dataesr/scientific-paragraphs-categorization huggingface dataset:
Brain and behavioural asymmetries have been documented in various taxa. Many of these asymmetries involve preferential left and right eye use. However, measuring eye use through manual frame-by-frame analyses from video recordings is laborious and may lead to biases. Recent progress in technology has allowed the development of accurate tracking techniques for measuring animal behaviour. Amongst these techniques, DeepLabCut, a Python-based tracking toolbox using transfer learning with deep neural networks, offers the possibility to track different body parts with unprecedented accuracy. Exploiting the potentialities of DeepLabCut, we developed Visual Field Analysis, an additional open-source application for extracting eye use data. To our knowledge, this is the first application that can automatically quantify left-right preferences in eye use.
Introduction Accurate quantification of animal behaviour is crucial to understanding its underlying mechanisms. Historically, behavioural measurements were collected manually. However, with technological progress, automated data collection and analyses have expanded  (Anderson & Perona, 2014) , making behavioural analyses more precise, reliable and effortless for the experimenter  (Lemaire et al. , 2021; Versace et al. , 2020a, b; Wood & Wood, 2019). Thus, computational ethology constitutes a promising research avenue, especially for research on laterality  (Vallortigara, 2021). Automated data collection may allow finer and more objective behavioural analyses than the ones provided by manual coding. However, many of the currently available tracking techniques and software can be complex to use and even inaccurate in some experimental conditions (such as in poor and changing illumination, low contrast, etc.
). The open-source toolbox DeepLabCut copes with these limitations  (Mathis et al. , 2018; Nath et al. , 2019). DeepLabCut exploits deep learning techniques to track animals' movements with unprecedented accuracy, without the need to apply any marker on the body of the animal  (Labuguen et al. , 2019; Mundorf et al. , 2020; Worley et al. , 2019; Wu et al. , 2019) , opening a new range of possibilities for measuring animal behaviour such as behavioural asymmetry data or preferential eye use. While different markerless software can track animals' body parts (e. g. EthoVision XT, ANY-maze), we focus on DeepLabCut because it is open-source, accurate, does not require a prespecified experimental setting and is widespread in behavioural research. It is now clear that structural and functional asymmetries, once believed to be unique to humans, are widespread among vertebrates  (Halpern et al.
Data acquisition To perform data acquisition, Visual Field Analysis requires three main inputs for each analysed subject (Fig. 2 ). The first input is the video recording of the animal's behaviour. The second input is a file containing information about the tracking (x, y coordinates) of specific body parts (output file provided by DeepLabCut). Visual Field Analysis focuses on three points located on the head: the closest points to the left eye, the right eye and the top of the head. For the current experiment, the positions of these three points were manually labelled on 100 frames so that DeepLabCut could accurately generalize each point of interest on all video recordings. The third input corresponds to a spreadsheet where the experimenter manually enters specific information about the observed animal.
Manual coding of the data To assess the reliability of the eye use data provided by Visual Field Analysis, we manually checked all the frames analysed by Visual Field Analyses (7639 frames in total). The frames were checked and saved using a built-in function provided by Visual Field Analysis. Each frame was inspected by two independent coders, each of whom coded all the frames independently from the other. Then, the two experimenters compared their output, re-inspected all the frames for which a different coding was assigned and agreed on a final labelling of these frames. The manual scores were attributed using the same score attribution method and the same visual field subdivisions as Visual Field Analysis. On each frame, the coders superimposed a transparent sheet, on which five lines originating from a central point represented the subdivisions of the chick visual field (see Fig.
Statistical analyses The scores obtained for each visual field (frontal left, frontal right, lateral left and lateral right) were compared between scoring methods (Visual Field Analysis vs manual coding) ual. An estimation of the of frames needed to achieve a significant power for the correlation analysis was run prior to the study, assuming an r value of 0. 9 (given the high accuracy of the tracking methodologies). According to this estimation, it would be sufficient to code only seven frames to achieve a statistically significant correlation. However, we deemed this to be insufficient to provide a reliable validation of our software. Thus, in order to provide the most precise estimation possible of the accuracy of our method, we decided to code all the frames available, greatly surpassing the minimum number required to achieve sufficient statistical power.
As some videos were better tracked by DeepLabCut than others, we report the reliability of our program in relation to the tracking accuracy (measured as the percentage of frames that received identical scoring in the manual and automated scoring). The statistical tests were performed using RStudio version 4. 0. 2 (RStudio Team, 2020). Fig. 4  Visualization of the projection lines defining each region of the visual field. The yellow line indicates the midline and delimitates each hemifield, providing the nasal margin of the frontal visual fields. The green lines delimitate the frontal visual fields from the lateral visual fields. The blue lines delimitate the lateral visual fields from the blind spot. In these pictures, referring to the setup of the current experiment, the visual field used to look at the stimulus is shown, which can be compared to the information reported on the left side of the pictures (within the dark rectangles on the top and bottom left corners of the images).
Eye use data reliability The Pearson's correlation tests revealed an almost perfect, and highly significant, correlation between scoring methods, both when the data for the whole sample were taken into consideration and when the analysis was run at the single subject level. This was true for each visual field (see Table  1  for statistics). The number of frames for which the manual scoring of the human coder was discrepant from that assigned by Visual Field Analysis (relabelled frames) is detailed for each subject in Table  1. No discrepancy between the manual coding and the automated coding was found for subjects 8 and 10. Thus, for these subjects, no frames were relabelled, and a perfect correlation was, of course, found between manual and automated scoring. One should note that the reliability of our application is directly dependent on the DeepLabCut tracking accuracy, which differs across conditions (different videos settings) and individuals (different behaviours).
The two types of coding (manual and automatic) were compared using Spearman's correlation tests, both at the individual level and for the whole sample of N = 10 chicks. This was done separately for each visual field (frontal left and right, lateral left and right). (green dot) and 'rightHead' (red dot) on an empty portion of the screen, close to the stimulus. In image 5B, the chick started to insert its head in the round opening, but most of it is still invisible. DeepLabCut incorrectly located the 'leftHead', 'topHead' and 'rightHead' labels on the animal's beak. 99. 9%. Nonetheless, the reliability of the program for scoring eye use remained relatively high all conditions (see Table  1  for statistics). Even when the tracking accuracy was at its lowest (65. 4% for subject 9), the correlation between the coding provided by Visual Field Analysis and the manual coding remained strong for most visual fields (Pearson's r ranging from 0.
It also excludes frames where the distance between the DeepLabCut's labels is higher than a given threshold. Note that the two measures are often correlated: when DeepLabCut tracking is imperfect, both the number of frames considered as unlikely to be well tracked and the number of frames considered as outliers with the threshold method are high. Therefore, most of the frames that could be wrongly tracked are excluded from the analysis. Moreover, using a built-in function of our application, the user can manually visualize a certain number of random frames of a video to check the program performance. We suggest visualizing at least 100 frames per individual with more than 90% of tracking accuracy in order to achieve a performance similar to what is described in the current study. If the tracking accuracy is lower than 90%, we suggest training DeepLabCut again using a new set of frames or a different labelling method.
In animals with frontally placed eyes and stronger inter-hemispheric coupling, eye use studies are less straightforward to interpret and thus less common. However, this is more reflective of the biological constraints of these species than a specific limitation of the current software, which in principle could be adapted to a variety of vertebrate species. For instance, even in the case of animals with frontally placed eyes, Visual Field Analysis can be used to measure the total amount of time spent looking toward a stimulus, regardless of the visual field used. This method could hence address issues of subjective coding on human and non-human animal playback experiments in which the position of the head is scored. One of the advantages of Visual Field Analysis is that it allows the accurate processing of vast amounts of data, which would normally be unfeasible to process with manual coding methods.
, 2015)  and insects  (Boeddeker et al. , 2010; Hateren & Schilstra, 1999). The use of our application to encode visual field data could be thus particularly beneficial for these kinds of research. Moreover, Visual Field Analysis not only assesses simple preferential eye use (whether the left or right eye is preferred); it can also investigate the use of sub-regions within each hemifield (frontal visual field vs lateral visual field), allowing the analysis of eye use behaviour at a fine level, which is very time-consuming if done manually as performed by  Lemaire and collaborators (2019)  in King Penguins. Alongside measuring eye use, the application allows the recording of other relevant behavioural measurements, such as the activity level of an animal's head, while keeping track of its positions in different areas of a testing environment (for this last function, the performance of Visual Field Analysis was already validated in a previous study, showing once again very high correlation with the measurements obtained by traditional manual coding methods;  Santolin et al.
, 2020). In addition, Visual Field Analysis can extract data on the movements of the heads of the animals, the amount of motion of this body part. This can be informative about changes in eye use and head saccades, a behaviour present in birds which is correlated with arousal  (Golüke et al. , 2019; Kjrsgaard et al. , 2008). These other behavioural measurements provide additional information that can be analysed in relation to eye use behaviour or independently from it, allowing for richer behavioural assessments and more flexible use in different experimental designs. However, our data acquisition technique is entirely dependent on tracking accuracy: a key component most tracking software still struggles with. To provide optimal results, in most tracking software the video recording quality has to be pretested and adjusted, while the experimental settings must be similar throughout the whole data acquisition phase.
By using deep learning techniques, DeepLabCut started to overcome those limitations. For instance, DeepLabCut can be trained to recognize animal body parts in a wide range of scenarios as long as the video recordings are of sufficient quality. Moreover, DeepLabCut even allows the tracking of multiple animals at the same time  (Lauer et al. , 2021). In its current version, however, Visual Field Analysis allows the tracking of only one animal at a time, within an orthogonal arena. Moreover, the current version of this application provides only limited flexibility in the shape of the experimental arena and in the placement of the stimuli within it (see  Josserand & Lemaire, 2020  for further details). Since this represents potentially the greatest limitation of this software, we are actively working to update Visual Field Analysis so that it can be used in a greater variety of experimental designs, both in terms of stimuli placement and in the shape of the experimental arena.
INTRODUCTION Current sequence databases contain hundreds of billions of nucleotides encoding genes whose functional profiling is a primary problem in genomics and metagenomics. This task is typically carried out by annotating protein domains, which are functional units, much shorter than proteins, with an average size of 100 aa  (1) (2) (3). Despite their short length, they are accurate enough to inform us about the potential functional activity of a protein. Recently, a marked improvement in annotation has been achieved with the multisource strategy, using multiple probabilistic models, compared to the mono-source strategy, using one single consensus model, employed by the two most commonly used annotation tools HMMER (4) and HHblits  (5, 6). CLADE  (7, 8)  and MetaCLADE  (9)  rely on millions of probabilistic models for Pfam domains  (10)  that have proven to be more specific and functionally predictive than the widely used consensus models  (11) (12) (13).
They have been shown to improve domain architectures in complete genomes  (8)  and the catalog of functions in microbiomes  (9, 14). A fine degree of accuracy has been achieved in domain annotation for both prokaryotic and eukaryotic organisms. Nucleic Acids Research, 2021, Vol. 49, Web Server issue W453 MyCLADE is an online server that allows the community to access domain annotations, based on a large dataset of probabilistic models, which enhance Pfam  (10)  and Inter-Pro  (15)  annotations. It finds domains for proteins that are annotated for the first time, it enriches known architectures with new domains and might provide alternatives for previously annotated domains. To reconstruct the most appropriate domain architecture, MyCLADE integrates a new improved version of MetaCLADE and DAMA  (16)  in the same environment.
Model library for Pfam domains MyCLADE uses a probabilistic model library that includes Pfam sequence consensus models (SCM)  (20) (21) (22) (23)  and at most 350 clade-centered models (CCMs)  (7, 9) , with an average of 161 models, per domain. Both SCM and CCM are profile Hidden Markov Models (pHMM) generated using HMMER v3, http://hmmer. org. To construct a CCM for a Pfam domain D i , we consider the FULL set of homologous sequences S i in Pfam  (10)  associated to D i , and for some representative sequences s j in S i (see below), we construct a model by retrieving with HHblits (5) a set of sequences similar to s j from the Uniprot Uniclust30 database. The probabilistic model generated in this way displays features that are characteristic of the sequence s j and that might be very different for other sequences s k in S i.
The more divergent the homologous domain sequences s j and s k are, the more models constructed from these sequences are expected to display different features. It is therefore important for a domain D i to be represented by several models that can characterise its different pathways of evolution within different clades. These probabilistic models are the CCMs used in MyCLADE. The details of their construction are described in  (9). For a domain, representative sequences are selected in order to span the tree of life as much as possible as in  (7)  by considering different clades. The rationale is that evolutionary patterns can be found in species that are very far apart in the tree. For this, we considered the tree of life and fixed a list of clades. The selection of representative sequences was designed as follows: given a Pfam32 domain, all Pfam32 sequences for the domain were clustered with MMseqs2 (https://github.
Note that this step allows to select sequences (possibly paralogs) sufficiently divergent from those already selected due to the initial clustering. This is equivalent to building >2. 5 million probabilistic models for the whole Pfam32 database (17 929 domains). A reduced model library was created, keeping only the first 50 models built from the aforementioned procedure. By construction, CCMs span regions of the protein sequence space that are usually not well represented in a SCM. These regions might highlight motifs, structural or physico-chemical properties characteristic of divergent homologous sequences. Thus, if a domain is associated with many divergent homologs, the CCMs are expected to describe properties that might not be detected by a SCM. For this reason, CCMs allow to find divergent homologous sequences in species that might be phylogenetically distant.
The output of this first step of MetaCLADE is a set of hits, each one defined by a domain family D, a probabilistic model M associated to D, a bit-score and a mean-bit-score. Since each domain can be represented by a large number of models, a large number of domain hits might be associated to each sequence. MyCLADE uses three criteria to filter them, based on the bit-score, the probability p of being a true positive, and the identity percentage of the hit  (9). The output of this filtering step is the sequence annotation where hits of different domains might overlap for at most 30 W454 Nucleic Acids Research, 2021, Vol. 49, Web Server issue aa and hits of the same domain are non-overlapping. Compared to the original version of MetaCLADE  (9) , in Meta-CLADE v2: (i) the code has been optimized, (ii) the selection of representative sequences spanning the tree of life follows a new strategy, as described above, (iii) the search for similar sequences is realized with hhblits instead of psiblast for the construction of HMMs instead of PSSMs.
MyCLADE input and parameters MyCLADE can be run on three different library types defined by: (i) few (at most 10) user-provided domains, (ii) all Pfam32 domains, (iii) a Pfam32 clan. Each of the three sets of domains characterizes a library of models that is based on either 350 or 50 models per domain in the set. An option allows the user to decide on the number of models per domain. The first and third library types require a list of up to 2000 sequences in FASTA format (possibly uploaded). The second library type requires a small dataset of up to 200 sequences. The list of input sequences is checked for format requirements. The help section in the online interface provides information on the expected format. An option allows the user to filter out all hits with an E-value <1 which is greater than the chosen threshold (by default set at 1e-3).
A presentation of the information on the annotated domain is also available through a tooltip, by hovering the mouse over the graphical representation of the domain. Three tables collect all details of domain annotation, the associated GO-terms  (31)  and the logos matching the hits. Multiple links to Pfam  (10)  and QuickGO  (32)  databases are available for an easy retrieval of general domain and clan information (Figure  1A, B, D ). For each domain hit, start and end positions of the model hit against the sequence are reported in the table presenting the logos. Note that these positions are different than the envelope positions, usually corresponding to a larger interval.
Construction of the testing dataset The Harmonizome database (http://amp. pharm. mssm. edu/Harmonizome/ dataset/InterPro+Predicted+Protein+ Domain+Annotations) (33) contains a set of 18 002 genes organised in 11 015 domains annotated with Interpro  (15). Pfam domains were randomly selected and the associated protein sequences were retrieved to create a FASTA file for testing: 200 sequences were recovered for a total of 64 domains. The set of sequences was subdivided into subsets to evaluate MyCLADE time complexity on very small (5, 10, 25 and 50) and larger (100 and 200) sets of sequences.
RESULTS The performance of MetaCLADE has been extensively evaluated across multiple genomic and metagenomic datasets  (9). On a dedicated page of the MyCLADE server, five annotations of known and less known proteins are discussed: the Mediterranean Fever gene MEFV and its pyrin protein, the human amylase AMY1A, a UDP-N-acetyltunicamine-uracil synthase TunB-like protein (B5GL39), the hypothetical protein YP 499998 in Staphylococcus aureus and the RING finger protein in Plasmodium falciparum PFE0100w. These case studies illustrate how the user can explore domain annotations and can acquire confidence in them. We also discuss the motivation to search for specific domains in datasets of a few thousand sequences and give an example of the application of MyCLADE to a case study. Here, we (re)annotate the entire Staphylococcus aureus genome with MyCLADE for the discovery of new domains.
Evaluation of the execution time of MyCLADE MyCLADE can annotate sequences with a limited number of targeted domains or with all Pfam32 domains. A runtime evaluation of MyCLADE was performed on these two use cases (Figure  2 ) for small (5, 10, 25 and 50 sequences) and large (100 and 200 sequences) protein datasets. On targeted domains, from 10 to a few hundred, MyCLADE annotates hundreds of sequences in less than a minute while on all Pfam32 domains it takes less than a hour. The estimates are realized with the complete model library. The three plots in Figure  2  (for 10 and 340 domains in Figure  2A  and all domains in Figure  2B ) show that by augmenting the number of sequences, for instance from 100 to 200, the ratio of the execution times decreases when the number of models increases: 3.
16 on ∼2000 models for 10 domains, 2. 5 on roughly 60 000 models for 340 domains and 1. 2 on more than 2. 5 million models, for all 17 929 domains of the Pfam32. This means that the bottleneck of the annotation pipeline resides on the alignment of the models against the sequences and not on the number of sequences. Therefore, the user should expect essentially the same time of execution for the annotation of few sequences (e. g. 1, 10, 20) with a given set of domains. To evaluate the dependence of the computation time on the number of available models, we considered the restricted library and observed that the computation time is greatly reduced: on 10 domains it is reduced by a factor of 6, on 340 domains by a factor of 3. 65 and on 17 929 domains of a factor of 2. 3 (Figure  2 ). The time reduction is obtained at the cost of an expected decrease of the number of annotated domains: on the 200 sequences of the test dataset, 637 domains are annotated with the complete library and 561 with the restricted one.
SOFTWARE AVAILABILITY For the analysis of large files, not allowed in MyCLADE, the user can locally install MetaCLADE (www. lcqb. upmc. fr/metaclade/) along with the corresponding model library. DAMA can be retrieved at www. lcqb. upmc. fr/dama/. Note that DAMA uses Pfam knowledge for building its architectures and the user can check whether the domains in a MyCLADE architecture are known to co-occur or not at http://pfam. xfam. org/search (on the 'domain architecture search').