dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis The morphological quantification results obtained by our software are analyzed with analysis of variance (ANOVA) to confirm the significant differences among the four taxonomical groups of Riccardia by taking one morphological variable at a time. Multivariate analysis of variance (MANOVA) assesses the statistical significance of the group differences by considering all of the variables simultaneously. Our analysis goal is to distinguish a group from the four groups by considering the variables; therefore, canonical discriminant analysis (CDA) was applied by finding the combinations of the variables that maximize the discrimination of the predefined groups, testing whether the means of those groups are significantly different, and computing classification rate. Statistical analysis was performed using RStudio Team  [38].
Results We used a dataset with 138 images from the four taxonomical groups of thalloid liverworts, which consist of 37 samples from R. amazonica collected from Africa, 26 samples of R. amazonica collected from South America, 25 samples of R. compacta collected from Africa, and 50 samples of Riccardia obtusa collected from Africa. The morphological variables described above were quantified with our software. The graphical result of some of the sample images of thalloid liverworts are shown in Additional file 2: Figure  SI1. Table  1  shows descriptive statistics of the six morphological measurement variables for each group. From Table  1a , means of some variables seem to differ noticeably among the four groups, which offer an opportunity to use the variables to distinguish samples in one group from the others.
The original image with overlapping branches was also measured in order to compare the data. The quantitative data of some morphological characters of African R. amazonica, R. compacta, and R. obtusa   from literature were reported (Table  8 ). At the species level, it seems difficult to compare the literature data in Table  8  with our data due to the imprecision of measurements and different protocols. However, if we just compare width (junction thickness) and length (branch length), our data (Table  1 ) seem to provide lower values than the literature data. This could be due to the measurements between skeleton nodes that are not taken in account in the literature data, unlike our methods which defined precisely how to measure them based on points defined by junctions or nodes in the image skeleton.
For this reason, having access to a 3D model of the art, which can be manipulated and annotated by archaeologists worldwide, would be useful. The tool described here, MAAP Annotate, forms part of the Megalithic Art Analysis Project (MAAP)  2. MAAP aims to explore how low cost 3D scanning devices (e. g. , the Xbox Kinect sensor) could be used by archaeologists to generate 3D models directly in the field. One of the goals of the project is to create a queryable database of annotated 3D models. In order to create this database, it is necessary to first have a tool which can be used to annotate the art. This was one of the primary motivations for the development of MAAP Annotate. While the current focus of the tool is megalithic art, it is intended that this tool could be used to annotate any other archaeological artefacts (i.
e. , any other type of rock art, pottery). This paper is divided into five sections. Section II introduces the reader to the archaeological context, and briefly review prior research in this area. Section III outlines the system design and the software used to create MAAP Annotate, before giving a detailed description of the tool. Section IV describes the evaluation of the tool by archaeologists, and summarises the results. Section V discusses how this tool could be improved in the future, and what features remain to be implemented. Section VI concludes the paper with a brief exploration of the potential of MR tools in archaeology.
These annotations will then be sent to a MAAP Annotate database, which will store models and annotations. However, this database is not yet functional. This is discussed in more details in Section V. c) Previous Annotations: although this is a fundamental part of MAAP Annotate, which will be developed before the final version of the application, it has not yet been implemented. Currently, the option is displayed, but cannot be selected on the menu. The annotation box to the left of the stone will be used in this feature.
B. Software Library Microsoft HoloLens offers a developer centre 5  with a Windows SDK (Software Development Kit) and resources such as documentation, tutorials, and source code. The recommended tools for HoloLens include: Visual Studio 2017  6  , HoloLens Emulator 7  and Unity 8. Note that Unity also offers a holographic emulator which is really helpful as it decreases the amount of time spent deploying the application onto the HoloLens. The open source 3D creation software Blender  9 was used primarily to process the 3D models offline in order to display them in the HoloLens. Indeed, in order to display 3D models in real-time in the HoloLens, it was often required to pre-process them to lower the mesh quality (i. e. , reduce the number of vertices and faces) and to add a high resolution texture as well as a normal map.
This allowed use to accommodate the HoloLens requirements while presenting the best possible visual representation of the stone, allowing archaeologists to examine the megalithic art in sufficient detail. HoloToolkit  10  is an open source toolkit for HoloLens development in Unity. It provides scripts and components that were extremely useful while developing this application. For instance, we can use events managers to know when a source (voice, hand, gesture) is detected or lost by the device. Moreover when that source is a hand we can access its position, which we use during the selection process.
For this reason, the suggestions about the placement of these items will be implemented. Future work will also involve creating a database in which all of the stones in Ireland and Northern Ireland will be stored. Eventually, it is intended that 3D models collected in other countries will also be available on this project. When this database is created, users will be able to select any country for which 3D models are available. After choosing a country, they may choose an archaeological site, and all the stones with megalithic art from that site will appear on the screen. They will then have the option to select the stone that they wish to annotate. Having opened the first model, it will be possible to select additional stones and view them all simultaneously. It may be possible to view these stones in a carousel, as suggested by one of the participants.
This would allow a user to explore a site without having too many models present in the room at one time. Of course, one of the challenges associated with this is that the memory on the HoloLens cannot support such a database. It will be necessary to store everything online, meaning that it will not be possible to use this application without internet access. It will also be possible to save annotations to this database, and to access all of the stones and annotations using MAAP Annotate. This will facilitate collaboration between archaeologists using this application, and will allow previous annotations to be fully implemented. "Previous annotations" includes both notes made by archaeologists, and the areas which they have highlighted. When users are viewing previous annotations, the area that is being discussed will automatically be highlighted.
However, it will be possible to turn off this emphasis in order to better view the art itself. When implementing the previous annotations option, notes will be ranked by expertise of the author possibly through a system of upvoting or accreditation. This will ensure users that the information they receive is legitimate. Upon selecting an annotation the user will be presented with the option of either reading or listening to it. In the final MAAP database, only verified archaeologists will be able to annotate. This will prevent non-specialists from adding inaccurate information that would decrease the value of the database. The high scores given to MAAP Annotate by archaeologists are significant because they imply that in the future MR tools, such as the HoloLens, can be accepted and will be more widely used in an archaeological context.
VI. CONCLUSION MAAP Annotate, although currently incomplete, provides an interactive and useful way to annotate 3D models. Currently, it is possible to manipulate a 3D model, and to create annotations. The results of our SUS demonstrated that the features implemented thus far are, for the most part, ready for use. There are some improvements to be made -the light and the drawing selection were the two most commonly criticised features. However, there remains work to be done to make this tool fully functional and usable by archaeologists. The main focus for future work will be to create a database into which annotations can be saved, and from which previous annotations can be accessed. In addition to this, a method of viewing multiple stones will need to be developed. The SUS was useful in this regard, as it provided confirmation that this was perceived as important by archaeologists.
The suggested carousel of megalithic art is an interesting idea, and is something that will be investigated. The largest obstacle to this is the lack of memory available on the HoloLens. It is likely that the database will be stored in the cloud, and will be accessed via the HoloLens connected to Wi-Fi. There are some other issues that need to be addressed, however these are mostly cosmetic -placement of menus for example -and are not the top priority for the completion of this program. One of the primary motivations for this project was to create an annotation tool which could be used to annotate models for a queryable database of megalithic art. When the application is complete, the MAAP project will utilise this tool to start annotating these models. This will allow further, more intensive, analysis of megalithic art by archaeologists.
It is interesting that 7 of the survey participants (70%) commented upon the potential of this tool for access and collaboration. This suggests that the database, which has similar goals, will be welcomed by the archaeological community. Of course, this tool could be used on any 3D model, and not simply megalithic art. Although the primary aim of this tool is to aid archaeologists studying megalithic monuments in their research, it could also be used in other fields. This will be explored once MAAP Annotate is complete and accessible. Annotating in MR is an interactive and straightforward method of sharing knowledge and adding information to 3D models. It is hoped that MAAP Annotate will be an aid to archaeological research, teaching, and public outreach.
Background. The availability of large databases containing high resolution threedimensional (3D) models of proteins in conjunction with functional annotation allows the exploitation of advanced supervised machine learning techniques for automatic protein function prediction. Methods. In this work, novel shape features are extracted representing protein structure in the form of local (per amino acid) distribution of angles and amino acid distances, respectively. Each of the multi-channel feature maps is introduced into a deep convolutional neural network (CNN) for function prediction and the outputs are fused through support vector machines or a correlation-based k-nearest neighbor classifier. Two different architectures are investigated employing either one CNN per multichannel feature set, or one CNN per image channel.
Results. Cross validation experiments on single-functional enzymes (n = 44,661) from the PDB database achieved 90. 1% correct classification, demonstrating an improvement over previous results on the same dataset when sequence similarity was not considered. Discussion. The automatic prediction of protein function can provide quick annotations on extensive datasets opening the path for relevant applications, such as pharmacological target identification. The proposed method shows promise for structure-based protein function prediction, but sufficient data may not yet be available to properly assess the method's performance on non-homologous proteins and thus reduce the confounding factor of evolutionary relationships.
INTRODUCTION Metagenomics has led to a huge increase of protein databases and the discovery of new protein families  (Godzik, 2011). While the number of newly discovered, but possibly redundant, protein sequences rapidly increases, experimentally verified functional annotation of whole genomes remains limited. Protein structure, i. e. , the 3D configuration of the chain of amino acids, is a very good predictor of protein function, and in fact a more reliable predictor than protein sequence because it is far more conserved in nature  (Illergård, Ardell & Elofsson, 2009). By now, the number of proteins with functional annotation and experimentally predicted structure of their native state (e. g. , by NMR spectroscopy or X-ray the PDB database into the l = 6 primary categories: oxidoreductases (EC1), transferases (EC2), hydrolases (EC3), lyases (EC4), isomerases (EC5), ligases (EC6).
Representation of protein structure The building blocks of proteins are amino acids which are linked together by peptide bonds into a chain. The polypeptide folds into a specific conformation depending on the interactions between its amino acid side chains which have different chemistries. Many conformations of this chain are possible due to the rotation of the chain about each carbon (Cα) atom. For structure representation, two sets of feature maps were used. They express the shape of the protein backbone and the distances between the protein building blocks (amino acids). The use of global rotation and translation invariant features is preferred over features based on the Cartesian coordinates of atoms, in order to avoid prior protein alignment, which is a bottleneck in the case of large datasets with proteins of several classes (unknown reference template space).
RESULTS The protein structures (n = 44,661) were collected from the PDB. Only enzymes that occur in a single class were processed, whereas enzymes that perform multiple reactions and are hence associated with multiple enzymatic functions were excluded. Since protein sequence was not examined during feature extraction, all enzymes were considered without other exclusion criteria, such as small sequence length or homology bias. The dataset was unbalanced in respect to the different classes. The number of samples per class is shown in Table  1. The dataset was split into five folds. Four folds were used for training and one for testing. The training samples were used to learn the parameters of the network (such as the weights of the convolution filters), as well as the parameters of the subsequent classifiers used during fusion (SVM or kNN model).
Effect of sequence redundancy and sample size Analysis of protein datasets is often performed after removal of redundancy, such that the remaining entries do not overreach a pre-arranged threshold of sequence identity. In the previously presented results, sequence/threshold metrics were not applied to remove sequence-redundancy. Although structure similarity is affected by sequence similarity, the aim was not to lose structural entries (necessary for efficient learning) over a sequence based threshold cutoff. Also, only X-ray crystallography data were used; such data represent a 'snapshot' of a given protein's 3D structure. In order not to miss the multiple poses that the same protein may adopt in different crystallography experiments, the whole dataset was explored. Subsequently, the performance of the method was also investigated on a less redundant dataset and the classification accuracy was compared in respect to the original (redundant) dataset, but randomly subsampled to include equal number of proteins.
This experiment allows to assess the effect of redundancy under the same conditions (number of samples). Since inference in deep networks requires the estimation of a very large number of parameters, a large amount of training data is required and therefore very strict filtering strategies could not be applied. A dataset, the pdbaanr (http://dunbrack. fccc. edu/Guoli/pisces_download. php), pre-compiled by PISCES  (Wang & Dunbrack, 2003) , was used that includes only non-redundant sequences across all PDB files (n = 23242 proteins, i. e. , half in size of the original dataset). This dataset has one representative for each unique sequence in the PDB; representative chains are selected based on the highest resolution structure available and then the best R-values. Non-X-ray structures are considered after X-ray structures.
As a note, the author also explored the Leaf algorithm  (Bull, Muldoon & Doig, 2013)  which is especially designed to maximize the number of retained proteins and has shown improvement over PISCES. However, the computational cost was too high (possibly due to the large number of samples) and the analysis was not completed. The classification performance was assessed on Architecture 2 by using 80% of the samples for training and 20% of the samples for testing. For the pdbaanr dataset, the accuracy was 79. 3% for kNN and 75. 5% for linear-SVM, whereas for the sub-sampled dataset it was 85. 7% for kNN and 83. 2% for linear-SVM. The results show that for the selected classifier (kNN), the accuracy drops 4. 4% when the number of samples is reduced to the half, and it also drops additionally 6. 4% if the utilized sequences are less similar.
DISCUSSION A deep CNN ensemble was presented that performs enzymatic function classification through fusion in feature level and decision level. The method has been applied for the prediction of the primary EC number and achieved 90. 1% accuracy, which is a considerable improvement over the accuracy obtained in our previous work  (73. 5% in Amidi et al. (2016)  and 83% in Amidi et al. (  2017 )) when only structural information was incorporated. These results were achieved without imposing any pre-selection criteria, such as based on sequence identity, thus the effect of evolutionary relationships, as confounding factor in the prediction of function from 3D structure, has not been sufficiently studied. Since deep learning technology requires a large number of samples to produce generalizable models, a filtered dataset with only non-redundant proteins would be too small for reliable training.
This is a limitation of the current approach, which mainly aimed to increase predictive power over previous methods using common features for structural representation and common classifiers such as SVM and nearest neighbor, rather than addressing this confounding factor in the prediction of protein structure. Many methods have been proposed in the literature using different features and different classifiers. Nasibov & Kandemir-Cavas (2009)  obtained 95%-99% accuracy by applying kNN-based classification on 1200 enzymes based on their amino acid composition. Shen & Chou (2007)  fused results derived from the functional domain and evolution information and obtained 93. 7% average accuracy on 9,832 enzymes. On the same dataset, The vertical axis at each plot corresponds to the 23 amino acids sorted alphabetically from top to bottom (ALA, ARG, ASN, ASP, ASX, CYS, GLN, MET, GLU, GLX, GLY, HIS, ILE, LEU, LYS, PHE, PRO, SER, THR, TRP, TYR, UNK, VAL).
Thus, the presented results are not directly comparable to the ones of previous methods due to the use of different features and datasets. If desired, the current approach can easily incorporate also sequence-related features. In such a case however, the use of non-homologous data would be inevitable for rigorous assessment. The reported accuracy is the average of five folds on the testing set. A separate validation set was not used within each fold, because the design of the network architecture (size of convolution kernel, number of layers, etc. ) and the final classifier (number of neighbors in kNN) were preselected and not optimized within the learning framework. Additional validation and optimization of the model would be necessary to improve performance and provide better insight into the capabilities of this method.
CONCLUSIONS A method was presented that extracts shape features from the 3D protein geometry that are introduced into a deep CNN ensemble for enzymatic function prediction. The investigation of protein function based only on structure reveals relationships hidden at the sequence level and provides the foundation to build a better understanding of the molecular basis of biological complexity. Overall, the presented approach can provide quick protein function predictions on extensive datasets opening the path for relevant applications, such as pharmacological target identification. Future work includes application of the method for prediction of the hierarchical relation of function subcategories and annotation of enzymes up to the last digit of the enzyme classification system.
High-throughput next generation sequencing (NGS) Background: technologies enable the detection of biomarkers used for tumor classification, disease monitoring and cancer therapy. Whole-transcriptome analysis using RNA-seq is important, not only as a means of understanding the mechanisms responsible for complex diseases but also to efficiently identify novel genes/exons, splice isoforms, RNA editing, allele-specific mutations, differential gene expression and fusion-transcripts or chimeric RNA (chRNA). We used , a tool that uses genomic locations and local Methods: Crac coverage to classify biological events and directly infer splice and chimeric junctions within a single read. Crac's algorithm extracts transcriptional chimeric events irrespective of annotation with a high sensitivity, and was CracTools used to aggregate, annotate and filter the chRNA reads.
The selected chRNA candidates were validated by real time PCR and sequencing. In order to check the tumor specific expression of chRNA, we analyzed a publicly available dataset using a new tag search approach. We present data related to acute myeloid leukemia (AML) RNA-seq Results: analysis. We highlight novel biological cases of chRNA, in addition to previously well characterized leukemia chRNA. We have identified and validated 17 chRNAs among 3 AML patients: 10 from an AML patient with a translocation between chromosomes 15 and 17 (AML-t(15;17), 4 from patient with normal karyotype (AML-NK) 3 from a patient with chromosomal 16 inversion (AML-inv16). The new fusion transcripts can be classified into four report report report ,.
Though RNA-Seq may enable new biomarker discovery, there is a lack of consensus on which analysis tool and algorithmic strategy should be used, especially in the detection of chRNA  13. We recently proposed Crac  14  , a novel way of analyzing reads that combines genomic locations and local coverage to classify the biological events, to directly infer splice and chimeric junctions within a single read. The Crac software is based on an innovative algorithm that allows extraction of transcriptional events, irrespective of annotation. The main advantage of using Crac to detect chimeras is its high sensitivity and specificity, allowing detection of rare events with confidence. We developed the complementary CracTools module to aggregate, annotate, and filter the chRNA reads. The procedure classifies reads into 4 classes, depending on the exon organization.