dataesr/scientific-paragraphs-categorization huggingface dataset:
evaluate specific properties of an unsupervised clustering outcome, given the true classes of the data samples and their pairwise distances. Assume that the samples of dataset A are being classified into a set of clusters ‫ܭ‬ ൌ ሼ݇ ଵ , ݇ ଶ , …, ݇ ሽ, using an unsupervised clustering algorithm. The amount of samples of class ݅ which are assigned to cluster ݆ is denoted as ห‫ܣ‬ ห. HS evaluates the quality of the derived K clusters to contain objects belonging to a unique classes, by measuring the conditional entropy of the classes' distribution given the proposed clustering: quantifies the uncertainty about the distribution of samples in the set of ‫ܥ‬ classes, given the K clusters. If each cluster ݇ is homogenous (i. e. containing samples from only one class), then the conditional entropy ‫‪ሻ‬ܭ|ܣ‪ሺ‬ܪ‬ is equal to zero.
Figure1: Schematic analytical workflow -(A) Process of data acquisition. Proteostasis-related gene lists were constructed manually for model organisms (step I). Then, public databases were used to collect genomic homologies to expand organisms' collection and concentrate specific data for rRNA, HSP40 and HSP70 sequences. Only organisms with ample annotation were selected in the final set (step II). (B) Analysis workflow. Gene lists were translated into networks of biological processes through pathway analysis of GO-BP corpus (step I). An unbiased version of GO was constructed to remove potential annotation bias from the results. Finally, semantic analysis was performed to calculate the differences among proteostasis-related networks (step II). (C) Construction of the unbiased GO-BP graph: Terms residing low in the branches of the graph bear high IC, meaning they are really specific with the leaves of ontological graph bearing maximum IC (red and blue nodes).
is used as an feature selection measure in data mining. Given a dataset A, with samples belonging to a set of classes ‫ܥ‬ ൌ ሼܿ ଵ , ܿ ଶ ,… , ܿ ே ሽ and D a subset of A, the entropy of the classes' distribution in subset D is ห is the amount of samples in partition D, which belong to class ‫ܥ‬. ‫ܪ‬ሺ‫ܦ‬ሻ quantifies the expected information, to classify correctly a sample ܽ in D. This entropy is maximized when each sample belongs to a different class. During the training process of decision trees, discrete or continuous variables (features) are examined to separate samples, with respect to their classes. Assume that a feature F is selected to separate further the samples in D, producing M groups. Then,.
Acknowledgements This work was funded by grants from  INCa (PLBio) ,  FRM  ( DEQ20180339169 ) and  ANR (ERAAT)  to EC; from  INSERM (International Research Project -TUPRIC ),  EU   H2020 MSCA RISE-734749  (INSPIRED) to AC and EC; from  ANR  ( ANR-15-CE12-0003-01 ) and  FRM  ( DBF20160635724 ) to BF and  ELIXIR-GR (MIS 5002780 ) to TK.
Code and Data Availability The main part of code written in support of this manuscript is publicly available on a GitHub repository at https://github. com/thodk/proteostasis_imprinting_across_evolution. Some tasks were performed using the software of e-NIOS Applications PC, so the respective code is not available. All the results and the data used to produce them, are available in a figshare collection at 10. 6084/m9. figshare. c. 5427279.
is used as an feature selection measure in data mining. Given a dataset A, with samples belonging to a set of classes ‫ܥ‬ ൌ ሼܿ ଵ , ܿ ଶ ,… , ܿ ே ሽ and D a subset of A, the entropy of the classes' distribution in subset D is ห is the amount of samples in partition D, which belong to class ‫ܥ‬. ‫ܪ‬ሺ‫ܦ‬ሻ quantifies the expected information, to classify correctly a sample ܽ in D. This entropy is maximized when each sample belongs to a different class. During the training process of decision trees, discrete or continuous variables (features) are examined to separate samples, with respect to their classes. Assume that a feature F is selected to separate further the samples in D, producing M groups. Then, defined as follows: which is the weighted mean entropy of the derived M subsets. Information Gain of feature F is equal ‫ܪ‬ሺ‫ܦ‬ሻ ൌ െ ห‫ܥ‬ , ห ‫|ܦ|‬ ‫݈݃‬ ቆ ห‫ܥ‬ , ห ‫|ܦ|‬ ቇ | ವ | ୀଵ ‫ܪ‬ ி ሺ‫ܦ‬ሻ ൌ ห‫ܯ‬ ห ‫|ܦ|‬ ൈ ‫ܯ‪ሺ‬ܪ‬ ሻ |ெ| ୀଵ to the reduction of entropy after the split: ‫‪ሻ‬ܨ‪ܽ݅݊ሺ‬ܩ݂݊ܫ‬ ൌ ‫ܪ‬ሺ‫ܦ‬ሻ െ ‫ܪ‬ ி ሺ‫ܦ‬ሻ Another useful measure is the Split Entropy, which is defined as the derived uncertainty due to the partitioning of samples: where ห‫ܥ‬ , the entropy of D is re-calculated, taking into account that partitioning: ‫ݐ݈݅ݏܪ‬ ி ሺ‫ܦ‬ሻ ൌ െ ห‫ܯ‬ ห ‫|ܦ|‬ ൈ ‫݈݃‬ ቆ ห‫ܯ‬ ห ‫|ܦ|‬ ቇ |ெ|ୀଵSplit Entropy value increases in function with the amount of the produced subsets and it is maximized when feature F creates a novel branch for each sample.
V. Methods Throughout this article the time (τ ) units are years, length (L) units are bp, while the rates (mutation µ and horizontal transfer ρ) are in units of yrs -1 bp -1. Data To validate the theoretical predictions we used taxa from the well known Enterobacteriaceae family and two outgroups: Serratia and Vibrio genera. We considered only chromosomal part of the genome. To filter out plasmids we used only complete genome and chromosome level assemblies in the RefSeq  [69]  and GenBank  [70]  databases using NCBI  [71]. We considered only species with at least 20 available assemblies. For species with smaller number of full assemblies we grouped the species to corresponding genera. Aligning the genomes To align pairs of genomes we used nucmer  [47]  with the default settings, using only unique matches in both genomes (--mum option).
The challenge is to make this merger be, by analogy, one language, rather than nouns and verbs from a dozen or more dialects which cannot be used directly in statements about some aspect of the farm to fork journey without expensive translation or substantial dialect education in order to understand a particular text or domain of knowledge. This work focuses on the ontology components -object and data properties and annotations -needed to model food processes or more general process modelling within the context of the Open Biological and Biomedical Ontology Foundry and congruent ontologies. Ideally these components can be brought together in a general process ontology that can be specialized not only for the food domain but for carrying out other protocols as well. Many operations involved in food identification, preparation, transportation and storage -shaking, boiling, mixing, freezing, labeling, shipping -are actually common to activities from manufacturing and laboratory work to local or home food preparation.
1. The opposite paradigm is exemplified by WikiData, a federated RDF graph database which merges databases that make use of over 9,000 properties  [25]. This landscape is permeated by diverse and not necessarily complete data models and datasets requiring a larger learning curve to navigate. Regarding logical consistency, OBO has tacitly promoted reasoning over a given ontology's logical structure merged with the Basic Formal Ontology (BFO)  [26]  to expose internal inconsistencies. (Currently, it is up to individual projects to test other pertinent combinations of OBO ontologies however. ) By analogy, BFO acts as a grammarian enforcing the proper use of noun and verb types. More recently an alternate compatibility starting point is with the OBO Core Ontology for Biology and Biomedicine (COB)  [27] , an upcoming ontology that has a handful of commonly used upper-level classes and relationships -material entity, process, characteristic, and informationcombined into a single resource with a principle aim of supporting RO relations' domain and range constraints.
COB is a more agnostic semantic territory that other ontologies might establish equivalencies with. A future harmonisation question would be to see, for our proposed OBO process model, how many properties it shares with other upper-level-ontologies such as Dolce-Ultra light  [28]. To fit in OBO, a process ontology and the components it imports must generally adhere to key OBO membership criteria. If useful components are not reusable as is in OBO, then they must be replaced with comparable terms that meet OBO criteria  [29] , examples of which are listed here: • Permanent URL (PURL) management: Each ontology term is given a URL and attached to a service which returns human and computer readable information about the term. The term URL is expected to exist in perpetuity; a deprecation and replacement term reference system exists which facilitates database updates in the face of evolving ontologies.
If a term resource is not completely OBO compatible, data translation could still be accomplished by term mapping using the "has database cross reference" (oboInOwl:hasDbXref) data property, or via SSSOM mapping  [31]  for finer semantic tuning that can differentiate between semantic broad/narrow/exact synonymy. Data interoperability is promoted when an ontology is developed as a set of micromodels each focused on an entity in a given context and level of abstraction  [32]  as illustrated in Fig. 1  which highlights Phenotypic Ontology (PATO)  [33]  term application; the expectation is that micromodels can plug into each other and be used for various purposes by other users outside a given curation community. This entails potentially splitting entity management across to other ontologies which is a more complex process requiring mutual understanding, a resource price tag, and stamina.
On a positive note, this upfront work avoids dependence on a patchwork of reference materials written in other dialects, and the problems that such vocabulary brings "Finding Our Way through Phenotypes"  [32]. in terms of comprehension and training time. Also, the more partners involved, the more easily an ontology goes from essentially a database solution for one project, to an interagency standard. However, the capabilities of the OBO curation model are being challenged as it evolves from its initial life science comfort zone into connected domains such as manufactured products and the build environment. Some other ontologies or model frameworks were considered but not given in-depth review: AMALQEIA  [34]  captures dish roles with respect to a meal (main course etcetera), style of cooking, ingredients, and their nutritional value, and mentions food process modelling but leaves it to a future task to "express precisely which ingredients undergo which process.
Process model nomenclature An ontology term label may cater to a specific ontology domain and its expert users, which may seem limiting if repurposed outside that community, but a solution is to augment it with a synonym used by a given application user interface. A "has quality" relation could have an exact synonym "has characteristic" for use within a particular community. Such moves attempt to preserve semantic web data exchange objectives by maintaining database views that can be queried using a reduced set of relations under-the-hood. A more difficult challenge semantically is to see how much one can rely on the more abstract "glue" or grammar of a generalist ontology -especially its relationships -in order to express specialist ontology concepts. If a generalist ontology suffices, then the chances of plug and play data harmonization across disciplines is increased.
Other aspects of an observation may be documented: the particular time and context it happened in, and the sensor (device or human or other organism organ) that was involved. This sense is similar to the Extensible Observation Ontology (OBOE)  [41]  observation and related terms. Some use "observation" for qualitative description of phenomena while reserving "measure" for quantitative recording of underlying characteristics of phenomena; our use has "observation" allow both as components. An observation data item requires a data structure to encompass the contextual information about the time and place etc. of a measure. A data set composed of these observations could adopt the characteristic values they have in common, for example, if all of a dataset's observations were made at the same location, then that location can become a characteristic of what the dataset is about, and is inherited rather than repeated at the observation level.
Process ontology implementation can reuse the above relations and terms directly into graph database representation (OWL ontologies provide the sentence structure for writing statements directly into RDF graph databases). For other kinds of databases, ontology terms can be used at a more basic level via lookup tables that name categorical variables and their choices, and numeric or date variables. From a data import/export perspective, the ontology structures discussed so far can translate into native relational database records and vice versa, though knowledge graph querying is most flexibly done in a native graph format. Object and data properties for holding values and units are important (examples are shown in Fig. 9 ), and vary widely between OBO Foundry ontologies and other ontologies and RDF schemes like Schema  [42]  and GS1  [43] ; their potential harmonization is not covered here.
Realization of planned process The instance vs. class distinction arises when looking at OBO entities for general or more specific plans. OBI defines a vague sense of "plan" (OBI:0000260) as the expression of a general intention of an agent to achieve an objective, but largely avoids using this in favor of IAO plan specification which is designed to capture a sense of a stepby-step plan; as well OBI provides "protocol" (OBI_0000272), "A plan specification which has sufficient level of detail and quantitative information to communicate it between investigation agents, so that different investigation agents will reliably be able to independently reproduce the process. " OBO currently does not specify the data structure details for this. Regardless, an instance of a plan specification or protocol -which details the needed device(s), settings and material or digital inputs -is executed by a process, and that instance presumably can reflect refinements regarding the possible tools at hand and their operation.
Note that unless a process is explicitly triggered by specific calendar dates (for example a Remembrance Day ceremonial process), modelling can occur entirely with reference to durations, and temporal dependencies between processes can be later calculated or reasoned based on instance data that includes process start or stop times. One modelling choice is whether to utilize the time:ProperInterval class to structure dependencies between processes. The alternative is to attach statements about process dependency directly to the processes themselves. In that scenario, RO is used to state that process B "ends with" process C, or process A "immediately precedes" B, as in the boiling carrots recipe model below. It would seem like a more intentional design to express dependencies on the processes themselves, and leave reasoning to infer impacts on associated OWL-Time namespace objects.
Process and Observation Ontology Based partly on the Sensor, Observation, Sample, and Actuator (SOSA) ontology  [13] , but also situated within the BFO hierarchy, PO2 is designed to monitor industrial food processing, and describe food formulation. PO2 can represent a food transformation process described by a set of experimental observations available at different scales and evolving in time through the different unit operations of a production process. The initial model called MS2O (Multi-scale Multi-step Ontology) was built to represent a bioprocess of yeast production and stabilization  [58]  and further developed to fit the need of dairy gel manufacturing and the integration of heterogeneous data  [59, 60]. Finally, the core model of PO2 was reengineered with SOSA/SSN to be able to adapt to any transformation process.
OBO -OWL-Time Rather than use OBO's interval and duration expressions, we suggest OWL-Time hasTime, hasDuration, hasBeginning, and hasEnd object relations, as well as Instant, ProperInterval and Duration classes. Upcoming COB work will provide guidelines for using OWL-Time data properties in a limited way, such as inXSDDate and perhaps numericDuration and unitType. Additionally, if OBI 'planned process' can be logically typed as a proper interval then OWL-Time's Allen relations could be considered for direct use.
The result is a data structure suited to informational retrieval algorithms with limited ontological logic. The most common Recipe class implementation is as a list, or a single narrative (free-text) string, of the recipe steps with no possibility of representing concurrent processes unless expressed explicitly within the narrative. The sequence of these steps is implicit to their physical ordering within the serialization itself which is not guaranteed outside of the specific context of schema. org Search Engine Optimization documents. Executing a recipe step involves quantities of a subset of the ingredients, which implies a relationship between the set of ingredients and each step. In a completely ontological framework, each step would reference the same individual ingredient nodes as the recipe ingredient list to ensure proper entity resolution.
These sets will be subclasses of IAO data set (IAO:0000100) which is simply a set of things of the same kind. Some amount of instruction set recursion is needed where recipes have overall stages or steps, with each step itself potentially having an instruction set component. Each ingredient specification references a food material (product, intermediary product, or additive, or choice thereof) but also a proportional, count or scalar measure of it. Device capabilities are stated by the device specification (pot, pan or bowl size, dish oven temperature rating etc. ). Free text commentary or instruction can potentially be linked to any of the class entities by using annotations such as "dc:description" or "rdfs:comment". The recipe model currently does not include serving procedures or portions, or derived nutrition or cooking duration information.
ACKNOWLEDGEMENTS This study was supported by an  ARC  project grant ( PJA20181208167 ) and the  ANR DroZeMyo  ( ANR-17-CE13-0024-02 ) (MF). AJK benefited from a 4 th year PhD fellowship from  La Ligue Contre le Cancer. Confocal microscopy was performed with the help of the iBV PRISM imaging platform. We thank  M. Gonzalez-Gaitan ,  C. P. Heisenberg ,  T. Lepage ,  S. Lopes , and  C. &  B. Thisse  for the sharing of reagents. We are grateful to  S. Polès ,  R. Rebillard  and  G. Dupuy  for technical assistance and excellent fish care.
1B ). To go further in the understanding of this unusual infection, we con rmed bacterial identi cation with whole-genome sequencing (Illumina®). Pair-end reads were de novo assembled with SPAdes-based  [10]  shovill algorithm (https://github. com/tseemann/shovill). Species identi cation was performed using the KmerFinder approach  [11]  in combination with phylogeny within the bacterial species belonging to ABC. We computed core-genome using Roary  [12]  using a MAFFT alignment. Maximum-likelihood phylogenies using Generalize Time Reversible model were set with PhyML  [13]  into i) ABC isolates (data not shown) and then ii) A. pittii isolates (Fig. 2 ) including external genomes previously published. Phylogenetic tree of 38 A. pittii isolates combining: 36 previously published genomes (assembly level: chromosome and complete genome), 1 outgroup (A.
Freshly hatched larva were transferred to regular vials at a density of 30 individuals / tube, and placed at 18 °C or 29 °C. Once arrived at the fluid pupal stage, individuals where switched temperature (from 18 °C to 29 °C and vice-versa). Upon hatching, flies were collected for wing dissection. Wing measurements were done using a semi-automated procedure for estimating the positions of 12 landmarks and 37 semi-landmarks along the wing outline and veins (Figure  4suppA ). This was done using Wings4 software  [25, 26]  which fits a B-spline model to the wing from which the coordinates of landmarks and semi-landmarks are extracted. Wings4 outputs were examined using CPR software  [26]  which allows to screen for outliers and to generate a consolidated dataset of landmarks and wing areas. Landmarks data were geometrically aligned within each experiment using the General Procrustes Analysis (GPA)  [42]  as implemented in R geomorph package  [43].
Short-term substitution rates of CfMV and RYMV We first assessed and confirmed the strength of temporal signal present in the CfMV dataset through the tip cluster randomisation test in root-to-tip regression (see Methods). We estimated the short-term substitution rate of the coat protein gene (ORF3) at 7. 8×10 -4 s/s/y (95% highest posterior density (HPD): 6. 4 -9. 5×10 -4 s/s/y) for RYMV and at 5. 0×10 -4 s/s/y (95% HPD: 2. 3 -8. 2×10 -4 s/s/y) for CfMV using the same substitution model in our Bayesian inference (Figure  1 ). The wider variation in the CfMV posterior rate distribution likely reflects the lower number of sequences and weaker clock signal compared to the RYMV dataset. The inferred substitution rate of RYMV for the polymerase gene was 6. 5 (5. 2 -7. 9) ×10 -4 s/s/y, similar to and slightly lower than that of the coat protein gene.
Phylogeny and taxonomy of recognized and tentatively new sobemovirus species We next examined a dataset of the 26 sobemovirus species isolated from plants (examined above) merged with 42 additional known or tentatively novel sobemovirus sequences collected from metagenomic analyses (hereafter referred to as metagenomic sequences; see Methods) to establish their taxonomy and phylogenetic relationships. Most sequences were obtained from gut metagenomes of herbaceous animals feeding mainly on Poaceae and Fabaceae (Table  2 ). This explains why many isolates were related to sobemovirus species found in Fabaceae and in Poaceae, particularly RGMoV (see Figure  2b ). In contrast, none of the metagenomic sequences clustered closely with sobemovirus species infecting the Solanaceae (see Figure  2b  and Table  2 ).
We found that although the great majority of the metagenomic sequences originated from China  [29] , they mostly cluster with isolates found in other continents, reflecting the worldwide distribution of many sobemoviruses. Nevertheless, there was some geographical association between the sequences. For instance, the metagenomic sequences close to CfMV (MN626425 and MW588040) and CnMoV (MW588173) were from Europe. The phylogeny for the collection of 68 sequences differed in several respects from that of the 26 selected samples representing the recognized and candidate sobemovirus species that we initially investigated. The overall genetic diversity of the merged dataset was noticeably higher as new subclades possibly representing the novel species were revealed, some of which have long independent evolutionary histories (see Figure  2b ).
There were also several sequences that are closely related to known sobemovirus species such as RGMoV. The RGMoV lineage includes strains of RGMoV and related species, likely at the subspecies level. Our analysis of the pairwise genetic identity of the merged dataset revealed a continuous change in pairwise identity of samples from interspecific to intraspecific levels. This contrasts with the earlier findings in the 26 selected samples of sobemovirus species where certain percentages of genetic identity had no corresponding pairs (see Figure  2a ). This discontinuity is no longer apparent in the merged dataset, confirming the broadened sobemovirus genetic diversity (Figure  2b ).