Shoulder fillets are widely used in the structural optimization design of metal dampers. However, the plastic deformation property of dampers affected by stress concentration, owing to different fillets, has not been explored in-depth. In this study, two typical metal damper materials with different plastic deformation, i.e., ordinary steel SS400 and low-yield-strength steel LYS160, were investigated. The strengthening effect of fillets under different loading is evaluated by comparing the mechanical properties of different fillet heights. Furthermore, the effect of the stress concentration caused by different fillet shapes, based on the failure mode of materials, is discussed. Subsequently, the fatigue degradation effect under the reciprocating shear loading is studied. Based on a series of studies on the deformation properties of fillets in different ductile materials, the basis for the structural optimization design under plastic deformation is provided. As an important energy-dissipating component, metal dampers are widely used in seismic engineering. It is crucial to optimize the design of metal dampers, to improve their energy-dissipation capacity, by fully utilizing the plastic deformation of metal materials.Different types of dampers can be designed, using different deformation mechanisms of metal, such as shear , tensionIn the case of dampers with tension and compression energy dissipation, the strength of the fixed part of the damper is generally significantly greater than that of the energy-dissipation core, to ensure that the latter can perform reliably. Accordingly, the thickness of the fixed part is greater than that of the energy-dissipation core ,10, and The fatigue performance of shoulders is generally evaluated by the stress concentration. Some studies focused on the stress concentration of shoulders under different loads, such as tension ,12, bendHowever, a large arc cannot be adopted in the thickness direction of a shear panel damper, owing to its thickness limitation. Therefore, a small arc fillet or a T-shape fillet  cannot bEnergy is typically dissipated by the plastic deformation of a damper instead of the elastic deformation. With the increase of loading cycle, the stress at the edge of shear panel decays rapidly when the plastic deformation reaches the critical value of fatigue . StudiesCompared with traditional ordinary steel, low-yield-strength steel is becoming more widely used in metal-damper design owing to its large plastic deformation. However, the effect of shoulder fillets in large plastic deformation on the structure’s performance has not been studied in-depth. Hence, the performance difference caused by shoulder fillets under different plastic deformation has not been compared and analyzed.In this study, ordinary carbon steel and low-yield-strength steel were selected as test materials. The mechanical properties of ordinary carbon steel and low-yield-strength steel with different shoulder fillet shapes and heights under different loads were analyzed. The effect of T-shape shoulder fillet on the plastic deformation ability of structures fabricated by using these two different ductile materials is discussed in detail.Low-yield-strength steel  and ordinary carbon steel SS400 were adopted to design the specimens, as shown in Based on the three different fillet heights, the effect of fillet shape on structural deformation performance is further discussed. The properties of the standard specimen with a large arc transition can be obtained easily, according to previous studies . Hence, Flanges  are typiThe research plan is shown in Tensile and shear tests were performed, using a universal material testing machine and a torsional fatigue testing machine with loading speeds of 1 mm/min and 180°/min, respectively . The forThe failure modes of specimens with different fillet heights under tensile loading are shown in 2/4; and d is the diameter of the specimen.All the specimens were necked and fractured in the middle parts. This implies that no stress concentration existed, or the stress concentration at the fillet was negligible under tensile loading. This phenomenon is primarily determined by the characteristics of tensile deformation, based on the following formula under tensile loading:As the diameter of the clamped ends is larger than that of the middle part, the corresponding stiffness of the clamping part is greater than that of the middle part. Once the tensile loading is applied, the middle part will deform first. With the increase in loading, the middle diameter of the specimen decreases and the stiffness becomes smaller. The diameter and rigidity of the clamped part at both ends remained unchanged. Hence, the reduction in stiffness of the middle part caused the deformation to be concentrated in the middle part. The reinforcement effect of the clamped end can be realized when the diameter of the middle deformation area is slightly larger than that of the middle deformation area. Moreover, the stress concentration of the structure under tensile loading has little correlation with the fillet shape and angle of the shoulder.Red and blue lines were drawn on the opposite sides of the specimen, along the axial direction, to observe the torsion deformation of the specimen. The failure modes of the specimens under shear loading are shown in t = πd3/16; d is the diameter of the specimen; and T is the torque.Compared with the tensile test, the maximum shear stress of the torsion test is expressed as follows:t remained unchanged. The torque increased with the deformation, owing to its stress-hardening effect. When the antitorque of the clamping end was lower than the loading torque, torsional deformation was produced in the clamping ends . The deformation capacity of the specimen would be decreased by 21% if the T-shape shoulder fillet was adopted. When the T-shape shoulder was inclined, the stress concentration was concentrated at one point . Compared with the T-shape shoulder specimens, the deformation capacity of the OBLIQUE T-shape shoulder specimen would be decreased by 17%. Compared with standard torsional specimen, the deformation capacity of SS400 decreased from 440% to 135% shear stain when the T-shape shoulder fillet was adopted. The reduction of deformation capacity was around 69% b. In theThe fatigue performances of the T-shape shoulder fillet and traditional standard specimens are shown in The red and green circles in the figure are the fatigue cycles when the fillets of specimen SS400 are arc and T-shape, respectively. The green curve is the fatigue life curve. Although the maximum shear strain of sharp transition specimen SS400 was 69% lower than that of the standard specimen under unidirectional shear, the fatigue performance did not differ, owing to the small loading amplitude. The transition form of the fillet imposed little effect on the fatigue behavior of specimen SS400 under a small deformation.Under reciprocating shear, the deformation in the middle of the LYS160 specimens was uniform, and the stress concentration was not significant. The stress concentration at the fillet caused the sharp decline of strain amplitude, and the advantage of large plastic deformation capacity of LYS160 could not be utilized. The fatigue performance of the specimens depends on the fatigue performance of the fillet, while it is less affected by the fatigue performance of the material. On the opposite, when the loading amplitude of SS400 is not more than 61% shear strain, the fatigue fracture occurs in the middle of the SS400 specimens. The fatigue performance of the specimens depends on the fatigue performance of the material, while it is not affected by the fillet. The difference of fatigue performance between different plastic materials is narrowed when the T-shape shoulder is adopted.(1)For LYS160 or SS400, its deformation capacity was not significantly affected by the transition form of fillets under tension loading.(2)The shear deformation capacity was affected by the fillet shape, while it was insensitive to the fillet radius.(3)Under reciprocating shear, the deformation capacity of large plastic material (LYS160) was affected by the T-shape fillet slightly, while the deformation capacity of small plastic material (SS400) was affected by the T-shape fillet greatly.(4)Under repeating shear, stable interlayer deformation could be obtained in the middle part, and the deformation capacity of LYS160 specimens depended on the T-shape fillet. There was no stable deformation in the middle part, the deformation capacity of SS400 specimens was not affected by the T-shape fillet.(5)When the fatigue cycle was 30, the corresponding amplitude of the LYS160 specimens with a T-shape fillet was 25% shear strain, which can meet most engineering applications.In this study, the deformation properties affected by different shoulder fillets were investigated by tensile and shear loading. The main conclusions are as follows:


It remains a mystery as to how neurons are connected and thereby enable use to think, and volume reconstruction from series of microscopy sections of brains is a vital technique in determining this connectivity. Image registration is a key component; the aim of image registration is to estimate the deformation field between two images. Current methods choose to directly regress the deformation field; however, this task is very challenging. It is common to trade off computational complexity with precision when designing complex models for deformation field estimation. This approach is very inefficient, leading to a long inference time. In this paper, we suggest that complex models are not necessary and solve this dilemma by proposing a dual-network architecture. We divide the deformation field prediction problem into two relatively simple subproblems and solve each of them on one branch of the proposed dual network. The two subproblems have completely opposite properties, and we fully utilize these properties to simplify the design of the dual network. These simple architectures enable high-speed image registration. The two branches are able to work together and make up for each other’s drawbacks, and no loss of accuracy occurs even when simple architectures are involved. Furthermore, we introduce a series of loss functions to enable the joint training of the two networks in an unsupervised manner without introducing costly manual annotations. The experimental results reveal that our method outperforms state-of-the-art methods in fly brain electron microscopy image registration tasks, and further ablation studies enable us to obtain a comprehensive understanding of each component of our network. Image registration is a long-standing problem in computer vision and medical image processing. The aim of image registration is to estimate the deformation field between two images and warp source images to reference images according to the estimated deformation field. This task has wide applicability in neuroscience. To obtain a comprehensive understanding of the circuitry mechanisms underlying brain neuron structures, mechanically cut sections are imaged by electron microscopy. Due to the large deformation and artefacts introduced in the data acquisition step, a registration method that can recover the connectivity among neurons is of great importance.Current learning-based methods generally directly regress the deformation field from two coarsely aligned images. Coarse alignment is usually conducted by off-the-shield software, and high alignment quality is not guaranteed. The whole process is not end to end and cannot fully exploit the advantages of deep learning. Most methods pursue the high-precision prediction of the deformation field at the expense of computational complexity. Many well-designed modules are progressively added to the networks, causing a decrease in inference speed.In this work, we suggest that a complex architecture is not necessary, and we simply need to reformulate the original problem. We split the problem into two parts: estimating a linear deformation and estimating the remaining nonlinear deformation, which can be considered as a residual of the previous linear deformation.The linear deformation can be parameterized by an affine transformation. A simple architecture is sufficient to regress its six parameters. Since linear deformations pay greater attention to the global transformation of the image, they do not focus on local details. Utilizing this property, we can use low-resolution inputs to reduce the computational complexity. After the linear deformation has been estimated, we warp the original images to eliminate this part of the deformation. The remaining nonlinear deformation is estimated from these linearly warped images. Since a large part of the deformation represented by the affine transformation has been eliminated, the remaining nonlinear deformation can be seen as small variations of fine details; such deformation focuses on local structures, no high-level semantic information is involved, and a shallow neural network is sufficient.To summarize, we decompose the original problem to reduce the difficulty in solving it. The two subproblems have completely opposite properties, and we utilize these properties to simplify the design of the corresponding estimation branches of the overall network, resulting in a simple architecture. The two branches are combined in a dual manner and make up for each other’s drawbacks. The resulting dual-network architecture is able to perform image registration with high precision and high speed.Furthermore, to eliminate the cost of manual annotation, we propose a training scheme for unsupervised image registration. No knowledge of ground truth is needed, and the registration quality is totally regularized by our specially designed losses.The experimental results reveal that our method outperforms state-of-the-art methods in the fly brain electron microscopy image registration task, and further ablation studies enable us to obtain a comprehensive understanding of each component of our network.In recent years, an increasing number of biological brain volumes have been reconstructed from serial-section electron microscopy images, e.g., the adult Drosophila melanogaster brain , the larAll traditional registration methods calculate a parameterized deformation field based on the matches of sparse features such as SIFT , SURF 66 and BRIWith the development of deep learning, deep learning-based methods have gradually been proposed in recent years. As a common way to estimate the deformation field, well-developed optical flow prediction networks ,13 have Our aim is to estimate the high-precision deformation field in minimal time. This task is not trivial since common practices trade off time for accuracy. To this end, we adopt a divide-and-conquer scheme to split a difficult deformation field estimation problem into two relatively simple subproblems. In this way, these two subproblems can be solved by neural networks with simple architectures.We divide the original deformation into a linear part and a nonlinear part. Although common deformations are nonlinear, we can still extract a linear part therein, and the remaining nonlinear deformation can be treated as a residual. In practice, linear deformations  are in the majority because sections are placed in various poses; this situation is inevitable but will not destroy the morphology of the brains. On the other hand, nonlinear deformation is often caused by sectioning and ruins the original brain structures, causing issues in subsequent analyses. With the development of various techniques, many precautions are being taken to largely avoid this type of artefact. Therefore, instead of directly regressing the deformation field from image pairs, we design a dual-network architecture whose two branches—LinearNet and NonlinearNet—are aimed at handling the linear deformation and the remaining nonlinear deformation, respectively. The combination of the estimated linear deformation and remaining nonlinear deformation forms the final deformation. Our workflow first estimates the linear deformation and then uses the estimated linear deformation to warp the source images to eliminate the majority of the deformation; consequently, the remaining nonlinear deformation is estimated as a residual.Before utilizing the different characteristics of the linear deformation and nonlinear deformation, we summarize their properties in the following.Properties of linear deformations:A linear deformation can be parameterized as a rigid transformation, similarity transformation, affine transformation etc. A small quantity of parameters need to be estimated, and the regression process is relatively simple.A linear deformation focuses on the global transformation of the whole image and is less focused on spatial variations of local details.A linear deformation pays more attention to high-level semantic features, which plays an important part in global transformation regression. Correspondingly, low-level semantic information becomes less important, and we can avoid unnecessary computations in this part.Properties of nonlinear deformations:Although some methods choose to parameterize a nonlinear deformation as thin-plate splines or B-splines to simplify the calculation, these forms can only cover finite types of nonlinear deformations. One flexible method is representing a deformation as a field; however, extra regularization terms, such as smoothness penalties, are needed, and this approach has difficulties regressing large deformations.A nonlinear deformation is not inclined to have global consistency. Every pixelwise spatial variation can be different; however, in real-world applications, it is usually locally smooth.The regression of nonlinear deformations relies more on low-level semantic features, and high-level semantic information is less useful.Through analysing the properties of linear and nonlinear deformations, we utilize these properties to guide the design of our dual-network architecture. The design principles of LinearNet and NonlinearNet, which fully explore the respective properties of two deformations, are summarized as follows.Design principles of LinearNet:LinearNet is designed to have a simple architecture, i.e., an encoder with sequential standard convolutional layers to regress a six-dimensional vector as the parameters of an affine transformation.Only the high-level semantic features will be leveraged to regress the final parameters, and low-level semantic features are merely used to generate high-level semantic features.The inputs of the network are resized to low resolution. Since a linear deformation focuses on a global transformation, high resolution is unnecessary.Design principles of NonlinearNet:NonlinearNet is designed to be shallow because nonlinear deformations attach minimal importance to high-level semantic information.Low-level semantic information is reused via skip connections .High-resolution images are needed; therefore, spatial variations at fine details will be well distinguished.Through fully utilizing the respective properties of the two deformations, we are able to simplify the architectures of LinearNet and NonlinearNet as much as possible. Unnecessary computations are abandoned, enabling us to increase the inference speed and retain satisfactory registration accuracy simultaneously. In the following, we will describe the detailed overall pipeline of our dual network.The overall pipeline of our dual network is illustrated in k refers to the W denotes a warping function that can warp the input image according to the input deformation field. The warping process is fully differentiated and can be implemented as a neural network layer; for detailed information, please refer to STN [The inputs of LinearNet are the low-resolution concatenated source and target images , which tend to be 3–30-times faster. For traditional methods, their optimization processes have to run once per input. Their optimization is usually conducted in an iterative manner, which is time consuming and inefficient. In contrast, once deep learning-based methods have been trained, their models do not need to go through the complex optimization process during use.Compared with the deep learning-based method ssEMnet, our DualNet is 7-times faster. Unlike DualNet, which uses grayscale images as inputs, ssEMnet is designed to have a substantially more complex architecture to receive feature maps extracted from original image pairs by a pretrained encoder. Its loss function is evaluated at the feature level, which means greater computational burden. In contrast, our proposed loss function is able to substitute the role of the feature-level comparison and will not cause excessive computational complexity.Accuracy. In terms of accuracy, our method outperforms all the state-of-the-art methods. The first metric, SSIM, is aimed at measuring the continuity between registered image pairs. From this aspect, our method achieves substantially more continuous results. As shown in The second metric, Dice, is designed to determine whether the proposed method is able to restore the connectivity between mechanically cut neurons; this ability is of great significance in the neuroscience field. Furthermore, unlike the previous SSIM metric, Dice is measured with ground truth, meaning it is more likely to reveal a method’s capability for real-world applications. From this perspective, our method is more applicable than the other methods.When removing NonlinearNet, it is obvious that the remaining LinearNet is not able to obtain satisfactory registered images, whereas when removing LinearNet, NonlinearNet will have trouble regressing the global transformation; when high-level semantic features are needed, the current simple architecture is not sufficient. However, the proposed divide-and-conquer scheme makes LinearNet and NonlinearNet excel in their advantages and make up for each other’s drawbacks, resulting in a simple but efficient architecture to accomplish the complex brain image registration task. Since the architectures of the two subnetworks are all simple, the overall dual-network architecture is also very simple, and it has much fewer learnable parameters than widely used architectures such as the flownet  and flowDuring our experiments, we observed that when the linear deformation estimation fails to obtain correct results, the subsequent nonlinear deformation estimation usually cannot provide promising results. Once the linear deformation has been well estimated, the further nonlinear deformation estimation becomes very easy. Based on this phenomenon, we can conclude that introducing a linear deformation estimation step will dramatically simplify the remaining nonlinear deformation estimation, and directly estimating a nonlinear deformation is very difficult.To better understand how different elements influence the overall performance, in Training Loss. To clarify how much each component of the overall loss contributes to the overall performance, we try different combinations of losses, as shown in We can see from Image resolution. Since in our setting the input image resolution has a large impact on the final performance, to determine the configuration of th input image resolution that will lead to the best performance with as short a computation time as possible, we test different combinations of input image resolutions in LinearNet and NonlinearNet, as illustrated in By comparing the figures in the top half and bottom half of From the results in the first three rows, we can conclude that the increase in input image resolution may not necessarily improve the performance of LinearNet. The most appropriate image resolution for LinearNet is Even though reducing the input image resolution will reduce the time required for prediction, our network has a simple architecture, it is sufficiently fast, and the difference between different image resolutions is not substantial; thus, we can choose a relatively high solution — In this work, we aim to solve the image registration problem, which is a key component of volume reconstruction from brain microscopy sections. In contrast to common methods that design complex models to regress the deformation between two given images, we suggest that complex models are unnecessary and propose a dual network with a simple architecture to solve this problem. We divide the original deformation field estimation problem into two subproblems. The two branches of the dual network fully utilize the individual properties of these subproblems and solve them in a simple yet efficient manner. To make our method applicable to real-world scenarios, we propose a series of loss functions to enable unsupervised learning, and no ground truth is needed during training. Experiments on fly brain electron microscopy images show that our method achieves the best performance with the lowest time requirement. Comprehensive ablation studies further verify the effectiveness of our work. In this work, simple convolution layers are used, some modules which have been proven extremely computation-efficient, e.g., EfficientNet , Shuffle


The Rvb1-Rvb2-Tah1-Pih1 (R2TP) complex is a co-chaperone complex that works together with HSP90 in the activation and assembly of several macromolecular complexes, including RNA polymerase II (Pol II) and complexes of the phosphatidylinositol-3-kinase-like family of kinases (PIKKs), such as mTORC1 and ATR/ATRIP. R2TP is made of four subunits: RuvB-like protein 1 (RUVBL1) and RuvB-like 2 (RUVBL2) AAA-type ATPases, RNA polymerase II-associated protein 3 (RPAP3), and the Protein interacting with Hsp90 1 (PIH1) domain-containing protein 1 (PIH1D1). R2TP associates with other proteins as part of a complex co-chaperone machinery involved in the assembly and maturation of a growing list of macromolecular complexes. Recent progress in the structural characterization of R2TP has revealed an alpha-helical domain at the C-terminus of RPAP3 that is essential to bring the RUVBL1 and RUVBL2 ATPases to R2TP. The RPAP3 C-terminal domain interacts directly with RUVBL2 and it is also known as RUVBL2-binding domain (RBD). Several human proteins contain a region homologous to the RPAP3 C-terminal domain, and some are capable of assembling R2TP-like complexes, which could have specialized functions. Only the RUVBL1-RUVBL2 ATPase complex and a protein containing an RPAP3 C-terminal-like domain are found in all R2TP and R2TP-like complexes. Therefore, the RPAP3 C-terminal domain is one of few components essential for the formation of all R2TP and R2TP-like co-chaperone complexes. Heat shock protein 90 (HSP90) is a molecular chaperone that assists the maturation, stability, and assembly of a number of cellular proteins and macromolecular complexes. HSP90 works with the help of co-chaperones, nonclient partners that interact with the chaperone and contribute to its function. Some co-chaperones contain tetratricopeptide repeat (TPR) domains that bind to EEVD motifs located at the C-terminus of each HSP90 monomer and help recruit HSP90 to specific clients . CrystalSaccharomyces cerevisiae and it was later found in mammalian cells . R,5. R4,5]A fully functional R2TP-based chaperone pathway requires the participation of additional proteins . R2TP asSome of the proteins that interact with R2TP work as adaptors connecting the R2TP co-chaperone machinery to specific clients . The comRecently, Houry, Bertrand, and Coulombe proposed renaming the chaperone machinery composed by R2TP, WDR92, RPB5 , and the URI1 prefoldin complex as PAQosome (particle for arrangement of quaternary structure) to better reflect the function of this co-chaperone machinery in the acquisition of the quaternary structure of a growing number of large macromolecular complexes  11]. So. So11]. Of all the components currently identified as part of the R2TP-based co-chaperone system, only the RUVBL1 and RUVBL2 ATPases are consistently found to be essential in all the activities described, suggesting that these are non-interchangeable components of the system. RUVBL1 and RUVBL2 are two closely related AAA-type ATPases that form hetero-hexameric rings where three copies of RUVBL1 and three copies of RUVBL2 subunits alternate  Figure A. This hSaccharomyces cerevisiae, Chaeetomiun thermophilim, and Homo sapiens [The atomic structure of RUVBL1-RUVBL2 has been studied by X-ray crystallography in several species:  sapiens ,31,32,33 sapiens ,34. In a sapiens ,29. RUVB sapiens ,36.In 2017, two cryo-EM structures of yeast R2TP (Rvb1p-Rvb2p-Tah1p-Pih1p) reconstructed at medium resolution revealed that Rvb1p-Rvb2p formed a hexameric ring that scaffolds a Tah1p (yeast RPAP3 homologue) in complex with Pih1p (yeast PIH1D1 homologue) ,39. Pih1A significant advance in our structural understanding of human R2TP was achieved in 2018  Figure B,C. CompThe cryo-EM structure of human R2TP revealed some similarities to the yeast complex. In human R2TP, PIH1D1 and most of RPAP3 are located in the DII face of the RUVBL1-RUVBL2 ring as in the yeast complex, and this allows R2TP to recruit clients and HSP90 in proximity, facilitating the action of the chaperone on its clients. In addition to the structural similarities with yeast R2TP, the human complex revealed unique structural features. RPAP3 is the only protein that interacts with all other components of the human R2TP complex, and it is therefore essential for R2TP assembly, a function performed by Pih1p in yeast. In addition, the N-terminal TPR containing half in RPAP3 is very flexible compared to the restricted flexibility of the components of yeast R2TP B,C. DiffThe structural characterization of the R2TP complex is completed by the characterization of the interaction of PIH1D1 with the DII domain of an RUVBL2 subunit and how this interaction affects the structure of RUVBL1-RUVBL2  and by aThe most noticeable difference between yeast and human R2TP complexes is the role of the RPAP3 C-terminal domain in the assembly of the full complex ,37. CryoThe atomic structure of the RPAP3 C-terminal domain has been determined as part of R2TP using cryo-EM  Figure A as wellThe cryo-EM structure of the R2TP complex shows regions of the RPAP3 C-terminal domain facing RUVBL2, and some residues that can be important for their interaction  Figure C. HeliceThe RPAP3 C-terminal domain performs at least two essential roles. On the one hand, it assembles R2TP by bringing together RPAP3 (or the RPAP3-PIH1D1 complex) and the RUVBL1-RUVBL2 ATPase complex. In vitro mapping experiments have shown that the interaction between the RPAP3 C-terminal domain and RUVBL2 is essential to hold the R2TP complex together . PIH1D1 situs inversus disease, suggesting a critical role of CCDC103 in the assembly of dynein arms on ciliary axonemes [RPAP3 exists in two isoforms, with isoform 2 being shorter as it lacks few residues essential for the interaction of RPAP3 with PIH1D1 . In addiaxonemes . A mutataxonemes .The canonical R2TP complex is made of RUVBL1-RUVBL2 hetero-hexamer in complex with RPAP3 and PIH1D1 , but theIn summary, RPAP3 C-terminal-like domains allow the interaction between an RPAP3-like protein and the RUVBL1-RUVBL2 ATPases forming several distinct complexes, collectively named as R2TP-like  Figure . The funPol II, U5 snRNP, and mTORC1 complexes are among several large macromolecular complexes that require the action of the R2TP-based chaperone system for their assembly, activation, or maturation. This chaperone system is versatile, with not all the components working for each client, and with several RPAP3-like and several PIH1D1-like proteins implicated. The interaction of the RUVBL1-RUVBL2 ATPases with a protein containing an RPAP3 C-terminal-like domain is the only element apparently conserved in all versions of the R2TP-based chaperone. Further progress needs to be made to identify all the proteins involved in the assistance to each client and their specific role. The role of ATP binding and ATP hydrolysis by RUVBL1 and RUVBL2 in the R2TP complex is still mysterious and this needs further investigation. Solving the structures of the URI1 prefoldin complex and its interaction with R2TP, as well as the structures of complexes between R2TP and adaptors, clients, and chaperones will be important to fully understand how R2TP functions.


The properties of potato flour will be different due to different processing parameters, which will affect their processing adaptability. In this paper, different potato flour were investigated to determine viscoelastic properties and structural transformation using thermodynamics, rheological and spectrum methods. Potato flour was prepared by drying at different temperature after soaking in citric acid, microwave and steamed respectively. The treated samples were dried by hot air and then compared with the freeze-dried potato flour. Four kinds of potato flour showed different properties after shearing at high temperature.Differential scanning calorimetry (DSC) results revealed that potato flour with low gelatinization had lower enthalpy and faster melting process than freeze-dried potato powder. RVA and texture results showed that potato flour with low gelatinization had the best retrogradation property and the stable gel. X-ray diffraction (XRD) patterns revealed that the crystalline properties of different potato flour after shearing at high temperature were the same. In addition, low gelatinization potato flour presented a crystalline structure or strong internal order. Fourier-transform infrared spectroscopy (FTIR) spectra showed that high temperature and shearing mainly caused δ-deformation of O–H in intact potato granules.Freeze drying and hot air drying at low temperature made potato flour had better gel stability than microwave and steamed treatment. Hot air drying at low temperature made potato flour had good retrogradation after hot shearing, which was more conducive to the formation of hot-processed products. Solanum tuberosum L.) is an annual herb of Solanaceae, its fresh tubers can be eaten after simple processing. As one of the world’s major food crops, potato contains ascorbic acid, phenolic substances and other important active ingredients [Potato  could significantly inhibit the activity of polyphenol oxidase (PPO). When vitamin C was 0.35%, citric acid was 1.2% and Na2SO3 was 0.25%, the inhibitory effect on PPO was the best. Zhao et al. [l-cysteine to remarkably inhibit the browning of potato powder.Browning of fresh potato during processing is an important factor affecting the quality of dry potato flour, which can be divided into enzymatic browning and non-enzymatic browning. Enzymatic browning makes potatoes murky grey, and non-enzymatic browning makes them yellowish-brown. In production, browning can be prevented by heat-treated or adding color fixative at room temperature. Vitamin C, citric acid and phytic acid are in common use in color protection operation. Wang et al.  found tho et al.  used 0.0High temperature can inactivate polyphenol oxidase, and thus inhibit browning. However, excessive heat treatment or mechanical shear stress can lead to cell wall degradation and free state of starch and other nutrients in potato tubers. The physical properties of the product are difficult to preserve, its dispersibility and rehydration are not good, and it does not have the characteristics of fresh potatoes. Li et al.  obtainedDespite several studies on the physicochemical, rheological, and thermodynamics properties of potato flour have been reported earlier in several studies , 10–12. Hot air, microwave and steam change the temperature, moisture and particle structure of potato flour, which resulted in different degree of starch gelatinization (DG) of the product . The DG As shown in Fig. The potato flour microparticles were prepared by suspension sampling technique. The results obtained for particle size and size distribution were given in Table Microparticles prepared from LGPF showed a mean size of 240.73 μm with a median size of 213.7 μm. About 90% of the microparticles were lower than 514.4 μm and 10% of the particles were lower than 29.54 μm. Particle size distribution of FDPF concentrated at 391.38 μm. A large amount of water in the sample before hot air drying was beneficial to enhance the binding force between molecules, leading to the formation of large masses.Microparticles prepared from MPF showed a mean size of 210.27 μm with a median size of 162.03 μm. About 90% of the microparticles were lower than 471.23 μm and 10% of the particles were lower than 33.61 μm. Particle size distribution of MPF concentrated at 185.49 μm. After microwave irradiation, the water content in potato chips was decreased, and the binding and aggregation of molecules was weakened.Microparticles prepared from SPF showed a mean size of 273.8 μm with a median size of 252.03 μm. About 90% of the microparticles were lower than 526.13 μm and 10% of the particles were lower than 48.37 μm. Particle size distribution of SPF concentrated at 356.48 μm. Similar to LGPF, a large amount of moisture during drying led to an increase in the particle size of the powder.According to the value of C.V., the particle size distribution of SPF was the narrowest, followed by LGPF, and FDPF and MPF were the widest.Figure The gel forming ability of MPF and SPF was weak and not suitable for the detection of texture analyzer. Table G. As can be seen from Table G, the gelatinization process of LGPF spend less thermal energy than that of FDPF.No peak of MPF and SPF image was found in the DSC results. The gelatinization properties of FDPF and LGPF were observed by DSC, and the results are shown in Table The pasting profiles of potato flour are shown in Table Figure −1 of the IR spectra represent ν-stretching of O–H [−1. However, this was opposite for LGPF. ν(O–H) of SPF was the weakest than other potato flour. The IR intensity of the peaks centered at ~ 1655 cm−1 represent δ-deformation of O–H [−1 represent the bending of C–H [−1. There was no significant difference in IR spectra between MPF and its paste.FTIR spectra for the potato flour and their paste were all similar in shape but not intensity Fig. , indicatg of O–H . For FDPn of O–H . For FDPg of C–H . For FDPThe effect of temperature and shear on the viscoelastic properties and molecular structure of potato flour prepared by different methods was investigated. The effect of high temperature shearing on the binding of covalent bonds in potato flour was different. Results of FTIR showed that the combination of temperature and shear stress had positive effect on ν-stretching of O–H in FDPF and SPF. However, the combination play negative role on ν-stretching of O–H in LGPF. ν(O–H) of SPF was the weakest than other potato flour. For FDPF, LGPF and SPF, high temperature shear had negative effect on δ-deformation of O–H. For FDPF and SPF, high temperature shear play positive role on bending of C–H. Results of XRD showed that the crystalline properties of different potato flour after shearing at high temperature were the same. In addition, low gelatinization potato flour presented a crystalline structure or strong internal order. Texture results showed that FDPF and LGPF had good gel stability, while MPF and SPF gel had poor stability. LGPF had good retrogradation after hot shearing, which was more conducive to the formation of hot-processed products.Potato tubers were purchased from Taste From The Field . Citric acid  was purchased from Beijing Chemical Works .Some fresh potatoes were cleaned and steamed for 40 min, peeled artificially, and then dryed by hot air (65 °C) in a Natural convection oven  for 12 h. In addition, some tubers were peeled using a fruit parer. Immediately, the peeled potatoes were kept in water to prevent browning. Following, the tubers were cut into slices (2.5 mm thick) using a fruit slicer. Finally, some potato slices were freeze-dried for 48 h, the others were soaked in a compounded anti-browning agents containing 0.4% citric acid for 30 min to avoid enzymatic browning and dried by hot air in Natural convection oven  for 12 h, or heated for 4 min with a microwave (P70D20AP-TE(W0), 700W, Galanz, China) and dried by hot air in natural convection oven  for 12 h. After microwave for 2 min, potato slices were turned over before microwave for the remaining 2 min.The dried potato mass or slices were smashed by a Mill  for 15 s and sieved with 60 mesh. According to Zhang et al.’s  method, The DG was determined according to the Chemical Industry Standards of the People’s Republic of China (HG/T 3932-2007). Firstly, two laboratory samples (1 g each) were weighted and placed in 150 mL conical bottles and labeled A and B, respectively. An empty 150 mL triangular bottle was regarded as control and marked C. 40 mL phosphoric buffer (pH = 6.8) was added to A, B and C. A was heat in boiling water bath for 30 min and then cooled down to below 60 °C. 5  mL β-amylase solution  was added to these three samples, respectively. Secondly, A, B and C were put in a water bath (40 °C) for 1 h and they were shook once every 15 min. 2 mL sulfuric acid solution  and 2 mL sodium tungstate solution (120 g/L) were added to A, B and C, and then shake well and diluted to 100 mL with distilled water. Solutions in 100 mL volumetric bottles were filtered with neutral filter paper. Thirdly, 5 ml filtrate was added to a 150 mL conical bottle, then 15 mL 0.1 mol/L alkaline potassium ferricyanide solution was added and boiled in water bath for 20 min. After cooling to room temperature, the samples were added 25 mL acetate solution  and 5 mL 100 g/L potassium iodide solution in turn. Immediately, the solution was titrated to light yellow with 0.1 sodium thiosulfate solution and added three drops of 10 g/L starch indicator solution, then titrated until the blue disappeared. The DG of the sample was calculated according to the following formula:Amylose contents were determined by iodine colorimeter at 620 nm using a potato starch standard mixture .The swelling properties were measured following the procedure by Zhang and Ma et al. with some modification , 18. 0.2The light transmittance was determined according to the method of Heo . 1%  was determined according to areas below the curves and was shown in the form of mJ.The gelatinization of potato flour was analyzed by differential scanning calorimetry  under ultra-high purity nitrogen atmosphere according to a conventional procedure stated by Cui’s team . 3 mg saParticle size distribution of potato flour was analyzed using a Laser particle size analyzer . Each value was mean of three replicates.According to a conventional procedure stated previously , the samThe pasting properties of potato flour were measured in accordance with the method as defined by the published literature with rapid viscosity analyzer  . 25 mL dThe textural properties were measured following the procedure by Li et al.  with somAccording to the procedure stated previously , XRD pat−1 and at an average of 4 scans per sample.FTIR spectra were collected for potato flour and their paste using a FTIR spectrometer . 1–2 mg samples were blended with 200 mg KBr and pressed into tablets before measurement. Spectra were collected at a resolution of 4 cmAll experiments were replicated at least thrice. All test data were statistically analyzed by one-way ANOVA using SPSS 19.0, and expressed as mean values ± standard deviations. Independent T-test and Duncan’s multiple range test was used to determine significant differences. Differences were regarded as significant at 95% (p < 0.05).


Chromism—color changes by external stimuli—has been intensively studied to develop smart materials because of easily detectability of the stimuli by eye or common spectroscopy as color changes. Luminescent chromism has particularly attracted research interest because of its high sensitivity. The color changes typically proceed in a one-way, two-state cycle, i.e. a stimulus-induced state will restore the initial state by another stimuli. Chromic systems showing instant, biphasic color switching and spontaneous reversibility will have wider practical applicability. Here we report luminescent chromism having such characteristics shown by mechanically controllable phase transitions in a luminescent organosuperelastic crystal. In mechanochromic luminescence, superelasticity—diffusion-less plastic deformation with spontaneous shape recoverability—enables real-time, reversible, and stepless control of the abundance ratio of biphasic color emissions via a single-crystal-to-single-crystal transformation by controlling a single stimulus, force stress. The unique chromic system, referred to as superelastochromism, holds potential for realizing informative molecule-based mechanical sensing. Color changes by external stimuli, so-called chromism, have been intensively studied to develop smart materials due to the stimulus-responsiveness of chromic materials. Here the authors demonstrate luminescent chromism during a mechanically controllable phase transition in a luminescent organosuperelastic crystal. Fundamental research has a fascination with chromism because color changes based on wavelength shifts in absorption, emission, or reflection correlate with stimuli–responsive structural changes of materials on the subnanometer to micrometer scale. Chromism is also attractive from a practical viewpoint since the color changes are easily detectable by the naked eye or simple spectroscopy.Chromism7, which is particularly important because the most fundamental stimuli in nature are commonplace mechanical forces that can be generated without any special apparatus. Mechanochromism based on elastic deformation has been demonstrated in amorphous gel materials10 and a few molecular crystals under high pressure (3–10 GPa)11 to show continuous changes in structural periodicity and color, which has led to quantitative mechanical sensing. Mechanically induced defects can also induce mechanochromism4. These systems exhibited spontaneously reversible chromism, but most chromic processes demand another stimulus for reverse chromism, i.e., reversion to the initial color.The process of color changes induced by pressing, shearing, cutting, and other mechanical forces is called mechanochromism13. Mechanochromism based on a phase transition, e.g., polymorphic14 or crystal-to-amorphous3, is spontaneously irreversible but shows a biphasic color change at a high resolution, which is useful for memory storage and sensing tiny forces.As we previously reported about the polymorph-dependent luminescence of organic crystals, the luminescence color depends on the difference in molecular packing in crystals, and no chemical reactions are involved. Information about such systems could prove fruitful for elucidating the crystal structure–property relationship15. Elasticity is a common physical property in the spontaneous shape recoverability of materials. Recently, research on the manner of elastic deformation of organic crystals has been intensive20 despite a general perception of their brittleness. In contrast, superelasticity or more specifically plastic deformation with spontaneous shape recoverability is a minor and unusual physical property, except in special kinds of metallic solids called superelastic alloys and shape-memory alloys22, and research is still in its infancy especially in organic crystals30. In the elastic deformation, the density-gradient distribution of components causes strain to accumulate, whereas the superelastic deformation can accommodate strain through orientation changes of domains upon phase transition or twinning; thus, superelastic deformation has a potential ability to abruptly change physical properties. Since superelastic materials were developed specifically for metal alloys, their electronic physical properties limited coupling to conductivity and, recently, magnetism31. In organosuperelasticity, organic solids even in the single-crystal state show spontaneously reversible mechanically induced phase transitions, indicating the potential functionality of superelasticity with dielectric and optical properties due to the organic materials’ characteristics. Very recently, organoferroelasticity—diffusion-less plastic deformation leaving spontaneous strain without spontaneous shape recoverability—in luminescent organometallic crystals was reported32. The luminescence is stable and unchanged during the organoferroelastic mechanical twinning, giving no mechanochromism.In this context, the focus of this work is organosuperelasticity, which we first discovered in 2014a]pyridine (7Cl)13 show superelasticity with luminescent color changes.Ideally, superelastic solids have optical functionality, and the occurrences and amounts of the phase transitions can be easily controlled in real time as material strain caused by mechanical stress, enabling biphasic color switching with excellent controllability in direction, region, timing, and velocity Fig. . We find7Cl derivatives is based on an excited-state intramolecular proton transfer (ESIPT)  light (365 nm)  Fig. 33–36, renm) Fig. .Fig. 2FlYG) domain was converted into a daughter (β) domain in association with the deformation of a YG crystal  crystal  measurements  domain confirmed by single-crystal X-ray diffraction measurements of a YO crystal in the state with coexisting α and β domains  domain under both PW and UV light βYO (or (120)αYG//βYO), resulting in a calculated bending angle of 42.1°, which agrees well with the measurement by optical microscope observation (ca. 42°). The αYG domain transformed into the βYO domain by a 68° (or 61° and 16°) rotation of 7Cl molecules, which also induced their displacive motions of 2.0 and 1.9 Å along the a- and b-axes, respectively, to optimize the herringbone arrangement at the interface  and reverse (σr) deformation, respectively; chemical shear (σc) of 1.092 MPa; energy-storage density (Es) of 553.6 kJ m−3; dissipated energy density (Ed) of 733.0 kJ m−3; energy-storage efficiency (η) of 0.430; a superelastic index (χ) of 0.505  . Organosuperelasticity offers the advantage of reversible and strict crystal deformation in association with molecular rearrangement against applied force. Mechanochromism can be induced at any position(s) at any time by superelasticity, or mechanically well-regulated structural transition. These characteristics differentiate superelastochromism from conventional chromisms, e.g., superelastochromism can detect in situ mechanical stress quantitatively: detection of the moment when mechanical stress is applied and removed, and how much work is done on a crystal specimen. The advantage of ESIPT molecules is their strong emissions with a color sensitive to solid-state molecular arrangement. Combining these advantages will open up the science of superelastochromism, which can expand the designability and applicability of chromic materials in the future.In conclusion, ESIPT-fluorescent crystals of 3  was refluxed for 20 h. After filtering off the insoluble solid, the filtrate was evaporated, and the residue was applied to a silica gel column (CHCl3/ethyl acetate = 100:0 to 40:1) to afford 7-chloro-2-(2′-methoxyphenyl)imidazopyridine . Then a cooled solution (0 °C) of well-dried 7-chloro-2-(2′-methoxyphenyl)imidazopyridine  in anhydrous dichloromethane was dropwise added to a dichloromethane solution (1.0 mol/L) of boron tribromide . The reaction mixture was allowed to reach room temperature and further stirred for 3 h. A saturated aqueous NaHCO3 was slowly added with stirring, and then separated with water and chloroform. The organic layer was washed with water and brine, and then dried over Na2SO4. The organic layer was evaporated to give the crude product. Purification by recrystallization from ethanol . M. p.: 196–197 °C. 1H NMR  δ: 12.38 , 8.05 , 7.81 , 7.59 , 7.54 , 7.23 , 7.02 , 6.87 , and 6.84   δ: 157.3, 146.3, 143.4, 131.7, 130.1, 125.9, 125.6, 119.2, 117.9, 115.8, 115.8, 114.9, and 106.9  solution of 2-(bromoacetyl)anisol , 4-chloro-2-amino-pyridine , and NaHCO 1.55 g, .45 mmol 7Cl were prepared by recrystallization from ethanol solutions, affording two polymorphic crystals: YG and YO. Single crystals in the YG form were selectively obtained from a 7Cl solution in 1:1 mixture of THF and toluene at r.t.Single crystals of An optical microscope  equipped with polarizing plates and a digital camera was used to record mechanical deformation of crystals using tweezers.7Cl crystals were carried out using a DSC-60 (Shimadzu Co.) and DSC 7020 (Hitachi High-Technologies Co.) instruments under a nitrogen gas flow (65 ml min−1). Crystals in YG and YO form with the size of a few hundreds of micrometers for the measurements were prepared by slow evaporation of a 7Cl solution in 1:1 mixture of THF and toluene and in ethanol, respectively, at r.t. Experimental conditions are summarized in Supplementary Table DSC measurements of YG and βYO domains) and YO crystals (coexisting state of αYO and βYG domains) were prepared in addition to as-prepared 7Cl single crystals in YG and YO forms. To avoid spontaneous dissipation of a βYO domain, the mechanically deformed YG crystal was partially cleaved at the αYG//βYO interfaces. Single-crystal X-ray diffraction measurements of the crystals were performed at 298 K (25 °C) with a CMOS detector (Bruker Photon III C14) with a nitrogen-flow temperature controller using a rotating anode X-ray source (MoKα radiation (λ = 0.71073 Å)). Multi-scan absorption corrections were applied using the SADABS program. The structure was solved by intrinsic phasing methods (SHELXT-2014/5) and refined by full-matrix least-squares calculations on F2 (SHELXL-2016/6). Non-hydrogen atoms were refined anisotropically; hydrogen atoms were fixed at calculated positions by riding model approximation. With respect to the mechanically deformed crystals, X-ray diffraction patterns were obtained around the interface of αYG//βYO and αYO//βYG and they were analyzed as twins. Crystal face indexing was carried out using APEX III Ver.2016.1-0 program package with a twin-resolution program  for VIS light and HLV-24UV365-4WPCLTL  connected to PD3-5024-4-EI(A) (CCS Inc.) for UV light. The experimental information and schematic representation of the setup are shown in Supplementary Table Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Movie 1Supplementary Movie 2


Ankyrin repeat (AR) domains are considered the most abundant repeat motif found in eukaryotic proteins. AR domains are predominantly known to mediate specific protein–protein interactions (PPIs) without necessarily recognizing specific primary sequences, nor requiring strict conformity within its own primary sequence. This promiscuity allows for one AR domain to recognize and bind to a variety of intracellular substrates, suggesting that AR-containing proteins may be involved in a wide array of functions. Many AR-containing proteins serve a critical role in biological processes including the ubiquitylation signaling pathway (USP). There is also strong evidence that AR-containing protein malfunction are associated with several neurological diseases and disorders. In this review, the structure and mechanism of key AR-containing proteins are discussed to suggest and/or identify how each protein utilizes their AR domains to support ubiquitylation and the cascading pathways that follow upon substrate modification.  Many proteins have evolved through gene duplication and recombination events to produce repetitive motifs in their primary sequences. These non-overlapping repeat regions, commonly referred to as tandem repeats, provide a high amount of sequence conservation that are generally thought to prevent deleterious residue substitutions that cause alterations to the global fold of the domain. Another tangible benefit of tandem repeats can provide multiple binding sites for various intracellular proteins that can play an important role in protein structural integrity. Tandemly occurring repeats within the primary sequence display specific characteristics in their three-dimensional structure, forming an integrated assembly which allows for tandem repeat domain characterization . The claSaccharomyces cerevisiae cell cycle regulators Swi6, cell division control protein 10 (Cdc10) and Notch in Drosophila melanogaster [AR domains were first discovered as a repeating sequence in nogaster . This ~3nogaster . Since inogaster . To datenogaster . With suComparing AR-containing proteins has proven to be difficult as each protein has acquired various characteristics through multiple evolutionary events. This is largely due to conservation within AR domains relying on various residue types rather than requiring highly conserved residues at specific sites. Given that specific residue types influence the protein’s secondary structure coupled with AR domains not relying on specific residue conservation, it suggests the AR domain is defined primarily on its 3D structural fold rather than its functional support for AR-containing proteins. For example, highly conserved regions of AR domains exist between each repeat, whereas variation of hydrophobic residues can occur while maintaining the structural integrity of the domain . ConservIn comparison to naturally occurring AR domains, there is little deviation from its typical helix–loop–helix–β–hairpin/loop structure, which is supported through conservation of residue type. Inter-domain interactions tend to be short distances, which aids in the linear solenoid packing the AR domain fold rather than a typical globular shape. Both hydrophobic interactions through non-polar regions of the inter and intra α-helices as well as hydrogen bonding through polar residues found near the N-terminus define and stabilize the AR domain’s structural integrity ,13 FiguA.N- and C-terminal AR domains has been observed. Recent insights in AR domain conservation has identified specific residues and motifs frequently occupying the primary sequence of an AR domain. This includes the identification of a G-X-TLPHLA motif and two conserved glycine residues that allow for antiparallel helix termination and β-hairpin loop formation. Throughout the ~33 amino acids, probability of conserved residues to occur within eukaryotic AR-containing proteins remains relatively high, with an increase in conservation within the mid-region  involves a highly regulated enzyme cascade that results in the covalent attachment of ubiquitin on to a substrate protein . First, ubstrate . This poubstrate . C-terminus (HECT), and RING-between-RING (RBR) E3 ubiquitin ligases [2+-binding RING domain as a scaffold that engages and properly orients the E2~ubiquitin complex for efficient transfer of ubiquitin onto a substrate protein [C-termini that contains an absolutely required cysteine residue for accepting ubiquitin from an E2 and the subsequent transfer on to its substrates [The human genome encodes for two E1 enzymes, 37 E2 enzymes, and hundreds E3 ligases allowing for exquisite specificity in substrate recognition for ubiquitin attachment . There a ligases . The RIN protein . The HECbstrates . HECT E3bstrates . There abstrates . Similarbstrates ,22. The specific attachment site(s) of ubiquitin on a substrate protein is decided by the E2-E3 pair , and depWith the prevalence of AR domains in eukaryotic proteins  coupled C-terminal HECT domain required for ubiquitylation, HACE1 was also the first member of the HECT E3 ubiquitin ligases to contain six predicted AR domains at its N-terminus , which was first identified in 2004 as a tumor suppressor caused by chromosomal translocation that leads to sporadic Wilms tumor with decreased HACE1 expression . Apart fterminus A.Besides being genetically linked to Wilms tumor formation, HACE1 has also been reported to play a role in the onset of liver, lymphoma, osteosarcoma, breast and colorectal cancers ,45,46,47N-terminal putative protein interaction domains have been annotated for each HECT E3 ubiquitin ligase family member, which gave rise to the sub-classification of this family. This variety is predicted to be involved in substrate recognition and recruitment [Various ruitment . For insruitment . The HACruitment . It is sruitment . HACE1 also acts as an adaptor protein that is critical in cardiac protection during hemodynamic stress. For example, HACE1 facilitates the transfer of ubiquitylated proteins p62 and microtubule-associated proteins 1A/1B light chain 3B (LC3) for autophagic degradation. This is accomplished through recognizing and binding to these modified proteins via its AR domain, independent of its typical E3 ligase activity . HACE1 aN-terminus  was shown to interact with heat shock protein 90 (Hsp90) to influence cranial mesenchymal cell behavior  through ubiquitylation. EMT is an integral process that allows for plasticity in a phenotype switch in epithelial cells to mesenchymal cells . The actBeyond EMT, HECTD1 has also been shown to play an essential in regulating Iκβα, a key regulator of the NF-kB transcription factor. Specifically, HECTD1 mediates Iκβα K48 polyubiquitylation through its direct interaction with the ribosomal protein subunit 3 (Rsp3). HECTD1 forms a ternary complex with latexin (LAX), Iκβα and ribosomal protein subunit 3 (Rps3); this triggers Iκβα ubiquitylation and upregulates the NF-κβ stimulated inflammatory stress response . InteresDeubiquitylation involves the enzymatic removal of ubiquitin or ubiquitin chains from a substrate protein, and arguably plays the most critical role in regulating ubiquitylated substrate fate . While tUbiquitin specific peptidase 9 X-linked (Usp9x) is a prominently known for acting as the DUB to protect SMURF1 and RNF115 from proteolysis ,72, and Intriguingly, TRAF-binding domain (TRABID) was found to contain two AR domains between a ubiquitin binding domain, named AnkUBD , which rGankyrin is a seven AR domain-containing liver oncoprotein that is involved in a myriad of cellular processes including cell cycle progression, liver regeneration, protein translocation and enzymatic regulation A 78,79,,79,80. WImportantly, gankyrin plays a critical role in the encouragement of mouse double minute 2 (MDM2), a RING E3 ubiquitin ligase, to ubiquitylate p53 for cytoplasmic localization and degradation . SpecifiGankyrin also has been demonstrated to be involved in EMT through its regulation of downstream cytokines interleukin 6 (IL-6) and transforming growth factor beta (TGF-β) that induce the EMT phenotype . In contThe mechanisms used by gankyrin to regulate transcription factor levels is currently unclear and need further examination. Likewise, a detailed comparison of gankyrin to a similar AR domain-containing protein that regulates nuclear factor-kappa-β (NF-κβ) expression levels is warranted. For instance, the Iκβα is a five AR-containing regulatory protein with capabilities of regulating NF-κβ expression levels A,C. OnceAR domain-containing proteins are ubiquitous and play essential roles in numerous biological processes that can also influence the onset of various diseases and disorders. Many AR domain-containing proteins have been identified in numerous occasions to support the USP and reverse the process of deubiquitylation through sequence alignment. Being essential scaffolds to mediate protein–protein interactions, it is intriguing that this domain is not functionally driven but rather dependent on the structural characteristics of the AR domain. Understanding how these AR-domain containing proteins and their role in ubiquitin signaling on the molecular level is a major challenge in our present understanding of their function(s) and activity. Expanded biochemical and biophysical examinations on the novel mechanisms used for AR-domain protein recruitment are needed. By experimentally identifying and validating potential substrates and interactors for these AR-domain proteins, we will be able to improve our knowledge of how these proteins work and will be able to better assess if these proteins could serve as novel drug targets or as biomarkers for disease.


Cross-domain recommendation is a promising solution in recommendation systems by using relatively rich information from the source domain to improve the recommendation accuracy of the target domain. Most of the existing methods consider the rating information of users in different domains, the label information of users and items and the review information of users on items. However, they do not effectively use the latent sentiment information to find the accurate mapping of latent features in reviews between domains. User reviews usually include user’s subjective views, which can reflect the user’s preferences and sentiment tendencies to various attributes of the items. Therefore, in order to solve the cold-start problem in the recommendation process, this paper proposes a cross-domain recommendation algorithm (CDR-SAFM) based on sentiment analysis and latent feature mapping by combining the sentiment information implicit in user reviews in different domains. Different from previous sentiment research, this paper divides sentiment into three categories based on three-way decision ideas—namely, positive, negative and neutral—by conducting sentiment analysis on user review information. Furthermore, the Latent Dirichlet Allocation (LDA) is used to model the user’s semantic orientation to generate the latent sentiment review features. Moreover, the Multilayer Perceptron (MLP) is used to obtain the cross domain non-linear mapping function to transfer the user’s sentiment review features. Finally, this paper proves the effectiveness of the proposed CDR-SAFM framework by comparing it with existing recommendation algorithms in a cross-domain scenario on the Amazon dataset. A recommendation system helps a user discover the information he/she wants such as products and content from the massive information produced by the Internet. Recommendation systems are primarily used in commercial applications. A recommendation system helps users find valuable information as the interested information can be recommended to users. This is a win-win situation for both consumers and manufacturers. A good recommendation system can not only accurately detect the user’s behavior, but also help users find the potential information they are interested in.There are a lots of achievements in recommendation systems, which try to enhance the accuracy, diversity and novelty of recommendation. For example, a collaborative filtering-based recommendation algorithm  is one oRecently, more and more researchers  have resHowever, the existing cross-domain recommendation methods are only based on the sharing and transfer of knowledge in the text information such as rating, tag or review, and ignore the latent sentiment information in the review. User reviews usually include user’s subjective views, which can reflect the user’s preferences and sentiment tendencies to various attributes of the item. Fully mining and using the implied sentiment information is helpful to solve the cold-start problems and data sparsity in the process of cross-domain recommendation. The existing cross-domain recommendation algorithms using user reviews do not make full use of the sentiment information in these reviews. They mixed positive sentiment, neutral sentiment and negative sentiment together to realize knowledge transfer, which will weaken or even lose some sentiment information of users, especially negative sentiment. Therefore, it has a great significance to make cross-domain recommendations by combining the user’s sentimental features implicit in the review information.A novel algorithm for cross-domain recommendation named CDR-SAFM is proposed for cold-start users in target domain. It employs sentiment analysis and latent feature mapping and it can transfer latent sentiment review feature from source domain to target domain and make recommendation for cold-start users in target domain.Basing on the idea of three-way decisions, we take into account neutral sentiment to generate the latent sentiment review feature from both the source and target domains, which can affect ratings in the two domains.The LDA models is used to generate user latent review features. When generating features, we consider the sentiment information from reviews, and generate the user sentiment review features in different domains.The Multi-layer Perceptron is employed to accurately map the latent sentiment review feature from the source domain to the target domain, which improves recommendation accuracy.To address the problem of cold-start in the process of recommendation, we propose a cross-domain recommendation algorithm based on sentiment analysis and latent feature mapping (shorted by CDR-SAFM) in this paper, by combining with the implicit sentiment information in user reviews. First, this paper divides the sentiment of user review information into three categories based on the theory of three-way decisions ,5, namelThe rest of the paper is organized as follows. Aiming at the cold-start problem caused by too sparse rating matrices in different domains, some scholars ,7,8 trieMoreover, in order to make full use of the hidden user and item relationships between domains, some works ,10,11 prFurthermore, Song et al.  believedText sentiment analysis refers to the process of analyzing, processing, and extracting subjective text with emotion using natural language processing and text mining technology . The senSentiment classification refers to the identification of subjective text in a given text, whether it is positive or negative, which is the most researched in the field of sentiment analysis. There are usually a lot of subjective texts and objective texts in network texts. Objectivity text is an objective description of things, without emotion color and emotional tendency, and subjective text is the author’s views or ideas on various things, with emotional tendencies such as the author’s likes and dislikes. The object of sentiment classification is subjective text with emotional tendency, so emotion classification must first be subjective and objective classification of text. The subjective and objective classification of texts is mainly based on the recognition of sentiment words. Using different text feature representation methods and classifiers for classification, subjective and objective classification of web texts in advance can improve the speed and accuracy of sentiment classification. Looking at the current research work on subjective text sentiment analysis, the main research ideas are divided into semantic-based sentiment dictionary methods and machine learning-based methods.In the semantic-based sentiment dictionary method, the construction of sentiment dictionary is the premise and basis of sentiment classification. At present, it can be classified into four categories: general sentiment words, degree adverbs, negative words, and domain words. The construction method of emotional dictionaries is to use existing electronic dictionary extensions to generate emotional dictionaries. English is mainly based on the expansion of the English dictionary WordNet to form SentiWordNet lexicon. Hu and Liu  have manThe tendency calculation of semantic-based sentiment dictionaries is different from the machine learning algorithms that require a large number of training datasets. It mainly analyzes the special structure and sentiment tendency words of text sentences by using sentiment dictionary and sentence lexicon, and uses weight algorithm instead of traditional manual discrimination or simply statistical method for sentiment classification. Emotional words with different sentiment intensity are assigned different weights, and then weighted summation is performed.Finally, the threshold is determined to judge the tendency of the text. In general, the weighted calculation result is positive to indicate positive tendency; the result is negative to indicate negative tendency, and the score is zero to indicate no tendency. Compared with the classification algorithm based on machine learning, the sentiment dictionary-based method is a coarse-grained tendency classification method, but because it does not rely on a well-labeled training set, implementation is relatively simple. It can effectively and quickly classify sentiment for web texts in universal fields.Sentiment analysis has been widely used in recommendation systems. Calculating the sentiment orientation of user reviews has been studied by some researchers. Diao  built a In natural language processing, the Latent Dirichlet Allocation (LDA)  is a powz represents subject assignment, while variable w is the observed word. R is the number of resources in the corpus, and N is the number of words in the resources. Parameter K indicates the number of topics suitable for corpus. K is allocated during initialization. Among the variables, only w is the observation variable and the rest is the latent variable.As shown in z. Based on topic z, the word w is obtained by sampling The process of LDA starts from the sampling of topic k represents the k topic of model sampling, and k topic in the dictionary. k. d for topic k. r is assigned to topic k. The joint distribution of the model is as follows:Variable k without including the currently assigned task.Equation  is used We suppose that there are two domains sharing the same user. Users who appear in one domain can appear in another domain. In this sense, the two domains share the same user. Without losing generality, one domain is called the source domain. The other is called the target domain.Define the recommended objects in the target domain as items. Let U across domains, we aim to analyze the sentiment information of the source domain and the target domain, use the common user as a bridge to realize the knowledge transfer from the source domain to the target domain, and solve the problem of rating prediction of cold-start users in the target domain. For this purpose, we propose a cross-domain recommendation algorithm, CDR-SAFM, based on sentiment analysis and latent feature mapping. This framework contains three major steps, that is, latent sentiment review features modeling, latent sentiment review features mapping and cross-domain recommendation, as illustrated in Given the review information At the first step, we aim to analyze the emotional orientation of user reviews in two domains through sentiment analysis methods, so that the original dataset in the two domains is divided into three parts: positive reviews, neutral reviews, and negative reviews. Reviews in the source domain are divided into In the second step, we aim to obtain a mapping function for modeling cross-domain relationship of sentiment review features. We assume that there is a latent mapping relationship between the sentiment review features from source domain and target domain, and then capture this relationship by mapping function. To avoid the mutual interference among the features of positive, neutral and negative sentiment review during the process of knowledge transfer, we use mapping function to model the cross-domain relationship of different sentiments respectively. In order to avoid the lack of different sentiment review features of users in the mapping process, we train the mapping function of different emotions by preprocessing the data and using the common users in the two domains whose sentiment review features are not missing.Algorithm 1: The CDR-SAFM algorithm.Require:Source domain U;Common User set Ensure:  Make recommendation for cold-start users in the target domain;  Sentiment Analysis1:Learn 2:Learn  Latent sentiment review feature3:Learn 4:Learn  Latent Sentiment Review Feature Mapping5:Learn the mapping function  Cross-domain Recommendation6:Get affine features 7:Make recommendation for target users.Finally, we recommend a cold-start user in the target domain. Using this method, we can get the corresponding latent sentiment review feature in the target domain and use these features to affect the final recommendation results. Different mapping results of sentiment review features of cold-start users have different influence on users’ ratings. Therefore, we set different weights for different results of sentiment review features to get the emotional ratings of cold-start users. The complete CDR-SAFM algorithm is presented in Algorithm 1.In this section, we aim to analyze the sentiment information hidden in the user’s review information and extract the user’s review features under different sentiments from the source domain and the target domain, as illustrated in According to general psychology, sentiment has an important influence on one’s behavior and choices. Sentiment analysis plays an important role in information retrieval. It clarifies people’s thoughts and feelings about something or someone in a certain situation. This kind of high level information can be used in many applications, such as customer review analysis, business and government intelligence, personalized recommendation and so on. User reviews on online platforms show similar sentiment expressions, which is generated by similar psychological stimulation. Therefore, it is valuable to combine the latent sentiment information in reviews with cross domain recommendation.Sentiment analysis (SA) is a process of analyzing, processing, summarizing and reasoning subjective characters with emotional color. Among them, sentiment analysis can also be divided into emotional orientation analysis, emotional level analysis, subjective and objective analysis, and so forth. The purpose of emotional orientation analysis is to judge the positive, negative and neutral meaning of the text. In most application scenarios, there are only two types. For example, the two words “love” and “disgust” belong to different emotional orientations.R, a sentiment classification algorithm can divide each sentence of a review However, in the past work based on sentiment analysis, most of the sentiment analysis problems are expressed as—given a set of review We extract “ratings” and “reviews” from the dataset for analysis. “Ratings” represents the user rating of the item in the review, and the rating range is 1–5. “reviews” is the text information of the user review. We randomly selected 1000 reviews each of 1–5, and performed basic de-punctuation, lowercase, and stop word processing on the text to count the word frequency to make a word cloud diagram. Based on the analysis of word cloud diagram with different ratings, most reviews rated as 1, 2 are negative sentiment reviews, and reviews rated as 4, 5 are mostly positive sentiment reviews. However, it is difficult to define the division of reviews with a score of 3, which also proves the idea of three-way decision ideas, so we regard them as a separate category. Therefore, we can divide the dataset into positive sentiment reviews with a rating of 4 or 5, neutral sentiment reviews with a rating of 3 and negative sentiment reviews with a rating of 1 or 2.Obtaining the latent sentiment feature of users is the key for improving the performance of recommendation algorithms. Review-based recommendation algorithms tend to extract latent feature by topic models. In this paper, we use an LDA topic model to extract latent feature. Each latent feature (topic) extracted by LDA is associated with a set of keywords. Thus, we can get interpretable recommendation results by matching the topic distribution of users and items.Take the analysis of user features under positive sentiment in source domain as an example (other similar). We use the review information of all users as a corpus for LDA model training, and use all the reviews of each user as a document. Finally, we find the word distribution of the topics and the topic distribution of the document under the positive sentiment of the source domain. The topic distribution of the document is the user’s latent sentiment review feature, which represented as How to transfer users’ sentiment information effectively is an important problem to be solved in this paper. Existing cross-domain recommendation algorithms that use user reviews do not make full use of the sentiment information in these reviews. They achieve knowledge transfer by mixing positive, neutral, and negative sentiment, which will weaken or even lose some of the user’s sentiment Information, especially negative sentiment. For example, users may be very concerned about the plot of a novel and make positive reviews on the plots of some novels in the field of e-books (source domain), while making negative reviews on the plots of other novels. If we transfer the knowledge obtained from user reviews from the source domain to the target domain without distinguishing the emotional orientation of these reviews, the underlying sentiment factors in the reviews will be mixed together and transferred to the target domain as user features. In movies (target domain), movies with poor plots will match user features transferred to the target domain. But users may not like the movie.To connect the source domain and the target domain, we assume that we can get the relationship between domains through a mapping function. The mapping architecture is shown in To obtain the mapping function, we can formalize the learning process into a supervised regression problem. In particular, we minimize the mapping loss to obtain the mapping function.Multilayer Perceptron (MLP) is a nonlinear transformation, which is more flexible than a linear mapping function. Thus, we choose mapping based on multi-layer perceptron to realize cross domain connection between different domains. It makes the user’s sentiment review feature in the source domain play a complementary and perfect role in the target domain recommendation.MLP can be optimized using back propagation. The optimization problem can be formalized asIn this paper, through sentiment analysis and feature extraction of the source domain and target domain datasets, we can get the user sentiment review features N is the number of common users. We formalized mapping problem as: given N training samples Let That is to say, Given a cold-start user, we cannot recommend it directly because of its sparse data. We first can get the user’s sentiment review feature in the source domain and get the sentiment review feature in the target domain through the learned sentiment feature mapping function. Then by combining the existing user benchmark recommendation results and the influence of sentiment information on user recommendations, we can get the final recommendation results. The specific formal description is as follows:Given a cold-start user Calculating the topic similarity under the same sentiment in different domains is defined as:Therefore, we can get the predicted sentiment rating of cold-start users. That is, the impact of sentiment information in user reviews on the final recommendation is defined as:In this section, we test the CDR-SAFM algorithm proposed in this paper with a real-world dataset. Firstly, the experimental dataset is introduced, and the possible rating of cold-start users in the target domain is predicted by using review data from two different domains—Electronics, and Movies and TV. Then we compare the proposed model with the common methods in the recommendation system. Finally, we randomly select different percentages of cold-start users and analyze the experimental results.Datasets. We employ Amazon cross-domain dataset [ dataset  in our eExperimental Settings. In the experiment, we set Electronics as the source domain and Movies & TV as the target domain. After data preprocessing, the number of common users in the two domains is 2406. The items in these two domains are very different, forming a cross domain user sharing scenario. To evaluate the validity and efficiency of the proposed algorithm on cross-domain recommendation task, we randomly remove all the rating information of a fraction of entities in the target domain and take them as cross-domain cold-start entities for making recommendation. For the sake of stringency of the experiments, we set different fraction for cold-start entities, namely, 20%, 50% and 70%. In addition, since different sets of cold-start entities may affect the final recommendation results, we repeatedly sample users for 10 times to generate different sets. Dimension K of latent sentiment review features is set as 50 and 100. For the MLP mapping function, we choose the structure of the MLP as one-hidden layer, the dimension of input and output of the MLP is set as K, whilst the number of nodes in the hidden layer is set as 2K. The weight and bias parameters of the MLP is initialized according to the rule in Reference [eference . FinallyModels for Comparison. We compare the CDR-SAFM algorithm with the following baseline models and algorithms for validating the performance.AVE: It predicts ratings by the following equation: MF: This is the single-domain matrix factorization algorithm proposed in [posed in .MF_MLP: This is a cross-domain recommendation algorithm based on MF and MLP, which is proposed by [posed by . In our Evaluation Metric. We adopt the Root Mean Square Error (RMSE) to evaluate the prediction performance. It is defined as:The experimental results in terms of RMSE on the Amazon dataset are presented in As the proportion of cold-start users increases, the recommended performance decreases. The decline of single-domain recommendation method MF is the most obvious, while the performance of cross-domain recommendation method is relatively good, which also proves the effectiveness of knowledge transfer in cross-domain recommendation. The result of MF_MLP is better than MF, which also shows that the mapping function based on MLP is feasible in knowledge transfer. Compared with MF_MLP, the CDR-SAFM method proposed in this paper has been improved in terms of RMSE. When the number of topics K = 50, the mean square errors of our method are reduced by 0.0346, 0.0109 and 0.004 respectively. When the number of topics K = 100, the mean square errors are reduced by 0.0509, 0.0786 and 0.0829, respectively. These results demonstrate that the CDR-SAFM is more suitable for making recommendations to cold-start users compared to other cross-domain baseline methods, and also proves the effectiveness of our method in the cross-domain recommendation scenarios.In the topic model, the number of topics is very important. The number of topics directly affects the results of the experiment. However, the number of topics is not directly proportional to the results. As the number of topics increases, more computing costs will be required and there will be a risk of overfitting. In our method, we select the number of topics K as 50 and 100, and analyze the results under different number of topics. As shown in A cross-domain recommendation algorithm is an effective recommendation method. Its purpose is to transfer the knowledge in the source domain to the target domain, so as to improve the quality of recommendations and alleviate the cold start problem in the recommendation system. However, existing works on cross-domain algorithms mostly consider ratings, tags and text information such as reviews, but cannot use the sentiments implicated in the reviews efficiently. In this paper, we propose a cross-domain recommendation algorithm (CDR-SAFM) based on sentiment analysis and latent feature mapping by combining the sentiment information implicit in user reviews in different domains. The results of comparative experiments show that our method is effective. At the same time, it also proves that it is feasible for us to consider the implicit sentiment information in the reviews into the cross-domain recommendation method. However, there are still many deficiencies in our work. In this paper, we only validate our method on one dataset, and need to validate our method on more datasets in different fields. At the same time, there is a lot of space for data preprocessing and method improvement, which is also the problem we need to solve in future work.In this paper, we address the cold-start problem in the recommendation process. We proposed a cross-domain recommendation algorithm based on sentiment analysis and latent feature mapping by combining the sentiment information implicit in user reviews in different domains. We first employed the latent feature model to project users in both source and target domains into two different feature spaces. Then, we learned an appropriate non-linear mapping function to capture the coordinate relationship across the two domains. To avoid mutual interference between different sentiment features during the process of knowledge transfer, we have learned three different types of sentiment mapping function, respectively based on three-way decision ideas, including positive, neutral and negative mapping functions. For a cold-start user in the target domain, we made recommendations by mapping a user’s sentiment features from the source domain to the target domain. Experimental results from a cross-domain recommendation scenario on the Amazon dataset demonstrate that the proposed framework can improve the quality of cross-domain recommendation. There are possible minimum biases in the rating process in relation to some factors as interests of raters. This work does not make a contribution to the bias. Developing an efficient method to preprocess the rating data is part of our planned future work.