dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2020; Ciftci, Demir & Yin, 2020; Qi et al. , 2020). Indeed, signals originating from biological action such as heart beat, blood flow, or breathing are expected to be (in large part) disrupted after faceswapping. Therefore, methods such as remote PPG can be adopted in order to evaluate their presence. In the following, it is shown how pyVHR can be effectively employed to easily perform a DeepFake detection task. To this end, we rely on the FaceForensics++ 4 dataset  (Rössler et al. , 2019)  consisting of 1,000 original video sequences (mostly frontal face without occlusions) that have been manipulated with four automated face manipulation methods. Each video, either original or swapped is fed as input to the pyVHR pipeline; then, the estimated BVPs and the predicted BPMs can be analyzed in order to detect DeepFakes.
CONCLUSIONS In recent years, the rPPG-based pulse rate recovery has attracted much attention due to its promise to reduce invasiveness, while granting higher and higher precision in heart rate estimation. In particular, we have witnessed the proliferation of rPPG algorithms and models that accelerate the successful deployment in areas that traditionally exploited wearable sensors or ambulatory monitoring. These two trends, combined together, have fostered a new perspective in which advanced video-based computing techniques play a fundamental role in replacing the domain of physical sensing. In this paper, in order to allow the rapid development and the assessment of new techniques, we presented an open and very general framework, namely pyVHR. It allows for a careful study of every step, and no less important, for a sound comparison of methods on multiple datasets.
pyVHR is a re-engineered version of the framework presented in  Boccignone et al. (2020a)  but exhibiting substantial novelties: • Ease of installation and use. • Two distinct pipelines for either traditional or DL-based methods. • Holistic or patch processing for traditional approaches. • Acceleration by GPU architectures. • Ease of extension (adding new methods or new datasets). The adoption of GPU support allows the whole process to be safely run in real-time for 30 fps HD videos and an average speedup (time seq /time parall ) of around 5. Besides addressing the challenges of remote Heart Rate monitoring, we also expect that this framework will be useful to researchers and practitioners from various disciplines when dealing with new problems and building new applications leveraging rPPG technology.
The left-most column collects the dataset names and introducing papers; second column, the number of subjects involved; third column, the task or condition under which data have been collected (Stationary: subject are asked to sit still; Interaction: emulation of a human-computer interaction scenario via a time sensitive mathematical game; Multiple: more than one condition has been considered while recording subjects, such as Steady, Talking, Head Motion etc; Physical Activities: subjects are recorded while performing activities such as speaking, rowing, exercising on a stationary bike etc; Stress Test: participants are subject to tasks with different levels of difficulty inspired by the Trier Social Stress Test; Emotion Elicitation: participants were shown fragments of movies and pictures apt at eliciting emotional reactions).
Data Availability The following information was supplied regarding data availability: The code is available at GitHub: https://github. com/phuselab/pyVHR. The UBFC data used in the experiments is available at: https://sites. google. com/view/ ybenezeth/ubfcrppg. The FaceForensics++ dataset used in the case study is available at GitHub: https: //github. com/ondyari/FaceForensics. The LGI-PPGI dataset is available at GitHub: https://github. com/partofthestars/LGI-PPGI-DB.
Comparison of predicted vs ground truth BPMs using the patch-wise approach. Predicted BPM (blue) for the Subject1 of the UBFC Dataset. The uncertainty is plotted in shaded blue, while the ground truth is represented by the red line. Full-size DOI: 10. 7717/peerjcs. 929/fig-9 per Frame Time Requirements CPU Architecture GPU Holistic Patches 0. 00 0. 02 0. 04 0. 06 0. 08 0. 10 0. 12 Time(s).
In the last column, datasets whose handling APIs are currently available in pyVHR have been checked. Dataset Subjects Task/Condition pyVHR UBFC1 (Bobbia et al. , 2019) 8 Stationary UBFC2 (Bobbia et al. , 2019) 42 Interaction PURE (Stricker, Müller & Gross, 2014) 10 Multiple LGI-PPGI (Pilz et al. , 2018) 25 (6 available) Multiple MAHNOB-HCI (Soleymani et al. , 2011) 27 Emotion elicitation COHFACE (Heusch, Anjos & Marcel, 2017b) 40 Stationary UBFC-Phys (Sabour et al. , 2021) 56 Stress test × AFRL.
Finally, sequences that cannot be classified by either of these criteria, are classified as "Genomic Unknown" (GU) when they may be found in the genomes in reference databases; and "Environmental Unknown" (EU) when only have been observed in environmental samples. By contextualizing the different categories with information from several sources (Figure  1C ), we hope this will prove an invaluable resource for including genes of unknown function in evolutionary and ecological studies  (Delmont et al. , 2020; Gaïa et al. , 2021; Holland-Moritz et al. , 2021)  as well as enhancing the current methods for its experimental characterization. The application of our approach to 415,971,742 genes predicted from 1,749 metagenomes and 28,941 bacterial and archaeal genomes revealed that the unknown fraction (1) is smaller than has been previously reported  (Salazar et al.
AGNOSTOS infers, validates and refines the GCs and combines them in gene cluster communities (GCCs). Then, it classifies them in one of the four conceptual categories based on their level of 'darkness'. Finally, we add context to each GC based on several sources of information, providing a robust framework for generating hypotheses that can be used to augment experimental data. (B) The computational workflow provides two mechanisms to structure sequence space using GCs, de novo creation of the GCs (DB creation), or integrating the dataset in an existing GC database (DB update). The structured sequence space can then be plugged into traditional analytical workflows to annotate the genes within each GC of the known fraction. With AGNOSTOS, we provide the opportunity to integrate the unknown fraction into microbiome analyses easily.
C) The versatility of the GCs enables analyses at different scales depending on the scope of our experiments. We can group GCs in gene cluster communities based on their shared homologies to perform coarse-grained analyses. On the other hand, we can design fine-grained analyses using the relationships between the genes in a GC, i. e. , detecting network modules in the GC inner sequence similarity network. Additionally, given that GCs are conserved across environments, organisms and experimental conditions give us access to an unprecedented amount of information to design and interpret experimental data. First, we used AGNOSTOS in a metagenomic context to show its ability to process complex and noisy datasets. We explored the unknown sequence space of 1,749 human and marine metagenomes. In total, we predicted 322,248,552 genes from the environmental dataset and assigned Pfam annotations to 44% of them (Appendix 1-figure  2A ).
Next, AGNOSTOS clustered the predicted genes in 32,465,074 GCs and flagged those gene clusters that contain less than ten genes  (Skewes-Cox et al. , 2014). Flagged gene clusters are not processed through the validation workflow because they don't have enough sequences to provide reliable results. We flagged 29,461,177 gene clusters, with 19,911,324 singletons (Appendix 1-figure  2A ; Appendix 2). A total of 3,003,897 GCs (83% of the original genes) will go through the validation steps. The validation process selected 2,940,257 good-quality clusters (Fig. 1B ; Appendix 1-table 1; Appendix 3), of which 43% were identified as unknowns after the classification and remote homology refinement steps (Appendix 1-figure  2A , Appendix 4). Lastly, we demonstrate how AGNOSTOS can integrate a new dataset into the already existing metagenomic database by integrating 28,941 genomes from the GTDB_r86 (Appendix 1-figure  2A ).
With this integration we can build links between the environmental and genomic sequence space by expanding the final collection of GCs with the genes predicted from GTDB_r86. Surprisingly, the integration showed that the environmental GCs (human and marine) not only included a large proportion of the genes predicted from GTDB_r86 (72%) but also that most of these genes (90%) are part of the known sequence space (Appendix 1-figure  2A ; Appendix 5). Only 22% of the GTDB_r86 genes created new GCs (2,400,037), with most of these genes (84%) classified as unknowns by AGNOSTOS (Appendix 1-figure  2A ; Appendix 5). Finally, only a small proportion of the genes from GTDB_r86 (6%) resulted in singletons (Appendix 1-figure  2A ; Appendix 5). The final dataset after being validated and categorized resulted in 5,287,759 GCs (Appendix 1figure  2A ), with both datasets sharing only 922,599 GCs (Appendix 1-figure  2B ).
The integration of GTDB_r86 into the metagenomic database increased the proportion of GCs in the unknown sequence space from 43% to the 54%. Additionally, AGNOSTOS identified a subset of 203,217 high-quality GCs (Appendix 1-table  2 ). In these high-quality GCs, we identified 12,313 clusters potentially encoding for small proteins (<= 50 amino acids). Most of these GCs are unknown (66%), which agrees with recent findings on novel small proteins from metagenomes  (Sberro et al. , 2019). We also observed that the KWP category contains the largest proportion of incomplete genes (Appendix 1-table  3 ), disrupting the detection and assignment of Pfam domains. But it also incorporates sequences with an unusual amino acid composition that have homology to proteins with high levels of disorder in the DPD database  (Perdigão et al.
Beyond the twilight zone with AGNOSTOS, communities of gene clusters To find relationships between gene clusters, we implemented in AGNOSTOS a method to group them in gene cluster communities (GCCs) (Fig. 2A ) using remote homologies. As we are dealing with the unknown, we identified GCCs independently in each category. AGNOSTOS uses the gene clusters from category K as a reference to automatically identify the best parameters for the clustering of the gene cluster homology network (Methods; Fig. 2B ; Appendix 7) and applies the learnt parameters to the other categories. The grouping reduced the final collection of GCs by 87%, producing 673,601 GCCs (Methods; Fig. 2B; Appendix 7 ). We validated our approach to capture remote homologies between related GCs using two wellknown gene families present in our environmental datasets, proteorhodopsins  (Olson et al.
3A ). The number of genes corresponding to EU gene clusters is higher in marine metagenomes; 12% of the genes are part of this GC category. We obtained a comparable result when we evaluated the genes from the GTDB_r86, 75% of bacterial and 64% of archaeal genes were part of the known sequence space. Archaeal genomes contained more unknowns than those from Bacteria, where 30% of the genes are classified as genomic unknowns in Archaea, and only 20% in Bacteria (Fig. 3A ; Supplementary file 1B). To further evaluate the differences between the known and unknown sequence space, we calculated the accumulation rates of GCs and GCCs combining the categories K and KWP as knowns, and GU and EU as unknowns to get a general overview of both fractions. For the metagenomic dataset we used 1,264 metagenomes (18,566,675 GCs and 282,580 GCCs) and for the genomic dataset 28,941 genomes  (9, 586, 109 GCs and 496, 930 GCCs).
Next, we wanted to know how much of the sequence space we integrated with AGNOSTOS was found in other databases (Appendix 9). Despite only including marine and human metagenomes in our database, we already cover, in average, 76% of the sequence space of seven datasets spanning different databases and environments (Appendix 9-figure  1 ). By screening MGnify  (Mitchell et al. , 2020 11 biomes; 843,535,6116 proteins)  we identified freshwater, soil and human non-digestive as the biomes less covered by our data (Appendix 1-figure  6 ). Two of the seven analyzed datasets are designed to study genes of unknown function (Appendix 9-table 1). On Wyman et al. (Wyman et al. , 2018) , where they defined Function Unknown Families of homologous proteins (FunkFams), we identified 20% of their FunkFams to be members of the known sequence space.
We performed an in-depth exploration of the Cand. Patescibacteria phylum, and we provide a collection of 54,343 lineage-specific GCs (283,874 genes) of unknown function at different taxonomic level resolutions (Appendix 1-table 7; Appendix 12). (Salazar et al. , 2019). The right side of the alluvial plot shows the new organization of the OM-RGC v2 sequence space based on the approach described in this study. The treemap in the right links the metagenomic and genomic space adding context to the unknown fraction of the OM-RGC v2 One of the strengths of AGNOSTOS is the possibility of bridging genomic and metagenomic data and simultaneously unifying the known and unknown sequence space. We further demonstrated this by integrating the new Ocean Microbial Reference Gene Catalog  (Salazar et al. , 2019)  (OM-RGC v2) into our database.
We assigned 26,170,875 genes to known GCs, 11,422,975 to genomic unknowns, 8,661,221 to environmental unknown and 520,083 were discarded. From the 11,422,975 genes classified as genomic unknowns, we could associate 3,261,741 to a GTDB_r86 genome and we identified 113,175 as lineage-specific. The alluvial plot in Fig. 5E  depicts the new organization of the OM-RGC v2 after being integrated into our framework and how we can provide context to the two original types of unknowns in the OM-RGC (those annotated as category S in eggNOG  (Huerta-Cepas et al. , 2019)  and those without known homologs in the eggNOG database  (Salazar et al. , 2019) ) that can lead to potential experimental targets at the organism level to complement the metatranscriptomic approach proposed by Salazar et al. (Salazar et al.
We can track this GC into the environment and explore the occurrence in the different samples we have in our database. As expected, the GC is mostly found in non-human metagenomes (Fig. 6B ) as Pseudomonas are common inhabitants of soil and water environments  (Heffernan et al. , 2009). However, finding this GC also in human-related samples is very interesting due to the potential association of P. fluorescens and human disease where Crohn's disease patients develop serum antibodies to this microbe  (Scales et al. , 2014). We can add another layer of information to the selected GC by looking at the associated remote homologs in the GCC GU_c_21103 (Fig. 6C ). We identified all the genes in the GTDB_r86 genomes that belong to the GCC GU_c_21103 (Supplementary file 1E) and explored their genomic neighborhoods.
It can briefly be summarized into (1) assembly, (2) gene prediction, (3) gene catalog inference, (4) binning, and (5) characterization. AGNOSTOS exploits recent computational developments  (Steinegger and Söding, 2018; Steinegger and Soding, 2017)  to maximize the information used when analyzing genomic and metagenomic data. In addition, we provide a mechanism to reconcile top-down and bottom-up approaches, thanks to the well-structured sequence space proposed by our framework. AGNOSTOS can create environmental-and organism-specific variations of a seed database based on gene clusters. Then, it integrates the predicted genes from new genomes and metagenomes and dynamically creates and classifies new GCs with those genes not integrated during the initial step (Fig. 1B ). Afterward, the potential functions of the known GCs can be carefully characterized by incorporating them into the traditional standard operating procedure described previously.
One of the most appealing characteristics of our approach is that the GCs provide unified groups of homologous genes across environments and organisms independently if they belong to the known or unknown sequence space, and we can contextualize the unknown fraction using this genomic and environmental information. Our combination of partitioning and contextualization features a smaller unknown sequence space than previously reported  (Salazar et al. , 2019; Thomas and Segata, 2019). On average, only 30% of the genes fall in the unknown fraction for our genomic and metagenomic data. One hypothesis to reconcile this surprising finding is that the methodologies to identify remotely homologous sequences in large datasets were computationally prohibitive until recently. New methods  (Steinegger et al.
However, not only splitting GCs, but also lumping unrelated genes or GCs owing to the use of remote homologies can be problematic. Although we use very sensitive methods to compare profile HMMs to infer GCCs, low sequence diversity in GCs can limit the method effectiveness. Moreover, our approach is affected by the presence and propagation of contamination in reference databases, a significant problem in 'omics  (Breitwieser et al. , 2019; Steinegger and Salzberg, 2020). In our case, we only use Pfam  (Finn et al. , 2016)  as a source for annotation owing to its high-quality and manual curation process. The categorization process of our GCs depends on the information from other databases, and to minimize the potential impact of contamination, we apply methods that weight the annotations of the identified homologs to discriminate if a GC belongs to the known or unknown sequence space.
Genomic and metagenomic dataset We used a set of 583 marine metagenomes from four of the major metagenomic surveys of the ocean microbiome: Tara Oceans expedition (TARA)  (Sunagawa et al. , 2015) , Malaspina expedition  (Duarte, 2015) , Ocean Sampling Day (OSD)  (Kopf et al. , 2015) , and Global Ocean Sampling Expedition (GOS)  (Rusch et al. , 2007). We complemented this set with 1,246 metagenomes obtained from the Human Microbiome Project (HMP) phase I and II  (Lloyd-Price et al. , 2017). We used the assemblies provided by TARA, Malaspina, OSD and HMP projects and the long Sanger reads from GOS  (Sanger et al. , 1977). A total of 156M  (156, 422, 969)  contigs and 12. 8M long-reads were collected (Appendix 1-table  5 ). For the genomic dataset, we used the 28,941 prokaryotic genomes (27,372 bacterial and 1,569 archaeal) from the Genome Taxonomy Database  (Parks et al.
Computational workflow development We implemented a computation workflow based on Snakemake  (Köster, 2018)  for the easy processing of large datasets in a reproducible manner. The workflow provides three different strategies to analyze the data. The module DB-creation creates the gene cluster database, validates and partitions the gene clusters (GCs) in the main functional categories. The module DB-update allows the integration of new sequences (either at the contig or predicted gene level) in the existing gene cluster database. In addition, the workflow has a profile-search function to quickly screen samples using the gene cluster PSSM profiles in the database.
Metagenomic and genomic gene prediction We used Prodigal (v2. 6. 3) (Hyatt et al. , 2010)  in metagenomic mode to predict the genes from the metagenomic dataset. For the genomic dataset, we used the gene predictions provided by Annotree  (Mendler et al. , 2019) , since they were obtained, consistently, with Prodigal v2. 6. 3. We identified potential spurious genes using the AntiFam database  (Eberhardt et al. , 2012). Furthermore, we screened for 'shadow' genes using the procedure described in Yooseph et al. (Yooseph et al. , 2008).
PFAM annotation We annotated the predicted genes using the hmmsearch program from the HMMER package (version: 3. 1b2)  (Finn et al. , 2011)  in combination with the Pfam database v31  (Finn et al. , 2016). We kept the matches exceeding the internal gathering threshold and presenting an independent e-value < 1e-5 and coverage > 0. 4. In addition, we considered multi-domain annotations, and we removed overlapping annotations when the overlap is larger than 50%, keeping the ones with the smaller e-value.
The predicted genes can have multi-domain annotations in different orders, therefore to validate the consistency of intra-cluster Pfam annotations, we applied a combination of w-shingling  (Broder, 1997)  and Jaccard similarity. We used w-shingling (k-shingle = 2) to group consecutive domain annotations as a single object. We measured the homogeneity of the shingle sets (sets of domains) between genes using the Jaccard similarity and reported the median similarity value for each cluster. Moreover, we took into consideration the Clan membership of the Pfam domains and that a gene might contain N-, C-and M-terminal domains for the functional homogeneity validation. We discarded clusters with a median similarity < 1. After the validation, we refined the gene cluster database removing the clusters identified to be discarded and the clusters containing ≥ 30% shadow genes.
We aligned the sequences of the non-annotated GCs with FAMSA  (Deorowicz et al. , 2016)  and obtained cluster consensus sequences with the hhconsensus program from HH-SUITE  (Steinegger et al. , 2019a). We used the cluster consensus sequences to perform a nested search against the UniRef90 database (release 2017_11) (The UniProt Consortium, 2017) and NCBI nr database (release 2017_12) (NCBI Resource Coordinators, 2018) to retrieve non-Pfam annotations with MMSeqs2 (Steinegger and Soding, 2017) ("-e 1e-05 --cov-mode 2 -c 0. 6"). We kept the hits within 60% of the Log(best-e-value) and searched the annotations for any of the terms commonly used to define proteins of unknown function (Supplementary file 1G). We used a quorum majority voting approach to decide if a gene cluster would be classified as Genomic Unknown or Known without Pfams based on the annotations retrieved.
We searched the consensus sequences without any homologs in the UniRef90 database against NCBI nr. We applied the same approach and criteria described for the first search. Ultimately, we classified as Environmental Unknown those GCs whose consensus sequences did not align with any of the NCBI nr entries. In addition, we developed some conservative measures to control the trade-off between specificity and sensitivity for the remote homology searches such as (1) a modification of the algorithm described in Hingamp et al. (Hingamp et al. , 2013)  to get a confident group of homologs to determine if a query protein is known or unknown by a quorum majority voting approach (Appendix 4); (2) strict parameters in terms of iterations, bidirectional coverage and probability thresholds for the HHblits alignments to minimize the inclusion of non-homologous sequences; and (3) avoid providing annotations for our gene clusters, as we believe that annotation should be a careful process done on a smaller scale and with experimental context.
Gene cluster remote homology refinement We refined the Environmental Unknown GCs to ensure the lack of any characterization by searching for remote homologies in the Uniclust database (release 30_2017_10) using the HMM/HMM alignment method HHblits  (Remmert et al. , 2012). We created the HMM profiles with the hhmake program from the HH-SUITE  (Steinegger et al. , 2019a). We only accepted those hits with an HHblits-probability ≥ 90% and we re-classified them following the same majority vote approach as previously described. The clusters with no hits remained as the refined set of EUs. We applied a similar refinement approach to the KWP clusters to identify GCs with remote homologies to Pfam protein domains. The KWP HMM profiles were searched against the Pfam HH-SUITE database (version 31), using HHblits.