dataesr/scientific-paragraphs-categorization huggingface dataset:
The latter assemblies were imported into Geneious v. 9. 1. 8 (37) for analysis. For annotated gene models, we obtained sequences derived from NCBI's Eukaryotic Genome Annotation (EGA) pipeline by BLASTing (discontiguous megablast) a human CHIA mRNA reference sequence (NM_201652) against the NCBI nucleotide collection and performing separate Basic Local Alignment Search Tool (BLAST) searches against all the placental taxonomic orders. We downloaded all relevant EGA gene models, which are denoted by an "XM_" accession number prefix and are annotated as CHIA or any annotation suggesting homology to this protein (for example, "acidic mammalian chitinase-like"). We considered the gene absent/nonfunctional under any of the following conditions: (i) we retrieved negative BLAST results; (ii) the gene model was annotated as a "low-quality protein," a designation given if construction of the model required correction for premature stop codons and/or frameshift insertions or deletions (indels) found in the reference genomic contigs; and (iii) the sequence lacked a canonical catalytic domain (DXXDXDXE)  (18).
Placental CHIA sequences clustered in five distinct clades, which we designated CHIA1, CHIA2, CHIA3, CHIA4, and CHIA5. On the basis of these clade assignments, sequences from all five CHIA subclades were realigned separately (that is, all CHIA1 sequences, all CHIA2 sequences, etc. ), and then each subclade alignment was aligned successively using the Geneious Translation align tool implementing the MUSCLE algorithm. We then performed a RAxML analysis on this finalized alignment (data set S1) in CIPRES using the same parameters (fig. S1 ).
Assembly of contig-derived sequences Given the inference of five distinct clades of CHIA within placental mammals, we directly queried the genomic contigs (WGS) of a subset of species to verify the presence of distinct CHIA genes (data set S2), determine their synteny, and validate the presence of inactivating mutations. Some genome assemblies present in WGS had not been annotated by EGA at the time of our study, so we examined additional genomes to broaden taxonomic diversity. We directly queried at least one member of each mammalian family for which there was a genome assembly available and, in some cases, examined additional species from a family if we were unable to recover a particular CHIA paralog or to check for inactivating mutations shared by confamilials. On the basis of the predicted gene models, very few mammals appeared to have all five predicted CHIA genes.
As we downloaded each sequence, we recorded the accession number of each contig, the position of the BLAST hits within each contig, and the position of flanking genes to determine synteny (tables S1, S2, and S8 and fig. S4 ). We imported each captured sequence into Geneious, aligned the imported sequence to the T. syrichta probe reference using MUSCLE, and successively aligned additional sequences derived from the same ordinal and/or superordinal taxon, frequently using alignments of outgroup taxa to anchor the new alignments (data set S2). We examined each of the gene sequences for putative inactivating mutations, including missense mutations to the conserved catalytic domain or chitin-binding domain residues, start codon mutations, frameshift indels, premature stop codons, and splice site mutations (table  S4 ).
We examined the master alignment by eye, adjusted it manually, removed sequence positions of dubious homology, and removed sequences that were likely to be phylogenetically uninformative because of missing data and/or minimal or no overlap with related species (for example, one exon only). We then executed a RAxML analysis through CIPRES on this contig-derived sequence alignment (data set S1) using the methods described above. The resulting tree (fig. S2 ) largely recapitulated the results from the gene model-derived analyses (fig. S1 ) with the major exception of CHIA4 being nested within CHIA5. Upon closer inspection, it became apparent that the chiropteran CHIA4 and CHIA5 sequences seemed to be influencing this aberrant topology. Specifically, chiropteran CHIA4 is paraphyletic at the base of the CHIA4 clade, and chiropteran CHIA5 sequences form the sister group to the entire CHIA4 clade.
Regression analyses We tested whether the number of intact CHIAs in a genome correlates with the amount of insect prey consumed by a species. We derived the latter metric from EltonTraits v. 1. 0  (36) , which provides the estimated percentage of invertebrate prey in the diet, a proxy for the amount of insects consumed. We performed PGLS regression analyses with the caper package in R  (43, 44) , testing the hypothesis separately with the gene model (EGA) and contig-derived (WGS) data sets. We assumed the timetree from Emerling et al. (24)  and implemented phylogenetic corrections using maximum-likelihood estimates of Pagel's l. We made taxonomic substitutions when species in our data set were not represented in the Emerling et al. (24)  phylogeny. Specifically, we assumed that species in the same taxon (for example, genus and family) could be interchanged if there were no other congeners or confamilials in the phylogeny.
The wide offering of cloud-based services brings alternatives to traditional approaches for developing modern information systems. In this work, we examine cloud spreadsheet services as an alternative for data backend in small scale, view-only web applications. We review 6 cloud-based spreadsheets offering data access APIs to third-party applications, then we present a set of performance tests over spreadsheets hosted on Google Sheets. Preliminary findings show a performance penalty for transferring JSON-formatted data and an expressive failed request rate for many simultaneous accesses.
INTRODUCTION Cloud computing is not only changing the way we use software, but also the way we build it. As more and more services migrate to the cloud, traditional components in software architecture may be provided by cloud-based services. Since the early days of cloud computing, the range of services has grown and spanned from personal to corporate applications. Examples of cloud services include word processors, spreadsheets, database managers and a lot more  (Miller, 2008). Databases are usually a core component of large information systems as well as of smaller web and mobile applications. Relational Database Management Systems (RDBMS) are well known for their efficiency in querying and filtering data or processing transactions. As so, they are the first choice for data backend in a wide range of web applications.
Modern web applications can vary greatly in their purposes and architectures. Some are highly interactive websites or mobile applications which perform frequent data read and write operations. Others are "applications that use backend data, supporting the searching, sorting, filtering and visualizing of the data based on the user input"  (Chang and Myers, 2014). These are view-only web applications, in the sense that the end user only reads data that have been previously registered in the storage backend. For such class of web applications, some developers are reportedly using cloud-based spreadsheets instead of relational databases as an alternative for data backend  (Fisher, 2014; Hankinson, 2015) , as many cloud providers offer APIs for reading or writing data onto the online spreadsheets.
Arguments in favor of this approach usually mention its simplicity for developers, which do not need to create administrative interfaces for simple operations, as the spreadsheet interface already meets this need. Against this approach, arguments emphasize that spreadsheets and databases are very distinct components and this solution might a prototyping-only alternative. In technical articles, arguments in favor or against this approach are not based on evidence from experiments and analysis in a given situation, but rather on general knowledge about the matter. Also, real applications relying on spreadsheets provide no public data on their performance. In this work, we aim to further the discussion around this approach by focusing on performance issues. We begin by reviewing the current offering of cloud-based spreadsheets and examine their service plans and their endpoints for developers.
It was launched around 2013 and its API was made available in 2015. Fieldbook 5  is a spreadsheet-database hybrid service in the cloud launched around 2013. Its interface resembles other cloud-based spreadsheets, but it lets one to link sheets so to view and edit related items, as in database powered applications. Rowshare 6  provide a cloud-based service for managing online collaborative tables. It provides finegrained capabilities (for example, sharing a row instead of an entire table) and customizable interfaces for table viewing and editing. It was launched in 2015. To develop web applications using the aforementioned cloud-based spreadsheets, one must rely on their provider's API. To illustrate this point, Table  1  presents example API requests for each of the services. Most of them provide an Uniform Resource Identifier (URI) to address an entire spreadsheet table.
Depending on the provider, authentication may be required even for reading data, as for example in Excel Online. Some APIs require more than a single request to get the data, as it may be necessary to query a spreadsheet id which may not be known in advance. This is the case with Rowshare and Excel Online. Also, there may be different ways to get the spreadsheet data, as in the case of Google Sheets, which offers a comprehensive API for data access. Each provider may have multiple service plans, which define prices and limits for data storage and API usage. Table  2  summarizes service plans for each cloud-based spreadsheet. Most providers offer free plans with lower limits than paid plans. In Table  3 , we present resource limits for the 6 cloud-based spreadsheet services, focusing on their free plans.
Spreadsheets as Data Backends Using cloud-based spreadsheets as a data backend for web applications may sound surprising and controversial, as traditional approaches rely on databases. Even so, there is evidence that such alternative has been explored in some cases, as for example in the news and media domain  (Fisher, 2014)  and even in game development  (Hankinson, 2015). For developers, there are libraries exploring this   Using a cloud-based spreadsheet in place of a database may be advantageous because it brings with it a simple yet effective, well-known web interface for data management. The web-based spreadsheet user interface support insert, update and delete operations, which would otherwise need to be implemented for managing data records. Also, as they are built for collaboration, cloud-based spreadsheets usually offer easy access management features for granting readwrite permissions.
A drawback of this approach is related to the limits imposed by the cloud service provider. This affects even paid service plans, so scalability is usually compromised. Also, performance may be a problem, as data transfers may take long and there is generally no guarantees in this regard. 7 http://jlord. us/sheetsee. js/index. html 8 https://github. com/jsoma/tabletop 9 http://www. tarbell. io/ 10 http://tabledo. com.
RELATED WORK Many technical articles present cloud-based spreadsheets as a simple yet effective solution for data backend in web or mobile applications  (Noble, 2015; Stanoev, 2015; Doubintchik, 2016; Dayagi, 2017). They highlight the ease of use of this approach and mostly recommend it for simple applications and prototyping. From a different perspective, some authors have conducted a thorough investigation on cloud-based spreadsheets as statistical software  (McCullough and Yalta, 2013). They point out many limitations, but do not focus on performance. Focusing on performance, some authors evaluate traditional relational database management systems in cloud environments  (Bini et al. , 2014; Litchfeild et al. , 2017). Findings indicate inefficiencies related to the cloud environment. To the best of our knowledge, no previous work has addressed the performance of cloud-based spreadsheets as a replacement for relational databases in small-scale, view-only web applications.
Using a free account of Google Sheets, we imported those tables as spreadsheets and adjusted sharing/publishing permissions as to make them available as JSON-formatted data using REST API requests. For the testbed, we used Apache JMeter to perform two test plans for simulating: (i) a single user requesting each one of the spreadsheets and (ii) multiple users requesting a single spreadsheet. All tests used a ramp-up period of 1 second. The first test plan allowed us to examine the impact of different table sizes on the service response and, consequently, on the elapsed response time perceived by the user. The second test plan was conceived to investigate how the service responds to different request rates. To favor the replication of the experiments in other environments, all test scripts are available online at: https://github.
Single User Requests In Table  4 , we present response times for a single user requesting spreadsheets of varied sizes from Google Sheets, in JSON format. The transferred size for each requested spreadsheet comprises all table data plus JSON-formatted metadata. This size grows as we add rows or cols, and different table dimensions (rowsXcols) lead to different transferred sizes, even for a constant number of cells (see, for example, dimensions 1000x100 and 10000x10, both with 100000 cells). As we can observe, the average response time grows with the transferred size, and even a relatively small table takes more than a second to be transferred. Also, minimum and maximum values for response times may vary significantly. This may be attributed to both the cloud service provider and the network traffic.
Multiple User Requests In Table  5 , we present response times for simultaneous requests over the smallest table (1000x10). As we increase the number of (virtual) users, we can observe the response times grow. This may be due to the cloud service delaying the response, but also due to a client overload. In either case, a web application running on a single machine will notice such response times when serving multiple users. As we exceed the API request limits, some requests may fail and return an error code instead of the actual data. In this experiment, there are no failed requests for up to 200 users. Starting from 250 users, error rates grow and can reach high percentages. Even so, we may expect some requests to be successful if they are sent just after a failed batch, as Google Sheets do not impose a waiting time before subsequent requests succeeds.
Methods We first describe the main methodological contribution of our work, the ADSEQ tool, followed by its application to the specific Anopheles genomes data set we analyze in detail. We begin by introducing some simple but important terminology. A gene, extant or ancestral, is seen as a directed DNA segment with two extremities. Genes are parts of larger segments containing one or several genes, which are contig, super-contigs, scaffolds or even chromosomes for well assembled genomes. Two genes that are contiguous along such a segment define an adjacency between one extremity of each gene, that we call a gene adjacency. Thus segments of genes, either extant or ancestral, are encoded by linearly organized sets of gene adjacencies. For extant genomes, such segments, corresponding to contigs, scaffolds or chromosomes, are observed, while for ancestral genomes, segments are reconstructed and are to be considered as hypothetical as they are not directly supported by sequence data.
Assembly recovery through detection of Coevolution with sequencing data (ADSEQ) ADSEQ builds upon a family of methods aimed at reconstructing the evolutionary history of gene adjacencies introduced with the DECO algorithm  [50]  and extended along several lines in  [51, 52]. It is implemented within the package DECOSTAR  [53]. The aim of ADSEQ is to jointly scaffold extant genomes and reconstruct ancestral gene orders, through the joint analysis of phylogenomics and sequencing data. ADSEQ takes as minimal input a species tree and a set of extant genomes data:gene adjacencies, homologous gene families and their associated reconciled gene trees (see for example  [54]  for background on reconciled gene trees). Reconciled gene trees implicitly yield the gene content of ancestral genomes. A key feature of ADSEQ is that the extant gene adjacencies can originate from genomes in various state of assembly, from fully assembled genomes -where gene adjacencies encode the gene order along the chromosomes-, to ambiguously assembled genomes represented as scaffolding graphs, through fragmented genomes assembled into contigs or scaffolds.
Tuning the pseudo-temperature kT provides a control over the probability to sample parsimonious solutions: a low pseudo-temperature tends to increase the probability to sample most parsimonious solutions while a large pseudo-temperature skews the Gibbs-Boltzmann distribution toward the uniform distribution over the set of all solutions (we refer to  [51]  for more details on the Gibbs-Boltzmann framework applied to the DECO algorithm). In the present work, the prior scores of extant adjacencies are either 1. 0 for adjacencies that are observed in a contig or scaffold, or a scaffolding score obtained from sequencing data using the scaffolding software BESST. Pairs of genes located at the extremities of contigs and for which sequencing data do not provide any evidence for a scaffolding adjacency receive a small prior score as described in  [52]  (see also Additional file 1).
Genomic data Most genomic data we used were produced by the Anopheles 16 Genomes project described in  [4]  and retrieved from VectorBase (https://www. vectorbase. org): genome assemblies (contigs, scaffolds, or chromosome arms), gene annotations, and gene sequences (CDS). For 16 out of the 18 considered Anopheles species, we retrieved from the NCBI Sequence Read Archive (SRA) paired-end Illumina libraries with an insert size of 180bp ('fragment' libraries) and mate-pair libraries with an insert size of 1. 5kbp ('jump' libraries), both obtained from a single female individual. Additional low-coverage long-range sequencing libraries ('Fosill' libraries) were obtained from pools of individuals. Details are available in Additional file 17: Table  S1.
Gene families and trees We retrieved homologous gene families from orthologous gene groups of 21 Culicidae species recovered from the OrthoDBmoz2 database (http://cegg. unige. ch/orthodbmoz2) generated using OrthoDB  [57]. We generated a multiple sequence alignment for each family, used RAXML  [58]  to compute draft gene trees with bootstrap supports, and then corrected these draft gene trees using PROFILENJ  [59]. PROFILENJ contracts branches with low bootstrap support and, using the species tree, resolves the polytomies in a way that minimizes the number of duplications and losses in a reconciliation. This resulted in 14,940 gene trees containing 183,680 genes. We refer to Additional file 1, Additional file 2: Figure  S1 , Additional file 3: Figure  S2 , Additional file 4: Figure  S3 , and Additional file 5: Figure  S4 , and Additional file 18: Table  S2  for a description of our preprocessing pipeline (including preprocessing of these gene families), a comparison of the newly inferred gene trees with the original ones computed by the Anopheles consortium and for statistics on the inferred gene duplications and losses in these gene trees.
Extant adjacencies and prior scores from sequencing data Sequencing reads from all libraries were filtered to discard low quality reads and trimmed to 75bp length using TRIM-MOMATIC  [60] , then mapped onto the contigs or scaffolds of the considered species using BOWTIE2  [61] ; for reads with multiple mappings, all of them were conserved. The scaffolding software BESST  [62, 63]  was then used to detect potential scaffolding adjacencies between pairs of contigs containing at least one annotated gene. Scaffolding adjacencies that were not supported by at least four pairs of reads were discarded. For remaining scaffolding adjacencies, we assigned a score defined as the arithmetic mean of the two scores computed by BESST, the link variation score and the link dispersity score. Detailed statistics on the scaffolding adjacencies so obtained are available in Additional file 6: Figure  S5  and Additional file 7: Figure  S6.
Conflict With both phylogenies we also measured the level of syntenic conflicts, defined as the sum of the posterior scores of the adjacencies discarded during the linearization phase (data shown in SI text). We observe a higher level of syntenic conflicts in the X phylogeny (7665) than in the WG phylogeny (6319). According to simulations (described in SI text), the level of conflict is higher with a wrong phylogeny, even if it is not with the same order of magnitude than what we observe on our data. This could be seen as a second element contradicting the sequencebased hypothesis that the X phylogeny is the true species phylogeny, although the high level of conflict observed with both phylogenies here again does not allow to draw reliable conclusions.
Funding This work is funded by the  Agence Nationale pour la Recherche ,  Ancestrome  project  ANR-10-BINF-01-01 , the  MITACS Globalink program  and the  Natural Sciences and Engineering Research Council of Canada (NSERC) ,  Discovery Grant   RGPIN-249834  and a resource allocation from  Compute Canada. Funding for the publication of this article was provided by the  Agence Nationale pour la Recherche ,  Ancestrome  project  ANR-10-BINF-01-01.
1  Since then, the synthesis process has been improved, regulatory toxicity tests have been performed and two clinical trials have been accepted by the French regulatory office ANSM (Agence Nationale de Sécurité du Médicament et des produits de santé): 36 (i) NanoRAD (NCT02820454) for the treatment of brain metastases by whole brain radiation therapy in association with AGuIX NPs at Grenoble Alpes University Hospital. (ii) NanoCOL (NCT03308604) for the treatment of locally advanced cervical cancer by chemoradiation and brachytherapy in association with AGuIX NPs at Institut Gustave Roussy, Villejuif. The object of the present review is to focus on the new developments of the AGuIX nanoplatform and its translation to the clinic.
3 and 25% for the irradiated group and irradiated in association with the AGuIX NPs group respectively (Figure  10 ). These encouraging results on two different models of brain cancer have led to the NanoRAD clinical trial ( Clini-calTrials. gov Identifier: NCT02820454) that is designed to treat multiple brain metastases by whole brain radiation therapy in association with AGuIX NPs. In parallel to these studies on tumours of the CNS, a complete study on pancreatic cancer (capan-1) was published in 2016 comparing the efficacy of AGuIX NPs in association with preclinical irradiation (220 kV) or clinical irradiation (6 MV) (Figure  10C, D ). 46 Irradiation at a dose of 10 Gy was performed 15 min after administration of AGuIX NPs (0. 25 mg of AGuIX NPs g -1 of animal). This irradiation time was determined by MRI.
FiRst in mAn The first in man administration of the AGuIX NPs was performed during the NanoRAD clinical trial (NCT02820454). 77 NanoRAD is a phase Ib clinical trial ongoing at CHU Grenoble Alpes aimed at treating multiple brain metastases by whole brain RT (10 × 3 Gy over 3 weeks max) in combination with AGuIX Nps (one injection 4 h before the first session of RT). The NanoRAD trial was designed as a dose escalation phase Ib with five doses tested (15, 40, 50, 75 and 100 mg kg -1 ). The NPs were injected in patients with brain metastases originating from four different types of cancer: lung (NSCLC), melanoma, colon and breast. For each type of cancer, a targeting of the metastases by AGuIX NPs was observed due to the EPR effect (Figure  13 ). Interestingly, all the metastases already detected by conventional T 1 MRI contrast agent were also detected by AGuIX NPs.