dataesr/scientific-paragraphs-categorization huggingface dataset:
All analyses were performed in R (v. 3. 6. 1)  (Team, 2017). We utilized the dada2 pipeline (v. 1. 12. 1)  (Callahan et al. , 2016)  with default parameters and the following adjustments to increase the stringency of the analysis: we set the truncLen parameter in lterAndTrim to c(270, 260), maxEE to c(1,1) and truncQ to 1, and increased the minOverlap parameter in mergePairs to 20. We obtained a total of 50,016 Amplicon Sequence Variants (ASVs) in 4,403,068 nonchimeric clean paired-end sequences, of which 19,909 ASVs in 4,262,444 reads remained after removal of taxa with <25 reads across the full dataset. This removal of taxa of low prevalence or relative abundance is commonplace in microbiome studies to avoid contaminants and many microbial dimensions, focusing on the most relevant microbes in the dataset.
Rarefaction plots indicated that we captured su cient community diversity to assess bacterial inheritance (Figure  1  -gure supplement 2). We assigned ASVs with taxonomic ranks using default parameters in the assignTaxonomy function in dada2. Taxa were classi ed with the Dict_db v. 3. 0 database  (Mikaelyan et al. , 2015) , after which ASVs without genus-level taxonomic assignments were reclassi ed with SILVA v. 132  (Quast et al. , 2013). The cellular mock community validated the detection of all eight expected taxa. Negative control samples lacked DNA and failed library preparation according to BGI-Hong Kong's sequencing service, suggesting the very low levels or absence of contaminants. The package Decontam was run to identify potential contaminant taxa (v. 1. 10. 0)  (Davis et al. , 2018)  despite the lack of negative controls.
Mature queen samples from parental colonies contained exceptionally low numbers of 16S rRNA copies, while workers, founding reproductives, kings, and mature colony queens contained 10 3 higher concentrations of 16S rRNA copies (Figure  1  -source data 2), creating a broad variation in bacterial loads for package to effectively run. The Decontam package suggested 1. 3% potential contaminant reads across samples. We did not remove them from the dataset considering: 1) we removed taxa with <25 reads across the dataset to reduce the in uence of contaminants, 2) the worker and founding reproductive samples on which the majority of analysis were based on carry high microbial loads (Figure  1  -source data 2), minimizing any effect of contaminants, and 3) the potential contaminants represented a very low number of total reads and some were identi ed taxa were common in fungus-farming termite microbiomes  (Otani et al.
Network construction We calculated ASV association networks for the offspring colony worker samples to infer ecological relationships using the SpiecEasi package v. 1. 1. 0  (Kurtz et al. , 2015)  and the "mb" algorithm, lambda. min. ratio = 1e-3, nlambda = 300. Prior to network calculation, following good network practices, we removed rare ASVs with less than 200 reads across all offspring colony samples. To improve visualizations, we only included nodes with six or more connections in the Figure  4B. We handled graphs using igraph v. 1. 2. 5, network v. 1. 16. 0 and plotted them using ggnet v. 0. 1. 0 and ggnetwork v. 0. 5. 8. To understand ecological interactions amongst and between inherited and non-inherited taxa, we calculated the assortativity coe cient for the variable 'inheritance', using the function with the same name within igraph.
To do this, we created a linear model where all interactions, positive or negative, were the response variables and the xed effects were whether the ASV was inherited or not, the log of ASV abundance, and the interaction between the two. The latter was not signi cant and was consequently removed from the nal model. Random Forest models to identify major features predicting inheritance We tted Random Forest classi ers (RF) implemented in python's scikit-learn 66 to uncover which experimental characteristics predict inheritance. We focused on the impact of bacterial taxonomy, abundance in maternal colonies, and network features on the propensity for inheritance of an ASV. This was done in six different models based on dependent variables: observed and null model inheritance to founding reproductives, offspring colonies, and overall.
Each ASV was classi ed eight times (once per stage of inheritance and per pedigree) with each taxonomical level recorded (the null model was assigned a dummy binary variable), which, along with stage of inheritance, network features and abundance (normalized as above) were the independent variables. ASVs containing missing data in any features were removed. The contribution of each predictor to inheritance was estimated in utilizing Gini importance values and SHAP values (Shapley Additive exPlanations)  (Lundberg et al. , 2020). We randomly split each dataset into a training set comprising 80% of the ASVs and a test set comprising 20%. We used the training dataset to form a 10-fold cross-validated grid search for the best number of features used in each split, while xing the number of trees used to 500.
Acknowledgments We thank  Jacobus J. Boomsma ,  Robert R. Dunn ,  Kasun H. Bodawatta , and members of the  Social and Symbiotic Evolution Group at the University of Copenhagen  for constructive comments. We are grateful to  David Sillam-Dussès  and  Alain Robert  for eld assistance and  Sylvia Mathiasen  for laboratory support. This work was funded by a Ph. D. stipend from the  Department of Biology, University of Copenhagen  to V. M. S. , the  International Human Frontier Science Program   RGP0060/2018  to M. V. -C. , and a  European Research Council   Consolidator Grant  ( ERC-CoG 771349 ) to M. P.
, 2020, p. 11). Such responses suggest that firms are considering making use of the crisis to open up unthinkable or unfeasible business opportunities  (Roy et al. , 2018). As a result of the pandemic, firms are exploring new avenues for collaboration with customers, digitalising core competencies, shifting business models, and adding integrated resources to their customer databases  (Seetharaman, 2020). Exit strategies refer to the discontinuation of business activities, such as a temporary closure in the times of crisis  (Wenzel et al. , 2020). A crisis can cause unexpected devastation and challenges such as disrupted financial balance and untenable business models  (Dahles & Susilowati, 2015; Seetharaman, 2020). Businesses that cease operations are able to retain some resources and build a better recovery after the crisis  (Carnahan, 2017; Ren et al.
In Study 1, we firstly identified a list of UK hotels by adopting three criteria: 1) the company has more than 250 employees (i. e. non SMEs); 2) the hotel has an official Twitter account; and 3) hotels have posted at least one COVID-19 related tweets on their official account in the pandemic duration. After employing the selection criteria, a total number of 49 UK hotels were included in the research dataset. The data were collected with a coverage from March 1, 2020 (2 weeks before the official lockdown started on 16th March) to 31st July (before the ease of lock down and the Eat Out to Help Out scheme placed from 3rd to 31st August). A total number of 262 COVID-19 announcements for the UK hotels and 2953 consumer comments associated with these announcements were collected. We also provide an example below to show what information we have extracted from Twitter in this study (see Fig.
2 ). The purpose of Study 2 is to complement the results of Study 1 by further including the restaurant sector. In Study 2, we followed the same line of thought, identified a list of UK restaurants from the FAME database, and followed the three criteria as listed in Study 1 in company selection i. e. non-SMEs, with official Twitter accounts, and with COVID-19 tweets on their Twitter accounts. With a coverage from March 1, 2020 to July 31, 2020, a number of 47 companies were included, and their 211 associated Twitter announcements together with 12,252 consumer comments were collected.
We selected themes as the unit of analysis, looking primarily for the explanation or expression of a concept embodied in the tweet related to COVID-19 posted by tourism organisations. The level of analysis in this study is the restaurants and hotels that post their announcements on Twitter. The purpose of this coding process was to evaluate the different types of response strategy for each announcement. We then established a structured coding instruction which guided and trained coders until they reached a specific level of reliability. Following suggestions from Krippendorff (2018), the coding instruction contains an outline, examples of the coding procedures, a general standard for data sheets administration and usage, and the definitions and examples of different types of response strategies  (Krippendorff, 2018).
IBM Natural Language Processing could provide overall sentiment score on specific sentences (e. g. , posts or replies on Twitter) ranging from 1 (extremely positive) to -1 (extremely negative) with a score of 0 representing neutral  (Carvalho et al. , 2019). As suggested in the product recall crisis literature  (Hsu & Lawrence, 2016) , the most market reaction occurs within six days after the product recall announcement date. We thus chose a 6-day as the time length to collect customer emotion to examine the combined effects of response strategy and linguistic cue within this time length. In total, the dataset of Study 1 contains 2953 online user comments regarding 262 COVID-19 announcements within 6 days were collected. Organisational characteristics. Hotel and restaurant age is measured by the difference between the year of observation and year of business opening  (Madanoglu & Ozdemir, 2016).
In this study, fsQCA can help answer 1) Which of the theoretically possible configurations of COVID-19 response strategy and linguistic cues can be found in our data, and which cannot, 2) Which configurations are the most or least frequent in the data, and how often 1 A list of 56 stative verbs can be found at https://www. perfect-english -grammar. com/stative-verbs. html. S. Li et al. do cases appear in those configurations, and 3) What COVID-19 response strategies, or combinations of response strategies and linguistic cues, should be adopted in order for positive emotion to occur. We followed Fiss (2011)'s three stages in the fsQCA analysis. We firstly calibrated measurements using membership scores in the first stage. Since all causal conditions are categorical data, they are transformed into crisp datasets in this study.
For the use of Pronoun (noted as "Pronoun"), frame with community identity is calibrated as 1 while frame with individuated identity is calibrated as 0. For the use of assertive language (noted as "Assertive"), assertive language frame is calibrated as 1 while non-assertive language frame is calibrated as 0. The outcome measurement of consumer emotion is regarded as fuzzy dataset, since it shows a varying degree to which consumers' emotional change towards the crisis response. We used Pineiro-Chousa et al. (  2016 )'s recommendations to assign full membership score to sentiment values above 0. 3, full non-membership score to sentiment values below -0. 3, and the crossover point to sentiment values around 0 (between -0. 3 and 0. 3). The second stage consisted of reducing truth table rows according to conditions of 1) minimum number of cases required for an outcome (i.
e. frequency threshold) and 2) minimum consistency level that represents that outcome (i. e. consistency threshold)  (Ragin, 2008). Fiss (2007)  notes that configuration with at least one case is considered as empirically pertinent, and  Ragin (2008)  argues that frequency threshold set as 1 is appropriate in small-N settings but should be set higher in large-N settings: for instance,  Pappas et al. (2016)  set 3 for a large dataset. Consistency indicates the degree to which cases under each combination of causal conditions are corresponded to the set theoretic relations in a solution  (Rihoux & Ragin, 2008). As  Ragin (2008)  recommended, the minimum threshold as the lowest acceptable consistency for solutions is 0. 75. To address the problem of limited diversity in configurational analysis, the third stage involved reducing the truth table to a simplified combination through counterfactual analysis.
General discussion Using real-world data from the UK restaurant and hotel industries, this study empirically examines how response strategy, linguistic cues, and organisational characteristics interact to create positive emotional reactions. Our study is among the first to use the fsQCA method for offering new insights into the complicated formulation of crisis responses. Our results extend some well-established findings in the tourism crisis literature by suggesting that combining innovative response strategy, argument quality, and assertive language can reinforce positive emotions. In the following sections, we discuss the theoretical and practical implications.
Different turbulence surrogated fields were thus proposed, e. g. , turbulent intensity, and then closure coefficients describing the transport of these fields were fitted from the experimental database of flows. Nonetheless, it was soon realized that the Boussinesq hypothesis was not accurate in many flow problems, e. g. , mixing, boundary later detachment, etc. , and research proceeded towards the development of algebraic Reynolds stress models  [12, 13]. In these models, an algebraic equation was determined for the components of the turbulent Reynolds Stress Tensor (RST), which provides the model the ability to capture the anisotropy and second-order diffusivity effects due to turbulence  [14]. Nonetheless, these models are still very dependent on the hypothesis made by the algebraic closure equation and thus on the flow field for which the algebraic stress balance was calibrated.
Moreover, with adequate extrapolations, it can be analyzed using standard methods. Nonetheless, it introduces two new drawbacks. Firstly, contrary to neutral networks and due to the randomness of genetic algorithms, a different "optimal" turbulence model is generally obtained for the same dataset at each new evaluation of the genetic optimization. This generates uncertainty on the appropriateness of the models learned. Secondly, standard genetic algorithms frequently converge to local minimums. Hence, different tailored variations need to be introduced to the standard process for trying to enforce its convergence to a global minimum  [23]  and not just a local one. This modification is frequently not well adapted for turbulent model optimization. In the present work, we improve upon our previous attempts using genetic algorithms by proposing a new method that solves these standard issues of genetic algorithms.
For this purpose, we developed a deterministic method to automatize the fitting of the structural form of the turbulent anisotropy tensor T. It is important to remark that this optimization model deals exclusively with the structural form of the anisotropy and thus allows us to retain standard models (i. e. , simple models) for the turbulent viscosity. Since the coefficients of ν t for standard turbulence models have already been calibrated on a large database of problems, we choose not to change them since this would change the applicability of the calibrated model. For example, modifying the fitting coefficients in the standard kmodel for calculating k, and ν t will lead to suboptimal predictions of the return to isotropy in turbulent shear flows and in the decay of turbulent kinetic energy in isotropic turbulence.
e. , T (q,j) r = f (T 1 ,. , T 10 ). As depicted in Figure  2 , the optimization procedure goes as follows. First, an initial random population of N models T (q,1) for the turbulence anisotropy tensor is proposed (Step A). Using a standard turbulence model in the literature, e. g. , the kturbulence model, and the description of the turbulence anisotropy tensor provided by each candidate, we compute the flow field predicted by each candidate. The predicted flow field for each candidate is then compared against a reference flow field (e. g. , from an experimental database) and the constants in each c (q,1) r function are fitted to minimize the difference between the predicted and reference flow field (Step B). This leads to a series of fitted candidates, which are ordered in an array from lower to higher fitting error.
Definition of the Error Metric Each of the generated turbulent candidates is then implemented in the RANS averaged Navier-Stokes Equations (3), (  4 ) and (  6 ), and a simulation is performed to yield an averaged velocity field u (q,j) (x, t), for model candidate q at iteration optimization iteration j. The predicted velocity field is then used for the definition of an error metric against available flow data for optimizing the candidates. This error metric is defined as follows: L (q,j) w = f u data (x, t) -u (q,j) (x, t) + µ l l MAX , (11) where L (q,j) w is the error function for the current model candidate, u data (x, t) is the velocity of the dataset used for calibrating the turbulent model, • is some scalar metric on the difference between the velocity fields, and f is a function of this metric.
In mutation operations, the complement flag is composed of a signaling number indicating the position of mutation, the length of the term mutated and a set of numbers indicating the mutations that should be introduced in the bases or in the constants. Finally, the complement flag in the recombination operation is composed of two numbers that signalize the terms combined and a third signalization number that indicates the position in the first term where the second term needs to be introduced. All models are represented through Open Reading Frames (ORFs) for simplicity when performing Bayesian optimization. We then use the Bayesian optimization implemented in the scikit-optimize Python library  [37]  to find optimal values for the TOTC flags during the optimization process. During the Bayesian optimization process, for each new model created in each Bayesian iteration, we fit the constants and insert it in the priority stack according to its fitting error.
A proportional-integral control system was designed in the LabVIEW software  [41]  to fix the flow velocity which was measured by an ultrasonic flowmeter right after the exit of the upstream tank. The action of the control system was an automatic pressure release valve in the downstream tank. An outward pipe of a length of 2. 0 m was used for the inlet to the test section to ensure that the flow field was fully developed before entering the Backward-Facing Step (BFS). The section was built in transparent, low-rugosity plexiglass to allow for optical measurements. Thus, a developed Poiseuille flow boundary condition can be assumed in the inlet of our simulations, no rugosity zero velocity conditions have been applied to the enhanced wall treatment, and standard a pressure-outlet condition has been assumed for the section's outlet.
A Particle Image Velocimetry (PIV) technique was used in the BFS test section to measure the flow field. In our PIV setup, the laser light sheet entered the channel by its top part and the high-speed, high-resolution camera was placed in front of the test section. A squared grid-pattern metal sheet with a spacing of 1cm was placed behind the test section for calibration and correction of the light diffraction errors during the data postprocessing. The measurement procedure was carried out using an overlapping sliding window technique. Each of the overlapping measurement windows used is depicted in Figure  7c. Time measurements were set between 2-5 min per window, which allowed us to obtain steady time-averaged profiles when postprocessing the measurements. An example of the postprocessed PIV measurements for Re = 1111 is presented in Figure  7d.
Conclusions In the present work, we have proposed a method based on genetic algorithms for the deterministic development of explainable, data-driven turbulence models. This method performs the structural optimization of the Reynolds anisotropy tensor with respect to a high-fidelity dataset, while it preserves the baseline models for the turbulent viscosity. Moreover, the integration of a Bayesian optimization loop for determining the order of the genetic operations in our optimization method guides the convergence of the method towards the global minima. The convergence to a global minimum has been observed for high and low aspect-ration backward-facing step flows. However, the convergence to a global minimum in more complex flow configurations, such as the one in the MSFR, must still be demonstrated.
The absence of correlation between SHM and nascent transcription is a global feature of SHM To extend our analysis to non-IG AID targets, we made use of a previously available SHM dataset from murine GCBs where mutation frequencies at 275 AID target genes were identified by deep sequencing the first 500 bp from the annotated transcription start site (TSS)  (17). In this study, mutation analysis was performed in GCBs from mice deficient in base excision and mismatch repair pathways (Ung -/-Msh2 -/-). In these mice, the processing of AID-induced U:G mismatches is abolished and, consequently, DNA replication over these lesions leads to CT and GA transition mutations that represent the direct targets of AID  (78). By comparing these data with PRO-seq and PRO-cap obtained from GCBs, we could compare mutation profiles with those of nascent transcription.
Importantly, at many genes, initiation sites or zones defined by PRO-cap are often located at considerable distances from the annotated TSS (defined by the RefSeq database and labeled as TSS in Fig. 5D , Fig. S3  and Fig. S4 ). Consequently, the 500 bp segment sequenced for mutational analysis  (17)  often begins further upstream or downstream of the initiation sites newly defined by PRO-cap. The large number of genes in this analysis led to new and unexpected observations regarding the nature of AID targeting. At virtually all genes, the differential mutation rates of WRCH motifs are evident with many such motifs being unmutated and many non-WRCH cytidines being as efficiently targeted (Fig. 5D  and several additional examples in Figs. S3  and S4 ). As we observed in variable regions, mutation frequencies at individual nucleotides and mutation zones did not necessarily correlate with enrichments of PRO-seq or PRO-cap (Fig.
The native loci were defined as the region between the earliest position belonging to an annotated V segment and the latest position belonging to an annotated J segment. Reads with a mapped location outside these areas were rejected. This filtering was implemented in a custom Python script. The qualifying reads were then classified as reads mapping uniquely to the particular variable region sequence and reads mapping to both the variable region sequence and to the native IGH/Igh region. Genome browser tracks were created by quantifying and scaling read coverage of the variable region sequence using bedtools ((109) v2-29. 0). Reads were split by strand, and strand labels for PRO-seq were inverted in order to match the strand designations of the respective PRO-cap reads. For both data types, only the first 5' base of the strand-adjusted reads was used for the tracks.
Additionally, the 5' end of PRO-seq reads was shifted by 1 nucleotide downstream, to compensate for the fact that the last nucleotide in the RNA was incorporated during the run-on reaction. For normalized tracks, read counts were scaled to RPM (reads per million). Finally, "-" strand coverage tracks were further scaled by -1, for visualization purposes. Code for the workflow and the custom scripts is available on Github at https://github. com/PavriLab/IgH_VDJ_PROcapseq.