dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of known allergens in AllergenOnline. org database using FASTA at specific E-score limits for significance The predicted proteins from some allergenic species were compared to AllergenOnline. org database at different E-scores, and we focused on the best E-score threshold for identification of known allergens using the official WHO/IUIS Allergen Nomenclature in AOL database. Table  6  illustrates the identified allergens using FASTA in different allergenic species at representative E-scores of 1e-7, 1e-30, and 1e-100. All known allergens of major and minor allergenic sources in the AllergenOnline. org database were detected using E-scores of 10, 1, 0. 001, and 1e-7. However, some potentially important matches to allergens were missed in the FASTA searches when the E-score was reduced less than 10e-7.
Minor allergens and noise of CODEX limits To consider protein identity matches to minor allergens, predicted proteins of the 23 species were compared to AOL version 18B by FASTA using the HCC supercomputer. Those proteins that had a match of >35% identity to proteins from at least 10 of these species were considered evolutionarily conserved minor allergens. Most of the minor allergens represented had sequence identities less than 50% when compared within the protein type. Matches of >35% identity were found to allergens listed in AOL, and those are considered minor also because they do not have published evidence of causing clinical reactions, only IgE binding. They all matched at least 10 different species out of the Identification of known allergens listed in the AllergenOnline. org database using full-length FASTA.
Predicted proteins of the allergenic species listed in this table were compared to the AOL database. Matches above CODEX limits to known allergens were found using the official WHO/IUIS Allergen Nomenclature in the AOL database identified E-scores from 1e-7, 1e-30 and 1e-100. A few allergens were missed between E-scores of 1e-7 and 1e-30. species. Supplementary Table  2  lists these minor allergens and shows the number of species matched out of 23. Searches that identify protein identity matches to proteins in 10 or more diverse species must be evolutionarily conserved and are unlikely to represent real risks of crossreactivity.
Conclusion It is becoming more common to use a whole genome or a proteome bioinformatics approach to identify potential proteins in a wide variety of species. Some regulatory agencies or risk assessment scientists have suggested using these predicted proteins against allergen databases to identify possible risks of allergenicity for food safety. The CODEX guideline (>35% identity over 80 amino acids to any known allergen) has become a standard for possible risks of cross-reactivity since 2003. The comparison to www. AllergenOnline. org was made available to the public in 2005 to assess individual proteins. The database is updated annually. The interpretation of identity matches over 35% over 80 amino acids or the equivalent is assumed to be a positive identity match that would require serum IgE binding tests sera from subjects allergic to the matched allergen.
Since we know that matches at that identity level can occur by random chance, we tested the use of protein sequences predicted from genomes, transcriptomes, or proteomes against AOL to estimate the commonality of false positive matches. We compared the predicted proteins from the genomes of 23 diverse allergenic and low-or non-allergenic species including plant sources, fungi, fish, insect and other animal sources as well as human sequences against the AOL database using standard CODEX criteria as well as full-FASTA alignments to provide identity matches. We used a wide variety of E score criteria to consider that as a variable as well. A number of housekeeping proteins across many species had moderate to high identities to minor putative allergens in AOL. However, many of these proteins are highly conserved in most eukaryotes and as a consequence would be expected to be found in any search using the standard CODEX criteria.
Examples using three predicted proteomes from three novel foods were assessed against the AOL database and many identity matches were seen. The comparison of predicted proteins from 23 test species demonstrated conclusively that the low-level match of >35% identity over 80 amino acids over-predicts potential risks of allergy. We have concluded that Chlorella variabilis, Galdieria sulphuraria and Fusarium strain flavolapis do not represent a significant risk of food allergy to the general population as matches to similar proteins from many diverse species are very common. Alternative strategies of increasing the match criteria above 35% identity, possibly to 45% identity; decreasing the E-score below 1e-7 or smaller; may be needed although matches to a few allergens may be missed at 1e-20 and ranking of allergens in AOL regarding risks of disease could markedly improve this assessment strategy.
FASTA comparison of predicted proteins of Chlorella variabilis NC64A compared to AOL V18B (E-score: 1e-07). The amino acid sequences of all proteins predicted from the genome of this species were used to search version 18B of the AllergenOnline. org database to find identity matches with proteins listed as allergens or putative allergens in the database using full-length FASTA searches to identify matches of >35% identity with different E scores, those from matches at 1e-7 are shown here.
Acknowledgements Genomic sequences of Galdieria sp. were provided by  Fermentalg  and for Fusarium sp. by  Nature's Fynd. The AllergenOnline. orgdatabase search routines were developed by  John Wise  as an employee of the  University of Nebraska. Jean-Jack Riethoven of the Holland Computing Center at the University of Nebraska  provided BLASTP supercomputer access for much of this work and loaded the NCBI non-redundant Protein dataset on HCC.
The results provide a graphic structure or a base of rules facilitates understanding and corresponds to human reasoning. Learning by decision tree is part of supervised learning, where the class of each object in the database is given. The goal is to build a model from a set of examples associated with the classes to find a description for each of the classes from the common properties between the examples. Once this model has been built, we can extract a set of classification rules. In this model, the extracted rules are then used to classify new objects whose class is unknown. The classification is done by travelling a path from the root to a leaf. The class returned (default class) is the one that is most frequent among the examples on the sheet. At each internal node (decision node) of the tree, there is a test (question) which corresponds to an attribute in the learning base and a branch corresponding to each of the possible values of the attribute.
However, it is generally less accurate than other machine learning models like neural networks. In brief, this learning algorithm has the following three essential characteristics: • Interpretability: Because of its flowchart-like structure, the way attributes interact to give a prediction is very readable. • Efficiency: The induction process is done by a top-down algorithm which recursively splits terminal nodes of the current tree until they all contain elements of only one class. Practically, the algorithm is very fast in terms of running time and can be used on very large datasets (e. g. of millions of objects and thousands of features). • Flexibility: This method does not make any hypothesis about the problem under consideration. It can handle both continuous and discrete attributes. Predictions at leaf nodes may be symbolic or numerical (in which case, trees are called regression trees).
In addition, the tree induction method can be easily extended by improving tests at tree nodes (e. g. introducing linear combinations of attributes) or providing a prediction at terminal nodes by means of another model. The review is organised into three parts. The first aims at introducing a brief history of decision tree induction. We present mathematically basics and search strategy used to train and build a decision tree. We discuss the supervised learning problem and the trade-off between a model's ability to minimise bias and variance. In this regard, we are extending our investigation to fundamental aspects, such as ensemble meta-algorithms and pruning methods, which we must put in advance for building an optimal decision tree. In the second section, we introduce some results obtained by means of the Scikit-Learn Python modules and Pima Indians Diabetes data in order to feed our discussions and our perspectives in terms of future developments and applications of Python community.
In regression tasks, the fit method takes inputs and target arguments as in the classification setting, only that in this case target, it is expected to have floating point values (continuous values) instead of integer values. For a leaf L i , common criteria to minimise as for determining locations for future splits are mean squared error (MSE), which minimises the L i þ 1 error using mean values at terminal nodes, and mean absolute error (MAE), which minimises the L i error using median values at terminal nodes. Several software for decision trees building are available, most of them referenced in the literature. We cite the chi-squared automatic interaction detector (CHAID) method implemented in the SIPINA 1 tool which seeks to produce a tree of limited size, allowing to initiate a data exploration.
WEKA 2 uses C4. 5 algorithm, and there is no need to discretize any of the attributes, and scikit-learn Python library uses an optimised version of the CART algorithm. The current (version 0. 22. 1) implementation of scikit-learn library does not support categorical variables. A data encoding is mandatory at this stage (the labels transform into a value between 0 and nbClasses-1). The algorithm options are described in the Python documentation  3. The algorithm below generally summarises the learning phase of a decision tree which begins at the top of the tree with a root node containing all the objects of the learning set: In order for the tree to be easily interpreted, its size must be minimum. Thus, the test selection procedure applied at each node aims to choose the test (the attributethreshold pair) which separates the objects from the current sub-sample in an optimal way, i.
(2) In the second phase, we try to reduce the tree by using another criterion to compare trees of different sizes. The building time of the tree is of course longer. It can be penalising when the database is very large while the objective is to obtain a tree that performs better in classification phase. The idea that was introduced by Breiman et al. [4]  is to construct a sequence of trees T 0 , ::, T i , ::, T t , which minimise a function called cost complexity metric (previously mentioned). This function combines two factors: the classification error rate and the number of leaves in the tree using α parameter. For each internal node, Ne and T 0 , the relationship is defined as: α p ðÞ¼ ΔR S emp |T p | À 1 (9) where ΔR S emp is the number of additional errors that the decision tree makes on the set of samples S when we prune it at position p.
Decision tree classifier building in Scikit-Learn Today there are several best machine learning websites that propose tutorials to show how decision trees work using the different modules of python. We quote for example three popular websites: Towards Data Science, 4 KDnuggets, 5 and Kaggle. 6  Developers offer in a few lines of optimised code how to use decision tree method by covering the various topics concerning attribute selection measures, information gain, how to optimise decision tree performance, etc. From our side, we choose Pima Indians Diabetes datasets (often used in classification problems) to examine the various tuned parameters proposed as arguments by Scikit-Learn package. The Pima are a group of Native Americans living in Arizona. A genetic predisposition allowed this group to survive normally to a diet poor of carbohydrates for years.
In the recent years, a sudden shift from traditional agricultural crops to processed foods, together with a decline in physical activity, made them develop the highest prevalence of type 2 diabetes, and for this reason they have been subject of many studies. The original dataset is available at UCI Machine Learning Repository and can be downloaded from this address, 7 "diabetesdata. tar. Z", containing the distribution for 70 sets of data recorded on diabetes patients, several weeks to months worth of glucose, insulin and lifestyle data per patient. The dataset includes data from 768 women with 8 characteristics, in particular: 1. Number of times pregnant (NTP) 2. Plasma glucose concentration in 2 h in an oral glucose tolerance test (PGC) 3. Diastolic blood pressure (mm Hg) (DBP) 4. Triceps skinfold thickness (mm) (TSFT) 5.
Two-hour serum insulin (mu U/ml) (HSI) 6. Body mass index (weight in kg/(height in m) 2 )(BMI) 4 https://towardsdatascience. com/decision-tree-algorithm-explained-83beb6e78ef4 5 https://www. kdnuggets. com/2020/01/decision-tree-algorithm-explained. html 6 https://www. kaggle. com/dmilla/introduction-to-decision-trees-titanic-dataset The last column of the dataset indicates if the person has been diagnosed with diabetes or not. Without any data preparation step (cleaning, missing values processing, etc. ), we partitioned the dataset into a training data (75%) to build the tree and test data (25%) for prediction. Then we kept the default settings which we can see through the profile class function (Figure  2 ). The Scikit-Learn documentation  8  explains in detail how to use each parameter and offers other modules and functions to search information and internal structures of classifier from training to building step.
We will see that we have good accuracy with this setting. Accuracy can be computed by comparing actual test set values and predicted values (Figures  5 6 7 8 ). Most important features of Pima Indians Diabetes dataset is shown in Figures  4, 6  and 8. We can see the root node is glucose, which can show the glucose has the max information gain, so it confirm the common sense and the clinical  diagnosis basis. Body mass index (BMI) and age are also found among the first important variables. According to consulting relevant information, we know there are three indicators to determine the diabetes mellitus, which are fasting blood glucose, random blood glucose and blood glucose tolerance. Pima Indians Diabetes dataset only has blood glucose tolerance. Prevalence of diabetes mellitus, hypertension and dyslipidaemia increase with higher BMI (BMI 25 kg/m 2 ).
Discussions Decision trees accept, like most learning methods, several hyper-parameters that control its behaviour. In our use case, we used Gini index like information criteria to   split the learning data. This criterion has directed the method to build a tree with a maximum of 15 levels and to accept a node as a leaf if it includes at least five learning instances. Impurity (entropy) is a measure of disorder in dataset; if we have zero entropy, it means that all the instances of the target classes are the same, while it reaches its maximum when there is an equal number of instances of each class. At each node, we have a number of instances (from the dataset), and we measure its entropy. Setting impurity to a given value (chosen according to expertise and tests) will allow us to select the questions which give more homogeneous partitions (with the lowest entropy), when we consider only the instances for which the answer to the question is yes or no, that is to say when the entropy after answer to the question decreases.
On the other hand, the graphical representation of tree is not included and requires the implementation of another free software like "Graphviz" and "Pydotplus" modules. The pros and cons of decision trees are known and described in almost all the articles and works developed in this field. We highlight some that we consider important for industrial applications. Selecting features is an extremely important step when creating a machine learning solution. If the algorithm does not have good input functionality, it will not have enough material to learn, and the results will not be good, even if we have the best machine learning algorithm ever designed. The selection of characteristics can be done manually depending on the knowledge of the field and the machine learning method that we plan to use or by using automatic tools to evaluate and select the most promising.
Another common problem with datasets is the problem of missing values. In most cases, we take a classic imputation approach using the most common value in the training data, or the median value. When we replace missing values, we should understand that we are modifying the original problem and be careful when using this data for other analytical purposes. This is a general rule in machine learning. When we change the data, we should have a clear idea of what we are changing, to avoid distorting the final results. Fortunately, decision tree requires fewer data preprocessing from users. It is used with missing data, and there is no need to normalise features. However, we must be careful in the way we describe the categorical data. Having a priori knowledge of the data field, we can favour one or more modalities of a descriptor to force the discretization process to choose a threshold, which highlights the importance of the variables.
Moreover, Geurts  [21]  has shown that the choice of tests (attributes and thresholds) at the internal nodes of the tree can strongly depend on samples, which also contributes to the variance of the models built according to this method. Decision tree can easily capture nonlinear patterns, which is important in big data processing. Nevertheless it is sensitive to noisy data, and it can overfit it. In big data mining, online data processing is subject to continuous development (upgrade, environment change, catching up, bugs, etc. ) impacting the results expected by customers and users. To this, the problem of variation that can be reduced by bagging and boosting algorithms (that we mentioned in Section 2. 1) is added. Decision tree is biased with imbalance dataset. It is recommended to balance dataset before training to prevent the tree from being biased towards the classes that are dominant.
Conclusions Decision trees simply respond to a classification problem. Decision tree is one of the few methods that can be presented quickly, without getting lost in mathematical formulations difficult to grasp, to hearing not specialised in data processing or machine learning. In this chapter, we have described the key elements necessary to build a decision tree from a dataset as well as the pruning methods, pre-pruning and post-pruning. We have also pointed to ensemble meta-algorithms as alternative for solving the variance problem. We have seen that letting the decision tree grow to the end causes several problems, such as overfitting. In addition, the deeper the tree is, the more the number of instances (samples) per leaf decreases. On the other hand, several studies have shown that pruning decreases the performance of the decision tree in estimating probability.
In this study, one method of using antennas based on fractals to cover few kinds of public protection and disaster relief (PPDR) communications was presented. Dedicated antenna forms, necessary for antenna design by 5G implementation, were enhanced to suit the requirements of specific applications. Employing fractal-shaped antennas have allowed us to accomplish all these actions, which request compact, conformal, and broadband high performance devices. Antennas derived from Koch's curve fractals are studied. In order to implement PPDR communications in 5G technology, frequency bandwidths of importance have been carefully selected and properly included in the antenna developments under MATLAB environment. Important information necessary for antenna designers, such as 360 degrees directivity at various frequencies, the impedance (resistance and reactance) along the bandwidth of interest, as well as voltage standing wave ratio (VSWR) along the bandwidth of interest for dipole, one-iteration, and two-iteration Koch's curves, respectively, have been obtained.
In the fifth chapter, the results obtained using our developed MATLAB programs are presented, in specific frequency bandwidth carefully chosen in order to implement PPDR communications in 5G technology. erefore, the reader is given an overview of important information necessary for antenna designers, such as 360 degrees directivity at various frequencies, the impedance along the bandwidth of interest, as well as voltage standing wave ratio (VSWR) along the bandwidth of interest for dipole, oneiteration, and two-iteration Koch's curves, respectively. e characteristic of directivity at selected frequencies is also highlighted. e study concludes in the sixth chapter.
Results In order to implement PPDR communications in 5G technology, few bandwidths were suggested by ANCOM in Romania [14]: 452. 5-457. 5 MHz, 462. 5-467. 5 MHz, 753-758 MHz, and 788-791 MHz. 5. 1. Antennas Derived from Koch's Curve Fractals. From this point of view, we designed in MATLAB R2018b a center-fed fractal-shaped antenna (Koch's curve format) to observe how well does this kind of solution fits in a 5G communication in comparison with a dipole. By default, MATLAB generates the dipole with a length of one meter each side and 0. 1 meters width. By adjusting the dimensions according to the desired bandwidths, we obtain a length of 0. 75 meters each side and a width of 0. 0583 meters. After making the first iteration, we observe that the antenna should be fatten in order to obtain a wider bandwidth  [13].
In this study, two ways of using fractal-based antennas to cover few kinds of PPDR communications are presented. e fractal shape suggested is a Koch's curve fractalshaped antenna, which was based on a dipole. is kind of  All results were obtained using MATLAB R2018b software development. For the studied antennas, we have obtained the following results regarding the maximum gains in dBi (which is the unit of the ratio between the gains of the antenna compared to the gain of an isotropic antenna). For the dipole antennas, the gains are 2. 73 dBi and 4. 76 dBi at 460 MHz and 770 MHz, respectively. e gains for one-iteration fractal Koch antenna are 6. 91 dBi and 4. 51 dBi at 460 MHz and 770 MHz, and finally, for twoiteration fractal Koch antenna, the gains are 4. 91 dBi and 3. 28 dBi at 460 MHz and 770 MHz, respectively.
Clinical and economic inputs were based on public US data from the CDC and national databases, while data on vaccine effectiveness were extracted from published literature and clinical trials. The impact of HD TIV was further explored in five scenario analyses and deterministic sensitivity analyses (DSA). Results: Over 10 years, it is estimated that HD TIV resulted in an averted 1,333,479 influenza cases, 769,476 medical visits, 40,004 ED presentations, 520,342 cardiorespiratory hospitalizations and 73,689 deaths and generated an absolute $4. 6 billion in savings, translating into a return on investment of 214. 4%. Hospitalizations costs represented 98. 4% and 98. 3% of the management costs in the 'with HD TIV' strategy and 'without HD TIV' strategy respectively. Hospitalizations and HD TIV relative vaccine efficacy vs SD TIV as a major cost driver were further confirmed in scenario analysis and DSA.