dataesr/scientific-paragraphs-categorization huggingface dataset:
The most rigorous approach is the time-independent close-coupling (CC) formalism, developed originally by Arthurs and Dalgarno  [3]  for inelastic atom-molecule scattering. From a knowledge of the PES for a given system, the Hibridon package provides the means to solve these close-coupled equations to obtain dynamical quantities of interest. Section 2 provides a brief description of the underlying scattering theory and the subsequent calculation of cross sections. Sections 3 and 4 describe the treatment of bound states and photodissociation, respectively. Section 5 discusses in more detail the types of collision systems that can be handled by Hibridon. Sections 6-9 provide information on the installation and use of Hibridon. The name Hibridon arises from the use of the hybrid integration scheme described in more detail below.
The original program package was first referenced in 1987  [4]. Over the years, many enhancements have been added, including the addition of a number of basis routines. In addition to the determination of integral and differential cross sections, the code treats photodissociation and allows the calculation of tensor, transport, and pressure broadening cross sections. Cross sections calculated by Hibridon have been successfully compared with those obtained using the widely employed Molscat program  [5]. This includes calculations on the CO-He and CO-H 2 systems for which the cross sections calculations by the two program suites differ by ∼ 1%. A recent check in Google Scholar revealed that since its introduction Hibridon has been referenced in over 350 scientific publications and in 10 review articles.
The Hibridon package has been utilized in the past successfully to guide and interpret experimental results. For example, in the case of the open-shell molecule of 2 character, Nizamov et al. [6]  studied experimentally and theoretically using Hibridon collisions of CN(A 2 ) radical with He. Later, de Lange et al. [7]  used Hibridon to compute -doublet propensities and steric asymmetry cross sections that compared well to the experiment.
Here, also V (R) is the total quasi-diabatic potential matrix, which for the case of closed-shell system described by a single Born-Oppenheimer potential operator is equivalent to the adiabatic potential. The diagonal matrix elements of k 2 and l 2 matrices are (k) 2 ii = 2m h2 (E -ε i ), ( 4 ) where E is the total energy, ε i is the internal energy of the i th channel and (l) ii 2 = h2 2mR 2 l i (l i + 1). (5) Here, l i is the relative orbital angular momentum of the i th channel. Note that we assume that the potential matrix vanishes as the particles separate, namely lim R→∞ V (R) = 0. (6) Because of the importance of inelastic collisions in a wide variety of physical phenomena, a number of techniques have been developed to solve the close-coupled equations (Eq. (  2 ))  [8]. The Hibridon package incorporates a hybrid integration scheme  [9].
At short range (small R) a solution-following method  [10]  is used, based on the log-derivative algorithm of Johnson  [11] , and later modified by Manolopoulos  [12]. In the Hibridon package, this propagator is designated LOGD. At long range, a potential-following method  [10]  is used, based on the linear-reference potential algorithm of Gordon  [13, 14] , and later modified by Alexander and Manolopoulos  [9, 15]. This propagator is designated AIRY. The Hibridon code combines these two propagators (LOGD and AIRY), which are both numerically stable and computationally fast. The numerical stability is achieved by propagation of the logarithmic derivative of the solution matrix F(R), namely Y(R) =F (R) F(R) -1 , ( 7 ) rather than the solution matrix F(R) itself. The speed arises from the ability of the AIRY propagator to take increasingly larger steps at long range.
Bound state energies and wavefunctions The Hibridon package uses a variational method based on the distributed Gaussian basis approach of Hamilton and Light  [40]  to determine energies and wavefunctions of bound states. Each element of the solution matrix, F ij (R), is expanded in terms of a set of functions in the separation coordinate R F ij (R) = M m=1 C ( j) mi χ m (R). ( 25 ) As in any standard linear variational technique, the energy and eigenfunction of the n th bound state is obtained by diagonalization of the matrix of the full Hamiltonian H(R) = T(R) + W(R), where T(R) is a radial kinetic energy operator, in the χ (R) basis. The dimensions of this Hamiltonian matrix are M × N ch , where M is the dimensionality of the χ (R) basis and N ch is the number of internal states (channels).
In the Hibridon package, the uniformly distributed Gaussian basis suggested by Hamilton and Light  [40]  is used, in which χ m (R) = e -α(R-R m ) 2 , ( 26 ) where R m is the midpoint of the m th Gaussian function. The Hibridon code will calculate bound-state energies if the flag BOUNDC is set. TRUE. This set of χ (R) functions is defined by several input parameters, as follows: 1. R1: smallest value of R m 2. R2: largest value of R m 3. SPAC: spacing between successive values of R m 4. C: parameter which determines the exponential scale factor of the distributed Gaussian functions, with α = (C/SPAC) 2. 5. EIGMIN: lower limit on the minimum allowed eigenvalue of the overlap matrix. If the minimum eigenvalue is less than this value, the parameter C should be increased.
Photodissociation The Hibridon code can also treat photodissociation, in a time-independent manner. The homogeneous set of close-coupling scattering equations [Eq. (2) ] are replaced by a similar set of inhomogeneous equations  [41] [42] [43] :  (27)  which are solved subject to the boundary conditions lim R→0 F(R) = 0,  (28)  and [I d 2 dR 2 + W(R)F(R)] = χ (R), lim R→∞ F(R) = O (R) τ. ( 29 ) Here O (R) is a diagonal matrix of flux normalized outgoing wave components in each fragment channel, the elements of which are related to the outgoing Ricatti-Hankel functions defined in Eq. (10) , by  [42]  O ii (R) = (hk i /m) ĥ(1) l i (R). ( ) 30 The i th row of the column vector τ gives the amplitude for dissociative excitation into internal state i of the fragments. In the case of photodissociation Eq.
As discussed in Sec. 2, the hybrid (LOGD + AIRY) propagation scheme  [9]  makes use of a potential-following method at larger R, which allows a dramatic increase in sector width. The determination of cross sections (and, subsequently, rate constants) requires integration of the coupled equations at many different values of the total angular momentum J and energy. This allows for straightforward parallelization. Hibridon can be run interactively, setting parameters in terminal mode and running short jobs. Alternatively, the program can be run in batch mode with a job script file. In either case, it is convenient to enter the parameters through an input file (*. inp); an example of an input file is given in Appendix A. 2.
Incorporating a PES Hibridon is designed to be used as a library with a user-supplied PES. Notwithstanding, for test purposes and to help in incorporating a user's PES, several PESs are included in the Hibridon package. The coordinates used to describe the geometry of the PES of the collision complex should be appropriate for the calculation of matrix elements of the vibration-rotation scattering basis functions. Usually, only the radial coefficients of the expansion of the PES are required and should be carefully implemented in the code. For each PES, the user is expected to provide three subroutines: loapot: Called only once, this subroutine's purpose is to initialize the PES data, possibly involving loading data from a file, pot: Computes the radial coefficients for a given interparticle separation distance R, driver: this subroutine is used upon the summoning of the TESTPOT command and provides an interactive prompt that prints the value of the radial coefficients for a given interparticle separation distance R.
Distribution of the code The Hibridon package is fully available as a Github repository  [44]  under the GPLv3 license  [84]. The compilation of the source files is cross-platform, compiler independent and automated using the cmake open-source software. Full instructions on how to build the Hibridon package can be found in the README. md file distributed along the source code. The proper building of Hibridon can be checked through automated testing using the ctest tool from the cmake software. Those tests cover most of Hibridon's source code and ensure that a given installation behaves as intended. Each of these tests also represent fully working examples of PES implementations, input files, and expected output files that are accessible in the tests subfolder at the root of Hibridon's source code.
Acknowledgements The authors wish to thank particularly  D. E. Manolopoulos  for his essential mathematical contributions to the two propagators discussed in Sec. 2 and to the application of Hibridon to photodissociation, discussed in Sec. 4. Other key collaborators were  A. Berning ,  G. C. Corey ,  A. Degli-Esposti ,  F. de Weerd ,  B. Follmeg ,  S. Gregurick ,  R. Gordon ,  B. R. Johnson ,  D. Lemoine ,  T. Orlikowski ,  B. Pouilly ,  C. Rist ,  G. van der Sanden ,  M. Yang , and  P. F. Vohralik. Support  for the development of Hibridon was provided by grants (to MHA) from the  U. S. National Science Foundation , the  U. S. Army Research Office , and the  Office of Scientific Research of the U. S. Air Force , (to HJW) by the  German Fonds der Chemische Industrie , as well as (to FL) from the  European Research Council  ( Consolidator Grant COLLEXISM, Grant  Agreement No.
01) and we recorded the male proportion at equilibrium. We made linear regressions and we tested the effect of predation pressure on the proportion of males at the equilibrium, using python packages scikit-learn ver. 0. 24. 1  (Pedregosa et al. , 2011)  and seaborn ver. 0. 11. 1  (Waskom et al. , 2017). Furthermore, we explored the effect of the direct protection provided by the sting for females (driven by 𝛼) as well as the cost of male proportion on predator learning (driven by 𝛽) on the population equilibrium. These two parameters are linked to the harmfulness of females and the investment in son production, and are specific to the Aculeata model. We chose values of λ1=0. 02 and p=0. 6 for which the population was maintained in the first experiment, then we varied 𝛼 within [0,10] with a step of 1, and 𝛽 within [0,1] with a step of 0.
Running simulations Simulations were performed using Python ver. 3. 8. 8 (Van Rossum & Drake, 2009)  and differential equations were solved using the function odeint from the package Scipy ver. 1. 6. 2 (Virtanen et al. , 2020). The scripts are available on the following link: https://zenodo. org/badge/latestdoi/553618533. We ran simulations during a number n of time intervals with Δt = 50 for each interval and a time-step of 0. 1, which makes 500 times values per interval. Simulations were stopped when population densities varied less than 10 -4 between two time intervals. Then a population was considered extinct at equilibrium when female (and male) densities were under 10 -3. Note that the equilibria obtained were the same when using the value 10 -6 as a threshold. The data were analysed and visualised using the packages Pandas ver  1.
| Sperm velocity measurement Sperm velocity assessments were realized thanks to videos processed using a Computer-assisted Sperm Analysis system called OpenCASA (available at https://github. com/calqu ezar/OpenCASA;  Alquézar-Baeta et al. , 2019). OpenCASA is a plugin to the opensource software Fiji (free software, https://imagej. net/softw are/fiji/ downl oads) that proved to be efficient to evaluate sperm parameters in fishes  (Sanches et al. , 2013). As advised by  Cosson (2019) , a dark field optical microscopy with a high magnification (400×) objective lens was used to record the videos. To prevent sperm from sticking to the microscope blade, a 0. 5% solution of bovine serum albumin (BSA) in distilled water was used. For every male, each 1 μL subsample of sperm was activated with the BSA solution at room temperature.
As a result, the average time we used to start the records was very short compared with the total duration of sperm motility. Sperm movements were registered with a Nikon D7500 camera adjusted on a Leica Leitz DMRB microscope (GMBH Germany 541,006). The recording speed was 60 frames per second. Videos were collected using Microsoft Photos (free software, https://www. micro soft. com/ enus/p/micro softp hotos/ 9wzdn crfjb h4?activ etab=pivot :overv iewtab) and cut just after microscope focusing to keep 3 s for analyses and exported in mp4 format. In order to have an uncompressed avi format for OpenCASA, videos were formatted in the open-source software PuTTY using the command "ffmpeg -i Video_name. mp4 -f avi -vcodec mpeg convertedFile. avi. " The videos were then opened in Fiji and analyzed using the OpenCASA plugin according to the procedures described by  Alquézar-Baeta et al.
Fertilization success of eggs exposed to sperm of both ecotypes was analyzed using GLMMs in the lme4 package or the MASS package when there was overdispersion. For L. planeri eggs, a GLMM with a binomial distribution and logit-link function was used. For L. fluviatilis eggs, a GLMM with a quasibinomial family and logit-link function was used because of overdispersion. The models included the male ecotype (LP or LF) as a fixed variable and female identity as a random effect. The significance of the fixed variable was determined using a likelihood-ratio chi-square test. To analyze embryo viability, a GLMM was performed with a quasibinomial family and logit-link function because of overdispersion (for both L. fluviatilis eggs and L. planeri eggs). The model included the cross-type (within-ecotype or between-ecotype) as a fixed effect and female identity as a random effect.
Sperm speeds were scaled to zero mean and unity standard deviation to avoid spurious correlations between intercept and slope. 1 is the random effect associated with the female identity, and 2 is the random effect associated with the male pair. For fixed effects the priors followed t distribution while random effects followed a Gaussian distribution: We estimated the parameters using JAGS v4. 3. 0  (Plummer, 2003)  implemented in R v3. 6. 2 through the package jagsUI that relies on the packages rjags and coda. We ran four independent MCMC chains of 200,000 iterations each with a burn-in of 100,000, and thinned chains at a period of 10 iterations. The convergence of the MCMC chains was assessed using the trace plots (Figure  S2 ), Gelman-Rubin R statistic  (Gelman & Rubin, 1992 ) and a Bayesian p-value  (Gelman et al.
The cap8 genotype is present in almost any type of host in a study involving isolates of mastitis S. aureus in cattle and goats  (Acosta et al. , 2018; Sheppard, Guttman and Fitzgerald, 2018; Oget, Tosser-Klopp and Rupp, 2019). At the genomic sequence level, although the number of complete S. aureus genomes deposited in the NCBI genome database has increased considerably in recent years, most are related to human isolates. More recently, only three ovine strains (ED133, NCTC1803, and NCTC9555) have been isolated from mastitis  (Guinane, ben Zakour, et al. , 2010). The information about peculiarities of the genome of small ruminant isolates is rare, and there is no knowledge of the genomic characteristics that may be involved in the onset of mastitis in sheep (Aires-  de-Sousa et al. , 2007b; Peton et al.
Sequencing, assembly, and annotation of the ovine S. Aureus genomes Sequencing of all total DNA the strains were performed using Hiseq 2500 platform, 150 bp, paired end (Illumina, San Diego, CA, USA). The two genomes previously sequenced (O11 and 046) was obtained with using an Illumina Genome Analyzer GAII (Le  Maréchal, Hernandez, et al. , 2011). The reads quality was checked with FastQC (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/). SPAdes, version 3. 9. 1  (Bankevich et al. , 2012) , was used for the ab initio assembly of all genomes, including O46 and O11. CONTIGuator version 2. 7  (Galardini et al. , 2011)  generated scaffolding. The gaps resulting from assemblies were manually filled using the CLC Genomics Workbench software, version 7. 0 (https://www. qiagenbioinformatics.
Genome average nucleotide identity analysis in the selected S. aureus strains To determine the genome similarity among the ovine strains and the strains isolated from various hosts, we used the Average Nucleotide Identity (ANI) analysis, specifically ANIm, based on MUMmer calculations  (Konstantinidis, Ramette and Tiedje, 2006; Kim et al. , 2014a). JSpeciesWS Online Service Taxonomic Thresholds (available at jspecies. ribohost. com/jspeciesws/) (Richter et al. , 2016)  were used with BLASTn  (Altschul et al. , 1990). This analysis method performs pairwise comparisons through the identity and similarity values implemented  (Goris et al. , 2007). The similarity heatmap was screened using Morpheus software (available at https://software. broadinstitute. org/morpheus/).
Pangenome analysis of ovine mastitis-associated S. Aureus genes and proteins Bacterial Pan Genome Analysis Tool (BPGA) version 1. 3  (Chaudhari, Gupta and Dutta, 2016a)  determines the ovine's core, accessory, and singleton genes. In addition, a group of complete genomes isolated from other hosts was added to the dataset for comparative purposes of the presence and absence of genes between hosts. This work considered two groups of genomes for pangenome analysis. The first group comprised twelve ovine strains (isolated from ovine, sequenced and assembled from herds in France), and three more ovine strains (ED133, NCTC1803, and NCTC9555) genomes sequences were downloaded from NCBI for the analysis. Moreover, genomes sequences of fifty-eight strains of S. aureus isolated from different hosts were retrieved from the NCBI database.
Genome plasticity analyses in the selected S. aureus strains The Genomic (GEIs), Pathogenicity (PAIs), and Symbiosis (SIs) Island predictions were performed using GIPSy (Genomic Island prediction Software), version 1. 1. 2 (Soares et al. , 2016) , with the genome of Staphylococcus warneri strain 16A (CP031269. 1), retrieved from the NCBI genome database, used as the reference. BLAST Ring Image Generator (BRIG), version 0. 95  (Alikhan et al. , 2011) , was used to generate a circular image of all ovine genomes with the predicted prophages, GEIs, and PAIs. Similar to the two clades (CC133 and CC130), strains 0268 and 082 were used (Table  S1 ). The PHASTER  (Arndt et al. , 2016)  was used to predict and annotate prophages in all strains' genomes. Considering the S. aureus methicillin resistance presence, we tried to identify the cassette chromosome mec (SCCmec) elements  (Zafalon et al.
, 2018). A search for SCCmec IV, a cassette chromosome carrying the mecA or mecC gene, was performed by SCCmecfinder version 1. 2  (Kaya et al. , 2018). For this analysis, the thresholds of 90 % and 60 % for minimum identity and minimum length were used to compare the genome against the reference database  (Kaya et al. , 2018).
Resistance genes analysis in the ovine S. aureus strains The Abricate (https://github. com/tseemann/abricate) tool was used to mass screen the genomes to find resistance: NCBI AMRFinderPlus  (Feldgarden et al. , 2019)  CARD  (Jia et al. , 2017) , Resfinder  (Feldgarden et al. , 2019) , ARG-NNOT  (Gupta et al. , 2014)  and MEGARES 2. 00  (Doster et al. , 2019)  and for virulence genes, using the Virulence Factors of Pathogenic Bacteria (VFDB)  (Chen et al. , 2016).
Using 50 % sequence identity as the cutoff value, the BPGA yielded 4200 distinct gene clusters. Out of which 2008 clusters respectively contain at least one gene from each genome and thus construct the core genome, 1559 of these genes are accessory genes (i. e. , genes that are present in at least two genomes but not in all the genomes) and 634 unique genes (exclusive genes) of S. aureus. The genome statistics, such as the gene family distribution and new gene distribution of the S. aureus pangenome, are shown in Fig. 3. In addition, the number of genes unique or exclusively present and absent in each strain of the S. aureus dataset is shown in the last two columns of Table  S2. At the level of functional genome analysis, the genes belonging to each group's core, accessory, and exclusive genomes were categorized into cellular processes and functions by Cluster of Orthologous Groups (COG) distribution (Fig.
aureus dataset may help identify host-specific or virulence-specific genes in S. aureus. The results achieved for gene family distribution in these two subsets are shown in Table  S3  and Table  S4. We observed the presence of unique genes in sheep strains. The genomes that presented the most exclusive genes were the genes corresponding to the complex clonal singletons, the SO55, SO217, and SO331 (72, 54, and 41) exclusive genes, respectively. The functional characterization resulted in products such as hypothetical proteins (39. 6 %), phages (6. 4 %), transporter (2. 9 %), toxins (6. 4 %), and others (44. 7 %). A very high percentage of exclusive ovine S. aureus genes encode proteins with unknown functions, evidencing the need for further studies. Also, genes encode proteins involved in different cellular functions, such as iron acquisition proteins, transferases, DNA helicases, and coagulases.
Interestingly the exclusive ovine genes are found in prophages and mobile genetic elements, suggesting that many of the genetic traits related to the ovine host specificity might be horizontally disseminated. 4. 6. Virulence genes in the S. aureus strains Eighty genes were observed, considered encoders of the virulence factors, and 57 to 72 genes were present in all seventy-three genomes. All virulence proteins predicted using the VFDB database are listed in (Fig. 4a  & 4b ). The exciting thing is the sharing of genes related to the type of clonal complex, regardless of its type of host. However, there are some signatures regarding the suborder, as in the case of ruminants and small ruminants (Fig. 4b ). When analyzed at the ovine subgroup level, we observed 59 to 70 virulence genes. CC130 and CC133 (large ovine clusters) have a repertoire of similar genes compared with singletons: CC30, CC59, and CC425.
Resistance genes in the ovine S. Aureus strains The analysis of the presence and absence of resistance genes was performed via ABRicate with the support of the five databases and other relevant data available in PATRIC, such as AMR phenotypes for genomes, AMR genes, and AMR regions, aimed at predicting resistance to some antibiotic (Fig. 5  and Fig. 6 ). ABRicate predicted 37 genes related to some antimicrobial resistance for the 73 genomes. PATRIC is classified into eight classes of antibiotic drugs (ciprofloxacin, clindamycin, erythromycin, gentamicin, methicillin, penicillin, tetracycline, and trimethoprim-sulfamethoxazole) from a database containing 97 antibiotics. Analyzing the repertoire of resistance-related genes for ovines, each database yielded different predictions, with some of these genes varying in nomenclature.
Discussion S. aureus is an important pathogen of ovine mastitis (Le  Maréchal et al. , 2011; Smith et al. , 2014). This infection is difficult to control since S. aureus can spread within the herd and become resistant to antibiotic treatment  (Oviedo-Boyso et al. , 2007). In addition, the number of ovineassociated S. aureus genomes available in the NCBI database and genomic studies involving these strains are scarce. Therefore, more information about S. aureus genomic characteristics is necessary to efficiently develop new strategies to control infection  (Peton et al. , 2014). The isolates from France are of different herds, cities, and years, yet they share a high similarity. For example, the strains SAR475 and O331 are grouped in the MLST analysis. Considering that SAR475 was isolated from humans and O331 from ovine mastitis, this result shows that these two strains are closely related.