dataesr/scientific-paragraphs-categorization huggingface dataset:
5). A subset of K eggs initiates the next generation, mirroring the census made in the experiment or a limited carrying capacity in the wild. At each generation, we record the proportions of the three genotypes in the eggs and the adults. The model was implemented in Rust 1. 32. 0 (9fda7c223 2019-01-16). The model outcomes were analysed at three levels. (i) First, we ran the model for five generations with parameters drawn from the experiment (Table  1 , Supplementary Table  7 ) and no limited duration of habitat availability (A mean = 30 days) while varying the relative male reproductive success (30 replicates per set of parameters). The fit of each simulation to empirical data was quantified by computing the normalized root-mean-squared error (nRMSE) for each genotypic proportion from generation 1 to 5.
Acknowledgements We are very grateful to  C. Babin ,  B. Labbé  and  L. Logacheva  for help in the field and with raising and genotyping the flies. We would like to thank  F. Larochelle ,  L. Johnson  and  J. -C. Therrien  for their support in establishing the fly rearing conditions. We would also like to thank  E. Berdan  for valuable discussion about Coelopa frigida biology and the inversion dynamics. M. Laporte  and  H. Cayuela  provided constructive comments on the manuscript. This research was supported by a  Discovery research grant  from the  Natural Sciences and Engineering Research Council of Canada (NSERC)  to L. B. , by the  Canadian Research Chair  in genomics and conservation of aquatic resources held by L. B. and by the  Swedish Research Council  grant  2012-3996  to M. W. C.
The initial temperature T, the temperature displacement coefficient k, the temperature probability coefficient k t , and the temperature decrease rate r d were investigated in this study to more precisely obtain the optimal operating conditions. Besides, multiple particles could enhance SA optimization performance. Therefore, the number of particles was also investigated. The adaptive neural fuzzy model and SA were coded and run in MATLAB R2016a with Inter(R) Core (TM) i7-9750H with NVIDIA GeForce GTX 1060.
Results and discussion This section introduces and discusses the virgin and the waste PE TG experimental results firstly. With equal time intervals, one-sixth experimental data was selected to conduct the adaptive neural fuzzy model predictions, another one-sixth experimental data, also with equal time intervals, was selected to evaluate the adaptive neural fuzzy model's accuracy. This data was not covered in the previous predictions. The virgin PE pyrolysis rate was served as the objective function to determine the SA parameters. Subsequently, SA in different temperature ranges was determined by the virgin and the waste PE optimal conversions and pyrolysis rates.
7 Examples that show large NTE and NLC include cyanide frameworks such as Ag ). In many cases, mechanical response can be predicted in these systems by considering the framework as a set of rigid connectors and hinges, sodepending on geometry and topologyNTE or NLC can be targeted. 8, 28 Despite no formal relationship between the two phenomena  15  (with at least one known example violating the correspondence, ref 29), the correspondence of NTE and NLC is found in many systems, and across several other structural mechanisms of anomalous mechanical response. 5 While up to 3% of 13,621 materials with elastic data recently analyzed in the Materials Project database are predicted to have NLC,  30  experimental reports of large and sustained NLC remain rare. A promising family of NTE/NLC coordination polymers are dicyanometallates with the general formula AM[M′(CN) 2 ] 3 (A = group 1 monocation; M = +2 transition metal; M′ = Ag, Au).
This mixture was then loaded in the preindented hole of a gasket along with a ruby sphere as for Raman experiments. High-Pressure Single-Crystal X-ray Diffraction. Singlecrystal X-ray diffraction measurements at high pressure were performed using a Rigaku Oxford Diffraction (Agilent) Gemini-S Diffractometer (Mo Kα1, λ = 0. 0. 71073 Å) equipped with a Sapphire CCD detector at 65. 00 mm from the sample. The same crystal was also studied at room pressure on a Rigaku Oxford Diffraction (Agilent) X'calibur diffractometer. A 170 × 80 × 30 μm 3 single-crystal plate was fixed to one anvil of a Merrill-Bassett DAC equipped with Boehler-Almax diamonds along with a ruby microsphere for pressure calibration. The opening angle of the cell was 80°to give maximum access to reciprocal space during data collection.
A tungsten gasket was placed on the other anvil, Daphne7474 oil  41  was added as a pressure transmitting medium, and the two parts of the cell were then assembled. Data collection, indexing, and reduction were performed using CrysAlisPro (Agilent) software along with absorption correction for the ambient-pressure data. High-pressure data were corrected for absorption using Absorb7. 0. 42 Structure refinements were performed using SHELXL97. 43 Structural models for the highpressure phases were obtained based on the structure of the ambient pressure phase and the observed group-subgroup relationships based on the unit cells and space groups of the high-pressure phases. 44 C and N atom positions were located using difference Fourier maps. The C-N distance was constrained in all refinements, making use of the average value determined in the ambient-pressure phase.
Due to the large number of variables for the two high-pressure phases, the Ag-C and Cd-N distances were also constrained. C and N atoms were treated using isotropic displacement parameters for the high-pressure data sets. High-Pressure Synchrotron Powder X-ray Diffraction. Several KCd[Ag(CN) 2 ] 3 single crystals were ground to a very fine powder and placed in the preindented and drilled cavity of a stainless steel gasket in an ETH Diamond Anvil Cell. A ruby microsphere was added to measure the pressure, and silicon oil was used as the pressure transmitting medium. X-ray diffraction data were obtained on beamline BM01A (Swiss-Norwegian beamline) at the ESRF. A wavelength of 0. 69412 Å was selected using a Si(111) monochromator with a 100 μm focused beam-size. Data were collected using a MAR345 image plate placed at ∼250 mm from the sample.
Calibration and integration of the images were performed using FIT2D. 45 awley fits to data were performed using TOPAS Academic v4. 1  46  at each pressure using space groups as determined from single-crystal experiments using a single TCHZ peak shape. 47 ietveld refinement was not possible due to the extreme preferred orientation within the pressure cell, especially at low pressure. Computational Methods. First-principles calculations were performed within the density functional theory framework, as implemented in the ABINIT package. 48 The exchange-correlation energy functional is evaluated using the local density approximation parametrized by Perdew and Wang. 49 The all-electron potentials are replaced by normconserving pseudopotentials generated according to the Troullier-Martins scheme.
57, 58 The challenge in high-pressure experiments is gaining enough intensity and resolution to resolve such distortions, where restraining the model to a higher symmetry is advantageous to reduce the number of variables in the refinement to increase robustness. Lattice parameters were extracted by Pawley refinement of powder diffraction data at all pressure points in the lowpressure regime, making use of the space groups determined from single-crystal analysis. The variation in the lattice parameters (powder and single crystal) across all phases is presented in Figure  6. By fitting our variable-pressure lattice parameters for phases I and II using PASCal,  59  we can extract the bulk mechanical response, as given in Table  3. For phase I, the small number of data points means the behavior is most robustly described using a linear fit to data.
Nationale de la Recherche  (project  ANR-09-BLAN-0018-01 ) for financing this study. A. B. C. and A. L. G. gratefully acknowl-edge financial support from the  ERC  (Grant Nos. 279705  and  788144 ) and  EPSRC  (Grant No. EP/G004528/2 ). A. B. C. thanks  Imperial College London  for financial support. We thank the  Swiss-Norwegian Beamline  at the  ESRF  for allocation of beamtime (Proposal No. HS4434 ).
| RNA-Seq data analysis Sequenced reads were trimmed with readtools (version: 1. 5. 2)  Schlötterer, 2018)  based on a quality score of 20 and mapped to the D. melanogaster reference genome (version 6. 03)  (Hoskins et al. , 2015)  using gsnap (version: 2018-03-25)  (Wu & Nacu, 2010)  with the following parameters (-A: SAM, -k: 15, -N: (Gómez-Sánchez & 1, -m: 0. 08). Quality checks for even gene body coverage with rseqc  (Wang et al. , 2012)  were used to exclude 3′-biased libraries from the analysis. To quantify the number of exon-aligned reads, we used rsubread (version: 1. 30. 9) (Liao et al. , 2013)  based on the annotation (version 6. 09) of the D. melanogaster genome  (Hoskins et al. , 2015). Differential expression (DE) analysis was performed with edger (version: 3. 22. 5) (Robinson et al.
| Weighted gene co-expression network analysis Because gene-wise differential expression analysis does not account for the nonindependence among functionally related genes it suffers from a lack of power, which is exacerbated by the required multiple testing corrections. Aiming for more statistical power, we performed a co-expression analysis to identify gene modules that were coregulated during the temperature adaptation. Normalized gene expression (log-scaled counts per million) of all genes were subjected to weighted gene co-expression network analysis (WGCNA) implemented in the R package wgcna  (Langfelder & Horvath, 2008). Briefly, Pearson's correlation coefficients were used to measure the co-expression between each gene. Based on them we generated an adjacency matrix by raising the correlation coefficients with a power of β.
A topological distance matrix was then calculated from the adjacency matrix and used for the hierarchical clustering to construct the network. We followed the developers' instructions and determined a β of 6 for our data and used the blockwiseModule function to construct a "signed" co-expression network with a minimum module size of 100. For all others parameters we used default values. To determine the evolutionary pattern of each module, we investigated the normalized mean expression pattern of the genes in each module and tested whether they are enriched for adaptive genes.
Seventy-nine of these genes were expressed at higher levels in hot-evolved populations while 124 genes were expressed more in cold-evolved populations. For simplicity, we will call these candidate genes temperature-adaptive genes throughout although we provide no empirical evidence that the expression changes directly affect temperature-specific fitness. Rather than acting in isolation, genes are usually organized in functional networks with complex interactions. Hence, in addition to differential expression analysis on the level of single genes, we reconstructed the gene regulatory network based on the co-expression pattern across the studied populations. Using wgcna, we identified 20 gene modules that exhibit different evolutionary patterns (Figure  S1 ; Table  2 ). Enrichment for biological processes (GO terms, Table  S5 ) of the genes in each module confirmed that these modules are biologically meaningful.
DATA AVA I L A B I L I T Y S TAT E M E N T Sequence reads from this study are available from the European Sequence Read Archive (http://www. ebi. ac. uk/ena/), accession no. PRJEB39854. Original data and scripts for the analysis are available upon publication on the GitHub repository of this study (https:// github. com/Sheng KaiHs u/Dmel_paral lel_tempe rature_adapt ation). [Correction added on 16-April-2021, after first online publication: Data Availability Statement has been updated. ] O RCI D Sheng-Kai Hsu https://orcid. org/0000-0002-6942-7163 Christian Schlötterer https://orcid. org/0000-0003-4710-6526.
, 2012)  with the ladder encoding of Junttila and  Kaski (2010)  which obtains the best results), and the libexact  (Kaski & Pottonen, 2008)  implementation of the dedicated DLX algorithm  (Knuth, 2000). libexact is always faster than EC DL+ : libexact is 3 times as fast as EC DL+ , and this ratio is rather constant when p increases. The gap between EC DL and libexact is explained (1) by the difference of support languages (Java for EC DL and C for libexact), and (2) by the cost of using a generic CP solver instead of a dedicated algorithm. EC DL+ is faster than SAT , and the gap between the two approaches increases when increasing p, showing that EC DL+ has better scale-up properties than SAT : EC DL+ is 10 times as fast as SAT when p = 20 and 31 times as fast when p = 35. When p = 40, SAT is not able to enumerate all solutions within the CPU time limit of 50, 000 seconds whereas EC DL+ needs 4, 036 seconds on average.
6). F intent subset of objects frequency size diameter split c 0 ∅ {o 1 , o 2 , o 3 , o 4 , o 5 } 5 0 1 0 c 1 {a 1 } {o 1 , o 2 , o 5 } 3 1 3/4 1/3 c 2 {a 2 } {o 1 , o 3 , o 4 } 3 1 3/4 1/2 c 3 {a 3 } {o 2 , o 4 , o 5 } 3 1 3/4 1/2 c 4 {a 4 } {o 1 , o 2 , o 3 } 3 1 3/4 2/3 c 5 {a 1 , a 3 } {o 2 , o 5 } 2 2 2/3 1/2 c 6 {a 1 , a 4 } {o 1 , o 2 } 2 2 1/2 1/3 c 7 {a 2 , a 3 } {o 4 } 1 2 0 2/3 c 8 {a 2 , a 4 } {o 1 , o 3 } 2 2 1/3 1/2 c 9 {a 1 , a 3 , a 4 } {o 2 } 1 3 0 1/2 c 10 {a 1 , a 2 , a 4 } {o 1 } 1 3 0 1/3 c 11 {a 1 , a 2 , a 3 , a 4 } ∅ 0 4 0 0 Table 2: The set F of all formal concepts contained in the dataset described in Table  7. • The intent of a subset of objects O i ⊆ O is the set of attributes common to all objects in O i , i. e. , intent(O i ) = ∩ o∈O i attr(o). • A subset of objects O i ⊆ O is a formal concept if it contains every object whose set of attributes is a superset of its intent, i.
e. , O i = {o ∈ O : intent(O i ) ⊆ attr(o)}. • A conceptual clustering is a partition of O in k formal concepts O 1 ,. , O k , i. e. , ∀o ∈ O, #{i ∈ [1, k] : o ∈ O i } = 1. Example 6. In Table  2 , we list all formal concepts associated with the dataset displayed in Fig. 7. Examples of conceptual clusterings are {c 2 , c 5 } and {c 5 , c 7 , c 8 }. Quality Measures Associated with Formal Concepts. Two classical measures for evaluating the quality of a formal concept O i ⊆ O are the frequency, which corresponds to its number of objects (i. e. , frequency(O i ) = #O i ), and the size, which corresponds to its number of attributes (i. e. , size (O i ) = #intent(O i )). Two other measures that are often used to evaluate the quality of a group of objects for clustering applications are the diameter and the split.
Optimization Criteria for Conceptual Clustering. There may exist different solutions to a conceptual clustering problem, and we may add an objective function to search for the best solution. In this paper, we consider the case where we maximize an objective variable denoted Min q (where q ∈ Q is a quality measure) which is constrained to be equal to the smallest quality among the selected formal concepts, i. e. , Min q = min O i ∈{O 1 ,. ,O k } q(O i ). By maximizing Min q , we ensure a minimal quality over all clusters, and this is well suited for many applications such as, for example, the ERP configuration problem addressed by  Chabert (2018). Example 7. In Table  2 , we give for each formal concept of the dataset the value of each quality measure defined above. For the conceptual clustering {c 5 , c 7 , c 8 }, we have: Min frequency = 1, Min size = 2, Min -diameter = -2/3, and Min split = 1/2.
Experimental Setup Benchmark. We describe in Table  3  six classical machine learning instances, coming from the UCI database, and six ERP instances coming from an ERP configuration problem described by  Chabert and Solnon (2017). Let us recall that to solve conceptual clustering problems with a two-step approach, we first compute the set F of all formal concepts with a dedicated tool such as LCM, and then we solve an exact cover problem (S, P ) such that S is the set of all objects (i. e. , S = O) and P is the set of all formal concepts (i. e. , P = F). The number of objects #O varies from 47 to 630, and the number of formal concepts #F varies from 1, 580 to 3, 788, 341. The time spent by LCM to compute F is smaller than one second for all instances but two. The two harder instances (ERP7 and UCI6) are solved in 5.
We compare ECQK and ECQ * ,sum with the following declarative approaches: • FCP1, the full CP model introduced by  Dao et al. (2015) , and implemented with Gecode (2005); • FCP2, the full CP model introduced by Chabert and Solnon (2017), and implemented with Choco v. 4. 0. 3; • ILP, the hybrid approach introduced by Ouali et al. (  2016 ), and implemented with CPLEX v12. 7. Note that ILP approaches dedicated to the set partitioning problem (such as  Rasmussen (2011) ,  Rnnberg and Larsson (2014) , or  Zaghrouti et al. (2014) , for example) cannot be used to solve our problem as the goal is not to minimize a weighted sum but to maximize the minimal weight of a selected subset. Performance Measures. We consider two different performance measures, i. e. , the number of choice points and the CPU time.
S7 ), as having exhibited a fire regime shift. Then, we estimate the system biomass loss per fire event for these areas as the BRI:FRI ratio in the year of the event multiplied by the total CO2 emissions from a given PFT in that year's fires: 𝑽𝑪 ∆𝑭𝒊𝑹𝒆𝒈 = ( 𝑩𝑹𝑰 𝑷𝑭𝑻 𝑭𝑹𝑰 𝑷𝑭𝑻 ^) * 𝑬 𝑭𝑪𝑶𝟐 (7) El Niño drought-induced tropical post-fire mortality (  , )  QGRS is estimated for El Niño years only given that current data on drought induced post-fire mortality extends from these years. For this estimate we do not consider belowground mortality losses since these are not estimated in the literature for this type of disturbance event. To get the per-PFT total aboveground fraction of biomass allocation, we extract annual gridded biomass allocation terms and sum them over the total biomass allocated to all vegetation C pools for each PFT ( TE 0&5 ).
Where index values cross 1. 5 for 2 consecutive years (double peak) we select both years. The resulting gridded  TE 0&5 dataset is then used to estimate the proportion of tropical vegetation affected by fire in an El Niño year by multiplying the probability that a fire in a given pixel comes from a given PFT by the burned fraction of that PFT: 𝑭𝒊𝒓𝒆𝑽𝑪 𝑨𝑮 𝑷𝑭𝑻 = 𝑽𝑪 𝑨𝑮 𝑷𝑭𝑻 * 𝒑(𝑭𝒊𝒓𝒆) 𝑷𝑭𝑻 * 𝒇𝑩𝒖𝒓𝒏 𝑷𝒊𝒙 (9) To capture only those areas that may have experienced drought and hence drought induced fire mortality, we assume that drought occurs in a pixel if annual precipitation for that pixel is at or below the 25 th percentile of precipitation for that pixel over the entire simulation period and mask out pixels in the dataset  TE 0&5 which do not satisfy this condition. We then assume that total post-fire mortality loss is approximated from the mean literature value of -24.
Local incision rate from paleomagnetic data (southern Grands Causses) A total of 100 clay-infilling samples distributed over of 13 sites (i. e. , profiles) was studied. The lowest sample elevation above sea level (a. s. l. ) is in the Garrel cave system (ca. 190 m) and the highest is in the Leicasse cave system (ca. 580 m a. s. l. ). In the Leicasse cave system, we sampled eight profiles totalizing 60 samples. Profiles elevations are located between ca. 200 and ca. 400 m above the base level (a. b. l. ), which corresponds to the elevation of the Buèges river spring at 170 m a. s. l. In the Garrel cave system, we sampled five profiles for a total of 40 samples that range between 20 and 80 m a. b. l. , defined by the Garrel spring at 180 m a. s. l. Given the very marginal difference in elevation between the local base levels from these two caves, we assume that they have the same local base level.
Code and data availability. Surface analysis was performed using QGIS version 2. 18, MATLAB ® code and IGN DEM (RGE Alti ® ) 5 m. Modeling was performed using ADELI code  (Hassani and Chery, 1996; Chéry et al. , 2016). Data for TCN and paleomagnetic analysis are provided in the paper itself or in the Supplement. Additional information for geologic background are available at http://infoterre. brgm. fr/ (last access: 21 February 2020) (French Geological Survey data visualizer) and in Karst3D Team (2019, https://doi. org/10. 15148/940c2882-49f1-49db-a97e-12303cace752). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/se-11-241-2020-supplement.
In our recent work, we presented molecular modeling studies meant to contribute to a better understanding of the reactivation mechanism of HssAChE inhibited by the nerve agent O-ethyl S-(2-(diisopropylamino)ethyl) methylphosphonothioate (VX)  [20]. In that case, classical molecular-dynamics (MD) simulations were performed, considering the near attack conformation (NAC) approach  [20] [21] [22] , on a model of HssAChE based on the experimental structure of mouse AChE (PDB code 3ZLV). As a result, a NAC frame of deprotonated-2-PAM in the CAS of VX-inhibited HssAChE was obtained  [20]. The deprotonation of the aldoxime (-C=NOH) group may happen under physiologic conditions in the pathway towards the NAC  [14, 19, [21] [22] [23] , or before its entry into the AChE active site, according to the pKa = 7.
However, there is no consensus yet about this mechanism due to the fact that none of the aldoximes developed so far are capable of reactivating AChE inhibited by all existing OPs  [13, 14, 19]. Therefore, the full elucidation of the mechanism of this AChE reactivation reaction is crucial for the development of new and more efficient antidotes. In our recent work, we presented molecular modeling studies meant to contribute to a better understanding of the reactivation mechanism of HssAChE inhibited by the nerve agent O-ethyl S-(2-(diisopropylamino)ethyl) methylphosphonothioate (VX)  [20]. In that case, classical moleculardynamics (MD) simulations were performed, considering the near attack conformation (NAC) approach  [20] [21] [22] , on a model of HssAChE based on the experimental structure of mouse AChE (PDB code 3ZLV).
Thus, it was possible to determine the energy barrier involved in the reactivation of VX-inhibited AChE. All calculations were performed through the exchange-correlation functional ωB97X-D  [37]  in the framework of the density functional theory (DFT)  [38] , using the aug-cc-pVDZ basis set  [39] , by employing the Gaussian09 package  [40] , as indicated in Figure  2. Biomolecules 2020,  10, 192  3 of 8.
Thus, it was possible to determine the energy barrier involved in the reactivation of VX-inhibited AChE. All calculations were performed through the exchangecorrelation functional ωB97X-D  [37]  in the framework of the density functional theory (DFT)  [38] , using the aug-cc-pVDZ basis set  [39] , by employing the Gaussian09 package  [40] , as indicated in Figure  2.
Methods We designed an observational, multicentre, nationwide cohort study. We retrospectively included all cases of percutaneous closure of pmVSD with or without aneurismal tissue using the Nit-Occlud Lê VSD Coil performed in France over the first 3 years of device availability (January 2015 to December 2018) among 20 catheterization laboratories of paediatric and adult congenital cardiology centres. Selection of patients for pmVSD closure was according to each centre's protocol. We ensured inclusion of all cases by cross-checking with data from the manufacturer. Written informed consent was obtained from patients or their legal guardians. Demographic data, clinical information and non-cardiac comorbidities were collected. All patients were examined in a paediatric and congenital cardiology tertiary centre (M3C network for complex congenital heart diseases).