. Moisture, protein, ash and lipid contents of flours were measured using AACC International standard methods 44-19.01, 46-30, 08-01 and 30-25, respectively . Total c−1 and 0.22 kg h−1, respectively. For air-assisted extrusion, an injector valve located 22.9 cm before the die exit was utilized for the injection of pressurized air  into the extruder barrel. A gas pressure regulator was used to set the air injection pressure at 150 or 300 kPa. Control extrudates were produced without air injection, i.e., 0 kPa. Torque and die pressure as extrusion process variables were recorded for each extrusion run. Specific mechanical energy (SME) during extrusion was calculated as described by Koksel and Masatcioglu (2018) [A laboratory-scale co-rotating twin-screw extruder  with a circular die (diameter: 2.3 mm) was used at a screw speed of 220 rpm. Three different temperature profiles were employed along the five different zones of the extruder barrel extending from the feeder towards the die: 60–75–90–105–120 °C, 80–95–110–125–140 °C, and 100–115–130–145–160 °C. These temperature profiles are referred to as die temperature (DT) 120, 140 and 160 °C, respectively. The flour and water flow rates were kept constant at 2.5 kg hExtrudates were collected in duplicate at each temperature profile, dried in an air oven  at 50 °C overnight and stored in zipped plastic bags until further analyses. The moisture content of extrudates after overnight drying was WS) and water binding capacity (WBC), 0.5 g raw or extruded flour was added to distilled water (10% w/v) and vortexed  at room temperature for 15 s, every 5 min, for 1 h. Then, the mixture was centrifuged  at 5000g for 10 min, and the supernatant was dried in an oven  at 100 °C. WS and WBC of the raw and extrudate flours were calculated using the following equations [For measurements of water solubility (quations :(1)WS %=EC) and emulsion stability (ES) of the raw or extruded flours were measured in triplicate using the method of Ahmedna, Prinyawiwatkul and Rao (1999) [g for 20 min. To obtain ES, each homogenized mixture was incubated in a water bath at 45 °C for 30 min, cooled down to 25 °C for 20 min and then centrifuged at conditions similar to those of the EC test. A digital caliper (accuracy 0.01 mm) was used to measure the height of the emulsified phase and the height of the whole mixture, the ratio of which was used to define EC and ES. Emulsion capacity (o (1999)  with som−1, hold at 95 °C for 3 min, cool down to 25 °C, hold at 25 °C for 5 min.A Rapid Visco Analyzer  was used to measure the pasting properties of the raw and extruded flours following the method of Li, Masatcioglu and Koksel (2019) . Extrude−1 from 25 to 125 °C. Onset temperature (To), peak temperature (Tp) and enthalpy (ΔH) were calculated using the TA Universal Analysis Software . Results were reported as means of triplicate analyses.Thermal properties of a select set of raw and extruded flours were analyzed using a differential scanning calorimeter . Briefly, ~10 mg of raw (or extruded) flour was weighed into an aluminum DSC pan, followed by the addition of deionized water (40 µL). The pan was hermetically sealed and stored for 48 h at 4 °C for equilibration. The equilibrated pan was placed in a standard DSC cell with an empty pan as a reference and heated at a rate of 5 °C minp Techno-functional and thermal properties were evaluated by one-way analysis of variance (ANOVA) and Tukey pairwise comparison test using Minitab Statistical Software  at a 95% confidence interval (p < 0.05) for the large particle size flour. The extent of the difference in d(0.1) and d(0.5) was not as dramatic for the two flours. As the average size of a flour particle increases, its total surface area to volume ratio decreases. Consequently, water penetration and diffusion to the center of the particles was expected to be slower for flour with relatively larger size particles. The particle size distribution of the raw flours is shown in p < 0.05) protein and ash content, and also lower total carbohydrates (by difference) content compared to the small particle size flour. These findings suggest that milling and grinding operations may impact the proximate composition of the flour, likely through their effects on flour yield. A recent study on roller milling of yellow peas and red lentils revealed that the mean flour particle size significantly affected the milling yield and the proximate composition of the flours produced [The small particle size flour contained 212.2 ± 2.4 g protein, 26.4 ± 0.0 g ash, 16.3 ± 0.1 g lipid and 745.1 ± 2.4 g total carbohydrates per kg of dry flour, while the large particle size flour contained 222.7 ± 2.6 g protein, 27.0 ± 0.1 ash, 16.3 ± 0.1 g lipid and 734.1 ± 2.5 g total carbohydrates per kg of dry flour. Based on these results, small and large particle size flours had similar lipid contents, but the large particle size flour had higher . Overall, air injection increased the die pressure but decreased the torque and SME values compared to extrusion without air injection, suggesting that air injection acted as a plasticizer due to its possible partial dissolution into the melt inside the barrel under pressure. Similarly, decreases in torque and SME values with gas injection were reported by Sinaki et al. (2021) [The effects of extruding yellow pea flour at different particle sizes, die temperatures and air injection pressures on torque, die pressure and specific mechanical energy (SME) values are presented in . (2021)  for air WS and WBC of extrudates as a function of flour particle size, die temperature and air injection pressure are shown in WS and WBC of raw yellow pea flour at both flour particle sizes  were significantly lower than those of their respective extrudates, showing that extrusion cooking improved WS and WBC. These findings are consistent with reports in the literature that extrusion enhances flour’s WS and WBC, as also reported for pea and kidney bean flours [n flours , yellow n flours , and corn flours . WS values compared to those produced with the large particle size flour. Although statistically insignificant, the increase in WS with decreasing particle size can be attributed to an increase in the ratio of the surface area of particles to unit volume [WS values for extrudates produced at 160 °C were at least as high as those that were produced at 120 °C . Increasing the die temperature increases starch gelatinization and/or melting and, therefore, WS values [WS values was observed, indicating the complexity of the changes in partial gas dissolution as a function of die temperature and how flour particle size affects the pressure build-up inside the barrel and at the die  affected by the flour particle size. The only exceptions to this trend were the extrudates produced at 120 °C and air injection at 300 kPa and the extrudates produced at 160 °C and air injection at 150 and 300 kPa, where the extrudates made from the smaller particle size flour had significantly (p ≤ 0.05) higher WBC compared to those made from large particle size flour. The fact that the WBC of extrudates made from small particle size flour improved only in the presence of the blowing agent indicated a mechanism that involves air, specifically oxygen due to its relatively higher solubility in water when compared to nitrogen [WBC of the extrudates produced with air injection. The increased starch gelatinization levels in extrudates produced by air injection were confirmed by thermal analysis. In The nitrogen . Oxygen nitrogen ,43,44,45nitrogen ,47, resuEC and ES values of extrudates as a function of flour particle size, die temperature and air injection pressure are presented in EC and ES values of raw yellow pea flour at both flour particle sizes were significantly higher than those of their respective extrudates, showing that extrusion cooking lowered EC and ES. These findings are consistent with reports in the literature that extrusion decreases EC and ES of the starting material, as was also reported for soybean meal [EC and ES can be due to a reduction in protein solubility after extrusion cooking, as protein solubility is positively correlated with emulsifying properties of proteins [ES decreased  higher EC compared to those made from the large particle size flour. When the effects of die temperature were considered, in general, extrudates produced at 160 °C showed higher EC values compared to those produced at 120 °C . Similarly, Martínez et al. (2014) [p ≤ 0.05) higher EC compared to those at 140 °C and 160 °C. The higher EC at a higher temperature may be attributed to the aggregation of protein molecules at a higher level due to enhanced protein–protein and protein–oil interactions at higher temperatures [EC of these extrudates, as gelatinized starch can act as a protective barrier between oil droplets in an emulsion [EC of extrudates was not significant (p ≤ 0.05) for most of the extrudates except for those produced with the small particle size flour at 120 °C, for which air injection significantly (p ≤ 0.05) decreased EC. It is possible that air injection at 120 °C reduced protein solubility, leading to a decrease in EC. However, a comprehensive investigation of the protein structural modifications is outside the scope of this study and can be explored further in a future study. The t 120 °C . In line. (2014)  showed teratures ,54. Addiemulsion ,55,56. TES values of the extruded large particle size flour produced at 120 and 140 °C were significantly (p ≤ 0.05) higher compared to those of the extrudates produced with small particle size at 120 and 140 °C, meaning that flour particle size was a significant factor affecting ES. In line with this finding, a positive correlation was observed between ES and flour particle size . When the effects of die temperature were considered, in general, extrudates produced at 160 °C showed higher ES values compared to those produced at 120 °C . The increasing temperature may result in changes in protein structure that produce relatively more flexible proteins  that can be more easily and rapidly adsorbed at the oil interface, resulting in higher ES values [ES values was air injection pressure- and flour particle size-dependent. Air injection at 300 kPa significantly (p ≤ 0.05) increased ES values of extrudates produced from the large particle size flour, while air injection could not significantly (p ≤ 0.05) affect ES values of extrudates produced from the small particle size flour. It is possible that with air injection, a higher dissolved oxygen concentration in the melt may have caused protein aggregation and exposure of originally buried hydrophobic sites of proteins, causing an increase in ES. Our results suggest that manipulation of extrusion conditions, especially air injection during extrusion cooking of the large particle size yellow pea flour, can be used to produce ingredients with better ES, and, therefore, better fit for applications including meat extenders for sausages, burger patties, frankfurters, etc.Based on t 120 °C . CorrelaS values ,50. The The pasting properties of extrudates as a function of flour particle size, die temperature and air injection pressure are shown in Extrusion cooking caused significant changes in the pasting properties of the flours. Extrusion resulted in an increase in the cold viscosity due to the starch gelatinization/melting that occurs during extrusion cooking . The extp ≤ 0.05). Similar results have been reported by Wang, Nosworthy, et al. (2019) [In general, extrudates produced at 140 and 160 °C showed higher cold, trough and final viscosities compared to those produced at 120 °C. In line with this, a moderate and significant correlation between the die temperature and the cold viscosity of the extruded flours was found  , who shoIt was clear that the interactive effects of flour particle size and air injection pressure were complex. For example, air-injected extrudates produced with small particle size flour at lower die temperatures of 120 and 140 °C in general had higher cold, trough and final viscosities compared to those produced at the highest die temperature of 160 °C. However, for the air-injected extrudates with large particle sizes, the trend was different. These extrudates had lower cold and trough and higher final viscosity values when compared to those produced with no air injection. The reason for differences in RVA viscosities at different air injection pressures and die temperatures in large and small particle-size flours is attributed to the presence of different levels of degraded starch in their counterpart extruded flours. A higher level of starch degradation might occur as a result of air injection into the extruder barrel, as confirmed by thermal analysis , which iThe utilization of pulses in the food industry can be broadened by improving their techno-functional properties. However, several factors, such as pulse flour particle size, lead to variability in the techno-functionality of different pulses in various food applications. To better understand the complexity that particle size variation brings to yellow pea flour techno-functionality, this study focused on its relationship to temperature profile and air injection pressure during extrusion cooking. An increase in raw flour particle size generally caused a decrease in torque, die pressure and SME during extrusion, meaning that larger particle-size flour required less mechanical energy input to be processed. Overall, water solubility was the highest for the extrudates produced at the highest die temperature and extrudates made from small particle flour, making them more suitable for products such as sports drinks and protein shakes. In general, extrudates produced with air injection and at the higher extrusion temperature had higher emulsion capacity and stability, making them more suitable for extending meat products such as burger patties and sausages. The impact of air injection was dependent on the feed particle size, extrusion temperature and injection pressure. Overall, the injection of air at 300 kPa had a higher impact on techno-functional properties compared to that at 150 kPa or no air injection. The results of this study indicate that a novel extrusion technique  combined with conventional modifications such as feed characteristics  and processing conditions  can be used to effectively manipulate techno-functional properties of extruded pulse flours. Future work will focus on changes in protein structure with extrusion and how these changes affect ingredient techno-functionality.


Motivated by a recent model for elasto-plastic evolutions that are driven by the flow of dislocations, this work develops a theory of space-time integral currents with bounded variation in time, which enables a natural variational approach to the analysis of rate-independent geometric evolutions. Based on this, we further introduce the notion of Lipschitz deformation distance between integral currents, which arises physically as a (simplified) dissipation distance. Several results are obtained: A Helly-type compactness theorem, a deformation theorem, an isoperimetric inequality, and the equivalence of the convergence in deformation distance with the classical notion of weak* (or flat) convergence. Finally, we prove that the Lipschitz deformation distance agrees with the  homogeneous Whitney flat metric for boundaryless currents. Physically, this means that two seemingly different ways to measure the dissipation actually coincide. The recent work  introducb  along the dislocation movement. Also, The 2-current in space given asently in .b) move from time s to time t, is derived in )≤C by  and so tH was assumed to possess T into In this section we consider progressive-in-time deformations of (boundaryless) integral currents. To see how one could generalize currents deformed via Then, by  we have\S itself.Unfortunately, the class of We first consider the case of Lipschitz homotopies in more detail. The following lemma also provides a further motivation for our definitions , 3.2) o o3.2) ofLet k-rectifiable carrier set R such that k-dimensional Jacobian of t, x) with respect to Z. It follows, see, e.g., [k-covector field k-covector field Let kby  and the sumption  now implThen, by , there eis shows ; the bouis shows  follows R being the carrier set of T), instead of  t t\documeo obtain   Let P, S satisfy the conclusion of the theorem for our P, T and S(t)  also follows from the estimates of Lemmas We have As a corollary, we obtain the following version of the isoperimetric inequality: Let P, S as in the deformation theorem withThe proof is similar to the one for the classical isoperimetric inequality and follows immediately from the deformation theorem: Assuming that k-currents via progressive-in-time deformations, namely Lip-integral currents. In all of the following, We now define a metric measuring the distance between two boundaryless integral (Lipschitz) deformation distance between k-currents.For With regard to the notion of convergence induced by the (Lipschitz) deformation distance, we have the following result:j from a subsequence of the j’s, there are  For every N depend only on the dimensions and the domain P that can possibly satisfy the conclusion of the deformation theorem for a For the first direction, assume T)→0. By  we have its, cf. . Thus,\dN sufficiently large , we may find a diagonal subsequence, still denoted by m bounded by Returning to our sequence T. Hence, also We now pass to the limit ur claim .Finally, taking the upper limit of the mass estimate,integral homogeneous Whitney flat norm in the bounded Lipschitz domain Finally, we investigate the relationship of the deformation distance to the (i)(ii)(iii)(iv)For every The equivalence of (i) and (ii) was proved in Theorem global Whitney flat norms For the We can now prove the main result of this section, namely that the integral homogeneous Whitney flat distance is equal to the deformation distance. For S yields the above inequality.First, the inequalityQ) as in . Taking k-face For the other inequality, let Q exists, the result holds trivially since in this case Q.Next, take any Q. According to this result, there is a polyhedral chain We now apply the approximation result of Proposition k-polytope V as required.We claim that there exists For Q with d-current in global boundary operator). Hence, by the constancy theorem (see, e.g., [R is a fixed multiple of Q with globally finite mass, which we denote as Note that in codimension 1, i.e.,


Domain adaptation methods are proposed to improve the performance of object detection in new domains without additional annotation costs. Recently, domain adaptation methods based on adversarial learning to align source and target domain image distributions are effective. However, for object detection tasks, image-level alignment enforces the alignment of non-transferable background regions, which affects the performance of important target regions. Therefore, how to balance the alignment of background and target remains a challenge. In addition, the current research with good effect is based on two-stage detectors, and there are relatively few studies on single-stage detectors. To address these issues, in this paper, we propose a selective domain adaptation framework for the spatial alignment of a single-stage detector. The framework can identify the background and target and pay different attention to them. On the premise that the single-stage detector does not generate region suggestions, it can achieve domain feature alignment and reduce the influence of the background, enabling transfer between different domains. We validate the effectiveness of our method for weather discrepancy, camera angles, synthetic to real-world, and real images to artistic images. Extensive experiments on four representative adaptation tasks show that the method effectively improves the performance of single-stage object detectors in different domains while maintaining good scalability. Object detection is a fundamental and widely used topic in the field of computer vision. In the past few years, deep neural networks (DNNs) have achieved satisfactory results ,3,4,5,6 An obvious solution is to retrain with data collected in a new environment. Unfortunately, real-world environments usually do not have corresponding annotated images, and finely annotating each object instance in each image is usually expensive and time-consuming. Unsupervised domain adaptation (UDA) , assuminIn recent years, domain adaptation has achieved remarkable success in many fields, and mainstream methods adopt the idea of generative adversarial to align the feature distributions of source and target domains through adversarial learning ,10,11. IIn this work, we address the problem of adaptively aligning background and instance features for single-stage detectors., and propose a new method suitable for single-stage object detection. The goal of this paper is to build an end-to-end single-stage domain-adaptive deep learning model based on YOLOv5, the key idea of which is to apply different attention to the background and target features when aligning them, setting different domain confidences for background and target, so that the network pays more attention to the target area and reduces the interference of the background, as shown in We propose a domain adaptation framework suitable for single-stage detectors, which combines image-level alignment and instance-level alignment. Balances the domain offset by applying different attention to the background and target through spatial domain alignment (SDA) and spatial consistency regularization (SCR);We improve a new target loss in the target domain loss calculation, reducing the negative impact of false-negative samples by suppressing the loss of false-negative samples.We validate the effectiveness of our method from four different perspectives . Experimental results show that this method can effectively improve the cross-domain detection performance of single-stage object detectors. Additionally, almost no increase in inference time and real-time detection can be achieved on GPU.The contributions of this work mainly include the following three aspects:Object detection—The task of object detection is to find out the categories and positions of all objects of interest in an image, which is one of the core problems in the field of computer vision. Object detection algorithms based on deep learning are mainly divided into two categories: two-stage detectors and single-stage detectors. The two-stage detector first performs region generation to obtain region proposals and then performs sample classification in the second stage through a convolutional neural network, such as R-CNN , Fast R-Unsupervised domain adaptation–Unsupervised domain adaptation ,27,28,29Domain adaptation in object detection—Domain adaptation has achieved satisfactory results in the fields of image classification and semantic segmentation. However, domain adaptation research in object detection is still in its early stages. Following the widespread success of adversarial learning-based methods in other domains, many adversarial-based domain adaptation frameworks have emerged in object detection. DA Faster R-CNN  is the fIn this section, we detail the technical details of the proposed method, adopting YOLOv5 as the baseline.Our research involves scenes from two domains, a source domain with images and annotations  and a target domain with only images. The final task is to use data from both domains to train a detector that performs well on the target domain. To this end, we propose a framework for selective domain adaptation based on spatial alignment, using YOLOv5 as the baseline. The overall architecture of the proposed method is shown in detL is the detection loss and dL and cstL are the domain identification loss and the domain consistency regularization loss, respectively. The network can be trained end-to-end. In addition, the antagonistic training of the domain adaptation component is realized by using a gradient reversal layer (GRL), which automatically reverses the gradient in the process of propagation. During inference, only the detection branch prediction results need to be used.Our framework does not rely on RPN to extract regions of interest, thus it can be directly applied to single-stage detectors to better align background and targets across domains and improve adaptive detection performance. Additionally, our framework is flexible and generalizable and does not depend on specific algorithms for image or instance alignment. The training loss of the network is the sum of each part, which can be written as:We detail the technical details of the proposed method below.We utilize the improved domain classifier for feature alignment of the source and target domains. Generally speaking, the domain classifier judges the input as a whole from the source domain or the target domain and then conducts domain obfuscation through a gradient reversal layer (GRL) to achieve feature alignment. As mentioned earlier, it is difficult and unnecessary to achieve alignment of background and target of interest using the same settings. Aligning the object of interest in the feature space is more important than aligning the background. To overcome this problem, the spatial domain alignment module is designed to align the background and instances of an image. Specifically, we introduce a domain classifier after the YOLOv5 backbone network, through the gradient reversal layer, the gradient direction is automatically reversed in the backpropagation process, and the identity transformation is realized in the forward propagation process, and then a series of volumes are performed. The product and downsampling finally obtain the feature output of the m × m dimension. Each value represents the domain confidence for that location. In addition, a residual attention module is added to the domain classifier to better extract spatially effective information.s, xt are images from source domain S and target domain T, respectively, and ys, yt are source domain label data and model-predicted pseudo-label data, respectively. According to , , the position of the target in the picture can be calculated. Then we divide the image into m × m grids, and assign attention to the corresponding positions according to the area where the target falls into the grid. We use the IOU as a measure, if the IOU is greater than 1/3, then the grid prediction is considered as foreground, otherwise, it is background. Different values for background and foreground are set as ground truth. Domain-consistent features are better learned by optimizing the domain classification loss to confuse the source and target domain features. We let i,j,kD denote the domain label of the i-th training image at the j-th row and the k-th column, 0 for the source domain target, 0.3 for the source domain background, 1 for the target domain target, and 0.7 for the target domain background. By denoting the output of the domain classifier as i,j,kp and using the cross-entropy loss, the spatial domain identification loss can be written as:For each image, we wish to identify important regions of the object of interest and reassign the region representations. Single-stage detectors do not have region proposals from RPN, and a natural idea is to use the final predictions. We assume that xs  and the target domain sample distribution is Pt , where I is the image representation, B is the bounding box of an object, and C represents the class of the object. Because of the domain offset, Ps ≠ Pt. We then use i,j,kP to represent the grid feature distribution of the i-th image row, the j-th row, and the k-th column of the image. Di represents the feature distribution of the i-th image. Since it comes from the same image, there should be i,j,kP = iD. Therefore, we can alleviate the prediction bias of different regions of the same image by strengthening the spatial domain classification consistency. The spatial consistency regularization (SCR) can be written as:We assume that the source domain sample distribution is PN represents the total number of image division areas, and ||.||2 represents the L2 regularization.Among them, objL is defined as:In the target domain, to enable the model to learn the features of the target domain, guided learning with high model prediction confidence is used. In our attempts, directly applying the detection loss to the target domain would result in a negative gain, which is since wrong negative samples would lead to a reduced model recall, we minimize the impact of false negatives by modifying the loss in the target domain, and further propose to stabilize the training of the model with false-negative suppression (FNS) loss. According to the definition in YOLO, the original loss is defined as:YOLOv5 judges whether there is a target in each prediction frame according to the label information and certain rules. If there is, the value of the corresponding position in the mask matrix is set to 1, otherwise, it is set to 0; obtaining the mask matrix of the image. The classification loss and bounding box loss are only calculated for the predicted box whose position in the mask matrix is 1. Additionally, all prediction boxes need to calculate the target loss, which will cause false-negative samples in the target domain to suppress the prediction that there is a target at that location. We can use the mask matrix to eliminate this effect and improve the target object loss as:In this section, we introduce the details of typical datasets and implementation of domain adaptation, from weather discrepancy, camera angles, synthesis to the real-world discrepancy, and real images to artistic images to validate our approach; and compare our results with other methods. Furthermore, the effectiveness of the proposed method is verified by a simple ablation experiment.Cityscapes —The CityFoggy Cityscapes —The FoggKITTI —KITTI isSIM10K —SIM10K iPASCAL VOC —The PASCClipart and Watercolor —Clipart In all experiments, we use YOLOv5-l as the baseline, keeping the default training settings and hyperparameters of YOLOv5. The input image size is adjusted to 640 × 640 by scaling and padding, and stochastic gradient descent (SGD) is used for training. The initial learning rate is 0.01, the weight decay is 0.0005, and the SGD momentum is 0.937. During the experiment, the data of two domains are loaded for training, and the source domain and target domain are scrambled before training. Pseudo-labels are updated for each iteration during training. In the initial 100 iterations α = 0, the target domain only computes the domain classification loss, after 100 iterations, α gradually increases. For all experiments, we evaluate the mean precision with a threshold of 0.5 for comparison with other methods.In this section, we evaluate the object detection of our proposed spatially adaptive model in four different domain transfer scenarios: weather discrepancy (Cityscapes → Foggy Cityscapes), camera angle (Cityscapes → KITTI), synthetic to real-world discrepancy (SIM10K → Cityscapes), and real to artistic (PASCAL VOC → Clipart and Watercolor).Setting—In this section we use the Cityscapes dataset for clear weather and the Foggy Cityscapes dataset for foggy weather to study the adaptation of weather discrepancy; taking Cityscapes containing images and annotations as the source domain, and Foggy Cityscapes containing only images as the target domain. The source domain and target domain data are scrambled when reading to participate in the training process. It is finally evaluated on the validation set of Foggy Cityscapes.Results—The comparison results are shown in Setting—In this section, we use Cityscapes as the source domain and KITTI as the target domain to verify the performance of our method in scenarios with different camera configurations. During training, the training sets of the KITTI dataset and Cityscapes dataset are used. It is finally evaluated on the validation set of Cityscapes. The final evaluation used the only common class between the two domains—car.Results—The results are shown in Setting—Since data acquisition can be difficult, the cost of sampling and labeling can be significantly reduced by generating synthetic data, thus this adaptation makes sense. During training, we use SIM10K as the source domain and Cityscapes as the target domain. It is finally evaluated on the validation set of Cityscapes. The evaluation of the results uses the only common class between the two domains—car.Results—The final results are shown in Setting—In this section, we will verify the effectiveness of adapting real images to artistic images. The source domain images are from Pascal VOC 2007 and 2012, and Clipart and Watercolor are used as the target domain, respectively. Finally, it is evaluated on the Clipart and Watercolor test set.Results—The performance comparison of PASCAL VOC → Clipark and PASCAL VOC → Watercolor is shown in In this section, to verify the effectiveness of different components in our method, thorough ablation experiments are performed. We design several variants of the model to verify the contributions of different components. The results are shown in We show an example of detection results in In this work, we investigate the alignment of background and object instances for unsupervised domain adaptation on a single-stage detector. Therefore, we propose a new UDA framework suitable for single-stage detectors with added attention to background and object instance settings for better feature alignment. Our key contribution is to address the problem of adaptively aligning background and instance features for single-stage detectors. We demonstrate the effectiveness of our approach from different perspectives, such as weather discrepancy, camera angles, synthetic data to the real-world images, and real images to artistic images. We conduct extensive experiments and ablation studies, which demonstrate that our method achieves competitive performance.


Neurodegenerative diseases are characterized by the progressive loss of neural structures instead of the selective neuronal loss caused by metabolic or toxic disorders. Alzheimer's, Parkinson's, Huntington's, and amyotrophic lateral sclerosis are among the several neurodegenerative diseases for which there is no treatment . New anThe authors declare no conflict of interest.


We present cryo-EM structures of CIII2 from Yarrowia lipolytica at resolutions up to 2.0 Å under different conditions, with different redox states of the cofactors of the high-potential chain. All possible permutations of three primary positions were observed, indicating that the two halves of the dimeric complex act independently. Addition of the substrate analogue decylubiquinone to CIII2 with a reduced high-potential chain increased the occupancy of the Qo site. The extent of Rieske domain interactions through hydrogen bonds to the cytochrome b and cytochrome c1 subunits varied depending on the redox state and substrate. In the absence of quinols, the reduced Rieske domain interacted more closely with cytochrome b and cytochrome c1 than in the oxidized state. Upon addition of the inhibitor antimycin A, the heterogeneity of the cd1-helix and ef-loop increased, which may be indicative of a long-range effect on the Rieske domain.Movement of the Rieske domain of the iron–sulfur protein is essential for intramolecular electron transfer within complex III Each protomer consists of three subunits that form the catalytic core of the complex, plus up to eight supernumerary subunits. CIII2 catalyses the transfer of electrons from the lipophilic two-electron donor ubiquinol (QH2) to the water-soluble, one-electron acceptor protein cytochrome c, contributing to an electrochemical proton gradient across the membrane. An ATP synthase then uses the gradient to generate ATP. Electron transfer within the complex occurs through the prosthetic groups of the three catalytic core subunits: heme bL and heme bH of cytochrome b (cyt b), heme c1 of cytochrome c1 (cyt c1) and the [2Fe–2S] cluster of the iron–sulfur protein (ISP), often called the Rieske protein after its discoverer , two protons are released into the intermembrane space and one proton is taken up from the matrix. In the Q-cycle mechanism, proton release results from the oxidation of QH2 at the Qo site. From the Qo site, one electron is transferred through the high-potential chain that consists of a [2Fe–2S] cluster and heme c1 to cytochrome c. The second electron is transferred through the two b-type hemes, which constitute the low-potential chain, to Q bound at the Qi site, a second Q/QH2 binding site. The intermediate semiquinone (SQ) radical generated at the Qi site is stabilized and then fully reduced to QH2 upon the oxidation of another QH2 at the Qo site in a second turnover of the cycle , which is formed by cyt b. Alternatively, the Fe–S cluster is positioned closer to cyt c1 . In intermediate positions, the tip of the Rieske domain is close to the ef-loop of cyt b  studies revealed redox-dependent shifts in the equilibrium of positions that are occupied by the Rieske domain  as well as two symmetric dimers  or decylubiquinol (DQH2). The enzyme–product complex was expected to form when the reduced Rieske domain is in the b position and quinone occupies the Qo site. Focused 3D classifications improved the density for the mobile Rieske domain, which allowed us to resolve several different positions in a conformationally heterogeneous sample as well as distinct changes in its position depending on experimental conditions.Our aim was to elucidate the structure of CIII2.2.1.2 was purified from detergent-solubilized mitochondrial membrane preparations after depletion of the His-tagged respiratory chain complex I by immobilized ion-affinity chromatography, as described by Parey et al.  and salt (40 mM) concentrations.In a protocol adapted from Pálsdóttir & Hunte 2003, CIII2 w2.2.2 was concentrated to ∼5 mg ml−1 (∼10 µM) in 20 mM Tris–HCl pH 8.0, 40 mM NaCl, 1 mM EDTA pH 8.0, 0.025% n-dodecyl-β-d-maltoside (DDM) . Potassium hexaferricyanide was added to 500 µM (sample +FCN) and sodium ascorbate to 25 mM (sample +NaAsc). Atovaquone  was added at 120 µM together with antimycin A  at 125 µM (sample +atovaquone+antimycin A). Decylubiquinol was added at 1 mM with or without 188 µM antimycin A (samples +DQH2 and +DQH2+antimycin A). Decylubiquinone  was added at 330 µM with or without 25 mM sodium ascorbate (samples +DQ and +DQ+NaAsc). Hydrophobic ligands were incubated with the sample for at least 30 min on ice before vitrification. Decylubiquinone was reduced with sodium borohydride to obtain decylubiquinol  equipped with a K3 camera (Gatan) in counting mode and a Gatan BioQuantum energy filter with a slit width of 20 eV. The fluence was set to ∼1.1 e Å2.3.RELION-3.1  were performed to remove bad picks before 3D autorefinement (C1). The resolution was improved by two iterations of CTF refinement, one before and one after per-particle drift correction and dose-weighting with Bayesian polishing. Polished particles were subjected to a final consensus 3D autorefinement (C1) that yielded high-resolution maps of CIII2 with blurred Rieske domains.Micrographs were processed in C2 symmetry-expanded (relion_particle_symmetry_expand) and signal outside a mask around the Rieske domain was removed (particle subtraction) for focused 3D classifications. Classifications were performed with six classes without alignment, with the mask used for particle subtraction as a reference mask, a 15 Å low-pass filtered consensus refinement map as a reference and a T value of 256. The particle subtraction was reverted and separate 3D autorefinements (C1) were performed with local searches and without a reference mask for selected classes.Particles were 2, particles were separated according to the classes of both the particle and its symmetry-expanded duplicate rotated by 180° prior to 3D autorefinement.To resolve both Rieske domains of CIIIC2 symmetry was applied to symmetric dimers and the map resulting from the 2.0 Å consensus refinement of combined data sets. Local resolution maps were calculated in RELION-3.1.Phyre2  reduced after purification . In addition to this apo state without additives, we prepared samples to investigate potential effects on the position of the Rieske domain. Additives included commonly used reductants or oxidants of the high-potential chain (sodium ascorbate and potassium ferricyanide), Qo- and Qi-site inhibitors (atovaquone and antimycin A), substrate and product analogues (DQH2 and DQ) and combinations thereof. The cryo-EM structure of CIII2 with bound atovaquone served as a reference for the complex with an immobilized Rieske domain, in addition to the corresponding X-ray structure  Apo: without additives .(ii) +Antimycin A: with antimycin A only.(iii) +NaAsc: reduced with ascorbate.(iv) +FCN: oxidized with potassium ferricyanide.f-type inhibitor atovaquone and the Qi-site inhibitor antimycin A.(v) +Atovaquone+antimycin A: with the P(vi) +DQH2: with the substrate analogue decylubiquinol.2 and antimycin A.(vii) +DQH2+antimycin A: with DQH(viii) +DQ: with decylubiquinone .(ix) +DQ+NaAsc: reduced by ascorbate and DQ.A total of nine CIIIa)]. The three subunits of the catalytic core Cob (cyt b), Cyt1 (cyt c1) and the transmembrane domain of Rip1 (ISP), as well as the seven supernumerary subunits of yeast CIII, Cor1, Qcr2, Qcr6–9 and the less tightly bound Qcr10, were well-resolved, consistent with other complete structures of CIII2 within respiratory supercomplexes from Saccharomyces cerevisiae  and 1d)]. The poor solubility of DQ limited the accessible concentration range, which may explain why we did not observe density in the Qi site in the consensus refinements. Atovaquone was resolved in the Qo site in the +atovaquone+antimycin A sample, with the Rieske domain fixed in the b position [Fig. 1d)]. In samples with added DQH2 or DQ, the densities in the Qo sites increased slightly but the density for the Rieske domain remained weak [Fig. 1c)].In the consensus refinements of the +atovaquone+antimycin A, +DQH2+antimycin A and +DQH2 samples, the Q3.1.et al., 20152 particles according to the positions of the two ∼14 kDa small Rieske domains. Firstly, particles after 3D refinement without imposed symmetry were C2 symmetry-expanded to allow the classification of both Rieske domains on one side of the complex. Then, all signal from particle images outside a small mask that encompassed the volume of the Rieske domain in the b, intermediate and c positions was removed by particle subtraction in RELION. This mask included some residues of cyt c1 and cyt b that line the cavity in which the Rieske domain moves, but it was designed to exclude as much of the other subunits as possible. Otherwise, classifications would be driven by these less flexible regions rather than by the Rieske domain. 3D classifications were then performed with particle orientations fixed as determined in the prior consensus refinements that had yielded high-resolution maps of CIII2 with poorly resolved Rieske domains . Classification improved the density of the Rieske domain, which was seen to assume one of three different positions .Focused 3D classification  was less than ∼10 Å. The cluster-binding tip of the Rieske domain was well-resolved and density for the [2Fe–2S] cluster was strong. Using the sample of CIII2 with an excess of DQH2, density for the Rieske domain in this position did not greatly improve  compared with the other samples.A closer look indicated subtle differences in the position of the Rieske domain and the occupancy of the Qte Fig. 3. In all b. As expected for a Pf-type inhibitor, the hinge region that tethers the mobile Rieske domain to the transmembrane domain was in an extended conformation . In addition, Cys189, His170 and Leu171 of the Rieske domain formed hydrogen bonds to Tyr279, Lys288 and Asn149 of cyt b, as previously observed .Atovaquone induced very tight binding of the tip of the Rieske domain to cyt f-type inhibitor-induced structure most closely when the high-potential chain was reduced by sodium ascorbate, with a similar hydrogen-bonding distance between the Rieske domain and cyt b. Density inside the Qo site was weak, suggesting that no substrate had remained bound during sample preparation.The Rieske domain approached the Po site increased, indicating a possible low occupancy of DQ in the Qo site. The additional density was in a position similar to atovaquone. The Rieske domain was in a position in which Tyr279 and Cys189 were further apart (3.7 Å) and His190 was closer to Tyr279 (3.4 Å) compared with the ascorbate-reduced sample without added DQ. His190 was ∼2.7 Å away from the DQ density in the Qo site.When DQ and ascorbate were added after purification (+DQ+NaAsc) density in the Qf-type inhibitors bound, and density distal to the tip was weak, indicating heterogeneity in these regions. A shift towards the b position in the equilibrium of positions was not observed .The Fe–S cluster-bearing tip of the Rieske domain was well-resolved in the two ascorbate-reduced samples. Density at the hinge, where the transition from helix to random coil occurs with P2, the Rieske domain was more distal, beyond hydrogen-bonding distance to cyt b, and the hinge formed an α-helix. Density in the Qo site was weak and more proximal compared with +DQ+NaAsc . When antimycin A was added with DQH2, the Rieske domain did not occupy the b position.In all the other tested conditions with the high-potential chain partially reduced, oxidized or reduced by DQH3.3.b or cyt c1. However, sample-dependent differences in the position of the Rieske domain  compared with fully oxidized samples (+FCN and +DQ)  His190 was within 3.8 Å of the propionic acid group, whereas it did not approach heme c1 closer than ∼8 Å in the fully oxidized complex (+FCN). At a lower threshold, an undefined, rod-shaped density was visible in the Qo site, which is accessible to solvent with the Rieske domain in the c position.Rigid-body fitting of the Rieske domain in the ) Figs. 2 and 4 ▸.cd1-helix and ef-loop of cyt b were weaker and modelling was more difficult when the Rieske domain was in the intermediate position .Interestingly, in samples without added reductant or oxidant, and especially in the presence of antimycin A, map densities for the on Fig. 5. In part3.4.2 with improved density for both ISP-ED protomers  and asymmetric (C1) dimers, indicating that the two Rieske domains in the dimer move independently.For the reconstruction of both Rieske domains in the dimer, the six classes of symmetry-expanded particles were further split into subsets after focused 3D classification. Every particle was sorted according to the combination of the two respective classes that were assigned to the symmetry-expanded pair during focused 3D classification. Local 3D refinements of these particle subsets yielded maps of CIIIrs Fig. 6. There a4.2 from Y. lipolytica with a focus on the small, ∼14 kDa iron–sulfur cluster-binding domain of the Rieske protein subunit. The Rieske domain plays a central role in electron transfer through CIII2. As expected, the Rieske domain was mobile and thus poorly resolved, except when the Pf-type inhibitor atovaquone was added to arrest the domain at the surface of cyt b (Birth et al., 20142 particles were classified according to the position of the Rieske domain in the dimer maps, thus reducing conformational heterogeneity in subsequent refinement steps. Due to the small size and weak signal of the Rieske domain separation was not perfect but, in the more restrained b positions in particular, details of the Rieske domain and density for the [2Fe–2S] cluster improved substantially. The higher resolution of the Rieske domain indicates that it has less conformational freedom in the b position than in the intermediate and c positions. The Rieske domain covered a range of positions between the b and the c position. We found a systematic effect of the redox state of the high-potential chain and of added inhibitors or substrate analogues on the position of the Rieske domain in the complex, with direct consequences for electron transfer. As expected, the distances between the [2Fe–2S] cluster and the Qo site or heme c1 are within 14 Å for the b and c positions, respectively, at which electron tunnelling between redox cofactors is not rate-limiting for CIII2 turnover (Zhang et al., 1998et al., 1998et al., 1999We report multiple high-resolution cryo-EM structures of CIIIet al., 2000b when the high-potential chain was reduced by sodium ascorbate compared with when it was fully or partially oxidized. The interaction between cyt b and the Rieske domain of the reduced high-potential chain appeared to be weaker when there was an excess of DQH2, as the Rieske domain was confined to a more distal position. This highlights the role of the hydrogen bond between the cluster-ligating His190 of the Rieske domain and the Qo-site ligand in positioning the Rieske domain on the surface of cyt b. In CIII2 with a reduced high-potential chain there was also a clear increase in density for bound quinone in Qo when DQ was added. The hydrogen bond can form in the enzyme–product complex when the [2Fe–2S] cluster is reduced and Q is bound or when the cluster is oxidized and QH2 is bound, forming the reactive enzyme–substrate complex (Crofts et al., 1999b position may arise through interaction of the backbone carbonyl of Cys189 or the cluster-ligating His190 with Tyr279 of cyt b. The His190–Tyr279 hydrogen bond is also seen with Pf-type inhibitors such as famoxadone that do not form a direct hydrogen bond to His190 but still arrest the Rieske domain in the b position (Gao et al., 2002b, as seen in Pf-type inhibitor-bound structures (Esser et al., 2006et al., 2014b positions of apo CIII2 or of CIII2 when a substrate analogue was added. Therefore, it remains unclear whether cyt b acts as a catalytic switch with native substrate, in the same way as Qo-site inhibitors.Consistent with previous EPR experiments (Brugna b, no shift in the equilibrium of positions in ascorbate-reduced samples was observed. Together with the weak density in the hinge region between the Rieske and transmembrane domains in these samples, this may indicate that the close interaction of the cluster-bearing tip with cyt b loosens the helical secondary structure of the hinge only transiently (Darrouzet et al., 2000b and the Rieske domain (Crofts et al., 2002et al., 2021et al., 2013Despite the closer interaction of the Rieske domain with cyt cd1-helix and the ef-loop were weaker and more difficult to model when the Rieske domain was in the intermediate position in CIII2 samples without added oxidant or reductant, indicating increased flexibility and heterogeneity. The addition of antimycin A reduced the resolution of these regions, whereas both the cd1-helix and the ef-loop were well-resolved in the intermediate positions of CIII2 oxidized with ferricyanide or reduced with ascorbate or DQH2. If Trp142 of the cd1-helix moves out from underneath the ef-loop this is likely to increase the mobility of this loop and thereby reduce the energetic barrier to the transition of the Rieske domain between the b and c positions. This might explain the increased mobility of the Rieske domain upon the addition of antimycin A (Valkova-Valchanova et al., 2000et al., 2005et al., 2009et al., 2003b position, where we also observed the majority of density for Trp142 in its common position. In our structures and in those previously published, no changes upon the binding of antimycin A were apparent that could explain long-range communication between the Qi and Qo sites. Also, increased heterogeneity was not observed in cyt b when antimycin A was added in combination with atovaquone or decylubiquinol. Therefore, further experimental evidence and higher resolution maps would be required to validate this observation. Trp142 is highly conserved (Esposti et al., 1993et al., 1993et al., 1995The densities of the 2, c positions closer to heme c1 were observed. The resolution of c positions was limited, but in the rigid-body fitted Rieske domain His190 came within hydrogen-bonding distance of the heme c1 propionate, as in previous crystal structures (Iwata et al., 1998When the high-potential chain was partially or fully reduced either by sodium ascorbate or an excess of DQHet al., 2021et al., 2022et al., 2016et al., 2016et al., 2021It was also possible to classify particle images according to the positions of both Rieske domains in the dimeric complex. We found both conformationally symmetric and asymmetric dimers, as reported by others (Steimle b and cyt c1. The reduced state of the high-potential chain favours closer interaction with both cyt b and and cyt c1 without apparent shifts in the equilibrium of positions, enabling rapid electron transfer between the cofactors of the high-potential chain. Under conditions that favour the enzyme–product complex we observe better density for a quinone substrate in the Qo site, which has been difficult using crystallographic studies. The heterogeneity of the cd1-helix and ef-loop increased upon the binding of antimycin A, which may affect the dynamics of the Rieske domain.The structures presented in this paper provide evidence that the redox state of the high-potential chain affects the interaction of the Rieske protein with cyt 2, consensus refinement, 8ab6CIIIPDB reference: with atovaquone and antimycin A bound, 8ab7PDB reference: b position, 8ab8ascorbate-reduced with decylubiquinone, Rieske domain in PDB reference: b position, 8ab9ascorbate-reduced, Rieske domain in PDB reference: ascorbate-reduced, Rieske domain in intermediate position, 8abaPDB reference: c position, 8abbascorbate-reduced, Rieske domain in PDB reference: b position, 8abeferricyanide-oxidized, Rieske domain in PDB reference: ferricyanide-oxidized, Rieske domain in intermediate position, 8abfPDB reference: c position, 8abgferricyanide-oxidized, Rieske domain in PDB reference: b position, 8abhwith antimycin A bound, Rieske domain in PDB reference: with antimycin A bound, Rieske domain in intermediate position, 8abiPDB reference: c position, 8abjwith antimycin A bound, Rieske domain in PDB reference: b position, 8abkwith decylubiquinol, Rieske domain in PDB reference: with decylubiquinol and antimycin A, consensus refinement, 8ablPDB reference: b position, 8abmRieske domain in PDB reference: Rieske domain in intermediate position, 8ac3PDB reference: c position, 8ac4Rieske domain in PDB reference: b position, 8ac5with decylubiquinone, Rieske domain in PDB reference: 10.1107/S2052252522010570/rq5008sup1.pdfSupplementary table and figures. DOI:


Covalent organic frameworks (COFs) are a novel class of porous crystalline organic materials with organic small molecule units connected by strong covalent bonds and extending in two- or three-dimension in an ordered mode. The tunability, porosity, and crystallinity have endowed covalent organic frameworks the capability of multi-faceted functionality. Introduction of fluorophores into their backbones or side-chains creates emissive covalent organic frameworks. Compared with common fluorescent organic solid materials, COFs possess several intrinsic advantages being as a type of irreplaceable fluorescence materials mainly because its highly developed pore structures can accommodate various types of guest analytes by specific or non-specific chemical bonding and non-bonding interaction. Developments in fluorescent COFs have provided opportunities to enhance sensing performance. Moreover, due to its inherent rigidified structures and fixed conformations, the intramolecular rotation, vibration, and motion occurred in common organic small molecules, and organic solid systems can be greatly inhibited. This inhibition decreases the decay of excited-state energy as heat and blocks the non-radiative quenching channel. Thus, fluorescent COFs can be designed, synthesized, and precisely tuned to exhibit optimal luminescence properties in comparison with common homogeneous dissolved organic small molecule dyes and can even compete with the currently mainstream organic solid semiconductor-based luminescence materials. This mini-review discusses the major design principle and the state-of-the-art paragon examples of fluorescent COFs and their typical applications in the detection and monitoring of some key explosive chemicals by fluorescence analysis. The challenges and the future direction of fluorescent COFs are also covered in detail in the concluding section. In addictronics . Howeverctronics .In 2001, with the milestone discovery of aggregation-induced emission (AIE) by Tang’s group, pure organic emissive materials have been continuously burgeoning and advanced into a new era . Various2O , being considered the next-generation star materials for multifunctional applications, have aroused considerable research focuses for decades since its first discovery in 2005 . COFs ar2O . In 20082O . These r2O , and sp2ated C=C  COFs madated C=C .COFs applied in the field of fluorescence will be discussed in detail in the following sections. First, this kind of highly porous materials possessing abundant interior pores can be effectively designed to selectively recognize the specific analytes and initiate responding processes by either fluorescence enhancing or quenching. Second, the controllable formation of appropriate covalent bonds in the two-dimensional plane and three-dimensional space also enhances the specific intermolecular interaction between analytes and COFs. Guided by these two major conclusions, we continue our discussion on the main examples of COF fluorescence materials applied in the field of explosive analysis. We divide our following discussion into three parts including fluorescence mechanisms of COFs, explosive sensing, and conclusion. In addition, we provide the current main challenges and the future developing directions of COFs for the fluorescence sensing of explosive analysis. We believe that this mini-review can help to provide novel insights and understandings to further design and synthesize more efficient fluorescent COF sensors.via heat-release routes  effects by greatly promoting the coupling effect to quench the excited-state energy e routes . Althouge routes . Both the routes , there ae routes . Thus, ivia confining the fluorophores into the rigid reticular structures, the fluorescence intensity and the photoluminescence quantum yield (PLQY) are still far from expectation. Inspired by the previous success of AIE and solid-state emission of organic solids formed from AIEgens, which can effectively inhibit the thermal energy dissipation routes of RIR or RIV in aggregate states, some scientists borrowed this concept to the COF research field. Also, there appeared two famous fluorescent COFs, namely, TPE-Ph-COF , Förster resonance energy transfer (FRET), Dexter energy transfer, and inner-filter effects (IFE) can occur to selectively turn on or block off the fluorescence signals. Otherwise, when the COF fluorescent monomer coordinated with the analyte, the structural changes such as deprotonation, protonation, and tautomerization will trigger the fluorescent change of COF materials. Thus, the linear relationship can be established based on the fluorescence intensity change with the concentration of the analyte. To realize meaningful COF chemo-sensing applications, enhancing the selectivity with the specific guest molecules and increasing the sensitivity by responding to more diluted analytes are the two major developing directions.Nitrobenzene explosives are used as important chemical compounds in mines, military, and safety administration. But they are also severe pollutants in the natural aqueous environment. Moreover, they are used as explosives by terrorists, causing panic and threatening the lives of citizens. The rapid and accurate detection of nitrobenzene and other explosives now becomes a particularly urgent issue . Due to As mentioned previously, most two-dimensional COFs are non-fluorescent or weakly fluorescent even by incorporating fluorophore monomer units due to their severe interlayer π–π-stacking-induced fluorescence quenching. This may also be due to the much greater kinetics rate constant of thermal deactivation channel in comparison with relatively slow fluorescence kinetics. Moreover, two-dimensional COFs exhibited a low fluorescence intensity compared with their organic small molecule partner mainly because of the higher non-radiative deactivation rate constant due to the strong interlayer coupling. Using a strong luminescent monomer such as pyrene can re-open COF fluorescence channels.Jiang et al. synthesized the first fluorescent COF sensor for explosives by condensing hydrazine with 1,3,6,8-tetra-(4-formylphenyl)pyrene under the solvothermal condition . The as-−5 M, and it functioned without pronounced response in the presence of other nitroaromatics such as TNT, DNT, DNP, NT, and NP. The good sensing performance relates with the aggregation state of CON, which on the one hand accommodated more available electrons to participate in the electron-transfer and energy-transfer process with TNP analyte. On the other hand, the few-layered CON greatly decreased the interlayer π–π stacking regenerating the fluorescence channel Apart from azine-COFs, Banerjee et al. exhibited that imide TfpBDH COF and CON  effectively detected TNP with a turn-on mode  see Fig. Moreove3CN dispersion, which is the highest recorded PLQY for the imine-bonded COFs. The Py-TPE COF acted as a selective and sensitive turn-off sensor for the detection of picric acid with the ppm level LOD. Other nitroarene interferences such as TNT, DNP, DNT, NT, and NP cannot generate remarkable fluorescence quenching. This indicated that the proper design and engineering of COF morphology can also provide the alternative effective strategy for nitrobenzene explosive sensing.Imine-linkages are the most common COF motifs. However, due to the relatively flexible single bonds near the imine bond, they usually suffer from total or partial fluorescence quenching. This property considerably limited their uses as chemo-sensors. Loh et al. utilized the morphology engineering tactic to increase the imine-COF fluorescence intensity . They fa2 formed a two-dimensional DTZ-COF, which exhibited high crystallinity with intense XRD peaks at 2θ < 10o and remained highly porous with a 1276 m2/g BET specific area  explosives, which lack the typical electron-accepting nitroarene motif, can also be efficiently detected by a novel 2D-conjugated COF. Perepichka et al. constructed the ketonenamine COF by a Michael addition–elimination cascade reaction between β-ketoenols and aromatic amines . The as-Apart from the experimental results on the application of fluorescent COFs for explosive detection, 2, RDX, and TATP are less concerned. Much more focuses can be encouraged to highlight these explosives, and more efforts can be devoted on developing more general and broad-spectrum sensors for more types of explosives. Another direction for COF fluorescence explosive detection is to develop more fluorescence turn-on sensors and naked-eye detection. Higher intrinsic fluorescence intensity and PLQY should be pursued. Possessing satisfactory sensitivity, selectivity, and stability, we believe that the future of fluorescence COFs will be brighter if we continue to involve efforts on this significant research area.As a class of novel materials, COFs have been developed rapidly for almost 2 decades since 2005 in Yaghi’s inaugurating work . The COF


Pseudoalteromonas tunicata “tam” biosynthetic gene cluster (BGC)identified a unique di-domain biocatalyst (PtTamH).Sequence and bioinformatic analysis predicts that PtTamH comprises an N-terminal, pyridoxal 5′-phosphate (PLP)-dependenttransaminase (TA) domain fused to a NADH-dependent C-terminal thioesterreductase (TR) domain. Spectroscopic and chemical analysis revealedthat the TA domain binds PLP, utilizes l-Glu as an aminedonor, accepts a range of fatty aldehydes (C7–C14 with a preference for C12), and produces thecorresponding amines. The previously characterized PtTamA from the “tam” BGC is an ATP-dependent, di-domainenzyme comprising a class I adenylation domain fused to an acyl carrierprotein (ACP). Since recombinant PtTamA catalyzesthe activation and thioesterification of C12 acid to the holo-ACP domain, we hypothesized that C12 ACPis the natural substrate for PtTamH. PtTamA and PtTamH were successfully coupled togetherin a biocatalytic cascade that converts fatty acids (FAs) to aminesin one pot. Moreover, a structural model of PtTamHprovides insights into how the TA and TR domains are organized. Thiswork not only characterizes the formation of the tambjamine YP1 tailbut also suggests that PtTamA and PtTamH could be useful biocatalysts for FA to amine functional groupconversion.Tambjamine YP1 is a pyrrole-containing natural product.Analysisof the enzymes encoded in the  Alternatively, a MAP derivative can beused.12 However, in the case of tambjamines,the MBC intermediate is condensed with an amine to form an enaminemoiety in place of the third pyrrole ring. The biosynthetic productionof these secondary intermediates can differ, along with the enzymesutilized for their production.Prodiginines are a class of secondary metabolites found in variousorganisms including the prodigiosin-producing iosin R1 1.8−11 Thfamilies 1. Extens13 These five-membered N-heterocyclic-containingproducts often exhibit antimicrobial, antiviral or anticancer bioactivity,which has led to their use as therapeutic agents.16Along with the prodiginine and tambjamine families,NPs’with pyrroles built into their scaffolds are ubiquitous in nature.The planar, electron-rich ring is able to form hydrogen bonds, chelatemetal ions, and participate in π-stacking interactions.Pseudoalteromonas tunicata.17 Bioinformatic analysis and sequencealignment with homologous red and pig proteins helped to identifya distinct BGC designated as the “tam”cluster, which is responsible for YP1 biosynthesis .18 Of the 19 genes predicted,only a few have been expressed and their encoded enzymes characterized.The biosynthesis of YP1 requires two converging pathways; the firstpathway produces the MBC core, whereas the second pathway producesthe fatty amine tail. Since MBC biosynthesis is highly conserved,the genes required to make this bipyrrole core can be confidentlypredicted and annotated. However, the production and attachment ofthe unsaturated fatty amine tail are less well-understood.A yellow-pigmentedalkaloid, identified as tambjamine YP1 (YP1),was extracted from tam” cluster.19 Our recent investigationsshowed that another enzyme, PtTamA [a di-domain enzymecomprising a class I adenylation (ANL) domain fused to an acyl carrierprotein (ACP)], found within the “tam”cluster catalyzed this step. This enzyme uses adenosine triphosphate(ATP) and a C12 fatty acid (FA) to catalyze the formationof a C12 adenylate, which is then captured by the 4′-phosphopantetheine(4′-PP)-modified ACP domain to form an acyl-ACP-bound PtTamA.21 This PtTamAanalysis led us to question how the bound acyl chain is released forfurther downstream tailoring. Two other enzymes in the BGC (PtTamH and PtTamT) are predicted to beinvolved in the formation of the amine tail after PtTamA, although their exact function is yet unknown. Here, comprehensivesequence, spectroscopic, and chemical analysis revealed that PtTamH is the unusual fusion enzyme that carries out theacyl chain off-loading function. We also show that recombinant PtTamA and PtTamH act together to convertfatty acids to the corresponding amines.Theinitial aim of this study was to characterize the formationof the fatty amine precursor to YP1. It was originally proposed thatthis second pathway in YP1 biosynthesis was initiated by AfaA, a fattyacid CoA ligase (FACL) not found within the “PtTamH was performed to identify and characterize its catalyticdomains. The deposited amino acid sequence of PtTamH is annotatedas a di-domain enzyme, with an N-terminal, pyridoxal 5′-phosphate(PLP)-dependent transaminase (TA) type III domain and a C-terminal,amino acid dehydrogenase domain . Approximately 50 amino acids with no annotated function connectthese two domains. An exhaustive phylogenetic analysis performed usingthe ConSurf26 server identified PtTamH homologues from other Pseudoalteromonas species including P. citrea and Pseudoalteromonas sp.A25. More distantly related homologues with similar domain functions/organizationsare present in Chitinimonas, Paucibacter, and Streptomyces species . It is currentlyunclear whether these distant homologues are involved in tambjaminebiosynthesis or the closely related prodiginine biosynthetic pathway.27An initial bioinformatic study on 28  by BLASTp,further sequence analysis suggests that the C-terminal domain (residuesK547–S941) is more accurately describedas a thioester reductase (TR). The proposed TR domain of PtTamH shares sequence homology with acyl-ACP reductases(AARs) involved in alkane biosynthesis. Such homologues originatefrom cyanobacterial species including Synechococcuselongates(Synechocystis sp. PCC6803 (27.6% identity), and Nostoc punctiforme(707xxG710xxG713)and a putative catalytic cysteine (C887) were identifiedin PtTamH, as well as other Pseudoalteromonas homologues . As reported inthe literature31 , the active residueC294 in S. elongatus AAR(SeAAR) is responsible for acyl chain transfer froma 4'-PP carrier such as holo-ACP or coenzymeA (CoA).The resulting thioester is optimally positioned for hydride attackby nicotinamide adenine dinucleotide phosphate hydrogen (NADPH), releasingan aldehyde. An identical mechanism was proposed by Warui et al. for N. punctiforme AAR.30 Giventhe apparent sequence similarity around this active site cysteine,it is plausible that the PtTamH TR domain employsa comparable mechanism to liberate 4'-PP-bound intermediatesas aldehydeproducts. Thus, from this sequence analysis, PtTamHwas putatively redefined a bifunctional ω-TA-TR fusion biocatalyst.While the N-terminal domain was confirmedto be a PLP-dependent ω-transaminaseelongates . The addition of 500 mM sorbitol to the growth media promotes correctprotein folding, improving protein solubility.32 The soluble PtTamH was isolated usinga combination of cobalt-/nickel-immobilized metal affinity chromatography(IMAC) followed by size exclusion chromatography (SEC), which enabledthe isolation of ∼1.5 mg/L. After initial isolation to characterize PtTamH, and due to high protein purity after IMAC, SEC wasomitted from the purification process, which led to a slightly higherprotein yield of ∼3 mg/L. The SEC retention volume confirmedthe homodimeric nature of PtTamH ; this observation was expectedas PLP-dependent enzymes often form dimers or tetramers.33Recombinant PtTamH exhibited a strong yellow color and displayed a characteristicPLP spectrum when studied by UV–vis spectroscopy . As exemplified by the homologueCrmG, class III ω-TAs display a preference for either l-Glu or l-Ala as the amino donor; therefore, both aminoacids were tested as substrates. The covalent binding of l-Glu to the key catalytic lysine (putative K340) was confirmedby UV–vis analysis, where the emergence of a peak at 330 nmindicates the formation of the pyridoxamine 5′-phosphate (PMP)intermediate . To confirm theactivity of the predicted N-terminal ω-TA domain, PtTamH was incubated with l-Glu or l-Ala and C12 aldehyde for 24 h, and the formation of the C12 amine product was monitored by liquid chromatography (LC) electrosprayionization-mass spectroscopy (ESI-MS). We observed a peak matchingthe retention time (16.2 min) of the C12 amine standardin both reactions. The extracted ion chromatograms (EICs) revealedan ion with m/z = 186.2222 Da, whichmatches with the predicted mass of C12 primary amine  , the correspondingamine products (except C6) were detected, and a clear preferencefor C12 aldehyde was also observed (S11). Thesedata show that PtTamH displays a broad acyl chainselectivity between C7–C14 fatty aldehydes.Since the NP YP1 contains a Cobserved 3 and S11PtTamH N-terminal ω-TAdomain, we next focused on the predicted C-terminal TR domain. Inspiredby the study of coelimycin biosynthesis by Awodi et al.—whichinvolves an analogous TR-aminotransferase cooperativity34—PtTamH was incubatedwith C12 CoA and either NADH or NADPH in the presence of l-Glu. Furthermore, the reaction mixture was supplemented with200 mM KCl and 10 mM MgCl2 as both magnesium and potassiumions have been found to drastically improve the activity of homologousAARs.31 However, no C12 amine product was observedin either the NADH or NADPH reactions. This suggested that the PtTamH TR domain is either inactive or does not accept acyl-CoAsubstrates .After demonstratingthe activity of the PtTamH TR domain to accept a free acyl-CoA thioester substratewas not entirely unexpected. As previously discussed, the tambjamineYP1 BGC encodes PtTamA, a di-domain ACP-ANL naturalfusion. Our earlier investigations showed that the PtTamA ANL domain can activate carboxylic acids of various chain lengths(C6–C14) and attach them to the 4′-PParm of the fused ACP domain.21 The presence of thisenzyme suggests that PtTamH may be acyl-ACP dependent,transferring the acyl chain directly from the PtTamAacyl-ACP domain to the PtTamH TR domain.The inability of the PtTamA , prepared with the ACPdomain in its 4'-PP-activated holo-form.21PtTamA was incubated with PtTamHin the presence of C12 acid, MgATP, KCl, NADH or NADPH,and l-Glu, and the production of the corresponding C12 amine was monitored by LC ESI-MS . Taken all together, our data not only confirm that the PtTamH TR domain is catalytically active but also confirmthat it exhibits an innate specificity for the C12 ACPsubstrate. The four domains are therefore working in concert to convertthe acid to an amine. The C12 acid is activated by the PtTamA ANL domain36 in an ATP-dependentreaction that leads to the formation of the C12 adenylateintermediate. The PtTamA holo-ACP4'-PP thiol acts as a nucleophile, attacking the C12 adenylateand releasing AMP. Once captured, the C12 ACP is then reducedby the PtTamH TR domain using NADH, forming the C12 aldehyde. The final step involves transamination to givethe final C12 amine, catalyzed by the PtTamH TA domain  andstudied by molecular dynamics simulation (MDS). The predicted ω-TAdomain shares structural homology with several ω-TA crystalstructures including YgjG40 ,PigE41 , ArgD ,and CrmG42 ,with the latter displaying the highest sequence coverage of the homologuesidentified . This critical lysinecovalently binds PLP via a Schiff base linkage, thereby enabling thecoenzyme to participate in transamination .44 Furthermore, the ω-TA domainsare predicted to comprise the homodimeric interface; identical interfacesexist across all solved ω-TA structures to date, and ω-TAdimerization is essential for defining the active site. Using thismodel, the C12 external aldimine could be docked in thepredicted ω-TA binding tunnel, with the PMP moiety flanked byK340 and F206 and the alkyl chain extendingtoward a hydrophobic pocket comprising F51, L470, and L473 from chain A and M365 from chainB .Following experimentalcharacterization, we sought to understand the structural logic underpinningthe activity of ied 90%, 6D. EvoluPtTamH TR modeland the crystalstructure of SeAAR could be comfortably superimposed, encompassing several hydrophobicresidues  complementary to the lipophilicity of PtTamH’snatural substrate . Like SeAAR, the putative catalytic cysteine C887 ispositioned at the intersection between the NADH- and alkyl-bindingsubdomains. Evolutionary conservation analysis suggests that the activecore of the reductase is highly conserved, including residues involvedin nucleotide binding as well as C887 itself . By comparison, the residues predictedto stabilize the acyl chain display greater variability, which maysuggest an additional substrate-recognition role for this pocket.With this knowledge, both NAD+ and C12 aldehydeligands were docked in this predicted active site. The top-rankedoutputs position the aldehyde carbonyl near C887 and theNAD+ pyridine ,with the alkyl chain extending into the predicted hydrophobic pocket. These poses are remarkablysimilar to the ligand orientation in the SeAAR crystalstructure  and suggest a conservedactive site architecture within this class of reductases.The predicted rimposed6D, enablPtTamH homodimer maintainedits structural integrity over the course of a 10 ns MDS . The simulated 208 kDa complex positionsthe TR domains laterally from the ω-TA core. Studying the electrostaticproperties of PtTamH revealed an electropositivesurface on the solvent-exposed substrate channel of the TR domain,which may underpin the recognition/docking of the comparatively acidic PtTamA ACP .45 Thus, the predicted domain organization of PtTamH would permit the facile transfer of acyl intermediatesbetween the ACP of PtTamA and the TR domain of PtTamH. Taken alongside the experimental data, the PtTamH TR domain can be confidently classified as an AAR.The fascinating predicted architecture of PtTamHmakes it an attractive target for further structural studies.Thefully predicted 46 is gaining in popularity.49 Once an enzymehas been discovered, its substrate specificity can be engineered towiden the substrate scope for a bespoke synthetic application.6 A rich source of these enzymes has been NP biosyntheticpathways which have evolved to efficiently transform simple buildingblocks into complex structures with a myriad of functionalities.50 These NPs are often encoded in BGCs where eachenzyme plays a specific role in the stepwise transformation alonga linear path. Along with these discrete, single-domain biocatalysts,the polyketide and nonribosomal peptide classes of NPs are membersof a large family whose biosynthesis is driven by large, multidomainassemblies.52 The fused domains within these molecular machines have clearly evolvedto efficiently transfer intermediates between active sites. A keyplayer within the complex is the acyl-ACP substrate which relays covalentlytethered substrates between domains.The application of biocatalysts for thesynthesis of a range ofhigh-value molecules PtTamA enzyme is a di-domainfusion of an N-terminal ANL to a C-terminal ACP.21 However, the details involved in the downstream processing of thenovel PtTamA acyl-ACP intermediate were unknown.In this study, the putative biocatalyst PtTamH wasanalyzed using comprehensive bioinformatic analysis. This predicted PtTamH to also be a di-domain fusion with an N-terminaldomain that displays high-sequence homology to a class III PLP-dependentTA. This is fused to a C-terminal domain whose sequence and structuralhomology suggest that it is a member of the AAR family. To assigna function to the individual domains, the recombinant PtTamH was initially shown to bind PLP and catalyze the conversionof C12 aldehyde to the corresponding C12 amine.The preferred amine donor was found to be l-Glu , and it also displaysa broad substrate promiscuity by being able to accept C7–C14 aldehydes. In future, the synthetic utilityof this TA could be further expanded by studying its activity withsmart amine donors such as cadaverine, o-xylylenediamine,and N-phenylputrescine (NPP).55The tambjamine YP1 BGCencodes a pathway to convert long-chainFAs to the essential hydrophobic fatty amine tails of these biologicallyactive NPs. The details of this part of the pathway were unclear untilwe reported that the PtTamH was unable to convert C12 CoA to the correspondingC12 aldehyde. However, we established that the PtTamH TR domain was catalytically active by successfullyconstructing a biocatalytic cascade which converted the C12 acid to the corresponding amine. Moreover, we also detected theformation of an aldehyde intermediate by omission of the amine donor.This suggests the four domains, present as two fusions within PtTamA and PtTamH, worked together andthat the PtTamH TR domain requires a specific acyl-ACP-boundthioester substrate. Defining the function of PtTamH,and coupling it with PtTamA, finally resolves theorigin of the fatty amine tail of tambjamine YP1.56The outstandingactivity to be defined was that of the origin ofthe aldehyde substrate, and we predicted that the TR domain wouldcatalyze the reduction of an acyl-thioester substrate, but PtTamA-PtTamH systemcould be carried out to fully explore the substrate scopeof this novel biocatalytic cascade. This would be enabled by determinationof the three-dimensional structures of both enzymes; our sequenceand structural analyses has provided initial insights into the chemistryand logic underpinning PtTamH-PtTamA cooperativity. The dimeric PtTamH is >200kDaand is also an excellent candidate for cryogenic electron microscopystudies.57 Further issues to be resolvedinclude the molecular details of how each domain interacts with eachother and how substrates and products navigate between catalytic sites.Tambjamine YP1 contains an oxidized acyl chain (with cis geometry between C3 and C4), and PtTamT within the P. tunicata BGC hasbeen proposed to perform this oxidation. The active biocatalytic cascadedescribed here will allow functional analysis of this remaining usefulenzyme. Furthermore, along with YP1, Picott et al. have recently characterizedthe macrocyclic tambjamine derivative MYP1 produced by P. citrea. Initial analysis identifies PtTamA and PtTamH homologues [TreaA (Uniprot: U1J4V2) and TreaH(Uniprot: U1KHB2)], in the BGC of this organism, which both show 71% identity to PtTamA and PtTamH, respectively. Our worklays the foundation to reveal the similarities and differences betweenthe two biosynthetic pathways.56Furtherwork on the 58 New tools suchas the database RetroBioCat should become a useful resource to incorporatesuch adaptable biocatalysts into synthetic routes for the preparationof a range of target molecules.59 The workdescribed here suggests that the functional group conversion displayedby the PtTamA–PtTamH biocatalyticcascade would be a valuable addition to this biocatalyst repository.Members of the ANL family are versatile biocatalysts that can beused to activate a range of FA substrates and prepare useful amides,esters, and thioesters.