dataesr/scientific-paragraphs-categorization huggingface dataset:
Retrospective identification of cholera alerts Because the cholera alert system was launched to prospectively monitor the epidemic, we used cholera alerts as an outbreak proxy to retrospectively evaluate the implementation of the alertresponse strategy. Hence, we could detect delays in notification and analysis of cholera routine surveillance data at DELR, as well as the capacity of response teams to directly get epidemiological information from the community, treatment institutions and departmental directorates of health. We computed retrospective cholera alerts classified as red, orange or green at commune-level and per epidemiological week, between July 2013 (27th epidemiological week, 2013w27) and June 2017 (2017w26), using consolidated databases of institutional cholera suspected cases, suspected deaths and of stool culture results, as well as alert criteria including results of stool culture for cholera (criteria #5) (Table  1 ).
Software and packages Data management was performed using Microsoft ® Excel for Mac v15. 32. The map was drawn using QGIS v2. 18 (http://www. qgis. org, accessed 26 January 2018). Graph design and statistical analyses were performed using RStudio version 1. 0. 136 for Mac (http://www. rstudio. com/, accessed 26 January 2018) with R version 3. 4. 2 for Mac (http://www. r-project. org/, accessed 26 January 2018) and the ggplot2  [53]  (https://cran. r-project. org/web/packages/ggplot2/index. html, accessed 26 January 2018), and lme4  [54]  (https://cran. r-project. org/web/packages/lme4/index. html, accessed 26 January 2018) packages.
Time-space distribution and description of cholera retrospective alerts Using completed cholera databases of case, death and stool culture, and, the DELR criteria (Table  1 ), we retrospectively computed a total of 4,378, 3,475 and 16,710 red, orange and green alerts, respectively, across all 140 communes and during the 209 weeks between mid-2013 (2013w27) and mid-2017 (2017w26). A median weekly number of 20 communes were in red alert (Table  2 ). But as expected, alerts exhibited a temporal evolution consistent with the dynamic of the epidemic (Figure  1  Panel B), with a weekly minimum of 5 red communes observed in March-April 2014, and a maximum of 49 in October-November 2013. The distribution of cholera alerts also showed a 10 marked spatial heterogeneity, as red and orange alerts mainly clustered in the departments of Ouest (DSO, especially in Port-au-Prince Metropolitan Area), Centre (DSC) and Artibonite (DSA) (Figure  2 ).
Therefore, some cholera outbreaks could have been overlooked, especially in remote areas. In addition, DELR prospectively communicated only 35% of cholera alerts during the same week, mainly because surveillance data were obtained with chronic delays. Communes in alert did not locate the actual patients' place residence but their treatment institution that notified cases. The weekly time scale of alerts was also too long compared with cholera median incubation of 1. 4 days  [56]. For these reasons, alerts were hardly used to rapidly guide field response interventions. Instead, mobile teams directly gathered their epidemiological data in treatment institutions, and peripheral health authorities progressively established and shared detailed line listings of suspected cholera cases, which included patient address.
Hence, response rates by WaSH mobile teams soon became higher than communication rates by DELR. Anyhow, identification and communication of alerts was progressively abandoned by DELR after departure of the epidemiologist in charge of this work. To monitor the implementation of the alert-response strategy, we chose to use alerts that were retrospectively computed based on consolidated surveillance databases. These alerts proved a practical and original indicator, but they brought several limits to our analyses. Their weekly time scale largely exceeded the 48h response deadline that mobile teams were requested to respect, which may have overestimated response rates. Interventions were linked with alerts, based on the commune where patients were treated, whereas they were conducted locally, at patients' homes and neighborhoods.
Audio signals are generally treated as superpositions of sinusoidal sub-signals in the form of a Fourier series. This approach has not been hitherto been applied to a reputation signal. We attempt to estimate the amount of noise by filtering out the high frequency noise. In general, low pass filters have only one parameter available for controlling which frequencies are filtered out. Therefore, they filter out all noise, not just measurement noise. They are therefore not as suitable as a means to assess measurement noise, but it is interesting to see the results of using this type of filter. Smith (2007)  (Chapter 1) has a simple account of how a low pass filter is intended to work, and  Hamming (1989)  has a more rigourous account. Practical guidance on programming digital filters using Matlab may be found in  Jackson (1996).
Signal to Noise Calculation Expressing the noise level in a time series in terms of the signal to noise (S/N) ratio enables a ready comparison with noise in an audio signal, which may be more familiar to some readers. We therefore include a way to measure the S/N ratio using the terms in Equation (  16 ). There are several established ways to calculate a S/N ratio. See, for example,  Smith (1999). A convenient method in this context is to use the square of the ratio of the signal amplitude to the noise amplitude. There is a direct implementation in Matlab: the function snr() (see https://uk. mathworks. com/help/ signal/ref/snr. html). Thus, if A denotes amplitude, S/N = ( A signal A noise ) 2 = ∑ τ t=1 {y 2 t ∈ J|r)} ∑ τ t=1 {y 2 t ∈ J|r)}. ( 23 ) Expressed in decibels, the S/N ratio is given by 10log 10 (S/N).
Named-entity recognition aims at identifying the fragments of text that mention entities of interest, that afterwards could be linked to a knowledge base where those entities are described. This manuscript presents our minimal named-entity recognition and linking tool (MER), designed with flexibility, autonomy and efficiency in mind. To annotate a given text, MER only requires: (1) a lexicon (text file) with the list of terms representing the entities of interest; (2) optionally a tab-separated values file with a link for each term; (3) and a Unix shell. Alternatively, the user can provide an ontology from where MER will automatically generate the lexicon and links files. The efficiency of MER derives from exploring the high performance and reliability of the text processing command-line tools grep and awk, and a novel inverted recognition technique.
If the user also wants to perform entity linking, a text file containing the terms and their respective Unique Resource Identifiers (URIs) can also be given as input. Therefore, adding a new lexicon to MER could not be easier than this. MER also accepts as input an ontology in Web Ontology Language (OWL) format, which it converts to a lexicon. MER is not only minimal in terms of the input but also in its implementation, which was reduced to a minimal set of components and software dependencies. MER is then composed of just two components, one to process the lexicon (offline) and another to produce the annotations (online). Both were implemented as a Unix shell script  [24] , mainly for two reasons: (1) efficiency, due to its direct access to high-performance text and file processing tools, such as grep and awk, and a novel inverted recognition technique; and (2) portability, since terminal applications that execute Unix shell scripts are nowadays available in most computers using Linux, macOS or Windows operating systems.
MER obtained the highest precision in both NER and NEL tasks, the highest recall in NER, and a lower processing time. Additionally, we compared MER with Aho-corasick  [29] , a well-known string search algorithm. MER obtained a lower processing time and higher evaluation scores on the same corpus. MER is publicly available in a GitHub repository  [30] , along with the code used to run the comparisons to other systems. The repository contains a small tutorial to help the user start using the program and test it. The remainder of this article will detail the components of MER, and how it was incorporated in the annotation server. We end by analyzing and discussing the evaluation results and present future directions.
2. Note that the word acid-adenine was replaced by acid. adenine, and the last file presents the first two words of each entry in the third file. Note also that all the above steps are performed offline and only once per lexicon. The online module of MER starts when the user provides a new input text to be annotated with a lexicon already pre-processed. The goal is to identify which terms of the lexicon are mentioned in the text. The first step of MER is to apply the same minor modifications to the input text as described above, but also remove stop-words, and words with less than a given number of characters. The file with the list of stop-words and the minimum entity length are parameters that the user can easily modify in the scripts. The list of stop-words used in this study are in the stopwords.
The terms matched to this lexicon were categorized as Unknown, as suggested by the organization. The software to extract the list of terms from the above data sources can be found in the GitHub repository branch dedicated to TIPS  [37]. Figure  12  shows the number of terms, number of words, and number of characters of each lexicon created. Our Annotation Server was then able to recognize more than 1M terms composed of more than 2M words and more than 25M characters. All lexicons are available for reuse as a zip file in the TIPS branch of our repository  [37].
Infrastructure Our annotation server was deployed in a cloud infrastructure composed of three Virtual Machines (VM). Each VM had 8 GB of RAM and 4 Intel Core CPUs at 1. 7 GHz, using CentOS Linux release 7. 3. 1611 as the operating system. We selected one VM (primary) to process the requests, distribute the jobs, and execute MER. The other two VMs (secondary) just execute MER. We installed the NGINX HTTP server running CGI scripts given its high performance when compared with other web servers  [47]. We also used the Task Spooler  [48]  tool to manage and distribute within the VMs the jobs to be processed. The server is configured to receive the REST API requests defined in the BeCalm API documentation. Each request is distributed to one of the three VMs according to the least-connected method of NGINX.
When a getAnnotations request is received, the server    13  Output example of MER using BeCalm TSV format for the sentence in Fig. 8  and the lexicon in Fig. 2  first downloads the documents from the respective sources and then processes the title and abstract of each document in the same VM. Two jobs are spawned in background, corresponding to the title and abstract. Each annotation server handles all the entity types mentioned in Fig. 12 , spawning a separate job for each entity type. The name of the entity type is added as another column to the output of Fig. 9. These jobs can run in parallel since they are independent from each other and the output of each job can be easily merged into a final TSV output file. When a job finishes processing, a script checks if the other jobs associated with the same requests have also finished processing.
Computational performance Table  1  shows the official TIPS evaluation data of our system  [50]. These results refer to the whole period of the competition, from February 5, 2017 to March 30, 2017. The evaluation process and metrics used are described in the workshop article  [26]. Each request consisted of one document that the server had to retrieve either from PubMed or a repository hosted by the organization. Our server was able to handle all 319k requests received during the evaluation period, generating a total of 7. 13M annotations (second best) with an average of 22. 5 predictions per document (MAD) (third best). In average, each prediction has been generated in 0. 129 s (MTSA). Our average processing time value (ART) was 2. 9 s, and the processing time per document volume (MTDV) was 0.
We used the same HPO lexicon for both methods, as well as the same documents. Unlike the comparison to BioPortal, the experiment was done using local installations of MER and of the Makefast tool  [54] , which provides an implementation of the Aho-corasick algorithm. Table  3  shows the results of this comparison. MER obtained higher precision, recall and F1-score, as well as a lower processing time per document and per annotation. MER obtained better evaluation scores since it was developed specifically for NER, while Aho-corasick is a generic string search algorithm. The processing time was also shorter, due to the lexicon pre-processing done by the offline module of MER. However, this pre-processing is quick (3. 671 s for the HPO ontology) and only has to be done once.
Conclusions We presented MER, a minimal named entity recognition and linking tool that was developed with the concepts of flexibility, autonomy, and efficiency in mind. MER is flexible since it can be extended with any lexicon composed of a simple list of terms and its identifiers (if available). MER is autonomous since it only requires a Unix shell with awk and grep command-line tools, which are nowadays available in all mainstream operating systems. MER is efficient since it takes advantage of the high-performance capacity of grep as a file pattern matcher, and by proposing a novel inverted recognition technique. MER was integrated in an annotation server deployed in a cloud infrastructure for participating in the TIPS task of BioCreative V. 5. Our server was fully developed inhouse with minimal software dependencies and is opensource.
Acknowledgements This work was supported by  FCT  through funding of the DeST:  Deep Semantic Tagger Project ,  Ref. PTDC/CCI-BIO/28685/2017 ,  LASIGE Research Unit, Ref. UID/CEC/00408/2013  and  BioISI, Ref. ID/MULTI/04046/2013. AL is recipient of a fellowship from  BioSys Ph. D. programme  ( Ref PD/BD/106083/2015 ) from  FCT (Portugal). This work was produced with the support of the Portuguese National Distributed Computing Infrastructure (http://www. incd. pt).
One of the mutants obtained was hsc82-K394E. This mutation was verified by DNA sequencing. In Vivo His 6 -Hsc82 and His 6 -Hsp82 Protein-Protein Interaction Assay. His 6 -Hsc82 (  56 ) and His 6 -Hsp82 (53) complexes were isolated from yeast cells grown at 30 °C and lysated as described. Samples were separated by SDS/ PAGE and were either stained with Coomassie blue or transferred to PVDF membranes and blotted with antibodies specific to Hsp82 (Enzo ADI-SPA-840) (57), Hsc82 (generated in the J. L. J. laboratory), Hsp70 (Enzo ADI-SPA-757), Ssa1 (a gift from E. Craig, University of Wisconsin, Madison, WI), or Sti1 (generated in the J. L. J. laboratory or the D. C. M. laboratory)  (58). Prism 7. 0a for Mac (GraphPad Software, https://www. graphpad. com/) was used for statistical analyses. One-way ANOVA followed by Dunnett's multiple comparisons test was used to establish significant differences (P < 0.
05). In Vivo Glucocorticoid Maturation Assay. The assay was performed as previously described  (53). Briefly, 5-FOA-resistant STI1 cells expressing the indicated hsp82 allele were transformed with plasmids encoding the rat GR and β-galactosidase driven by a glucocorticoid response element. Log-phase liquid cultures were incubated at 30 °C for 5 h with 10 μM deoxycorticosterone, and the β-galactosidase activity was measured. Prism 7. 0a for Mac (GraphPad Software) was used for statistical analyses. One-way ANOVA followed by Dunnett's multiple comparisons test was used to establish significant differences (P < 0. 05). Proteins. Substitution mutations of Hsp82 were introduced into pET24b-Hsp82 with the QuikChange Lightning Mutagenesis Kit (Agilent). All mutations were verified by DNA sequencing.
Association of Ssa1 (2. 5-70 μM) with the Hsp82-biotin-bound sensor tip in BLI buffer was monitored over time. Last, dissociation was monitored in BLI buffer alone. For each experiment, nonspecific binding was monitored using a reference biosensor subjected to each of the above steps in the absence of biotinylated Hsp82, and the nonspecific binding signal was subtracted from the corresponding experiment. For steady-state analysis of kinetic association data, the association curve at each Ssa1 concentration was fit using a single exponential equation without constraints in Prism 7. 0a for Mac (GraphPad Software), and the plateau value determined from the fit was plotted versus the concentration of Ssa1. The resulting binding curve was analyzed using a one-site specific binding model in Prism to determine the K d (equilibrium binding constant) and B max (maximum specific binding) values.
For assays in the presence of Ydj1 and ATP, reactions mixtures contained 10 mM MgCl 2 and 2 mM ATP where indicated and were incubated in the presence of NeutrAvidin agarose for 10 min at 30 °C with mixing. Reactions mixtures were centrifuged for 1 min at 1,000 × g, and the recovered agarose beads were washed twice with 0. 5 mL PD buffer and processed as above. Where indicated, protein band intensities from replicate gels were quantified using ImageJ (https://imagej. nih. gov/ij/). The ratio of Ssa1 bound to each of the Hsp82-biotin mutants relative to Ssa1 bound to wild-type Hsp82-biotin was calculated and plotted. Modeling Ssa1-Hsp82 Interaction. Starting with the ADP-bound conformation of DnaK [Protein Data Bank (PDB ID code 2KHO] (61) or, separately, the apo conformation of Hsp90 Ec (PDB ID code 2IOQ) (  16 ), we constructed homology models of the yeast proteins Ssa1, Hsp82, and Hsc82 using the I-TASSER structure-prediction software  (62).
Within each set, individual images were captured with a minimum overlap of 66% using a digital single-lens reflex camera with a 50 mm f/2 lens. Aperture and focus were constant to ensure consistency between sets of photographs. Each set was then aligned and scaled to create a 3D model using the photogrammetric software Agisoft PhotoScan Professional. Individual surface models were exported to the 3D software Pixologic ZBrush version 4R7, which was used to merge the different meshes and improve the quality of the final merged 3D surface. Missing areas were modeled according to mirrored bones when available. Missing bone elements and broken areas in the available material were reconstructed using P. petaurista as reference. The morphology of the axial skeleton follows that of P. petaurista but has been modified according to the proportions of the recovered vertebrae.
Finally, carpal bones of extant Sciurus vulgaris, P. petaurista and Hylopetes sagitta, as well as those from M. neogrivensis were digitized for visualization and comparison. All extant specimens are stored in the collections of the NBC and were scanned there using a mCT scanner Skyscan system model 1172 (Table  5 ). Raw CT data from each scanning were imported directly (as stack of TIFF 16bit files) to Avizo 7. 0.
, 2016)  or measured by one of the authors (I. C. V. ). BM average data were computed from individual published values  (Bertrand et al. , 2016)  or taken from the literature  (Hayssen, 2008; Thorington and Heaney, 1981; Zahler, 2001). The ordinary least-squares regression method, selected as the most suitable for prediction  (Ruff, 2003; Smith, 1994) , yielded the following equation with SPSS v. 17: ln BM = 4. 369 ln SL -11. 384 (N = 33, p<0. 001, r = 0. 972, SEE = 0. 256). The logarithmic detransformation bias was corrected using the quasimaximum likelihood estimator  (Ruff, 2003; Smith, 1993) : QMLE = 1. 033. 50% confidence intervals (CI) for the prediction were computed.
Phylogenetic analyses and divergence dating To infer the phylogenetic placement of M. neogrivensis and assess its impact on the dating estimates of the phylogeny of flying squirrels, we performed a new phylogenetic analysis of tribe Pteromyini by means of two alternative approaches: total evidence (Ronquist et al. , 2012) and node dating  (Ronquist et al. , 2016). These approaches relied on the combination of morphological and molecular datasets, or on molecular datasets alone, respectively. For the molecular dataset, we retrieved from GenBank the combination of the four genes more frequently used in previous phylogenetic studies of the Sciuridae (12S, 16S, cytochrome b, and irbp)  (Mercer and Roth, 2003) , which were downloaded for all Sciurinae species available in GenBank (Supplementary file 2).
This dataset comprises 58 of the 89 extant Sciurinae species  (Koprowski et al. , 2016 ) and all genera but Biswamoyopterus. The Pteromyini are represented by 29 species, comprising 65% of extant diversity. We also obtained the sequence for Aplodontia rufa, sole extant member of the Aplodontiidae (the sister group of the Sciuridae; Fabre et al. , 2012;  Huchon et al. , 2002) , which was used as outgroup in the total evidence analysis. Each gene was aligned using two procedures: ribosomal coding genes were aligned by means of MAFFT v6  (Katoh et al. , 2002)  and protein coding genes were aligned using the translation alignment algorithm implemented in TranslatorX (using MAFFT to align proteins)  (Abascal et al. , 2010). Poorlyaligned regions in the ribosomal coding genes were eliminated with Gblocks  (Castresana, 2000)  under low stringency options  (Talavera and Castresana, 2007).
The morphological dataset consisted of 105 characters comprising the dentition, skull and postcranial skeleton of 36 extant taxa plus two fossil squirrels: M. neogrivensis and Douglassciurus jeffersoni (Appendix 2; Supplementary file 3). Douglassciurus is chosen because it is the earliest squirrel represented by abundant postcranial material and is currently recognized as an outgroup to all other sciurids (Appendix 1. 1). The character list is mostly based in Thorington et al. , 2002 with numerous additions of diagnostic pteromyin characters from the limb bones after Thorington et al. , 2005. Finally, a set of new characters was also added to resolve the relationships of A. rufa and D. jeffersoni with other taxa (Appendix 2, Supplementary file 3). For the total evidence dating we used MrBayes v.
3. 2. 6  (Ronquist et al. , 2012). The analysis included 36 species for which we had both molecular and morphological data plus two fossil species with only morphological data. The molecular data consisted in a total of 3345 base pairs (bps) distributed in each gene as follows: 12S (505 bps), 16S (521 bps), cytb (1140 pbs), irbp (1179 pbs). The protein coding genes were split in codon positions and the most appropriate partitioning scheme and the model of molecular evolution for each partition were estimated by means of the software Partitionfinder (Lanfear et al. , 2012) (Table  6 ). The five partitions derived from Partitionfinder were then concatenated with the previously described morphological dataset. We modeled node ages   6 ). For the morphological data (only variable characters included), we used a k-state Markov (Mkv) model  (Lewis, 2001)  with a rate variation modeled by means of a discrete gamma model.
We distinguished between ordered and unordered characters. We also used an alternative Bayesian program, BEAST v. 1. 8. 4  (Drummond et al. , 2012) , to provide an independent estimate of the time of the Pteromyini/Sciurini divergence and the onset of Pteromyini crown diversification by means of node dating. This analysis included all species of Sciurinae available in GenBank (58 taxa) and consisted in 3225 pbs distributed in each gene as follows: 12S (383 bps), 16S (523 bps), cytb (1140 pbs), irbp (1179 pbs). Protein coding genes were subsequently split in codon positions and we used Partitionfinder to determine the best set of molecular partitions and models of molecular evolution (Table  6 ). We estimated the tree in time units using an uncorrelated lognormal clock applied to each of the five partitions derived from Partitionfinder and two different calibration points (one located in each tribe; Table  4 ).