dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, our toolbox is built on an expression vector scheme containing only 6 module types (versus 8-11 for alternative strategies  [25] ). Moreover, we decided to use only one type IIS restriction enzyme (BsaI) for expression vector assembly. This further simplifies the toolbox since a smaller number of modules needs to be assembled, and there are less constraints regarding the presence of specific restriction sites in the module sequence. Last, our custom software facilitates the design of primers for the generation of new module variants, especially when cloned sequences feature unwanted BsaI restriction sites: in this case, the software provides assistance to the user by proposing potential design strategies to remove them. Altogether, this toolbox provides a simple and efficient expression system that might be of particular interest in labs with little experience in yeast molecular biology.
The COSPLAY toolbox also allows for single integration using a single module that can be easily designed with our software, instead of the 3 modules required in alternative methods  [25]. To conclude, the COSPLAY toolbox is extremely flexible and efficient, simple of use, easily expandable and complementary to alternative Golden Gate cloning-based systems for yeast genetic editing. Single copy chromosomal integration. A) A sample plasmid containing the indicated modules is linearized by cutting either within the target locus (using AscI, see Fig  4  and corresponding text) or within the auxotrophic marker to compare the number of copy integrated at each locus. B) Flow cytometry analysis of GFP expression on yeast transformed with URA3, ChRVI or ChrIXintegrating plasmids. Each colored line represents the distribution of fluorescence obtained with a given clone.
Acknowledgments We thank  Maxime Diem  and  Vincent Hisler  for contributions to this project during their stay in the lab as undergraduate interns. This work was supported by the  ATIP-Avenir program  (G. C. ), and by the grant  ANR-10-LABX-0030-INRT , a  French State  fund managed by the  Agence Nationale de la Recherche  under the  frame program Investissements d'Avenir   ANR-10-IDEX-0002-02.
This work was supported by the  ATIP-Avenir program  (G. C. ), and by the  Agence Nationale de la Recherche  under the  frame program Investissements d'Avenir   ANR-10-IDEX-0002-02. This work was also supported by the  Agence Nationale pour la Recherche ,  ANR-10-LABX-0030-INRT  (https://anr. fr)to GC and  ANR-10-LABX-0030-INRT  to BR. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Therefore, in this paper, we go beyond entailment reduction and introduce derivation reduction. We also consider more general fragments of metarules, such as a fragment of metarules sufficient to learn Datalog programs. Cropper and Tourret  [16]  introduced the derivation reduction problem and studied whether sets of metarules could be derivationally reduced. They considered the exactly-two-connected fragment previously considered by Cropper and Muggleton and a two-connected fragment in which every variable appears at least twice, which is analogous to our singleton-free fragment (Section 5. 3). They used graph theoretic methods to show that certain fragments could not be completely derivationally reduced. They demonstrated on the Michalski trains dataset that the partially derivationally reduced set of metarules outperforms the entailment reduced set.
Materials To obtain the experimental data, we first generated 8 random target train programs where the programs are progressively more difficult, where difficulty is measured by the number of literals in the generated program from the easiest task T 1 to the most difficult task T 8. Figure  2  shows the background predicates available to Metagol. We vary the metarules given to Metagol. We use the S-, E-, and D-reductions of the fragment {1,2} 5 (Table  13 ). In addition, we also consider the {1,2} 2 fragment of the D-reduction of {1,2} 5 , i. e. a subset of the D-reduction consisting only of metarules with at most two body literals. This fragment, which we denote as D * , contains three fewer metarules than the D-reduction of {1,  2} 5. Table  16  shows this fragment. 19 https://github. com/metagol/metagol/releases/tag/2.
3. 0 20 Experimental data is available at http://github. com/andrewcropper/mlj19-reduce has_car/2 has_load/2 short/1 long/1 two_wheels/1 three_wheels/1 roof_open/1 roof_closed/1 zero_load/1 one_load/1 two_load/1 circle/1 triangle/1 rectangle/1 Fig. 2 : Background relations available in the trains experiment. P(A) ← Q(A) P(A, B) ← Q(B, A) P(A) ← Q(A), R(A) P(A, B) ← Q(A), R(B) P(A) ← Q(A, B), R(B) P(A, B) ← Q(A), R(A, B) P(A) ← Q(A, B), R(A, B) P(A, B) ← Q(A, B), R(A, B) P(A, B) ← Q(A, C), R(B, C).
By contrast, when using the D * set of metarules the hypothesis space contains approximately 10 14 programs. As explained in Section 3. 2, assuming that the target hypothesis is in both hypothesis spaces, the Blumer bound  [3]  tells us that searching the smaller hypothesis space will result in less error, which helps to explain these empirical results. Of course, there is the potential for the D * set to perform worse than the D set when the target theory requires the three removed metarules, but we did not observe this situation in this experiment. Figure  3  shows the target program for T 8 and example programs learned by Metagol using the various reduced sets of metarules. Only the D * program is success set equivalent 23 to the target program when restricted to the target predicate f/1.
In all three cases Metagol discovered that if a carriage has three wheels then it is a long carriage, i. e. Metagol discovered that the literal long(C2) is redundant in the target program. Indeed, if we unfold the D * program to remove the invented predicates then the resulting single clause program is one literal shorter than the target program. Overall, the results from this experiment suggest that we can reject the null hypothesis, both in terms of predictive accuracies and learning times.
String transformations In  [38]  and  [14]  the authors evaluate Metagol on 17 real-world string transformation tasks using a predefined (hand-crafted) set of metarules. In this experiment, we compare learning with different metarules on an expanded dataset with 250 string transformation tasks. 21 A statistical test on paired nominal data https://en. wikipedia. org/wiki/McNemar%27s_test 22 A statistical test on paired ordinal data http://www. biostathandbook. com/pairedttest. html 23 The success set of a logic program P is the set of ground atoms {A ∈ hb(P)|P ∪ {¬A} has a SLD-refutation}, where hb(P) represents the Herband base of the logic program P. The success set restricted to a specific predicate symbol p is the subset of the success set restricted to atoms containing the predicate symbol p.
Materials Each string transformation task has 10 examples. Each example is an atom of the form f (x, y) where f is the task name and x and y are strings. Figure  4  shows task p6 where the goal is to learn a program that filters the capital letters from the input. We supply Metagol with dyadic background predicates, such as tail, dropLast, reverse, filter_letter, filter_uppercase, dropWhile_not_letter, takeWhile_uppercase. The full details can be found in the code repository. We vary the metarules given to Metagol. We use the S-, E-, and D-reductions of the fragment.
Inducing game rules The general game playing (GGP) framework  [25]  is a system for evaluating an agent's general intelligence across a wide range of tasks. In the GGP competition, agents are tested on games they have never seen before. In each round, the agents are given the rules of a new game. The rules are described symbolically as a logic program. The agents are given a few seconds to think, to process the rules of the game, and to then start playing, thus producing game traces. The winner of the competition is the agent who gets the best total score over all the games. In this experiment, we use the IGGP dataset  [9]  which inverts the GGP task: an ILP system is given game traces and the task is to learn a set of rules (a logic program) that could have produced these traces.
Materials The IGGP dataset contains problems drawn from 50 games. We focus on the eight games shown in Figure  5  which contain BK compatible with the metarule fragments we consider (i. e. the BK contains predicates in the fragment 2 m ). The other games contain predicates with arity greater than two. Each game has four target predicates legal, next, goal, and terminal, where the arities depend on the game. Figure  6  shows the target solution for the next predicate for the minimal decay game. Each game contains training/validate/test data, composed of sets of ground atoms, in a 4:1:1 split. We vary the metarules given to Metagol. We use the S-, E-, and D-reductions of the fragment {1,2} 5. We again also use the D-reduction of the fragment If no program is found in 10 minutes then no program is returned and every testing example is deemed to have failed.
Limitations and future work Theorem 7 shows that certain fragments of metarules do not have finite D-reductions. However, our experimental results show that using D-reduced sets of metarules leads to higher predictive accuracies and lower learning times compared to the other forms of reduction. Therefore, our work now opens up a new challenge of overcoming this negative theoretical result. One idea is to explore whether special metarules, such as a currying metarule  [12] , could alleviate the issue. In future work we would also like reduce more general fragments of logic, such as triadic logics, which would allow us to tackle a wider variety or problems, such as more of the games in the IGGP dataset. We have compared the learning performance of Metagol when using different reduced sets of metarules.
Rifampicin-resistant TB diagnosis in Papua New Guinea The CPHL is the country's national reference laboratory for TB, HIV and malaria. All newly diagnosed RR-TB patient specimens detected using Xpert instruments around the country are sent to the CPHL, which refers specimens to the Queensland Mycobacterial Reference Laboratory (Brisbane, QLD, Australia) for culture and drug susceptibility testing. The CPHL TB laboratory was also the diagnostic centre for PMGH until February 2018, performing acid-fast bacilli microscopy and Xpert for all PMGH patients with presumed TB. Testing and results were recorded in the laboratory TB register. Xpert testing was piloted in four provinces in 2012, with expansion to 30 Xpert instruments in 2018. The GxAlert software was introduced in May 2015 as a pilot project in two provinces (NCD and Western Province).
Data variables, collection and analysis A structured data collection form was used to collect data from the MDR-TB treatment register at the TB clinic at PMGH and the laboratory register at the CPHL. Data were double-entered in EpiData v 3. 1 (Epi-Data Association, Odense, Denmark). Collected data were cross-linked with the data extracted from the existing electronic laboratory register. The data were reviewed extensively and validated using Stata v 15 (StataCorp, College Station, TX, USA) before analysis. The variables collected from the laboratory register were name, age, sex, sample collection date, sample receipt date in the laboratory and the date of Xpert testing. The variables collected from the MDR-TB register were name, age, sex, risk category for Xpert testing, date of treatment initiation and outcome at 6 months.
The date the clinician received the Xpert result was not recorded in either register. Pre-treatment attrition was defined as a patient diagnosed with RR-TB on Xpert, recorded in the laboratory register but not recorded as commencing treatment in the MDR-TB register within 30 days of diagnosis, with additional analyses performed at 60 days and 6 months. Time to treatment initiation was defined as the number of days between sputum collection and treatment initiation. An operational definition was used for interim treatment outcomes at 6 months with an 'unfavourable' outcome defined as patient death or patient LTFU at 6 months after treatment initiation. Analysis was undertaken using EpiData v 3. 1 and Stata v 15. Categorical variables were described by frequency (percentage), and continuous variables by median (interquartile range [IQR]).
Public Health Action GxAlert implementation. We also found high rates of pre-treatment attrition and unfavourable outcomes in RR-TB patients, with no differences in the interim clinical outcomes between these two periods. The majority of the RR-TB patients diagnosed with Xpert were RR-TB contacts. This is one of the first studies to assess the impact of an electronic results notification tool for Xpert on the time to treatment initiation and patient outcomes. One of the strengths of the study is the use of the national reference laboratory register and database, which ensured inclusion of all patients diagnosed with RR-TB using Xpert over the study period. A limitation of the study was the lack of a record of the date that clinicians received notification of the Xpert result. This prevented an assessment of the effect of GxAlert on the component of the diagnostic cascade that it auto-mates and bypasses.
Furthermore, the study findings are not generalisable to the whole country. In a centralised model of care, it is likely that GxAlert would be most effective in reducing the time to treatment initiation in geographically distant regions where Xpert is not available on site. As the data collection was retrospective, some data on patient outcomes were missing. In addition, GxAlert was implemented at the same time as other interventions, making the attribution of benefits to specific interventions difficult. Subgroup analysis of TB-HIV patients would have provided an additional assessment of programmatic care and changes; however, HIV status was not recorded in the study. Although the median time to treatment initiation decreased by over two thirds between the 12-month period before the introduction of GxAlert and the 12-month period after its introduction, this decrease had already occurred prior to its introduction, and the time to treatment initiation in the 6-month period immediately before GxAlert implementation was similar to the subsequent period.
CONCLUSION While the time to treatment initiation improved for patients with RR-TB at PMGH and pre-treatment attrition decreased, our study findings do not suggest that GxAlert was directly associated with this decrease or that it led to improved interim patient outcomes. However, the continued programmatic strengthening of the TB diagnostic cascade, including the use of rapid diagnostics and integrated remote monitoring software, allows many barriers to timely TB treatment to be bypassed and will likely improve outcomes for patients with TB and RR-TB in PNG in the longer term.
| INTRODUC TI ON Studying global biodiversity patterns is at the core of macroecological research  (Costello, Wilson, & Houlding, 2012; Kreft & Jetz, 2007; Wiens, 2011) , since their exploration may provide insights into the ecological and evolutionary processes acting at different spatiotemporal scales  (Ricklefs, 2004). The opportunities engendered by the compilation of large collections of biodiversity data into widely accessible global (GBIF, www. gbif. org) or continental databases (e. g. ,  BIEN, www. bien. nceas. ucsb. edu/bien) have recently advanced our understanding of global biodiversity patterns, especially for vertebrates, but also for vascular plants  (Butler et al. , 2017; Engemann et al. , 2016; Lamanna et al. , 2014; Swenson et al. , 2012). Although this development has led to the formulation of several macroecological theories  (Currie et al.
, 2004; Pärtel, Bennett, & Zobel, 2016) , a more mechanistic understanding of how assembly processes shape ecological communities, and consequently global biodiversity patterns, is still missing  (Lessard, Belmaker, Myers, Chase, & Rahbek, 2012). Understanding the links between biodiversity patterns and assembly processes requires fine-grain data on the co-occurrence of species in ecological communities, sampled across continental or global spatial extents  (Beck et al. , 2012; Wisz et al. , 2013). For example, such co-occurrence data have been used to compare changes in vegetation composition over time spans of decades  (Jandt, von Wehrden, & Bruelheide, 2011; Perring et al. , 2018). Unfortunately, up to now information on fine-grain vegetation data has not been readily available, as most of the continental to global biodiversity datasets have been derived from occurrence data (i.
e. , presenceonly data), and after being aggregated spatially, have a relatively coarse-grain scale (e. g. , one-degree grid cells) without information on species co-occurrence at the meaningful scale of local communities  (Boakes et al. , 2010). In contrast, vegetation-plot data record the cover or abundance of each plant species that occurs in a plot of a given size at the date of the survey, representing the main reservoir of plant community data worldwide  (Dengler et al. , 2011). Vegetation-plot data differ in fundamental ways from databases of occurrence records of individual species aggregated at the level of grid cells or regions of hundreds or thousands of square kilometers (Figure  1 ). First, vegetation plots usually provide information on the relative cover or relative abundance of species, allowing for the testing of central theories of biogeography, such as the abundancerange size relationship  (Gaston & Curnutt, 1998)  or the relationship between local abundance and niche breadth  (Gaston et al.
Occurrence data (e. g. , from distribution atlases, GBIF, etc. ) can be used to calculate mean trait values in grid cells G1-G3. However, community weighted means (CWMs) of traits differ across local plots (P1-P6), while the mean values of CWMs in the grid cells differ from the unweighted values calculated in the grid cells. This example is simplified by showing few species and few plots. In reality, differences are generally more pronounced organisms from different trophic groups sampled in the same plots (e. g. , multiple-taxa surveys) and related processes and services both below (e. g. , decomposition, nutrient cycling) and above ground (e. g. , herbivory, pollination) (e. g. ,  Schuldt et al. , 2018). Recently several projects at the regional to continental scale have demonstrated the potential of using vegetation-plot databases for exploring biodiversity patterns and the underlying assembly processes.
By collating >40,000 vegetation plots sampled in European beech forests, Jiménez-Alfaro et al. (2018) found that current local community diversity and species pool sizes calculated at different scales were mainly explained by proximity to glacial refugia and current precipitation. Although large collections of vegetation-plot data are now available from national to continental levels (e. g. ,  Chytrý et al. , 2016; Enquist, Condit, Peet, Schildhauer, & Thiers, 2016; Peet, Lee, Jennings, & Faber-Langendoen, 2012; Schaminée, Hennekens, Chytrý, & Rodwell, 2009; Schmidt et al. , 2012) , they are rarely used in global-scale biodiversity research  (Franklin, Serra-Diaz, Syphard, & Regan, 2017; Wiser, 2016). This is unfortunate because vegetationplot data may reveal important patterns that cannot be captured by grid-based datasets (Table  1 ).
Functional composition patterns, for instance, may differ substantially when considering vegetation-plot data rather than single species occurrences aggregated at the level of coarse-grain grid cells. Using plant height as an illustration reveals that the trait means calculated on all the species occurring in a grid cell may differ strongly from the community-weighted means (CWMs) averaged across local communities (Figure  1 ). Nevertheless, only the gridbased approach has been used to date in studies of the geographic distribution of trait values (e. g. ,  Swenson et al. , 2012 Swenson et al. , , 2017;; Wright et al. , 2017). Here, we present sPlot, a global database for compiling and integrating plant community data. We describe (a) main steps in integrating vegetation-plot data in a repository that provides taxonomic, functional and phylogenetic information on co-occurring plant species and links it to global environmental drivers; (b) principal sources and properties of the data and the procedure for data usage; and (c) expected impacts of the database in future ecological research.
| Vegetation-plot data The sPlot consortium currently collates 110 vegetation-plot databases of regional, national or continental extent. Some of the databases have previously been aggregated by and contributed through TA B L E 1 Types of information provided by single vegetation plots, vegetation plots aggregated within grid cells (or other geographic units) and single species occurrence records aggregated within grid cells. The three levels are illustrated in Figure  1  Information from… Single vegetation plots Set of vegetation plots aggregated within grid cells.
Plot level Grid cell level Grid cell level two (sub-)continental database initiatives (Table  2  and Appendix   S1 ). All data from Europe and nearby regions were contributed via the European Vegetation Archive (EVA), using the SynBioSys taxon database as a standard taxonomic backbone  (Chytrý et al. , 2016). Three African databases were contributed via the Tropical African Vegetation Archive (TAVA). In addition, multiple U. S. databases were contributed through the VegBank archive maintained in support of the U. S. National Vegetation Classification  (Peet, Lee, Boyle, et al. , 2012; Peet, Lee, Jennings, & Faber-Langendoen, 2012). The data from other regions (South America, Asia) were contributed as separate databases. We stored the vegetation-plot data from the individual databases in the database software TURBOVEG v2  (Hennekens & Schaminée, 2001).
| Taxonomic standardization To combine the species lists of the different databases in sPlot, we constructed a taxonomic backbone. To link co-occurrence information in sPlot with plant traits, we expanded this backbone to integrate plant names used in the TRY database  (Kattge et al. , 2011)   et al. , 2013; iPlant Collaborative, 2015) , using the five TNRS standard sources ranked by default. We allowed for (a) partial matching to the next higher rank (genus or family) if the full taxon name could not be found and (b) full fuzzy matching, to return names that were matched within a maximum number of four single-character edits (Levenshtein edit distance of 4), which corresponds to the minimum match accuracy of 0. 05 in TNRS, with 1 indicating a perfect match. We accepted all names that were matched, or converted from synonyms, with an overall match score of 1.
One potential shortcoming of our taxonomic backbone is that for most regions it was necessary to standardize taxa using standard sets of taxonomic synonyms. Thus, if a taxonomic name represents multiple taxonomic concepts, e. g. , such as created by the splitting and lumping of taxa, or a name has been misapplied in a region, we must trust that this problem has been addressed in our component databases  (Franz, Peet, & Weakley, 2004; Jansen & Dengler, 2010). However, different component databases may have applied different taxonomic concepts for splitting and lumping taxa.