dataesr/scientific-paragraphs-categorization huggingface dataset:
Overall, the Pearly Beach record represents an important new contribution to a growing body of data that provides evidence for great spatial complexity in regional climatic responses, and more specifically, provides evidence that AMOC variability influence extended eastwards along the southwestern Cape coast at least as far as the modern boundary of the WRZ. It also provides a rare look at the response of lowland sectors of the mega-diverse Cape Floristic Region to major long-term climatic transitions. (Parkington et al. , 1988; Cowling et al. , 1999; Parkington et al. , 2000) , 2 -Grootdrift  (Meadows et al. , 1996) , 3 -Verlorenvlei  (Kirsten et al. ; Stager et al. , 2012; Carr et al. , 2015) , 4 -Klaarfontein Springs  (Meadows and Baxter, 2001) , 5 -Pakhuis Pass (Scott and Woodborne,    Aridity index  (Zomer et al.
, 2008; Trabucco and Zomer, 2019)  and the temperature of the wettest quarter (used as a proxy for the temperature of the growing season) and the minimum temperature of the coldest month  (Fick and Hijmans, 2017). These responses were calculated following the probability density function (pdf) approach of  Chevalier et al. (2014)  and using data from the SANBI database  (SANBI, 2003) , restricted to the plant species that currently live in the study area (defined here as a box between 17° and 29°E and between 32° and 35°S). (Jouzel et al. , 2007) , c) record of Atlantic overturning circulation strength  (Ng et al. , 2018) , d) the coastal thicket pollen record from Pearly Beach, shading from proxy ghost analysis using rbacon software  (v. 2. 3. 6; Blaauw and Christen, 2011)  to express chronological uncertainties, e) sea-surface temperature record from the GeoB 1023-5 marine core  (Kim et al.
Acknowledgements L. J. Q. acknowledges the financial assistance of the  National Research Foundation (NRF) of South Africa, PAST (Palaeontological Scientific Trust)  and the  University of Cape Town. This study was funded in part by the  European Research Council (ERC)  under the European Union's  Seventh Framework Programme  (FP7/2007-2013)/ ERC Starting Grant "HYRAX", grant  agreement no. 258657. We thank  Carmen Kirchner  for conducting the particle size analysis at the  Department of Geography, Friedrich-Schiller-University, Jena. We also thank  Rachid Cheddadi  and  Graciela Gil-Romera  for assisting with recovery of the core.
Micro-channel and pore formation To form micro-channels, hydrogels were casted using a mold in which clinical-grade polyamide-6 filaments (Ethicon) were included (Fig. 1 ). This mold made of poly-lactic acid was conceived by Fusion 360 Autodesk software and 3D printed using an Ultimaker Original + printer. Pores were obtained with a freeze-drying protocol. Hydrogels were embedded in PBS: water (1:6) salt solution and transferred to the freeze dryer (Cryotec). Controlled freezing phase of the samples at -20 °C was followed by a sublimation phase under controlled low pressure with an increase of temperature. For specific coating of the channels, samples were incubated for 2 h at room temperature in a collagen type I from rat tail solution (BD-#354236) (0. 5 mg/mL -1 ) in 0. 01 N HCl. Collagen fiber deposition on the channels was done by pH neutralization through incubation in PBS: water (1:6) at pH 7.
Channel diameter measurement Hydrogels containing FITC-Dextran and channels with different diameters were immersed in water, PBS or culture media. Media were exchanged until the ionic force remained constant (Conductivity Meter Thermo Orion model 145). Then, confocal microscopy of hydrated samples was used and image acquisition was done with a confocal microscope LSM 510 Zeiss and optical sections comprising the whole channel were collected with a step-size of 5 μm. Images were acquired with ZEN lite software and image analysis was done with ImageJ software. Channel diameter in different media was established measuring the maximal distance between channel walls. Analysis was done in triplicate.
Collagen coating assessment To confirm collagen coating in the micro-channels of non-porous and porous hydrogels, two methods were followed. First, hydrogels were stained using picrosirius red dye that binds collagen. After 2 h incubation at room temperature, hydrogels were extensively washed with PBS until complete removal of the unbinded dye (this was confirmed by using hydrogels without collagen as controls). Then, confocal microscopy was employed based on the fluorescence emitted by collagen fibers after picrosirius red staining. Confocal microscope LSM 510 Zeiss was used and images were acquired with ZEN lite software and image analysis was done with ImageJ software.
Cell staining and confocal imaging Actin filaments were labeled by incubation with Alexa-Fluor 647 Phalloidin (Invitrogen) (1/200, 1 h incubation time at room temperature) after fixation in paraformaldehyde 4%. Laminin was stained using rabbit polyclonal antibody (ab19012) (1/400, overnight incubation at 4 °C) followed by secondary antibody Alexa-Fluor 647 anti-rabbit (ab150079) (1/500, 2 h incubation time at room temperature). Nuclei were stained with DAPI (1/50,000). Image acquisition was done with a confocal microscope LSM 510 Zeiss. Images were acquired with ZEN lite software and image analysis was done with ImageJ software.
Cell density quantification within the channels Number of cells/mm 2 within the channels was quantified using ImageJ software. At different times after cell seeding, samples were fixed and actin filaments and DNA stained as previously described. Samples were observed and images were acquired by confocal microscopy. Cell density was calculated normalizing the number of nuclei per channel area. The ratio of adhered and non-adhered cells was also estimated by this method. Analysis was done in triplicate.
Acknowledgements This work was support by  Inserm ,  Université de Paris  and  Université Sorbonne Paris Nord. M-N. L. postdoctoral fellowship was supported by  RHU  iLite (grant number  ANR-16-RHUS-0005 ). The authors greatly acknowledge  Samira Benadda  of the  CRI U1149 Imaging Facility. Jean-Noël Rey  is also acknowledge for his kind help with the 3D printing.
Thermo-hydraulic analyses For the thermal and thermo-hydraulic analyses, the study has been conducted with two MS: FLiBe and ClLiK (structural materials and multiplier chosen for each case are given in Table  3 ). Besides the development of an analytical thermal model, the option has been taken to start the development of a Finite Element Method (FEM) calculation via the Cast3M software  [31]  to analyse the Coolant/Breeder MS distribution and recirculation at the scale of the outboard BB segment. The major purposes are to evaluate the temperature fields in the fluids and the structure and the pressure drops ΔP through the BB segment. The 3D-massive FEM model developed for the thermal analysis respects the dimensions of the outboard BB segment with the DEMO 2017 Baseline: its characteristics are given in Table  3.
Data Our case study is based on two instances of the GasLib  (Schmidt et al. 2017 ) and different corresponding bookings. On the one hand, we study GasLib-134 (version 2), which is a tree-shaped network with 134 nodes, one compressor, and one control valve. It roughly represents the Greek gas network. The flow thresholds m are set to 0 for the compressor and to -10 -2 for the control valve. The latter value is chosen to guarantee the feasibility of the zero nomination in GasLib-134. Since the zero nomination is always booking-compliant, its feasibility is a necessary condition for the feasibility of any booking. Bookings for networks in the GasLib can be obtained by setting the corresponding nominations contained in the GasLib as bookings. For GasLib-134, these nominations reflect actual demand scenarios over several years in the past.
Computational setup All models have been implemented in Python 3. 8. 0 using Pyomo 5. 7. 1  (Hart et al. 2017). We performed all computations using the Kaby Lake nodes with 32 GB RAM of the compute cluster Regionales Rechenzentrum Erlangen (2021). The time limit is 2 h. In Sect. 7. 3, when treating nonlinear gas physics, we use ANTIGONE 1. 1 (Misener and Floudas 2014) and BARON 17. 4 Tawarmalani and Sahinidis 2005) within GAMS 24. 8 (GAMS Development Corporation 2020) to solve the occurring MINLPs. We perform the computations on a single thread and set the optCr parameter in to 10 -4. In Sect. 7. 4, we use Gurobi 9. 0. 1 (LLC Gurobi Optimization 2020) to solve linear approximations of the gas physics. We again perform computations on a single thread and set Gurobi parameters IntFeasTol to 10 -9 and NumericFocus to 3.
The linear case Except for the nonlinear gas physics at the right-hand side of (1b), all considered models are linear with mixed-integer variables. In this section, we consider linear approximations of gas physics to obtain mixed-integer linear problems (MILP) to be solved by Gurobi. To this end, we replace |q a | for every a ∈ A pipe by cM, where c ∈ (0, 1] is a scaling factor and M := min{ u∈V + b u , u∈V -b u } is an upper bound on the flow on each arc. Thus, we replace Constraints (1b) with π u -π v = ξ a q a , ξ a = c a M, a = (u, v) ∈ A pipe. Table  3  shows the results for GasLib-134 and the 2011-11-06 booking, where Appr. indicates the different scaling factors c ∈ {0. 2, 0. 4, 0. 6, 0. 8, 1. 0}. F-OVF is clearly outperformed by the shown methods. The same holds for F-CHAR both w. r.
Acknowledgements  Martine Labbé  has been partially supported by the  Fonds de la Recherche Scientifique -FNRS  under Grant no PDR  T0098. 18. Fränk Plein  thanks the  Fonds de la Recherche Scientifique -FNRS  for his Aspirant fellowship supporting the research for this publication. This research has been performed as part of the  Energie Campus Nürnberg  and is supported by funding of the  Bavarian State Government. Martin Schmidt  and  Johannes Thürauf thank the Deutsche Forschungsgemeinschaft  for their support within projects  A05 ,  B07 , and  B08 in CRC TRR 154.
The integral time absolute error was used as the objective function. Moreover, the controller was also tuned to ensure that the tie-line power and the frequency of the multi-source power system were within the acceptable limits. A two-area power system was designed using MATLAB-Simulink tool, consisting of three types of power sources, viz. , thermal power plant, hydro power plant, and gas-turbine power plant. The overall efficacy of the proposed algorithm was tested for two different case studies. In the first case study, both the areas were subjected to a load increment of 0. 01 p. u. In the second case, the two areas were subjected to different load increments of 0. 03 p. u and 0. 02 p. u, respectively. Furthermore, the settling time and the peak overshoot were considered to measure the effect on the frequency deviation and on the tie-line response.
Moreover, K pr , K i and K d represent the proportional gain, the integral gain, and the derivative gain, respectively. We considered the ITAE as the objective function. The tuning of the parameters of the PID controller was achieved by minimizing this objective function. The objective function can be described by Equation  (2). ITAE = t sim 0 t×|e(t)|dt (2) A test system consisting of two areas was considered for the LFC analysis. The test system used multiple sources for power generation, such as the hydro power plant, the gas turbine power plant, and the thermal power plant. The modeling of this test system was done in MATLAB SIMULINK using linearized approach, and the model is depicted in Figure  1. Some of the parameters of this test system are mentioned in the Appendix A. In each area, all the three different plants were connected and one PID controller was used for the AGC.
Results and Discussion The model for the MSPS was developed using the concepts of controls' systems. Different plants, such as thermal, hydro, and gas turbine, were represented using their equivalent transfer functions. Two different areas were considered with these power plants. Using Equation  (1)  and Equations (  4 )-(  7 ), the test system was designed to evaluate the performance. On the other hand, the proposed hybrid intelligent optimization technique was designed using Equations (  2 ), (3), and (  8 )-(  16 ). The test system was implemented as a Simulink model using the MATLAB (R2016a) software on an i5-6200 CPU@ 2. 30 GHz with 8 GB RAM. Also, the proposed algorithm was implemented using MATLAB. Furthermore, the other optimization techniques such as PSO, FA, and GSA were also implemented using the MATLAB.
All these four algorithms were integrated with MATLAB Simulink model of the test system to obtain a comparative performance evaluation. Some other key parameters of the test system and parameters related to the optimization techniques are elaborated in the Appendix A of the manuscript. Based on several case studies, the effectiveness of the proposed controller was analyzed under dynamic system operating conditions. Based on the four optimization techniques, the parameters of PID controller was tuned by minimizing the ITAE. Specifically, the optimization techniques were used to find the optimal parameters of PID controller, which are used for the LFC of the MSPS. To elucidate the efficacy of the proposed optimization techniques, we considered two cases, which are described below. Case Study I: Both the areas of the test system were subjected to a load increment of up to 0.
Work by Sahl et al. sought to expand on the original B. anthracis classification system, and generated an SNP database used to characterize the branching structure of isolates based on 193 genomes  [28]. More recent genomic analyses that comprise the largest global phylogeny of B. anthracis to date (356 genomes) has redefined B. anthracis population genomic structure, resulting in six primary clusters and 18 nested clades. This new classification system uses an intuitive, simplified naming system and allows for linkable, rapid classification  [27]. Two of the major genotype clusters, cluster 1 (C Branch) and cluster 2 (B Branch), are vastly underrepresented in terms of prevalence and have been hypothesized to be less fit than the majority of B. anthracis specimens isolated and sequenced  [26, 29, 30].
However, the link between genomic architecture, and the scarcity of these genotypes remains largely unexamined. In addition, some of the individual clades identified are geographically specific, whereas others seem to be widely distributed, raising numerous questions about what factors are driving evolutionary success in this species  [26] [27] [28]. Understanding the relationships among spatial variation, population stability, and genomic architectural variation is particularly important for B. anthracis, as it is a major threat to wildlife, livestock and public health globally  [31]. In this study, we explore genomic variation and selection in a global whole-genome dataset of B. anthracis isolates spanning 39 countries and six continents. In addition, we apply an ensemble machine learning method [random forest (RF)] to elucidate the ways in which isolation source, geography, phage diversity and AMR genes may be shaping genomic diversity and genotypic persistence.
Whole genome mapping and assembly The population genomic dataset used in these analyses was previously developed and published by Bruce et al. [27]  consisting of 356 B. anthracis whole genomes collected from the NCBI sequence read archive (  [27] , File S1, available in the online version of this article). Each read pair was mapped to the fully annotated Ames Ancestor genome (accession AE017334. 2), using the RedDog pipeline (https:// github. com/ katholt/ RedDog). Mapped reads were then subjected to extensive post-processing to remove calls (a) found in regions with large 'inexact' repeats, (b) within prophage regions of the reference genome, (c) from regions that were found to be invariable in all but the outgroup, (d) from regions potentially resulting from recombination and (e) potentially related to stutter.
Identification of AMR genes and phage sequence variation We screened each assembly for AMR genes employing The Resistance Gene Identifier (RGI) tool provided by the Comprehensive Antibiotic Resistance Database (CARD)  [33]. RGI can be used to predict resistomes from protein or nucleotide data based on homology and SNP models. In addition to identifying AMR genes, we identified prophage sequences within the contigs of each assembled genome using the Phage Search Tool Enhanced Release (PHASTER)  [34]. PHASTER is a web-based application that is designed to rapidly and accurately identify, annotate and graphically display prophage sequences within bacterial genomes or plasmids. The full phylogenetic tree of B. anthracis isolates from Bruce et al. [27]  was then annotated using iTOL  [35]  with both phage sequence variation (scored as either intact, questionable or incomplete; see Table  S1  for details), and presence (or absence) of AMR genes.
Classifying population genomic architecture using RF To understand how various factors may be influencing the genomic architecture of B. anthracis we used an RF approach  [37] , incorporating AMR gene data, phage diversity data and isolate metadata (continent of isolation and source) accessed through the NCBI biosample database  [38]. RF has gained increased attention over the past several decades given its ability to produce excellent classification results while also being computationally inexpensive  [39, 40]. The RF classifier produces valid classifications using predictions derived from a group of decision trees and can also be used to select and rank those variables, allowing the user to successfully discriminate between the target classes  [41]. RF was carried out using the R package randomForest  [37]  to construct a multitude of decision trees and determine the mean prediction of each individual tree pertaining to the six primary population clusters  [27].
The R package SPM was then used to carry out a 5-fold cross-validation  [42]. We first removed samples with missing values for independent variables, and additionally removed three variables (AMR gene mphL, and phage sequence Bacillus virus 1 and Bacillus phage PfEFR-5) which exhibited no variation across the dataset. The final dataset resulted in 20 independent variables (Table  S2 ). We divided the dataset into a training dataset including 75 % of the samples and a validation dataset including the other 25 %. To determine Mtry and Ntree (number of variables and number of trees), we used a 5-fold cross-validation and grid search. To carry out 5-fold crossvalidation, we randomly assigned each sample to one of five groups. For each pass of cross-validation, RF classifiers were trained with a test dataset of which one group was held out  [43].
The model with the highest correct classification rate and Kappa index of the classification was selected for determining values of Mtry and Ntree. We used the best combination of the Mtry and Ntree for the final RF model. To assess the model fit of the RF we subjected the model to the validation dataset and estimated the accuracy. To determine the contribution of the variables to the classification in the model, the importance of variables was evaluated by the mean decrease in accuracy. The mean decrease in accuracy was computed with the difference between the out-of-bag (OOB) error (training observations not included in the bootstrap) from a dataset with the selected variable permuted and the OOB error from the original dataset  [41].
Recombination, high-impact SNPs and candidate genes for selection To determine how recombination may be influencing population genomic structure across our dataset, we first used the program Gubbins to iteratively identify loci containing elevated densities of base substitutions in the SNP dataset (prior to removal of recombinant sequences)  [44]. To analyse selection in non-recombining regions, we analysed SNPs (post-removal of recombinant sequences) using the program SnpEff  [45]. SnpEff annotates and predicts the effects of genetic variants on genes and proteins (such as amino acid changes). To assess 'high-impact' SNPs influencing population genomic structure, we compiled a list of SNPs that produce significant changes to protein structure in the B. anthracis chromosome and plasmids, specific to each primary cluster and groups of primary clusters, such as mutations that result in the gain of a stop codon, the loss of a start codon and splice region variants.
A world map depicting the prevalence of AMR genes is depicted in (b). Each circle represents an AMR gene coloured according to the key and figure above. Percentages represent the total proportion of isolates from each continent where the respective AMR gene was identified (including North America, South America, Europe, Asia and Oceania). See Fig. S1  for a key to previously established classification schemes. were found across the majority of isolates tested. AMR gene mphL was identified in every isolate examined. AMR   In addition to AMR genes, we also identified 11 prophage sequences across our global dataset (Fig. 2 , Table  S4 ). Prophage sequences were scored as intact, questionable or incomplete. Criteria related to this categorization can be found in Table  S1. Additional information regarding the phage sequences and their lineages is provided in Table  S5.
Bacillus virus 1, Bacillus phage PfEFR-5 and Staphylococcus phage vB_SepS_SEP9 sequences were detected across all of our samples. Bacillus virus 1 was determined to be intact in all isolates examined. Bacillus phage PfEFR-5 was determined to be questionable across most isolates, but incomplete for all isolates comprising primary cluster 1 (C Branch, N=5), while Staphylococcus phage vB_SepS_SEP9 was determined to be questionable across all isolates, but incomplete for all isolates comprising primary clusters 1 and 2 (C and B Branches; N=18). The eight remaining prophage sequences were scattered in comparatively minimal amounts across the global dataset, with the exception of Bacillus phage phBC6A52 which was intact in a large number of the isolates examined (N=91), with seemingly no link to relatedness or geography among isolates.
Explaining global genomic clusters with RF The RF model was trained using 5-fold cross-validation with a training dataset. The best model parameter (where Ntree and Mtry equalled 400 and 9 respectively) produced a cross-correlation rate that showed a high value of 83. 6, while kappa equalled 0. 764. Variables examined include presence of AMR genes and phage sequences, as well as sample source (details provided in Table  S2 ). With this combination of Ntree and Mtry, the OOB error based on the confusion matrix was 16. 89 %. Applying the model to the validation dataset and comparing the observations and predictions, the overall accuracy was 0. 861. The model always failed to predict primary cluster 1 (C Branch) for the validation dataset (N=1), and primary cluster 2 (B Branch) for both the training set (N=9) and the validation dataset (N=2), probably due to the reduced number of representatives comprising these clusters.
The AMR gene vmlR, the isolation source (host, environment or industry) and the continent of isolation were the most important variables in explaining genomic clusters across the entire dataset (Fig. 3a ). The variable importance based on the mean decrease in accuracy for each individual cluster is shown in Fig. 3b. The absence of AMR gene fosB was the strongest predictor for primary cluster 1 (C Branch), whereas the vmlR gene in primary cluster 2 (B Branch) acted as the strongest predictor. Nevertheless, both of these models exhibited negligible accuracy in the confusion matrix, suggesting more data are needed for accurate classification for these two clusters (Table  S6 ). In cluster 3 (Ancient A) the continent of isolation was the strongest predictor by a large margin, as the vast majority of the samples that make up this population were isolated in Africa.