Grain boundary (GB) plasticity dominates the mechanical behaviours of nanocrystalline materials. Under mechanical loading, GB configuration and its local deformation geometry change dynamically with the deformation; the dynamic variation of GB deformability, however, remains largely elusive, especially regarding its relation with the frequently-observed GB-associated deformation twins in nanocrystalline materials. Attention here is focused on the GB dynamics in metallic nanocrystals, by means of well-designed in situ nanomechanical testing integrated with molecular dynamics simulations. GBs with low mobility are found to dynamically adjust their configurations and local deformation geometries via crystallographic twinning, which instantly changes the GB dynamics and enhances the GB mobility. This self-adjust twin-assisted GB dynamics is found common in a wide range of face-centred cubic nanocrystalline metals under different deformation conditions. These findings enrich our understanding of GB-mediated plasticity, especially the dynamic behaviour of GBs, and bear practical implication for developing high performance nanocrystalline materials through interface engineering. Grain boundary can change its structure upon deformation. Here, the authors show that during this process, grain boundary mobility can be tuned dynamically via a self-stimulated twinning process. However, nanocrystalline materials have long been suffering from their poor ductility and strain softening4, due to the plastic instability and thereby premature necking induced by GB deformation5. Numerous studies have reported the GB-dominated plasticity via GB migration6, GB sliding8, and grain rotation/coalescence9 in nanocrystalline materials. Nonetheless, the dynamic deformability of GBs upon mechanical loading has been largely overlooked in these models. Generally, GB mobility depends not only on the intrinsic GB geometry and atomic structure 13, but also on the local stress condition and thermo-mechanical loading history15. During plastic deformation, GB configuration and deformation geometry evolve dynamically with the emission or absorption of defects at GBs. For instance, GB-mediated deformation twinning changes the GB structure significantly, resulting in an instant modification of GB dynamics17. The variation of GB dynamics should bear an impact on the GB-dominated deformation and even plastic instability. A systematic exploration of the dynamic deformability of GBs is thus critical for a thorough understanding of the plastic instability of nanocrystalline materials, as well as on the application of GB engineering in nanomaterials design.Nanocrystalline materials possess a large volume fraction of grain boundaries (GBs), which can substantially modify their physical, mechanical and chemical properties in comparison with the coarse-grained polycrystalline counterparts19, where GBs act as the effective twin nucleation sites. In previous studies, deformation twinning is simply deemed as an intragranular deformation mode that is important for the mechanical properties and plasticity of nanocrystalline materials21. Given that deformation twinning is a reorientation process that not only changes the local lattice orientation dynamically but also tunes the GB structure and thereby GB kinetics simultaneously, the GB-correlated deformation twinning may impose critical influences on GB-dominated deformation16, rather than simply acting as twin nucleation sites. Such dynamic GB behaviour resembles the common approach of GB engineering, where vast Σ3 boundaries  were introduced into the polycrystalline materials to regenerate the overall GB networks into a crack-resistant interconnection with a higher portion of special GBs22. Hence, the twinning-modified GB structure and geometry underscore the intrinsic GB dynamics during plastic deformation, which can greatly tune the GB mobility and facilitate GB plasticity, as exemplified by twinning-correlated nanograin coarsening or coalescence in face-centred cubic (FCC) nanocrystalline metals under uniaxial tensile loading16, cyclic loading24 or creep test25. A comprehensive understanding of the atomistic mechanism underlying twinning-assisted GB motion is of general significance for the plasticity and GB engineering of nanocrystalline materials, which, however, remains largely elusive due to the lack of quantitative experimental studies.In nanocrystalline materials, the twinning-modified GB dynamics should become pronounced due to an increased tendency for deformation twinning, even in metals with high stacking fault energiesHere, the dynamically adjusted deformability of GBs under mechanical loading has been unambiguously demonstrated in metallic bicrystals using integrated in situ high-resolution transmission electron microscope (HRTEM) nanomechanical testing and atomistic simulations. The as-fabricated high-angle GBs (HAGBs) with relatively low mobility instantly tune their local lattice orientations and atomic configurations via a GB-stimulated twinning process. Such self-driven dynamic adjustment of GB structure changes the GB dynamics with enhanced GB mobility, leading to an increased GB migration rate in subsequent deformation. A geometry-based model was further proposed to quantitatively describe the dependence of self-driven dynamic adjustment of GBs on GB misorientation and inclination, by considering the resolved shear stresses on twinning and slip systems. This twinning-assisted adjustment of GB dynamics can well explain the GB-associated twins in a wide range of FCC nanocrystalline metals, offering critical insights into GB-dominated plasticity for GB engineering in nanomaterial design.1–2, with the corresponding atomistic structure shown in Fig. 1–2 contains a few pre-existing GB facets and a nanograin (G3) with a diameter below 2 nm. Subsequently, a tensile loading was applied on this Au nanowire along its axial direction (with an inclination of ~12° to the (002) plane of G2) at a constant rate of ~0.005 nm s−1.Nanoscale Au bicrystals with designed GB structures provide a model system to study the dynamic deformability of GBs. Figure 1–2 migrated rightward via the lateral motion of pre-existing GB facets . Associated with the lateral growth and the thickening of the deformation twin were the continuously increased segment of GBT-2 and the reduced segment of GB1–2, leading to the gradual truncation and complete annihilation of G3  resulted in a consecutive thickening of the deformation twin  across the GB, which greatly promoted the shear-coupled GB migration via a disconnection-mediated mechanism, favouring the growth of the deformation twin. These experimental results clearly demonstrate that GB can dynamically tune its deformability via self-driven deformation twinning, which should be strongly correlated with the GB structure and local stress state.Upon tensile loading, the deformation of this Au bicrystal was accommodated by extensive GB migration towards the right, as presented in Fig. ets Fig. , resultiage Fig. . Subsequtal Fig. , which e G3 Fig. . Associa GB Fig. . Namely, −1 Fig. , in comp23° Fig.  was unfa1–2 segments to the GBT-2 between the twin and G2 grain  simulations were conducted to explore the governing factors from both dynamic and energetic perspectives. Simulation was first performed on the sample with an inclined 23° GB  under uniaxial tension to validate the twinning-assisted adjustment behaviour of the GB Fig. . Upon teain Fig. . These T47° Fig. . To furtent Fig. . Such GBd C Fig. . Accompad C Fig. . Eventua TB Fig. .Fig. 2At3 . Subsequently, the remaining segments of GB1–2 were transformed into GBT-2 via the continuous formation and interlink of atomic twin embryos . Such twinning-assisted GB structure adjustment was associated with decreased GB energy from 816.3 mJ m−2 for GB1–2 to 810.3 mJ m−2 for GBT-2. The resultant GBT-2 migrated steadily in subsequent shear loading , fully consistent with our experimental observations and MD simulations  method33, given that GB migration is a thermally activated deformation process. Starting from the initial GB structure, four consecutive migration steps along the GB normal direction have been performed for both GBT-2 and GB1–2  ranging from 10° to 70°, while fixing the lattice orientation of G2  were composed of 1/2 〈110〉 dislocation arrays and deformed by the collective motion of GB dislocations along the slip planes in G1 or G2  is predicted to possess a higher twinning tendency, i.e., TT = 1.28, compared with the minor facets B (θ = 23° and φ = 36°) with TT = 0.53 and C (θ = 23° and φ = 16°) with TT = 0.38, respectively, which is consistent with the preferential twinning behaviour of the terrace A  to 805.7 mJ m−2 (for 22.5° GB). Above MD simulations and in situ experiments have provided a comprehensive understanding of the geometry effect (including both misorientation and inclination) on self-adjusted GB mobility. The self-adjust twinning-assisted GB deformation is general for a wide range of GBs and plays a key role in tuning the GB structure and mobility. The threshold misorientation/inclination varies with the local stress state37, implying that the dynamic GB deformability adjustment is a stress-dominant process.To further validate our geometry-based model, additional testing was performed on a bicrystal with a flat 87° [1s−1 Fig. . With a ing Fig. . MD simu39. However, current theories of GB dynamics mainly rely on analysis of the original GB geometry, which cannot reflect the full landscape of GB plasticity in view of the dynamic evolution of GB structure during deformation. As such, controversial conclusions exist on the mobility of GBs and the stability of nanocrystalline materials40. For example, several studies revealed that the HAGBs generally possess higher mobility than that of the LAGBs42, while others reported no clear correlation between misorientation and the mobility of HAGBs43. Our experimental and simulation investigations unambiguously demonstrated that certain GBs in FCC metallic materials can tune their deformability dynamically via a self-driven twinning process. In this process, GB decomposition stimulates the deformation twinning, which, in turn, modifies the GB structure and the associated deformation dynamics, leading to dynamically-adjusted GB mobility.GB-dominated plasticity has been widely reported in literature, which exhibited sharply different behaviours under a range of GB configurations/geometries, including misorientation, inclination, curvature etc.More importantly, this self-driven twinning-assisted dynamic GB plasticity is independent on surface effects and crystal size, and thus should be quite common in nanocrystalline FCC metals. Fig. 36. Some studies have shown that deformation twins in nanocrystalline metals are more likely to nucleate at a lower grain growth rate44, and the increase of 45. Similar to the GB dissociation induced twin and thereby self-stimulated adjustment of GB mobility, deformation twins nucleated at other sources can impinge the GBs at the twin front, leading to GB dissociation, segmentation or partial replacement by incoherent TBs24. All of these processes could markedly modify the GB structure/misorientation, contributing to the dynamic change of GB mobility44. These observations suggest that the GB-associated twins (including both nucleation and growth) in nanocrystalline metals, under either mechanical loading or thermal annealing, are more likely to be dominated by the dynamic GB deformation processes, rather than through the migration of TB itself. Taken the 〈110〉 tilt GBs as an example  were often coupled with TBs, indicating the dynamic transformation between HAGBs and LAGBs. In subsequent loading, the GBs would move or react with other pre-existing defects to further adjust the GB structures46. The synergistic motion of GB sliding and migration can also be activated to dynamically adjust the GB networks49. The GB dislocations generated from such self-driven GB dynamics may further promote grain growth by contributing to the self-driven grain rotation46. These dynamic mechanisms call for a rethink of the role of deformation twinning in nanocrystalline materials, especially the ones interlinked with GBs.To further confirm this, we have summarized in Fig. In conclusion, the self-driven twinning-assisted dynamic adjustment of GB mobility was systematically investigated using Au bicrystals with HAGBs as model systems. During GB migration, GB structures can frequently experience self-adjustment by shear-dominated twinning, which, in turn, modify the GB structures and effectively promote GB deformability to accommodate migration by producing lower coupling factors and larger driving forces. Such self-adjusted GB migration is a common deformation mode for GBs among a range of FCC nanocrystalline metals under mechanical loading. A GB geometry-based twinning tendency model considering the GB misorientation and inclination was developed to predict the possibility of the twinning-assisted GB adjustment. Our findings provide deep insights into the fundamental understanding of self-adjusted dynamic behaviours of GBs, which predict the stability and evolution of microstructures of metals and alloys with low stacking fault energies, as well as the manipulation of interface dynamics to achieve optimal performance of nanocrystalline materials.θ = 23° and 87°) were successfully fabricated for tensile testing. During in situ experiments, the Au probe was moved backward slowly at a constant rate of 0.005 nm s−1 to realize the tensile/shear loading, giving an estimated strain rate of 10−3 s−1. In all experiments, the TEM was operated at 300 kV with low current density to minimize the potential beam effect on deformation. The in situ deformation processes were recorded in real-time by a CCD camera at a rate of ~0.3 s per frame.In situ nanofabrication and tensile testing of the Au bicrystals with [150 used to compute the interatomic forces has been proven to be reliable in describing the fundamental properties of Au. The Au samples containing an inclined  and with periodic boundary conditions to avoid the influence of free surfaces. In addition, the quasi-three-dimensional polycrystalline sample in Fig. 8 s−1. OVITO51 was used to visualize the simulated samples, and the common neighbour analysis method was employed to determine the position and structural evolution of the GBs.Atomistic simulations were performed to explore the microstructural origin of the twinning-assisted dynamic adjustment of GB mobility. The embedded atom method potentialned Fig. /flat . The final state was minimized to set the system energy close to the corresponding initial states. And a series of replicas were created by linear interpolation to connect the two end-states. The activation energies were calculated by finding the minimum energy paths (MEP) and transition states of the migration process.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Movie 1Supplementary Movie 2Supplementary Movie 3Supplementary Movie 4


Cross-domain sentiment classification could be attributed to two steps. The first step is used to extract the text representation, and the other is to reduce domain discrepancy. Existing methods mostly focus on learning the domain-invariant information, rarely consider using the domain-specific semantic information, which could help cross-domain sentiment classification; traditional adversarial-based models merely focus on aligning the global distribution ignore maximizing the class-specific decision boundaries. To solve these problems, we propose a context-aware semantic adaptation (CASA) network for cross-domain implicit sentiment classification (ISC). CASA can provide more semantic relationships and an accurate understanding of the emotion-changing process for ISC tasks lacking explicit emotion words. (1) To obtain inter- and intrasentence semantic associations, our model builds a context-aware heterogeneous graph (CAHG), which can aggregate the intrasentence dependency information and the intersentence node interaction information, followed by an attention mechanism that remains high-level domain-specific features. (2) Moreover, we conduct a new multigrain discriminator (MGD) to effectively reduce the interdomain distribution discrepancy and improve intradomain class discrimination. Experimental results demonstrate the effectiveness of different modules compared with existing models on the Chinese implicit emotion dataset and four public explicit datasets. Sentiment analysis is considered one of the fundamental problems in natural language processing (NLP), and with social media developing rapidly, it is widely applied in real scenarios such as comment analysis, food safety monitoring, and public opinion mining. Such tasks are usually defined as identifying the emotional polarity  of a given text, sentence, or aspect.2. We exploit the following examples to show the difference of two expressions:Explicit:你做的蛋炒饭太好吃了,我很喜欢! Implicit:这家蛋炒饭有种妈妈的味道! (English translation: “The rice fried with eggs in this restaurant reminds me of my mother!” label=positive)Example 1 uses the word ‘喜欢 (like) ’ to show a clear positive tendency. To express their views, people also use Example 2 . In this sentence, no explicit emotional words are used and the individual’s emotional tendency is embedded in the semantic meaning of the text. This phenomenon creates an exceptional challenge for implicit sentiment classification tasks. Moreover, accompanied by the absence of a large-scale labeled corpus, even with an advanced deep learning model, the classification accuracy of implicit sentiment classification tasks is not ideal3.The expression of emotion can be explicit or implicit. The implicit expression of emotions is defined as ’A language fragment  that expresses subjective sentiment but contains no explicit sentiment word’4. These methods attempt to reduce the discrepancy between domain-specific latent feature representations. Inspired by this idea, most existing adversarial-based methods, e.g., domain adversarial neural network (DANN) ]5, reduce feature differences by fooling a domain discriminator and have achieve promising results7. However, to achieve explicit-to-implicit positive transfer, these methods still have two major inherent drawbacks that need to be addressed:8. Figure Existing studies mostly focus on learning the domain-invariant information (e.g. ‘喜欢 (like)’, ‘坏 (bad)’, ‘差 (weak)’), rarely consider the usage of domain-specific semantic information (e.g. ‘蛋炒饭 (the rice fried with eggs)’, ‘抵抗力 (resistance)’), which is also helpful for cross-domain implicit sentiment classification(DANN), the features near the decision boundary may be ambiguous and even tangled together with traditional domain discriminator training, thus blocking adaptation performance.Traditional adversarial-based models merely minimize the marginal distribution of the two domains and ignore maximizing the class-specific decision boundaries. As shown in Fig. 2To tackle the above limitations identified above, we aim to use graph convolutional networks (GCNs). GCNs have a multilayer architecture, with each layer aggregating the information of nodes in the graph structure using features of immediate neighbors. Nevertheless, sequential free texts are unstructured data. Therefore, GCN-based text learning must conduct graph representation learning from the free text before graph convolution. Different from sequence learning models, GCNs can directly represent complex structured data. A GCN has the potential to capture domain-specific semantical information with GCN layers. Recently, GCN models have gained widespread attention and have been successfully deployed on text-word relationships11, and explicit sentiment analysis13. However, these graph-based models only considered intrasentence hierarchical dependency relationships and ignored intersentence semantic associations.One solution to this problem is cross-domain sentiment classification, which aims to exploit the rich labeled data in the source domain, e.g., explicit sentiment corpus, to help the sentiment analysis task in another domain lacking for or even without labeled data, e.g., implicit sentiment corpus. Recently, relevant models on cross-domain sentiment classification have mainly focused on learning domain-invariant features whose distribution is similar in the source and target domains5 and MGD.Therefore, in this paper, we propose a novel context-aware semantic adaptation network (CASA) for cross-domain implicit sentiment classification via GCNs. To obtain inter- and intrasentence semantic associations, we build a context-aware heterogeneous graph (CAHG). CAHGs build graphs in each document by regarding tokens and sentences as nodes (hence heterogeneous graph). The intrasentence propagation is constrained by the syntactic dependency tree, and intersentence propagation is constrained by the sentence-free sequence and term frequency-inverse document frequency (TF-IDF) local token cooccurrence information. The information propagates inter- and intrasentences via the GCN layers, followed by an attention mechanism that keeps high-level domain-specific features. We also conduct a multigrain discriminator (MGD) which is imposed during domain adaptation to minimize domain distribution and maximize class identification. The domain adaptation layer makes the source domain and target domain inseparable e through adversarial training, reduces the representation distribution gap between source and target domain data in a coarse-grained manner. The class adaptation layer utilize a classifier to judge different domain samples, whether class consistent, to distinguish samples in different classes. Figure We proposed a new transfer learning model CASA for the implicit sentiment classification task via GCNs, which is the first attempt to transfer explicit sentiment information to implicit sentiment.Our model provides a context heterogeneous graph, which can effectively extract inter- and intrasentence semantic information. Moreover, CASA improved the model’s generalization ability for implicit classification tasks and increased each sentiment’s polarity discrimination in the domain.We evaluate CASA on the Chinese implicit sentiment analysis dataset (SMP-ECISA 2019). CASA outperforms existing models in four different source domains. We also provide a visualization to demonstrate that CAHG can capture domain-specific information, and MGD can make features near decision boundaries more distinguishable.In short, the main contributions of this paper are as follows: 14.The existing sentiment calculation and sentiment analysis methods can be divided into three categories: knowledge-based methods, statistical methods and hybrid methods15, multi-label sentiment analysis model16, aspect-level sentiment analysis research based on Reinforcement Learning17, etc. On the other hand, more and more scholars have realized the particularity of Chinese characters and tried to model Chinese radicals22. The hybrid method aims to describe the rules of emotional expression better and realize the machine’s perception of semantics23. The model CASA in this paper considers contextual semantic perception and introduces cross-domain explicit emotional knowledge.Knowledge-based methods are popular because of their simplicity and ease of use, but their effectiveness is limited mainly by the depth and breadth of the established knowledge base. Statistical methods based on machine learning and deep learning have been widely used in Chinese sentiment classification, such as fine-grained sentiment analysis framework27. They exemplified recent research on attention-based sentiment analysis.Attention mechanism shows good performance in sentiment analysis tasks. It improves the interpretability of neural networks by letting people know the location of the focus28. However, these sentence-level sentiment analyses cannot be directly applied to implicit sentiment tasks because, in implicit sentiment tasks, the emotion of the target sentence holds different polarities for different contexts.Various sentiment analysis tasks usually focus on realizing binary classification (positive and negative classification), which cannot better describe emotions. In contrast, Wang proposed a multi-level emotion perception method with contradiction processing30 and CNNs31 in deep neural networks are widely used in sentiment classification tasks. In RNN-based models, an attention mechanism is usually introduced because each word in the text contributes differently to the classification task34. CNN-based models36 use character-level CNNs to extract semantic information from text. However, these models lack an effective mechanism to capture the information in the dependency tree structure. In works of39, structural and semantic information extracted from the tree structure of sentences, such as a dependency tree or grammar tree by LSTM or BiLSTM, was used for the sentiment classification task. Although Tree-LSTM can extract more accurate semantic information from text, it is difficult to perform parallel computing and requires a longer time to train. After that, CNNs were introduced into the tree structure information encoding process by41. In their work, a phrase structure tree and syntax-dependent tree were used to encode the semantic information of the target sentence and the context, respectively. However, in the above tree-based convolutional neural network model, sentences are considered to be independent of each other, so information regarding the relationship between sentences is lost. To solve this problem, our model extracts semantic information via GCNs9.Models based on RNNs10 builds a corpus-level text graph by word-word co-occurrence and document-word relations for text classification. Zhang et al.42 introduces a TreeGCN, where the GCN is used to encode the dependency syntactic structure. Zhang et al.12 presented an aspect-based GCN to demonstrate that GCNs can achieve long-range word dependency. Zhang et al.43 employ gated graph neural networks document-level graph word interaction. In contrast to their works, we regard the tokens and sentences in each document as graph nodes. The graph maintains inter- and intrasentence constraints to capture semantic information. It can obtain more accurate text semantics while increasing the interpretability of the model.GCN models have attracted widespread attention and have been successfully deployed in NLP tasks. Yao et al.41. Yosinski et al.44 discusses the application of transfer learning in deep neural networks for single domains. It draws an important conclusion: adding fine-tuning will improve the performance of the deep transfer network. In the cross-domain scenario, the difference between the probability distribution of the source domain’s data and the target domain’s data is significant. The use of fine-tuning alone may lead to negative transfer. The purpose of domain adaptation, also known as cross-domain learning, is to reduce the difference in distribution representation of the data of the source and target domains. In domain adaptation, a direct way to solve the above problem is to use a certain distribution distance measurement method to measure the distance between distributions and reduce the distance in the model training process. However, the calculation of distance measurements is difficult. Many methods based on domain adversarial models have been proposed6.Even with a strong deep learning model, the classification accuracy of implicit emotion classification problems is not ideal in the absence of sufficient labeled data46 without considering implicit situations. Due to data scarcity and the task’s value, transfer learning is more urgent for implicit sentiment analysis. To the best of our knowledge, CASA is the first explicit-to-implicit transfer learning model. MGD improved the model’s generalization ability to implicit emotion classification and the discrimination of each sentiment polarity.However, existing domain adversarial models for sentiment analysis focus on explicit sentiment classification or aspect-level sentiment classificationD consists of marginal distribution P(x) and m-dimensional feature space X, namely A domain D, the task is composed of a classifier f(x) and a class label set Y, namely Given the domain Given a training data come from source domain The overview of the CASA framework is described in Fig. 47 to encode the text input from the source or target domain to obtain contextualized word-level representation m denotes the vector dimension and i. The reason for employing this layer is to correct the information of the syntactic dependency tree, which was built by HANLP. We use HANLP for this: https://www.hanlp.com.First, we use the bidirectional gated recurrent unit (Bi-GRU)V, E represents the node set and the edge set of graph G, respectively. We construct G based on the token-token dependency relationship, TF-IDF, and sentence order. The token’s TF-IDF determines the edge weight between the sentence node and the token node in the sentence. We define TF as the number of words that appear in the sentence, and IDF as the logarithm of the overall sentence number in the text to the sentence number, which contains the token. The formal definition is as follows formula  is the relationship between token nodes p and q in the dependency tree. The information propagates inter- and intrasentences via the GCN layers, followed by a hierarchical attention layer that keeps high-level domain-specific features. The final text representation is defined as follows:5. Then In formula –8, \docu49. Therefore, adversarial-based transfer learning is widely used to solve the domain adaptation problem. Although it has considerable advantages, we observed that the domain discriminator could only reduce the marginal distribution distance.Ben-David and Ganin proved that to perform domain adaptation to reduce the target domain’s prediction error, maximizing the discriminator error between the source and target is necessary50, minimizing the difference between conditional distributions is critical to the robustness of distribution adaptation. We propose MGD, which consists of a domain discriminator T and an emotional polarity discriminator D. T makes the source domain and target domain not separable through domain confrontation. D maximizes the difference between different labels through label consistency identification. When the class discriminator D can accurately identify whether the sample labels from the source domain and the target domain are the same, the model can learn the class invariant features from the two domains. The formula is derived as follows:s and t represent the source and target domains, respectively.However, the relationship between marginal distributions and conditional distributions is uncertain. As indicated in5, this min-max game is implemented by a gradient reversal layer. Specifically, when the network is undergoing the gradient back-propagation process, we will change I denote an indicator function:To maximize the extraction of domain invariant features, we hope to maximize the discrimination error Here, C is the number of classifications, and The vector D is the document’s index with the label, and Y is the real label matrix. Therefore, we can obtain the classification loss of the source domain 42, who proposed mutual learning in supervised single-domain tasks. The Kullback Leibler divergence of the predicted source class distribution and predicted target class distribution is calculated and vice versa. The two KL divergences measure the similarity of the two distributions. Finally, the overall loss function in CASA consists of both source and target loss, which are given as follows: Before the training stage, we were motivated byhttp://biendata.com/competition/smpecisa2019/, http://www.pudn.com/Download/item/id/3993718.html, http://www.searchforum.org.cn/tansongbo/corpus1.php, https://smp2020.aconf.cn/.Our target domain data set is The Evaluation of Chinese Implicit Sentiment Analysis task in SMP2019 . The dataset contains two types of content in each document: context and target sentence. We chose four benchmark datasets of explicit sentiments as the source domain. They are the Weibo-60000 dataset, the hotel review dataset, the SMP-2020’s virus Weibo dataset, and the SMP-2020’s general Weibo dataset. The data is available at: To keep intact the dependent syntax structure, filter out sentences, which have no subject-predicate structure.51, sentiment polarity consistency exists between the context semantic background and the target sentence. To match the target domain data granularity, we randomly select a sentence in each source domain document as the target sentence and the rest as the context.As suggested byTo clean the dataset, we performed some preprocessing. The construction of a heterogeneous graph requires contextual sentence nodes and a dependency tree structure. We performed the following preprocessing, and Table P(x) is the predicted label and Y(x) is the actual label of sample x, respectively.We compute every model classification accuracy and F1 score in test dataset as evaluation indicator. The F1 score calculation and accuracy are shown as follows:To fully verify and understand CASA, we divide the baseline models into two groups for comparison:TextCNN31, TextRNN52 and BiLSTMAtt53: These are the basic deep neural networks in sentiment classification.TreeLSTM54: An LSTM network based on a tree structure, which solves the problem of the emotional classification of nonlinear systems such as dependent trees.TreeGCN39: BiLSTM is used to encode the input word vector to obtain the hidden state with context information. It then uses a GCN convolution to obtain the neighboring node information, which enhances the GCN’s robustness.CASA-T-D: The CASA feature extractor part for examining the ability of CAHG to express text information.To demonstrate the benefits from heterogeneous graphs, we compare with the following methods without transfer:Fine-tuning: Initialize the CASA-T-D parameters randomly, then train on the source domain dataset, and finally fix the parameters and fine-tune the model in the target domain dataset.DANN5: The model adopts the idea of domain adversarial, which has a feature extractor and a domain discriminator.CCSA55: A unified framework for supervised domain adaptation is created.d-SNE56: d-SNE is a novel technique method based on the distance metric that has achieved great transfer results on the image benchmark datasets.DAS45: This model employs two regularizations, entropy minimization and self-ensemble bootstrapping to refine its classifier while minimizing the domain divergence.DAAN57: DAAN is an adaptation network with dynamic adversarial.SAFN58: SAFN is the state-of-the-arts across many visual domain adaptation benchmarks.ML42: We apply the standard mutual learning in our task directly. ML can make the source domain and target domain collaborative and teach each other throughout the training process.The original DANN, DAS, DAAN and SAFN are unsupervised domain adaptation models. As suggested in59, we use the source code of DANN, DAS, DAAN and SAFN and extend them to DANNTo investigate the effectiveness of each part in the CASA, we also compare the following frameworks for experiments. For a fair comparison, we use CASA-T-D as a feature extractor in other methods.60 is used to train up to 30 epochs on the dataset, and the loss value is output every 100 batches. The training is stopped when the verification loss does not decrease for ten consecutive times.The word embeddings dimension is initialized with 200-dimensions. The GRU hidden layer size is 64 dimensions. The batch size is 64. The GCN hidden layers size is 128 dimensions, and the initial learning rate is 0.001. We compared the model in the context modeling stage with the current nontransfer model to explore the heterogeneous graph’s presentation ability in CASA. The results are shown in Table We note that (1) the CASA-T-D results are far better than those of the other models. Despite the same tree structure, the TreeGCN results are 7.4% higher than that of TreeLSTM, which shows that GCN captures depth features more effectively. (2) CASA-T-D and TreeGCN are both GCN convolutions but different in representation learning. In comparison, the result of CASA-T-D is 2.02% higher than that of TreeGCN. (3) The BiLSTM It can be observed from the experimental results in Table (4) mentclass2pt{minimTo further compare each component CASA’s contribution, we sorted out the data of the ablation study part of the main experiment and plotted it into a line chart, as shown in Table (1) Removing the CAHG, class discriminator, and domain discriminator of CASA separately, the experimental results drop by 1.44%, 1.65%, and 2.06% and are still higher than those of the nontransfer model CASA-T-D (82.59%). This shows that the fine-grained adjustment contributes to this transfer task, and the coarse-grained adjustment has greater performance than the heterogeneous structure. (2) When CASA-T-D removes CAHG and CASA* removes MGD, the accuracy drops by 0.78% and 2.16%, respectively, indicating that the CAHG and MGD we proposed can have a great impact on the experimental results.In this section, we will present how to choose the value of hyperparameters 5, 5; The relative value p of the iterative process, that is, the current number of training steps / the total number of training steps, changes from 0 to 1 with the progress of training. The above formula means that at the beginning,Inspired byWhich are selected through the validation set. First we have to judge whether Figure We want to explore what domain-specific information, which has been learned from the source domain, would enhance implicit emotion classification. In Table The first two samples contain only one context and the target sentence. Although the two models’ attention scores are different, the critical points of emotional judgment are noted, such as ‘差 (poor)’ and ‘伤害 (harm)’. Obviously, these tokens are domain-independent words, which are usually used in many domains. In the third example, the CASA model learned the virus domain-specific information ‘抵抗力 (resistless)’, but CASA-T-D could not.Then, we examine the impact of long contextual text on the two models. As shown in the last example, CASA-T-D focuses most of its attention on the token ‘不到 (not)’ in the target sentence and does not notice the token ‘不到 (good)’ in the context. In contrast, the CASA attention score is relatively scattered, conducive to the judgment of emotional polarity, which may benefit from source domain knowledge.61 to reduce the dimensionality of the feature to two and visualize the data distributions after domain adaptation Virus To better illustrate how the CASA works, we used t-SNEFigure In contrast, benefitting from our proposed MGD, CASA’s intraclass boundary is significantly more significant, conducive to intraclass identification.This paper proposes a CASA network via graph convolution for the cross-domain implicit sentiment classification problem, first building a relation between explicit and implicit sentiment. Existing studies either rarely consider using domain-specific semantic information or ignore maximizing class-specific decision boundaries. We aim to address the above two drawbacks. First, CASA provides a CAHG, effectively extracting domain-specific semantical information for both sources and targets. Hence, CASA improved the model’s generalization ability for implicit classification tasks. The case study shows that our model can effectively capture high-level domain-specific features. Second, CASA conducts an MGD to adapt the domain distribution, enhancing class distinction in each sentiment polarity decision boundary during domain adaptation. The feature visualization results show that CASA clarifies samples’ boundaries from different classes while adapting to the domain.Moreover, there are several worthy challenges in cross-domain implicit sentiment tasks, such as transferring between the different single-domain topics, fine-grain sentiment transferring, ambivalence handling, and transferring explicit-to-implicit topics where the target domain tags are not given. We believe that all these factors can help us comprehend the link between explicit and implicit sentiment and that implicit sentiment analysis will be solved more effectively in the future.


A substantial fraction of genes identified within bacterial genomes encode proteins of unknown function. Identifying which of these proteins represent potential virulence factors, and mapping their key virulence determinants, is a challenging but important goal.To facilitate virulence factor discovery, we performed a comprehensive analysis of 17,929 protein domain families within the Pfam database, and scored them based on their overrepresentation in pathogenic versus non-pathogenic species, taxonomic distribution, relative abundance in metagenomic datasets, and other factors.https://pathfams.uwaterloo.ca.We identify pathogen-associated domain families, candidate virulence factors in the human gut, and eukaryotic-like mimicry domains with likely roles in virulence. Furthermore, we provide an interactive database called PathFams to allow users to explore pathogen-associated domains as well as identify pathogen-associated domains and domain architectures in user-uploaded sequences of interest. PathFams is freely available at The online version contains supplementary material available at 10.1186/s12864-021-07982-8. Bacterial virulence factors are proteins that facilitate pathogen adherence, colonization, and survival with the host. Despite a long history of virulence factor characterization, new virulence factors and mechanisms are continually being discovered, even in well characterized organisms. Given the rapidly growing availability of genomic sequences across the bacterial tree of life, there is a potential explosion of virulence protein diversity to be discovered in genomes .A key question in the bioinformatic identification and analysis of virulence factors is how to detect candidate virulence related proteins from sequence information. Common strategies include the use of online virulence factor databases such as the VFDB, which is a comprehensive, curated resource of virulence factors across the best-characterized bacterial pathogens  Protein Although virulence factors can be grouped into protein sequence families, a more fundamental level of analysis is to assess proteins at the level of domains. Even if most of a protein sequence might be unrecognizable, the identification of key domains within that sequence may be sufficient to identify it as a candidate virulence factor . DomainsAs an alternative to homology-based functional annotation methods, functional insights into protein families can be obtained by detecting statistical associations between families and various biological traits. Quantification of the relative abundance of a protein family across different environments can help provide insights into its functional context –16. For https://pathfams.uwaterloo.ca) to allow researchers to analyze their proteins of interest and explore our pathogen-associated domain families.In this work, we have applied several association-based methods to analyze 17,979 Pfam domain families in terms of their pathogen-association, taxonomic distribution, enrichment in the human gut, and other factors. Our analysis identifies hundreds of pathogen-associated domains, which include known and potentially novel candidate virulence factors for future characterization. We provide an online database  domains (including 517 DUFs), in the pathogenic set, out of 11,299 domains present in bacterial proteomes . As expected, pathogenic lineages such as the Enterobacteriaceae had the highest frequency of pathogen-associated domains per proteome . Among the top-scoring pathogen-associated Pfam families are numerous domains from known toxins and virulence factors   Table S. To iden) Table S to filte) Table Sb. This iLegionella which mimics host Sec7 guanine exchange factors (GEFs) (PF01369) [Legionella secreted effectors, such as a protein family containing a eukaryotic RAS-GEF domain (PF00617), are also included in this list. Other interesting predictions include the Latrotoxin_C domain (PF15658) found in the black widow spider insecticidal latrotoxin, but also present in Wolbachia species. Each of these cases implies an ancestral horizontal gene transfer event from a eukaryotic species to bacteria.A subset of virulence factors modulates or disrupts host function by “mimicry” of eukaryotic proteins , 32, 33.PF01369) . Additio− 15, we identified a set of 2061 domains with significant enrichment in the human gut. Additionally, we identified 1050 in soil, and 1246 in marine systems , followed by soil-associated families , and were underrepresented in marine-associated families . Example DUFs with extreme environmental specificity are shown in Fig. To further focus our predictions toward domains with potential human specificity, we examined domain family abundance in human gut metagenomes and compared it to that in soil and marine metagenomes Data S, S3. UsiThe top enriched GO terms for human-gut associated protein families included the phosphoenolpyruvate-dependent sugar phosphotransferase system, O-glycosyl hydrolase activity, and carbohydrate metabolic process Table S. Also, aYersinia yop operon for secretion of virulence factors, BNR_3 , HrpB7 , Glyco_transf_52 (PF07922) which produces lipooligosaccharide (a pathogenicity determinant), the toxin family Thiol_cytolysin (PF01289), and the virulence factor Pertactin (PF03212). BNR_3 is a domain family that includes trans sialidases from the parasitic protist Trypanosoma cruzi  and other Trypanosoma spp., as well as sialidases from human gut bacteria including Prevotella spp. and Bacteroides spp. Sialidases allow pathogenic bacteria and commensals to release free sialic acids in the gut as a nutrient source, and sialic acid catabolism has been demonstrated to promote the growth of gut pathogens  and drive intestinal inflammation and dysbiosis [To identify candidate human gut virulence factors, we intersected the list of pathogen-associated domain families with domain families more enriched in the human gut microbiome than other environments Data S. The topysbiosis .C. difficile, Enterococcus, and S. pneumoniae. DUF3173 (PF11372) is largely restricted to Firmicutes including numerous pathogens, and appears to be conserved near phage integrase genes. DUF families identified by this analysis are of particular relevance and we suggest should be prioritized for functional characterization in the context of human gut pathogenesis.DUFs within the list of pathogen-enriched and gut-enriched domains include DUF2492 (PF10678), DUF1430 (PF07242), and DUF3173 (PF11372). Based on InterPro descriptions for entries IPR019620 and IPR006541, DUF2492 appears to be a metal binding sulfatase and may play a role in sulfated mucin metabolism. DUF1430 appears to be a transporter and occurs in numerous pathogens including In order to provide these analyses to the community, we constructed an online database (pathfams.uwaterloo.ca) which facilitates interactive exploration of all Pfam domain families. Included are measures of abundance and taxonomic breadth, as well as indicators of structural determination feasibility see . As an eClostridium taxonomic lineage, a botulinum neurotoxin (BoNT) like protein was identified in the gut commensal organism Enterococcus faecium [Chryseobacterium piperi, but this protein appears to possess structural differences from BoNTs and displays cytotoxic versus neurotoxic function [Clostridium haemolyticum as well as in diverse bacterial taxa [C. haemolyticum provides flagellar filaments with proteolytic activity against extracellular host substrates [Clostridium difficile (TcdA and TcdB) are highly abundant outside of the Clostridium difficile lineage [Serratia marascens, which is currently annotated in the NCBI database as a “hypothetical protein” (NCBI accession WP_073532240.1). Three domains common to TcdA and TcdB are predicted within this protein, all of which are pathogen-associated. As S. marascens is an insect pathogen and the top homologs of this protein according to BLAST occur in insect pathogenic Photorhabdus spp., we suggest that protein is likely an insecticidal toxin. We anticipate that, similar to these cases, other candidate virulence factors may be identified using PathFams tool by assessing domain architectures of uncharacterized proteins and identifying those that show significant pathogen associations.PathFams also allows the user to query the database with a protein sequence of interest. A submitted protein sequence will then be scanned against all Pfam models in the VirFam DB using either a “strict-mode” or “sensitive-mode”, and the predicted domain architectures will be visualized along with the pathogen-association scores for each identified domain Fig. . Domains faecium . The Patfunction . The Patial taxa . Flagellbstrates . Consist lineage . These TIn this work, we analyzed all 17,929 protein domain families in the Pfam v32.0 database in order to rank them based on several biological criteria. We were able to identify significant lineage, pathogen, and/or environment associations for 1675 out of 4049 (41 %) of all DUFs. These associations provide a biological context from which uncharacterized domain families (DUFs) can be prioritized for future virulence factor studies. In addition, by combining different scores, it was possible to identify Pfam families with specific phenotypic or functional associations, such as candidate virulence factors in the human gut microbiome, as well as candidates predicted to be feasible for structure determination. PathFams provides a clear and interactive way to explore this data, allowing users to assess the virulence factor potential of a domain family and/or submitted protein. Future work will update and expand our computational analysis of the Pfam database to include new metadata and phenotypic associations, and new domain families as they continue to be uncovered through ongoing sequencing efforts.https://www.patricbrc.org) [354 proteomes in Pfam v32.0 were designated as bacterial pathogens Data S based onbrc.org)  bacteriabrc.org) . Specifibrc.org)  set werebrc.org)  were incp-values were FDR corrected with p.adjust using the Benjamini-Hochberg model. The enrichment of DUFs in pathogen-associated domains was calculated in the same way as with the environment-associated domain set. For this paper, Pfam domains called UPF (uncharacterized protein family) were also treated as DUFs. The frequency of the pathogenesis GO term in domains identified as pathogen-associated and other Pfam domains present in bacterial proteomes were compared with the hypergeometric test (phyper in R). Eukaryotic-like domains in bacterial pathogens were identified as being most common in eukaryotic proteomes as well as pathogen-associated  or with hits in bacterial pathogens but without hits in non-pathogen proteomes. We expanded past the pathogen-associated domain set in this case, to capture domains present in a low number of proteomes  that seemed like promising “mimicry” candidates.Enriched pathogenic domains were detected with the hypergeometric test (phyper in R) based on the number of pathogenic proteomes in Pfam where the domain is present, compared to non-pathogenic bacterial proteomes in Pfam where the domain is present. The http://www.phidias.us/victors/downloads/gen_downloads_protein.php and http://www.mgc.ac.cn/VFs/Down/VFDB_setB_pro.fas.gz, respectively on Jul. 28, 2021). These proteins were annotated with PfamScan  using HMMER3 v.3.1b2 [http://www.mgc.ac.cn/VFs/Down/Comparative_tables_from_VFDB.tar.gz) were used to extract the virulence factor category (e.g. “toxin” or “adherence”) to gene mapping. The prevalence of proteins with at least one pathogen-enriched domain (as determined previously) was calculated in the two different virulence factor databases, and within various virulence factor categories from VFDB.In order to examine domain prevalence in virulence factor databases, the Victors  protein  v.3.1b2  against The taxonomy ID and taxonomic lineage of proteomes with Pfam domain matches were extracted, respectively, from PfamA_ncbi.txt.gz and taxonomy.txt.gz at Pfam’s ftp server . We calculated the sensitivity and precision of the Pfam domain distribution across the NCBI taxonomy system using these taxonomy ids and taxonomic lineages. The total number of proteomes within any one taxonomic group is based on the taxonomy ids in the PfamA_ncbi.txt file. These scores were calculated for the most common taxon (presence/absence counts of a domain hit per proteome) in each domain family at the Superkingdom, Kingdom, Phylum, Class, Order, Family, and Genus taxonomic levels. The best taxonomic level to describe a domain’s lineage specificity was chosen based on the F1 score: 2*(sensitivity*precision)/(sensitivity + precision). In the case of a tie between taxonomic levels, the higher level in the taxonomic hierarchy (i.e. Superkingdom) was given preference. If the majority of proteomes that the domain was present in did not have any classification at a certain taxonomic level, this taxonomic level would not be considered for “best taxonomic level.” The enrichment of DUFs in extreme lineage-specific cases was determined in the same way as with the environmental-associated domain set.− 3 was performed on the remaining sequences. The annotated region of each metagenomic sequence  was clustered with CD-HIT v4.6.8 [p-values were adjusted with p.adjust using the Benjamini-Hochberg model. The logarithm of the normalized adjusted family size (base 10) and the subsequent scaling across the domain hits  was done in R v3.3.3 for the heatmap. Enrichment of DUFs in the environment-associated domain sets compared to the background frequency of DUFs in Pfam was tested using the binomial test (pbinom in R). To determine GO term enrichment within the environment-associated domain sets, a Pfam to GO term map was retrieved from http://geneontology.org/external2go/Pfam2go . The frequency of GO terms in domains associated with one of the three environments  and the frequency of GO terms corresponding to other Pfam domains present in at least 5 % of the selected samples were compared with the hypergeometric test (phyper in R), with p-values again adjusted with the Benjamini-Hochberg model.Metagenomic assemblies and raw reads were taken from public repositories Data S. No sampT v4.6.8  to 99 % T v4.6.8 , and theT v4.6.8 , the ChiT v4.6.8 , and theT v4.6.8 , respectwww.ftp.ebi.ac.uk/pub/databases/Pfam/current_release/Pfam-A.full.ncbi . The proteins that were aligned to Pfam domains and the total number of hits were taken from this file. An environmental average of the normalized adjusted family size for each domain (see Lineage specificity section in Methods). The percentage of species where each domain is present, and the corresponding percentage for the Genus, Family, Order, Class, Phylum, Kingdom and Superkingdom taxonomic levels are included in Data SNCBI, the number of protein family members in the NCBI sequence database; Nspecies, the percentage of species containing the domain family in the Pfam proteome collection; and Nmeta, the number of non-redundant matches in a diverse dataset of metagenomes.The NCBI sequence database domain alignments were sourced from.All data was taken from Pfam v.32.0 . A list of Pfam families with PDB structures was taken from.www.ftp.ebi.ac.uk/pub/databases/Pfam/current_release/database_files/pdb_PfamA_reg.txt. Domain architectures were sourced from.www.ftp.ebi.ac.uk/pub/databases/Pfam/current_release/database_files/architecture.txt.Predicted transmembrane and disordered regions in sequences with Pfam domain alignments were retrieved from.www.ftp.ebi.ac.uk/pub/databases/Pfam/current_release/database_files/other_reg.txt. Overlap of predicted transmembrane or disordered regions with an annotated domain was evaluated by comparing to www.ftp.ebi.ac.uk/pub/databases/Pfam/current_release/Pfam-A.regions.uniprot.tsv. The standard deviation for domain family percentage disorder was calculated using std from the NumPy package v1.16.1. Domains that were prioritized for structural feasibility had no representatives in the PDB, an average across the domain family members of less than 10 % of the domain sequence predicted to be disordered, less than 10 % of their members with a predicted transmembrane region , and less than 10 % of their members with transmembrane-domain overlap.Additional file 1.Additional file 2.


The large-size lattice truss panel structure (LTPS) is continually increasing for higher upsizing, but the roles of its connected structures on the mechanical properties are always ignored during the previous structural integrity assessment. Thus, in this paper, a series of mechanical tests, including the fabricating of panel-to-panel LTPSs, monotonous tensile, and three- and four-point bending tests, were performed to comprehensively understand the mechanical behavior. Furthermore, a theoretical model including the role of connected structures was developed to predict both the elastic and plastic deformation behavior of panel-to-panel LTPS. Results show that the connected structure has a very significant effect on the mechanical properties of panel-to-panel LTPS during the three-bending tests, and I-beam element depresses its carrying capacity. The developed theoretical model was proved to accurately predict the experimental results, and the maximum error was limited within 20%. Finally, the dimensional effects of the connection components on mechanical properties were also analyzed by the theoretical model, and indicated that the panel-to-panel LTPS will present better mechanical performance than the intact structure when the width of I-beam element exceeds 12.2 mm or the its length downgrades to 39.1 mm, which provide a comprehensive guidance for the engineering design of large-size LTPS. The lattice truss panel structure (LTPS) ,2 has a At present, several methods have been developed to manufacture the PLTPS, including the investment casting (IC) ,9, stampd and length w) on mechanical properties were also analyzed to provide a comprehensive guidance for the engineering design of large-size LTPS.Therefore, in this study, the mechanical performance of the LTPS with panel-to-panel construction was investigated by the experimental and theoretical methods. The three-point and four-point bending mechanical tests were carried out for both the I-beam and intact panel-to-panel LTPSs to comprehensively reveal the role of connected structure. Additionally, a theoretical model was developed to accurately predict both the elastic and plastic deformation behavior. Finally, the dimensional effects of the I-beam  and four-point (D4) bending tests was calculated by Equations (1) and (2), respectively.δ is the increment of the deflection. L, S, and Hc denote the supporting span, loading span and structure height, respectively; The mechanical properties of both I-beam and intact panel-to-panel LTPSs were characterized by the three-point and four-point bending tests on a MTS electro-hydraulic machine (1200 kN) in accordance with the standard ASMT D7249-18, as show in Hc + 2t and B, respectively, as shown in d) is equal to the thickness (t) of the metal sheet, and the length w of the frange-plate is equal to the length (Lc) of the unit cell. The schematic of the typical unit cell and I-beam are illustrated in xl, ly and lz are the dimension of the unit cell in X, Y and Z direction, respectively.The key dimensions of the LPTSs involved in the analytical model are firstly introduced. The construction consists of two single sandwich structures and an I-beam structure, and the height and width of the single structure are N and tangential force Q on the truss 1 and 2 can be obtained by Equations (3) and (4), respectively,F and FR are loaded force and counter-force at the truss tip, respectively. θ denotes the inclination angle of the truss.M at truss end isThe equilibrium equation of bending moment In the horizontal direction, both the displacement and rotation angle of the truss are zero due to the clamped supporting.Deflection:Rotation angle:The corresponding longitudinal force, tangential force, and moment on the truss 1 and 2 can be written asSince the total displacement is the superposition of deformation controlled by longitudinal force, tangential force, and bending moment, the deformation contribution in X direction can be calculated by the dummy-load method:T1,2 imposed on the truss tip is expressed asAc is the area of the unit cell. However, it is worth noting that the deformation of the truss 3 (or 4) are dominated by the tangential deformation, and that the longitudinal deformation remains zero. The corresponding longitudinal force, tangential force, and bending moment at truss tip are written as Equations (13)–(15).Then, the shearing force T3,4 imposed for the 3 (or 4) truss is:Then, the equivalent force xyT and γc are the shearing force and strain of the unit cell. Finally, the equivalent shearling modulus of a unit cell is obtained.Gc and τc are the shearing modulus and stress of the unit cell.The relation between force and deformation can be expressed asγI and γ* are the shearing strain of the I-beam and whole structure. τ* and [G]* are the shearing stress and modulus of the whole structure, respectively. AI denotes the area of the I-beam element.The shearing deformation of the panel-to-panel construction is the superposition of the shearing deformation of the single LTPS and I-beam. In addition, based on the equivalent homogenization theory, the equivalent shearing strain is written as:GI and G are the shearing modulus of the I-beam and material.The equivalent shearing modulus of the panel-to-panel construction isEc of the single LTPS in Z direction is derived.E is the elastic modulus of the material.Based on this method, the equivalent elastic modulus ε* denotes the equivalent compressive strain of the unit cell. In addition, [E]* and EI are the equivalent elastic modulus of the whole structure and I-beam element. σ*, σc, and σI are the equivalent compressive stress on the whole structure, unit cell, and I-beam element, respectively. The equivalent elastic modulus of the panel-to-panel construction isThe overall compressive force of the panel-to-panel construction is the superposition of the force shared on the of the single LTPS and I-beam. Besides, the overall compressive strain is equal to the strain of the single LTPS, which is also equal to the compressive strain of the I-beam, thusI and ρ denote the moment of inertia and radius of curvature.Under the three-point bending load, the total deformation of the panel-to-panel construction contains of the bending deformation from metal sheet and the shearing deformation from lattice cores, as shown in D is the flexural rigidity of the inner core. τ and γ are the shearing stress and strain of the inner core, respectively. According to the stress-strain relation, the balance equation is written asc is the deformation of the inner core.The shearing deformation sketch of inner core is shown in f is the deformation of the inner core.Since the total central deflection is the superimposition of the bending deformation of the sheet metals and the shearing deformation of the lattice core, the deflection in Z direction is:x and Δz denote the total deformation in X and Z direction. Furthermore, 1′  represent the deformed trusses. The corresponding rotation angle of the plastic hinge is α1,1.Based on the experimental results ,23, abouα1,1, α3,1, and α4,1 are the plastic rotation angle of the truss 1, 3, and 4, respectively. Δx denotes the displacement of the truss in X direction.Under shearing force, the rotation angles of the plastic hinge for the truss 1, 3, and 4 are easily calculated by Equations (32) and (33).However, for the truss 2, the balance relation between rotation angle and deformation can be expressed as:α2 the plastic rotation angle of the truss 2.The rotation angle of the truss 2 is obtained by solving Equation (34).xF and shearing deformation Δx is calculated by Equation (40).yσ is the yield strength of the base material. Δl denotes the decrement of the truss under shearing load. By differentiating Equation (40), the relation between shearing force xF and shearing deformation Δx can be expressed as:According to the energy balance between work from the loading force and the absorption energy from the structural deformation, the relation between applied shearing force z and zF are the compressive deformation and force in Z direction. zF. α is the plastic rotation angle. By differentiating Equation (43), the equivalent compressive force in Z direction isSimilarly, for compressive condition, the rotation angle of the plastic hinge and energy balance equation is expressed as:For the whole structure, an equilibrium requires that the work carried by the load is related to the bended sheet metals, shearing cores and compressive cores.W and overall deformation Δz can be expressed as Equation (46).By differentiating Equation (45), the relation between total force For four-point bending, the decomposed forces can be expressed as8 N·mm2 and 1.37 × 108 N·mm2, and the corresponding initial yield loads were 68.54 kN and 53.90 kN, respectively. Then, the slope decreased significantly with the increase in imposed displacement after the plastic bending deformation became aroused by the contact between the upper metal sheet and the indenter, and the displacement loading reaches the inflection point (stage II). The bending deformation of the metal sheets were significantly larger than the shearing deformation of the inner core. It implies that the metal sheets yielded earlier than the inner core. The plastic collapse loads of the I-beam element and intact structure were 80.15 kN and 82.29 kN, respectively, and the corresponding ratio was 97.40%.The experimental load–displacement curves for both intact and I-beam panel-to-panel construction under three-point and four-point bending tests are plotted in 8 N·mm2, and the corresponding yield loads were 46.58 kN and 43.63 kN, respectively. However, the curves elevate unceasingly with a smaller rate due to the yield of the inner core. The measured plastic collapse loads of the intact and I-beam element were 68.58 kN and 65.53 kN. Appreciable differences arose with respect to the extent of the load drops and fluctuations near the peak values which corresponded to the dominant failure mode, i.e., debonding. Fortunately, the butted joints were intact after the three-point bending and four-point bending tests.After a peak, the load decreased gradually with the further increase in displacement due to the debonding between metal sheet and lattice core (stage III) Similar characteristics were observed in four-point bending tests until the peak, and the major inflections and nonlinearities of intact and I-beam element structure were initiated at similar displacement, indicating the similarity of underlying damage mechanisms in that region. The difference of flexural rigidity between intact and I-beam element structure was 0.52 × 10As a conclusion, the butted joints present good mechanical performance, and the overall deformation is the superimposition of the bending deformation of metal sheets, and the coerced shearing deformation of inner core. Elastic deformation of the metal sheets and inner core is defined in stage I. The overall deformation in stage II is dominant by the plastic deformation of the sheet metals and elastic deformation of the inner core. Both the metal sheets and inner core yield in stage III and the debonding failure reduces the bearing capacity of those structure further. For the investigated dimension, the I-beam element structure have slight poorer performance due to the weakening effect of the I-beam. Furthermore, the comparison of mechanical response for two structures is worth more attention.Mm is located at mid of the metal sheet, and the plastic deformation occurs when the metal sheets attain the yield strength, givingFor both three- and four-point bending, the yield load is determined by equating the maximum bending moment within the structure to the plastic collapse moment of the section. For example, in case of three-point bending, the maximum bending moment Wf) of the metal sheet isHence, the critical load  and (57), respectively.To summarize, the theoretical load-displacement curve was divided at the third stage.Stage I: loading range Stage II: loading rangeStage III: loading range 8 N·mm2 and 1.52 × 108 N·mm2, respectively, and the corresponding error between analytical model and experimental are 10.47% and 10.95%, respectively. However, in the second stage, the deformation is the superimposition of the plastic deformation of the metal sheets and the elastic deformation of the inner core. The plastic deformation of the inner core is behind of the plastic deformation of the metal sheets because the shearing deformation Δx  and length (w) on the mechanical properties of the panel-to-panel construction are exhibited in The effects of I-beam width , the force on the sheet metal under three-point and four-point bending are Equations (59) and (60), respectively.In this paper, the mechanical properties of the panel-to-panel LTPSs were investigated by the experimental and theoretical methods. The roles of connected structure were revealed to provide an extensive guidance for the engineering application and design of the large-size LTPS. The main conclusions are shown as the following:Under three-point bending, the connected structure had a very significant effect on the mechanical properties of panel-to-panel LTPS, and I-beam element greatly depressed its carrying capacity. The degradation of the flexural rigidity, initial yield load, and plastic collapse load are 20.59%, 21.38%, and 1.30%, respectively. Nevertheless, this effect became inconspicuous (less than 5%) under four-point bending, whereby the external loading was far from the I-beam element. This implies that the external loading being set at the location away from a connected structure is a positive for engineering LTPS in order to attain a greater carrying capacity.(1)The analytical models, including flexural rigidity, initial yield, and plastic collapse, were proposed. The maximum error is 18.78%, which reveals that the models were proven to accurately predict the deformation behavior and provide more convenience for the engineering safety assessment.(2)d and decreasing the I-beam length w, which are more sensitive to the length. In addition, those models provide a guidance for the engineering design of large-size LTPS. The I-beam element connected panel-to-panel LTPS presents better mechanical performance than the intact structure when the d exceeds 12.2 mm or the w downgrades to 39.1 mm.The dimensional effects of the connection components on mechanical properties were discussed by the analysis models. The mechanical properties were enhanced by elevating the I-beam width


Bordetella pertussis adenylate cyclase toxin, an RTX leukotoxin essential for B. pertussis colonization, have been shown to target the RTX domain and prevent binding to the αMβ2 integrin receptor. Knowledge of the mechanisms by which antibodies bind and neutralize RTX leukotoxins is required to inform structure-based design of bacterial vaccines, however, no structural data are available for antibody binding to any T1SS substrate. Here, we determine the crystal structure of an engineered RTX domain fragment containing the αMβ2-binding site bound to two neutralizing antibodies. Notably, the receptor-blocking antibodies bind to the linker regions of RTX blocks I–III, suggesting they are key neutralization-sensitive sites within the RTX domain and are likely involved in binding the αMβ2 receptor. As the engineered RTX fragment contained these key epitopes, we assessed its immunogenicity in mice and showed that it elicits similar neutralizing antibody titers to the full RTX domain. The results from these studies will support the development of bacterial vaccines targeting RTX leukotoxins, as well as next-generation B. pertussis vaccines.RTX leukotoxins are a diverse family of prokaryotic virulence factors that are secreted by the type 1 secretion system (T1SS) and target leukocytes to subvert host defenses. T1SS substrates all contain a C-terminal RTX domain that mediates recruitment to the T1SS and drives secretion via a Brownian ratchet mechanism. Neutralizing antibodies against the  Bordetella pertussis adenylate cyclase toxin (ACT) mediates leukocyte targeting via binding to the αMβ2 integrin receptor, and antibodies that block receptor binding neutralize toxin activity. However, ACT also contains multiple non-neutralizing epitopes, and precise knowledge of the sites targeted by neutralizing antibodies is desirable for vaccine design. Here we determine the crystal structure of an ACT fragment in complex with two neutralizing antibodies and define the key neutralization-sensitive sites within the RTX domain. This first structure of a heterotypic protein–protein interaction formed by an RTX domain suggests the linker regions between β-roll segments engage binding partners.Diverse bacterial pathogens use the type 1 secretion system (T1SS) to secrete RTX leukotoxins, which target host leukocytes during infection. T1SS substrates all contain a repetitive C-terminal ‘RTX’ domain that adopts a characteristic β-roll fold and is involved in secretion. Notably, The RTX domain of  B. pertussis colonization, as well as to optimize the presentation of their most vulnerable epitopes on vaccine antigens. The adenylate cyclase toxin (ACT) is a promising vaccine antigen as it has been shown to be essential for lung colonization in mouse intranasal models. In . In 37].ing turn . InteresCa2+ ion . OverallM2B10 engages L1 of the RTX domain using both its heavy and light chains, with the light chain contacting the short α-helix in the first loop of L1 . Specifi2 and 146 Å2 for the heavy and light chain interfaces, respectively. Tyr100B of the CDRH3 forms a hydrogen bond with Asp1234, a residue that has been implicated in αMβ2 binding/[A490 (lysis control)—A490 (untreated cell control)].Murine macrophage-like J774A.1 cells (ATCC) were cultured at 37°C and 5% COdescribed. BrieflyS1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


Oilseed cakes are produced as a by-product of oil pressing and are mostly used as feed. Their use for human consumption is due to the functional properties and benefits for human health. Herein, oilseed cake flours of eight species  were sieved into fractions above (A250) and below (B250) 250 µm. The chemical composition, SDS-PAGE profiles, colour, functional properties and antioxidant activities of these flours were evaluated. The B250 fractions were evaluated as being protein and ash rich, reaching crude protein and ash content ranging from 31.78% (milk thistle) to 57.47% (pumpkin) and from 5.0% (flax) to 11.19% (poppy), respectively. A high content of carbohydrates was found in the flours of hemp, milk thistle and safflower with a significant increase for the A250 fraction, with a subsequent relation to a high water holding capacity (WHC) for the A250 fraction . The A250 milk thistle flour was found to have the richest in polyphenols content (TPC) (40.89 mg GAE/g), with the highest antioxidant activity using an ABTS•+ assay (101.95 mg AAE/g). The A250 fraction for all the species exhibited lower lightness than the B250 fraction. The obtained results indicate that sieving oilseed flour with the aim to prepare flours with specific functional characteristics and composition is efficient only in combination with a particular species. Brassica species are the second-largest oilseed crop after soybean in the world production, surpassing peanut, sunflower and cottonseed during the last two decades [Oilseed species are an important group of crop plants whose oil is edible and suitable for human consumption. Only a few species are significant in the total world trade out of about 40 different oil seeds . The maj decades . Minor oAs a by-product of oil pressing, oilseed cakes are produced, which are mostly used as feed for farm animals because they contain significant amounts of protein and other valuable substances ,4,5. HowPredominantly, oilseed cakes and subsequently oilseed cake flour are valuable sources of gluten-free proteins  that areLinum usitatissimum L.), hemp (Cannabis sativa L.), milk thistle (Silybum marianum L.) Gaertn.), poppy (Papaver somniferum L.), pumpkin (Cucurbita pepo L. var. oleifera), rapeseed (Brassica napus L.), safflower (Carthamus tinctorius L.) and sunflower (Helianthus annuus L.). This work brings new information regarding the composition, functional properties and potential usability of various fraction sizes of the oilseed cake flours in relationship to the species. The use of oilseed cakes and flours for the fortification of various types of food products is already well described ,14,15,16Linum usitatis-simum L.), hemp (Cannabis sativa L.), milk thistle (Silybum marianum (L.) Gaertn.), poppy (Papaver somniferum L.), rapeseed (Brassica napus L.), safflower (Carthamus tinctorius L.) and sunflower (Helianthus annuus L.) were done using direct saturated steam injection at 95 °C with a cold screw pressing operated at a temperature below 50 °C. The industrial processing of pumpkin seeds (Cucurbita pepo L. var. oleifera) consisted of milling, moistening at 70 °C, roasting  and cold pressing using a hydraulic press that was operated at a temperature below 50 °C. Cold oilseed cakes of eight oilseed species were vacuum packed and kindly donated by AGRO-EL Ltd.  for further determination of the functional properties. Industrial moistening and screw pressing of oilseeds, namely, flax  under 10,000 rpm for 1 min and subsequently separated by sieving into size fractions . Finally, three size fractions of the oilseed cake flour were obtained: whole oilseed cake flour (WF) and oilseed cake flour fractions above 250 μm (A250) and below 250 μm (B250). The yields of the sieving fractions is given in The ground and homogenised samples were analysed for water content using the gravimetric method (AOAC 925.10), crude protein content (N × 6.25) using the Dumas method, crude fat content using the Soxhlet method (AOAC 935.38) and ash content (AOAC 923.03) . The moisture of the samples (2 g) was determined via drying them to a constant weight in a drying oven , which was maintained at 105 °C for 3 h. The crude protein content was determined via the modified combustion method according to the Dumas method using the rapid N elementary analyser . Each sample was analysed in triplicate and the crude protein (protein nitrogen) was calculated as nitrogen content multiplied by a factor of 6.25. The crude fat content was measured via the Soxhlet extraction method with petroleum ether using an automatic extraction system ANKOM XT 10 Extractor  according to the producer’s manual. Crude fat values were calculated from the weight difference before and after extraction. The ash content was measured as the total amount of inorganic residues remaining after the organic matter incineration by heating oilcake samples in a muffle furnace  at 550 °C for 5 h. The carbohydrates content was calculated as the remainder up to 100% of the sample dry matter (DM) after subtraction of the sum of all determined other component contents. g, 20 °C) . The supernatant was discarded and tubes with pellet were weighed, freeze-dried   and weighed again. The solubility and water holding capacity were computed from the obtained weight data according to the following formulas (Equations (1)–(3)):Water solubility (WS) and water holding capacity (WHC) of the oilseed cake fractions were determined by mixing 300 mg of flour dry matter (DM) with 5 mL of deionised water. The tubes with the mixture were allowed to stand at room temperature for 30 min under continuous shaking. The samples were centrifuged ) and water holding capacity were computed from the obtained weight data: The fat-holding capacity (FHC) was determined similarly to the WHC with some differences: 5 mL rapeseed oil was used instead of water. The supernatant (oil phase) was discarded after centrifugation and the tubes with the pellet were weighed. The FHC values were computed from the weight of the fatted pellet (Equation (5)): ® XTH colourimeter , which is based on the CIE  system and provides L*, a* and b* parameters . The analyses were performed in triplicate.The colours of the oilseed cake flour samples were measured using a ColorEyeg, 10 min), filtered and subsequently refrigerated at −20 °C until analysis. The extracts were analysed according to Lachman et al. [w/v) sodium carbonate. The absorbance was read at λ = 750 nm  after 2 h. Gallic acid was used for calibration. All measurements were repeated three times and the results were expressed as milligrams of gallic acid equivalent (GAE) per gram of sample fresh matter (FM). The total phenolic content (TPC) was determined spectrophotometrically using Folin–Ciocalteu’s reagent after the previous extraction of phenolics using 80% ethanol with a solid-to-solvent ratio of 1:20. The extraction was performed over 24 h at room temperature and the mixture was then centrifuged  was diluted in 5 mM phosphate buffer to an absorbance of 0.800 ± 0.01 at λ = 734 nm. The absorbance of the reaction mixture was measured at λ = 734 nm 1 min after the addition of 100 μL of sample extract to 1 mL of the radical solution. Radical scavenging activity with the ABTS•+ method: ABTS·+ radical was produced by dissolving 54.8 mg 2,2′-Azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS) and 1.0 g MnOv/v) DPPH• stock solution and 25 µL of the analysed extract. The absorbance of the reaction mixture was measured at 515 nm after incubation at room temperature for 30 min.Radical scavenging activity with the DPPH• method: 2,2-Diphenyl-1-picrylhydrazyl (DPPH) radical in an amount of 0.025 g was dissolved in 100 mL of methanol to obtain a stock solution. The reaction mixture was prepared from 975 µL of 10% (w/v) SDS, 5% (v/v) 2-sulphanylethanol). Protein separation was carried out in duplicate using cooled dual vertical slab units SE 600  with a discontinuous gel system (4% stacking and 12% resolving gel) in reduction conditions [Oilseed cake flour samples were extracted at 4 °C for 4 h using an SDS extraction buffer  was used for the data analysis. Data were subjected to analyses of variance using the two-way ANOVA method and the means were compared using the Fisher test. Differences between the variants were considered significant at The approximate compositions of all variants that were derived from the oil cake flours are given in The effect of size fractionation on crude protein content was limited for other species , which can be given in connection with the absence of pericarp. This conclusion is well documented regarding carbohydrates content and their distribution between the size fractions for individual species. Carbohydrates contents, including fibre, were significantly lower in the group of flax, poppy, pumpkin and rapeseed and differences in carbohydrates content between the A250 and B250 fractions were more pronounced in the group of oilseed species with a pericarp. The lowest content of carbohydrates  and the highest content of protein (ranged from 57.7 to 60.4%) was observed for pumpkin. The usual level of crude protein content in pumpkin seeds range from 20 to 40% ,23,24. NIn contrast, we found a high content of carbohydrates in the flour of hemp, milk thistle and safflower, with a significant increase for the A250 fraction. Differences in carbohydrates content were minimal and statistically insignificant for the flours of poppy and pumpkin. Dietary fibres are considered to be one of the major ingredients that are used to develop products with a functional purpose ,27 and oResidual crude fat content in the WF fraction varied from 4.84% (milk thistle) to 16.16% (flax) of the FW. The amount of residual fat increased with decreasing flour particle size. The observed differences for the WF and A250 fractions were statistically significant, except for sunflower. The second exception was safflower flour, with the highest content of residual fat in the WP fraction and the lowest level in the B250 fraction. The highest differences in fat content between the A250 and B250 fractions were determined for flax and rapeseed. A high content of residual fat in the B250 fraction appears to be problematic due to the application of this protein-rich source. On the one hand, the increased fat content of flax flour can be used as a substitute for animal fat sources , thereby increasing the health, environmental and economic benefits of such products. On the other hand, it is necessary to take into account the instability of flax fat components and the possible link to flour technological quality. Bochkarev et al.  proposedThe tested oilseed cake flours also differed regarding the ash content. The species with a pericarp  exhibited higher differences in ash content between individual size fractions. The lowest contents were determined in the A250 size fraction, and the highest level was found in the B250 fraction. This phenomenon was also observed for poppy and rapeseed flours; however, the differences between the fractions were minimal in comparison with the seed-coated species. Ash content in pumpkin flour was specific in comparison with other species. The lowest content of ash (8.94% FM) was examined in the B250 fraction, while 9.14 and 9.42% FM were found in the WF and A250 fractions, respectively. The ash content in the protein-rich fraction (B250) ranged among the species from 5.0% (flax) to 11.19% FM (poppy). For wheat flour , the tecThe SDS-PAGE profiles of oilseed cakes within individual size fractions are shown in The protein profiles that were detected within other species could be similarly characterised into the globulin and albumin protein groups with various manifestations. Globular edestin, which is the main storage protein of hemp as the hexamer protein, was visible in all hemp variants in the form of acidic and basic subunits that were detectable in the zones of 34 and 20 kDa . This was in agreement with Kotecka-Majchrzak et al. . The difSimilar SDS-PAGE profiles to those for milk thistle were detected for safflower, sunflower and partly similar for rapeseed. The protein profiles ranged from ~10 to 100 kDa, where a high number of individual protein bands cumulated in the area of subunits alpha and beta. The safflower profiles also showed a different intensity of protein bands in these major areas, with a higher intensity for the B250 fraction. Zones of cruciferin  and napin  were found in the rapeseed SDS-PAGE profiles. Cruciferin, oleosin and napin are the main storage proteins of rapeseed, with high potential for use in functional foods and food substitutes production ,34. The The poppy and pumpkin SDS-PAGE profiles were similar to the hemp and flax samples. A somewhat higher intensity of protein bands was found in the B250 fraction. Bučko et al.  determinSimilarly, the dominant albumin protein fraction was described in milk thistle  and the The water-holding capacity values ranged within the evaluated set from 1.44 g/g FW  to 4.90 g/g FW . Low WHC values (<2.00 g/g) were found for hemp, pumpkin and safflower. The second group with WHC values between 2.19 and 3.3 g/g FM consisted of milk thistle, rapeseed and sunflower. The highest WHC was observed for all size fractions of flax flour, ranging from 3.17 g/g FM (B250 fraction) to 4.15 g/g FM (A250 fraction). This was probably related to the content of flaxseed gum, which is a constituent of dietary fibre present in the flaxseed hull . This flVery similar values were found within the evaluated variants for fat-holding capacity. The lowest FHC values were obtained for pumpkin flour variants with values ranging between 0.65 g/g FM (WF and A250 fractions) and 0.71 g/g FM (B250 fraction). Higher FHC values were obtained for flax, ranging from 1.09 g/g FM (A250 fraction) to 0.91 g/g FM (B250 fraction). As for the WHC, the presence of flax gums seemed to be related to the fat-binding ability. The values of flax FHC corresponded with the carbohydrates content. Comparable high values of FHC were also recorded for poppy and sunflower, while the lowest FHC values were recorded for WF in the majority of the species, except for flax and rapeseed. However, it is necessary to notice that the differences in the FHC between the size fractions were minimal and, in many cases, not statistically significant . The total polyphenols content and values of antioxidant activities  are presented in The highest TPC was found in the flour of milk thistle, which ranged between 17.68 mg GAE/g FM B250 fraction) and 40.89 mg GAE/g FM (A250 fraction). The key component of milk thistle polyphenols are bioactive flavonolignans, which are referred to as silymarin or the silymarin complex ,48. Sily fractionA similar trend as for TPC was also found for antioxidant activity of the analysed flour samples within the individual evaluated variants . Using tProtein-rich B250 fractions with high antioxidant activities of 20.51, 33.82 and 31.03 mg AAE/g flour were found for rapeseed, safflower and sunflower, respectively. The antioxidant activities of these fractions fit with the observed TPC values. The data of antioxidant activities that were obtained using the DPPH• radical method approximately followed the trend of antioxidant activity that was determined with the ABTS radical; however, a decrease in measured values was observed. The lower sensitivity of the antioxidant assay using the DPPH radical is typical and was published previously ,60. The Interesting in this case may be the comparison of the content of polyphenols and the antioxidant activity of the oilseeds with other known sources of antioxidants. Wine, for example, is considered to be an important donor of polyphenols with a high value of antioxidant capacity. The TPCs and antioxidant activity that was obtained by using DPPH• in grape seeds were 546 mg/g DM and 1.0 AAE mg/mL, respectively . In contThe concentration of biologically active components alongside the antioxidant activities  is often connected with the colour characteristics. However, there has been some disagreement about the relationship between the total phenolic content and antioxidant activity. Liu et al.  found thThe greenish colour (−a*) was found only for whole seed flour of rapeseed and a very low a* value was found for sunflower. The size fractionation also significantly affected the a* and b* colour parameters. The greenish tones (−a*) did not appear in any sample of either A250 or B250 size fraction. However, a very low a* value was found for rapeseed and sunflower in both the size fractions. The highest value of reddish tones in the A250 fraction was found for flax flour (a = 6.06) and for poppy in the B250 fraction (a = 5.40). The fractionation had a significant effect on yellowness, with the exception of sunflower. In general, with the increasing fineness of the flour, the lightness and yellowness increased. This dependence was very distinct in rapeseed, with the yellow values a* being 8.46 and 26.81 for the A250 and B250 fractions, respectively. Within the B250 fraction, rapeseed flour also showed the most yellow colour compared with the other species. The lowest value of the b* parameter was found in the B250 fraction of sunflower. Flour of this species showed yellow and red intensities within the B250 group, as well as in the A250 fraction, with minimal differences between the size fractions. The obtained results indicated that sieving oilseed flour with the aim to improve the functional and visual properties was efficient only in combination with a particular species. The significant effect of supplemented oilseed flour on colour scores was previously described, e.g., flaxseed flour addition significantly decreases the lightness and increased the redness ; green aThis study produced complex information about the composition, functional and antioxidant properties of oilseed cakes of eight oilseed species  in three size flour fractions: whole oilseed cake flour, flour size fraction above 250 µm (A250) and below 250 µm (B250). Such complex data has not been published before. In conclusion, the content characteristics were significantly influenced both by the oilseed species and by the size fraction of the flour samples. The highest content of proteins, as the key component from a nutritional and functional perspective, was found in pumpkin flour with an equal level in all three fractions. With the exception of pumpkin and poppy, the B250 fraction could be described as being the richest in protein, where sieving is a way of increasing the protein content. Antioxidant activity was affected by both the species and the fraction. Flax, hemp and milk thistle species exhibited significantly higher polyphenols content and antioxidant activity in the A250 fraction, while for safflower and sunflower, these properties were the highest in the B250 fraction. In all the tested species, the fractionation significantly influenced the lightness of the flour samples. The lightness was improved by the sieving: the finer the flour, the lighter the colour. Yet even regarding the colour, species specificities emerged.The obtained data confirmed the potential of mechanical sieving for some of the tested oilseed species to produce flours with specific characteristics, e.g., flour rich in protein or flour rich in antioxidants that are usable in various applications.