Accidents of various types in the construction of hydropower engineering projects occur frequently, which leads to significant numbers of casualties and economic losses. Identifying and eliminating near misses are a significant means of preventing accidents. Mining near-miss data can provide valuable information on how to mitigate and control hazards. However, most of the data generated in the construction of hydropower engineering projects are semi-structured text data without unified standard expression, so data association analysis is time-consuming and labor-intensive. Thus, an artificial intelligence (AI) automatic classification method based on a convolutional neural network (CNN) is adopted to obtain structured data on near-miss locations and near-miss types from safety records. The apriori algorithm is used to further mine the associations between “locations” and “types” by scanning structured data. The association results are visualized using a network diagram. A Sankey diagram is used to reveal the information flow of near-miss specific objects using the “location ⟶ type” strong association rule. The proposed method combines text classification, association rules, and the Sankey diagrams and provides a novel approach for mining semi-structured text. Moreover, the method is proven to be useful and efficient for exploring near-miss distribution laws in hydropower engineering construction to reduce the possibility of accidents and efficiently improve the safety level of hydropower engineering construction sites. Construction is a high-risk industry, and until recently, construction sites have continued to pose a serious threat to workers' lives and health . In partNear misses have been defined as a dangerous state in production that may lead to accidents, such as the unsafe behavior of people, an unsafe state of things, unsafe factors in the environment, and defects in management . In partWith increasing attention to safety issues in the hydropower industry, the frequency of safety inspections has increased rapidly, and numerous near-miss text data have been accumulated. However, text data are both semi-structured and unstructured; accordingly, traditional methods of mining text data are time-consuming and labor-intensive. With the development of artificial intelligence (AI) technology, automatic mining and analysis of near misses will inevitably replace relying on manual work to structure text data and find near-miss laws . Thus, iIn the field of construction, text mining application research mainly adopts text classification methods to classify housing construction accidents, subway construction near misses, and construction contract documents. Shallow machine-learning algorithms, such as the support vector machine (SVM), naive Bayes (NB), and K-nearest neighbor (KNN) algorithms, have been used to classify housing construction accidents  and consIn contrast, deep learning algorithms  , BidirecPrevious text information expression and big data mining technology have laid a very important foundation for intelligent analysis of text information. All kinds of text intelligent analysis technology have been widely used in housing construction, subways, and so on. However, there are few studies on the intelligent analysis of big data in the field of safety knowledge in hydropower engineering construction addressed in this study. Although the core algorithm of text big data analysis has not changed much, due to the unique characteristics of safety knowledge in hydropower engineering construction, the data analysis framework that focuses on hidden trouble needs to be reformulated. The main reason is that hydropower engineering construction involves a wide range of engineering types, and there are huge differences between different engineering types, leading to the necessity of reexploring the distribution of near misses in this kind of engineering. Moreover, these studies only classified the text without discussing how to further mine the more detailed construction knowledge contained in the classified text. Safety managers cannot intuitively and quickly acquire near-miss knowledge due to the poor visualization effect of near-miss distributions.Against this contextual backdrop, we develop a near-miss classifier based on a CNN, associate the classified results, and visualize them with a network diagram  and a SaA classifier based on deep learning and a CNN combined with the apriori algorithm and a Sankey diagram can automatically classify text and associate the “location” and “type” of the classification results. Consequently, safety management personnel can implement corresponding near-miss measures for specific near-miss locations, eliminate near misses in advance, and improve the safety level of hydropower project construction.Hydropower engineering construction has the characteristics of a complex construction environment, including a wide range of cross-work and high labor intensity. Moreover, it has a low level of safety management and, more generally, a lack of safety supervision and personnel training . AccidenNatural language processing (NLP) is a technology in which a computer is used to process and analyze human language, including text classification, information extraction, and information retrieval . Text clMachine learning is a popular method to realize text classification . For insDeep learning (DL) can learn complex functions and extract higher-dimensional features from input data. The DL method has been identified as an appropriate method to automatically extract features for text classification without manually creating features . CompareCNNs have been applied in NLP and have achieved good results in semantic processing , sentencDo  proposedAssociation rules contain the rules of occurrence between things. It is imperative for people to understand detailed information about the research object. Agrawal  proposedAssociation rule mining has been widely used in construction safety fields. Cheng et al.  used assUsing the apriori algorithm for data mining, these researchers obtained valuable association rules that are difficult to find by subjective experience. The algorithm involves counting the number of terms. For unstructured text, items that have the same meaning but different expressions are considered different when they are counted. Therefore, items with the same meaning need to be classified into the corresponding preset categories to obtain structured text. Finally, the number of items in each category is counted as the operation data of the apriori algorithm. However, the association rule algorithm is only applicable to mining structured data, it is necessary to carry out structured data tasks to mine unstructured text, and text classification plays such a role. Since the near misses of hydropower projects are recorded artificially, they are random and nonstandard, and all belong to unstructured texts. To mine the association rules of near misses of unstructured texts, it is necessary to obtain structured texts that are easy to calculate by classifying near misses.A Sankey diagram is a data flow diagram that shows the flow of information among multiple attributes . The SanThe data preparation section is divided into 4 steps: (1) collecting near-miss data from the Crane Beach Hydropower Station projects and storing them in the database, (2) cleaning up noncompliance data and obtaining word segmentation, (3) labeling the training data, and (4) assigning the labeled dataset for training the model. This process is shown in The 32,370 safety records of the Crane Beach Hydropower Station from 2015 to 2020 were taken as the data source. The 24,325 collected semi-structured records were uploaded by the site construction operator through WeChat-based near-miss check software. The 8045 paper unstructured records were collected from the safety management personnel at the construction site and manually entered into the database. Some examples of raw data are shown in Each near-miss record includes its check date, near-miss description, and near-miss location. In the near-miss records, “description” and “location” belong to semi-structured data, which are characterized by lengthy sentences and inconsistent expressions. The fields of a semi-structured record are related to each other, but the data stored in the fields are unstructured text. The “description” contains the information of the type of near misses, and the “locations” contain the information of the near-miss places. However, the information is unstructured text and cannot be associated with the association rule algorithm. To automatically find the association rules between the near-miss type and the near-miss location, these two fields need to be transformed into structured text. The CNN DL algorithm is used to transform these two fields into structured data, which are 11 near-miss types and 35 near-miss locations.Empty items, numbers, and punctuations such as “3#,” “/,” “,” and “6–2” in a sentence are considered noise, and regular expressions (REs) are used in Python to remove the noise. In particular, “3#” describes the location information of hydropower projects in a more specific way. In different # hidden trouble locations, the impact on hidden trouble types can be ignored, so 3# is not considered.Jieba   CNN classification: the “type” classifier and “location” classifier are obtained by training the tag dataset. (2) Association analysis: the trained classifier classifies the “type” and “location” of new near misses to generate structured data of “type” and “location” for statistical analysis. An association rule network diagram is created to visualize the mining results. (3) Sankey diagram: the Sankey diagram adds detailed rules to the near-miss association rules. The specific steps are shown in The CNN is a supervised learning method in DL. The weight sharing of a convolutional layer in a CNN can reduce the number of trainable parameters in the network and the complexity of the network model. A text classification method based on CNN can learn complex functions and related features from a given text without the need to select effective features through tedious manual text analysis. This can greatly save on labor and time . With thThe context information of each word in the near-miss text is crucial for the CNN model to capture the near-miss features. By introducing word2vec to the input layer, the near-miss text is transformed into a word embedded with a specific numeric expression containing the relationship between words in a near-miss text. This serves as the input layer of the CNN model. In the convolutional layer, the feature mapping of near-miss text is learned in parallel using different sizes of convolution kernels. A fixed-length near-miss feature mapping is acquired by performing the max pool operation at the pooling layer. The final near-miss classification task is handled by the full connection layer. This is equivalent to classifying the features extracted by the convolution layer and pooling. The model structure is shown in Vdescription=8001 and Vlocation=1919.To make full use of the word characteristics, 19,143 “description” and 18,010 “location” instances in the dataset are divided into multiple words separated by line breaks with Jieba. Different words in the “description” and “location” datasets constitute the “description” word vector space and “location” word vector space, respectively. The numbers of words in the two word vector spaces are The text dataset of hydropower project construction near misses has the characteristics of a large word space, short sentences, and high frequency of professional vocabulary . To bettx1,  x2, ..., xC} to predict the central word Wi, where C is the window value (set to 5), Wi is the i word in word vector space, and {x1,  x2, ..., xC} is the one-hot coding . The model calculation is divided into two processes: forward propagation and back propagation.The main idea of the CBOW model is to use context words {(1) Forward propagation.H, which is a 1 × N-dimensional vector. N is the dimension of each word vector. The value is set to 100. The calculation formula is described as follows:Step 1: Calculate the hidden layer W0 is a V × N-dimensional matrix that connects the input vector and hidden layer, and V is the size of the word vector space. In the figure, the value is set to 6.where Y of size 1 × V. Y (the word vector for “bottle” in this image) is a distributed representation of the predicted central word. To facilitate the calculation of errors during back propagation, the softmax function is used to normalize H × W1. The calculation formula is described as follows:Step 2: Calculate the output vector W1 is the weight matrix with a size of N × V to connect the hidden layer and the output layer.where (2) Back Propagation. The back propagation error is calculated according to Y of the center word and the one-hot encoding vector of this word. The values of W0 and W1 are continuously adjusted using the gradient descent method. During the training, each word is used as a central word; that is, W0 and W1 are modified V times. After the training, the one-hot coding vector of each word is computed in steps 1 and 2 and united with the trained W0 and W1 to accomplish the word vector of all words in the entire dataset.k of the word embedding, the convolution kernel slides in only one dimension. We illustrate the process of convolution in h is set to 4. The process is divided into three steps. Step 1: the 4 × 4 matrix X1:4 corresponding to “氧气(oxygen)”/“乙炔(acetylene)”/“瓶(bottle)”/“无(no)” and convolution kernel W are substituted into formula C1.In the NLP domain, since the width of the convolution kernel is generally equal to the dimension X1:4 with X2:5 corresponding to “乙炔(acetylene)”/“瓶 (bottle)”/“无 (no)”/“安全 (safe).” Step 3: according to the first and second steps, an iterative operation is performed to obtain the feature mapping matrix C: 3 × 4. The calculation formula is described as follows:w is the convolution kernel matrix representing the shared weight, and xi:i+h−1 is the connection matrix of the word embedding from the i word of a “description” or “location” to the i+h − 1 word. b is an offset term.  f is a nonlinear function, and in this study,  f is set to a rectified linear unit (ReLU). Step 2: due to the sliding step s=1, the window slides down one slot. We perform the same calculation by replacing Pi. Pi is the maximum value of the vector by the i convolution operation, and m is the number of convolution kernels.To represent richer features, the convolution kernel is set to different windows, and the same convolution kernel will run parallel operations . TherefoP={P1, P2,…, Pm} to calculate Y={Y1,…, Yt}. In this study, t is tdescription (the number of “descriptions” tags) and tlocation (the number of “locations” tags). After vector Y passes through the softmax layer, the probability distributions L={L1, L2,…, L3} of different labels are acquired by normalization calculations. Dropout technology is adopted in the fully connected layer to prevent hidden layer neurons from self-adapting and to reduce overfitting . The weiAccording to the hyperparameter settings of CNN text classification in existing studies and through multiple comparison tests, the hyperparameters of this study are determined as shown in F1 score are used to evaluate the performance of the DL classification model. Formulas  defi defiF1 srecision .(4)accurD is a set of all “types” and “locations.” If there is an association rule “location1 ⟶  type1” in which “location1” contains the “pipeline” item and “type1” contains the “electric shock” item, then there is a high probability of an electric shock accident occurring in the pipeline. “Location 1” and “type 1” (hereinafter abbreviated as P1 and T1) are both near-miss data item sets.This study utilizes the apriori association rule algorithm to analyze the associations between “type” and “location” classified by a CNN-based classifier. P1⟶T1,” its support supP1⟶T1) is the number of simultaneous transactions between P1 and T1, and count(D) is the total number of transactions.For association rule “P1⟶T1)( measures the degree of credibility of “P1⟶T1”:1) is the number of transactions occurring in P1.Confidence confRules whose support and confidence are both greater than a given threshold are called strong association rules .In this study, the front and back items of association rules are “locations” and “types,” respectively, and each near-miss record is a single safety check record. The front and back items of association rules are limited. That is, there is only one item. Therefore, the algorithm can be improved to reduce the time cost of scanning by lowering the number of scans.Step 1: When finding the frequent 1-item set (the number of items contained in the frequent item set is 1), different from the traditional apriori algorithm, only the “location” item set is scanned instead of the item sets of “type” and “location” at the same time, thus saving scanning time. The corresponding support degree of each item is calculated, and an item set below the support threshold value is cut off to obtain a frequent 1-item set. The frequent 1-item set is connected with the “type” item set to obtain the candidate frequent 2-item set, and the candidate frequent 2-item set below the support degree is screened out to obtain the frequent 2-item set and its item statistics.Step 2: According to all frequent item sets mined in step 1, the confidence of each frequent item set is filtered whose value is greater than the small confidence; then, the frequent item set is a strong association rule.This algorithm can be divided into two steps as follows:x and y are the values before and after normalization, respectively, and xmax and xmin are the maximum and minimum values of the samples, respectively.This study explores what types of near misses may occur in a specific “location.” To show the relationship between them more intuitively, a network diagram is used to visualize them, as shown in The apriori algorithm can determine the strong association rule of “location ⟶ type” but cannot determine the distribution law of specific near-miss objects. The text corresponding to categories in strong association rules is processed by the Chinese word segmentation to obtain a more detailed near-miss distribution. For example, for “dam shoulder slot ⟶ fall from height,” (1) all descriptions of this association rule are collected as shown in To train the “location” and “type” classifiers with strong generalization ability, the dataset allocated according to The 8990 “description” data and the corresponding “location” data without labels generated in the Crane Beach Hydropower Station are taken to classify the “type” and “location,”, respectively, using the “type” classifier and the “location” classifier. The 8990 structured data are obtained for mining association rules “location ⟶ type.”The average accuracy, average precision, average recall rate, and average F1 score rate of the “location” classifier were 90.19%, 81.90%, 84.43%, and 81.93%, respectively. The evaluation results of each category of the “location” classifier are shown in Figures In In 1 scores indicate that the CNN performs better than other algorithms, they are unable to provide any information about how each category of “type” and “location” is misclassified. Thus, confusion matrices are introduced to focus on categories that are misclassified. In Figures Although precision, recall, and FAs shown in Existing studies show that the short text classification effect of shallow machine learning is worse than that of DL . ConsequNear-miss short texts on hydropower engineering construction have the characteristics of limited sentence length, compact structure, and independent expression, which make it possible for CNNs to handle such tasks . Five DLAs the number of categories classified in this study is too high to fully display the evaluation metrics of each category, the average value of each evaluation metric of the classifier is used for comparison with the CNN algorithm and other DL methods. As can be observed from To acquire more objective association rules, labeled data are added to the association analysis dataset for more comprehensive data. Due to the large amount of data and the large number of label categories, the threshold of support and confidence were set at low levels of 0.02 and 0.20, respectively. A total of 31 strong association rules were mined. Some of the results are shown in To display more association information using a network diagram, we set the support degree and confidence degree lower at 0.001 and 0.01, respectively, and a total of 235 association rules are output. As shown in Knowing which places are prone to accidents, safety managers can search for the corresponding original near-miss description data and perform a more in-depth and detailed analysis based on the specific association rules. For example, a tunnel is prone to collapse due to arch cracking, no anchor, nonstandard support, and so on. More valuable near-miss prevention objects can be learned by combining raw near-miss data with an association rule network.Compared with the network diagram, the Sankey diagram displays more detailed and specific content and visually presents the frequency distribution and information flow of the specific near-miss objects, near-miss locations, and near types. We exhibit one of the Sankey diagrams in In addition, the “traffic ladder” of the “dam shoulder slot” has great potential to cause near misses of “falling from height.” Referring to the “traffic ladder” in the original text, we can find that the main reason for “fall from height” is that “there is no traffic ladder,” “the traffic ladder handrail is missing,” and “the traffic ladder has no protective railing.” Safety managers can quickly find the details of near misses and implement measures to prevent the emergence of these near misses through the Sankey diagrams combined with original text data.The construction safety management of hydropower engineering is mainly based on the analysis of safety text data, but the recorded data are often inconsistent and messy data, so it is particularly difficult to directly obtain knowledge that can guide safety early warnings. In recent years, NLP technology combined with AI has provided the possibility for rapid and automatic analysis of text data in all walks of life.To mine the valuable information hidden in the data of hydropower engineering construction near misses, this study developed a new model combining text classification and association mining. The purpose of text classification is to aggregate near misses in the same category and lay the foundation for subsequent data statistics. The association algorithm can be used to calculate the results of structured classification and find the association rules with strong practical significance.To overcome the shortcoming that the association algorithm cannot analyze the near-miss description field that contains the most near-miss information, the method of word segmentation combined with the Sankey diagram was used to add abundant near-miss information to the association rules. Intuitive near-miss distribution visualization helps safety managers quickly find the causes of near misses and take measures to control them to reduce the possibility of accidents and improve the safety level of hydropower engineering construction sites. The model can mine massive texts and obtain more detailed rules and is also applicable to other fields of text mining.Our research can better examine near-miss associations, but there are still some limitations. First, the work of making near-miss labels is completed by different people, which may lead to different classifications of the same near-miss types due to respective subjective opinions. Second, it is still necessary to manually check the near-miss classification results with poor performance in the classifier to ensure the accuracy of data involved in association rule mining. Third, the CNN-based model proposed was only used to evaluate the near-miss text dataset obtained from the Crane Beach Hydropower Station project. Future study is required to use unsupervised learning to improve the accuracy of near-miss data classification. In addition, the consistency of near-miss dataset classification models for different hydropower engineering projects can be further discussed.


Prevotella and Treponema. Our results suggest that protozoa contribute to the maintenance of prokaryotic diversity in the rumen possibly by mitigating the effect of competitive exclusion between bacterial taxa. Our findings put forward the rumen protozoa populations as potentially important ecosystem engineers for future microbiome modulation strategies.Unicellular eukaryotes are an integral part of many microbial ecosystems where they interact with their surrounding prokaryotic community—either as predators or as mutualists. Within the rumen, one of the most complex host-associated microbial habitats, ciliate protozoa represent the main micro-eukaryotes, accounting for up to 50% of the microbial biomass. Nonetheless, the extent of the ecological effect of protozoa on the microbial community and on the rumen metabolic output remains largely understudied. To assess the role of protozoa on the rumen ecosystem, we established an in-vitro system in which distinct protozoa sub-communities were introduced to the native rumen prokaryotic community. We show that the different protozoa communities exert a strong and differential impact on the composition of the prokaryotic community, as well as its function including methane production. Furthermore, the presence of protozoa increases prokaryotic diversity with a differential effect on specific bacterial populations such as Gammaproteobacteria,  Microbial community assemblages are determined by both abiotic and biotic factors driven by the environmental conditions and a complex network of microbial interactions between diverse microorganisms.Microbial eukaryotes—such as protists—are ubiquitous in a wide range of environments and play a pivotal role in regulating microbial community structure and function as well as their physicochemical environment . ProtisBlastocystis was suggested to play a critical role in maintaining bacterial diversity in the gut microbiome [In host-associated microbial communities, protists were suggested to play a beneficial role for their host. For example, in the rhizosphere of plants, predation by protists promotes changes in microbial composition that accelerates nutrient cycling and supports the removal of pathogenic species –13. Thoucrobiome . Such ev6 protozoa cells per ml [One of the most densely populated gut environments is the upper digestive tract of ruminants, termed rumen. The rumen hosts one of the most complex microbial communities comprising bacteria, archaea and microbial eukaryotes, the latter being dominated by ciliate protozoa . Ciliates per ml . DefaunaDespite the perceived importance of protozoa communities on the rumen ecosystem and our environment, the direct effect of protozoa community and its composition on the prokaryotic community structure and function was never examined. In this study, we aimed to assess the role of rumen protozoa in shaping prokaryotic community structure and function. We hypothesize that various protozoa populations exert differential effects on the native prokaryotic community in the rumen. To this end, freshly sampled rumen prokaryotic community was exposed to rumen protozoa sub-communities in order to characterize the resulting metabolic output as well as the microbial prokaryotic community dynamics. Our findings suggest that rumen protozoa play a central role in defining characteristics of the rumen ecosystem shaping rumen microbiome structure and metabolism.The experimental procedures used in this study were approved by the Faculty Animal Policy and Welfare Committee of the Agricultural Research Organization Volcani Research Center approval no. 737/17 IL, in accordance with the guidelines of the Israel Council for Animal Care.Rumen fluid was sampled 2 h after feeding, from three cows kept under the same high fiber diet (70% roughage and 30% grains), which is the standard diet for cows during the dry period in our institute  and accog to remove potential remaining protozoa. Only the upper half of the supernatant was used to minimize contamination of protozoa after centrifugation. It is important to note that the methodological setup of this study focuses on the liquid-associated fraction of the prokaryotic community as these were suggested to more likely be predated than the particulate-associated prokaryotes [In order to obtain different populations of protozoa, the rumen samples underwent a series of size filtration and washings similar to the procedure performed in . Brieflykaryotes .g for 15 min. Cells were lysed using beat-beating performed three times for 30 s at maximun speed at 4 °C. The lysate was centrifuged at 4700 × g for 15 min and the supernatant was used to quantify protein content using the Bradford protein assay [Protozoa of each fraction were twice washed using anaerobic phosphate-buffered saline and centrifuged at 4700 × in assay . A Bradf4/ml of protozoa from each community, one treatment with the full native protozoa community (similarly adjusted to 104/ml cells) and one treatment without protozoa was named ‘protozoa-free’. The number of protozoa was chosen to reflect the typical abundance of protozoa in the rumen and was also based on a previous experiment showing that this number shows a visible change in methane production. Furthermore, we opted for a similar number of protozoa in order to allow for a direct comparison between the fractions. The requirement for such protozoa numbers hindered our ability to always obtain the aimed triplicates for all the cows and fractions, thus some groups were performed with two replicates . One additional all protozoa community was removed from the analyses following as it showed contamination with a species of Betaproteobacteriales, not native to the rumen. The microcosms were incubated for 96 h tilted at 20°. Methane quantification was performed after each day for four days. After methane quantification, 5 ml of the upper fraction of the microcosms was removed and kept frozen at −80 °C for quantification of volatile fatty acids (VFAs) and sequencing of the prokaryotic community. The microcosm was complemented with 5 ml of medium M [The prokaryotic community was distributed evenly in 20 ml anaerobic screw-cap glass tubes equilibrated in the anaerobic glove box. The rumen fluid containing the prokaryotic community was inoculated with 100 mg of ground feed of the same composition the cows received as substrate. The protozoa fractions were centrifuged twice and concentrated in order to inoculate the microcosms with the smallest amount of volume to minimize the carryover of additional ruminal factors that might affect our experiment . The overall volume of each microcosm was 10 ml containing 10–1 and an oven temperature of 200 °C. The oven temperature remained steady for a total run time of 5 min. A standard curve was generated using pure methane gas. After the daily measurement 5 ml of fluid from each microcosm was removed for VFA quantification and microbiome analysis. For VFA measurement, the removed fluid was centrifuged at 10,000 × g in order to first separate the microbial community from the incubated fluid. The supernatant was transferred to a new tube and the pellet was used for further DNA extraction. Eight hundred microliters of the supernatant was mixed with 200 μl of 25% metaphosphoric acid solution (w/v in DDW) followed by 1 min vortex and then incubated at 4 °C for 30 min. The samples were then centrifuged for 15 min at 10,000 g and the supernatant was removed into new tubes, then 250 μl methyl tert-butyl ether (Sigma-Aldrich) was added and the tubes were vortexed for 30 s. Another cycle of centrifugation was performed for 1 min at 10,000 g. The upper phase, which contained methyl tert-butyl ether +short chain fatty acids, was analyzed using an Agilent 7890B GC system  with a Flame ionization detector. The temperatures at the inlet and detector were 250 °C and 300 °C, respectively. Aliquots (1 μl) were injected with a split ratio of 1: 11 into a 30 m × 0.32 mm × 0.25 μm ZEBRON ZB-FFAP column  with helium carrier gas set to a flow rate of 2.4 ml min–1 and initial oven temperature of 100 °C. The oven temperature was held constant at the initial temperature for 5 min, and thereafter increased at 10 °C min–1 to a final temperature 125 °C, and a final run time of 12.5 min. Individual injections of each pure VFA was performed in order to identify their retention in the column and a calibration curve was generated by preparing an equimolar solution of all the VFA and serially diluting it from 100 to 0.1 mM.Methane and VFA quantification was performed following the protocol from Shabat et al. . For metDNA extraction was performed as previously described . In brieThe V4 region of 16S rRNA was amplified by PCR from DNA extracts using barcoded primers 515F 5′-CCTACGGGAGGCAGCAG-3′ and 806rcbR 5′-CCGTCAATTCMTTTRAGT-3′ . The barpost-hoc Aligned Rank Transform Contrasts (ART-C) was performed to determine which paired groups differed from each other using the Artool package in R [The relative contribution of protozoa to metabolites production was done by assessing the difference between each microcosms from a specific cow to the corresponding protozoa-free microcosms and subsequently normalized based on protein quantification of the washed protozoa fractions. Differential production of metabolites was assessed using ANOVA two-way performed on transformed data , in ordage in R , 44.Downstream processing of the 16S rRNA data, up to the generation of the amplicon sequence variant table (ASV) was performed in QIIME v.2 . DADA2 wpost-hoc Aligned Rank Transform Contrasts (ART-C) was performed to determine which paired groups differed from each other [Centered log-ratio transformation was performed for the statistical analysis of compositional data, using the ‘compositions’ R package . The anach other  using thch other , Kruskalp values of <0.05 after false discovery rate correction via the Benjamini Hochberg procedure were considered significant, unless otherwise stated in the text or figure. Statistical tests and data analysis across the different fractions were performed in R version 3.5.3 [For all the analyses, on 3.5.3 . Multiplon 3.5.3 , with LGon 3.5.3 .Polyplastron being a key member of this population type [Ophryoscolex and Polyplastron genera, with P-60 including Isotricha. The P-40 fraction was almost exclusively composed of Isotricha. Fractions P-10 and P- < 10 were dominated by Dasytricha, and to a lesser extent by Entodinium. The Isotricha genus was detected in all size fractions albeit in different relative abundances   Fig. .Fig. 1Ex4 cells/ml per tube  and exposed them to identical prokaryotic communities. Each protozoa community coming from each of the three cows were reintroduced to their respective native prokaryotic community of the cow they originated from. The resulting microcosms consisted of replicates of each communities along with replicate for the rumen prokaryotic community incubated without protozoa (hereafter referred to as”protozoa-free” microcosms) and a fraction in which the whole protozoa population was reintroduced . The P-40 community dominated by Isotricha exhibited a 1.5-fold higher methane output compared to fractions P-100, which exhibited the second highest methane production. Furthermore, P-40 protozoa exhibited a ~3-fold higher methane production compared to the protozoa-free microcosms after 48 h , but was still significantly higher when compared to the protozoa-free microcosms , and dynamics across the days of sampling , driven by a higher ASV richness , bacterial genera enriched within the Gammaproteobacteria were Succinivibrionaceae, her Fig. . These gher Fig. . These gOur results show that the presence of protozoa had a recurrent effect on the bacterial composition, regardless of its source community, particularly with regards to the enrichment of specific taxonomic lineages.p < 0.001; Fig. Our results of the prokaryotic taxonomic distribution so far show that protozoa had a stark effect on the microbial community composition. To assess the protozoa-mediated effect on the microbial species level, we conducted a phylogenetic analysis of ASV-level taxa Fig. . This rePrevotella and Treponema, which were either significantly higher in abundance or exclusively found in protozoa-containing microcosms (Fig. Prevotella overall exhibited a decrease in abundance in most of the protozoa-containing microcosms (Fig. Prevotella diversity significantly increased in the presence of protozoa (protozoa communities P-100, P-60, P-40 and all-protozoa, ART-C test, p < 0.05; Fig. Prevotella ASVs found in the protozoa-free microcosms (Fig. Treponema genus (Fig. Treponema in protozoa-free microcosms was limited to a small number of ASVs, while it accounted for significantly more ASVs in the all protozoa-containing microcosms (ART-C test, p < 0.05; Fig. Further analysis of the distribution of taxa that are differentially abundant when incubated with protozoa, we observed that the largest proportion of ASVs belonged to sms Fig. . Notablysms Fig. . Nonethesms Fig. . A similnus Fig. . This genus Fig. . HoweverHost-associated microbial communities can be altered by several factors including the host species or genetics , 57, hosSuccinivibrionaceae have been characterized as hydrogen utilizers, which protozoa produce in high abundance via hydrogenosome organelles or cytosolic hydrogenases [Methanobrevibacter, a methanogenic archaea genus, in which most of its known species are hydrogen utilizers (Fig. Succinivibrionaceae were observed to be associated with higher feed efficiency and lower methane emissions in ruminants and other foregut hosts [The protozoa-mediated enrichment of specific taxa, such as the expansion of Gammaproteobacteria families and genera, suggests metabolic interactions between bacteria and protozoa. Indeed, species of ogenases , 61. Thiers Fig. . Nonetheut hosts , 70. TheIsotricha prostoma preferentially ingested bacteria of specific morphologies [Entodinium caudatum had no preference in bacterial prey when offered bacterial mixtures with differing proportions [In their role as microbial predators, a large body of theoretical framework as well as empirical evidence show that protozoa modulate the relationship between microorganisms by exerting top-down control on the overall structure of prey communities , 71–73. hologies . In contportions . Based oportions .Prevotella or Treponema, concomitant with an increase ASV abundance of phylogenetically related ASVs and overall increasing within genus diversity (Fig. Microcosm experiments as well as theoretical models previously demonstrated that exposure to predation or grazing pressure mitigates competition between species with overlapping niches in a competition-predation trade-off , 76, 77,ity Fig. . These gInterestingly, the increase in diversity parameters was generally most pronounced in fractions containing intermediate sized protozoa (P-40, Fig. Isotricha enriched fraction exhibited the highest methane production, even when protozoa contribution was normalized by biomass. Holotrich protozoa, such as Isotricha, have been previously shown to play a role in supporting methanogenesis that is likely due to the observed higher activity of their hydrogenosomes [Our results further show that protozoa play a pivotal role in the rumen microbiome end-product output comprising VFAs and methane Fig. . While tenosomes , 61. TheMany experiments studying the effect of micro-eukaryotic predators on the bacterial community use artificial prey communities that comprise only a low number of different species or a simplified bacterial community , 71, 83.Supplementary material


Estimation of state of saturation with stone-forming salt represents a reliable tool to assess the overall risk. The available methods are based on computer-assisted ab initio calculations. Our earlier method URSUS was subsequently substituted by Lithorisk®, a software including visualization of risk profiles. Unfortunately, Lithorisk does not adapt to new versions of Windows® and Macintosh® Apple, neither runs on smartphones or tablets. We propose a novel version of the software which can be directly used online on any device equipped by different operating systems. Upon online connection and after registration, the software is ready for unlimited accesses, in either Italian, English or French. After digiting input variables  in a fixed dashboard, state of saturation is promptly given. In addition to state of saturation (ß) with calcium oxalate, brushite and uric acid, ß struvite and cystine are available. Both input variables and ß results are graphically depicted as green or red horizontal bars to indicate recommended values. The software was implemented with equations allowing to omit sulphate and ammonium excretion for users with difficult access to these measurements. This simplified version, tested for ßCaOx and ßBsh on 100 urine samples showed close correlation with the full version. The software gives a list of total and free concentrations and soluble complex species distribution. Results can be printed or saved as PDF. So, we propose an easily accessible software to estimate state of saturation usable on any operating system and personal device. The essential condition for a renal stone to form is supersaturation of urine with respect to lithogenic salts –3. Whilethermodynamic stability constants, conditional stability constants of soluble complex species, reassessed at actual ionic strengths, were used [Years ago, we had developed our own method for calculating urine saturation with stone-forming salts, called URSUS and distributed as CD-ROM in our country . This meere used . To bettere used . HoweverThe source code has been completely re-written based on the original files . DespiteEach input variable must be digited in a fixed dashboard in the preferred units. If urine volume is available, input values, given as 24-h urine excretion, are automatically transformed as concentrations per liter. At any value, a green or red horizontal bar will correspond, to evidence normal or abnormal values according to their positive or negative effects on supersaturation, respectively. The sense of the effect conforms to arbitrary though widely accepted reference ranges, which are delimited by a thin yellow bar –20. UppeFigure Results of ß which promptly appear are depicted as horizontal bars, green in case of ß < 1 for uric acid and struvite. Thresholds of normal range for calcium oxalate, brushite and cystine, denoted by a small yellow bar, are of course arbitrary, and refer to recommended levels of saturation emerging from the literature , 16–22. Many potential users had complained to be unable to use the first version of Lithorisk® because their laboratories did not supply measurements of inorganic sulphate and urinary ammonium. To obviate this drawback, in this last version, the software was implemented with equations, whereby estimates of the above components were derived, by means of multiple regression analysis, from measurements commonly available everywhere. Regression analysis was performed by using a previous database . From thTo test the reliability of the above equations, we calculated ßCaOx and ßBsh using both actually measured and predicted values of sulphate and ammonium. The correspondence between the two sets of measures made on 100 urine collections appeared quite satisfactory as evidenced in Figs. Nephrolithiasis is a disease with a great epidemiological impact, since up to 10% of the general population is expected to form at least one stone in their lifespan  and, in Some contend that to achieve saturation estimates requires that many urinary components be measured with the consequent increase in cost. This explains why several urine-based indices have been proposed trying to simplify the assessment of the stone risk. These include the Tiselius Index , the RobThere is a direct access to the software through web on users’ devices, including PC and laptop, tablets and smartphones, independently of the operating system. The software is based on the earlier version  and usesThere is a completely renewed graphic presentation.It is now possible to get estimates of struvite and cystine saturations.Species distribution is available, including concentration and percentage of both cations and anions and soluble complexes as well. In expert hands, this could be an important option to evaluate the impact of varying ion concentrations on species distribution to optimize effects on state of saturation. For instance, it may be relevant to assess effects of different citrate and magnesium concentrations on ionic equilibria and proton concentration .The new software is equipped with subroutines that generate ß values even omitting the sulphate and ammonium concentrations. Despite their marginal influence on the calculations, sulfate and ammonium determinations are generally included in ab initio calculations, including ours. Indeed, changing concentrations from the lower to upper limits of normal distribution, the effects become not irrelevant. For instance, changing sulphate concentration from 10 to 40 mmol/L, leaving other components unchanged, results in 22% variation of ßCaOx. The correlation of Eq. ” sessionHerein, we report a new version of the software Lithorisk® which is characterized by several novelties:To ease the task of producing this version of Lithorisk®, it was decided to maintain the original list of soluble complexes. This may suffice in the routine management of patients, namely, to improve and control their compliance to treatment . In addiIn conclusion, the essential novelty of this software is that it can be easily available for use in any operating systems and personal devises. Also, the great simplification of the input variable procedure, including the option to change units, and the novel graphical visualization of risk profiles are likely to ease the use and widen the understanding of the concept of saturation in the general practice of stone disease.


The cervical extension of the thymus is the most common variation. However, this may be mistaken for a soft tissue mass in the neck particularly by the radiologists who are not familiar with the pediatric population and not aware of this variation, leading to unnecessary surgery and increased medical costs. Since the rates of cervicaly extended thymus in children in clinical practice are lacking in Turkey, this study aimed to evaluate the prevalence of cervical extension of the normal thymus in the pediatric population.This descriptive cross-sectional study included all pediatric patients who were referred to the radiology department for neck ultrasonography between August–October 2018. A high-frequency probe was implemented and 220 patients  with a mean age of 8.7 ± 4.39 years (ranging from 1 month to 18 years of age) were examined.Cervical extension of the thymus was detected in 103 patients (46.8%). The age of the patients was found to be significantly lower than the age of the patients whose thymus was not extended . The mean craniocaudal length of the thymus that cervically extended was 6.41 ± 2.31 mm. There was no significant difference in the length of the thymus between males, females (6.48 ± 2.12 mm and 6.37 ± 2.46 mm. p = 0.924), and different age groups (p = 0.442).Approximately half of the children have the cervical extension of the thymus. Thus, radiologists and clinicians should be aware of this entity to avoid unnecessary imaging studies and interventional procedures. •The cervical extension of the thymus in children is the common anatomic variation.•It may be mistaken for a soft tissue mass in the neck.•The clinicians should be aware of this entity to avoid unnecessary imaging and intervention. It is formed by the fusion of the right and left thymic rudiments, originating from the ectoderm of the third branchial cleft and the endoderm of the third branchial pouch, and moves caudally and medially from the pharyngeal region to the superior anterior mediastinum . DefectiAccording to our clinical experience of neck ultrasound (US), the cervical extension of the thymus is the most common variation of the thymus. This may be mistaken for a soft tissue mass in the neck particularly by radiologists who are not familiar with the pediatric population and not aware of this variation, potentially leading to unnecessary surgery and increased medical costs ,4. To ou2This descriptive cross-sectional, single-centered study was approved by the Institutional Review Board for Ethical Issues in Clinical Research and is compatible with the Declaration of Helsinki. The patients referred to the radiology department for the neck US with various clinical indications between August–October 2018 were included in the study. The informed consent was taken from the parents of the patients. The patients who had any systemic illness, treated for malignancy, or had a history of lower neck or chest surgery, were excluded.The neck US was performed by two radiologists . The scan was performed using a 7–12 MHz high-frequency linear transducer on the Aplio-500 US device . The probe was placed perpendicular in the suprasternal notch in the transverse plane. Without any angulation towards the mediastinum, the cervical extension of the thymus was considered to be ‘positive’, when the thymus-located in the anterior mediastinum abutted the upper border of the manubrium sterni through the neck. The thymus was distinguished with its homogeneous hypoechoic background echogenicity compared to the thyroid with scattered hyperechoic foci resembling a ‘starry sky’. With increasing age, due to physiological fatty replacement, the echogenicity of the cervically extended thymus could increase.The length of the extended thymus, from the upper margin of the manubrium to the superior edge of the thymic tissue on the transverse plane was measured in millimeters and recorded. The images containing the most upper extent of the thymus at midline or near the left or right of the midline were used for measurement.Patients were grouped according to sex and age intervals  and compared statistically in terms of the length of the cervically extended thymus.Statistical analysis of the data was performed in SPSS, Version 25 program . Pearson Chi-Square Test was used in comparing categorical data between groups. Due to the lack of normal distribution of continuous data, Kruskal Wallis H was implemented for comparisons between more than two groups and Mann Whitney U statistical analysis for comparisons between two groups. p<0.05 was considered statistically significant.This work has been reported in line with the STROCSS 2021 criteria .32 = 3.743, p = 0.442).220 patients  with a mean age of 8.7 ± 4.39 years were included in the study. 103 patients (46.8%) had cervically extended thymus. The location of the extended thymus was in the midline and inferior portion of the neck not exceeding the thyroid lobes . The ageIn 4 patients , and vascularity of any mass, as well as its relationship to nearby anatomic structures .With theIn the literature, there is a limited number of studies focusing on the cervical extension of the thymus. In a study reviewing PET/CT examinations acquired in children and young adults with malignancy and under the age of 20 years for 2 months, the superior thymic extension was reported as 11% . HoweverThe thymus has a unique US appearance, a homogeneous hypoechoic background echogenicity, compared to the thyroid, with scattered hyperechoic foci resembling ‘starry sky’ due to the mixture of lymphoid tissue and fat. Continuity with the thymus located in the anterior mediastinum and the US characteristics resembling normal thymus tissue is the features helping establish the diagnosis of 'cervically extended thymus' .The cervical thymic extension above the manubrium in the midline should not be confused with a pathologic condition. Criteria for differentiating the normal finding of cervical thymic extension from pathologic mass is that cervical extension appears as a soft tissue lobe, extending above the upper margin of the manubrium anterior to the trachea and great vessels with direct vicinity of the mediastinal thymic tissue and should not deform or compress vessels or airway ,15. In oIn the current study, we demonstrated no significant difference between the sex and age groups in terms of frequency of cervical extension. However, the patients without cervically extended thymus were significantly older than those in whom cervical extension was detected. The increased echogenicity of the thymus due to increasing fatty replacement with the age may interfere with discrimination of the thymus from the fatty tissue of the neck .Fig. 2ThThere were several limitations in this study. Although this study, to our knowledge, is one of the few studies implementing the US to determine the prevalence of superior cervical extension of the thymus in children, the study had a relatively small study population. Future US studies with a larger sample size would emphasize the entity of the cervical thymic extension. Since the echogenicity of the thymus increases with age due to fatty replacement, discrimination of the cervically extended thymus from surrounding adipous tissue may be challenging. More than half of the pediatric patients were older than ten years of age and this might have been interfering with the detection of cervical extension of the thymus.5The findings of our study shows that approximately 50% of the children have a cervical extension of the thymus. The extended thymus tissue is often located in the midline at the suprasternal notch level in the neck and continued with the mediastinal thymus, having the typical thymus echogenicity. However, with increasing age due to fatty replacement the thymus echogenicity increases and cervical extension may not be detected on US examination. Thus, radiologists and clinicians should be aware of the high frequency of cervical extension of the thymus as a variation in pediatric patients to avoid unnecessary imaging studies and interventional procedures.Not commissioned, externally peer reviewed.The authors declare no conflict of interest.


Our findings provide the first evidence that ubiquitinated p21 in the cytoplasm can be recycled through USP11-mediated deubiquitination, and we identified the USP11-p21 axis in the cytoplasm as a potential therapeutic target for breast cancer control.Breast cancer ranks as the most frequently diagnosed cancer among women worldwide. Elevated cytoplasmic p21 levels are often found in breast cancer tissues and related to a poor prognosis. However, the underlying mechanisms that lead to the stabilization of cytoplasmic p21 protein, which normally has a very short half-life, remain obscure. In this study, we found that there was a strong correlation between p21 and USP11 in the cytoplasm of breast cancer tissues and cells. Furthermore, we revealed that ERK1/2 phosphorylated USP11 at the Ser905 site, which promoted the cytoplasmic localization of USP11. In the cytoplasm, USP11 colocalized and interacted with p21. As a result, USP11 catalyzed the removal of polyubiquitin chains bound to cytoplasmic p21 and resulted in its stabilization. Functionally, USP11-mediated stabilization of cytoplasmic p21 induced breast cancer cell proliferation Breast cancer was the most common cancer diagnosed among women worldwide in 2020, with an estimated 2.3 million new cases CDKN1A gene, which can function as an oncogenic protein or a tumor suppressor, mainly depending on its subcellular localization p21 is encoded by the CDC20, CRL4CDT2, and SCFSKP2, have been shown to promote ubiquitination and degradation of p21. Our previous study further demonstrated that USP11 could reverse the nuclear p21 degradation mediated by SCKSKP2, APC/CCDC20 and CRL4CDT2 by removing the polyubiquitin chains bound to p21, and stabilize p21 LRR1 has been revealed to promote p21 degradation via ubiquitination p21 protein levels are mainly regulated by two posttranslational modifications, namely phosphorylation and ubiquitination USP11 belongs to the ubiquitin-specific processing protease family of deubiquitinases, which regulates DNA damage repair, proliferation and metastasis in multiple cancer types by specifically interacting with and deubiquitinating target proteins The biofunction of a protein is always associated with its subcellular localization In this study, we found that there was a strong correlation between p21 and USP11 in the cytoplasm of breast cancer tissues and cells. We provided evidence that ubiquitinated p21 in the cytoplasm could be reversed and stabilized by USP11-mediated deubiquitination. We also demonstrated that the cytoplasmic localization of USP11 was associated with its phosphorylation mediated by ERK1/2. As a result, USP11-mediated stabilization of cytoplasmic p21 promoted the proliferation of breast cancer cells. Our results reveal an important mechanism regarding the regulation of cytoplasmic p21 stability, and indicate that the USP11-p21 axis in the cytoplasm could be a potential therapeutic target for breast cancer control.We conducted USP11 and p21 protein expression analysis using the UALCAN portal TM siRNA Transfection Reagent. To stably knock down endogenous USP11 in MCF-7 cells, cells were infected with negative control (NC) lentivirus or USP11-shRNA lentivirus (purchased from GenePharma) for 72 h and subsequently selected with 2 μg/mL puromycin for 5 days. The shRNA target sequences were shown in Supplementary Plasmids were transfected into cells using Lipomax . The sequences of the small interfering RNAs (siRNAs) used in this study were shown in Supplementary U0126  and EGF  were purchased from Beyotime Biotechnology. Mitoxantrone  was purchased from MCE. TureColor three-color pre-stained protein Marker  was purchased from Sangon Biotech. BSA  was purchased from US EVERBRIGHT. UltraSignal ECL  was purchased from 4A Biotech Co., Ltd. Antibodies information: anti-USP11 ; anti-p21 ; anti-Flag ; anti-Myc ; anti-HA ; anti-ERK1/2 ; anti-phospho-ERK1/2 ; anti-phospho-MAPK/CDK Substrates ; anti-phosphoserine/threonine ; anti-GAPDH ; Dylight 488 ; Dylight 594 .MDA-MB-468, HEK293, MCF-7 and 4T1 cells were purchased from ATCC. SKBR3 and BT474 was obtained from Jining Corporation. MCF-7, MDA-MB-468, HEK293, and 4T1 cells were cultured in DMEM containing 10% FBS and 1% Pen/Strep. BT474 and SKBR3 cells were cultured in RPMI-1640 containing 10% FBS and 1% Pen/Strep. All cells were maintained at 37 °C with 5% CO2. Cells were stored at -80 °C using CELLSAVING (New Cell & Molecular Biotech).Total RNA was acquired using RNA Extraction Reagents . Then total RNA was subjected to reverse transcription using Nova UScript First-Stand cDNA Synthesis SuperMix . qRT-PCR reaction consisted of the resulting cDNA, primer, and 2× SYBR Green qPCR Master Mix . The primer sequences for qRT-PCR were shown in Supplementary Cells were seeded in confocal dishes for 1 day, then washed once with DPBS. The cells were fixed with absolute ethyl alcohol for 20 min. The fixed cells in confocal dishes were washed 3 times with DPBS, then incubated in 0.2% Triton X-100 solution for 10 min. Next, the cells were washed three times and incubated in 5% bovine serum albumin (BSA) solution for 1 h, followed by incubating in antibody solution  for 8 h at 4 °C. Then the cells were washed three times and subsequently incubated in secondary antibody solution  at room temperature for 2 h. Subsequently, the cells were incubated in Hoechst solution for 5 min followed by 3 washes with DPBS. Images were acquired with confocal microscope (NIKON).Cells were harvested from the 6 cm dishes, then were resuspended in 500 µL of buffer A containing the phosphatase inhibitor (Roche 4906845001) and protease inhibitors (Roche 4693159001). Buffer A was prepared according to the reference procedure The procedures of western blot analysis and immunoprecipitation were implemented according to previously reported The procedure of ubiquitination assay was performed as previously described Cells infected with indicated lentiviral shRNAs were seeded in 12-well plates . Later, the cells were cultured in medium containing 50 μg/mL CHX  or 20 μM MG132  for the indicated times, followed by western blot analysis.MCF-7 cells were seeded in 12-well plates. After two weeks, the cell clones were fixed with absolute ethyl alcohol for 20 min. Next, the cell clones were stained with 0.2% crystal violet solution, and the number of colonies was counted using ImageJ.Usp11-/- mice were generated by Cyagen Biosciences Corporation. To generate breast cancer xenografts in female mice, 1×107 MCF-7 cells stably expressing the indicated shRNA or 1×106 4T1 cells were harvested and washed three times with DPBS. After suspending in serum-free media (100 μL), the cell suspension injected into the mice. WT mice were treated with DMSO or MTX ; Usp11 KO mice were treated with DMSO. Treatments were given by intraperitoneal injection. The tumor diameter was measured using a vernier caliper, and the tumor volume (V) was calculated according to the following formula: V = (L × W2)/2. The L represents the longest diameter and the W represents the shortest diameter.All animal experiments were approved by the Experimental Animal Ethics Committee of Hunan University (no. 1107271911007500) and carried out following the National Guidelines for Animal Usage in Research (China). Female BALB/c mice and female nude mice were purchased from Hunan SJA Laboratory Animal Corporation. Breast cancer tissue microarray (HBreD090CS01) was purchased from Shanghai Outdo Biotech Company. Immunohistochemistry was performed using standard protocols ***p < 0.001, **p < 0.01 and *p < 0.05.Quantitative variables were analyzed by Student's t-test between groups. One way analysis of variance (ANOVA) was used for comparisons each group. All of the data were processed by GraphPad Prism 8. A value of p < 0.05 was considered to be statistically significant. The significance level was presented as To investigate the potential physiological and pathological functions of p21 and USP11 in breast cancer, we performed bioinformatic analysis using the Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset. As shown in Previous studies have uncovered that the ERK signaling pathway plays key roles in the malignancy of breast cancer. To investigate the association between USP11 and ERK, a co-immunoprecipitation assay was carried out using an anti-USP11 antibody. Interestingly, ERK1/2 was present in the USP11 immunoprecipitates  were significantly decreased after U0126 treatment, and the USP11 mutant (USP11S905A) with a serine to aspartic acid at position 905 could not be phosphorylated by ERK1/2  As mentioned above, USP11 was mainly localized in the cytoplasm of breast cancer cells, which is different from previous studies describing its nuclear location To identify whether ERK1/2-phosphorylated USP11 affected its intracellular distribution, cytoplasmic and nuclear fractions were extracted from MCF-7 cells treated with U0126 or control. Strikingly, inhibition of USP11 phosphorylation mediated by U0126 caused a decrease of USP11 in the cytoplasm and an increase of USP11 in the nucleus, accompanied by consistent changes of p21 Figure A. Of notwt or USP11S905A. As shown in Figure S905A was reduced in the cytoplasm and increased in the nucleus compared with USP11WT, which was consistent with immunofluorescent staining results indicating the subcellular location of USP11wt and USP11S905A  and p21 (Myc-p21-ΔNLS). Immunofluorescent staining images indicated that p21-ΔNLS and USP11-ΔNLS were colocalized in the cytoplasm Figure A. FurtheTo confirm that cytoplasmic p21 indeed interacts with USP11, Flag-USP11-ΔNLS or Myc-p21-ΔNLS plasmids were transfected into MCF-7 cells, and coimmunoprecipitation (co-IP) was carried out. As shown in C275S/C283S showed no effects on total p21 levels or cytoplasmic p21 levels . As shown in Figure C275S/C283S failed to prevent cytoplasmic p21 ubiquitination (Figure To validate whether USP11 affects cytoplasmic p21 expression in a proteasome-dependent manner, MCF-7 and BT474 cells with or without USP11 were treated with the proteasome inhibitor MG132 to block protein degradation. As expected, downregulation of total p21 or cytoplasmic p21 levels caused by USP11 knockdown A-B and un Figure H, suggesin vivo, USP11-depleted and/or p21-ΔNLS-overexpressing MCF-7 cells (Cytoplasmic p21 was reported to act as an oncogenic protein in various cancers -7 cells B were suUsp11-WT or Usp11-KO mice. The results showed that USP11 deficiency suppressed the growth of 4T1 cells in vivo (Figure The tumor microenvironment is important for tumor progression o Figure E-G. Thesin vivo through cytoplasmic p21, and USP11 inhibitor MTX has promise for potential applications in the treatment of breast cancer.To further test the feasibility of developing USP11 inhibitor for breast cancer therapy, we used the USP11 inhibitor MTX to treat mice for two weeks. The results showed that USP11 inhibition effectively attenuated the volume and weight of the tumors formed by 4T1 cells Figure E-G. To fThe ERK1/2 pathway plays central roles in cell proliferation. A previous study showed that overexpression and hyperphosphorylation of ERK1/2 is frequently found in breast cancer SKP2, APC/CCDC20 and CRL4CDT2, are involved in p21 ubiquitination and degradation. Our previous study further demonstrated that USP11 could block p21 degradation mediated by SCKSKP2, APC/CCDC20 and CRL4CDT2 by removing the polyubiquitin chains bound to p21, and thus stabilize p21 in the nucleus, revealing that p21 levels in the nucleus are regulated in a dynamic balanced manner p21 is a labile protein with a half-life of about 30 min LRR1, has been reported to specifically target cytoplasmic p21 for its ubiquitination and subsequent degradation. In this study, we found that USP11 was mainly located in the cytoplasm of breast cancer cells. Similar to its function in the nucleus, USP11 interacted with and stabilized cytoplasmic p21 by removing p21 polyubiquitination chains. To our knowledge, these results are the first evidence that ubiquitinated p21 in the cytoplasm can be recycled through deubiquitination. It would be interesting to further test whether USP11 protects cytoplasmic p21 from the degradation mediated by CRL2LRR1, which will contribute to elucidating the dynamic regulatory mechanism of cytoplasmic p21 levels.Unlike the three identified E3 ligases in the nucleus, only one E3 ubiquitin ligase, CRL2It has been reported that USP11 functions as a tumor suppressor and an oncogenic protein. The tumor-suppressive activities of USP11 were found in non-small cell lung cancer In summary, we provide the first evidence that ubiquitinated p21 in the cytoplasm can be recycled through USP11-mediated deubiquitination. Based on our current and previous results Supplementary figures and tables.Click here for additional data file.


Recently, with the popularization of camera tools such as mobile phones and the rise of various short video platforms, a lot of videos are being uploaded to the Internet at all times, for which a video retrieval system with fast retrieval speed and high precision is very necessary. Therefore, content-based video retrieval (CBVR) has aroused the interest of many researchers. A typical CBVR system mainly contains the following two essential parts: video feature extraction and similarity comparison. Feature extraction of video is very challenging, previous video retrieval methods are mostly based on extracting features from single video frames, while resulting the loss of temporal information in the videos. Hashing methods are extensively used in multimedia information retrieval due to its retrieval efficiency, but most of them are currently only applied to image retrieval. In order to solve these problems in video retrieval, we build an end-to-end framework called deep supervised video hashing (DSVH), which employs a 3D convolutional neural network (CNN) to obtain spatial-temporal features of videos, then train a set of hash functions by supervised hashing to transfer the video features into binary space and get the compact binary codes of videos. Finally, we use triplet loss for network training. We conduct a lot of experiments on three public video datasets UCF-101, JHMDB and HMDB-51, and the results show that the proposed method has advantages over many state-of-the-art video retrieval methods. Compared with the DVH method, the mAP value of UCF-101 dataset is improved by 9.3%, and the minimum improvement on JHMDB dataset is also increased by 0.3%. At the same time, we also demonstrate the stability of the algorithm in the HMDB-51 dataset. In the past several years, video information has been widely used because of its richer content and it is easier to understand compared with other media. Due to the rise of various short video platforms and video sharing websites, a large amount of video information is uploaded to the Internet every day, which is widely shared through various social media and news platforms. People can also use various editing software to edit the video while browsing, such as inserting icons, changing the brightness, resizing, clipping video, and so on, which also increases the amount of video data. This not only leads to copyright infringement, but also makes video retrieval difficult, that is, people can hardly find the best matching videos. Usually, the results retrieved by computer need to be manually selected to find the most appropriate ones, which will increase the workload of users and affect the user experience.As a result, efficient video retrieval algorithms have become nowadays an important component in the applications of copy detection , video rCompared with other kinds of content-based information retrieval, the difficulty of video retrieval lies in the fact that video information has more features that need a large amount of storage space and computing consumption. Benefiting from the XOR operation in binary space, hashing methods have advantages in retrieval efficiency and memory cost, but but previous hashing methods ,23,24,25(1)We design an end-to-end framework for fast video retrieval using the idea of a Deep Supervised Video Hashing. By learning a set of hash functions to transfer video features extract by 3D CNN to a binary space.(2)We choose a fixed number of frames for each video to represent the characteristics of the entire video, which can greatly reduce the computation.(3)We apply the idea of transfer learning and use a 3D CNN model with residual links pre-trained on large-scale video dataset to obtain the spatial-temporal features in videos.(4)We conduct a great quantity of experiments over three datasets to demonstrate that the proposed method outperforms many state-of-the-art methods.To handle these problems, we propose in this research a novel method called deep supervised video hashing (DSVH) for large-scale video retrieval. First, we apply a pre-trained 3D CNN model to extract the temporal and spatial features in videos. Then, by using supervised hashing, the hash functions are trained, and features extracted by 3D convolution are mapped to binary space. Finally, by calculating the similarity of query video and dataset video in low dimensional space, the retrieval efficiency and accuracy can be improved greatly. The contributions of the proposed method are listed below:The following sections of the paper are arranged as follows: We begin with introductions of some related video retrieval works in Convolutional neural networks have been used in many fields of computer vision due to their powerful feature processing capabilities, including video retrieval. Siamese convolutional neural network (SCNN)  is propoIn previous video retrieval methods using CNN for feature extraction, operations are usually carried out on single video frames. In this way, a significant drawback in processing video information is that the relationship between frames is ignored which certainly affects the precision of retrieval. Therefore, by performing 3D convolutions we expect to get spatial-temporal features of videos directly. 3D CNN  is firstHashing is widely used for a variety of multimedia retrieval tasks with the purpose of transform the information from higher dimensional to lower dimensional, and it has attracted much interest in multimedia retrieval due to its search efficiency and memory saving features. According to whether label information is needed during the training step, we divide the existing hash methods into supervised and unsupervised hashing. Supervised hashing learns compact representations with the help of labels. Well known supervised hashing methods include: supervised hashing with kernels (KSH)  by minimWith the development of multimedia information technology, people often need to retrieve videos from the Internet, and video retrieval technology has become a hot topic. Common types of video retrieval include text-based queries , audio-bThe two most important parts of video retrieval are feature extraction and similarity comparison. In recent years, many deep learning-based approaches have been proposed. Kumar et al.  propose The first step of processing video data is to extract video frames. A video is composed of several video scenes, a video scene is composed of various shots, and a shot is composed of many video frames. As we can see from If we carry out feature extraction on all frames, the amount of computation is too large, and it is not necessary. Therefore, to reduce the computational amount and increase the retrieval efficiency, some representative video frames will be selected for feature extraction by certain strategies defined using Equation (1):The CNNs pre-trained on ImageNet are used in various computer vision tasks and achieved great success. The application of CNN models pre-trained on large datasets that uses the idea of transfer learning greatly saves workload, saves time, and reduces the problems caused by insufficient training data. Inspired by the huge success of the pre-trained model, we adopt a pretrained 3D CNN model  for featAfter obtaining video features from the network with stacked convolutional layers, we need to map features extracted by 3D CNN to Hamming space for quick retrieval. We build a hash layer with fully connected structure as show in j-th bit is greater than or equal to 0, its corresponding hash code is 1; otherwise, it is −1:In the test retrieval step, in order to use hamming distance to measure similarity between videos, we need binarize the hash code of videos, so we define Equation (2) to generate the hash code. If the output value of the gIn our proposed algorithm, we use the triplet ranking loss in  during t1, P2, …, Pn} that belongs to the same class and shears the same label as it in the dataset in a mini-batch. This creates a set of query-positive video pairs , , …, . The rest of the videos {V1, V2, …, Vn} in the mini-batch are not similar to the query video. Finally, we can form a series of triples consisting of a query-positive video pair and a dissimilar video like , , …, , etc.For a query video Q, we first select the videos {PIn this section, we conduct experiments on three public video datasets to prove the effectiveness of our method. We start with introducing the datasets and the pre-trained network then show our experimental results with comparison to some representative hashing methods on video retrieval task.UCF-101 Dataset This consists of 101 categories realistic videos collected from YouTube. UCF-101 gives the largest diversity of classes among video datasets, the videos are divided into 25 groups, each consisting of 4–7 videos. Groups of videos have some common features, such as similar backgrounds, similar perspectives, and so on. The clip duration for most videos in UCF101 is less than 10 s. The training set of the original dataset is used for supervised learning and test set is used for retrieve.JHMDB Dataset This database contains a total of 928 videos grouped into 21 categories. Most videos involve single actions such as throwing, walking and kicking a ball, and each category has 36–55 samples containing 15–40 frames. We follow the setting in , 10 videHMDB-51 Dataset It consists of a total of 7000 videos in 51 categories collected from YouTube, and each category contains at least 100 samples. The video samples come from a wide range of sources, most of which are from movie clips. The training sets are used for supervised learning and test sets for retrieve.Pretrained modelWe use the pre-trained 3D ResNet model  on the K−4, and the learning rate was reduced to 1/10 of the original rate every 40 epochs. The training epochs of UCF-101, JHMDB, HMDB are 150, 100, 150 the batch size of UCF-101, JHMDB, HMDB are set to 80, 60, 80.Three datasets are used to measure the performance of our method, split of the datasets is shown in Mean Average Precision (mAP)AP) is calculated by Equation (5):AP.We employ mean average precision (mAP) to evaluate the performance of the proposed video retrieval algorithm, where average precision :N is number of the top-N retrieved results, if the retrieved video similar to the query video This represents the proportion of correct retrieval results in the top-To verify the performance of our algorithm, several state-of-the-art video retrieval methods are used to compare, including DVH , ITQ 4242, DCNNHThe experimental results and the comparison with other hashing-based methods on UCF-101 dataset are shown in SPDTH uses CNN + RNN to extract features and generate hash codes through temporal-aware hashing, so that temporal information can be well preserved, but it is not easy to train due to too many parameters. Compared with SPDTH, our method still achieves an increase by 4.4–5.7% in mAP and 1.7–3.0% in precision with a simpler loss function.In order to explore the relationship between the network depth and the retrieval accuracy, we also use 3D ResNet-18 under the UCF-101 dataset for the experiment, and the results are shown in In order to demonstrate the effect of the hash layer, we remove the activation function in the hash layer, and directly use the full connection structure to map the video features into 64, 128 and 256 dimensions, and then compare the result with the previous one with a hash layer as shown in The experimental results and the comparison with other hashing-based methods on JHMDB dataset are shown in We also use different pre-trained networks on the JHMDB dataset to conduct experiments, and the results are shown in The influence of the hash layer is also listed in The experimental results and the comparison with other hashing-based methods on HMDB-51 dataset are shown in In order to prove the robustness of our method, we test the precisions of top-10, top-50 and top-100 respectively, the results are shown in n on the performance of our method, we test the mAP when n equal to 12,16,20 respectively, the results are shown in In order to verify the influence of the number of video frames All the results prove that DSVH outperform many advanced video retrieval methods not only in accuracy but also in stability By selecting video frames and using 3D CNN, we can directly obtain the spatial-temporal feature in videos, and then get compact binary codes of the whole video under the supervised training, without considering how to maintain the temporal information after the convolution operation like other frame-based video retrieval methods, the use of pre-trained model also saves a lot of work and time.In this paper, we have proposed an efficient video retrieval method named DSVH, which combines 3D convolution and supervised hashing for efficient video retrieval. By using 3D convolution for feature extraction from a series of video frames arranged in chronological order, and then using the supervised hashing method to map video features into Hamming space for similarity comparison, the retrieval performance is greatly improved. Compared with traditional methods such as ITQ , the mAP


Malnutrition (MN) in nursing home (NH) residents is associated with poor outcome. In order to identify those with a high risk of incident MN, the knowledge of predictors is crucial. Therefore, we investigated predictors of incident MN in older NH-residents.n = 3), function (n = 4), nutrition (n = 1), diseases (n = 5) and medication (n = 4)) with incident MN (weight loss ≥ 10% between nD and follow-up (FU) or BMI (kg/m2) < 20 at FU) was analyzed in univariate generalized estimated equation (GEE) models. Significant (p < 0.1) variables were selected for multivariate GEE-analyses. Effect estimates are presented as odds ratios and their respective 99.5%-confidence intervals.NH-residents participating in the nutritionDay-project (nD) between 2007 and 2018, aged ≥65 years, with complete data on nutritional status at nD and after 6 months and without MN at nD. The association of 17 variables , a quarter (2.15 [1.56–2.97]) or half of the meal eaten (1.72 [1.40–2.11]) (vs. three-quarter to complete intake), the lowest BMI-quartile (20.0–23.0) (1.86 [1.44–2.40]) (vs. highest (≥29.1)), being between the ages of 85 and 94 years  (vs. the youngest age-group 65–74 years)), severe cognitive impairment  (vs. none) and being immobile (1.28 [1.00–1.62]) (vs. mobile) predicted incident MN in the final model.10.5% of non-malnourished NH-residents develop MN within 6 months. Attention should be paid to high-risk groups, namely residents with poor meal intake, low BMI, severe cognitive impairment, immobility, and older age. The number of nursing home (NH) residents was estimated by the number of beds at about 1.6 million in the US in 2015 and 4.0 million in Europe in 2017 . TherefoMany factors are associated with malnutrition in cross-sectional studies. In a systematic review, including studies with at least 100 NH-residents each, depression, poor oral intake, swallowing disorders, chewing problems, and eating dependency were associated with weight loss (WL) in 6 studies, and immobility and dependence in activities of daily living (ADL), poor oral intake, chewing problems, dysphagia and female sex were associated with low Body Mass Index (BMI) in 7 studies . Since tBased on repeated measurements data, a decline in nutritional status  was observed in 23% of NH-residents within 1 year . A 2-yeaIdentification of factors increasing the risk and predicting the development of malnutrition is of utmost importance to help prevent malnutrition and its negative consequences. Thus, our aims were to examine the incidence of malnutrition and to identify predictors of incident malnutrition in a large sample of NH-residents.This manuscript adheres to the reporting standards for observational studies . The nD-project was approved by the ethical committee of the Medical University of Vienna, Austria, by the ethical committee of the Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany and by local ethical committees as required by national rules. All residents or their legal representatives gave written or oral consent prior to participation and all information was assessed completely anonymised.This analysis is part of the Joint Action Malnutrition in the Elderly Knowledge Hub (MaNuEL) of the European Joint Programming Initiative A Healthy Diet for a Healthy Life (JPI‐HDHL) that investigated determinants and risk factors for (incident) malnutrition in older people in different settings –24 and i2 and/or WL of >5 kg in the past year (assessed via questionnaire)) were excluded to gain data on real incidence.We included NH-residents aged 65 years or older participating in the nD-project between 2007 and 2018. Residents without data on body weight at FU were excluded as well as residents from Japan due to major differences in the nursing home system  and a co2. Height at nD was used for calculation of BMI at nD and FU.Questionnaires were completed by local NH staff. Body weight in kilogram (kg) was measured using scales from the NH wards at nD and FU, or the most recent weight documented in the nursing records was used for both dates. NH staff was asked to measure body height using a stadiometer for residents who were able to stand. If this was not possible, height was extrapolated based on the measurement of knee height or was recorded from the information in nursing records or identification cards. BMI was calculated according to body weight (kg)/height (m)2 and/or WL ≥ 10% between nD and FU, which was calculated as body weight difference between the two points in time.Incident  malnutriSeventeen variables in 5 categories were included as potential predictors of incident malnutrition:2).General characteristics (3 variables): age , sex , BMI : mobility chair and unable to stand up without any help by other persons), cognitive impairment , dysphagia (yes/no), chewing problems (yes/no).Nutrition (1 variable): intake at lunch .Diseases (5 variables): diagnosis of cancer (yes/no), neurologic diseases  (yes/no), musculoskeletal diseases  (yes/no), cardiovascular/pneumological diseases (yes/no), other diseases (yes/no).Medication (4 variables): intake of antibiotics (yes/no), opiates (yes/no), psychoactive substances (yes/no) and number of drugs (<5/≥5).Potential predictors are described by median with minimum and maximum for continuous variables (age and BMI) and by absolute numbers, percentages for categorical variables.For categorical variables with more than 0.1% missing values, we added a missing category. Cases with missing values in a variable with less than 0.1% missing values  and cases with missing values for age or sex were excluded.p-value p < 0.1 were tested for multicollinearity (variation influence factor (VIF)) and those with a VIF < 5 were added to the multivariate model. A p-value of p < 0.005 was defined as significant in the final model ) (vs. highest BMI-quartile (29.1–64.8 kg/m2)), the age-group 85–94 years  (vs. 65–74 years)), severe cognitive impairment  (vs. none) and being immobile (OR 1.28 [1.00–1.62]) (vs. mobile) predicted incident malnutrition in the final model , associations and effect sizes were similar, only immobility could not retain significance  global consensus definition , are widThe incidence of malnutrition at 6 months according to our definition was 10.5%, with 8.0% according to the WL-criterion and 4.9% according to the low BMI-criterion. Data on incident malnutrition in NH-residents is scarce and hardly comparable due to different definitions of malnutrition, different methods to assess malnutrition (e.g. measurement vs. self-report), and different FU-periods. Beck reported a WL of ≥10% within 6 months in only 2.1% of NH-residents  and MamhResidents aged 85–94 years were more likely to develop malnutrition compared to residents aged 65–74 years. While chances for the other age groups were also higher compared to the reference group, they did not show significance which could be due to the statistical power: nearly half of all participants were in the age group 85–94 years  had a 1.86 times higher chance of incident malnutrition  to develop malnutrition compared to mobile residents. This is in line with results from a systematic review of cross-sectional studies which reported an association between low BMI and immobility in several studies . Two lonDepending on the malnutrition screening tool used in the NH-setting , our ideOur study has several strengths. First, we could include a very large sample from different countries and different care systems. The questionnaires were standardized and available in different languages. Second, we present data on incident malnutrition which are very rare so far by excluding residents with malnutrition at baseline to reduce the risk of wrong causation. Third, we were able to consider a large set of variables from different domains to investigate the potential prediction of incident malnutrition. Moreover, we report our analyses according to a standardized reporting guideline (STROBE-statement) to ensure transparent and thorough reporting.However, when interpreting our results, several limitations must be considered. First, data were assessed by local staff and not by trained scientific personnel. Data presented here are mostly assessed as part of daily routine with simple and quick methods and not via validated scales (such as Katz or Barthel Index for ADL). Our analyses are limited to the variables available in the nD dataset, and eventually relevant potential predictors, e.g. ADL, multimorbidity, or hospitalisation, could not be taken into account. Food intake on nD was assessed estimating the consumed amount of food via a plate diagram and not via dietary records or other more valid—but also more time-consuming—dietary assessment methods . ThirdlyIn this large nD data set, 6-month incidence of malnutrition in nursing-home residents was 10.5%. A low intake at lunch, a low BMI, immobility, severe cognitive impairment and older age were identified as predictors for the development of malnutrition.are on physical and cognitive status are well known, which might also affect the nutritional status positively. In case of severe functional or cognitive impairment, at least special attention should be given to these residents. Food intake also represents a modifiable risk factor, and if food intake (at lunch) is low, effective nutritional care strategies with a potential to prevent malnutrition  are available and should be offered.While older age represents a non-modifiable factor, it is also of importance since special attention should be given to older residents to timely start with respective preventive interventions. In contrast, functional and cognitive impairments represent at least partly modifiable risk factors. Beneficial effects of physical exercise and cognitive training interventions Further prospective, large-scale cohort studies in nursing homes with longer follow-up periods and repeated measurements of nutritional status including reasons for WL (e.g. voluntary vs involuntary) and reasons for low intake should be conducted to complement and extent our results. In addition, food intake should be assessed more detailed, and reasons for drop-out should be recorded. Finally, it should be investigated whether inclusion of our identified risk factors in malnutrition screening tools will improve nutritional care and health outcomes.Supplemental material