dataesr/scientific-paragraphs-categorization huggingface dataset:
This was observed in phages infecting bacteria from the Enterobacteriaceae family and the Rhodococcus and Bacillus genera. However, Phirbo assigned the highest score to the correct host species (Additional file 2: Table  S3 ). Additionally, it refined the host prediction for the Cronobacter phage ENT39118 sequence, which BLAST assigned to the Escherichia coli genome. Phirbo revealed Cronobacter sakazaki as the primary host species, as the BLAST list of the Cronobacter phage is more similar in content and order to the BLAST list of C. sakazaki (Phirbo score = 0. 50) than E. coli (Phirbo score 0. 48) (Additional file 1: Figure  S2 ). As Phirbo links virus to host through common sequences, the content of the sequence database was the main factor defining host prediction quality. Since the similarity between viruses may indicate a common host  [19, 29] , we expanded the two BLAST databases of prokaryotic sequences obtained from Edwards et al.
3 ). The host prediction accuracy of Phirbo was examined using the expanded BLAST database of both prokaryotic and viral full-length sequences. To ensure fairness, for each tested viral contig, we removed its corresponding full-length sequence from the BLAST database and recalculated Phirbo scores between the viral contig and every prokaryotic sequence. This approach outperformed BLAST at every contig length across all taxonomic levels in both data sets (Fig. 3 ). Generally, the host prediction accuracy of Phirbo improved by 5-11 percentage points compared to the BLAST results. For example, when the contig length was 3 kb, the prediction accuracy of Phirbo was 8-11% higher than BLAST at the family level and 8-17% higher than WIsH (Fig. 3 ; Additional file 2: Table  S4 ). Phirbo also achieved the highest AUC and AUPR scores when discriminating between interacting and non-interacting virus-host pairs (Additional file 1: Figure  S3 ).
Implementation and availability Predicting hosts from phage sequences using BLAST is accomplished by querying phage sequences against a database of candidate hosts. However, Phirbo also uses information about sequence relatedness among prokaryotic genomes. Therefore, it requires ranked lists of prokaryote species generated by BLAST for the virus and host genomes. The computational cost of querying every host sequence against the database of all candidate hosts using BLAST may still be a limiting factor. However, for mass host searches, the computational cost of all-versus-all host comparisons becomes marginal, as it must be done only once. After the relatedness among host genomes is established, the time required for Phirbo host predictions is negligibly higher than the time for typical BLASTbased host predictions.
However, no clear pattern emerges after analyzing the functions of the remaining, over-represented proteins. In this study, we attempted to expand the information content of a single local alignment of virus and host sequences by incorporating the results of multiple alignments between a viral sequence and different prokaryotic genomes. This approach may more closely resemble a manual assignment of virus-host pairs, where an expert analyst not only considers a top-ranked matching prokaryote in the BLAST results, but also uses the information contained in other, less significant, matches and their sequence and taxonomic similarity. Through a taxonomically aware stratification scheme, this approach tracks the multilateral dynamics of horizontal gene transfer. This dynamics is reflected by the fact that BLAST lists obtained by querying a database of prokaryotic genomes with viral sequences tend to cover more taxa than similar lists based on prokaryoteprokaryote comparisons (Additional file 1: Figure  S5 ).
Virus and prokaryotic host data sets The data sets analyzed in this study were retrieved from three previously published virus-host studies  [14, 17, 19]. The first set  [14]  contained 2699 complete bacterial genomes obtained from NCBI RefSeq and 820 RefSeq genomes of phages for which the host was reported. The data set encompassed 16,757 known virus-host interaction pairs and 2,196,424 pairs for which interaction was not reported (non-interacting phage-host pairs). The second data set  [17]  contained 3780 complete prokaryotic genomes of the KEGG database and 1420 viruses for which host species were reported in the RefSeq Virus database. The data set consisted of 26,024 interacting-and 5,341,576 non-interacting virus-host pairs. The third data set  [19]  included 1462 viruses and 62,493 candidate prokaryotic hosts encompassing 113,250 interacting-and 91,251,516 non-interacting virus-host pairs.
Host prediction tools The host prediction tools BLAST  [21] , WIsH  [17] , and Phirbo were run separately on the Edwards et al. and Galiez et al. data sets. For each tool, sequence similarity scores were calculated across all combinations of virusprokaryote pairs. BLAST 2. 7. 1+  [44]  was run with default parameters (task: blastn, e-value threshold 10) to query each virus sequence against a database of candidate host genomes. For each BLAST alignment, the highest bit-score between every virus-host pair was reported (for virus-host pairs that were absent in the BLAST results, a bit-score of 0 was assigned). For RBO host prediction, an additional BLAST search was performed to establish ranked lists of genetically similar host genomes. Specifically, a nucleotide BLAST was run with default parameters to query each host sequence against a database of candidate host genomes.
As an alternative to BLAST, Mash 2. 1  [31]  was used with default parameters (k-mer size = 21, sketch size = 1000) to establish ranked lists for each host by comparing its sequence against the database of candidate host genomes. RBO scores were calculated between all pairwise combinations of virus and host ranking lists. WIsH 1. 0  [17]  was used with default parameters to calculate loglikelihood scores between all pairwise combinations of virus-host sequences. To have comparable values between different phages, log-likelihood scores returned by WIsH were converted into z-scores. VirHostMatcher-Net 1. 0  [19]  and Prokaryotic virus Host Predictor (PHP)  [20]  were run using default parameters.
Phage genome annotation To define phage genes potentially exchanged between phage and host genomes, we re-annotated 485 phage genomes that were correctly assigned to host species by both Phirbo and BLAST. The genes were classified into predefined pVOGs (prokaryotic Virus Orthologous Groups)  [46]  and RNA families  [47]. Briefly, open reading frames (ORFs) in the analyzed 485 phage genomes were identified using Transeq from EMBOSS  [48]. The ORFs were then assigned to the respective orthologue group by HMMsearch (e-value < 10 -5 ) against the database of hidden Markov models (HMMs) created for every of 9518 pVOG alignments using HMMbuild of HMMER v3. 3. 1  [49]. Non-coding RNAs (ncRNAs) were predicted in the phage genomes (e-value < 10 -5 ) using Rfam covariance models v14. 3  [47]  and the Infernal tool v1.
Acknowledgements We thank  Bas Dutilh ,  Rob Edwards ,  Clovis Galiez ,  Johannes Söding ,  Fengzhu Sun , and  Weili Wang  for providing us with the benchmark data sets used in their studies. We likewise acknowledge  William Webber  for assistance with modifying the RBO formula to account for tied ranks. The computations were performed at the Poznan Supercomputing and Networking Center.
Availability of data and materials Phirbo is available at https://github. com/aziele/phirbo [52]. Accessions of viral and prokaryotic sequences used in this study were retrieved from  [25, 26, 30]. Sequence accessions from all three data sets (Edwards et al. , Galiez et al. , Wang et al. )  as well as information about virus-host assignments are available at https://github. com/aziele/phirbo/tree/main/datasets [53]. Raw genomic sequences of viruses and prokaryotes used in this study are available from the authors upon request.
digtrace. co. uk) that enables the capture of tracks in 3D using the open source software OpenMVG  (Bennett and Budka 2018). Not only can you create 3D models, but the freeware provides a series of analytical workbenches with tools specific to the analysis of footprints. Commercial alternatives exist in the form of such things as PhotoScan by Agisoft (http://www. agisoft. com), and while they create excellent 3D models, they do not provide analytical tools specific to footprint analyses, and the user has to rely on expensive 3D modelling software or freeware tools such as  MeshLab (www. meshlab. net) or CloudCompare (www. danielgm. net/cc). Structure from Motion relies on multiple oblique digital photographs from which individual pixel clusters are placed in 3D space. Almost any digital camera can be used provided its sensor size is known or can be calculated  (Bennett and Budka 2018).
Methods We employ the robust method of statistical parametric mapping (SPM), a topographical statistical approach first developed by  Friston et al. (1995)  for functional brain imaging and extensively validated by that group (open source). The algorithms have been further developed for foot pressure studies and incorporated into our opensource software pedobarographic statistical parametric mapping (pSPM), which has been further and extensively validated (e. g. Pataky 2010;  Pataky and Goulermas 2008; Pataky et al. 2008 Pataky et al. , 2011)). In its analogous extension to footprint depth, statistical comparison of the samples also uses pixel-level pairwise t-tests  (Pataky and Goulermas 2008; Pataky et al. 2008 ) but here after normalization by plantar surface maximum depths. Methods follow  Crompton et al.
Results and Interpretations Figure  3. 1 presents the mean footprints and the results of topological statistical comparisons of 11 prints from the G1 trail at Laetoli  (Leakey and Hay 1979; Leakey and Harris 1987) , with 9 prints from the upper surface at Ileret (FwJj14E; Kenya)  (Bennett et al. 2009 ), a 32 print sample from the Holocene trail at Walvis Bay, Namibia  (Kinahan 1996; Morse et al. 2013) , and a modern Western (thus habitually shoe wearing) dataset collected on a treadmill (N ¼ 100 pressure records registered to create one mean print each from 10 individuals). Habitually shod Western modern and presumably habitually unshod Holocene modern human footprints (Fig. 3. 1a) show no areas of significant difference. The Ileret dataset differs significantly from that for modern Western humans ( p ¼ 0.
2016). Similarly, the relatively greater number of deep prints from Holocene modern humans (from a wetter substrate, mean maximum plantar depth was 45 mm and range 23-77 mm) compared to Laetoli could readily account for deeper hallux impressions in Holocene human footprints. Crompton et al. (2012)  used computer modelling to simulate contact pressures under the foot in upright and flexed knee walking. They showed that bent knee or flexed knee walking produced higher forefoot than hindfoot pressures because of the anterior shift of the centre of mass (CoM). Their analysis of a larger dataset including all of those prints analysed by  Hatala et al. (2016)  revealed consistently deeper hindfoot than forefoot impressions, indicating full extension at the knee during upright walking  (Ferris et al.
At N ¼ <10 records this increases to more than 75%, indicating a high probability that such a small individual trial N would not reflect either the range of variation or the habitual mean pressure that would be represented by a larger dataset of consecutive foot print records. Acquiring a sample of more than 500 is clearly unfeasible for footprints and even for foot pressure in the infirm. However, samples of 100-138 would deliver around 95% confidence and might be achievable in the future at Laetoli S. But even assuming a close link between pressure and footprint depth, a sample of five, as used by  Hatala et al. (2016) , offers well under 25% confidence of assessing pressure characteristics, i. e. a probability of unreliable assessments. Indeed, our own sample of 11 would allow no better than 50% reliability; however, we are accounting for the effects of variability in our interpretations and not making confident claims for a new locomotor mode, from what we know to be a biomechanically unsatisfactory sample size.
2013) , and the modern Western, habitually shoe-wearing dataset (N ¼ 100 footprints from 10 individuals). The first two images in each set are the site means, the third is their subtraction to show where they differ, while the fourth identifies those areas where the difference is statistically different using pixel-level pairwise t-tests  (Pataky and Goulermas 2008; Pataky et al. 2008)  after normalization by plantar surface maximum depths and their probability level These tests were carried out using the same methods used in previous studies  (Crompton et al. 2012; Bates et al. 2013).
Additional Footprint Discussion Comparison of footprint topology between sites with different substrates and geological properties is potentially difficult since the biomechanical signature of a trackmaker is mediated through the geotechnical properties of the substrate, and the substrate may also influence taphonomic modification  (Craig 1997; Ditchfield Fig. 3. 5  An example of within-subject registration using ten prints from the modern, Western human dataset; (a) the ten prints are preprocessed as explained in Fig. 3. 4. An initial registration is then performed that individually (i. e. one at a time) aligns the last nine prints with the first print in the dataset (not depicted above); (b) subsequently a second registration is performed in which all ten prints are individually (i. e. one at a time) aligned with their mean image; (c) the mean image itself.
They were photographed using a Nikon D7100 with a 60 mm focal length lens. Then each footprint was 3D digitized using an Artec EVA 3D light scanner 2013 (Artec Group, Luxembourg). This scanner uses the structured light triangulation technique to reconstruct a 3D model of the footprint. The accuracy achieved by this scanner is 0. 5 mm at a working distance of 40 cm to 1 m, and the 3D resolution goes up to 0. 1 mm. The scanner takes up to 16 frames per second and transfers them to the Artec Studio software  (Modabber et al. 2016)  which aligns the frames in real time. Post-processing was performed on the Artec Studio 9 software, which recreated a colour texturized 3D mesh. The 3D models of the footprints at different moments of the experiment were visualized and compared with CloudCompare (2.
We do not imply that the indigenous knowledge we work with necessarily has to be a pristine knowledge, but instead we involve San ichnology because it is the highest standard we can get today and it promises to get the maximum possible information that can be retrieved from an imprint for which there are not yet any equally yielding machine-based analytical devices. Further research is necessary to determine the smallest analytical steps of the methodology applied by Ciqae, Thao and Kxunta to each single imprint. We do have some indications about the procedure of extracting data from a footprint, manifested in the sections of a foot that are part of the rating process and which are also used in orthopaedics or forensics (Fig. 6. 3). But this can only be the start and in order to collect first data on this topic, the entire determination process in each cave was recorded as audio protocols.
Data processing started with checking topographic measurements in plan and height, which is preliminary to the definition of the control point coordinates. The trilateration method was used to obtain x, y coordinates of the control points in plan. For each test-pit, six measurements were taken at the same height: the length of the four sides of the perimeter and the length of the two diagonals. Redundant measurements were used to compute the errors. In addition to a preliminary graphical control by CAD software, we used an automatic calculation software to adjust a new set of x, y coordinates and heights of the control points by least squares technique. The residues of adjustments never exceeded 10 mm, which is fully acceptable considering the size of the test-pits. We used the adjusted x, y, z coordinates of the control points to scale and locate in the 3D space the SfM models.
A check on point cloud density was also carried out by a software for 3D point cloud and mesh processing and analysis. The average density found in the Laetoli point clouds is around 20 points/cm 3 for the test-pits and 1500 points/cm 3 for the detailed footprints  (Masao et al. 2016; Menconero et al. 2019). The 3D models obtained by SfM were also used in the morphometric analysis of the hominin tracks. We used a contouring and modelling software that transforms x, y, z data into maps. The x, y, z-format files were imported into the software and transformed into grid files. The software uses randomly spaced x, y, z data to create regularly spaced grids composed of nodes with x, y, z coordinates. The Triangulation with Linear Interpolation gridding method was applied, because it works better with data that are evenly distributed over the grid area.
Future plans must include a programme of continuous monitoring of the footprints, especially with the involvement of the local community  (Ichumbaki et al. 2019). Once established a comprehensive plan of conservation and valorization of the Laetoli footprints in close collaboration with all the involved Tanzanian (e. g. Ministry of Natural Resources and Tourism, NCAA) and international (e. g. UNESCO) institutions, new systematic excavations can be carried out. This would allow unearthing the entire S1 and S2 trackways and also opening up the intriguing possibility of discovering tracks of other individuals. This new research would noticeably improve the available dataset on the foot anatomy, locomotor pattern, body size variation, social structure, and behaviour of the Laetoli australopithecines.
2012; Pablos et al. 2012 ). The 0. 15 ratio suggested a height range for the Happisburgh hominins of between 0. 93 and 1. 73 m, indicating the presence of adults and children. For the orientation studies, a larger dataset of 49 prints was analysed, showing a preferred south-north orientation. In 29 cases where the arch and the front/back of the foot could be identified, the direction of movement was also assessed, showing a preferred direction of movement to the south. Unfortunately, there are no human fossils from Britain that date to this period, but the closest comparison is Gran Dolina (TD6) at Atapuerca in northern Spain where bones and teeth dating to c. 800 ka have been named as Homo antecessor or Pioneer Man  (Carbonell et al. 2005 (Carbonell et al. , 2008)). This attribution has recently been examined using 2D morphometrics on a range of footprints from Pliocene, Pleistocene and Holocene sites  (Wiseman et al.
180 tracks including 170 footprints were digitized in 3D. Seventy-seven footprints were digitized by using photogrammetry with the Agisoft Protocan software (v. 1. 4. 0) and a Canon EOS 1300D camera. 137 footprints were 3D modelled by using a Noomeo OptiNum surface scan. The use of these different acquisition techniques required that we run morphometric comparisons between them prior to analysis. These comparisons did not detect any differences between the types of acquisition  (Duveau et al. 2019). Besides, each track was measured in situ. These measurements were controlled and specified on the tracks digitized in 3D by using Geomagic Studio 2013. The length was measured along the longitudinal axis. For footprints, this axis is from the most proximal point of the heel to the distal end of the second toe.
• Reliability of identification: Particularly important for the comprehensibility of the analysis is the judgement of its reliability on the basis of preservation and visibility. For this purpose, a subjective five-stage classification was carried out from very good (1) to unsatisfactory  (5). The intermediate stages are good (2), satisfactory (3) and sufficient (4). • Remarks: An open field for comments of any kind. The position of every spoor was located on plans or sketches. All work sequences were recorded on film. In this way, not only the results can be checked and compared with each other, but also further linguistic research can be carried out. At the end stands a database (catalogue) with the results of the morpho-classificatorical analysis and event identification. For future work, photogrammetric records of the examined footprints will be generated with the help of Structure from Motion (e.
1). This concerns the number of subjects as well as age and sex. Events are identified by both groups of specialists, whereas those of western academic scientists are much more fragmentary than the often consistent event identifications of experienced trackers. These aspects have to be discussed in detail but make more sense on a broader database from Tuc d'Audoubert that is under construction as part of the multi-stage studies on the prehistoric human spoors. Based on photogrammetric images, prominent landmarks of the plantar imprints will be systematically measured. In addition, complex morphometric analyses and plantar pressure analyses will follow. In the same way, the events related to drawing activities will be investigated. Only on the basis of all data available a more intensive analysis of the differences and similarities of the results of the indigenous ichnologists and western academic scientists seems to be reasonable.
Morphometric Analysis Principal component analysis (PCA) represents a really powerful tool to summarize in a two-dimensional scatter plot the morphological variability for the studied specimens, even starting for a large number of morphological variables. The scores and loadings provided by the PCA help to identify the morphological variables that greatly influence the variance of the considered dataset and thus the more important anatomical landmarks for morphological description and grouping of similar objects. A morphometric study has been performed on the best-preserved human and canid footprints. To build the raw dataset, we use the anatomical foot landmarks proposed by  Robbins (1985for human)  and  Ledoux and Boudadi-Maligne (2015for canids) , and the log-transformed data that have been subjected to a principal component analysis in software PAST 3.
Footprints and Trackways This precise cartography allows the position of the tracks to be observed in relation to the morphology of the passages by identifying not only the human footprints but also the topographic elements (boulders, rocks, speleothems, crusts, channels, etc. ) that form the surface and condition the presence of human footprints. This enables the reconstruction of some of the paths and movements made by these ancient visitors. All the measurements and most of the observations of the footprints have been made over the resulting three-dimensional and cartographic restitution with AutoCAD software, as it is not possible to measure them in situ, owing to the nonconsolidation of the sediment recording the traces. Once the footprints were scanned, each of the prints was individualized.
g. raw data, interview data, media recordings). Access to research data and results should be provided for all stakeholders and interested parties through reports, databases, open access publications, etc. Type and extent of data use and data access should be discussed with all participants and implemented jointly. As far as reciprocity is concerned, an appropriate remuneration for indigenous participants negotiated in advance and the calculation of travel costs, visa procurement and similar should be a matter of course. The sharing of research capacity, knowledge and skills and other intangible forms of reciprocity, for example, support for indigenous knowledge transfer and research beyond the scope of one's own project, are also frequently mentioned. The evaluation of the research project should include an exchange on the continuing benefits and significance of the research results for the indigenous community.