dataesr/scientific-paragraphs-categorization huggingface dataset:
The GST tag was proteolytically separated from CTI2 using thrombin treatment. Proteins were quantified by Bradford assays using bovine gamma globulin as a standard. 100 nM α-CT-His protein was fluorescently labeled using His-tag labeling kit (NanoTemper, MO-L008) according to the user manual. After labeling, 10 μL of labeled α-CT protein was mixed with 10 μL of CTI (serial dilutions in PBS buffer). The microthermophoresis was carried out using 100% LED power and high MST power with a NanoTemper monolith NT. 115 instrument. The data were analyzed by MO. Offinity Analysis (X86). Immunoblotting. For quantification of the htACCase subunit content, proteins were extracted from crude chloroplasts using a protein extraction buffer (50 mM HEPES-KOH pH 7. 6, 150 mM NaCl, 1 mM EDTA, 10% (v/v) glycerol, 1 mM 2mercaptoethanol, 1% (v/v) Triton X-100, 1X complete protease inhibitor cocktail).
To analyze total fatty acids in leaves or seeds, total lipids were transmethylated into fatty acid methyl esters (FAMEs). FAMEs were analyzed by a Hewlett Packard 6890 gas chromatograph. Leaves from 4-week-old plants were collected and dried by centrivap SpeedVac overnight. Mature seeds were harvested and dried at room temperature for 1 week prior to analysis. Lipidomics. Lipid extracts were dissolved in chloroform, and appropriate amounts of internal standards were introduced. The lipids were detected by triple quadrupole mass spectrometer (Applied Biosystems API 4000) with an autosampler (LC Mini PAL; CTC Analytics)  59. Data processing was performed in a Lipidome DB Data Calculation Environment (http://lipidome. bcf. ku. edu:9000/Lipidomics) with normalization to internal standards. In vivo acetate labeling.
Acknowledgements We thank  N. Nesi  and  J. Jouhet  for helpful discussion and the  Kansas State University Lipidomics Research Center  for lipidomics analysis. This research was funded by  National Science Foundation  (grants  MCB-1716688  and  PGRP IOS-1829365  to J. J. T. ), the  Agence Nationale de la Recherche  (grant  ANR-11-BTBR-0004  to S. B. ), and the IJPB benefits from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007  to S. B. ).
Data availability The authors declare that the data supporting the findings of this study are available within the article and its Supplementary Information files. The datasets and plant materials generated and analyzed during the current study are available from the corresponding author upon reasonable request. Sequence data from this article can be found in TAIR under accession numbers: AT2S2, At4g27150; BAN, At1g61720: BC, At5g35360; BCCP1, At5g16390; BCCP2, At5g15530; CTI1, At1g42960; CTI2, At3g02900; CTI3, At5g16660; α-CT, At2g38040; β-CT, AtCg00500; EF1αA4, At5g60390; MYB118, At3g27785; ODD, At1g04380; PTST2, At1g27070; TGD2, At3g20320; TOC33, At1g02280; WRI1, At3g54320; WRI2, At2g41710. Source data are provided with this paper.
Running the game and data collection The game sessions took place in March 2015 and lasted one full day. During the morning session, eight participants -the seven farmers who participated in the ethnographic study, and a nature conservationist, played the classical scenario. In the afternoon, the alternative scenario was played with one fewer player as one farmer had to leave for unexpected personal reasons. Participants were assigned a role corresponding to their role in real life. Table  2  introduces participants' role in the game, their profile/farming system in real life and their "type" as determined during the ethnographic fieldwork (inclination towards science, politics and/or environmental conservation). We documented players' behavior and equipped players with microphones to record their narrative during the two game sessions and the debriefing.
Quantitative data analyses To support the qualitative data collected during this study, we also performed quantitative data analyses on the participants' narrative of the two game sessions. We perform a content analysis  (Carley and Palmquist, 1992) , i. e. we examine the use of language of the farmers and of the nature conservationist in their daily-life as well as during a role-playing game session, and categorize words and phrases into key concepts, to elicit mental models. We chose this method over influence diagram, arguably the most common approach  (Abel et al. , 1998; Dray et al. , 2006; Mathevet et al. , 2011; Prager and Curfs, 2016) , because of its non-intrusive nature, thereby allowing uninterrupted game session. The analyses were conducted with IRAMUTEQ, a linguistic software based on R and Python  (Ratinaud, 2009).
parameters differ: the individual vs collective form and the action vs result-based form. It would have been interesting to change one parameter at a time and test a collective actionbased AES and an individual result-based AES as well 3. This opens scope for further research. ACKNOWLEDGEMENT This work was carried out with the financial support of the «ANR-Agence Nationale de la Recherche -The French National Research Agency» under the «Programme SYSTERRA», project «FARMBIRD» and with the financial support of the BASC Labex. The authors wish to acknowledge the stakeholders who participated in the joint construction of the RPG and in the RPG sessions. The authors wish also to thank Cécile Helan, Marion Sorrieul, Patricia Grené, and Brigitte Rémy for their involvement in the development and the implementation of this methodology.
The formalism of pattern structures  [4, 5, 6]  extends FCA to deal with non binary data provided by space description organised as a semi-lattice in order to maintain a Galois connection between objects and their descriptions. Therefore a pattern lattice represents the data where concepts are composed of objects together with their shared descriptions. This space description must be organised and defined as a semi-lattice in a preliminary step, independently of the data, often with a large number of generated concepts and unreasonably large lattices that are uneasy to interpret. Otherwise, pattern structures do not allow an easy management of heterogeneous datasets where several kinds of characteristics describe data. In this paper, we present the NextPriorityConcept algorithm that computes a concept lattice from heterogeneous data, where: • Patterns are locally selected and discovered: Indeed, patterns of each concept are locally discovered, and predecessors of a concept can be filtered according to a specific strategy.
Data: • G, M, (α, β) be a formal context Output: • concepts ( Property. Algorithm NextPriorityConcept-Basic computes the concept lattice of G, M, (α, β) in O(|G||M | 2 ). We will introduce our NextPriorityConcept algorithm in two steps: In Section 3, NextPriorityConcept-Basic algorithm is first modified in order to introduce the possibility to filter the new attributes considered during the immediate predecessor process according to a strategy σ of exploration. In Section 4, the final version of NextPriorityConcept, inspired from pattern structures, extends the computation of concepts to heterogeneous dataset, where attributes P are predicates deduced from each characteristics of data according to a specific description.
Example We can observe that this second concept lattice contains only 9 concepts instead of 14. The concepts for attributes p and s are not generated as immediate predecessors of the top concept since their support is not maximal. However, p appears in concept $5, generated as a predecessor of concept $3 equal to ({1,  3, 5, 7, 9}, o) , thus introduced only for the odd digits {1, 3, 5, 7, 9}. Therefore, p means prime property only for the odd digits, denoted p|o. In the same way, s appears in concept $6 as a predecessor of concept $4 equal to ({0, 4, 6, 8}, ec), meaning square property for the even and composite digits, denoted p|ec. The classical concept lattice produced without strategy is displayed in Figure  2. As second example, we consider the Lenses dataset from the UCI Machine Learning Repository 1.
This dataset is composed of 24 objects/patients described by 4 categorical attributes: • age of the patient: young, pre-presbyopic, presbyopic • spectacle prescription: myope, hypermetrope • the patient should be fitted with hard contact lenses, • the patient should be fitted with soft contact lenses, • the patient should not be fitted with contact lenses. We consider the formal context composed of 9 binary attributes, i. e. the modalities of the 4 categorical attributes. The classical concept lattice contains 109 concepts. With the entropy strategy σ entropy using the class information and by keeping only the two best entropy measures for the predecessors, we obtain a more compact lattice of 28 concepts displayed in Figure  3. As long as a concept contains only one class, no new predecessors are generated by the strategy.
Next Priority Concept algorithm: the final version In the previous section, the new set P of attributes is introduced to store the selected attributes from which predecessors of a concept (A, D) are generated. It is easily observed that it is also possible to introduce new attributes at each iteration without changes. For example, we can consider σ neg (A) = {b, b : b ∈ M -α(A)}, a strategy adding negative attributes as selector. Our final NextPriorityConcept algorithm exploits this possibility to manage heterogeneous data as input. We use predicates describing objects independent of their types. In order to avoid confusion with classical binary attributes, we will use characteristic instead of attribute. More formally, we consider a heterogeneous dataset (G, S) as input where each characteristic s ∈ S can be seen as a mapping s : G → R s where R s is called the domain of s.
However, characteristics on the same domain can be processed together or separately, and some characteristics may not be considered, or considered several times. Therefore, characteristics are given by a family S = (S i ) i≤d , where each S i contains characteristics on the same domain. For example, for the well-known Iris database 2 composed of class information and four numerical characteristics S = {sepal-length, sepal-width, petal-length, petal-width}, we can consider the petal characteristics together, and the sepal characteristics together (S 1 = {petal-length, petal-width} and S 2 = {sepal-length, setal-width}). We can also only consider the petal characteristics, and separately (S 1 = {petal-length} and S 2 = {petal-width}). Predicates are provided according to a given S i , both to describe a set of objects, but also to compute the predecessors of a concept: A description δ i is an application δ i : 2 G → 2 P which defines a set of predicates δ i (A) describing the characteristics of S i for any subset A of G.
σ i (A) = { contains X as subsequences} where sequences of X are obtained by a reduction process of the longest most common subsequences of δ i (A). NextPriorityConcept and Predecessors-Description algorithms are very similar to the previous versions. NextPriorityConcept algorithm considers as input • a heterogeneous dataset (G, S) • (S i ) i≤d a family of S • δ a description • σ a strategy This algorithm computes the formal context G, P, I P and its concepts, where P is the set of predicates describing the characteristics, I P = {(a, p) : p(a)} is the relation between objects and predicates, and (α P , β P ) is the associated Galois connection. Predecessors-Description is a modified version of Predecessors-Strategy to compute predecessors of a concept (A, D), where we consider σ(A) and δ(A) given as input.
4. 2. 4. 4. Digit dataset with minimal logical formula as descriptions. As last example, we consider the digit described by their properties composed, even, odd, prime and square given in Table  1. These 5 characteristics are considered together, and we compute their minimal boolean formulae as description using the Quine-McCluskey algorithm  [11]. The strategy consists in trying to add an attribute or its negation at each step. The concept lattice is displayed in Figure  8. We can observe that the maximum number of predecessors cannot exceed 5 since the predecessors are maximum per inclusion.
Primary neuronal culture and neurite outgrowth measurement Primary cortical neurons were extracted from E17. 5 embryos using as previously described. Approximately 30, 000 cells were plated per well in the 96-well plate for live cell imaging (IncuCyte® system, Essen Bioscience). For each well, 9 images were taken at fixed spots every 3 h during 6 days. Media was change 1 day after plating to remove debris, then every 3 days thereafter. In post analysis, a mask was designed using the inbuilt Neurotrack software module to detect neurites and cell body. The same mask was applied to all time points and all repeats to record neurite extension over time.
The analog signal was transmitted to the Freezing software module through the load cell unit for recording purposes and analysis of time active/time immobile (Freezing) was performed. The CFC procedure took place over two consecutive days. On day 1, mice were placed in the conditioning chamber, and received 3 foot-shocks (1 s, 0. 5 mA), which were administrated at 60, 120, and 180 s after the animals were placed in the chamber. They were returned to their home cages 60 s after the final shock. Contextual fear memory was assessed 24 h after conditioning by returning the mice to the conditioning chamber and measuring freezing behavior during a 4-min retention test. Freezing was scored and analyzed automatically using Packwin 2. 0 software (Bioseb, France). Freezing behavior was considered to occur if the animals froze for a period of at least two seconds.
Light-to-dark transition test (D/LT) This test is based on the innate aversion of rodents to brightly illuminated areas and on their spontaneous exploratory behavior in response to the stressor that light represents. The test apparatus consists of a dark, safe compartment and an illuminated, aversive one. The lit compartment was brightly illuminated with an 8 W fluorescent tube (1000 lx). Naive mice were placed individually in the testing chamber in the middle of the dark area facing away from the doorway to the light compartment. Mice were tested for 10 min, and two parameters were recorded: time spent in the lit compartment and the number of transitions between compartments, indices of anxietyrelated behavior and exploratory activity. Behavior was scored using an infrared light beam activity monitor using actiMot2 Software (PhenoMaster Software, TSE), and it was statistically analyzed using Prism program.
Open-field est (OFT) This test takes advantage of the aversion of rodents to brightly lit areas. Each mouse is placed in the center of the OFT chamber (43 × 43 cm chamber) and allowed to explore for 30 min. Mice were monitored throughout each test session by infrared light beam activity monitor using actiMot2 Software (PhenoMaster Software, TSE). The overall motor activity was quantified as the total distance traveled (ambulation). Anxiety was quantified by measuring the time and distance spent in the centerversus periphery of the open-field chamber.
Funding This work received a state subsidy managed by the  National Research Agency  under the  "Investments for the Future" program  bearing the reference  ANR-10-IHU-01  and the " ANR-SAMENTA 2012" program. This work was also supported by the  Fondation pour la Recherche Médicale  ( DEQ20160334938 ) and the  Fondation de France. LC is supported by the  Centre National de la Recherche Scientifique. JF is supported by  Crédit Agricole d'Ile-de-France Mécénat. FO is supported by the  Fondation pour la Recherche Médicale (FRM) ,  Human Frontier Scientific Program-CDA, ATIP-AVENIR Program, AGEMED INSERM program ,  ANR ,  Fondation Schlumberger pour l'Education et la Recherche (FSER) , and  Emergence de la Ville de Paris.
Modern SAT-solvers We have presented above the two most important ideas to tackle the SAT problem, but there are many other innovations used in modern SATsolvers. There is substantial engineering such as the two watched literals rule, heuristics for variable selection / branching (e. g. the VSIDS variable activity score), restart strategies, and techniques for clause database management (e. g. the LBD measure). Also of great importance are the preprocessing and inprocessing procedures, such as variable elimination, subsumption, and blocked clauses elimination. The best single reference for an overview of these topics is the Handbook of Satisfiability ?.
There have been 10 separate contributions of benchmarks linked to the SC 2 project, accounting for a third of the recent library growth. This has included a more diverse range of applications such as those from the life sciences (?) and economics (?). Also, Maple now supports the SMT-LIB language and integration with SAT/SMT solvers (?) as does Reduce which further, has a fully featured CDCL solver implemented and won the NRA category of the 2017 SMT competition 5. The project also funded a variety of proof of concept studies including on integrations: between computer algebra system Redlog and SMT-solver VeriT, between computer algebra systems CoCoA and Maple and SMT-solver SMT-RAT, and between computer algebra system CoCoA and SMT-solver MathSAT; as well as development on new incremental versions of symbolic algorithms.
Articles of this Special Issue The special issue comprises seven articles. A call was made following the 2017 SC 2 workshop, and submission was open to all work on the the intersection of the two communities (no requirement for a liation with the EU project). Of the seven articles, four have authors funded by the project (although all had taken part in the project workshops and special sessions). An average of 3. 3 reviews per article was obtained with all reviewed by at least one expert in each of the two communities. All accepted articles underwent a multi-stage revision process to take into account the high standards of this journal.
4. Their algorithm builds a CAD incrementally, by performing one projection step at a time and then moving to lifting to perform the additional real root isolation and cell decomposition required by the new additions. They maintain a history of computation allowing for backtracking, developing data structures quite di↵erent to existing CAD implementations. Their work is implemented in their SMT-solver SMT-RAT. The article by England, Bradford and Davenport considers how the process of CAD construction can be improved when the input contains equational constraints (equations which must be satisfied for the formula to hold). Intuitively each of these decrease the solution space by one dimension, and the authors show that a corresponding decrease in the theoretical complexity of CAD can be achieved.
Chisel (Constructing Hardware In a Scala Embedded Language) is an open source Scala based language dedicated to hardware generation, with high level programming functionalities, and an ever growing community. It can be used to generate Verilog code, insuring compatibilty with the standard flow, and ease design reutilisation thanks to the software constructs it embeds. Google used Chisel for the design of their Edge TPU  [1] , and two RISC-V implementations have been proposed -Rocket Chip and BOOM -showing that the initiative can be integrated in both industrial and academic worlds. Works like  [8]  showed that Chisel can be used to explore different implementations of a circuit, here designed for BLAS (Basic Linear Algebra Subroutines) dot product acceleration. BLAS introduces a set of linear algebra operations that can be used to evaluate implementation performances on this kind of applications  [5].
Experimental setup For the experimentations, we targeted a Xilinx VC709 board which includes Block RAMs and integer DSPs, as specified in section 3. It embeds a xc7vx690 FPGA with 433k LUT6s, 866 FFs, 3600 DSPs and 1470 BRAMs (6. 45 MB). We developed a tool flow implementing the methodology proposed in section 2, based on Chisel (latest 3. 2 version) as entry point to generate multiple Verilog designs with respect to the parameters defined in section 3. The flow simulates generated designs behavior using verilator to ensure functionality, comparing it to a software reference defined in Scala. It also uses simulation to extract design latency ∆ c as defined in the temporal behavior model (figure  2 ). After generation and simulation steps, we use Xilinx vivado (2017. 3 version) to synthesize designs and extract performance and resource metrics.
Purpose: Although retractions are commonly considered to be negative, the fact remains that they play a positive role in the academic community. For instance, retractions help scientific enterprise perform its self-correcting function and provide lessons for future researchers; furthermore, they represent the fulfillment of social responsibilities, and they enable scientific communities to offer better monitoring services to keep problematic studies in check. This study aims to provide a thorough overview of the practice of retraction in scientific publishing from the first incident to the present. Methods: We built a database using SQL Server 2016 and homemade artificial intelligence tools to extract and classify data sources including RetractionWatch, official publishers' archives, and online communities into ready-to-analyze groups and to scan them for new data.
After data cleaning, a dataset of 18,603 retractions from 1,753 (when the first retracted paper was published) to February 2019, covering 127 research fields, was established. Results: Notable retraction events include the rise in retracted articles starting in 1999 and the unusual number of retractions in 2010. The Institute of Electrical and Electronics Engineers, Elsevier, and Springer account for nearly 60% of all retracted papers globally, with Institute of Electrical and Electronics Engineers contributing the most retractions, even though it is not the organization that publishes the most journals. Finally, reasons for retraction are diverse but the most common is "fake peer review".
Future researchers can learn from the reasons behind retraction  [5]. Publicly available retraction notices represent the fulfillment of the social responsibilities of journals and publishers  [6]. Open review communities, such as PubPeer, can offer better monitoring services to keep problematic studies in check. Specific goals: To better facilitate this truly powerful and posi- tive function of retraction, a comprehensive database of retraction will be highly beneficial. Useful insights can be drawn from retraction data by asking questions. How old is the phenomenon? Are some specific publishers/journals more prone to retraction? Are retractions concentrated in certain fields? When did retractions begin to become more visible to the world? How long does it take for a journal to issue a retraction? Hence, by extracting insights from a homemade retraction database, of which the sources were RetractionWatch, official publishers' archives, and online communities, we aimed to answer the above questions and provide suggestions for changes in scientific publishing.
Methods Ethics statement: No informed consent was required because this is a literature-based study. Study design: This is a descriptive study that utilized database analysis. Setting: A rise in scholarly publication retractions has been seen in recent years, according to sources of information such as RetractionWatch and publishers' retraction notices, which have fostered open discussions of retracted publications categorized by author, country, journal, subject, and type  [7, 8]. Yet, the large amounts of data stored in different systems may easily lead to omissions in results obtained by searching manually. To bolster the value of retraction data, we embarked on a project to replicate data retrieved from online platforms, such as RetractionWatch, online journal archives, and online discussion communities.