dataesr/scientific-paragraphs-categorization huggingface dataset:
8. Statistically tested result was significantly difference between balanced and imbalanced sequences (p < 0. 05). Besides, each algorithm also showed a significant difference in sensitivity, specificity, accuracy, and macro Fscore when used in both groups (p < 0. 05). Several k-mers clustered from heat map were discovered to have biological functions and motifs at the different positions like interior loops, terminal loops and helices. They were validated to have a biological function and some are riboswitch motifs. The analysis has discovered the importance of solving the challenges of majority bias analysis and overfitting. Presented results were generalized evaluation of both balanced and imbalanced models, which implies their ability of classifying, to classify novel riboswitches. The Python source code is available at https://github.
Recently, almost over twenty diverse classes of riboswitches have been found in bacteria, archaea  [11, 12]  and eukaryote. The majority of the riboswitch classes are in bacteria  [12, 13]. Thiamine pyrophosphate (TPP) is the only eukaryotic riboswitch. It is detected in Arabidopsis thaliana. TPP was also found in some fungi  [13]  for instance Neurospora crassa, in algae  [14, 15]. The last two decades have revealed incredible advancement in big and complex omics data due to emerged novel high-throughput experimental technologies such as next-generation sequencing  [16, 17]. Numerous bioinformatics databases are available to gather data for riboswitches analyses and assemble the information regarding diverse functionality of RNA molecules  [18] , including GenBank, National Center for Biotechnology Information (NCBI), Rfam  [19] , Protein Data Bank (PDB), RiboD  [20]  and European Bioinformatics Institute (EMBL-EBI).
Imbalanced class on classification performance After feature selection, sequences containing 156 k-mers values were split into 70% training dataset and 30% test dataset. Improved cross-validation method in training dataset was used to validate both imbalanced models and balanced models, while the remaining test set was applied to test generalizations of those models. All the following results are results based on the test set, which can demonstrate their ability to classify novel sequences. Classifiers on minority class resulted in F-score value from 0. 50 (NB) to 0. 94 (MLP), while on majority class, the range is from 0. 91 to 1. 00, as indicated in Table  1  and Fig     for classification were present in S1 Table. The average performance of each classifier is computed using mean and standard deviation for parameters: accuracy, specificity, sensitivity and macro F-score.
Methodology We showed a complete evaluation of different machine learning approaches for classification and predicting regulatory riboswitches. First of all, we present the benchmark sequences and data mining approach followed by feature engineering that was done through testing. Besides, model selection methods were used to model and compare balanced and imbalanced sequences as well as determine the best combination of hyperparameters for each classifier  [65]. These methods are implemented in an open-source machine learning platform called WEKA 3. 8  [66, 67] , SMOTE  [31]  and Python 3  [68] , which allow evaluating different parameters and algorithms for classification and prediction of the riboswitch. Lastly, we described the results of classifications from the learned models. The workflow for the analysis of imbalanced and balanced sequences used for performance evaluation of different machine learning algorithms found in Fig 1.
Feature selection FASTA format sequences were used for k-mer (1 � k � n) frequency counts through executing in the R package called kcount  [69]. In order to obtain a sufficiently informative k-mer counting matrix for the task  [70] , we set k value to 6 and finally got 5,460 features. This k-mers composition was used to make frequencies of each riboswitch. This avoids unnecessary computing power consumption and dimensional disaster caused by extremely sparse matrices due to high k values as well. Attribute evaluators CfsSubsetEval and BestFirst were used for dimensionality reduction and searching of the space of attribute subsets by greedy hill-climbing augmented with a backtracking facility  [71] , which was consistent with some other researchers  [26]. WEKA 3. 8 was used to implement the task  [66, 67].
Machine learning models A crucial step in machine learning is model selection, as the performance of algorithms is sensitive to the calibration parameters. Configuration and choice of the hyper-parameters are found to be crucial. For our data, we calibrated a model using 10-fold cross-validation. Firstly, the complete feature selection of k-mers sequences was divided into two parts randomly: 70% of data were training set, while 30% of data as the test set. The 70% training set was used to build multiclass classification models and determine the hyper-parameters through 10-fold cross-validation. Then, the test set was used to test the final generalization performance of the balanced and imbalanced models. In order to increase the credibility of comparison results and to ensure the repeatability of the results, all sequences were chosen randomly.
Input data and model parameters except for the step of SMOTE processing were strictly consistent for both balanced and imbalanced models. This task was left to make pipeline module and Pipeline object in Python package imblearn (0. 5. 0), which ensures that in cross-validation or generalization testing, SMOTE only treats the training data used to build the cross-validation model or the final model. By this means, the validation set in each fold cross-validation was consistent in all models just as in the case of the 30% test set. During the model selection process, for each algorithm, the grid search method was applied to traverse all hyper-parameter combinations, while 10-fold cross-validation method for evaluating each parameter combination. Specifically, the program randomly divided the 70% training set into ten straight sections.
Evaluation In order to evaluate the performance of the classifiers, the confusion matrices were used to compute sensitivity, specificity, accuracy and F-score  [32, 87]. Most researchers used a weighted F-score to evaluate the classifier's overall performance. However, it leads to assessment bias between majority families and minority families. In this evaluation, we used macro F1 instead, which gives an arithmetic mean of the per-class F1-scores and avoids assessment bias to some extent. A statistical test was carried out in GraphPad Prism 8. 3. 0 using the Wilcoxon rank test and multiple Wilcoxon rank test at p < 0. 05, 0. 01, 0. 001 level ("Wilcoxon rank test were performed using GraphPad Prism version 8. 3. 0 for Windows, GraphPad Software, La Jolla California USA, www. graphpad. com").
In Section 3, we prove several combinatorial properties, which form the basis of our new technique. In Sections 4 and 5, we present our main results. Our experimental results are presented in Section 6. Section 7 concludes the paper with some remarks for further investigation. A preliminary version of this paper appeared as  [1]. Compared to the preliminary version, we have extended the work by adding a simplified space-efficient version of the algorithm (see Section 4). We have also added additional experimental results using real-world datasets, which further substantiate our contribution.
Proof-of-Concept Experiments In this section, we do not directly compare against the fastest internal  [7]  or external  [18]  memory implementations because the former assumes that we have the required amount of internal memory, and the latter assumes that we have the required amount of external memory to construct and store the global data structures for a given input dataset. If the memory for constructing and storing the data structures is available, these linear-time algorithms are surely faster than the method proposed here. In what follows, we rather show that our output-sensitive technique offers a space-time tradeoff, which can be usefully exploited for specific values of , the maximal length of MAWs we wish to compute. The time-efficient algorithm discussed in Section 5 (with the exception of storing and searching the reduced sets of words explicitly rather than in the constant-space form previously described) has been implemented in the C++ programming language  1.
The correctness of our implementation has been confirmed against that of  [7]. We have also implemented the algorithm discussed in Section 4 but as it was significantly slower, its results are omitted from here. As input datasets here we used: the entire human genome (version hg38), which has an approximate size of 3. 1GB; the entire mouse genome (version mm10), which has an approximate size of 2. 6GB; and the entire chimp genome (version panTro6), which has an approximate size of 2. 8GB. All datasets were downloaded from the UCSC Genome Browser  [26]. The following experiments were conducted on a machine with an Intel Core i5-4690 CPU at 3. 50 GHz and 128GB of memory running GNU/Linux. We ran the program by splitting the genomes into k = 2, 4, 6, 8, 10 blocks and setting = 10, 11, 12. In accordance to Theorem 4: graph (a) in all figures shows an increase of time as k and increase; and graph (b) in all figures shows a decrease in peak memory as k increases.
Notice that the space to construct the block-wise data structures bounds the total space used for the specific values and that is why the memory peak is essentially the same for the values used. This can specifically be seen for = 10 where all words of length 10 are present in all three genomes. The same datasets were used to run the fastest internal memory implementation for computing MAWs  [7]  on the same machine for = 12. Notice that the algorithm of  [7]  takes the same time and space irrespective of. It took only 2934 seconds to process the human genome but with a peak memory usage of 75. 40GB (it took 2844 seconds to process the mouse genome with a peak memory usage of 66. 54GB; and 2731 seconds to process the chimp genome with a peak memory usage of 69. 49GB). These results confirm our theoretical findings and justify our contribution.
To the best of our knowledge, there are no studies addressing numerical computations of the properties of viscoelastic composites by varying the microstructure within the periodic cell. In addition, the manuscript presents some differences with respect to previous works of some of the authors  ( Otero et al. , 2020; Penta & Gerisch, 2016; Rodríguez-Ramos et al. , 2020 )  and with others in the literature  ( Chen, Wang, Chen, & Geng, 2017; Chen et al. , 2020; Li, Chen, Liu, & Wang, 2019; Wang & Pindera, 2016a  ). Specifically, the numerical solution of the local problem is tackled by means of the finite element software COMSOL Multiphysics® and LiveLink TM for Matlab® scripting, and the solution's convergence is analyzed through three types of mesh discretization. We compute the effective coefficients for several types of fiber and inclusion reinforced structures with isotropic components and perfect contact at the interfaces.
Conclusions In this paper, we use a semi-analytical technique that naturally appear as a combination of the two-scale asymptotic homogenization method and a three dimensional computational study, by means of the finite element software COMSOL Multiphysics®. Particularly, the methodology is framed to the investigation of the effective creep and relaxation behavior of non-aging linear viscoelastic composite materials. The results derived from the AHM's procedure are based on the elasticviscoelastic correspondence principle and the Laplace-Carson transform, and comprised the local and homogenized problem, and the expression for the effective coefficients. Moreover, the stress jump conditions and the corresponding interface loads for composites with discontinuous material properties and perfect contact at the interfaces as well as significantly details of the three dimensional computational study are provided.
Acknowledgements We gratefully acknowledge to  Ph. D. Qiang Chen  for the valuable assistance in providing us with the data for the comparisons. OLCG kindly thanks to  Ecole Doctorale  no. 353   de L'Université de Aix Marseille and L'équipe Matériaux & Structures du Laboratoire de Mécanique et d'Acoustique   LMA -UMR 7031 AMU -CNRS -Centrale Marseille 4  impasse  Nikola Tesla   CS 40 0 06 13453 Marseille Cedex 13 , France. RRR thanks to  Departamento de Matemáticas y Mecánica, IIMAS  and  PREI-DGAPA  at  UNAM , for its support to his research project and the  Aix-Marseille Université. ART kindly acknowledges the  Dipartimento di Scienze Matematiche (DISMA) "G. L. Lagrange " of the  Politecnico di Torino , " Dipartimento di Eccellenza 2018-2022 ", Project code:  E11G180 0 0350 0 01. RP is partially supported by  EPSRC  grant ( EP/S030875/1 ).
Identification of Suitable Reference Genes The geNorm RG Selection Kit (PrimerDesign) was used to evaluate expression stability of 12 commonly used RGs in HepaRG control and hepatotoxin treated cells to identify a set of stably expressed RGs to be used in the normalisation of data. The tested RGs are detailed in Table  2. The geNorm output ranked the candidate reference genes according to their expression stability (M). Using this approach, we identified a minimum set of the most stably expressed genes in various experimental settings as RG candidates. These were used for the analysis of the changes in the expression of the genes of interest. Table  2. The 12 housekeeping gene candidates and function. The genes function description is provided based on the information from the human genome database (GDB, http://www.
Quality Control on Post-RT-qPCR Data The quality of RT-qPCR data was carefully investigated during multiple processing steps of RT-qPCR. The samples were treated with DNAase before cDNA synthesis. The assessment of the shape of sigmoidal amplification curves showed reactions of a high quality and an absence of inhibitors. Where the melting curve showed non-specific amplification products, the results were excluded from analysis. The melting curve analysis showed that all primer pairs led to the amplification of the desired product in the different samples.
Statistical Analysis For statistical analysis, the Graphpad prism6 software package (GraphPad Software, Inc. , La Jolla, CA, USA) was utilized. As there were multiple groups of differently treated samples, analysis of variance (ANOVA) was used to determine the differences across the groups. The statistical significance of the RT-qPCR results was assessed by the Repeated-Measure ANOVA with Tukey correction to evaluate the differences between means. This analysis was carried out on the Cq values after the calculation of the transcript copy number (fold change). The graphical representation of the RT-qPCR results represents the calculated transcript copy number, and the statistics shown in the graphs represent analysis based on the Cq values. The results were considered significant at p < 0. 05.
RT-qPCR Efficiency To compare the different RNA transcription levels, within and between experimental groups Cq (quantitation cycle) values of RT-qPCR reactions were compared directly. The Cq is defined as the number of cycles needed for the fluorescence signal to reach a specific threshold of detection and is inversely correlated with the amount of template nucleic acid present in the reaction  [21]. Cq comparison relies on the RT-qPCR efficiency of primers for all RG and GOI (Gene of Interest) being comparable. This was calculated by the primer supplier (PrimerDesign) as between 90% and 100%, indicating that primers were suitable for the delta Cq method of data analysis.
Stability Analysis Per Experimental Condition geNorm analysis of all samples showed that, for different experimental conditions, a different set of reference genes was required. Applying the default criteria of the geNorm module in qbase+ 4 reference gene expression stability (M) and the number of reference genes (V) required for the normalisation of GOI data was determined for each drug by grouping samples according to experimental conditions. In each of these groups, the number of samples, which fulfilled the criteria for geNorm analysis, is given (Figure  2 , Table  3 ). values for APAP-treated and untreated cells. The optimal number of reference targets in this experimental setting is 3 (geNorm V < 0. 15 when comparing a normalization factor based on the 2 or 3 most stable targets). As such, the optimal normalization factor can be calculated as the geometric mean of reference targets ATP5B, SDHA, and CYC1.
Normalising Data for Low and Highly Abundant Liver Gene RT-qPCR analysis was also applied to comparing the normalisation of the expression levels of low and highly abundant liver gene transcripts using VRG, GAPDH, or 18S. Albumin shows the highest RNA transcription level in liver20, and cyclin-dependent kinase inhibitor 2A (CDKN2A), an essential regulator of the cell cycle and cancer-related transcription factor, is not expressed in healthy hepatocytes. Increasing CPZ concentration induced significant Albumin expression (p < 0. 05) when results were normalized using VRG. Normalisation to 18S showed a trend in upregulation at 25 mM and downregulation at 50 mM (p < 0. 0005). Using GAPDH showed the opposite result to that seen using VRG, with significant downregulation of albumin induced by 50 mM CPZ concentration (p < 0.
These analyses showed that the application of single reference genes, e. g. , GAPDH or 18S, results in significant GOI RNA transcription differences that could even vary 100-fold, especially for low abundance gene transcripts as compared to using the geNorm-validated panel. These analyses showed that the application of single reference genes, e. g. , GAPDH or 18S, results in significant GOI RNA transcription differences that could even vary 100-fold, especially for low abundance gene transcripts as compared to using the geNorm-validated panel.
The geNorm analysis of all samples, APAP-and CPZ-treated, at all concentrations and untreated cells showed that the commonly used house-keeping RG genes, 18S and GAPDH, were not stably expressed across all treatments and therefore were ranked by the geNorm software as unsuitable as candidates for HepaRG transcriptional analysis unless used in a set of RGs (Figure  2 ). Both 18S and GAPDH have been commonly used as single internal transcriptional controls: in the vast majority of studies using HepaRG cells, normalisation to 18S expression has been used without the verification of expression stability under different conditions  [27, 28]. Our results show that stable RNA transcription for CPZ or APAP treatment is only met by three selected RGs: (CPZ: GAPDH, UBC, and TOP1) or (APAP: ATP5B, SDHA, and CYC1).
Using the geNorm-validated panel of reference genes, we found no upregulation of CYP3A4 following APAP treatment samples, also reported following a single APAP treatment in vivo  [34]. Comparing the expression level of low and high abundance liver gene transcripts CDKN2A and albumin respectively, normalisation to single reference genes, GAPDH, or 18S showed variation in transcription. In the case of low abundance gene transcripts CDKN2A, this reached up to 100-fold. This emphasizes the need to identify candidate RGs in HepaRG cells specific to experimental conditions as reference genes are differently regulated under different conditions. Collectively, these data showed that using GAPDH or 18S as a single RG for normalisation gene expression in HepaRG cells may result in misinterpretation of RT-qPCR data.
In contrast, another study showed that GAPDH, eEF-1, and UBC were suitable reference genes for porcine dorsal root ganglia samples, whereas ACTB, SDHA, and UBC were more appropriate for spinal cord samples  [43]. In conclusion, we assessed the expression stability of 12 commonly used RGs. Future studies may consider different gene combinations, including the TPB gene or other well characterised genes for metabolic function. We have demonstrated that application of 18S or GAPDH commonly used as RGs for normalisation of RT-qPCR data were consistently unreliable and should be used with caution as individual genes in studies involving HepaRG cells. Testing a panel of 12 RGs by using the geNorm tool to validate expression stability can identify as few as three RGs to serve as a good standard for normalizing RT-qPCR data from differentiated HepaRG cells exposed to hepatotoxins, such as CPZ and APAP.
Symmetry Plane Symmetry Plane CC → → ↘ ↘ → ↗ CS → ↘ → ↗ ↗ → down the space of possible dimensions of the CS-CPW for a given target specification, the design of state-of-the-art CS-CPWs still requires a number of iterations in an optimization process to minimize the losses. This issue can be addressed using two approaches: (i) using time-consuming full-wave 3D EM simulations or (ii) using an electrical model considering lossy CS-CPWs. The required time for full-wave 3D EM simulations of these structures is very long. For instance, using commercial software such as ANSYS HFSS the processing time can go from few minutes to 8-10 hours, depending on the architecture (i. e. uncut, CC-shielding or SC-shielding) and the computing power. Indeed, long design and optimization times are one of the main obstacles for the adoption of the CS-CPW structures.
At this frequency, the analytical model predicts -3. 6 dB at the through port, -3 dB at the coupled port and around 22 dB of isolation and return loss. Finally, the 3D EM simulation predicted -3. 6 dB at the through port, -3. 5 dB at the coupled port and around 26 dB of isolation and return loss, in a good agreement with the measurement results and the proposed analytical model. Above 180 GHz it can be observed that the EM simulation and the model-based results diverge. This can be easily explained by a difference in the magnitude of some of the parameters in the distributed model, which make the coupler resonate at a lower frequency than the one given by EM simulation. Fig. 12  shows the phase difference between the through port and the coupled port seen from the input (i. e. ∠ «# -∠ $# ), for the measurement (solid-line), model-(dashed-line) and EMbased (dots) datasets.
7, 4. 2 and 3. 2 dB, respectively. The through port presents -2. 9, -2. 6 and -3. 1 dB for the measurement, EM-and modelbased results, respectively. The EM-and model-based isolation (return loss) are 20 dB (23 dB) and 37 dB (37 dB), respectively. Next, in the 220-360 GHz band, the through and coupled Sparameters present good agreement between the analytical model and the 3D EM simulations. However, as for the previous 120-GHz coupler, the isolation and return loss between these datasets diverge when considering frequencies for which the coupler electrical length gets closer to  1 /2. This is especially remarkable above 300 GHz. Fig. 16  displays the phase difference between the through and coupled ports seen from the input, for the measurement (solid-line), EM-(dots) and model-based (dashed-line) datasets.
The proposed model was then validated using two 3-dB couplers with central frequencies of 120 GHz and 185 GHz, respectively. These couplers showed state-of-the-art or beyond state-of-the-art performance while keeping comparable sizes as compared to their counterparts. Couplers with different coupling could be easily designed thanks to the developed analytical model. By varying the value of the electrical and magnetic coupling coefficients, which is made possible by the floating ribbons cutting technique, forward and transdirectional type couplers could also be investigated. Finally, authors hope that the development of a simple to use analytical model will allow designers to take advantage of this efficient topology of coupled lines. The model was implemented on MATLAB and can be distributed to the readers upon request.