dataesr/scientific-paragraphs-categorization huggingface dataset:
Source, document type, chronological and geographical distribution of the articles The 908 articles included in the systematic map were mainly retrieved from publication databases (85. 8%) but specialist websites gave a substantial number of additional articles (6. 5%, Table  3 ). Articles were mostly written in English (97. 8%), and a large portion were journal articles (87. 8%), then conference proceedings (5%), PhD, MSc or BSc thesis (3. 6%) and reports (3%). The two oldest articles dated back to 1971, and half (49. 6%) Fig. 1  ROSES flow diagram  [32]  reporting the screening process of the articles of the systematic map of the articles have been produced since 2010 (Fig. 2 ). The corals studied were mainly from Southeast Asia (22% of the articles), Australia (13. 2%) and Middle Eastern Seas (13.
g. China, Japan). During the screening process, we excluded 64 articles due to language which are listed in Additional file 3 and could be screened for eligibility and used by others. Thirdly, some of the outcome categories defined by the review team were very broad and gathered very different outcomes (e. g. the outcome "physiology" may refer to either photosynthetic efficiency, enzyme activity, or mucus production). For these broad categories, defining more specific outcome categories may be necessary before conducting syntheses; this could be done directly from the map database using the detailed raw description of the outcomes. Finally, considering exposure to nutrients and according to our eligibility criteria we included nutrient enrichment arising from human activities but we excluded natural nutrient enrichment (stemming from guano, or upwelling).
Implication for policy/management This structured compilation of all available literature will help guide decision-makers on which clusters to focus on, in a regulatory context, and which priority research topics to support in the future. In addition, one way for decision-makers to take action for coral reef protection from chemicals is to assess risk, which is the result of chemical toxicity and exposure. From the map, it will be possible to extract evidence of toxicity from experimental studies, and exposure data from observational studies. Finally, this systematic map can help local stakeholders identify the body of literature that is relevant to their particular concern. For instance, if local stakeholders are concerned with the impact of nickel mining on corals, they will be able to easily search the map database under the metal exposure category and select studies relevant to their issue.
Acknowledgements The authors would like to thank  Nicolas Da Rocha  for his help during the search for full-texts. We are also grateful to  Omar Alaoui  (TAAF),  Jean-Pierre Allenou ,  Magali Duval  and  Emilie Gauthier  (Ifremer),  Jimmy Le Bec  ( DEAL Guadeloupe ),  Corinne Caroff  and  Nicolas Tamic (CEDRE) ,  Clément Lelabousse  (OFB), and  Pascal Talec  ( DEAL Réunion ) for the information, contacts and/or articles they provided during the call for literature.
Package morse can be used to get estimates of LC x (x% Lethal Concentration) or EC x (x% Effective Concentration) by fitting standard exposure-response models on toxicity test data. Risk indicator estimates as well as model parameters are provided along with the quantification of their uncertainty. Package morse can also be used to get estimates of the N EC (No Effect Concentration) by fitting a Toxicokinetic-Toxicodynamic (TKTD) model (namely GUTS models, that is General Unified Threshold models of Survival). Using GUTS models also allow to get estimates of LC (x,t) (whatever x and t) and LP  (x,t)  , this later being defined by EFSA as the x% multiplication factor leading to an additional reduction of x% in survival at the end of the exposure profile. Above all, GUTS models can be used on data collected under time-variable exposure profiles.
Statement of Need Package morse (Baudrot et al. , 2021) has been tested using R (version 3. 5 and later) on macOS, Linux and Windows machines. Regarding the particular case of toxicokinetictoxicodynamic (TKTD) models for survival, namely GUTS models (Jager & Ashauer, 2018), package morse was ring-tested together with nine other GUTS implementations under different software platforms. All participants to the ring-test received the same data sets and tasks, carried out their simulations independently from each other and sent the results back to the coordinator for analysis. Giving very similar results than the other implementations, package morse was thus confirmed as fit-for-purpose in fitting GUTS models on survival toxicity test data. All functions in package morse can be used without a deep knowledge of their underlying probabilistic model or inference methods.
Main features The main functions in package morse are survData(), reproData() and plotDoseResponse() to visualize raw data. Functions survFitTT(), reproFitTT(), survFit() allow to fit a model on data in order to estimate toxicity indicators, the choice depending on the type of data. Fitting outputs can be either displayed with plot() or synthesized with summary(). Functions are available to check the goodness-of-fit, namely ppc() and plot_prior_post(). Predictions can be performed with predict(), predict_ode(), predict_Nsurv() and predict_Nsurv_ode(). At last, function LCx()and MFx() allow to get x% lethal concentrations or profiles, respectively. The morse package currently handles binary and count data, as for example survival and reproduction data. Functions dedicated to binary (resp. count) data analysis start with a surv (resp.
Minimal Working Example Loading morse and its dependencies In order to use package morse, you need to install it with all its dependencies, including JAGS and C++ (see below), as well as other R-packages: mandatory ones (coda, deSolve, dplyr, epitools, graphics, grDevices, ggplot2 (⩾ 2. 1. 0), grid, gridExtra, magrittr, methods, reshape2, rjags (⩾ 4. 0), stats, tibble, tidyr, zoo) and suggested ones (knitr, rmarkdown, testthat). For this purpose, you can use the two classical R commands: ### install themorse`package, if needed if(is. element('morse', installed. packages()[,1]) == FALSE){ install. packages('morse') } ### load themorse`package library(morse).
JAGS The morse package is linked to JAGS http://mcmc-jags. sourceforge. net/ that is the Bayesian sampler used to perform inference with all implemented models. So, you need also to download and install JAGS at https://sourceforge. net/projects/mcmc-jags/. Then you must test that your R graphical user interface has access to JAGS, and, if not, to specify where JAGS can be found on your computer. Indeed, once installed, JAGS can be lost in the PATH. To help solving this issue, you can use package runjags which is not within morse so that you have to install and load it too.
Survival analysis We assume hereafter that morse and all the above dependencies have been corrected installed. To illustrate the use of morse, we will use a standard survival data set coming from a chronic laboratory toxicity test with Gammarus pulex, a freshwater invertebrate, exposed to increasing concentrations of propiconazole (a fungicide) during four days. Eight concentrations were tested with two replicates of 10 organisms per concentration. Survival was monitored at five time points (at day 0, 1, 2, 3 and 4) (Nyman, Schirmer, & Ashauer, 2012). We will used the reduced version of the GUTS model based on the stochastic death hypothesis (namely, the GUTS-RED-SD model), as recommended by the European Food Safety Authority (EFSA) for the environmental risk assessment (ERA) of plant protection products potentially toxic for aquatic living organisms  (EFSA PPR Panel, 2018).
Research using morse Package morse was recently used to evaluate the added-value of using TKTD models in comparison with classical dose-response models, based on a case study with the snail Limnaea stagnalis when exposed to increasing concentrations of cadmium  (Baudrot, Preux, Ducrot, Pavé, & Charles, 2018). Also based on morse, we proposed some recommendations to address TKTD assessment using uncertainties in environmental risk models  (Baudrot & Charles, 2019).
The computational experiments are carried out on a large benchmark including random artificial instances, and instances representing possible applications in structural biology, sensor network location  [2]  and interdiction problems. These experiments allow us to study the sensitivity of the solution methods on U , which was never previously analyzed (the value of U was set to L + 1 in all previous works due to the needs of the considered application). The experiments show that the new branch-and-bound widely outperforms existing methods and even allows us to solve medium-scale instances where the graph has 500 vertices and U = L + 1. The codes used for the experiments (see Section 6), as well as all our instances, are available on GitLab. The remainder of the article is organized as follows.
Algorithm 1: Computation of an upper bound on the number of fully-referenced vertices in a clique Procedure maxfull(K), whose pseudo-code is given by Algorithm 1, is based on this idea. In the description of the algorithm, vertices of K are iteratively added to a set O of ordered vertices. At each iteration, either one vertex can be ordered so that it has at least U references or we choose the vertex with lowest degree. Notice that maxfull(K) represents the number of vertices that can be fully-referenced, and not the set containing such vertices. Proposition 3. Let K be a clique of G, then at most maxfull(K) vertices of K are fully-referenced in any referenced order of G. 1 function maxfull(K): 2 MF K ← 0, O ← ∅; 3 while O = K do 4 if max u∈K\O {d • (u) + |O|} ≥ U + |K| -1 then 5 v ∈ argmax u∈K\O {d • (u)}; 6 MF K ← MF K +1; Proof.
Greedy search for a referenced order As already mentioned in Section 2. 1, a greedy algorithm was initially proposed for finding discretization orders in the context of distance geometry  [11]. The algorithm constructs the order starting from a given initial clique in O (n log(n) + m). A certificate of non-existence is delivered when no discretization order starting with this clique exists. The close relationship between discretization and referenced orders makes it easy to adapt this greedy algorithm for revorder. We give the pseudo-code of this greedy search in Algorithm 2. Algorithm 2: Greedy completion algorithm. input : An initial set S ∈ S output : A completion of S or the proof that none exist 1 σ(S) ← {1,. , |S|}; 2 while |σ| < n do 3 Let v ∈ argmax u / ∈PreIm σ {|R σ (u)|} ; 4 R max ← |R σ (v)|; 5 if R max < L then 6 stop: no referenced order starts with S; 7 else 8 σ ← [σ|v] // extend σ with v 9 return σ; Notice that the initial clique S is preselected, so that a complete enumeration of the solution set can actually be achieved only by invoking the greedy algorithm for every possible initial clique  [7].
We will call such an extension the propagation of full candidates in σ and denote it as Π(σ). Algorithm 3 gives the pseudo-code of function propagate(σ), which executes this operation. Algorithm 3: Propagation of an incomplete referenced order 1 function propagate(σ): 2 Π(σ) ← σ; 3 while Full (Π(σ)) = ∅ do 4 v ← any element of Full (Π(σ)); 5 Π(σ) ← [Π(σ)|v]; 6 return Π(σ); The recursive application of Proposition 9 immediately shows that full candidates can always be propagated without loss in the objective function value. We thus get the following fundamental motivation for an enumeration scheme based on the greedy search. Indeed, it implies that suboptimal extensions of an incomplete order can only be made if there is no full candidate. Proposition 10. Let σ be an incomplete referenced order, and σ = Π(σ) the propagation of full candidates in σ.
Computational experiments We have tested the branch-and-bound framework given in Algorithm 5 on five different families of instances (Random, Synthetic, Protein, SensorNetwork and Interdiction), and we compared it with the other methods discussed in this article. For reproducibility of our results and more efficiency in future research on the topic, our implementation of Algorithm 5 is publicly available on GitLab 2. The same repository also contains the instances used in our computational experiments, together with the bash scripts that run the tests.
As a result, this benchmark includes 180 instances. Every experiment is run on a workstation equipped with an Intel(R) Xeon(R) CPU E5-2630 v3 @ 2. 40GHz, 16GB RAM 4. Every IP and constraint programming formulation is solved with CPLEX 12. 9. The time limit is set to 1000 seconds, and for a fair comparison, every run is done on a single thread. In this section the reader can find the corresponding performance profiles: developed by Dolan and Moré  [6] , performance profiles allow us to scale the execution times of the compared algorithms with respect to the best solver in the set. For instance, Figure  1  shows that bb performs best for about 75% of the datasets and solves more than 95% instances less than 5 times slowlier than the best solver, whereas ccg is at least 5 times slower than the best performing algorithm for more than 50% of the datasets.
All our performance profiles were constructed with a dedicated Julia package  5. For more details about the computational results, the reader can refer to Appendix B. Our implementation of the branch-and-bound algorithm corresponds to the sketch given in Algorithm 5, where lowerbound(σ) returns the trivial lower bound, LB trivial (σ) (see Section 5. 3). The value of the parameter ∆ z is set to 1 in all the experiments. The nodes in the branch-and-bound queue are explored in a best-first fashion, where the best node is the one minimizing the ratio ∆ (σ) / |σ|. These two parameters have been set to these values according to preliminary tests. In the remainder, we will refer to this default implementation of our branch-and-bound as bb. The first performance profile (see Figure  1 ) considers all the experiments (the logarithmic scale on the x-axis is in base 2).
Reactive transport simulations We employ an isotope-enabled, multicomponent reactive transport model using the open-source CrunchFlow/CrunchTope software  (Druhan et al. , 2013; Steefel et al. , 2015)  to simulate the reactive flow of infiltrating precipitation (δ 7 Li = +31 ; Supplementary Text S1. 1) through present-day bulk regolith composing the Rivendell hillslope (δ 7 Li = -0. 57 ; Table  1 ). Here, we integrate Li and its isotopes into a previously established reactive transport model for the Rivendell vadose zone, which focused on cation solute profiles and solid-phase mineralogy  (Wang, 2019). Parameterization of hydraulic properties and initial geochemical conditions are detailed in this prior work. Notably, the present simulations focus on the evolution of the dissolved phase over a relatively short timespan (∼5 pore volumes or 6 yr; Supplementary Text S1.
Acknowledgements This research was partially funded by the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme FP7 /2007-2013/ under REA agreement [608069] (ITN "IsoNose"), the  IPGP multidisciplinary program PARI , and the  Region île-de-France SESAME  Grant No. 12015908. J. K. G. is supported by the  National Science Foundation (NSF) Graduate Research Fellowship Program. M. L. K. was supported by a joint  OZCAR (French CZO Network) -CZEN (Critical Zone Exploration Network)  fellowship. J. K. G. and J. L. D. acknowledge funding support from  NSF -EAR-2047318. The  Eel River Critical Zone Observatory  is supported by  NSF -EAR-1331940. M. J. H. and M. L. K. thank  Jutta Schlegel  and  Josefine Buhk  for assistance in the laboratory. We thank  Gunnar Reith  and  Will Speiser  for help with sample collection.
Data availability statement The source code for the CrunchFlow/CrunchTope software is available on Bitbucket (https://bitbucket. org /crunchflow). Please contact Dr. Jennifer Druhan (Assistant Professor at the University of Illinois at Urbana-Champaign) for more information about and access to the updated CrunchFlow/CrunchTope version used in this work. The associated model input files can be accessed on GitHub (https://github. com /jkgolla /ERCZOLithiumCrunchInput) and are archived on Zenodo (https://doi. org /10. 5281 /zenodo. 4642871).
Probabilistic programming languages aid developers performing Bayesian inference. These languages provide programming constructs and tools for probabilistic modeling and automated inference. Prior work introduced a probabilistic programming language, ProbZelus, to extend probabilistic programming functionality to unbounded streams of data. This work demonstrated that the delayed sampling inference algorithm could be extended to work in a streaming context. ProbZelus showed that while delayed sampling could be effectively deployed on some programs, depending on the probabilistic model under consideration, delayed sampling is not guaranteed to use a bounded amount of memory over the course of the execution of the program. In this paper, we present conditions on a probabilistic program's execution under which delayed sampling will execute in bounded memory.
A key innovation of ProbZelus was to demonstrate that delayed sampling  [Murray et al. 2018 ] could be extended to work with streams to provide high-quality inference procedures. Delayed sampling is an inference algorithm that combines both exact and approximate inference; it takes advantage of exact inference when efficient known closed-formed solutions exist and falls back on sampling-based, approximate inference when required. Specifically, delayed sampling combines Bayesian networks -graphs that encode exact distributions of probabilistic models -with particle filtering  [Del Moral et al. 2006 ]an approximate inference algorithm. The challenge in adapting delayed sampling to computations on streams is that such computations run for indefinite periods of time and are often subject to stringent limits on resources, such as memory.
Implementation We implemented our analysis framework and the -consumed and unseparated paths analyses in OCaml. Our implementation takes as input a  program and outputs either true or false for each analysis. It also accepts a parameter for the iteration bound for the unseparated paths analysis. The implementation goes beyond the type system laid out in the paper by supporting functions that have probabilistic effects as well as interfaces for list and array operations. programs can further be compiled to OCaml and executed using the ProbZelus delayed sampling runtime. The code is available at https://github. com/psg-mit/probzelus-oopsla21.
Methodology We executed the analysis on example programs from  Baudart et al. [2020]  originally written in ProbZelus, a probabilistic programming language featuring probabilistic data streams and delayed sampling. We manually translated them to  , and they reflect a range of realistic control problems with different memory usage characteristics. For the unseparated paths analysis, we set an iteration count bound of 10, which was sufficient for these programs. We compared the outputs of the analysis to our manual logical reasoning about the ability of each of the following programs to execute in bounded memory. We provide source code for all benchmarks in Appendix F. Kalman is the simplified core model of Figure  1  and models an agent that estimates position from noisy observations. Applying delayed sampling on this model is equivalent to a Kalman filter  [Kalman 1960]  where each particle returns the exact solution.
Baudart et al. [2020]  developed ProbZelus, a reactive probabilistic programming language which operates over streams of data and supports inference at each stream iteration. It uses an implementation of delayed sampling designed to provide bounded-memory inference for a class of reactive probabilistic programs. However, ProbZelus provides no static guarantee of bounded-memory inference. In this work, we define a language that can be used as a target for the compilation of ProbZelus and identify the semantic conditions and a static analysis that makes it possible to provide a static guarantee. Delayed Sampling and Bounded-Memory Inference. The mechanism of delayed sampling in probabilistic programs was introduced by  Murray et al. [2018]  and implemented in the Anglican and Birch programming languages, neither of which supports inference over streams.
ACKNOWLEDGMENTS We would like to thank  Cambridge Yang ,  Alex Renda ,  Jesse Michel , and  Ben Sherman , who all provided feedback on drafts of this paper. This work was supported in part by the  MIT-IBM Watson AI Lab  and the  Office of Naval Research  ( ONR N00014-17-1-2699 ). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the  Office of Naval Research.
AnnoMiner's fourth function performs transcription factor or histone modification enrichment analysis for user-provided gene lists by utilizing hundreds of public, high-quality datasets from ENCODE for the model organisms human, mouse, Drosophila and C. elegans. Thus, AnnoMiner can predict transcriptional regulators for a studied process without the strict need for chromatin data from the same process. We compared AnnoMiner to existing tools and experimentally validated several transcriptional regulators predicted by AnnoMiner to indeed contribute to muscle morphogenesis in Drosophila. AnnoMiner is freely available at http:// chimb orazo. ibdm. univ-mrs. fr/ AnnoM iner/. Transcriptional regulation is a highly complex process involving a combination of various molecular players and biochemical mechanisms, such as transcription factors (TFs), histone modifying enzymes, DNA methylases, as well as a structural reorganization of chromatin.
Results The AnnoMiner web server. The AnnoMiner web server is a new tool for the convenient annotation and integration of epigenetic, and transcription factor occupancy data for the wet-lab researcher. Based on an underlying library of java classes developed for the handling of annotated genomic peak or ranges datasets, the interactive graphical user interface of the web application provides functionalities for the upload of datasets, choice of analysis mode and model organism, visualization and download of analysis results (see Fig. 1  for a schematic of AnnoMiner functions and Supplementary Figure  S1  for implementation details). Genomic peak annotation functions. AnnoMiner's three annotation functions (peak annotation, peak integration and nearby genes annotation, Fig. 1 ) take as input one or more files containing genomic coordinates (in BED format), for example from ChIP-seq, with the aim of finding their associations with annotated gene features.
This is done by determining the overlap between the genomic coordinates of a peak and the attributes of annotated gene features. The user can set the following parameters for the search: the minimum required overlap among the gene feature attributes and peak features (in bp or %); whether only the longest isoform of a gene or all its isoforms will be considered; the gene's directionality; the organism and the genome resource. Optionally, a user-provided annotation file can be uploaded, containing for instance differential expression data which will be integrated with the gene lists generated from AnnoMiner annotation. The user-provided dataset is accepted in csv (comma separated values) or tsv (tab separated values) format. It can contain up to 6 columns, without any constraints in content, except the first column has to contain gene IDs to allow integration with AnnoMiner results.