Hierarchical topic modeling is a potentially powerful instrument for determining topical structures of text collections that additionally allows constructing a hierarchy representing the levels of topic abstractness. However, parameter optimization in hierarchical models, which includes finding an appropriate number of topics at each level of hierarchy, remains a challenging task. In this paper, we propose an approach based on Renyi entropy as a partial solution to the above problem. First, we introduce a Renyi entropy-based metric of quality for hierarchical models. Second, we propose a practical approach to obtaining the “correct” number of topics in hierarchical topic models and show how model hyperparameters should be tuned for that purpose. We test this approach on the datasets with the known number of topics, as determined by the human mark-up, three of these datasets being in the English language and one in Russian. In the numerical experiments, we consider three different hierarchical models: hierarchical latent Dirichlet allocation model (hLDA), hierarchical Pachinko allocation model (hPAM), and hierarchical additive regularization of topic models (hARTM). We demonstrate that the hLDA model possesses a significant level of instability and, moreover, the derived numbers of topics are far from the true numbers for the labeled datasets. For the hPAM model, the Renyi entropy approach allows determining only one level of the data structure. For hARTM model, the proposed approach allows us to estimate the number of topics for two levels of hierarchy. The large flow of news generated by TV channels, electronic news sources and social media is very often represented as a hierarchical system. In such a system, news items or messages are divided into a number of global topics, such as politics, sports, or health. Within each of the main topics, the documents usually demonstrate a considerable diversity and can be divided into subtopics, such as COVID-19, healthcare availability or healthy food within the global topic of health. The hierarchical division of information content is highly convenient and seems to reflect fundamental cognitive features of humans . TherefoIn this work, we investigate the behavior of three hierarchical models, namely, hierarchical latent Dirichlet allocation (hLDA) , hierarcTo simplify the structure of this work, an overview of hierarchical models and existing metrics of quality is provided in Appendix A. Thus, our work consists of the following parts. ‘Entropic approach to hierarchical topic modeling’ describes the entropic approach for estimating the quality of topic models and introduces the adaptation of this approach to hierarchical models. ‘Computational experiments’ outlines the design of our computer experiments for each of the considered models. ‘Numerical results’ contains an analysis of the behavior of hierarchical models under variation of hyperparameters and the number of topics for the three models on four datasets. ‘Discussion’ interprets the obtained results and reviews the possible limitations of the proposed approach. ‘Conclusion’ summarizes our findings and contains practical recommendations for choosing between hierarchical models. Appendix B contains additional experiments with different types of pre-processing that investigate the influence of the latter on behavior of Renyi entropy.S = − I  that change the value of entropy by ordering the data. Based on the fact that entropy equals minus information S = − I , one canΦ = {ϕwt}, where ϕwt is the probability of word w in topic t; for more details we refer the reader to Appendix A). Calculation of Renyi entropy for a topic model is based on two observable variables: (1) Gibbs-Shannon entropy of the model; (2) internal energy of the model. Gibbs-Shannon entropy S can be calculated as follows: N is the number of words with ϕwt > 1/W, W is the number of unique words in the dataset, T is the number of topics. The internal energy E of a topic model can be expressed in the following way: W. Then, Renyi entropy can be calculated through free energy (F = − qE + S) in the following way:Recent research  has demoq = 1/T is the inverse number of topics. Thus, Renyi entropy of a topic model explicitly contains ’number of topics’ as a deformation parameter. Application of Renyi entropy for tuning different non-hierarchical topic models is considered in work , and one distribution of probabilities of all documents over all topics (represented in matrix Θ), hierarchical topic modeling constructs one pair of these matrices per each level of hierarchy . The number of levels, and most often the number of nodes-topics per each level, is a value pre-selected by a user. In some models, topics of a lower level are united in clusters each of which is nested in only one topic of a higher level; in other models, hierarchy levels are unrelated and only differ by the number of topics, so that lower levels contain more topics. At all levels, matrices Φi, Θi use the same number of words W and documents D which equals the total number of words and documents in the collection, respectively. However, the share of words with high probabilities  is the lower, the higher is the level of hierarchy.As shown in L. Each solution can be characterized by the following variables: (1) the number of topics Ti on level i; (2) the number of words (Ni) with high probabilities: Ei and Gibbs-Shannon entropy Si of the the solution of level i with respect to the equilibrium state of that level: Si and Ei, one can determine free energy and Renyi entropy of level i. Free energy is expressed as follows: Fi = Ei − Ti · Si. Renyi entropy of level i can be expressed in the following way: q = 1/Ti is the deformation parameter characterizing each level of the hierarchy.As it can be seen, instead of a single topical solution, hierarchical topic modeling obtains a set of solutions whose number equals to the number of levels By measuring the value of entropy at each level while consecutively increasing the number of topics, it is possible to find a solution optimal in terms of information theory. In such a design, the process of clustering of words by topics starts with the minimum information (maximum entropy) when all the elements (words) of the statistical system are assigned to one or two topics and ends also with the maximum entropy when all the elements are almost uniformly distributed over topics (when the number of topics is large). The locations of the global minimum and of several possible local minima of Renyi entropy, as a function of the number of topics, are determined by the features of the data. Thus, Renyi entropy https://www.kaggle.com/yutkin/corpus-of-russian-news-articles-from-lenta). This dataset contains 8,630 documents with a vocabulary of 23,297 unique words in the Russian language. Each of these documents is manually assigned to a class from a set of 10 topic classes. Since some of the topics are closely related to each other, 7–10 topics can describe the documents of this dataset.‘Lenta’ dataset  is a widely used dataset in the field of topic modeling. It consists of 15,425 news articles in the English language with 50,965 unique words. Each of the news items is assigned to one of 20 topic groups. Since some of these topics might be combined, 14–20 topics can describe this dataset’s documents according to ‘20 Newsgroups’ dataset (https://data.mendeley.com/datasets/9rw3vkcfy4/1) is a dataset in the English language with a two-level hierarchical mark-up. The original dataset contains 46,985 abstracts of published papers available from the Web of Science. The first level contains seven categories (domains): computer science, electrical engineering, psychology, mechanical engineering, civil engineering, medical science, and biochemistry. The second level is comrpised of 134 specific topics (areas), each of which belongs to one of the categories of the first level. The number of unique words is 80,337. This dataset is often used as a benchmark for hierarchical classification  is a class-balanced subset of the ‘WoS’ dataset, which contains 11,967 abstracts. The first level contains seven categories and the second level consists of 33 areas.‘Balanced WoS’ dataset  is a dataset with a three-level hierarchical mark-up. It contains 40,000 product reviews in English from Amazon. The vocabulary of this dataset consists of 31,486 unique words. Level 1 of the hierarchical mark-up contains six categories, level 2 contains 64 categories, and level 3 contains 510 categories. We consider only the first two levels since the third level contains ‘unknown’ labels. Let us note that the original dataset is highly imbalanced. Some sub-categories contain less than 50 documents, while other sub-categories contain more than 2,000 documents. Therefore, a balanced subset of this dataset is also considered.‘Amazon’ dataset , and the implementation of hARTM model from BigARTM package, version 0.10.1 (https://bigartm.readthedocs.io/en/stable/). With each of them, we performed topic modeling on both types of our datasets—those with the hierarchical and the flat mark-up. Correspondingly, the results are divided into two parts. In the first part, we analyze the applicability of the models for the datasets with flat structure, while focusing on the datasets with hierarchical mark-up in the second part. Since topic modeling possesses a certain level of instability that leads to fluctuations in the word probabilities, all calculations were performed at least six times (for each combination of hyperparameters), and then the results were averaged. For each model we calculated Renyi entropy, perplexity and log-likelihood, the two latter having been chosen as the most common metrics for hyperparameters tuning in topic modeling. However, as perplexity is in fact a reciprocal geometric mean of log-likelihood (see Appendix A), its behavior has turned out to be nearly identical to the latter. Therefore, we do not report it in our analysis. In this paper we also leave out metrics related to semantic coherence or semantic stability, such as standard topic coherence  hyperparameter α. The number of topics at the first level of hPAM model is always set to one. Moreover, in this model, the user can only set the initial value of parameter α, and then the algorithm tunes it during the modeling. Our experiments demonstrated that variation of the initial value of α (α0) does not influence the results of modeling, namely, different values of α0 lead to almost the same topic solutions and the same final value of α. Therefore, in the rest of the paper, we fix α0 to 0.0001. For a more detailed description of the model, we refer the reader to Appendix A.The hPAM model depends on the following parameters: (1) number of topics at the second and the third levels; (2) hyperparameter T2 = 1 while hyperparameter η was varied in the range  and the number of topics on the second level (T1) was varied in the range . For each solution, we calculated Renyi entropy and log-likelihood of the second level of the hierarchy. Then, we found and fixed several pairs of the values of T1 and η that were the closest to minimum Renyi entropy. Second, we investigated the third hierarchical level, where the number of topics T2 on the third level was varied under condition of a fixed pair of T1 and η. For each combination of T2 and the pair , Renyi entropy of the third level was calculated.In our work, hPAM model was studied in two stages. First, we investigated the second hierarchical level in the following way: the number of topics on the third level was fixed α, which is tuned by the model automatically; (3) hyperparameter γ; (4) hyperparameter η. hLDA model is non-parametric; therefore, it infers the number of topics on each level automatically.The hLDA model has the following parameters: (1) depth of the hierarchy; (2) hyperparameter η, which was varied in the range . The depth of the hierarchy was set to three as in the experiments in the original work , and the number of topics on the first level was varied in the range of  topics. Based on the minimum Renyi entropy location, the optimal T1 was chosen. Second, the number of topics on the second level was varied under the condition of fixed T1. For each run of the model, the parameter seed was randomly selected to investigate the variability of the model output.The hARTM model has the following parameters: (1) number of topics at each level of the hierarchy; (2) seed—a parameter describing the initialization procedure (it defines the work of the random number generator). This model was also studied in two stages. First, the number of topics on the second level was fixed  and large (about 100–200) numbers of topics. These ranges correspond to the two extreme states of the statistical system characterized by entropy maximum. Moreover, one can see that the location and the value of minimum Renyi entropy significantly depend on the parameter η. Large values of η (η > 0.7) lead to significant fluctuations in the Renyi entropy for large numbers of topics that complicates finding entropy minimum the more, the higher the number of topics is. Correspondingly, the further increase of η is inadvisable.η by means of selecting the values that correspond to the minimum entropy. Since we test the model on the datasets with human mark-up, we can estimate the error of the found approximation of the number of topics. For non-hierarchical datasets (Lenta and 20 Newsgroups), the error corresponds to ±2 topics . Potentially interesting combinations of parameters that were used in our calculations at the second stage are highlighted in bold.The list of values of T1 and η demonstrates that this metric is not useful for selecting the optimal values of η and T1 since it has large fluctuations in the entire range of T1  minimum of Renyi entropy approximately corresponds to the first level of the hierarchical mark-up of the dataset and the second minimum corresponds to the second level. For instance, Renyi entropy for the balanced WoS dataset has a global minimum at the value of 10 topics, while human mark-up suggests seven topics. The second  minimum of Renyi entropy corresponds to 36–42 topics, and the mark-up discerns 34 topics. For the balanced Amazon dataset, the first minimum of Renyi entropy corresponds to 10 topics, with 6 topics on the first level provided by the mark-up. The second minimum is found at 38 topics, whereas the mark-up indicates 27 topics on the second level. Thus, the estimation of the number of topics on the second level has a larger error compared to that of the first level. However, to the best of our knowledge, Renyi entropy approach provides the best accuracy of estimating the number of topics for this hierarchical model. Moreover, our approach allows us to determine the very presence of hierarchical structure in the data. Let us note that balancing the datasets allows improving the results of topic modeling in terms of topic salience, making them strongly pronounced. This is achieved by obtaining higher probabilities of the most probable words . This, in turn, leads to the formation of a more pronounced entropy minimum and, on average, to lower values of entropy across the runs of the algorithm with different parameters. η, which controls the sparsity of topics. However, how to choose the optimal value of η is an open question since neither log-likelihood nor Renyi entropy allow us to tune this parameter.As our experiments demonstrate, hLDA model is very unstable which means that its different runs with the same parameters produce radically different topical structures of the same data. This instability can be significantly reduced by changing parameter η and the number of topics on different levels. Variation of these parameters in experiments demonstrated that Renyi entropy allows determining only one level of data structure for this model, namely, the number of topics on the second level. Variation of the number of topics on the next level leads to significant fluctuations of Renyi entropy for both non-hierarchical and hierarchical datasets. Correspondingly, it seems that sharp jumps of Renyi entropy on the third level are related to the model’s features rather than to the datasets’ structure. Additionally, the behavior of log-likelihood as a function of the number of topics and parameter η does not allow determining the dataset structure since the maxima and the minima of log-likelihood do not correspond to the structure of mark-up either for hierarchical or non-hierarchical datasets.A somewhat more promising result has been obtained for hPAM model which depends on the parameter Among the considered models, hARTM is the most stable since the procedure of initialization of this model has almost no effect on the obtained values of Renyi entropy. Moreover, Renyi entropy for this model demonstrates that for the datasets with non-hierarchical mark-up (for both the English-language and the Russian-language datasets), there is only one minimum, which is located close to the number of topics from the mark-up. Further increase in the number of topics does not lead to jumps (in contrast to hPAM model) but leads to a smooth growth of Renyi entropy which coincides with the results of modeling with non-hierarchical topic models . At the same time, the location of a global minimum stays almost unchanged. However, extensive pre-processing may lead to disappearance of local minima at the second hierarchical level. Therefore, it is recommended to remove short words and not to remove words with a frequency above 5. In general, cutting off the long tail of the frequency distribution affects the results of the entropy calculation, since Renyi entropy is calculated as the difference between high frequency words and low frequency words.The proposed Renyi entropy approach has some limitations related to the method of Renyi entropy calculation and to the distribution of words in text collections. First, these distributions have long and heavy tails. Words from such tails are assigned to topics with small probabilities. Since Renyi entropy measures the difference between words with high probabilities and words with small probabilities, it can only detect topics that are comprised of highly probable words. However, lower levels of hierarchy tend to contain narrower topics, each of which is comprised of words with smaller probabilities. Correspondingly, the ability to distinguish the sets of topics disappears as the number of hierarchy level increases. Thus, Renyi entropy approach is suitable for determining the number of topics and values of hyperparameters for one or two levels of hierarchical structures. Another limitation of our approach is the presence of fluctuations in Renyi entropy values. These fluctuations are related to fluctuations of word probabilities, which, in turn, are caused by the stochastic nature of topic modeling. The problem of minimizing the instability of topic modeling has not yet been solved, but the stabilization of topic models through word embeddings can be potentially interesting. The application of Renyi entropy approach to topic models with word embeddings is beyond the scope of this work and could be considered a next potential stage in the development of the entropic approach to topic modeling.In this work, we investigated the ability of hierarchical topic models to correctly determine the hierarchical structure in data by applying three topic modeling algorithms to four datasets with a human mark-up of topics. Two of these datasets have a flat mark-up, while the mark-up of two others is two-level. Three of the datasets are in the English language, and one is in Russian. Thus, we analyze the models and test our approach on a variety of data structures and languages. We first formulated the principle of Renyi entropy calculation for hierarchical topic models. Next, we consecutively analyzed the chosen topic models using log-likelihood metric and Renyi entropy. Based on the results of our calculations, we can conclude the following.First, our Renyi entropy-based approach can be extended to hierarchical topic models since the accuracy of the approximations of the optimal topic number for some of such models is not inferior to that for non-hierarchical models demonstrated in hARTM model is thus the easiest for tuning and, additionally, the most stable among the considered algorithms, while Renyi entropy is obviously a better quality metric compared to log-likelihood. We can also conclude that Renyi entropy is better than perplexity as the latter behaves very similar to log-likelihood whose derivative it is. Thus, for the purposes of topic number optimization it is recommended to select other model parameters based on the minimum of Renyi entropy.The main limitation of Renyi entropy approach is that it can detect the true number of topics only for one or two levels of a topic hierarchy. Topic numbers at the subsequent levels are hard to predict since the probabilities of words at these levels get smaller and more uniform, thus determining the formation of less pronounced topics with unclear boundaries that, furthermore, are much more prone to random shifts from one run to another. This, in turn, hinders discrimination between genuine local minima of Renyi entopy and its random fluctuations. The problem of detecting poorly pronounced topics and the related task of topic model stabilization can possibly be solved by means of word embeddings technology, which appears a promising direction for a future research.α, η or the number of topics) are those which produce a solution with the best value of one or more metrics of quality. However, when such metric cannot be optimized directly, as it is in the case of the number of topics, or its optimization is very time-consuming, an optimizable substitute metric should be found, of which Renyi entropy is a promising example. While in this work we have shown the utility of Renyi entropy metric for finding an optimal number of topics, our earlier work  and, in addition, the order of documents in the collection is neither important. Third, a conditional independence assumption states that document d and word w are independent conditioned on the latent topic t, i.e., words, conditioned on the latent class t, are generated independently of the specific document identity d   and its  follows :t is a topic, p(w|t) is the distribution of words by topics, and p(t|d) is the distribution of topics by documents. The output of TM is represented with two matrices, namely, matrix Φ: = {ϕwt}≡ {p(w|t)} containing the distribution of words by topics and matrix Θ: = {θtd}≡ {p(t|d)} containing the distribution of topics by documents . Many modifications of the LDA model were developed for various specific applications. However, these models share the same practical issue: to build a model, the user has to set the number of topics unknown in advance in many cases.where D documents can be calculated as follows  =  is a list of M most probable words in topic t, D(v) is the number of documents containing word v, and D is the number of documents where words v and v′ co-occur. The typical value for M is 5–20 words.W from the data. Theoretically, nonparametric models are able to select the number of topics automatically according to available data. Such models introduce a prior distribution on potentially infinite partitions of integers using some stochastic process that would give an advantage in the form of a higher prior probability for solutions with fewer topics. In works , a topicThe next important step in the development of topic models was dictated by the intention to organize topics into a hierarchy. It resulted in the development of hierarchical topic models. In contrast to flat topic models (such as pLSA or LDA), the hierarchical topic models are able to discover a topical hierarchy, which comprises levels of topical abstraction. Usually, each node in the hierarchy corresponds to a topic, which, in turn, is represented by the distribution over words. Different hierarchical topic models are based on different prior assumptions on the distribution of topics and on the type of hierarchical structure to be inferred. The two most widely used in practice hierarchical topic models  are hierL-level trees. To illustrate nCRP, assume that there are infinitely many Chinese restaurants with infinitely many tables. One restaurant is associated with a root (level 1) and each table of this restaurant has a card with a reference to another restaurant (level 2). Tables of those restaurants, in turn, have references to other restaurants (level 3), and this structure repeats. Each restaurant is referred to exactly once, therefore, the structure produces an infinitely-branched tree of restaurants. Imagine that a tourist enters the root restaurant and selects a table according tohLDA model is a hierarchical and nonparametric extension of LDA model. In the framework of this model, it is assumed that each sub-topic (child topic) has a single parent topic, thus, providing a tree of topics. Moreover, it is assumed that all topics of a document are found within a path in that tree. It imposes significant restrictions on the inferred topical components of documents. Thus, for instance, a document can not be devoted to several specific sub-topics within the assumptions of the model. Another feature of this model is that the first level of the hierarchy always contains one topic (the root of the hierarchy). hLDA model learns topic hierarchies based on the nested Chinese Restaurant Process (nCRP), which specifies a prior for the model. Nested Chinese Restaurant Process is a hierarchical version of the Chinese Restaurant process and is used to express uncertainty about possible mi is the number of previous customers at table i, m is the number of customers in the restaurant, including the tourist, and γ is a parameter that controls how often a customer chooses a new table. The next day, the tourist goes to the restaurant identified on the card of the table chosen the day before and selects a table according to L days. After M tourists proceed this process, the collection of their paths describes a particular L-level tree. In terms of topic modeling, customers correspond to documents, and restaurants correspond to topics. In the framework of hLDA model, the following prior distributions are assumed: (1) nCRP prior with hyperparameter γ on possible trees; (2) symmetric Dirichlet prior with hyperparameter η on the distribution of words by topics ϕwt; (3) L-dimensional Dirichlet prior with hyperparameter α on mixing proportions θtd of the topics along the path from the root to the leaf.where t, draw ϕ·t∼ Dir(η).For each node d = {cd1,…,cdL} according to nCRP with parameter γ d ∼ Dir(α). For each position n of word in the document, choose the level assignment zdn∼ Mult(θd) , then draw a word from the chosen topic wdn∼ Mult(φ· cdnzd).For each document d: draw a path of topics cThe generative process of hLDA model can be described as follows:dn and cd variables are the following:Thus, a document is drawn by choosing an L-level path through the restaurants (topics) and then sampling the words from the L chosen topics. The inference can be approximated by means of Collapsed Gibbs sampling W is the vocabulary (set of words), V is the vocabulary size, z = {zd}d = 1D, ¬ dn means all the tokens excluding token wdn, l in document d excluding token wdn, v assigned to topic cdl excluding the current token wdn, cdl excluding the current token wdn, cl excluding the tokens of document d, d that were assigned to topic cl, B(·) is the multivariate beta function and Φ is calculated according towhere cwt equals the number of instances word w was assigned to topic t.where a counter α is a parameter of Dirichlet distribution, which controls smoothing over levels in the tree; (2) η is hyperparameter of another Dirichlet distribution, which controls the sparsity of topics; (3) γ is a parameter of the nested Chinese restaurant process, which controls how often a document will choose new, i.e., not previously encountered paths.We would like to note that hLDA model has three hyperparameters: (1) i.e., it infers the number of topics on each level automatically while hPAM model is parametric, therefore, a user has to manually select the number of topics on each level. Thus, the number of topics on each level of the hierarchy is a model parameter. Moreover, hPAM model has two hyperparameters: η is a parameter of a prior Dirichlet distribution for ϕt, which controls sparseness of topics, and α is a parameter of a prior Dirichlet distribution for θ. Let us note that since hPAM constructs a directed acyclic graph, each node (topic) at a given level has a distribution over all nodes on the next lower level. Let T1 be the number of ‘super-topics’ and T2 be the number of ‘sub-topics’.In hPAM model, in contrast to hLDA, it is assumed that each child topic can be related to each node (topic) from the upper level, thus, resulting in a directed acyclic graph of topics. Therefore, in hPAM model, a child topic has several parent topics. It reduces the necessity of inferring the correct tree structure that is crucial in hLDA. Moreover, it allows the documents to contain several specific sub-topics. Moreover, hPAM model always infers a three-level hierarchy, where the first level corresponds to a root topic, the second level corresponds to super-topics, and the third level contains sub-topics. Another important difference between hLDA and hPAM models is that hLDA is a nonparametric model, t of the graph, draw ϕ·t∼ Dir(η).For each vertex (topic) d, sample θ0 from a T1 + 1-dimensional Dirichlet distribution with hyperparameter α0 and distribution θT from a T2 + 1-dimensional Dirichlet distribution with hyperparameter αT for each super-topic. The first component of θ0 determines the probability of the event that a word is generated by the root topic while the other T1 components of θ0 determine the distribution of the root topic over super-topics. Analogously, the first component of θT defines the probability of the event that a word is generated by the super-topic T, and the other T2 components determine the distribution of the super-topic T over sub-topics.For each document w: sample a super-topic zT ∼ Mult(θ0). If zT = 0, sample a word from ϕ·0 ∼ Dir(η). Otherwise, sample a sub-topic zt ∼ Mult(θzT). If zt = 0, then sample a word from ϕ·Tz∼ Dir(η). Otherwise, sample a word from φtz.For each word The generative process of hPAM model is as follows (hPAM model 2):W × (T1 + T2 + 1). The inference algorithm is based on Gibbs sampling method w in document d is as follows:Thus, matrix of the distribution of words by topics has dimension x ∈ {0,1,…, T1} is the index of super-topic if x ≠ 0; y ∈ {0,1,…,T2} is the index of sub-topic if y ≠ 0; z¬w is the topic assignments for all other words, counter dnw) in document d that were assigned to super-topic x, x ≠ 0, and to the root topic if x = 0; 0,xα means the x-th component of vector 0α; counter dnw) in document d that were assigned to super-topic x and sub-topic y; counter k, where k is the root topic if x = 0, k = 1, …,T1 is a super-topic if y = 0 and k = T1 + 1, …, T2 + 1 is a sub-topic otherwise. Then, a pair of indices  is sampled from the distribution dn. Matrix Φ is calculated according towhere cwt equals the number of instances word w was generated by topic t. One can use the fixed point update equations described in α. Thus, in most of the publicly available model implementations hyperparameter η is fixed and has to be set by a user while hyperparameters αt are optimized during the training step.where a counter Besides hLDA and hPAM, many other hierarchical topic models have been developed, for instance, nested hierarchical Dirichlet process (nHDP) , hierarcT. From a root Dirichlet process Gi0, a path is followed by drawing ϕl+ 1 ∼ Gil  for l = 0,1,2,…, where i0 is the root index, and il =  indexes the Dirichlet process associated with the topic ϕl = θil. For each document d, a tree Td is constructed, where for each Gil, a corresponding il, a document specific beta random variable is drawn. Namely, given that the path for word wd,n is at node il, stop with probability Ud,il ∼ Beta. If we do not select topic θil, then continue by selecting node il+ 1 according to wd,n in document d, the probability that it is generated by topic ϕdn = θil is as follows il, the right term is the probability that the word selects the lth topics, but not the first l − 1 topics, im denotes the first m values in il (m≤ l). The authors propose a stochastic variational inference  from a predefined number of classes. Second, a document d is chosen according to conditional probability p(d|α). Third, a topic for each word position is chosen according to class-conditional probability p(t|α). Forth, a word is sampled according to p(w|t). In the hierarchical structure of hPLSA, the document classes are the leaves of the hierarchy while topics occupy non-leaf nodes of the hierarchy. pLSA model is a special case of hPLSA since if only one topic per class is sampled then the result corresponds to the flat topic solution of pLSA. If a topic is shared among classes, then it is placed at a higher level of the hierarchy. hPLSA model has certain limitations, which are analogous to limitations of pLSA, namely, it possesses a large number of parameters that have to be estimated, and this number grows linearly with the size of the dataset. It can lead to model overfitting.In the framework of hPLSA, the generative process is as follows. First, a document class H, α, γ) that can potentially lead to different hierarchies in terms of the depth and the number of nodes (topics) on each level.In the framework of hHDP, two models are proposed. The first one (hvHDP) results in a hierarchy, where internal nodes are represented as probability distributions over topics and over words. Thus, in hvHDP model, all nodes can be considered as topics. In the second model (htHDP), only leaf nodes are represented as distributions over words. Thus, only the leaf nodes are essentially the topics. Both models are non-parametric and exploit the mixture model of hierarchical Dirichlet processes (HDP). At each level of the hierarchy, there is a Dirichlet process for each document and a global Dirichlet process over all the Dirichlet processes at that level. Thus, each level is associated with a HDP. These assumptions allow inferring the number of nodes on each level automatically. According to the authors , hPAM ani.e., by a set of words that are the best ones to characterize the difference between the two clusters due to the fact that their occurrence probabilities in the two clusters differ the most. Latent variables at high levels of the hierarchy correspond to more general topics, while latent variables at low levels correspond to more specific topics. The construction of the hierarchy is based on the subsequent application of flat models. The details of the model construction can be found in the original work  yielding a hierarchy of nested stochastic block models, where each level clusters the groups of the levels below. A detailed description of adaptation of stochastic block models to topics modeling can be found in via regularization. Thus, for already learned ϕl, i.e., the matrix containing the distribution of words by topics for topics on level l, it is proposed to implement matrix decomposition ϕl ∼ ϕl+ 1ψ, where matrix ψ = {p(tl+ 1|tl)} contains interlevel distributions of sub-topics tl+ 1 in parent topics tl, ϕl+ 1 is the matrix containing the distribution of words by sub-topics tl+ 1 with additional sparsing regularizers. So, one can infer the hierarchy level by level via finding parent topics for each sub-topic using interlevel regularizers. A more detailed description of the model and the model inference can be found in work  approach. In the framework of hARTM, it is allowed for a topic to have several parent topics and, moreover, the authors claim that the model can automatically determine the number of sub-topics for each topic . However in work .In addition to a wide variety of unsupervised hierarchical topic models, many semi-supervised and supervised extensions of hLDA have been proposed. For instance, supervised hierarchical latent Dirichlet allocation (SHLDA) , constraAlthough many hierarchical topic models have been developed, there are still no well-established quality metrics for topic hierarchies . TherefoTo estimate the influence of datasets pre-processing on the behavior of Renyi entropy in hierarchical models, the following experiments were conducted. First, let us describe additional pre-processing steps. At the first stage, words of length less than three letters were removed. At the second stage, in addition to short words, rare words with frequencies below 5 were also removed. For instance, after the initial pre-processing used in the main text of the paper, the 20 Newsgroups dataset consists of 50,965 unique words. After the first stage of additional pre-processing, the size of vocabulary is 25,956, and after the second stage it is 19,086 unique words. Analogously, Balanced WoS dataset contains 36,488 unique words in the experiments described in the main text. After the first stage of additional pre-processing it contains 31,725 unique words and 14,526 words after the second stage. For the obtained datasets with different types of pre-processing, hPAM and hARTM models were implemented to study the behavior of Renyi entropy in the same way as in section ‘Computational experiments’ of the main body of the article.η. Here and further, “pre-processing 0” refers to the initial pre-processing; “pre-processing 1” refers to the first stage of additional pre-processing; “pre-processing 2” refers to the second stage of additional pre-processing. Based on the calculations, one can conclude the following. Removing only short words slightly worsens Renyi entropy, with the worsening being only in raising the entropy curve. The range of topics, where minima of Renyi entropy are observed, corresponds to 6–12 topics for the dataset with the first stage of additional pre-processing. The closest result to the human markup is observed at η = 0.3. Removing short and rare words leads to entropy curve raising and shifting of minima points. The range of minima points for the second stage of pre-processing corresponds to 8–14 topics. Since human mark up corresponds to 14–20, one can conclude that additional pre-processing with removing of short and rare words has a positive effect on the results of topic modeling on a dataset with a non-hierarchical labeling.i.e., there is a small variation associated with the change in parameter η. Removing of short and rare words leads to a back-down shift of Renyi entropy. Moreover, variation of parameter η does not influence significantly the minimum location under condition of this type of pre-processing. In general, Renyi entropy minimum corresponds to six topics that, in turn, is very close to close to the human mark-up. Thus, extensive pre-processing is necessary to obtain a better result of topic modeling.η and T1. The results demonstrate that removing short and rare words improves Renyi entropy. For instance, sharp fluctuations appearing for hPAM model may disappear with a more extensive pre-processing.i.e., the dataset with non-hierarchical structure. For all types of pre-processing, the minimum corresponds to 14 topics, which is close to the human markup. Thus, hARTM model is stable and reproduces the results in terms of Renyi entropy. For balanced WoS dataset, we obtain that Renyi entropy curves are similar for initial and for the first additional pre-processing, namely, they have two clear minima corresponding to two hierarchical levels. However, more extensive pre-processing leads to the disappearance of the second local minimum in the region of 38 topics and the appearance of a small, not clearly pronounced minimum in the region of 16-18 topics. In addition, Renyi entropy curve for more extensive pre-processing is lower on average, which means a slightly better model.10.7717/peerj-cs.608/supp-1Supplemental Information 1Each row contains lemmatized texts of product reviews from Amazon.Click here for additional data file.10.7717/peerj-cs.608/supp-2Supplemental Information 2Each row contains lemmatized news texts.Click here for additional data file.10.7717/peerj-cs.608/supp-3Supplemental Information 3Each row contains lemmatized texts of product reviews from Amazon in CSV format.Click here for additional data file.10.7717/peerj-cs.608/supp-4Supplemental Information 4Each notebook takes a lemmatized dataset as an input, runs a hierarchical topic model for a range of model parameters and calculates Renyi entropy on each level of hierarchical solution.Click here for additional data file.10.7717/peerj-cs.608/supp-5Supplemental Information 5Each row contains lemmatized texts of news articles from Lenta.ru news agency in csv format.Click here for additional data file.10.7717/peerj-cs.608/supp-6Supplemental Information 6The versions of the packages used in experiments.Click here for additional data file.10.7717/peerj-cs.608/supp-7Supplemental Information 7Each row contains lemmatized texts of news texts from 20 Newsgroups dataset in CSV format (short words are removed).Click here for additional data file.10.7717/peerj-cs.608/supp-8Supplemental Information 8Each row contains lemmatized texts from 20 Newsgroups dataset in CSV format after removing short and rare words.Click here for additional data file.10.7717/peerj-cs.608/supp-9Supplemental Information 9Each row contains lemmatized texts of abstracts from Web of Science in csv format.Click here for additional data file.10.7717/peerj-cs.608/supp-10Supplemental Information 10Each row contains lemmatized texts from balanced WoS dataset in CSV format after removing short and rare words.Click here for additional data file.10.7717/peerj-cs.608/supp-11Supplemental Information 11Each row contains lemmatized texts from balanced WoS dataset in CSV format after removing short words.Click here for additional data file.10.7717/peerj-cs.608/supp-12Supplemental Information 12Click here for additional data file.10.7717/peerj-cs.608/supp-13Supplemental Information 13Each row contains lemmatized texts of abstracts from Web of Science in csv format.Click here for additional data file.10.7717/peerj-cs.608/supp-14Supplemental Information 14The first column corresponds to unique words, other columns conatin probability distributions for topics.Click here for additional data file.


Lysozyme is a key effector molecule of the innate immune system in both vertebrate and invertebrate. It is classified into six types, one of which is the goose-type (g-type). To date, no study on g-type lysozyme in crustacean has been documented. Here, we report the identification and characterization of a g-type lysozyme (named LysG1) from the shrimp inhabiting a deep-sea hydrothermal vent in Manus Basin. LysG1 possesses conserved structural features of g-type lysozymes. The recombinant LysG1 (rLysG1) exhibited no muramidase activity and killed selectively Gram-negative bacteria in a manner that depended on temperature, pH, and metal ions. rLysG1 bound target bacteria via interaction with bacterial cell wall components, notably lipopolysaccharide (LPS), and induced cellular membrane permeabilization, which eventually caused cell lysis. The endotoxin-binding capacity enabled rLysG1 to alleviate the inflammatory response induced by LPS. Mutation analysis showed that the bacterial binding and killing activities of rLysG1 required the integrity of the conserved α3 and 4 helixes of the protein. Together, these results provide the first insight into the activity and working mechanism of g-type lysozyme in crustacean and deep-sea organisms. The innate immune system is an important mechanism of the host to defend against pathogens, especially for invertebrate, which lack adaptive immune systems. Lysozyme  is one of the key molecules of the innate immune system. It catalyzes the cleavage of the beta-1,4-glycosidic bond between the C-1 of N-acetylmuramic acid (MurNAc) and the C-4 of N-acetylglucosamine (GlcNAc) in peptidoglycan . In receAccording to their difference in source, amino acid sequence, chemical property, and biological activity, lysozymes are classified into six types, including chicken-type (c-type), goose-type (g-type), plant-type, and invertebrate-type (i-type) . The g-tAlvinocarididae shrimp (Rimicaris sp.) collected from a deep sea hydrothermal vent in Desmos, Manus Basin, at ~1883 m depth [Alvinocarididae family of shrimp is known to be a dominant organism in the communities of various hydrothermal vents [In this study, we identified a g-type lysozyme (named LysG1) from the  m depth . In deep m depth ,30,31. Tal vents . To dateal vents ,34,35, bRimicaris sp. from a hydrothermal vent in Desmos, Manus basin. LysG1 protein is composed of 188 amino acid residues, with a calculated molecular weight of 20.0 kDa and a predicted pI of 9.06. It contains the conserved residues of the sugar binding site  and catalytic site (E72 and D96) of g-type lysozyme. LysG1 was predicted to form 8 α-helixes and 2 β-pleated sheets  have been deposited in GenBank, which are 52.98% and 53.76%, respectively, identical to LysG1  showed that the enzyme rapidly increased the permeability of the outer membrane of ormation . It alsoormation . In our ormation ,48,49. Tormation . In the In addition to its direct bacteriolytic effect, lysozyme also plays a part in limiting inflammation . During Staphylococcus aureus, Streptococcus iniae, Micrococcus luteus and Bacillus subtilis and the Gram-negative bacteria Pseudomonas fluorescens, Escherichia coli, Edwardsiella tarda, Vibrio harveyi, and Vibrio anguillarum have been reported previously [Bacillus thuringiensis, Bacillus mobilis, and Bacillus toyonensis, and the Gram-negative bacteria Psedoalteromonas hodoensis, Vibrio neocaledonicus, Alteromonas abrolhosensis, and Pseudidiomarina donghaiensisare were isolated from deep sea environments. S. iniae was cultured in TSB medium  at 28 °C. The deep-sea bacteria were cultured in marine 2216E medium at 28 °C. All other bacteria were culture in Luria-Bertani broth (LB) medium at 28 °C  or 37 °C .The Gram-positive bacteria eviously . The Grahttp://www.ncbi.nlm.nih.gov/blast (accessed on 3 December 2020)). Signal peptide search was performed using the SignalP program ). Multiple sequence alignment was performed with the ClustalX software version 2.1. The output picture was generated using ESPript 3.0 (https://espript.ibcp.fr/ESPript/ESPript/index.php (accessed on 1 December 2020)). The structure modeling of LysG1 was predicted with the SWISS-MODEL (https://swissmodel.expasy.org/interactive (accessed on 14 December 2020)). The phylogenetic tree was constructed using neighbour-joining method with the MEGA software version 7.0.26, and the reliability of the tree was assessed by bootstrapping (1000 replications).The sequence of LysG1 was obtained from the transcriptome of Alvinocarididae shrimp . The seqEscherichia coli BL21 (DE3) by transformation. The transformants were cultured in LB medium at 37 °C to OD600 of 0.4, and then 0.04 mM Isopropyl-β-D-Thiogalactopyranoside (IPTG) was added. The culture was continued at 16 °C for 15 h with shaking (100 rpm). The cells were then harvested by centrifugation. The proteins with His-tag were purified with nickel-nitrilotriacetic acid (Ni-NTA) columns  as described previously [To construct the plasmid pET28a-LysG1, which expresses recombinant LysG1 (rLysG1) with a His-tag, the coding sequence of LysG1 was inserted into the expression plasmid pET28a at the NdeI/XhoI sites, resulting in pET28a-LysG1. The pET28a-LysG1 and pET32a (for purification of His-tagged Trx as a control protein) were separately introduced into eviously . The recThe DNS  method was used to detect the muramidase activity of rLysG1 as reported previously . Briefly3 CFU/mL with PBS. Then 0.5 μM to 16 μM (2-fold dilution) of rLysG1 was added to 200 μL bacterial suspension. PBS was added to the control bacterial suspension. The samples were incubated at 37 °C for 4 h, and bacterial survival was determined by plate count [E. coli was incubated with or without (control) rLysG1 (8 μM) at different temperatures  or pH  for 4 h. To examine the effect of metal ions, E. coli was incubated with or without rLysG1 (8 μM) in PBS containing 100 μM FeCl2, CuCl2, CaCl2, MgCl2, MnCl2, CoCl2, or ZnCl2. To determine the effect of LPS on the bactericidal activity of rLysG1, rLysG1 (8 μM) was pre-incubated with LPS   at 37 °C for 1 h. E. coli was then added to the mixture, and the mixture was incubated at 37 °C for 4 h. To examine the bactericidal activity of the rLysG1 mutants, E. coli was incubated with or without each of the rLysG1 mutants (8 μM) at 37 °C for 4 h. In all cases, bacterial survival was determined as above. To determine the minimal bactericidal concentration (MBC) of rLysG1, bacteria were grown to logarithmic phase and resuspended in PBS to 10te count . To exam2CO3 and 2.93 g NaHCO3/L, pH 9.6) at the final concentration of 108 CFU/mL. The plate was incubated at 4 °C overnight. LPS or PGN  dissolved in the coating buffer to 200 μg/mL was added to a 96-well microtiter plate, followed by incubation at 4 °C overnight. Lipid A  (200 μg/mL in methanol) was added to a 96-well microtiter plate, and the plate was left to dry at room temperature for 2 h. All the plates were washed 3 times with PBST (PBS with 0.05% Tween 20) and blocked with 150 µL 5% skim milk powder  at 37 °C for 1 h. The plates were washed as above, and different concentrations of rLysG1 or PBS was added to the wells. The plates were incubated at 37 °C for 1 h and washed as above. The HRP-conjugated mouse anti-His antibody  (1/1000 dilution) was added to the plates. The plates were incubated at 37 °C for 1 h and washed as above. Color development was performed using TMB substrate solution  and terminated by adding ELISA stop solution . Finally the absorbance at 450 nm was measured. To examine binding of rLysG1 to bacteria by microscopy, E. coli and P. hodoensiswas were incubated with rLysG1 or rTrx for 1 h at 37 °C and washed 5 times with PBS. Then the cells were successively treated with the mouse anti-His antibody  (1/1000 dilution) and FITC-labeled goat anti-mouse secondary antibody  (1/1000 dilution) for 1 h at 37 °C. After washing 5 times with PBS, the cells were observed with a fluorescence microscope . Bacterial cells were placed into a 96-well microtiter plate containing coating buffer (1.59 g NaE. coli and P. hodoensiswas were cultured to OD600 of 1.0 and resuspended in PBS to 5× 108 CFU/mL. The cells were incubated with rLysG1 or rTrx for 2 h at 37 °C and washed 5 times with PBS. The cells were stained with propidium iodide (50 μg/mL) for 5 min and washed as above. The cells were then observed with a fluorescence microscope as above. For electron microscopic examination of bacterial structural change caused by rLysG1, E. coli and P. hodoensiswas were incubated with rLysG1 or rTrx and washed with PBS as above. The cells were fixed and processed as reported previously [eviously . The sam2.RAW264.7 cells were cultured in DMEM  supplemented with 10% fetal bovine serum  and 1% penicillin and streptomycin  at 37 °C and 5% COTo examine the effect of rLysG1 on LPS-induced inflammatory gene expression, LPS (1 μg/mL)  was incubated with or without rLysG1  for 1 h at 37 °C. RAW264.7 cells were incubated with rLysG1-treated LPS or untreated LPS for 24 h at 37 °C. The expression of TNF-α, IL-6 and IL-1β was determined by qRT-PCR as reported previously .In Summary, we identified and investigated the activity of a g-type lysozyme from deep-sea shrimp. rLysG1 killed selectively Gram-negative bacteria in a non-enzymatic mode by damaging bacterial membrane. The bactericidal activity of rLysG1 was regulated by multiple factors and required the integrity of the α3 and 4 helixes of the protein. The results of our study provide insights into the characteristics and function of g-type lysozyme in crustacean and deep-sea organisms.


The lack of new drugs for Gram-negative pathogens is a global threat to modern medicine. The complexity of their cell envelope, with an additional outer membrane, hinders internal accumulation and thus, the access of molecules to their targets. Our limited understanding of the molecular basis for compound influx and efflux from these pathogens is a major bottleneck for the discovery of effective antibacterial compounds. Here we analyse the correlation between the whole-cell compound accumulation of ~200 molecules and their predicted porin permeability coefficient (influx), using a recently developed scoring function. We found a strong linear relationship (74%) between the two, confirming porins key in compound uptake in Gram-negative bacteria. The analysis of this unique dataset aids to better understand the molecular descriptors behind whole-cell accumulation and molecular uptake in Gram-negative bacteria. From the discovery of the first antibiotics in the 1930s to the present time, antibiotics have been a significant relief for the global health burden caused by pathogenic bacteria around the globe ,3. The mDespite the advances in genomics and sequencing, high-throughput screening (HTS), automated chemical synthesis, and structural biology, no new classes of antibiotics against Gram-negative species ,7,8,9,10Enterobacteriaceae (>105 copies/cell) and thus they play a key role in compound permeation [The OM creates an additional physical/mechanical barrier that protects the cell against external agents ,14,15. Armeation ,18. Molermeation ,20,21. Mrmeation ; while srmeation ,23. CellEnterobacteriaceae [E. coli. Nevertheless, from the 68 primary amines presents in the dataset only 36 of them accumulate in E. coli. Using a chemoinformatic approach the authors calculated 297 molecular descriptors to train a random forest classification algorithm to predict accumulation. From this analysis, the shape and flexibility of the molecule emerged as a determinant for molecules with a primary amine to overcome the OM and accumulate in E. coli [Molecular uptake in Gram-negative bacteria can be a one-step or two-step process, depending on the location of the targets . Such a eriaceae  and conderiaceae . With a  E. coli , summari E. coli .E. coli, have been combined with deep neural networks to find molecules with bactericidal activity against a wide phylogenetic spectrum of pathogens [P. aeruginosa was better classified by electrostatic and surface area properties, whereas topology, physical properties, and atom or bond counts capture best the behavior in E. coli [E. coli and P. aeruginosa are different, the former has different general porins that allow the passive diffusion of polar molecules while the latter is known for only having specific channels that are narrower and which exhibit specific motifs for substrate recognition [Other microbiological assays, such as bacterial growth inhibition in athogens , but no  E. coli . This isognition .E. coli, we considered here the above database for which accumulation data are available [With the aim to find the molecular determinants for predicting intracellular accumulation in vailable  and we cE. coli: bad accumulators (<250 nmol per 1012 CFUs), good accumulators (>550 nmol per 1012 CFUs), and excellent accumulators (>1000 nmol per 1012 CFUs). Molecules that are electrostatically neutral (light blue) or negatively charged  are bad accumulators in E. coli  are not always good accumulators, as it can be clearly observed in the number of data points falling into the bad accumulators category . Both the total dipole moment and the transversal dipole moment , it is very rigid and with low transversal dipole moment (6.4 Debye).In The other three molecular descriptors show wide distributions for compounds exhibiting the same permeability level. The permeability coefficient depends on the delicate balance among the pore-molecule size distributions   in E. coli [To estimate the importance of permeation via porins in compound accumulation, we evaluated the correlation between the experimental whole-cell accumulation data and the predicted compound permeability coefficient calculated with the scoring function . Using a E. coli , the abiE. coli (bad accumulators) and with a predicted low permeability through OmpF (2 ± 1.8 Å2): it is big and rigid and its translocation through porins is unlikely. Molecules with high predicted permeability but negligible experimental accumulation are small , polar , and positively charged , two main conclusions can be stated: (i) the transversal dipole moment of excellent accumulators is always larger than 10 Debye  gmulation , moleculmulation . Howevermulation ,37 to efp values were calculated using the alogps 2.1 server [All compounds used in this work were obtained from the Supporting Information of Richter et al. . We extr1 server .We calculated the permeability coefficient of each compound in the dataset through OmpF using a previously published scoring function :(1)POmpFd porins ,32; Qmolnsidered ,25,33,44mall MPA . Further12 CFUs. Ampicillin is rapidly covalently appended to penicillin-binding proteins, preventing measurement by LC-MS/MS, and therefore all molecules with experimental accumulation lower than 45 nmol per 1012 CFUs were discarded  the correlation between the permeability coefficient through porins and experimental accumulation (0.74) highlights general porins as the main permeation pathway to cross the OM. (ii) Good/excellent accumulators are polar and have a high permeability coefficient through porins. (iii) Apart from being positively charged (net charge), the charge distribution of excellent accumulators maximizes the transversal component of their dipole moment with respect to its main axis of inertia. Therefore, the only way to operate on permeation to obtain good accumulation is to optimize the position of charged groups in the molecular scaffold. (iv) Small compounds are predicted to have good/excellent permeation, but excellent accumulators are bigger than good accumulators.


TERT Promoter (92%), PTEN (66%), and TP53 (60%). Of the 19 common genes in gliosarcoma, 6 were also common in both GBM and soft tissue sarcoma, 4 in GBM alone, 0 in soft tissue sarcoma alone, and 9 were more distinct to gliosarcoma. Of these, BRAF harbored an OncoKB level 1 designation, indicating its status as a predictive biomarker of response to an FDA-approved drug in certain cancers. EGFR, CDKN2A, NF1, and PTEN harbored level 4 designations in solid tumors, indicating biological evidence of these biomarkers predicting a drug-response. Gliosarcoma contains molecular features that overlap GBM and soft tissue sarcoma, as well as its own distinct genomic signatures. This may play a role in disease classification and inclusion criteria for clinical trials. Gliosarcoma mutations with potential therapeutic indications include BRAF, EGFR, CDKN2A, NF1, and PTEN.Gliosarcoma is an aggressive brain tumor with histologic features of glioblastoma (GBM) and soft tissue sarcoma. Despite its poor prognosis, its rarity has precluded analysis of its underlying biology. We used a multi-center database to characterize the genomic landscape of gliosarcoma. Sequencing data was obtained from 35 gliosarcoma patients from Genomics Evidence Neoplasia Information Exchange (GENIE) 5.0, a database curated by the American Association of Cancer Research (AACR). We analyzed genomic alterations in gliosarcomas and compared them to GBM  and soft tissue sarcoma . 30 samples were included . Nineteen common genes were identified in gliosarcoma, defined as those altered in > 5% of samples, including  Despite aggressive treatment consisting of surgical resection, radiation, and chemotherapy, survival for gliosarcoma remains poor with a median survival of 9 months compared to a median 15-month survival for other forms of GBM1. It is essential to accurately distinguish gliosarcoma from GBM in order to better inform patient prognosis with this distinct malignancy, as well as to allow for focused translational and clinical investigation into therapies that will improve outcomes for gliosarcoma specifically.Gliosarcoma is a rare tumor histologically characterized by both glial and sarcomatous features7. Gliosarcoma can be divided into primary and secondary gliosarcoma, with secondary subtypes thought of arising from previously treated GBM8. Primary gliosarcoma is historically perceived as a rare subtype of malignant glioma with similarly poor prognosis to glioblastoma. The rarity of this disease, however, has made it difficult to unequivocally define distinguishing features.Gliosarcoma presents a diagnostic challenge due to the lack of clear guidelines for clinicians and dedicated scientific focus on gliosarcoma as a separate entity from GBM; it is not unusual for clinical trials to group GBM and gliosarcoma together in patient inclusion criteria without subsequent stratification in analysisIn this study, we use a genomic database from an international consortium to characterize molecular alterations in gliosarcoma, GBM, and soft tissue sarcomas to better delineate shared and distinguishing features between these tumors. To help guide therapeutic strategies, we also highlight genetic alterations in gliosarcoma that are currently targeted with existing therapies for other indications.9. Patient age in the cohort is captured as "< 18″ or the exact age when > 18. For two patients with two tumor samples each, the sample with a later date of collection was excluded, as well as two others labeled as local recurrence or metastasis. Three primary gliosarcoma cases which shared a patient identification number with GBM tumor samples were also excluded. In total, 30 tumor samples from 30 distinct patients remained for analysis, derived from Memorial Sloan Kettering Cancer Center , MD Anderson , Dana-Farber Cancer Institute , and Johns Hopkins University  (Supplementary Table We identified 37 samples from 35 patients annotated as gliosarcoma from the American Association of Cancer Research (AACR) Genomics Evidence Neoplasia Information Exchange (GENIE) databaseWe compared these thirty gliosarcoma samples to the genomic profile of GBM (n = 1449) and soft tissue sarcoma (n = 1042) in GENIE. As with the gliosarcoma cohort, we excluded samples which were not primary tumors and limited our analysis to one tumor per patient, selecting the earlier specimen in cases of multiple samples belonging to a single patient.Institutions contributing samples to the present study  reported minimum depths of sequencing coverage of 750 ×, 250 ×, 500 ×, and 200 ×, respectively; with an average tumor sequencing coverage depth of 200 × to 4000 × and average depth of variant coverage of 10 × to 500 × . Individual variant-calling parameters were employed by each institution: MSK sample alterations were detected from matched tumor-normal sequence data, with sequence mutations reported for > 5% allele frequency for novel variants or > 2% allele frequency for recurrent hotspots. MDA sequence variant identification also incorporated germline variant subtraction, with variant filters of > 5% allele frequency, minimum variant coverage > 25, and absence of the variant in paired germline DNA. JHU specimens had multiple variant filters including a total read depth filter ≥ 100, variant allele coverage ≥ 10, variant allele frequency for substitutions ≥ 0.05, and variants seen in greater than 20% of a set of non-neoplastic control tissues were excluded. DFCI variant identification similarly used a panel of historical normal samples to filter putative germline variants.9. Genes that were altered in greater than 5% of assayed samples for each tumor type, with a minimum a genetic alteration in > 2 samples, were considered common alterations for the purpose of this study.An average of 247 genes were assayed per sample, with some degree of variability in the total and specific genes assayed at each institution  an FDA-recognized biomarker predictive of response to an FDA-approved drug for a specific indication; (2) standard care biomarker recommended by an oncology expert panel predictive of response to an FDA-approved drug for a specific indication; (3) clinical evidence supports the biomarker being predictive of response to an investigational drug in a specific indication or an FDA-approved drug in another indication; and (4) biological evidence supports the biomarker being predictive of response to a drug11.Fisher’s Exact Test was used to compare the frequency of altered genes between gliosarcoma, GBM, and soft tissue sarcoma. We then performed a Benjaminin-Hochberg  correction for multiple hypothesis, with a q-value cutoff of 0.1. All analyses were performed using the R programming language, version 3.6.0TERT Promoter (92%), PTEN (66%), TP53 (60%), and NF1 (41%),  harbored copy number alterations, and two (EGFR and CREBBP) contained both mutations and copy number alterations with nearly equal frequency  and a slight predilection for male patients (63%)  were indicated as potentially targetable genes present in the OncoKB database . Another four targetable alterations  were classified as level 4 for solid tumors, indicating the existence of compelling biological evidence for predictive value of response to an existing drug.We assessed the potential clinical impact of observed mutations by scoring them based on the four categories of targetability defined by OncoKB, a precision oncology database collating data on the treatment implications of specific cancer gene alterationsTo help delineate unique and shared features of gliosarcoma compared to other tumors, we next analyzed genes that were altered in greater than 5% of GBM n = 1449) and soft tissue sarcoma n = 1042) samples in GENIE , but none maintained significance when corrected for multiple hypothesis testing   mutation type and frequency contrast gliosarcoma from GBM. EGFR is thought to be a key oncogenic driver in GBM, amplified in 35–45% of IDH wildtype glioblastomas24. Investigation of EGFR-targeted therapies for GBM has been robust and diverse, including anti-EGFR antibody-loaded nanoparticles, anti-EGFRvIII CAR-T therapy, antibody drug conjugates like depatuxizumab mafodotin, and clinical trials investigating monoclonal antibodies or tyrosine kinase inhibitors against EGFR like erlotinib and lapatinib29. According to OncoKB, lapatinib has a level 4 indication for glioma, suggesting biological evidence for potential success as a targeted therapy. In gliosarcoma, no independent trial with EGFR targeted agents has been published to our knowledge. In a comprehensive whole-genome copy number analysis of gliosarcoma, a study found EGFR amplification was uncommon, but found frequent gains of chromosome 7, which contains the EGFR locus, among other genes including CDK6, PDGF-A, and c-Met22. It is unclear if the EGFR pathway is indirectly activated in gliosarcoma through other mutations. Therapies targeting EGFR mutations are unlikely to be important treatment options in gliosarcoma due to low frequency of genetic alterations.In our study, we found TP53 mutations in gliosarcoma to be correlated with worse prognosis, treatment resistance, and epithelial to mesenchymal transition of the sarcomatous cell population, making it a prospective marker for prognostic categorization1. Other mutations seen in high frequency in this particular analysis of gliosarcoma include TERT promoter, CDK2NB, RB1, and STAG2.Interestingly, one study found PTEN, NF1, and CDKN2A belonging generally to the Ras/PI3K/AKT pathway. Overall, these mutations remain of unclear clinical and prognostic relevance, though present an interesting avenue for further development as markers of prognosis or tumor-specific therapy.Other genes, elicited in our analysis, showing potential opportunities for investigation include PTEN had an alteration frequency of 67% among gliosarcoma samples. PTEN alterations have previously been detected in 26% of HGG in TCGA data, and identified in 45% (9/20) of previously investigated gliosarcomas31. Loss of PTEN function through deletion, mutation or down regulation has been found to potentially enrich sensitivity to small molecule inhibitors of PI3Kβ such as AZD8186 and GSK2636771, as studied in cell line panels and early clinical trials of patients with PTEN-deficient advanced solid tumors including GBM, respectively33.In our study, NF1 in 41% of gliosarcomas via damaging mutations and copy number losses. Loss of NF1 function enhances RAS activity, inducing RAS/RAF/MEK/ERK pathway activation. Single-agent MEK inhibitors (PD0325901 and AZD6244) have been shown to be effective against a subset of NF1–deficient GBM cells dependent on RAF/MEK/ERK signaling34.We further observed alterations in CDKN2A had an alteration frequency of 31% among the gliosarcoma samples, specifically representing copy number losses and an inactivating mutation. CDNK2A loss is common in 35–60% of GBMs and independently associated with worse overall and progression-free survival in both molecularly and histologically defined IDH-wildtype GBM37 In a microarray study of gliosarcomas, homozygous loss of CDKN2A was previously detected in 14 of 18 gliosarcoma specimens studied38. Given its significance as a poor prognostic marker in GBM, further investigation into the implications of CDKN2A loss in gliosarcoma is warranted.We found 39. Several studies suggest that these populations may be monoclonal in origin given their similar expression of early known glial mutations including p5339. A greater fraction of the sarcomatous component in comparison to a predominantly glial component has also been associated with survival benefit (71 vs 63 weeks) in small case series40. Intratumoral heterogeneity between these distinct cellular subpopulations in gliosarcoma harbingers acquired resistance to a single targeted therapy, as suggested in other glial tumors41. This provides another opportunity for further study to characterize the potential heterogeneous composition of this tumor.Our findings demonstrate a noteworthy distinction between gliosarcoma and GBM, as well as illuminate the potential for robust genomic and histologic analysis of this unique tumor type for prognostication and therapeutics. Histologically, gliosarcomas demonstrate intratumoral heterogeneity, notably of a biphasic composition with both glial-like and mesenchymal/sarcomatous cell populationsBRAF, EGFR, PTEN, NF1, and CDKN2A are potentially targetable according to OncoKB. Limitations of this study include the finite data set collected and presented within the GENIE database used for our analysis. Specifically, no clinical data was available to correlate genomic alterations with clinical outcomes in this study, nor were further details on histologic characterization of these tumors available. Additionally, institutions reporting to the GENIE database utilize differing parameters in their genetic assays, including in the average depth of coverage and quality parameter thresholds for variant reporting. This heterogeneity within the database itself limits an ideal level of standardization in our determination of significant variants.With a better understanding of the unique genomic landscape of gliosarcoma, scientific focus may be given to developing gliosarcoma-specific therapeutics. Among the most frequently altered genes found in gliosarcoma, While gliosarcoma is categorized by the WHO as a variant of glioblastoma and often grouped with GBM in clinical trials, this study suggests that gliosarcoma has a genomic landscape distinct from GBM and soft tissue sarcoma. These differences should influence disease classification as well as guide targeted therapy for this aggressive tumor.Supplementary Information.


Kaposi’s sarcoma (KS) was peculiarly described in the first notified cases of the acquired immunodeficiency syndrome as an opportunistic condition. However, the medical progress and the development of active antiretroviral therapy allowed the control of the HIV/AIDS epidemic, although the features of KS have changed throughout the past decades. The purpose of our study is to assess the epidemiological and clinical features of AIDS related KS in Romanian patients. A retrospective follow-up study was achieved in a single infectious diseases’ clinic from Galati—Romania, between 2001 and 2021. Referring to 290 new HIV diagnosed cases from our clinic retained in care, the prevalence of KS was 3.4%. The main characteristics of patients with KS are a median age of 33, a predominance of males, prevalent severe systemic forms of diseases, frequent association of past or concomitant tuberculosis, and context of immune reconstruction syndrome. The mortality rate was 70%. KS has occurred in patients with delayed HIV diagnoses and inadequate adherence to therapy. Early recognition of both infections, the close monitoring of latent or symptomatic tuberculosis, improving the antiretroviral adherence and raising the access to oncologic procedures in Romanian HIV patients could improve their prognosis related to KS. Moritz Kaposi has described angioproliferative tumours in the elderly since 1872, which have been called Kaposi’s sarcomas (KS). Usually involving the skin, KS in evolution is multifocal, and the lesions can be found in any organ. The main dermatological lesions appear as purplish-red infiltrative plaques or nodules, but lymphangioma-like, paucilesional or solitary forms are also possible appearances, with difficult clinical differentiation from other solitary lesions, telangiectatic, cavernous, ecchymotic, bullous or keloid injuries [Four subtypes of KS are presently recognised: the classical form, found in the Mediterranean region, predominantly affecting the lower limbs, assimilated to the initial description of Kaposi in the elderly people; the endemic form, specific to the children from the African area, with severe evolution, including lymphadenopathic manifestations; the iatrogenic form, occurring in patients post-transplant or other patients receiving immunosuppressive treatment; and the AIDS related KS, usually with generalized, severe, even fatal evolution ,4.KS in young men who had sex with men (MSM) was reported in 1981, among the first defining opportunistic diseases related to acquired immunodeficiency syndrome (AIDS), with an estimated frequency of over 30% .The overall incidence of KS was assessed in a comprehensive meta-analysis, which compared data for different population categories, proving that HIV-positive people have an annual incidence of this neoplasm of 481.54/100,000, significantly higher than people with organ transplants , children with HIV infection  and compared to the general population 1.53/100,000) [/100,000 The epidemiology of AIDS-related KS has changed in the era of HAART, regarding the decrease of the incidence and the changing of clinical features, either appearing like the classic form, or developing in the relationship with inflammatory immune reconstitution syndrome ,8. KS isThe aetiology of KS is multifactorial. The infection with human herpes virus-8 (HHV-8) acts as a co-factor associated with KS, as with other tumour diseases, such as primary effusion lymphoma, KSHV associated diffuse large B-cell lymphoma, multicentric Castleman’s disease, and KSHV inflammatory cytokine syndrome . HHV-8 bSeveral KS staging systems have been proposed, but no consensus has yet been reached. The AIDS Clinical Trials Group (ACTG) uses a prognostic scoring system based on three variables: tumour extension (T), immune status (I) and systemic symptoms (S). In the HAART era, the LCD4 level is no longer considered a prognostic value. Two evolutionary risk categories of KS are identified: unfavourable (T1S1) or favourable  ,14.2 laser therapy and cryosurgery, silver nitrate cauterization, intralesional injections with vincristine, bleomicine, α-interferon, and other topical applications of imiquimod or alitretinoin [A management strategy of KS associated with AIDS is limited to HAART, given that 50% of tumours are potentially regressing under this treatment . Treatmeretinoin . Electroretinoin ,16. Systretinoin . Other rretinoin . By confThe purpose of our study is to assess the epidemiological and clinical features of AIDS related KS in Romanian patients.This is a retrospective statistical analytical study on the epidemiology and clinical characteristics of patients with AIDS related KS.Data were collected after evaluation of the medical records between June 2001 and June 2021, belonging to the patients monitored in HIV/AIDS Daily Clinic from Galati, located in the South-East of Romania. Informed consent to agree to the use of the personal data was signed by each patient. According to their medical condition in the end of the study, the patients were classified as retained in care, lost from care or deaths. The inclusion criteria were to follow up until death or until the end of the study. Patients who were unable to be followed up during the study or were transferred to another site were excluded. We have identified the cases with HIV/AIDS and diagnosis of KS. Demographic data, clinical examination, opportunistic diseases, the dynamic of LCD4 number, the evolution and surviving length were analysed. We included cases of KS in patients with known HIV diagnosis and HAART, and also patients with indicative KS for immunosuppression and new HIV diagnosis, without HAART. According to the guidelines of antiretroviral therapy guidelines, HAART consists of combinations of two nucleoside (-tide) reverse transcriptase inhibitor (NRTI) and a third molecule of the class non-nucleoside reverse transcriptase inhibitor (NNRTI), protease inhibitor (PI) or integrase inhibitor (II). Cases of KS in paradoxical or unmasked immune reconstitution syndrome related to the administration of tuberculosis drugs or antiviral therapy have been identified according to clinical, virological and immunological definition.From July 2001 to June 2021, there were 290 new diagnosed HIV/AIDS patients retained in care. We identified 10 AIDS related KS (P1-P10), meaning a prevalence of 3.4% and an average annual incidence of 0.17% .All diagnoses of KS were confirmed by anatomopathological examinations of cutaneous/mucosal biopsy pieces (4/10), lymph nodes (5/10), gastric sample (1/10) and/or bronchial samples (2/10). Immunohistochemistry (IHC) was available in two women  with severe forms (T1S1), both with evidence of markers for HHV-8 infections . No PCR-Among the significant co-morbidities, co-infections with HBV (4/10), concomitant or past tuberculosis (5/10) and venereal warts (7/10) were noted. According to The AIDS Clinical Trials Group (ACTG) criteria, high severity index of KS was evidenced in 8/10 patients, due to the extent of the lesions , severe immunosuppression (I1: LT-CD4 < 200/mmc) or association of systemic manifestations (S1).In relation to the diagnosis of HIV, KS was the indicator of immunosuppression, diagnostic in four cases . All these cases were “late presenters”, with poor outcomes of KS in three of four cases . The other six cases  were severe forms of KS (T1S1) that occurred after HIV diagnosis, mostly in patients non-adherent to HAART and who had persistent immunosuppression . The interval between HIV and KS diagnosis ranged from 3 months to 18 years.Mycobacterium tuberculosis. After 8 weeks of tuberculosis treatment, KS lesions had spread to the skin, oral, pleural and pulmonary mucosa, and the death occurred during a pneumococcal meningoencephalitis (P8).Five cases of KS were related to IRIS and four of them died. Two patients with KS  experienced paradoxical worsening, explained by an immune reconstitution inflammatory syndrome, developed 3 to 13 weeks after initiation of HAART. A special case of paradoxical worsening of KS was diagnosed in a patient with HIV-HBV-VHD coinfection, and non-adherent to HAART, who was investigated for cervical lymphadenopathy, with bronchoscopy KS lesions  and positive bacteriology for HAART was ongoing for at least 1 year at the time of diagnosis of KS in four patients, but they were non-adherent . The initiation of HAART was decided in the other six patients as an option for the control of KS by suppression of HIV replication, but two cases  developed IRIS. Oncological chemotherapy with liposomal doxorubicin was achieved in only two of the patients with severe prognosis , and one of them survived. Extending lesions led to unfavourable evolution with a mortality rate of 70%.The average annual incidence of KS in the study group was over 400 times higher than the incidence in the general population, reported in the oncology database of Romania .Men predominated, with a ratio of F:M ratio of 1:2.3, in keeping with the most data in the medical literature ,7.3), with a severe form and unfavourable prognosis (T1I1S1), without oncological therapy and fatal evolution  [The results of our evaluation are concordant with a Brazilian study on 39 patients with AIDS related KS, predominantly men, with similar mean ages (36 years vs. 32.5 years) and significant immunosuppression at the time of diagnosis of KS (LCD4 = 95.25/mm2.6/mm3) . Differe2.6/mm3) .Concomitant or past tuberculosis was notified in half of the patients from our study, concordant with other reports on KS and tuberculosis’ significant association ,23,24,25human herpesvirus-8, cytomegalovirus, varicella-zoster virus, JC virus, hepatitis B or C viruses), fungal  or bacterial  [3 in the beginning of HAART, rapidly increasing levels of LCD4, with effective suppression of HIV-RNA after HAART, and early initiation of HAART, under 30 days from the treatment of an opportunistic infection [Highly active antiretroviral therapy (HAART) was the main strategy for the treatment of KS, knowing the potential for remission of lesions with effective control of HIV replication. The use of HAART dramatically improved the life expectancy and quality of life for people living with HIV, but chronic inflammation and early aging associated with persistent viral infections have changed HIV-associated morbidity, by decreasing the opportunistic diseases with advanced immunosuppression and increasing other chronic comorbidities. Additionally, initiation of HAART could be accompanied by a dysfunctional response of the immune system, with incompletely known pathogenesis, such as IRIS. Inflammatory immune reconstitution syndrome is defined by inflammatory disorders developed after initiation of HAART on HIV patients, due to recovering the ability to express an inflammatory response, with clinically paradoxical worsening or unmasking of a pre-existing infection . Pre-exicomplex) ,29. The nfection . An estiThere are limited studies on the reactivation of HHV-8 in IRIS, with unmasking KS ,33. FromCorticosteroids are frequently used in cases of IRIS, but contradictory results on KS are reported, either clinical stabilization or aggravation ,32. FurtDoxorubicin chemotherapy was used in only two patients, with a 1:1 survival/death rate. Increasing access to cancer diagnosis and therapy, using new biological cancer therapies, and personalization, the therapeutic strategies could improve the survival of patients with AIDS-associated KS.The limit of the study is the small number of cases and irrelevant statistical analysis.KS continues to occur in patients with severe immunosuppression, even in the era of highly active antiretroviral therapies, with higher risk on patients with delayed HIV diagnostic or non-adherent patients. Early clinical recognition of KS and support for oncologic diagnostics and treatment are required to improve the prognosis of these patients. Latent or symptomatic tuberculosis is a negative prognostic factor for AIDS related KS and requires close monitoring. In Romania, patients with HIV infections need a support program for the surveillance, diagnosis and treatment of KS, in a multidisciplinary and integrative approach.


Cucurbita Linn. vegetables have a long history of cultivation and have been cultivated all over the world. With the increasing area of saline–alkali soil, Cucurbita Linn. is affected by salt stress, and calmodulin-binding transcription activator (CAMTA) is known for its important biological functions. Although the CAMTA gene family has been identified in several species, there is no comprehensive analysis on Cucurbita species. In this study, we analyzed the genome of Cucurbita maxima and Cucurbita moschata. Five C. moschata calmodulin-binding transcription activators (CmoCAMTAs) and six C. maxima calmodulin-binding transcription activators (CmaCAMTAs) were identified, and they were divided into three subfamilies  based on the sequence identity of amino acids. CAMTAs from the same subfamily usually have similar exon–intron distribution and conserved domains . Chromosome localization analysis showed that CmoCAMTAs and CmaCAMTAs were unevenly distributed across four and five out of 21 chromosomes, respectively. There were a total of three duplicate gene pairs, and all of which had experienced segmental duplication events. The transcriptional profiles of CmoCAMTAs and CmaCAMTAs in roots, stems, leaves, and fruits showed that these CAMTAs have tissue specificity. Cis-acting elements analysis showed that most of CmoCAMTAs and CmaCAMTAs responded to salt stress. By analyzing the transcriptional profiles of CmoCAMTAs and CmaCAMTAs under salt stress, it was shown that both C. moschata and C. maxima shared similarities against salt tolerance and that it is likely to contribute to the development of these species. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) further demonstrated the key role of CmoCAMTAs and CmaCAMTAs under salt stress. This study provided a theoretical basis for studying the function and mechanism of CAMTAs in Cucurbita Linn. The information on the coding sequence and protein sequence of CmoCAMTA and CmaCAMTA is listed in To identify the CAMTAs in uGenDB1) . The proterature , and theCmoCAMTAs and CmaCAMTAs include the theoretical isoelectric point (pI), the length of amino acids (aa), and theoretical molecular weight (MW). All of them were analyzed by ExPASy5. The subcellular locations of CmoCAMTA and CmaCAMTA were predicted by Plant-mPLoc   .C. moschata and C. maxima, the protein sequences of CmoCAMTAs and CmaCAMTAs were used. The conserved motifs were presented on the Multiple Expectation Maximization or Motif Elicitation (MEME7)  , while t (MEME7)  of motifC. moschata and C. maxima, the protein sequences of CmoCAMTAs and CmaCAMTAs were used. The location information of the conserved domain was extracted from Batch Web CD-Search Tool8  was calculated on a Ka/Ks calculator according to Gojo-bori and Nei’s previous method (Ks > 2.0 (T) of the duplicated genes was calculated based on T = Ks/2λ × 10–6 million years ago (Mya), λ = 1.5 × 10–8  were extracted from the Cucurbit genomics database. These sequences were analyzed on the PlantCARE program10 and finally displayed by the Simple BioSequence Viewer in TBtools   . In thisCucurbita Linn. to salt stress, the transcript profiles of CAMTAs in leaf veins and leaf mesophylls were analyzed according to the transcriptome data (BioProject: PRJNA464060)  . “N12” iBaimi 9” from C. moschata and “Beiguan” from C. maxima were used as research materials to analyze the expression of CAMTAs under salt stress. The seeds were provided by the pumpkin team of the School of Horticulture and Landscape Architecture, Henan Institute of Science and Technology. The seeds were first sown in a tray with a matrix meteorite (3:1) mixture and then placed in a plant growth chamber for cultivation. The artificial growth conditions were set as follows: daytime temperature of 25°C, 16 h of light, light intensity of 350 μmol/m2/s, night temperature of 16°C, and a relative humidity of 65%. When the seedlings have grown to 2 months, healthy and neat seedlings were selected and cultured in Hoagland’s solution at pH 6.5. After 5 days of adaptation, 50 healthy and consistent seedlings of each variety were selected for NaCl treatment (the concentration of NaCl was 75 mM), and the remaining 50 seedlings were used as control. The veins and mesophyll were collected after 12 h of salt treatment, and each treatment had three independent biological replicates, and 10 seedlings per biological replicate were selected for mixed sampling. All samples were frozen in liquid nitrogen and stored at −70°C for quantitative real-time PCR (qRT-PCR) analysis.In this study, “@ reagent (Omega) and reverse-transcribed into cDNA with PrimeScriptTM RT Master Mix (TaKaRa) after DNase treatment, which was used as a template for qRT-PCR determination. The primers of CmoCAMTAs, CmaCAMTAs, and internal reference gene (β-actin) were first designed on Prime premier 6.0 and then blasted in the Cucurbit genomics database to verify the specificity of primers . The melting curve was 95°C for 15 s, 60°C for 60 s, and 95°C for 15 s. Each sample was performed with three technical replicates, and the data were analyzed with the 2Ct–ΔΔ method , these genes were named CmoCAMTA1-CmoCAMTA5 and CmaCAMTA1-CmaCAMTA6, respectively.Through the BLASTP program of six AtCAMTA proteins in the CmoCAMTAs ranged from 2,745 (CmoCAMTA4) to 3,270 bp (CmoCAMTA5), and the corresponding translated amino acids ranged from 914 to 1,089 aa (pI) were 103.36 (CmoCAMTA4) to 121.38 kDa (CmoCAMTA5) and 5.69 (CmoCAMTA5) to 7.7 (CmoCAMTA2), respectively. Similarly, the coding region of CmaCAMTAs ranged from 2,748 (CmaCAMTA5) to 3,273 bp (CmaCAMTA6), and the corresponding translated amino acids ranged from 915 to 1,090 aa (pI were 103.44 (CmaCAMTA5) to 121.64 kDa (CmaCAMTA3) and 5.66 (CmaCAMTA3) to 7.71 (CmaCAMTA5), respectively. Both CmoCAMTAs and CmaCAMTAs revealed diversity in the coding region, amino acid sequence, and MW and had low isoelectric points. Subcellular localization prediction analysis showed that all CmoCAMTAs and CmaCAMTAs were localized to the nucleus.The physical and chemical characteristics of five CmoCAMTAs and six CmaCAMTAs are listed in 1,089 aa . Their t1,090 aa . Their tA. thaliana CAMTA , it showed that all CAMTAs contained 12–14 exons  and five  out of 21 chromosomes, respectively (Cucurbita Linn. Ka/Ks indicated that the duplicated gene pairs had diverged at 8.62–9.64 million years ago (Mya) (To predict the location of ectively . In addigo (Mya) .cis-acting elements are specific motifs that exist in the promoter region of a gene sequence that regulates gene transcription. For instance, TGA-element, G-box, TGACG-motif, ABRE, GT1-motif, and MBS were all related to salt stress  TGACG-motif  ABRE compared with other gene promoters. At the same time, CmoCAMTA4, CmaCAMTA5, CmaCAMTA2, and CmoCAMTA2 contain a higher number of (four to six) G-box than other genes , the expression of these genes was similar to that in C. moschata (“N15”)  . Overallcultivar , so it wCmoCAMTAs and CmaCAMTAs in leaf vein and leaf mesophyll under salt stress, we treated C. moschata “Baimi 9” and C. maxima “Beiguan” with a NaCl solution. After 12 h of salt stress, phenotypic observation showed no significant difference between the saline-treated seedlings and the control. However, in the leaf vein of “Baimi 9,” the results showed that after 12 h of NaCl treatment, the relative expression profiles of CmoCAMTA1, CmoCAMTA2, CmoCAMTA4, and CmoCAMTA5 in treated samples were 1.27–1.9 times that of the control samples  in Cucurbita Linn. were identified using bioinformatics tools.So far, six thaliana , six CAMarboreum , seven Caimondii , nine CA Z. mays , 15 CAMTm G. max , eight Cgaris L. , five CAcuminata , seven Cpersicum , and eighinensis  have beeThe identified CmoCAMTA and CmaCAMTA proteins ranged from 914 to 1,089 aa, which was similar to the CAMTA proteins from other different plant species . Except CAMTA gene families in Cucurbita Linn., a total of five CmoCAMTA, six CmaCAMTA, and six AtCAMTA proteins were analyzed. The phylogeny tree showed that these genes were assigned to three subfamilies   , which w protein . The phythaliana . Based oCAMTAs in C. moschata and C. maxima; the exon number of CAMTAs in Cucurbita Linn. was between 12 and 14, which was similar to the exon number of CAMTAs in G. max, and Z. mays  genes responded significantly under NaCl treatment  of Cucurbita Linn. leaves under salt stress  can be found in the article/Cucurbitaceae CAMTAs and analyzed the gene structure. CS and XL studied chromosome distribution and gene duplication of Cucurbitaceae CAMTAs. JY supervised the research. CS, AS, and JY revised the manuscript. All authors have read and approved the manuscript.CS conceived, designed, and analyzed the data. CS and JY wrote the manuscript. BC and XL identified The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Direct Information (DI) [Mutual Information have been applied and are particularly useful for the case of molecular complexes and interactions [Historically, information theory has been closely interconnected with evolutionary theory. The work of Ronald Fisher in population genetics  and the ion (DI) ,4 used iractions ,6.L. Particularly, these studies are centered under the premise that a preprocessing step for multiple sequence alignment analysis might reduce phylogenetic bias and could improve the inference procedure. These methods, ultimately, improve prediction of amino acid contacts and functional connections among amino acid sites.This special issue focuses on important aspects of the study of molecular evolution through the statistical features of sequence data, molecular simulation, and evolutionary convergence towards specificity in signaling networks. Three articles ,8,9 inveIn , HockenbA second set of articles in this issue ,11,12 deIn a final study, Sinner et al.  construcEntropy will help advance the study of molecular evolutionary dynamics through the lens of information theoretical metrics and a combination of structural modeling and molecular dynamics simulations.All together, the methodological contributions presented in this issue of