We found that PAAs have a high affinity for domains I and IV of TCCs and a low affinity for domains III and IV of the LCC. Therefore, they should be considered as favorable candidates for TCC blockers. The new homology models were validated with some commonly recognized TCC blockers that are well characterized. Additionally, examples of the TCC blockers created were also evaluated using these models.To establish a computer model for evaluating the binding affinity of phenylalkylamines (PAAs) to T-type Ca Tgulation ,6,7. For1G (Cav3.1) pore domain structure is similar to that of α1S (Cav1.1) [vAb model, constructed based upon Arcobacter butzleri crystallization [3p49) may swing the aspartic acid (D3p50) away from the center of the calcium filter and change the preferred calcium ion and drug binding sites from domains III and IV for LCCs to domains I and IV for TCCs. Therefore, we used the ZMM molecule modeling program [1C LCC were determined by scoring their free energy in binding to the channels [TCCs have a close evolutional relationship with LCCs. A recent report from a cryo-electron microscopy study reveals that the frame of the α(Cav1.1) . This silization , in the  program ,11,12 tochannels .vAb blocking by phenylalkylamines (PAAs) [vAb [V3.1 and CaV1.1 are superimposed [The new TCC models are adopted from a drug–protein interaction framework for modeling Cas (PAAs) . This isAs) [vAb ,14. It iAs) [vAb . We reasrimposed . As a rerimposed  will makvAb open channel 3D structure as the input, ZMM generated the first template of the calcium channel, which was then modified with S5-P-loop-S6 segments of α1C, and α1G, α1H, and α1I  in α1G TCCs. This may exclude glutamic acid E2p50 as a Ca2+ binding candidate, leaving the Ca2+ to bind either E1p50 to D4p50 or D3p50 to D4p50 in TCCs. Additionally, to validate the normality of the remodeling data, we conducted a nonparametric test for the α1G P-loop remodeling data and confirmed that all the sampling processes  came from the same distribution  in domains III and IV [2+ [3p49) located at the 5′ end adjacent to D3p50 in domain III. It is reported that the replacement of lysine (K3p49) with Phe or Gly causes the activation curve to shift to the right [3p50) plays a significant role in the kinetic/dynamic mechanism of Ca2+ interaction with the inner environment of the central cavity of TCCs. This positively charged lysine alters the negative charge field distribution of D3p50 to attract Ca2+ (3p49) swings aspartic acid (D3p50) away from the original Ca2+ binding position, thus causing the Ca2+ to bind glutamic acid or aspartic acid in other domains, probably to domains I and IV since domain II has a configuration deviation. As a result, the phenylalkylamine may also switch its binding region from domains III and IV to domains I and IV.A previous study indicated that when a calcium ion enters the selectivity filter region of a LCC, it binds to the selectivity-determining glutamic acids  on overall electrostatic potential (E) for given TCC homology models, we calculated the electrostatic potential (E) for the tailed P-loop of domain I to IV. coul) becomes more negative in each domain as the number of testing amino acids is reduced from seven to five. In TCCs, Ecoul for domain I is more negative than that for domain IV in the five amino acid-reduced sequence, indicating a possible switching of the Ca2+ binding site from domains III and IV to domains I and IV.To determine the effect of lysine (K2+ and aspartic acid (D3p50). According to the equation in 3p49) has the least effect on the Ca2+-D3p50 attraction when K3p49 is located on the opposite side of the Ca2+ and when D3p50 is at the center. When  = 2.489 × 10−9 N and F = 0.702 × 10−9 N; thus, lysine, at a minimum, reduces the attraction force between Ca2+ and aspartic acid by more than 28%. The attraction force between Ca2+ and D3p50 will reduce further or reverse into a repellent force as the distance from the Ca2+ to K3p49 decreases; therefore, the preferred binding position of Ca2+ will likely be switched to domains I and IV in TCCs. This limits the PAA binding region on TCCs to domains I and IV. We could use the amino acid structure of domains I and IV to evaluate the affinities of the PAAs (and their derivatives) for TCCs  and use the amino acid structure of domains III and IV for evaluating their binding affinities to LCC (using the model of α1C).We used Coulomb’s electric force equation to quantitively analyze the influence of lysine on the electrical attraction force between Ca1C model predicts that one hydrogen atom from the nitrogen (N3) on the cyclopentadiene connects to methionine (M4i27) on domain IV of α1C LCC, as shown in 1C homology model does not predict that hydrogen bonds to NNC 55-0396. NNC 55-0395 inhibits both L- and T-type calcium channels [1C homology model predicts that NNC 55-0395 has one hydrogen bond that connects nitrogen (N3) to the glycine (G3p49) on P-loop domain IV. For NNC 55-0397, the α1C homology model also predicts that RO 40-5966, a hydrolyzed metabolite of mibefradil [4p49) at domain IV. No hydrogen bond has been found between the LCC and the TCC blocker SKF-96365.Mibefradil is reported to have an inhibitory effect on both LCC and TCCs . The α1Cchannels . The α1Cbefradil , has one1G, α1H, and α1I. The α1G model predicts that the fluorine atom from the compound NNC 55-0395 forms a halogen bond to glycine (G1p51) in domain I. Our α1G model also predicts a binding site of NNC 55-0396 to asparagine (Nli20) in domain I (1p46) at the P-loop of domain I. For mibefradil, one oxygen atom from the side chain of the compound forms hydrogen bonds with asparagine (N1o4) at S5 of domain I. For RO 40-5966, one hydrogen atom from the nitrogen (N3) on the cyclopentadiene forms a hydrogen bond with alanine (A1i27) in domain I. For SKF-96365, the center oxygen atom forms a hydrogen bond with asparagine (N1o4) at S5 of domain I. Using TCCs as templates, we have revealed some current TCC blockers of αdomain I C,D. For 1H model predicts that one hydrogen atom from the nitrogen (N3) on the cyclopentadiene of NNC 55-0395 interacts with valine (V1p46) at the P-loop of domain I to form a bond. The fluorine atom from NNC 55-0396 forms a halogen bond with isoleucine (I1i8) from the α1H S6 of domain I. For NNC 55-0397, one hydrogen atom from the nitrogen (N3) on the cyclopentadiene forms a bond to asparagine (N4p51) at the P-loop of domain IV. The hydrogen atom from the nitrogen (N3) on the cyclopentadiene of mibefradil finds asparagine (N1o4) to form a bond at S5 of domain I. For RO 40-5966, the fluorine atom from the compound forms a halogen bond with histidine (H4i29) at S6 of domain IV. One oxygen atom from the side chain of SKF-96365 forms a hydrogen bond with asparagine (N1o4) at S5 of domain I.Our α1I homology model predicts that NNC 55-0395 forms a halogen bond between the fluorine atom from the compound and isoleucine (I1i8) at S6 of domain I. The hydrogen atom from NNC 55-0396 forms a bond to asparagine (N1o4) at S5 of domain I. For NNC 55-0397, one hydrogen atom from the nitrogen (N3) on the cyclopentadiene interacts with valine (V1p46) at the P-loop of domain I to form a bond. For mibefradil, there is a halogen bond formed between a fluorine atom from the compound and a hydrogen atom from asparagine (N4p53) at the P-loop of domain IV. For RO 40-5966, one hydrogen atom from the ammonia on the cyclopentadiene interacts with asparagine (N1o4) at S5 of domain I to form a bond. Our model does not predict the hydrogen bond formed when docking SKF-96365 to α1I. Our αd and experimental measurements of IC50 for given TCC blockers are listed in A comparison of the predicted binding affinity KThe Vina  models w1G, α1H, and α1I, respectively. Compound TC 7 has the highest binding affinity, as well as a lower (water–octanol partition coefficient) logP and a higher Quantitative Estimation of Drug-likeness (QED) than existing TCC blockers. The 3D binding plots between TC 7 and α1G are shown in 1C LCC are shown in After performing virtual screening, we found that the compounds TC 7, TC 4, and TC 2 satisfied our screening criteria for αv3.1) 3D structure has already been modeled with cryo-electron microscopy [v3.1-Δ8b) shows 1.5-2-fold conductance increases when compared with the full-length form in human and rat preparations. Both activation and steady-state inactivation curves are shifted in the human preparation [v3.1-Δ8b is smaller than the biophysical measurement [v3.1 TCC. Therefore, the 3D structure of Cav3.1-Δ8b may not be the most suitable template for the general modeling of TCCs, especially for PAA binding, which is highly dependent on the position of Ca2+ interacting with the selectivity filter of TCCs. In this study, we chose to use CavAb as the model template since this channel is blocked by PAA and therefore is suitable for establishing a model for evaluating PAAs that inhibit TCCs selectively over LCCs.A TCC  difference between LCCs and TCCs, we have created a strategy that can distinguish the affinity of PAAs to TCCs and LCCs, respectively. The models have been validated by measuring the affinities of existing TCC blockers to LCCs and TCCs. We also used these models for evaluating the specificities of novel PAAs and phenylalkylamine derivatives in terms of their binding affinities to TCCs and LCCs.We chose to build models for the interaction between TCCs and PAAs since the binding mechanism of these compounds to LCCs has been studied extensively ,24,25,261G, α1H, and α1I TCC and a1C LCC structures with four domains, each containing three segments: segment 5, a P-loop, and segment 6. In contrast, the ab initio modeling method failed to produce a suitable calcium channel structure compared to ZMM.Using ZMM, we simultaneously created α1 subunit in each domain. In this region, there are two negatively charged glutamic acid residues likely to attract one Ca2+ in the space close to domains III and IV [2+, while the other parts of the molecule form affiliated interactions with the amino acids in the P-loop and segments 5 and 6 in domains III and IV of the calcium channel. This causes a physical blockage of ion flow through the channel. In the case of TCCs, there is a lysine (K3p49) located adjacent to D3p50 in domain III, and the ionized electric potential distribution of the aspartic acid is altered by lysine, which attenuates the electric attraction of aspartic acid (K3p49) to Ca2+ at the minimum binding distance (4.3 angstroms) and may swing K3p49 away from the selectivity filter. Based on this analysis, we suggest that Ca2+ will not bind to domain III but to domain I of TCCs. This prediction is consistent with the Cav3.1 structure estimated with cryo-electron microscopy [2+ ion is closest to Glu354 of Cav3.1  further away from the Ca2+ binding site. Since the movements of PAAs will follow the location of Ca2+ docking, our models are built for evaluating the affinity of candidate compounds binding to domains I and IV. The compounds that are predicted to have a higher affinity to bind domains I and IV of TCCs but not domains III–IV of LCC (α1C) are considered to be ideal selective TCC blocker candidates. This strategy screens out the compounds that are unlikely to bind LCC and TCCs as well as the compounds that are likely to bind both LCC and TCCs. To test whether Ca2+ docking is consistent on domains I and II across different TCCs, a molecular dynamics study should be conducted with modified membrane conditions and simulation environments [The selectivity of the calcium channel is dependent on the critical glutamate residues located in the selectivity filter of the P-loop of the αI and IV . When a croscopy , which s1 E1p50, . Additioronments .2+; this function persists if the nitrile is replaced with other elements with high electronegative potentials, such as oxygen or sulfur [2+ channels may not be explained by our new models. For example, by using the input template SKF-96365, we obtained 14 unique structures  as CavAb [1G, α1H, and α1I may have smaller diameters than CavAb, since the unitary conductance of TCC currents is smaller than that of LCC. Further statistical analyses of P-loop remodeling data show that a minimal structural difference exists in the P-loop region remodeling data  amino acid sequences  program. The final structure of the target peptides was optimized by using the Monte Carlo minimization protocol. The maximum iteration time for finding the global minimum was set to 5000. During the energy optimization, structural similarity between target and template was maintained by a flat-bottom parabolic energy penalty function that allows for penalty-free deviations of alpha-carbons up to 1 atom distance from their respective positions in the template, and a penalty was imposed with a force constant of 10 kcal mol-1A-2 for larger deviations [1H (UniProt id: O95180), α1I (UniProt id: Q9P0X4), and α1C (UniProt id: Q13936) were also built with this method.Three classes of calcium channel families have been discovered: Cafrom BAM  using trviations . The homcomplex = Gsolution + GCoulomb. The GCoulomb for LCC and TCCs in the P-loop region was calculated by using PyGBe with pre-defined parameters , and lysine (K), respectively. The effect of the repellent force on Ca2+ by lysine in the direction of the Ca2+ and the aspartic acid attracting force is defined by2+ to aspartic acid and from Ca2+ to lysine; 2+ and lysine, which is calculated by2+ and aspartic acid, and lysine and aspartic acid (ϕ) is less than 90°, 2+ and aspartic acid isTo calculate the electric double layer-related local electrostatic potential while a channel protein interacted with a surrounding water molecule, we generated the corresponding meshes using MSMS (v2.6.1)  and set rameters . To compThe ab initio modeling modules from Rosetta were employed to find the three-dimensional structure of target fragmental peptides by sampling and assembling a large candidate pool containing 22,000–27,000 decoy structures for every inputted amino acid sequence . The out1G was remodeled using a Rosetta loop modeling module combined with the FastRelax protocol. Twenty-seven amino acids in the P-loop were selected from domains I, II, III, and IV of the TCCs. The modeling used phenylalanine as the starting amino acid and tyrosine, tryptophan, proline, and isoleucine as the ending amino acids. The effective sample size used for subsequent statistical analysis was validated by two groups of data for every remodeled domain. The first group contained 500 output structures, and the second group had 20,000 output structures. The results were analyzed using the Calibur and energy-based clustering methods.The P-loop region of αWe used the de novo drug generation package “chemical vae” developed by Gomez-Bombarelli et al.  to creatThe 2D structure of mibefradil was employed as a redesigned template for new compounds. Based on its structure, we recreated 129 PAAs and their derivatives. Their corresponding 3D structures (involving up to 800 isomers) were created by Frog 2.1. We used the same program to find the 3D structures for NNC 55-0395, NNC 55-0396, and NNC 55-0397, and combined them with SKF96365 and RO 40-5966, whose 3D structures were downloaded from PubChem, for use as reference compounds for testing and validating the faithfulness of our TCC models.Some of the candidates of screening compounds may contain oxygen, which replaces the role of nitrile; this structural formula has been reported in certain PAAs such as falipamil, BRL-32872, and tiapamil .1C and 58, 48, and 50 for α1G, α1H, and α1I, respectively, when simulating the ligand–receptor interaction for existing TCC blockers. The number of mesh elements in the X, Y, and Z directions was increased to 126 when conducting virtual screening for newly designed compounds. To achieve repeatable docking results using Vina, the seed number was fixed at –1460306363. As the grid number for every direction was set to the maximum, Vina had to search a very large three-dimensional space. To find the local minimum, the exhaustiveness was set to 2000 for new compound screening cases and 8 for existing TCC blocker screening cases. The number of predictable binding models expressed as the output was limited to 3 for new compound screening cases and 20 for existing TCC cases.Virtual drug screening was conducted by using AutoDock Vina  with useThe Vina output results were checked using PyMOL to ensure the binding locations for existing and newly designed compounds. The predicted binding affinity for the testing compound was calculated as:+ [The 2D ligand–receptor interaction plot was created using LigPlot+ .The Anderson–Darling normality test and the Kruskal–Wallis one-way ANOVA test  were conAll the new identified compounds in this study are patented by the Office of Technology Transfer and Intellectual Property Development at Tulane University .


Prof. Donglin Jiang is a full professor at the National University of Singapore and is recognized as a pioneer in the field of 2D polymers and covalent organic frameworks (COFs). With a background in chemistry and studies on dendrimers for ten years, the beauty of dendrimers inspired him to consider the possibility of constructing other types of polymers with well-defined shapes and structures. After taking up an associate professorship in 2005 to set up an independent laboratory at the Institute for Molecular Science at the National Institute for Natural Science, he started to dedicate his work on the design, synthesis and functional exploration of 2D polymers, COFs and conjugated microporous polymers. Tell us a little bit about your research background and how you became interested in the chemistry of porous materials and COFs.As a chemist I have worked on the synthesis and functional exploration of dendrimers since 1995 at the University of Tokyo and from that time I became very interested in polymers with well-defined structures. The beautiful shape of dendrimers inspired me to consider the possibility of constructing other types of polymers with well-defined shapes and structures. In 2005 I moved to the Institute for Molecular Science at the National Institute for Natural Sciences and I developed supramolecular polymers that have self-assembled 2D sheet structures and stacked to form layer structures. From that study I got inspired on whether I can develop 2D polymers that are covalently linked. We succeeded in finding the phenazine linkage to covalently connect π building units into stable 2D polymers and layer frameworks via one-pot polymerization. This was the starting point where we involved in developing COFs by our own strategy and ideas.Looking back on 15 years research on COFs, what were the highlights in COF research and which expectations remained unfulfilled?Personally I think that COFs are a new and amazing class of materials that bridge the gap between organic chemistry and materials science. The most attractive feature is that COFs enable the predesign of both primary- and high-order structures. For traditional polymers, we can design primary-order chain structures by controlled or living polymerization, but we hardly design their high-order structures as polymer chains aggregate to form disordered materials. This has been a long-term challenge in polymer science. COFs make it possible as they merge both covalent bonds and noncovalent interactions into one polymerization system. The covalent bonds develop ordered polymeric skeletons (primary-order structure) while the noncovalent interactions control folding or stacking to construct frameworks (high-order structure). I think one distinct feature is that COFs allow us to construct both primary- and high-order structures in a predetermined way.I think the field has already exerted a lot of impacts on chemistry. We have now a high freedom to design polymers and organic materials with long-range ordered structures. From the very beginning we could not envision what the possibilities of COF structures are and after 15 years there are many clear visions and new findings which we have explored. For example, we designed well-defined skeletons with periodically ordered π columns, which enable us to establish a new type of organic semiconductors. These systems form topologically defined π architectures with the ability to form unidirectionally orientated π arrays. These semiconductors are unique compared to assemblies of conventional polymers and even single crystals of organic π compounds. I love the idea to develop COFs as functional porous materials. This is another distinct feature of COFs where we can design the pore shape and size and we even constructed the pore interface in a tailor-made way. These predesignable pore parameters control the interaction with molecules and ions and thus determine the property of pores, such as molecular storage or mass transport. We employed a complementary way to develop molecular systems for energy conversion and storage by designing the skeleton and pores to connect the involved chemical and physical processes into a seamless chain of events.In your opinion, which challenges need to be tackled in order to move the field further?I think there are still a lot of fundamental issues which need to be addressed in future. From a synthesis point of view, I think the key is to decrease the hurdles to access high quality framework materials or to develop a general way to produce single crystals. From physics perspective, I think the important issue is to understand the chemical or physical events at different time and spatial scales. Especially, disclosing interplays of COFs with photons, excitons, phonons, electrons, holes, ions, and molecules to identify their nature is a key point, which will lead to the findings of new phenomena and mechanisms. From materials science perspective, I believe the exploration of properties and functions that are specific to the structures of COFs is a major challenge in the future. I think COFs need to develop unique property and function which are inaccessible with other polymers and framework materials. In my view, this point is determining the impact of the COF materials and will definitely lead to large scale applications.What needs to be done in order to make COFs more widely adopted and to get a foothold in industry and industrial applications?I think that COFs have already established as a new class of polymers or molecular frameworks. I think it is highly possible that COFs see an uptake from industry because this system is based on a clean polymerization system to synthesise and is basically scalable. The question is which functions are truly unique or specific to COFs and can be developed for large scale applications. As example: COFs can be used for photoconversion or reducing carbon emission.Where do you see the field going next?I think while the field has made a great progress to achieve the diversity of structures and functions, and some unique structures and functions could be developed next. I think one of big things is to explore quantum phenomena by designing specific π electronic structures. Another one is related to the pores to realize unusual storage and transporting properties and functions. By complementary use of the skeleton and pore, designing unique molecular systems that connect various chemical and physical processes can be expected. I envision that with these further progresses, we will have the chance to disclose their hidden nature and to show their important role in addressing environmental and energy problems.The interview was conducted by Senior Editor Dr. Johannes Kreutzer.


Online review platforms are a popular way for users to post reviews by expressing their opinions towards a product or service, and they are valuable for other users and companies to find out the overall opinions of customers. These reviews tend to be accompanied by a rating, where the star rating has become the most common approach for users to give their feedback in a quantitative way, generally as a Likert scale of 1–5 stars. In other social media platforms like Facebook or Twitter, an automated review rating prediction system can be useful to determine the rating that a user would have given to the product or service. Existing work on review rating prediction focuses on specific domains, such as restaurants or hotels. This, however, ignores the fact that some review domains which are less frequently rated, such as dentists, lack sufficient data to build a reliable prediction model. In this paper, we experiment on 12 datasets pertaining to 12 different review domains of varying level of popularity to assess the performance of predictions across different domains. We introduce a model that leverages aspect phrase embeddings extracted from the reviews, which enables the development of both in-domain and cross-domain review rating prediction systems. Our experiments show that both of our review rating prediction systems outperform all other baselines. The cross-domain review rating prediction system is particularly significant for the least popular review domains, where leveraging training data from other domains leads to remarkable improvements in performance. The in-domain review rating prediction system is instead more suitable for popular review domains, provided that a model built from training data pertaining to the target domain is more suitable when this data is abundant. We use three different data sources to generate our datasets: (1) a publicly available collection of 6 million reviews from Yelp  a sentiment word that judges the object or action concerned and (2) a non-sentiment word that refers to the object or action being judged. To restrict the context in which a tuple can be observed, we consider segments of the reviews, i.e., sentences or parts of sentences that are independent from each other. We perform the extraction of aspect phrases by following the next steps: 1.POS tagging: We extract the part-of-speech (POS) tags of all words in the reviews by using NLTK’s POS tagger  and coordinating conjunctions  as tokens indicating the end of a segment. Text at either side of these tokens are separated into different segments. 4.Extraction of aspect phrases: At this stage, we only act within the boundaries of each segment. Within each segment, we identify pairs of words made of (1) a sentiment word labelled as positive or negative and (2) a word labelled as noun or verb by the POS tagger and identified as a non-sentiment word, i.e., not matching any of the keywords in the sentiment lexicon. Each pair of words matching these criteria within a segment is extracted as a tuple pertaining to an aspect phrase.ease of use for homeware, comfort for clothing, food quality for restaurants or happy hour for nightlife, among others.Through the process above, we extract aspect phrases for all review domains. Despite the ability of our method above to capture aspect phrases independent of the domain, these still vary in terms of vocabulary. To achieve semantic representations of aspect phrases extracted for different domains, we make use of Word2Vec word embeddings . We trai •Aspect Phrase Embeddings (APE): we aggregate all the aspect phrases extracted for a review. We generate the embedding vector for each review by adding up the word embeddings for all words included in those phrases. •Polarised Aspect Phrase Embeddings (PAPE): we aggregate the aspect phrases for a review in two separate groups, one containing positive phrases and the other containing negative phrases. Following the same method as for aspect phrase embeddings, here we generate a separate embedding representation for each group, which leads to an embedding representation for positive aspect phrases and another embedding representation for negative aspect phrases. We then concatenate both embeddings to get the combined vector.d ∈ {1..12} into 10 equally sized folds, f ∈ {1..10}. Each time one of these folds is considered as the test data, Testij, while the training data depends on the setting, i.e., (1) ∑f∈{1..10},f≠j,d=i∀Traindf for in-domain experiments, and (2) ∑d∈{1..12},d≠i,f=j∀Traindf for cross-domain experiments. We ultimately average the performance scores across all 10 folds in each setting.We perform different sets of experiments for comparing the performance of our rating prediction systems in in-domain and cross-domain settings for different domains. As we are interested in performing 10-fold cross-validation experiments for both settings, we randomly split the data available for each domain In setting up the experiments for our analysis, we tested all the classifiers proposed by We implement four different sets of features, which include two baselines and two methods that we propose: •3NCF):Baseline : we implement a second baseline consisting of a word embedding representation of the entire text of a review by averaging all the embeddings. We use the word embedding model we trained from our review datasets, which serves as a comparable baseline to measure the impact of aspect phrase embeddings on the performance of the rating prediction system. •Aspect Phrase Embeddings (APE): we concatenate the word embedding vectors obtained for the entire texts of reviews with the aspect phrase embeddings, i.e., word embedding representations of the aspect phrases extracted from a review. •Polarised Aspect Phrase Embeddings (PAPE): we concatenate the word embedding vectors obtained for the entire texts of reviews with the polarised aspect phrase embeddings, i.e., a concatenation of the word embedding representations of positive aspect phrases and the word embedding representation of negative aspect phrases.Note that baseline methods from more recent works are not directly applicable to our task as they make use of user data, which is not applicable in cross-domain scenarios where users are different across domains and platforms. Because our objective is to build a review rating prediction system that would then be applicable to other sources on the Web, relying on user metadata is not realistic, given that we often do not know the author of a text on the Web or the authors are different from those in the training data. Hence, we use the state-of-the-art text-only review rating prediction system by The review rating prediction task can be considered as a rating task with five different stars from 1 to 5. As a rating task, we need to consider the proximity between the predicted and reference ratings. For instance, if the true star rating of a review is 4 stars, then the predicted rating of 3 stars will be better than a predicted rating of 2 stars. To account for the difference or error rate between the predicted and reference ratings, we rely on the two metrics widely used in previous rating prediction work : the Roowhere:n denotes the total number of reviews in the test set.yi is the predicted star rating for the ith review.ri is the real star rating given to the ith review by the user.We report both metrics for all our experiments, to facilitate the comparison for future work.The results are organised in two parts. First, we present and analyse results for in-domain review rating prediction, where the training and test data belong to the same domain. Then, we show and analyse the results for cross-domain review rating prediction, where data from domains that differ from the test set is used for training. A comparison of the performance on both settings enables us to assess the suitability of leveraging a cross-domain classifier as well as the use of aspect phrase embeddings.3NCF) by All in all, these results show that the use of either form of phrase embeddings combined with word embeddings leads to improvements in the review rating prediction when the training and test data belong to the same domain. The main goal of our work is however to show their effectiveness on cross-domain review rating prediction, which we discuss in the next section. The in-domain results presented in this section enable us to perform a comparison with the cross-domain results presented next.3NCF for all of the non-popular review domains, with some variation for the popular review domains. This again reinforces the potential of our methods combining phrase embeddings for cross-domain review rating prediction applied to review domains with little training data available. These experiments show a clear shift in the performance of in-domain classifiers when the amount of training data decreases, encouraging the use of out-of-domain data for training in those cases.Results show a remarkable difference between popular and non-popular review categories. For the 6 most popular review categories , the best performance is obtained by the best in-domain (BID) classifier, which indicates that for review categories with large amounts of training data, it is better to use in-domain data. However, when we look at the bottom 6 review categories , we observe that the cross-domain classifier leveraging out-of-domain data for training achieves higher performance. While the sole use of word embeddings (w2v) leads to improved performance for the least popular categories, the improvement is even better when we incorporate either phrase embeddings or polarised phrase embeddings. The results are also positive for the w2v+PAPE over the w2v+APE; even if the results are not better in 100% of the cases, w2v+PAPE tends to outperform w2v+APE in most cases, with just a small difference when APE is better, showing that one can rely on PAPE for all cases dealing with non-popular domains. Our combining methods (w2v+APE and w2v+PAPE) also outperform the state-of-the-art baseline ALikewise, this motivates the use of the cross-domain classifier for new domains where no labelled data is available for training, for instance because reviews are collected from a website where no ratings are given by the user, such as Facebook, Twitter or comments on blogs.3NCF and the BID classifiers, both for the MAE and RMSE metrics. Review domains are sorted by popularity on the x axis, with the least popular domains on the right. The relative improvement on each review domain is represented by the blue line. The line of best fit (in black) shows the overall tendency of our cross-domain classifier to achieve an increasing relative improvement over other methods as the popularity of the review domain and subsequently the amount of in-domain training data increases. This reinforces our cross-domain classifier as a suitable alternative to achieve the best results on the least popular domains, outperforming any other in-domain alternative.Further to this, Since cross-domain experiments benefit from larger training sets, we assess the effect of the size of the training data on the performance of the rating prediction system. We evaluate the performance of cross-domain rating prediction by using different percentages of the training data available, ranging from 1% to 100%, which we then compare with the best in-domain rating prediction system. The subset of the training data is randomly sampled until the percentage is satisfied.As we observed before, the in-domain prediction system performs better for the top 6 review categories based on popularity, thanks to the availability of more training data. We are particularly interested in seeing how much data we need with the less popular categories for the cross-domain rating prediction systems to perform better than their in-domain counterparts. Looking at the 6 least popular review categories, we observe that using only 5% of the training data suffices in all cases to outperform the best in-domain system, with the percentage dropping up to 2% for casinos and hair salons and up to 1% for resorts. Note that out of the (up to) 58 million reviews available for cross-domain training, a subset of 5% only represents (up to) 2.9 million reviews, which are easy to obtain through online review platforms.We also observe that, while performance keeps improving as we increase the size of the training data, it generally shows a tendency to plateau after 25% of the reviews are used for training. This reflects that after about 14.5 million reviews the system has enough training data and can hardly improve its performance.In this work we have proposed a novel method that leverages aspect phrase embeddings for predicting the star rating of online reviews, which is applicable in in-domain and cross-domain settings. We have also shown that our cross-domain approach is effective for making predictions in review domains with a paucity of training data, where training data from other domains can be successfully exploited. Previous work on review rating prediction had been limited to popular reviews domains, such as restaurants or hotels. Our study broadens the findings of previous works by experimenting on 12 different datasets pertaining to 12 different review domains of very different levels of popularity, and collects from different sources including Yelp, Amazon and TripAdvisor. Given that some of these review domains have very limited availability of labelled data for training, our aim has been to propose a cross-domain review rating prediction system that would perform well for those non-popular domains. Likewise, a cross-domain review rating prediction system can be used to predict ratings of reviews gathered from platforms where users do not assign ratings, such as Facebook or Twitter.Our review rating prediction system leverages both POS taggers and sentiment lexicons to extract aspect phrases from reviews, i.e., phrases referring to different features of a business. To enable generalisation of aspect phrases to different domains, we make use of universal representations using word embeddings; we propose two different models for feature representations, (1) Aspect Phrase Embeddings (APE), which aggregates all aspect phrases of a review, and (2) Polarised Aspect Phrase Embeddings (PAPE), which considers positive and negative aspect phrases separately to create an embedding representation for each. We compare our results with those of the best-performing classifier by Our experiments show that both of our methods leveraging phrase embeddings lead to improvements over the rest of the baseline methods, both in the in-domain and the cross-domain settings. Interestingly, a comparison of results for these two experiment settings shows that performance scores are higher for the in-domain classifier when we make predictions on the most popular domains, however the cross-domain classifier leads to substantial improvements for the least popular domains. Our results indicate that a classifier trained from in-domain data is more suitable for popular review domains, whereas unpopular review domains can be improved when out-of-domain data is used for training along with our aspect phrase embedding representation. Further looking into the amount of data needed for training a cross-domain rating prediction system, we observe that a small sample of about 5% of the data available  for the other domains is enough to outperform baseline methods.Our work defines the state-of-the-art and the first approach to cross-domain review rating prediction, expanding the hitherto limited research focusing on in-domain settings for popular domains. Research in cross-domain review rating prediction is still in its infancy, and we hope that our work will encourage further research in the task. Future work on cross-domain review rating prediction could further focus on the detection of implicit aspect phrases for improving performance, as well as in capturing cultural differences in writing reviews, given that users from different countries may express themselves differently when writing positive or negative reviews. To further test additional methods to cross-domain rating prediction, our plans for future work include testing methods for transfer learning and domain adaptation.


The study finds that the droplet can deform into a flat shape but does not break even at 600 m/s2. Besides, the properties of the material in the domain surrounding the droplet and the contact angle also affect the deformation of the droplet. This work demonstrates the deformation of the liquid metal droplets under acceleration and provides the basis for the design of MEMS droplet acceleration sensors.Microelectromechanical system (MEMS) liquid sensors may be used under large acceleration conditions. It is important to understand the deformation of the liquid droplets under acceleration for the design and applications of MEMS liquid sensors, as this will affect the performance of the sensors. This paper presents an investigation into the deformation of a mercury droplet in a liquid MEMS sensor under accelerations and reports the relationship between the deformation and the accelerations. The Laminar level set method was used in the numerical process. The geometric model consisted of a mercury droplet of 2 mm in diameter and an annular groove of 2.5 mm in width and 2.5 mm in height. The direction of the acceleration causing the droplet to deform is perpendicular to the direction of gravity. Fabrication and acceleration experiments were conducted. The deformation of the liquid was recorded using a high-speed camera. Both the simulation and experimental results show that the characteristic height of the droplets decreases as the acceleration increases. At an acceleration of 10 m/s Liquid droplets have been wildly used as a component in many MEMS sensors and actuators, such as the droplet touch sensors reported by Kim in 2017 . Such drDroplet deformation is modelled with two phases: the droplet itself and the domain surrounding it. Therefore, multiphase methods need to be applied in the numerical process to track the connection surface of each domain. The level set multiphase method is one of the popularly used methods in analyzing such multiphase fluids, proposed by Sussman et al in 1994 . In comp2.This paper presents a numerical and experimental study of the deformation of a mercury droplet in an annular groove at acceleration. The mercury was selected as the research object mainly because of the high surface tension and conductivity of the mercury. Besides, mercury is widely used in current MEMS liquid droplet sensors. The numerical model consists of the air domain and the mercury domain. The direction of the acceleration is perpendicular to the direction of gravity. The Laminar level set (LS) method was used in the study to show the profile of droplets during the deformation process. The relationship between the height of the droplet after deformation and the applied acceleration was studied. Then, experiments were conducted to verify the simulation results. The results of simulation and experiments both show that the droplet deforms under acceleration, and the characteristic height of droplet decreases with the increase in acceleration. Meanwhile, the droplet does not split, even when the acceleration reaches 600 m/sThis research work contributes to the research community of liquid droplet sensors in the following aspects: the deformation of droplets under the influence of acceleration was presented by using Laminar level set method; the deformation results in both the experiment and simulation show that the characteristic height of a droplet will decrease as the acceleration increases, which provides a basis for the dynamic design of the metal droplet acceleration sensors.u is the velocity vector; ρ is the density; p is the pressure; and F is the volume force, including gravity and body force caused by acceleration; stF is the surface tension. The Equation of F is as follows:g is the gravitational acceleration and α is the acceleration of the whole flow.During the deformation, the Reynold number does not reach the critical value of turbulence (less than 150). Therefore, the Navier–Stoke equation for incompressible laminar flow was used to solve the flow velocity and pressure change in the fluid flow ,17:(1)ρ∂ϕ is given in Equation (4):As the mercury droplet is surrounded by air, the model becomes a two-phase flow to count for both air and liquid droplet in simulation, and the change of the interfacing profile between the two phases needs to be considered and updated in simulation ,19. Hencϕ of the level set function as shown below:airρ and airμ are the density and viscosity of air domain, respectively; mercuryρ and mercuryμ are the density and viscosity of mercury domain, respectively; and H(ϕ) is the Heaviside function, which is used to smooth approximation and avoid the problem of non-convergence caused by the Gibbs phenomenon. The value of the H(ϕ) is 1 in the droplet phase, 0 in the air phase, and a smooth transition at the interface of the two phases.The density and viscosity of the fluid are represented by the n, and curvature κ of the interface of two-phase are shown as follows:The unit normal vector of the interface σ is surface tension coefficient and δ(ϕ) is Dirac delta function.Here, the scalar surface tension can be expressed as a function of the curvature of the interface:X, Y, and Z are shown below:It can be seen from Equations (1) and (3) that the total force in flow consists of surface tension, gravity, and the body force generated by acceleration. Therefore, the total forces of the fluid in the three directions of This numerical method requires both the incompressible Navier–Stokes equation module and the LS module for capturing the profile of the droplets in the two-phase flow . Since tXY plane view, the simulation of a droplet in an entire annular groove and half of the groove was first performed. The initial conditions, boundary conditions, and material properties of the two models were the same, and the acceleration was given as 20 m/s2. The results after 0.3 s time step are shown in The numerical model was coded in COMSOL software, and 3D geometry was used in the model design. The model has accounted for the air and liquid phases and their interactions, as shown in The characteristic size of the droplet deformation in the entire annular groove and half annular groove is shown in The deformation of the droplet in the entire annular groove is the same as that of a half annular groove. Hence, a half annular channel was used for the air domain as equivalent to the complete annular channel in numerical simulations, as illustrated in For the module of incompressible laminar flow, the initial condition of the velocity vector is 0 m/s in each direction, the pressure is 0 Pa, and the temperature is 20 °C.For the module of LS, the LS function of the droplet domain is 1, and the air function is 0. Besides, the surface tension at the interface of the two-phases is considered.The initial position of the droplet is at the bottom surface of the annular groove.The simulation was carried out using both the incompressible laminar flow module and the LS two-phase flow module. The boundary conditions of the model are shown in Phase transition mainly occurs inside the droplet and at the boundary surfaces between the droplet and air. Therefore, a special mesh refinement process was required for these regions. A mesh consisting of 96,976 elements was generated using the free tetrahedral meshing method, and the average cell quality of the mesh reached 0.6.XY plane, and the C-length is the contact length of the droplet and the upper surface in the XZ view. Besides, 2, the droplets will flatten at 0.1 s, 0.2 s, and 0.3 s. It can be seen from the XZ view that the flattened droplet expands and reaches the upper surface of the annular groove.The characteristic-height (named C-height) and the characteristic-length (named C-length) of the droplet after deformation were recorded, as shown in 2, 200 m/s2, 300 m/s2, and 400 m/s2. Here, the steady-state deformation of the droplet is the desired result, so that the relationship between the deformation of the droplet and the time step needs to be investigated. The transient performance of the step size from 0–0.3 s are simulated using the models with four accelerations of 100 m/sThe results show that the C-height of the droplet from the time steps of 0.01 s to 0.18 s will increase and decrease periodically, which indicates that the droplet is flattened after the initial acceleration, and rebounds with an increased time step. This oscillation in height repeats for half a second before it settles. Acceleration is always present and does not change with time, but the deformation of the droplet oscillates at the beginning. When the time step exceeds 0.18 s, the height change of the droplets gradually becomes stable, which means the influence of the time step on the C-height gradually fades away. Therefore, the results after 0.3 s are used in the study.2, and several characteristic results at accelerations 30, 300, 500, and 600 m/s2 are shown in More types of acceleration were applied to study the deformation of the droplet and verify whether the droplet will split at a large acceleration. The acceleration varies from 10 to 600 m/s2, and 0.246 mm at 600 m/s2. Besides, it is observed that even when the acceleration reaches 600 m/s2, the droplet remained in one piece and did not break.The simulation results also show that as the acceleration increases, the droplet is gradually flattened and the C-length increases. The relationship between acceleration and C-length is shown in 2, the little deformation is not enough for the droplet to be in contact with the upper surface of the annular groove. At this time, the C-length of the deformation is 0. When the acceleration reaches 60 m/s2, the droplet is deformed enough to contact the upper surface of the groove, and the C-length at this time is 0.863 mm. The length of the droplet gradually increases as the acceleration increases. When the acceleration is 600 m/s2, the C-length reaches 2.776 mm.When the acceleration is lower than 50 m/s2, the viscosity is four times larger than air, (7.16 × 10−5 Pa.s), the density is three times bigger than air (3.9 kg/m3), and the surface tension is 1.44.In this section, the material properties surrounding the domain of the droplet are simulated. The relationship between the deformation characteristic size and acceleration of the droplet in different materials property domains is shown in It can be seen from The experiments for verifying the deformation of an accelerating droplet in the annular groove were designed, as shown in a is the acceleration, ω is the angular velocity of turntable, and r is the distance between the initial position of the droplet and the center of the turntable.The centripetal acceleration is generated using the turntable. The distance between the initial position of the droplet and the center of the turntable is 20 mm, and different additional accelerations can be obtained by changing the speed of the turntable. The acceleration can be calculated with the following Equation:2, respectively.Since the steady-state deformation model was required, the image of the droplet was acquired 60 s after the speed of the turntable became steady. For comparison of the C-height in the experimental results with the C-height in the simulation results, the boundary conditions in the simulation needed to be improved. The modified boundary conditions, including the contact angle, are shown in 2 are shown in The wetted wall was added as a boundary condition in the Multiphysics coupling, reflecting the contact situation between the droplet, and the annular groove and other initial conditions and material properties remain unchanged in simulation. The simulation and experimental results of the droplet deformation when the accelerations were 42, 242, 438, and 640 m/s2, 4.72% at 242 m/s2, 2.05% at 438 m/s2, and 11% at 640m/s2. The avenge amount of the relative error between the simulation and experimental results is 4.26%. 2, and the C-length at this time was 1.148 mm. As the acceleration increases, the value of the C-length increases as the liquid is squeezed. When the acceleration value reaches 100 m/s2, the increasing trend of C-length gradually slows down and stabilizes at about 2.24 mm. The results show that considering the contact angle boundary  condition will increase the droplet C-height and decrease the C-length, which represents a reduction in the deformation amount of the droplet.By measuring the C-height of the droplet in This paper presents numerical and experimental research of a liquid metallic droplet deformation in an annular groove channel under the influence of accelerations. The Laminar LS method was used in the numerical simulation. The simulation model consists of two phases: the air phase of a half annular groove of 2.5 × 2.5 mm in cross-section, and the mercury liquid phase with a diameter of 2 mm. Accelerations were applied to the fluid in a direction perpendicular to the direction of gravitational acceleration. Initially, the droplet is resting in the groove and only in contact with the bottom of the groove. The characteristic size of the droplet after deformation was proposed to show how acceleration will deform the droplet, and the deformation of a mercury droplet in different material parameter domains was analyzed. In addition, the experimental results were used to verify the results of the simulation work. An experimental setting was designed to match the simulation model. A turntable was used to generate centripetal acceleration in the experiment work as the acceleration in the simulation model, and a high-speed camera was used to capture the image of droplet deformation.2, it was reduced to 0.246 mm. Meanwhile, the C-length of the droplet increases with the acceleration increase. The length was 0.863 mm at acceleration 60 m/s2 and increased to 2.776 mm at 600 m/s2. Besides, the simulation results show that the deformation of the droplet is different in domains of different material properties. A larger viscosity, density, and surface tension will cause the reduction of the droplet deformation. Hence, the droplet is less deformed in water than air. The simulation results also show that droplet deformation requires initial oscillations of <0.18s. Therefore, for the droplet micro-switches and droplet acceleration sensors, the initial oscillation of the droplet under acceleration needs to be considered during the sensor design process. Appropriately reducing the deformation requirements can eliminate the effect of initial oscillation and further improve the sensitivity. The experimental results also show that droplet will be squeezed under the effect of accelerations. Both the experimental and simulation results show the same droplet deformation trend. This research work contributes to the research community of liquid droplet sensors in the following aspects: firstly, for the droplet inclination sensors, which require a small deformation of the droplet when affect by acceleration, the material around the metal droplet can be changed to reduce the deformation of the droplets when affected by acceleration; secondly, for the droplet acceleration sensors, which use the principle of droplet deformation under the action of acceleration to output an acceleration signal, the structures of the droplet acceleration sensor can be reasonably designed by the C-length and C-height results of the droplet.The numerical simulation results show that the C-height of the droplet decreases as the acceleration increases. At 10 m/s2, the height of the droplet was 1.658 mm, and at 600 m/s


This paper deals with the mechanical behaviour, especially the permanent deformation and resilient deformation of an unbound granular material (UGM) from Bréfauchet quarry which is used as base layer material in low traffic pavements for full-scale tests at IFSTTAR in France. Medium-scale repeated load triaxial tests (RLTT) are carried out at different water contents and the results show that both permanent and resilient deformations increase with water content. Besides, two techniques of fixing the sensors in materials with large particles for RLTTs are proposed and compared with each other. The results suggest that the tube method is more suitable for the UGM for an accurate measurement and a good adaptability is obtained during the RLTT. Based on the test results of UGM Bréfauchet, the modelling work is performed with improved models used previously for a sandy material. It appears that both the permanent and resilient behaviours of different unbound granular materials can be well captured by the proposed equations considering the effects of water content and anisotropy. This study is helpful to understand the evolution of permanent and resilient deformation in different granular layers, especially for the base layer, in low traffic pavements. The verified models can be used for other similar granular materials and this will lead to reducing the number of tests required to predict the deformation behaviour of granular materials. Flexible road pavements consist in upper asphalt layers over one or more unbound granular layers which are together compacted over a suitable soil subgrade. They represent approximately 60% of the road network in France.Unbound granular materials (UGM) are continuously graded granular materials, consisting in general of crushed rock particles. They usually contain a certain amount of fines  and water and they are generally partially saturated. In flexible pavements, the response of unbound granular materials gives rise in resilient and permanent deformations when subjected to repeated loading. As it is well known, resilient deformations are related to the stiffness characteristics of the material that should be sufficiently high in order to avoid the fatigue cracking of overlying asphalt layers. On the other hand, the gradual accumulation of permanent deformations, although they are very small during each loading cycle, could lead to large ruts at the top of the structure due to excessive rutting ,3. As a In recent years, the effect of moisture content on deformation behaviour, which was equally important, has been studied by many researchers ,11,12,13As a result, in present study, a new material, UGM Bréfauchet, which is used as base layer material in low traffic pavements for full-scale tests at IFSTTAR in France, will be tested with repeated load triaxial tests (RLTT) at various water contents to describe accurately the permanent and resilient deformation behaviour. Then, based on the test results, the models or equations suggested for Missillac sand could be extended or checked.Mr values with actuator LVDT, external LVDT, internal LVDT and Hall effect sensors based on a comprehensive comparative study. As a consequence, in this study, two technics for fixing the Hall effect sensors in the UGM for RLTTs will be proposed and compared with each other.Besides, the large UGM particles bring difficulties to fix the Hall-Effect strain measurement sensors, which is an accurate measurement method, on the triaxial sample. As a result, the actuator LVDT, external LVDT or internal LVDT are often applied in RLTTs to replace the Hall effect sensors ,15. HoweThe objective of this work is to verify the evolution mechanism of permanent and resilient deformation (based on a subgrade sand) with a coarser UGM. Once the relationships can be reproduced, the results will be very meaningful to understand the evolution of UGM deformation in different layers in low traffic pavements. Finally, the findings will be helpful to reduce the number of tests required and to provide the parameters for pavement design.The studied UGM Bréfauchet is a crushed gneiss aggregate from the site of Bréfauchet, near Nantes, France, as shown in 3) can be obtained based on the result.The modified Proctor test is completed with the automatic method : the UGM3 (96% of maximum dry density).In order to study the influence of water content on the mechanical behaviour of UGM Bréfauchet, the water content of triaxial test sample preparation is varied from 3% to 6% with a dry density of 2.13 Mg/mAs some researches mentioned above, the effect of water content is related to the suction in unsaturated soil. In fact, the suction corresponds to not only the moisture state but also to the microstructure of aggregates which can affect significantly the deformation. As a result, the SWRC of UGM Bréfauchet (wetting path) is investigated with the filter paper method . The soiw is the actual soil water content at the suction s (kPa); sw (%) and rw (%) are the saturated water content and the residual water content; α is a parameter related to the air entry suction; m and n are the model parameters with the relationship: m = 1 − 1/n. Besides, the van Genuchten model  is used It should be mentioned that, due to the time consuming and the limitation of device, two samples are tested for each studied water content. Finally, the test results of eight samples (lost two) from 2.2% to 5.2% are shown in RLTT is a common test to investigate the mechanical behaviour of granular materials. In the present study, it is used to simulate the in situ pavement conditions under cyclic loadings to describe the resilient deformation or permanent deformation of UGM at different initial hydraulic states.3 (96% of Maximum dry density). This is the maximum dry density which can be reached with this compaction method.All the UGM Bréfauchet specimens are prepared, in the same way, before being subjected to triaxial tests. Specimens are prepared firstly by oven-drying for 24 h, then cool down aggregates are mixed with water using a large mixer to reach the target water contents. The wet materials are then stored in a well-sealed plastic bag for at least 24 h for moisture homogenization. Compaction is performed using a vibrating hammer following the French standard  in 7 layTo test the UGM Bréfauchet with the largest particles whose diameter can reach 20 mm, a medium-scale triaxial device , as shown in σ1) and a confining pressure (σ3) are applied on the specimen in the triaxial cell, simulating the loading state under pavement to measure the vertical strain (σ1) and the radial strain (σ3). Both the axial stress and the confining pressure are supplied by the pneumatic servo loading system.During a triaxial test, an axial stress ;An inner Hall effect transducer .The principal measurement system is a composite of stress/pressure transducers and displacement transducers. The typical transducers used in this study are:ε1) and radial deformation (ε3) will be measured by the Hall effect transducers.As mentioned above, the axial deformation . The shear test is controlled with a speed of 0.1%/min and terminated with 5% axial strain.To determine the shear strength parameters of the UGM Bréfauchet (friction angle and cohesion), monotonic undrained triaxial tests are firstly performed at water content of σ3, the peak deviator stress is more significant, as expected. The friction angle is 54.6° and the cohesion is 72.9 kPa. The rupture line, as shown in σ1 and σ3 are the major and minor principal stresses.In this study, the large particles (the diameter of grains can reach or be larger than 20 mm) bring difficulties to fix the three displacement transducers on the UGM sample and to have an accurate measurement and deform consistently with the sample, comparing with the sand samples .To solve this problem, authors developed two sets of fixing devices for the displacement transducers to fit the larger particles.For the first method , the increase of deformation  can be divided into two parts in each cycle. The unrecoverable part is called permanent/plastic deformation, which accumulates gradually as number of cycles increasing. Another recoverable part is called reversible deformation. After adequate cycles, the increase of the unrecoverable part is quiet small. It means that the accumulated permanent deformation tends to stabilization and then the recoverable part can be treated as resilient deformation.q/Δp = 3  from an initial stress state  is applied at a frequency of f = 0.5 Hz. The number of cycles is chosen as N = 20,000 cycles. When the permanent deformation rate Δε1p/ΔN is lower than 1 × 10−7, we can state that the permanent deformation achieves the equilibrium state [For the conditioning phase, the cyclic stress path of Δum state .q/Δp = 0; 1; 1.5; 2; 2.5) are applied on the same sample in sequence from an initial stress state . Each stress path contains 100 loading and unloading cycles, with a frequency of f = 0.05 Hz.For the following resilient phase, five stress paths , a conditioning phase under CCP (constant confining pressure) loading to stabilize the accumulation of permanent strains and a following resilient test phase under VCP (variable confining pressure) loading to investigate the non-linear elastic behaviour are applied on a specimen.ε1p increases with an increase of the number of cycles at different water contents as illustrated in w = 5.6%, the sample is rapidly destroyed under the repeated loading. The evolution of permanent axial deformation rate versus the accumulated permanent axial deformation at various water contents is also presented in ε1p/ΔN) at the end of each test are less than or close to 10−7/cycle, except at water content of 5.6%. As a result, we can state that the permanent axial deformation has achieved the equilibrium state after 20,000 cycles and the following resilient phase can be applied on the samples.The results of conditioning phase are used to determine the permanent deformation behaviour. It can be stated that the permanent axial deformation ε3p also increases with the number of cycles at different water contents as shown in ε3p/ΔN at the end of each test, it can be also stated that the permanent radial deformation at end of each test achieves the equilibrium state after 20,000 cycles.For permanent radial deformation, the results show that the w = 4.6%-screw and T8-w = 4.6%-tube, it can be also stated that the effect of different methods of fixing displacement transducers on the permanent deformation behaviour: for the permanent axial deformation, tube method has 20 × 10−4 deformation larger than screw method; for the permanent radial deformation, these two methods accord with each other, which can be explained with the constant confining pressure (CCP) in conditioning phase: the radial sensor is always pressured on the sample by the constant confining pressure for both methods, the difference of radial deformation between both methods cannot be observed.Besides, based on the results of T1-vrε) and the resilient deviatoric deformation (qrε) defined asε1r is the resilient axial deformation and ε3r is the resilient radial deformation.In this study, the resilient deformation behaviour is described by the resilient volumetric deformation  at different water contents . Based on the results, following observations can be stated:p and the stress ratio Δq/Δp. It can be stated that qrε increases while vrε decreases with the ratio Δq/Δp increases.The cycles show that the behaviour of the UGM is nonlinear, and depends on the mean stress vrε and qrε increase with the water content and present the largest values at the water content (4.6%) in all stress paths nearly.The effect of water content on resilient deformations is observed: The very slight hysteresis for the cycles suggests that the behaviour is close to pure elasticity and the measurements with tube method is more accurate enough compared to that with the metallic inserts/screws.After tests, all the specimens are cut into seven equal pieces to measure the water content and the water distribution. As shown in In this section, the proposed/modified models for permanent behaviour  and resiAs mentioned above, the prediction models of permanent axial deformation proposed in previous research  are avaiε1p is permanent axial deformation; w is water content; N is number of cycles; N0 is number of cycles before the first measurement; a, v, o and v’ are parameters.Based on water content:S is suction value; S* is suction value corresponding to the intersection point of wetting and drying paths; b, d, e, and f are parameters; Sa is equal to 100kPa. In this study, the S* is estimated in a range from 1.5kPa to 3.5 kPa for the lack of drying path data.Based on suction:Besides, it is obvious that the water content based model can describe the permanent deformation behaviour and its evolution more accurately than suction based model. The phenomenon can be explained as follows, for the granular materials, the matric suction value and the influence of suction on mechanical behaviour decrease with the increase of coarse particles. In this study, the suction of UGM Bréfauchet is less than 70 kPa with a reasonable water content range, which is far less than the maximum applied loads during the RLTT. Besides, as we mentioned in the former study for Missillac sand ,13, the It can be concluded that the models proposed for granular materials have a good capacity to capture the general trend of the evolution of the permanent deformation with the number of cycles. The modelling accuracy is decreasing gradually with increasing particle size with suction based model.1. The modified model was expressed asKa, Ga, and n are parameters; γ1 is the axial anisotropy coefficient, γ1 = |ε1/ε3|; Pa is 100 kPa.Boyce (1980)  proposedγ3 was suggested by authors [γ1 to fit the changing anisotropy behaviour of granular materials under the repeated loading. The improved model was expressed as3γ is the radial anisotropy coefficient, γ3 = |ε3/ε1|.Based on the results of Missillac sand, the radial anisotropy coefficient  authors  to replaγ3.In this section, based on the resilient test results of UGM Bréfauchet as shown in γ1 is presented in γ3 is presented in w = 4.2%, T7-w = 4.3%, and T8-w = 4.6%, it can be stated that both sets of equations have good correlation coefficients for the resilient behaviour and the Equations (12)–(15) show a more accurate modelling result. The curves of T6-w = 3.5%, in different stress paths, are close together as shown in γ1 and 1/γ3. It can be confirmed that the reciprocal relationship between γ1 and γ3 for UGM can change under a repeated loading as observed for Missillac sand [The parameter optimization of Equations (8)–(11) for UGM Bréfauchet with ns (12)–1 show a mvrε and qrε values for UGM Bréfauchet at different water contents based on Equations (12)–(15). As described by correlation coefficients above, the very good estimated results (both vrε and qrε) can be obtained.-Both approaches based on water content/suction have a good capacity to describe the permanent deformation behaviour of UGM at different water contents. However, the modelling accuracy is decreasing gradually with increasing particle size with suction-based model.-For the resilient behaviour of UGM, the improved Boyce–Hornych model has a good ability to capture the changing anisotropy caused by the various water contents or fine contents under repeated loading.-The two methods of fixing the sensors on the materials with large particles for RLTTs are proposed and compared with each other. The results suggest that the tube method is more suitable for the large particle UGM as the accurate measurement and good adaptability during the RLTT.Granular layers play an important role in the overall performance of the structure, especially for the bearing capacity. In the previous studies, the performance of subgrade granular material— Missillac sand has been well analyzed and a series of prediction models was proposed or modified to describe the permanent and resilient behaviour at different water contents and different fine contents. In the present work, a much coarser UGM Bréfauchet, which is used as base layer material in low traffic pavements for full-scale pavement tests, has been studied to check the evolution mechanism. Based on the investigation of these two granular materials, it can be concluded that:Consequently, this study is helpful to understand the evolution of permanent and resilient deformation of a wide range of different granular materials. The models can be used for other similar granular materials directly and reduce the number of tests required to predict the deformation behaviour.It should be also mentioned that, due to the time consuming and the complexity of the operation, the number of the test in this study is not so abundant. As a result, for the future work, plenty of repetition tests will be continued for further verification and statistical analysis. Besides, further numerical modelling will be also conducted.


Interestingly, some protein domains are intrinsically disordered (abbreviated as IDD), and the disorder degree of same domains may differ in different contexts. However, the evolutionary causes and biological significance of these phenomena are unclear. Here, we address these issues by genome-wide analyses of the evolutionary and functional features of IDDs in 1,870 species across the three superkingdoms. As the result, there is a significant positive correlation between the proportion of IDDs and organism complexity with some interesting exceptions. These phenomena may be due to the high disorder of clade-specific domains and the different disorder degrees of the domains shared in different clades. The functions of IDDs are clade-specific and the higher proportion of post-translational modification sites may contribute to their complex functions. Compared with metazoans, fungi have more IDDs with a consecutive disorder region but a low disorder ratio, which reflects their different functional requirements. As for disorder variation, it’s greater for domains among different proteins than those within the same proteins. Some clade-specific ‘no-variation’ or ‘high-variation’ domains are involved in clade-specific functions. In sum, intrinsic domain disorder is related to both the organism complexity and clade-specific functions. These results deepen the understanding of the evolution and function of IDDs. According to our empirical knowledge, protein domains, as conserved units that can evolve, fold7 and function8 independently, are the structured and compact regions9 in contrast to IDRs. However, some protein domains indeed can exist and function without a stable three-dimensional structure. This type of protein domains was named as intrinsically disordered domains (IDDs). During the last two decades, an increasing number of IDDs have been reported14.Proteins containing intrinsically disordered regions (IDR)et al.15 found that the binding rates of disordered domains are faster than those of structured domains. Stanley et al.16 showed that in the cAMP response element binding protein (CREB), the disordered kinase inducible domain (KID) could become structured after its phosphorylation. Furthermore, a research has revealed that IDDs can participate in the formation of dynamic protein complexes and prevent additional protein monomer polymerization to maintain protein stability17.Previous studies mainly focused on the special physical–chemical properties and functional characteristics of certain IDDs. For example, Dogan et al. explored the distribution pattern of the conserved predicted disordered regions in domains and domain families18, and this kind of IDDs can mediate protein–protein recognition19. However, the distribution pattern of the IDDs across the species of the three superkingdoms and its functional significance are unclear. To this end, the calculation of the number of IDDs encoded by a genome and a comprehensive exploration of their evolutionary and functional features are important; such research will enrich our understanding of the evolution of the protein repertoire.The preceding studies mainly focused on different individual disordered protein domains. With the advancement of genome sequencing of human and model organisms, the characteristics of IDDs at the entire proteome level have been explored by some studies. Chen 25 and IDPs substantially contribute to the increasing organism complexity27. It is known clearly that eukaryotes have more proportion of intrinsically disordered proteins than prokaryotes31. However, the proportion of disorder at protein level in eukaryotes has no good correlation with species complexity27. Here, we investigated the relationship between the proportion of intrinsic disorder at domain level and eukaryotic organism complexity.More specifically, this analysis will also provide valuable insights into the mechanism of the increasing organismal complexity during evolution. Previous studies suggest hub proteins usually have higher content of intrinsic disorder to interact with various partners and form complex networksIn this study, there are two measures of the degree of disorder: the domain structural disorder ratio (DSDR), represented by the percentage of disordered residues in one domain; and CDRN, the number of consecutive disordered regions (CDR). CDRs are the regions where the consecutive disordered residues are more than 20 in the domains longer than 50 residues, or more than 40% of the total residues in the other domains. The relationship between these two measures had not been explored before. We investigated their relationship and compared the relationship patterns among different evolutionary lineages.33. Each domain family has a unique domain identifier and name (See Method section for its detailed definition). Based on usual understanding, the protein domains belonging to a same family should have the same structural characteristics. To test whether this intuitive understanding is correct, the variation of the disorder degree of the same protein domains was investigated in our analysis. Furthermore, we investigated the factors that affect the variation of domain disorder and the biological significance of their variation.Protein domains usually repeat in the same proteins or are shared among different proteins. The unique protein domain types are defined as domain familiesIn order to achieve a complete representation of the distribution pattern of IDDs across the three superkingdoms, one representative species was chosen from each genus. In total, 1,870 species were involved in the calculation and analysis of domain disorder, including 97 species of archaea, 1224 species of bacteria and 549 species of eukaryotes . We found that the percentages of IDDs  are highest in eukaryotes compared with the other two superkingdoms Fig. . The med34. However, another literature reported that this phenomenon was due to methodological bias35. Our results show that the original conclusion is valid even if the method is corrected 37, the organismal complexity of arthropods is much higher than fungi of these two phyla. Thus, intrinsic domain disorder in a species is not only related to organismal complexity, but may also be related to special functions, which will be discussed in the following text (Part 4 results).However, the comparison of different species of eukaryotes showed that the percentage of IDDs in simpler organisms is even higher than that of complex organisms Fig. . For exaThe preceding results show that, in general, the percentage of IDDs in species is positively related to organism complexity, that is, there is a greater proportion of IDDs in complex species; but in some instances, there are also more IDDs in some simpler species compared to complex species. What is the reason for the uneven distribution of the proportion of IDDs across species? This may be related to two aspects of protein domain evolution: firstly, some clade-specific domains (only appear in the species of a certain evolutionary clade) may tend to be more disordered, leading to the higher proportion of IDDs in these species, especially in complex eukaryotic species; secondly, the degree of disorder of some common domains  diverged during evolution, that is, some domains from the same families may have remarkably different DSDR values in different species.In order to test these two hypotheses, we first classified protein domains into 14 categories according to the species distribution width Fig. B. The diFor the second hypothesis, we compared the degree of disorder of the same domains among different species. For the domains common in metazoans and fungi, 15.1% of them had higher degrees of disorder in metazoans, whereas 20.0% of them had higher degrees of disorder in fungi Fig. D. Thus, These two factors led to the fact that each evolutionary clade may have its own specific young IDDs and special ancient IDDs.As domains are the functional units in proteins, we assume that the domains with different degrees of disorder have different biological functions. In order to test this hypothesis, all of the IDDs  and the completely structured domains (DSDR = 0 in no less than 50% species of each group) were subjected to the over-representation analysis of gene ontology (GO). We found that the completely structured domains tend to localize at membranes, and participate in the biological processes related to metabolism, including various biosynthetic and catabolic processes, as well as oxidation–reduction process Fig. A. In con41 (http://www.phi-base.org), which includes over 7000 proteins from over 200 species with phenotype annotation based on mutation experiment, and we explored the specific mutant phenotypes related to the proteins with IDDs in ascomycetes and basidiomycetes (two phyla of fungi), compared with other species. As the result, the proteins with IDDs of these two phyla of fungi significantly enriched in proteins whose mutation would lead to ‘reduced virulence’ compared with non-IDD domains  usually play important roles in protein–protein interaction and regulation of the activities of most of the protein machines in cells. Previous studies revealed IDRs are frequently subjected to PTMs that increase the functional statesins Fig. . The proins Fig. A, or CDRins Fig. B] than tins Fig. C. The siins Fig. . Phosphoe.g. fungi and metazoan, clearly form two lines  the proportions of each grade of domain structural disorder ratio (DSDR); (2) the number of consecutive disordered regions (CDRN). The relationship between DSDR and CDRN was explored in the three superkingdoms. We calculated the proportion of IDDs in each species according to DSDR or CDRN. Scatter plots of the percentage of IDDs in each species show a very interesting phenomenon: The data points of both archaea Fig. A and bacnes Fig. C. Similanes Fig. . But thenes Fig. . This reAccording to the values of DSDR and CDRN, all of protein domains were divided into 4 classes: I, II, III, IV Fig. D–G. The According to the protein domain repetition features, all the protein domains were divided into four categories Fig. A. A compAccording to the common understanding, the same domain family may share the same or a similar structure; thus, their degree of disorder may be similar. Surprisingly, the DSDR values of some repeating domains from the same domain family are completely different. Thus, we investigated the variation of disorder degree of all repeating domains in each whole proteome to get a complete understanding of this phenomenon and its evolutionary and functional significance. The DSDR values were normalized using the interpolation method Figure B. The pr44. Repeating domains that had a low sequence similarity exhibited the low variable DSDR values (< 1)  and DSDR variation degree was explored. We found that a large proportion of repeating domain pairs have a sequence similarity of less than 40% Figure A–D, whic) Figure A–D, thatAlthough only a small part of the repeating domains has high variations of DSDR values, it’s of great significance to explore the distribution pattern of the variation of DSDR across different species and to decipher the functional difference between the domains with constant and high-variable DSDR values. The number of ‘high-variation’ domains in a species has a significant positive correlation with organism complexity. But after normalized by the total number of repeating domains, this correlation disappeared Table B, S21E. 46. The stable three-dimensional structure of the PsaA_PsaB domain ensures that PsaA and PsaB can specifically recognize and bind to P700 to form a trimeric complex 48, acting as a plastocyanin/cytochrome c6-ferredoxin oxidoreductase in the photosynthesis.The DSDR variation of the protein domains is closely related to their functions. We found the functions of ‘no-variation’ and ‘high-variation’ domains are quite different which may reveal the functional constraints on DSDR variations Fig. F. PsaA aHigh-variation domains in prokaryotes tend to localize at intracellular part and function in translation and protein metabolism. In contrast, high-variation domains in eukaryotes tend to localize at extracellular region and nucleus participating in many regulation processes, such as transcription regulation. This type of domains needs variable DSDR values to meet with the diversified functions. For example, bZIP_1, a bZIP transcription factor, has a wide variety of DSDR values (from 0 to 82.5% in human) Fig. D. Its va3 and they usually have special evolutionary features2. For example, genes encoding IDPs tend to be evolutionarily young49; the disordered regions in IDPs tend to have higher amino acid replacement rates50 and biased amino acid composition affected by the GC content51. In general, these disordered regions in proteins are usually the linking regions between different protein domains51. However, IDDs indeed exist. The functions of IDDs have been investigated in previous studies55, but the comprehensive analysis of the distribution and functional significance of IDDs is lacking. In this study, we investigated the functional and evolutionary characteristics of IDDs at the entire proteome level across three superkingdoms. Our analyses revealed that the degree of domain disorder is not only related to organism complexity, but is also related to the clade-specific special functions.IDPs play important roles in various biological processes56. Nido et al. found that protein disorder degree in the centrosome correlates with species complexity57. Yruela et al. explored the role of disordered proteins in chloroplast and nucleus of plants58.Some previous studies have investigated the contribution of protein disorder to the formation of complex regulation mechanisms in eukaryotes. For example, evolution from unicellular organisms to multicellular organisms in all major eukaryotic lineages significantly benefited from IDPs, alternative splicing (AS) and post-translational modification (PTM), in which IDPs conferred multifunctionalities and tightly associated with AS and PTMs55. However, in that analysis55, the number of intrinsically disordered protein residues was not normalized by the total size of proteome. The more complex the species, the larger the total amount of amino acid residues, and the more disordered residues26. Therefore, it can’t be said that the increase of disordered residues contributes to the species complexity. If we want to evaluate the contribution of protein disorder to the complexity of organisms, we should use the percentage of disordered residues to total residues for correlation analysis. By this way, both Schad’s26, Xue’s27 and our results indicated that there is no significant correlation between biological complexity and protein disorder in eukaryotes. In Yruela et al.’s work, they also reported that if they use the fraction of disordered residues in transcription factor sequences to analyze the correlation with organism complexity, the correlation was poor59. For the first time, our analysis revealed that the degree of disorder of protein domains correlates with organismal complexity with a significant correlation coefficient. The reason for this phenomenon may lie in the more important functionality of protein domains than other regions of proteins. The protein domains are the structural and functional units of proteins. If a protein domain has a disordered region or has a high proportion of disordered residues, the structure and function of this domain will be more flexible, which in turn has a strong influence on the function of proteins. Thus, complex organisms need more intrinsic disorder domains to perform complex regulatory functions.From the general viewpoint, it was reported that there is a significant positive correlation between the disorder content of proteins and organism complexity measured by cell type numbers60. Studies have shown that halophiles prefer coil formation to alpha-helix formation61. Halophiles may rely on disordered domains to maintain their adaptability to high-salt environments. In eukaryotes, the virulence of ascomycetes and the cilium formation of green algae is also regulated by the functions of a large number of disordered domains.However, not all species are in line with the general positive correlation between the degree of domain disorder and organism complexity. For example, halobacteria, ascomycetes and green algae have higher proportions of IDDs than the species with similar or even higher organism complexity. These phenomena indicate that the degree of disorder of protein domains is also influenced by certain special functions evolved in a specific evolutionary lineage. In halobacteria, the protein adaptations toward the high-salt pressure result in special characteristics of the protein sequence : the acidic and polar amino acid residues are over-represented significantly, and the hydrophobic residues are remarkably under-representedAs for the causes of formation of disordered protein domains, the long length of the domain maybe a prerequisite for it to have a CDR. We found that the IDDs having a CDR but a low disorder ratio (DSDR < 30%) (Class II) tend to be much longer than those containing higher disorder ratio (DSDR > 30%) but no CDR (Class IV) Figure . In eukaIn the viewpoint of the disorder variation of the same protein domains, the clade-specific ‘no-variation’ and ‘high-variation’ domains may contribute to the clade-specific functions which need constant or variable DSDR values respectively.Notably, although the accuracy of the disorder prediction methods has been greatly improved these years, it is still difficult to guarantee 100% accuracy. This may not affect the core conclusion of large-scale statistical analysis, as in this study. In addition, we used two disorder prediction methods based on different principles and similar conclusions were obtained. Thus, the core conclusions of this study are reliable. However, if we want to explore the functions of a certain IDD in a certain protein, we should do experiments to confirm its structural properties. Another limitation in this analysis is the number of species having cell type number data. But the 51 species are also representative across eukaryotes, including deuterostomes (16 species), protostomes (4 species), sponges and cnidarians (3 species), protists (6 species), fungi (8 species), plant (13 species).In conclusion, this study focused on the uneven distribution pattern and functional characteristics of the IDDs across 1870 species of the three superkingdoms. We found that intrinsic disorder of protein domain contributes to both of organism complexity and clade-specific functions: 1) complex species tend to have higher proportions of IDDs, contributing to the increasing organism complexity; 2) some clade-specific IDDs, clade-specific domains with specific DSDR/CDRN ratios and clade-specific ‘no-variation’ or ‘high-variation’ domains are involved in clade-specific functions. These new findings enrich our knowledge about the evolution of protein domains and provide valuable new insights for the functional studies of disordered domains.62 , Ensembl Metazoa Database , Ensembl Bacteria Database , Ensembl Plants Database , Ensembl Fungi Database , Ensembl Protists Database , Ensembl Tardigrades Database (http://download.tardigrades.org/v1/sequence/) and NCBI Refseq Database (ncbi.nlm.nih.gov/protein)63.The protein sequences were retrieved from the Ensembl Database40. In brief, to identify the domains presented in the whole sequences of each species, we used HMMER-scan program64 to search the Pfam-A databases65. And the full sequence E value and C-Evlaue were kept less than 0.01. According to previous definitions33, domain families in our study refer to the unique domain types. Protein domains belonging to a same family share the same Pfam IDs and domain names, but are located in different contexts, including different proteins and different regions of the same protein.The protein domains were identified as described previously66(default parameters) and IUPred67 . SPOT-D and IUPred are popular programs for the prediction of structural disorder scores. SPOTD is based on deep recurrent and convolutional neural networks. IUPred is based on the physicochemical properties of amino acids. Both of them were used to calculate the structural disorder value of each amino acid. An amino acid with a high structural disorder value which exceeds the default threshold was considered to be disordered. ESpritz68  was used as a corrected method for halobacteria. PSI-BLAST can avoid over-prediction of disorder scores35.Intrinsically disordered regions were identified using SPOT-Dhttp://mobidb.bio.unipd.it/)69, was used. The protein sequence and disorder region information were downloaded using API method (https://mobidb.bio.unipd.it/help/apidoc). 39 species were included in the analysis of the correlation between disorder degree and organism complexity . For the rest of the domains, if the number of consecutive disordered residues in a region is more than 40% of the total number of the domain then this region is considered as a consecutive disordered region. From the viewpoint of CDRN, the protein domains were divided into three classes: 0, ‘domain without CDR’; 1, ‘domain with one CDR’; ≥ 2, ’domain with multiple CDRs’. High-DSDR (DSDR > 30%) domains and domains containing one or more CDRs are considered as IDDs. Notably, this definition of IDD is different from the ‘disordered’ type in Pfam database. In Pfam, only fifty-five entries with more than 100 residues and conserved sequences in primate proteins are regarded as ‘disordered’a, b and c, and the constant d, of the equation x and y in the equation respectively, and then, the following values were determined: a = 1/78,750, b =  − 137/63,000, c = 253/2100 and d = 0. Therefore, the normalization equation can be expressed as follows:x represents the DSDR value, and y denotes the normalized DSDR value.The DSDR values were normalized using the interpolation method. Four points, , ,  and  were used to calculate the coefficients The normalized curve is shown in Figure Each domain family was assigned a DSDR dominant category according to the DSDR distribution of the domain family. As mentioned above, DSDRs were divided into four grades. For each domain family, the number and proportion of each DSDR grade was calculated. If the proportion of a DSDR grade was greater than 0.5 then the domain family was assigned to this DSDR dominant category Figure . Accordi37 . Due to the limitation of the current known PTM information in the database, only a few representative model species could be included in this analysis. The text format file of Swiss-Prot was downloaded and the data of seven species  were extracted from it. The MOD_RES type information was selected from FT (Feature Table) lines. Meantime, the protein sequences of these seven species were also extracted from the text format file of Swiss-Prot. The disorder scores were calculated and protein domains were identified using SPOTD and HMMER-scan (mentioned above) respectively. The PTM site number and type in each type of protein domains/regions were calculated for the further comparison and over-representation analysis.PTM data were retrieved from the Swiss-Prot database  was used. The E value threshold was set at the default value of 10. The formula to calculate the sequence similarity degree can be expressed as follows:‘Match_query_percent’ denotes the percentage of the matched sequence in the query sequence. ‘Match_subject_percent’ represents the percentage of the matched sequence in the subject sequence. ‘Query_first_percent’ indicates the percentage of the region from the domain start site to the matched first site in the domain sequence. ‘ABS’ refers to the calculation of absolute values.P value74. The logarithm of the P value (log P), positive or negative, was used to represent the strength of the over- or under-representation of proteins containing certain domain characters. When the heat maps were used to represent the over- or under-representation strengths, the values of ± log(P) were transformed into 14 grades (− 7 to + 7): − 7, log(P) ≤  − 12; − 6, − 12 < log(P) ≤  − 9; − 5, − 9 < log(P) ≤  − 6; − 4, − 6 < log(P) ≤  − 3; − 3, − 3 < log(P) ≤  − 2; − 2, − 2 < log(P) ≤  − 1.301; − 0.25, − 1.301 < log(P) ≤ 0; 0.25, 0 <  − log(P) < 1.301; 2, 1.301 ≤  − log(P) < 2; 3, 2 ≤  − log(P) < 3; 4, 3 ≤  − log(P) < 6; 5, 6 ≤  − log(P) < 9; 6, 9 ≤  − log(P) < 12; 7, − log(P) ≥ 12. Note: the grading criterion for figure The over- or under-representation analysis of domain GO annotations (Dataset 5) was based on the hypergeometric distribution model and the Benjamini–Hochberg method was used to correct the Supplementary InformationSupplementary Information2