dataesr/scientific-paragraphs-categorization huggingface dataset:
Structural analysis Structural analysis of the p. (Gly726Val) variant was performed based on the crystal structure of murine PPFIBP1 (PDB: 3TAD 3 ), which exhibits 97% sequence identity to its human ortholog in the region of the SAM domains. The exchange was modeled with SwissModel  17  and RasMol  18  was used for structure analysis and visualization. Structural analysis for the other variants was not necessary as all are LoF variants and predicted to lead to a loss of protein.
Multiple in silico tools consistently predicted a damaging effect of the variant (Table  S3 ). Structural analysis showed that Gly726 is located in a tight turn of the second SAM-domain of PPFIPB1 (Figure  2B ). At this position, a valine can only be accommodated in a strained backbone conformation resulting in domain destabilization. In addition, the longer valine sidechain causes steric problems with Asn803 located in the third SAM domain (Figure  1B ), which are predicted to disrupt the domain interface. All of the identified variants are very rare in the general population, represented by the gnomAD database. 14 The variants detected in the families 1, 2, 3, 4, 6, 8, 9, 10 and in the fetus are absent from gnomAD. Five alleles are reported for the variant identified in family 5 (MAF of 0.
Acknowledgements We thank all families that participated in this study. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 Research and Innovation Program  (Grant Agreement No. 714853 ) and was supported by the  Medical Research Council  through Grant  MC-A658-5TY30. HT was supported by the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under grant agreement no. 608473.
Data and code availability The code used for tracking and extracting C. elegans behavioural features is available at https://github. com/Tierpsy and code for performing statistical analysis and generating figures is available at https://github. com/Tom-OBrien/Phenotyping-hlb1-disease-model-mutant. The associated C. elegans datasets are available at https://doi. org/10. 5281/zenodo. 6338403. The Morbid Genes Panel is available here https://morbidgenes. org/ and here https://zenodo. org/record/6136995#. YiYvI-jMKUk.
22 * (L/4) 2. 5 and T H + = 3. 5 * (L/4) 2 , respectively, from  (Sittler et al. 2006). The average ion mass of the dominant ion species ions is calculated as m i = 32n O + 2 + 2n H + 2 n O + 2 + n H + 2 , (3. 9) using the modelled ion densities by  Tseng et al. (2013). On average, we have m i ∼ 29 amu, T H + ∼ 0. 07 eV and T W + ∼ 1. 4 eV. There was no measurements of the dust temperature (T d ) in the F ring region. However, because the F ring and the Cassini Division (CD) have similar optical depth values (τ ∼ 0. 1), we assume that T d ∼ T F ∼ T CD ∼ 88 K as observed by the Composite Infrared Spectrometer (CIRS) during the ring-grazing orbits of Cassini  (Tiscareno et al. 2019). 4. Data analysis: Cassini's F-ring-grazing orbits 4. 1. RPWS/LP data Using the data collected by the RPWS/LP instrument ( § 3) during Cassini's F-ring-grazing orbits, we focus the analysis on the near-equatorial plane (|Z| 0.
Nevertheless, on average the charged dust are observed between -0. 13 R s Z 0. 2 R s , https://doi. org/10. 1017/S0022377822000186 Published online by Cambridge University Press slightly shifted towards the northern hemisphere. However, to confirm this offset as the effect of the ambipolar electrostatic field, the current dataset is not sufficient. This overall picture is in agreement with the LP measurements presented by  Morooka et al. (2018)  (see figure  2  in their work) who showed that the ions are concentrated on the equatorial plane (below the magnetic equator), whereas the electrons are more dominant above the ring plane than below it. However, the reason for the electron density asymmetry with respect to the kronographic equator was not clear until now.
Moreover, a large part of the risks endured by migrants during their journey comes from the inhospitality of the Sahara Desert, leading to dehydration, starvation, lack of healthcare and exposure to other human threats, especially against women. The International Organization for Migration (iom) missing persons database shows for instance that a large majority of deaths are caused during the trans-Saharan journey. 3 Moreover, the coronavirus crisis irremediably led to the closure of the borders of many states, including Morocco, and to the increase of territorial controls. These new restrictions put migrants crossing the desert under even greater threat to their security. For example, many decided to change their route, at the risk of getting lost  (iom, 2020). Despite all these threats to the safety of migrants, African citizens continue to head to Morocco.
The hybrid recommendation is performed by combining collaborative filtering and content-based filtering. As a result, the advantages of collaborative filtering and content-based filtering are utilized. Moreover, the intelligent behavior of Crows' assists the proper selection of neighbors, rating prediction, and in-depth analysis of the contents. Accordingly, an optimized recommendation is always provided to the target users. Finally, performance of the proposed model is tested using the TripAdvisor dataset. The experimental results reveal that the model provides 58%, 58. 5%, 27%, 24. 5%, and 25. 5% better Mean Absolute Error, Root Mean Square Error, Precision, Recall, and F-Measure, respectively, compared to similar algorithms.
However, with the increasing amount of movie content and users on a specific platform, this combination isn't considered efficient. Katarya et al. [5, 6]  have used FCM (fuzzy C-means), a soft clustering technique, to increase its efficiency. Recommendation Systems are used for filtering the required information from a large bucket of information for every individual. However, while filtering the information, it is also very important that the retrieved information is relevant and useful for end-use. Gray wolf optimization and fuzzy c-means techniques are used on MovieLens data clusters to achieve high precision and accuracy. Some of the popular RSs are Movie recommendation on Netflix, Music recommendation on Pandora, Product recommendation on Amazon, Tourism recommendation on Trip-advisor, the Jokes recommendation in Jester, and Social recommendation on Facebook.
Today, all tourism industries deploy recommendation techniques based on the concept of the Big Data model. In this paper, the concept of Big Data in the domain of tourism industries is considered. The tourism data set is analyzed first, and a structured form is made for further prediction. Crow search-based, hybrid recommendation techniques are applied to implement the proposed model. The proposed model combines collaborative and content-based filtering techniques to achieve more effective recommendations for clients in a real scenario. Finally, a comparison is performed with two different existing related works. The performance of the proposed model is also evaluated. The remaining parts of the article are organized as follows. Related Works are described in Section 2. Section 3 illustrates the relation of Big Data with Tourism Industries.
Katarya et al. [8]  described the recommendation systems work based on rating predictions on several items. It becomes equally important to focus on sequential information. To test this novel approach, an MNSBC data set was used, which contained 5000 entries per user. On the same line, to enhance the existing systems, another research was implemented on movie recommendations using K-Means and the Cukoo Search Optimization algorithm by Katarya et al. [9]. The final result was compared with the existing approaches and was found to be more precise. However, to enhance its quality, bio-inspired algae algorithms have also been used. To find the similarities between any two users' multilevel, the Pearson Correlation Coefficient (PCC) was used  [10]. Meanwhile, most music recommendation systems focus more on contextual information, such asmusic ratings and user-user interaction dynamics.
[14]  introduced a matrix factorization-based hybrid recommendation algorithm consisting of collaborative filtering and content-based filtering. Shamri et al. [15]  proposed a hybrid filtering technique using the the Fuzzy-Genetic algorithm. The algorithm provides less Mean Absolute Error and more Coverage. Lee et al. [16]  proposed a neural network-based optimized hybrid recommendation algorithm on the MovieLens dataset to improvise Mean Absolute Error and Scalability. Campos et al. [17]  introduced a hybrid recommendation algorithm with the help of the Bayesian Network using MovieLens and IMDB datasets, providing few Mean Absolute Error. Shinde et al. [18]  proposed a hybrid recommendation framework, applying the Centering-Bunching-based Clustering algorithm using the Jester dataset, resulting in less Mean Absolute Error and higher Precision, Recall, and F-Measure.
Jung et al. [19]  performed user behavior analysis to accomplish a hybrid recommendation. The framework was successfully validated with the EachMovie dataset's help, providing top-n recommendations and less Mean Absolute Error. Christakou et al. [20]  introduced a neural network-based two-way hybrid filtering technique. The proposed algorithm was validated using the MovieLens dataset, resulting in higher Precision and Recall. Geetha et al. [21]  developed a K-Means clustering-based hybrid filtering technique. The experimental results show an accurate and personalized recommendation. Yang et al. [22]  proposed a job recommendation system based on hybrid filtering. The system is validated using User-Skill, User-City, Most Recent Company, and Most Recent Job Title features. Iaquinta et al. [23]  proposed a word net-based hybrid recommendation technique to optimize the electronic support system's performance by using Each Movie dataset.
The results reveal the proposed algorithm results in low Mean Absolute Error compared to the popular Pearson coefficient. Recent research by Ashami et al. [24]  introduced a clustering-based approach to finding the best neighbor of the target user. The clustering is accomplished using the hybridization of the Crow Search and Uniform Crossover Algorithm and the Jester dataset is used as a test-bed for providing significantly less Mean Absolute Error. Recently, Tesfaye et al. [25]  proposed a hybrid algorithm to accomplish intelligent Collaborative Filtering. The hybridization is achieved using the Crow Search Algorithm and K-Means Clustering. The MovieLens dataset is used as a test-bed and the results are promising (small Mean Absolute Error) compared to similar techniques. A new proposed model was read that used Newton's third law of gravitation and was applied to the data set taken from the user-item matrix  [26].
Proposed Model Today, big data is useful in the domain of tourism industries to better serve the clients in a more realistic way. In this research paper, an Intelligent Tourist Recommender System (ITRS) is proposed to guide tourists based on their preferences. The tourism industries can deploy the model to recommend the tourists regarding their entire plans. This proposed ITRS is beneficial for both tourists as well as tourism industries. To implement the ITRS model, a Big Data tourism database is used. The tourism dataset supports the '3V' properties of Big Data  [27, 28] , i. e. , volume, variety, and veracity. A tourism dataset is statistically analyzed  [29, 30]  and a balanced, structured matrix is formed to perform the prediction and recommendation. Sequential analysis is performed as a pilot survey to generate sub-matrices where all the sub-matrices are orthogonal.
Statistical Analysis To validate the proposed model, data analysis is required. Real-time data is used as a test-bed for this model. The data is analyzed by statistical approach. Using the tourism data set as a test-bed, it is analyzed based on the user's choice. The dataset is extremely large, with unstructured data patterns and integrity issues to deal with. Assuming that all the records of the dataset have n number of features with their different parameters' value as {u 1 , u 2 ,. u n }, the dataset is continuous then determines the density function ϕ(u 1 , u 2 ,. , u n ) for selecting the data sample randomly. A small sample of data, ∆v, belongs around the data points {u 1 , u 2 ,. u n }. In the dataset, the values of users' choices are categorized and near each other. To get the whole dataset to equilibrium, the users with a high frequency of traveling are distributed with possible equidistribution over the whole dataset with kinematic restriction.
The users with a high frequency of traveling will influence their co-centric neighbors. The first target is to distinguish the users who travel very frequently through any third-party travel agencies and determine the users with similar choices. The proposed model is validated using the tourism dataset as a test-bed. The tourism dataset is unstructured and contains noisy data. Thus, the first thing is to filter the dataset and structure through an analysis. The data sample of frequent users of position {u 1 , u 2 ,. u n } have E(u 1 , u 2 ,. u m ). The mean of frequent users per sample is determined as EU dv = d (1) where, dv indicates the unit volume of the sample dataset and d denotes the entire dataset. ϕ is determined and evaluates the degree of similarities for each sample distribution using a uniform distribution.
Since the pattern of a dataset of tourism industries is Big Data, a high degree of similarities is required to find more similar users. To determine the entropy of the sample, Equation (  2 ) is used. -U log u dv (2) Then, the solution of Equation (  2 ) is maximized using Equation  (1). The optimum selection of data sample U, for which -U log u dv is maximized and subjected to EU dv = d, is determined using Equation  (3). U = e φE+µ = βe φE ( 3 ) where β is selected such that U dv = 1. Using the inequality U log u q dv ≥ 0 (4) the sample data, ∆v, are selected randomly from the whole dataset for every iteration. For any two alternative densities, the inequality in Equation (  4 ) will be defined as -U log u dv ≤ -p log q dv = -U(φE + µ) dv = -(φd + µ) ( 5 ) where U denotes the data samples.
The variable U will have unity if ∀ incident of Equation (  5 ) is applied for Equation  (1). The data samples of frequent users are considered for the tourism industries. The degree of travel frequency is different for the different data samples. The sampling distribution is deployed to transform the degree of travel frequency for different samples, as per linear transformation Y = BU, ∀U → Y. If B is considered a non-singular matrix, then DY DU = |B| is a positive sign (6) As per the relational equation, the relation between the users is defined as Equation (  7 ): dy 1 dy 2. dy n = |B| du 1 du 2. du n (7) As per Equation (  6 ), dY = |B|dU ∀ B is an orthogonal matrix and |B = 1| (8) Y = BU which transforms into a quadratic form U I U → Y I Y (U -µ) (U -µ) → (Y -η) (Y -η) where, µ = mean and η = Bµ (9) The dissimilarity between frequent users and rear users is firmly determined and a threshold value is evaluated by using a partitioning matrix.
B =       B 1. B k       where, B i = n i × n and ∑ n i = n (10) The partitioning matrix in Equation (  10 ) is partitioned into kth sub-matrices where all the sub-matrices are orthogonal to each other but are not orthogonal by themselves. This is a pilot statistical analysis for the data. After statistical analysis, the dataset is visualized in Figure  2. The visualization of the dataset in Figure  2  represents the distributions of the data points. Data analysis is performed by using Algorithm 1. -U log u dv ≤ -p log q dv = -Z(ϕE + µ) dv = -(ϕd + µ) Step 7: Sampling distribution is deployed to transform the degree of travel frequency for different samples Step 8: The interrelated differential elements are defined as dy 1 dy 2. dy n = |B| du 1 du 2. du n Step 9: Determine dissimilarity between frequent users and rear users as dY = |B|dU ∀ B is an orthogonal matrix and |B = 1|, U I U → Y I Y (U -µ) (U -µ) → (Y -η) (Y -η) where, η = Bµ B =       B 1.
Computational Analysis As per the concept of Big Data, the tourism dataset has a large number of dimension values as a matrix form with some known coefficients. If Y and α denote the column vectors of the variables, y i and the parameter α j can be represented as a matrix notation. Y = Xα + ε, E(ε) = 0 and D(ε) = σ 2 I ⇒ E(Y) = Xα , D(Y) = σ 2 I ( 11 ) where D denotes the dispersion, and I stands for the unit matrix of order n. The objective of this analysis is to estimate the unknown parameter α j based on the observations of y i. In this research, the user's choice is estimated based on their other profile data. Now, established correlations among all observations of the different sample can be deduced by Equation (  12 ): E(Y) = XαD(Y) = σ 2 T, |T| = 0 (12) where, T is supposed to be a known matrix and both α and σ 2 are unknown.
Proposed Collaborative Filtering Approach Once the statistical analysis is completed, a new Collaborating Filtering  [31] [32] [33] [34] [35] [36] [37]  approach is proposed, which is used for the purpose of recommendation. In fact, a memory-based collaborative filtering approach is applied to determine the users who are similar to the target user. The proposed collaborative filtering helps to effectively predict user feedback based on the feedback of like-minded users. Therefore, it performs in-depth analysis of user profiles. As a result, sparsity and scalability problems are substantially minimized with respect to the dataset chosen in this research. Technically, the proposed approach consists of finding a new similarity measure, a threshold to determine the similar users, and the rating prediction technique.
The proposed approach is as follows: Assuming the total number of users is defined as n and the total number of hotels is m where n, m > 0, the dimension of the data set is n × m. Let us consider that the user gives a rating for t number of hotels among m. The rating of each user is determined by R in Equation (  13 ): R = t m log m ∑ j=1 r j (13) where r and t indicate the rating of each hotel and number of hotels, respectively. Similarly, it computes the rating for n number of users as R 1 , R 2 ,. , R n. Rating R is used to determine each user's similarity index with respect to the remaining users in the dataset. The similarity index R sim is evaluated using Equation (  14 ). (R sim ) i,j = t ln R i -R i-j m t ∑ i=1 R i -R j ∀ 1 ≤ j ≤ t (14) A hotel dataset is used with n number of users giving a rating.
Data Preparation The TripAdvisor [43] dataset has been used to accomplish experimentation for the proposed model. The dataset is comprised of 29,799 reviews of 21,851 unique customers. The reviews were collected from September 2007 to September 2009, including every hotel from all regions of Ireland. In addition to the survey text, each audit accompanies a lodging identifier, a general rating, and discretionary viewpoint explicit appraisals for the accompanying seven angles: Rooms, Cleanliness, Value, Service, Location, Check-in, and Business. All review-level appraisals are on a discrete ordinal scale from 1 to 5 (with 1 demonstrating that a viewpoint's explicit rating was not given by the commentator). The proposed model is also validated by two more datasets as well. The Datafiniti's Business dataset  [44]  has been used for validation purposes.
It consists of 10,000 reviews of users to the 2000 hotels in the United States. The dataset is made up of several attributes, such as hotel id, name, address rating, reviews, and username. The user reviews basically contain textual descriptions describing the user experience. On the other hand, user ratings are numeric values in the range between 1 and 5. Moreover, TripAdvisor hotel review datasets contributed by Barkha Bansal has also been considered for validation purposes  [45]. It consists of 20,491 reviews as well as ratings of users. The user reviews are textual information depicting user experience, whereas, user ratings are numeric values between 1 and 5. Table  2  contains a sample of 15 customer ratings from the TripAdvisor dataset. The value of customer ratings lies between 1 and 5.
Results and Simulations The tourism dataset is used as a test-bed to validate the proposed model. The data set is very large and has an imbalanced structure. As such, statistical analysis is performed to fit the dataset of the proposed model, and the simulation results are shown in Figure  2. The original data set, before the statistical analysis shown in Figure  2a , has data patterns that are unstructured and have some missing data fields. After the statistical analysis, the dataset is filtered and freed from noise data. As per the requirement, the data is filtered by the analysis. The filtered and structured dataset is shown in Figure  2b. The required data values are all in a single group other than noise data. In Figure  2b , the required data points are shown in the high-density section.
Performance Analysis To evaluate the proposed model's efficiency, performance analysis is derived based on the different parameters of the confusion matrix. In this research paper, a four-fold cross-validation technique is applied to the given data sets. The ratio of the training and testing dataset are selected as various ratios for four iterations. The precision and recall values for dataset 1, dataset 2, and dataset 3 are shown in Tables  14 15 16 respectively. after every iteration, the error is corrected to enhance the model more accurately. The proposed model is applied for a good recommendation for users based of their preferences. The data sets are validated by the proposed model through four-fold cross-validation techniques. The values of the performance metrics are different for four different strategies.
Analytical Comparison To measure the proposed model's efficiency, a comparison is performed with two recent research studies that deal with the crow search-based hybrid recommendation algorithm. The comparisons are shown in Tables  17  and 18. The evaluation of efficiency is carried out using standard performance metrics, that is, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Table  17  shows a comparison between the proposed model, HCSUC, Tesfaye et al. , collaborative filtering, content-based filtering, collaborative-content hybrid filtering, and particle swarm optimization (PSO)-based collaborative-content hybrid filtering with respect to MAE. To begin, the dataset's 500,1000,2000,3000, 4000, and 5000 user ratings are considered for rating prediction. Then, on unseen objects, the users' ratings are anticipated.
RMSE is more accurate than MAE for detecting prediction mistakes. To begin, user ratings of 500, 1000, 2000, 3000, 4000, and 5000 are picked from the dataset. The users' ratings on unrated things are consistently anticipated. Finally, the RMSE is calculated based on the normalized difference between the expected and actual ratings. The suggested model has an average RMSE of 0. 33, according to the experimental results. HCSUC and TESFAYE et al. , on the other hand, report average RMSE as0. 86 and 0. 97, respectively. On the other hand, collaborative filtering, contentbased filtering, collaborative-content hybrid, and PSO-based hybrid filtering results in an average RMSE of 0. 64, 0. 57, 0. 61, and 0. 71, respectively. As a consequence, the preceding data show that the suggested model is more efficient than similar techniques.