dataesr/scientific-paragraphs-categorization huggingface dataset:
Code implementation, dependencies, and availability The code was ported to Python 3 (>=3. 7). Many unit and functional tests were implemented to reach a coverage of the code of 97%. In terms of dependencies, the program requires the HMMER suite (>=3. 1b2) for the search of proteins. It also uses several well-established and stable Python libraries to facilitate models' packaging (pyyaml, packaging), deal with output files (colorlog, https://pandas. pydata. org/), and search for the best solution (https://networkx. org/, see below). The code and macsy-model packages are available on Github under the GPL v3 license: https://github. com/gem-pasteur/macsyfinder and https://github. com/macsy-models. In addition, a pypi package, a conda package and a Docker container were created to enable the easy deployment of the MacSyFinder suite.
In addition, the 9 genomes were large, encoding between 4053 and 12492 genes. For this reason, we introduced a "-timeout" option (not set by default) to stop the search after a user-defined amount of time. It can be suitable for users who have a collection of genomes to analyse and do not want to be delayed by one, while other users may be interested in obtaining a result even if this means longer run times. The second bottleneck in MacSyFinder is the search of genes with HMMER (hmmsearch) that consists in the most computationally intensive part of a MacSyFinder run (Fig. 5C ). The timing of this step grows with the number of genomes in the dataset ("gembase" search mode). To tackle this problem, the user can benefit from available multi-core CPU to search several genes in parallel with the "-worker" option.
Using this option, the time spent in the genes search stage decreases significantly, even with a dataset containing a single genome. This technique is very efficient for macsy-models containing numerous genes, such as TXSScan (340 genes) (Fig. 5C ). In addition to the multi-core implementation for the genes search, we designed and made available a parallel version of MacSyFinder that consists in a workflow based on NextFlow (https://www. nextflow. io/) recommended for the analysis of datasets containing many genomes and easily deployed on a computer cluster. The memory footprint of MacSyFinder v2 for the analysis of one genome is around 100 MB of RAM (with the heavy TXSScan macsy-model), and remains very contained (<500 MB) even when analysing hundreds of genomes ("gembase" mode). This relatively low memory footprint is mainly due to the NetworkX's maximal clique search algorithm, which is implemented as a generator that never stores all cliques in the memory at once (Fig.
B. Although the algorithm runs in exponential time, the broad majority of the runs complete the best solution resolution within 1 second. C. The mean time spent by MacSyFinder searching genes with hmmsearch (HMMER) is large compared to the other parts of the program, and grows with the number of genomes in the dataset ("gembase" mode). However this time can be significantly reduced when using multiple workers. D. The mean maximum resident memory used by MacSyFinder v2 is typically 100MB to analyse one genome and this grows with the size of the dataset to analyse, while being still manageable on a laptop, with less than 500 MB used for 500 genomes analysed. For the 1, 10, 20, 50, 100, 200 and 500 genomes datasets (panels C and D), respectively 1500, 150, 75, 30, 15, 7 and 3 non-redundant replicate datasets were sampled and analysed.
Acknowledgements The authors are grateful to  Yoann Dufresne  for the suggestion to use the NetworkX library to address the weighted maximum clique search problem. The authors thank  Amandine Perrin  for providing the genomes' lists for the performance tests. This work used the computational and storage services (TARS cluster) provided by the  IT department at Institut Pasteur, Paris. Preprint version 2 of this article has been peer-reviewed and recommended by  Peer Community In Genomics  (http://doi. org/10. 24072/pci. genomics. 100233).
Funding EPCR lab acknowledges funding from the  INCEPTION  project ( ANR-16-CONV-0005 ),  Equipe FRM (Fondation pour la Recherche Médicale ):  EQU201903007835 , and  Laboratoire d'Excellence IBEID Integrative Biology of Emerging Infectious Diseases  ( ANR-10-LABX-62-IBEID ). SSA received financial support from the  CNRS  and  TIMC lab (INSIS "starting grant ") and the  French National Research Agency , " Investissements d'avenir" program   ANR-15-IDEX-02.
Data, script and code availability MacSyFinder Github repository is available here: https://github. com/gem-pasteur/macsyfinder and the hereby presented release version 2. 1 here (Néron et al. , 2023) and here at the Software Heritage Archive at the following permalink: https://archive. softwareheritage. org/swh:1:snp:be6f94d82f11702214af35de13a426a280c77353;origi n=https://github. com/gem-pasteur/macsyfinder. The hereby macsy-model packages are available from the following Github organization: https://github. com/macsy-models. Two sets of examples with corresponding command lines and expected input and output files are provided on the Figshare platform: https://doi. org/10. 6084/m9. figshare. 21581280 (Abby, 2022a)  and https://doi. org/10. 6084/m9. figshare. 21716426. v1 (Abby, 2022b).
In relation to SC, the tortuous route corresponds to diffusion of a hydrophobic molecule in a membrane with impermeable corneocytes. The present analysis consider both cases of impermeable and permeable bricks, thus accounting for diffusion of both hydrophobic, hydrophilic and intermediate polarity molecules in the SC brick and mortar structure. It is thus an extension of a previous study that only treat the case of turtous diffusional transport of molecules that are not soluble in the bricks  [8]. In order to validate the relevance of the present model, the calculated concentration profiles are compared with experimental data on the distribution of model compounds in porcine stratum corneum, as measured by multiphoton laser scanning microscopy. The model compounds include one hydrophobic molecule, rhodamine B hexyl ester (RBHE), and one more hydrophilic molecule, sulphorhodamine B (SRB).
005% RBHE in PBS is judged to be close to the saturation limit. Multiphoton laser scanning microscopy (MPM) was performed using a Zeiss LSM 710 META NLO microscope (Carl Zeiss, Germany), equipped with a tunable mode-locked femtosecond pulsed Ti:Sapphire laser (Mai Tai, Spectra Physics, Mountain View, USA) operating at 780 nm using an earlier reported procedure. [20]  For imaging a water immersion objective lens (20x, NA = 1. 0) and excitation of 780 nm was utilized. The fluorescence was recorded in both the green (300-485 nm, autofluorescence) and the red (565-610 nm, RBHE and SRB) using non-descanned spectral channels. Only data from the red channel is presented, corresponding to the signal from the applied fluorophore. The spectral crosstalk of autofluorescence in the red spectral channel given these settings was found negligible, as confirmed previously.
After calving, high-yielding dairy cows mobilize body reserves for energy, sometimes to the detriment of health and fertility. This study aimed to estimate the genetic correlation between body weight loss until nadir and daily milk production (MY24) in first-(L1) and second-lactation (L2) Holstein cows. The data set included 859,020 MY24 records and 570,651 daily raw body weight (BWr) phenotypes from 3,989 L1 cows, and 665,361 MY24 records and 449,449 BWr phenotypes from 3,060 L2 cows, recorded on 36 French commercial farms equipped with milking robots that included an automatic weighing platform. To avoid any bias due to change in digestive content, BWr was adjusted for variations in feed intake, estimated from milk production and BWr. Adjusted body weight was denoted BW. The genetic parameters of BW and MY24 in L1 and L2 cows were estimated using a 4-trait random regression model.
MATERIALS AND METHODS This study was based on data routinely collected in commercial farms. We did not perform any experiment on animals; therefore, no ethical approval was required. The data used for this study originated from 36 commercial dairy farms located in western France. The average herd size was 92 cows (range: 46-189). These herds are all members of the INNOVAL DHIS network (http: / / www. innoval. com). In total and before any editing, data were recorded for 5,372 Holstein cows  [4,464 and 3,364  cows in first (L1) and second lactation (L2), respectively; 2,456 cows had data in both L1 and L2], who calved from July 2014 to July 2018. Barns were equipped with A3 and A4 Lely milking robots (Lely; https: / / www. lely. com) that included an automatic weighing platform. At each visit of a cow to the milking unit, the robot recorded the date and time of the visit, the weight of milk collected (MILK), and the BWr of the cow at the end of the visit.
Daily Milk Yield Consider a cow i in lactation j (1 or 2). At day in milk t, this cow goes to the milking unit k it times, and the robot records its k it milk yields, called MILK i,j,t,k , respectively. Then, for each cow i in lactation j, the sum of the k it milk yields of the day was calculated and standardized as a 24-h milk yield (MY24r): MY r i j t M ILK i j t k k k time it 24 1 440 1 , , , , , , , ( )= ( ) × = ∑ ∆ where Δ time is the time interval (in minutes) between the last milking of the previous day and the last milking of the current day. Because MY24r data were rather noisy, the following cleaning procedure was used. A third-order polynomial was fitted to the MY24r data within each 30-d period of each lactation of each animal, and outlier phenotypes deviating from the prediction by more than 3 standard deviations (SD) were discarded.
This procedure was complemented by a visual inspection of the discarded data. Using monthly data on fat content (FC) and protein content (PC) obtained from the national database CTIG INRAE (6. 7 records on average per cow and lactation from calving to 316 DIM), we predicted for each animal i in lactation j the FC(i,j,t) and PC(i,j,t) at DIM t using Wilmink's lactation models: FC(i,j,t) = a i,j + b i,j × t + c i,j × exp(-0. 05 × t) and PC(i,j,t) = d i,j + e i,j × t + f i,j × exp(-0. 05 × t), where t is the DIM, and a i,j , b i,j , c i,j , d i,j , e i,j , and f i,j are coefficients to be estimated for each lactation of each cow. The median of the R 2 of these models was 0. 67 for FC and 0. 83 for PC. These daily FC and PC predic-tions were then used to convert MY24r(i,j,t) into fatand protein-corrected MY24, based on 4.
Moving averages were used instead of the daily records at DIM t to exclude daily variability, mostly for BWr, and to obtain smoother DMI predictions. Finally, BWr(i,j,t) was corrected for the change in feed intake between DIM t and calving, assuming a digestive content change of 4. 5 kg per kilogram of DMI change  (Faverdin et al. , 2017) : BW(i,j,t) = BWr(i,j,t) -4. 5 × [DMI(i,j,t) -DMI(i,j,0)]. Estimates of genetic parameters were obtained using WOMBAT software  (Meyer, 2007)  by applying random regression models across the lactation and considering 3 generations of ancestors for the cows. Pedigrees and dates of insemination of the animals were extracted from the French national database (SIG, INRAE CTIG). For each test date of a cow, the corresponding number of days in milk (DIM = test date -calving date) and of days carried calf [DCC = test date -date of conception (if the cow became pregnant during lactation)] were calculated.
Design and Data For the purposes of performing a genetic study of the evolution of body reserves, the ideal approach would be to obtain body condition scores recorded at several stages of lactation from a large number of cows. Unfortunately, such data are difficult to obtain on a large scale. To overcome this problem, we used the abundant daily data automatically recorded by milking robots on commercial farms to evaluate the genetic relationships between body mobilization in early lactation and milk production. These data are rather noisy due to random variation in digestive content and water intake, but this random noise can be accounted for thanks to the sheer abundance of data. The main issue with this approach is the lack of data on individual daily feed intake, which is not monitored in commercial herds.
ACKNOWLEDGMENTS This work was performed in the framework of the  DEFFILAIT  project ( ANR-15-CE20-0014 ), which is funded by the  French Agence Nationale de la Recherche  ( ANR, Paris, France ) and  APIS-GENE (Paris, France). INNOVAL (Noyal sur Vilaine, France) and Eliance (Paris, France ) are gratefully acknowledged for managing the data from the milking robots. The authors have not stated any conflicts of interest.
This means that the two spectra in Figure  3  recorded after the end of the illumination essentially reflect this spectral component. To better understand the nature of this spectral component, time-resolved FTIR spectra were also recorded in D 2 O. Spectra obtained soon after the illumination period in H 2 O and D 2 O are compared in Figure  4. From top to bottom: green spectrum, taken from  [7] , obtained after a Multivariate Curve Resolution treatment of time-resolved FTIR difference data on chromatophores. Blue spectrum, recorded between 1, 3 and 3 s after strong illumination (last bottom trace of time-resolved FTIR spectra shown in Figure  3 ). Black spectrum: spectrum recorded on chromatophores between 1 and 3 s after strong illumination in D2O. Peak position should be considered to have a ±1 cm -1 uncertainty.
Kentucky ST198 To further investigate the co-occurrence of IncHI2 replicon genes with aadA, aph (6), bla CTX-M-14. 1 , cml, fosA3, and sul3 AMR genes and an IncI plasmid carrying the bla CMY gene, the complete and closed whole-genome sequence of strains NM17-19 and NM17-56 was determined using long-read sequencing. A contiguous assembled sequence of approximately 157 kb containing an IncHI2 replicon (PTU-HI2) and the aadA, aph(6)-ld, bla CTX-M-14. 1 , cml, fosA3 and sul3 resistance genes, present on a composite transposon was identified and designated pGTZIM1 (Fig. 3 ). Alignment of the sequence to the PLSDB plasmid database indicated that a plasmid pF218CHI2 (accession NZ_CP043545. 1) from an E. coli strain as the closest known relative. Plasmid pF2_18C_HI2 also carried the aadA, aph(6), cml and sul3 resistance genes found in plasmid pGTZIM1, but lacked the bla CTX-M-14.
1 and fosA3 genes. The bla CTX-M gene present in pF2_18C_HI2 differed from bla CTX-M-14. 1 by a non-synonymous mutation resulting in a predicted I17F substitution in the primary amino acid sequence (Supplementary Fig. 2 ). A second contiguous assembled sequence of 92. 5 kb from NM17-56 contained an IncI replicon (PTU-I1) and carried a bla CMY gene and was designated pGTZIM2 (Fig. 4 ). Alignment of the sequence to the PLSDB plasmid database indicated that plasmid p92 (RefSeq NZ_023376. 1) first identified in an E. coli strain was the closest known relative. Three other plasmids from S. Kentucky strains in the database were also close relatives, sharing the same backbone, but only one had a bla CMY gene (GCA_006339875. 2), a second carried the tetC, tet, and tetR tetracycline resistance genes (GCA_011480175.
2), while the third lacked resistance genes (GCA_007862665. 2). The long-read assembly of both NM17-19 and NM17-56 revealed the presence of a 3. 3 kb ColpVC plasmid (PTU-E1), which also shares similarities with an E. coli plasmid (pCFS3313-4, RefSeq accession number NZ_CP053654. 1), that we designated pGTZIM3 (Supplementary Fig. 3 ). Alignment of the sequence to available databases failed to identify known AMR or virulence genes in pGTZIM3. Maximum likelihood phylogenetic tree constructed using sequence variation in the core genome of 37 S. Kentucky strains isolated in Zimbabwe and fifteen reference strains of diverse S. enterica subspecies I serotypes rooted on Salmonella bongori ASM25299v1 as the outgroup (not shown). The position of S. Kentucky ST198 (red taxa labels) and ST152 (blue taxon label) and representative strains of diverse serotypes (black taxa labels) are shown.
b Maximum likelihood phylogenetic tree constructed based on nucleotide sequence variation in the shared genome sequence with reference to the whole-genome sequence of strain 201001922 (GenBank accession number CP028357). The tree was rooted on S. Typhimurium SL1344 as the outgroup (not shown). The population structure organized into eleven clades based on three orders are indicated by integers and colored blocks, and the source, location, year of isolation and the presence of antimicrobial or plasmid replicon genes are color coded as indicated in the inset key. The presence of mutations resulting in S83F and D87Y substitutions in gyrA (*) or S80I in parC (#) are indicated. which is present as a single contiguous insertion in the trmE/ydiY locus, SGI1-KIV was present in two sections inserted into the rbsK locus and trmE/ydiY loci separated by 50 kb of the core genome sequence.
This lack of genetic diversity and wide geographical distribution within Zimbabwe suggests that the clone has spread rapidly to many farms across the country. The presence of strains of S. Kentucky ST198 in feed that were closely related to strains isolated from poultry implicates this as a potential source of transmission. Nonetheless, the relative contribution of livestock transfer, other animal species or environmental factors and feed in the transmission of S. Kentucky ST198 between farms in Zimbabwe cannot be assessed with these data. The close genetic distance between isolates are also consistent transmission of S. Kentucky from poultry to humans, but due to a small dataset and the limitation of sampling, no case of direct transmission could be inferred with high confidence. Strains isolated in Zimbabwe formed a distinct clade within a globally dispersed ST198 population that emerged in Egypt in 1989 and was associated with multidrug resistance conferred by the acquisition of SGI-1 and resistance to fluoroquinolones due to mutations in the gyrA and parC genes  17, [35] [36] [37] [38] [39] [40] [41] [42].
Ethics approval for the study was granted by the University of Pretoria, South Africa (779/2018) and the Medical Research Council of Zimbabwe (MRCZ/A/2369). Strains are Fig. 6  Population structure of S. Kentucky ST198 strains isolated from Zimbabwe in the context of 364 globally sourced S. Kentucky strains. a Maximum likelihood phylogenetic tree constructed based on recombination-purged SNPs in the shared genome with reference to S. Kentucky strain 201001922 (GenBank accession number CP028357). The tree was rooted on S. Typhimurium SL1344 strain SL1344 as an outgroup (not shown). The region (inner circle) and year (middle circle) and presence of a bla CTXM-14 gene (outer circle) are indicated by colors (inset key). Deeply rooted lineages (gray lines), lineages of strains isolated from Zimbabwe (red lines) and closely related strains isolated elsewhere (blue lines) are indicated to assist interpretation.
ACKNOWLEDGEMENTS We thank the entire network of the  One Health AMR Surveillance laboratories , especially  National Microbiology Reference Laboratory and Central Veterinary Laboratory  for providing the isolates and the core sequencing team at the  Quadram Institute Bioscience. We also thank  Muzhuzha Marclay C. ,  Tamika Munetsi  and  Mafuka Paidamwoyo V. for helping in culturing the strains isolated in Zimbabwe. RK and GT were supported by  BBSRC   Institute Strategic Programme Microbes in the Food Chain   BB/R012504/1  and its constituent projects  BBS/E/F/000PR10348  and  BBS/E/F/000PR10349.
Cryo-EM data acquisition Immediately prior to preparing cryo-EM grids, 3 μM Fos-Choline-8 (Anatrace) was added to the protein sample. The sample was then applied to a gold Quantifoil R 1. 2/1. 3 holey carbon grid (Quantifoil) that was glow discharged for 35 sec using PELCO easiGlow glow discharge cleaner. The grid was blotted for 3-4 sec using Whatman No. 1 filter paper and plunge frozen using Vitrobot Mark IV (FEI) set at 4°C and 100% humidity. The yeast Sec complex dataset (1,578 movies) was acquired on FEI Talos Arctica electron microscope operated at an acceleration voltage of 200 kV, with Gatan K2 Summit direct electron detector. A magnification of 36,000x under super resolution mode (with the physical pixel size of 1. 14 Å) was used with a nominal defocus range that was set between -0.
8 to -2. 2 μm. Each micrograph was composed of 42 frames with total exposure of 50 e -/pixel. The human Sec complex dataset (3,499 movies) was collected on FEI Titan Krios G2 microscope operating at an acceleration voltage of 300 kV and equipped with a Gatan Quantum Image Filter (slit width of 20 eV) and a Gatan K3 direct electron detector. A magnification of 64,000x under the super-resolution mode (with physical pixel size of 0. 91 Å) was used at a defocus range that was set between -0. 8 and -2. 0. Each micrograph was composed of 42 frames with total exposure of 50 e -/pixel. Exposures were performed with beam shifts onto 9 holes (3 by 3) per stage movement. All chimeric Sec complex datasets were acquired on an FEI Titan Krios G3i electron microscope operating at an acceleration of 300 eV, with a Gatan K3 Summit direct electron detector and a Gatan Quantum Image Filter (with 20 eV slit width).
Model building and refinement Atomic model building and refinement were performed using Coot 70 and Phenix 71. An initial model was built by docking an ScSec complex model (PDB ID 7KAH; ref. 17  ) into the cryo-EM map of the cotransin-bound complex using UCSF chimera  72  and rebuilding the polypeptide chains. For building and refining of Sec61 and inhibitor models, we used maps from focused (local) refinements as they typically showed better protein side-chain and inhibitor features than full maps. The initial model was further improved by using our highest-resolution map, which was obtained from the apratoxin F-bound complex. This model was then used to build atomic models for apo and other inhibitor-bound complexes by docking the model to the map using UCSF chimera and locally adjusting it into the map in Coot.
Acknowledgments We thank  Dan Toso  for support for electron microscope operation,  Guanghui Zong  for ipomoeassin F synthesis,  Philippe Mathys  and  Ralph Riedl  for help acquiring the IC50 data. E. P. was supported by the  Vallee Scholars Program  (E. P. ) and  Pew Biomedical Scholars Program. S. I. and L. W. were supported by a  National Institutes of Health  training grant ( 5T32GM008295 ). M. S and T. J. were supported by the  Swiss National Science Foundation  ( 31003A-182519 ). N. B was supported by  Fondation Raoul Follereau  and  Fondation Pour Le Développement De La Chimie Des Substances Naturelles Et Ses Applications. W. Q. S. was supported by an AREA grant from  National Institutes of Health  ( GM116032 ). C. F. and L. X. were supported by the  Ohio State University.
Replicating pins used for inoculation from freezer stock plates were sterilized in a series of ethanol baths in between plates. To ensure against dilution effects that can reduce the effectiveness of alcohol-based disinfection, the ethanol baths were completely replaced after inoculation of 4 deep-well plates. Replicating pins were also autoclaved each night. The original SmarT library layout included a mapped series of known blank/empty wells in each plate  (5). These were manually inspected in the deep-well plates, and any plates with contamination were discarded and reinoculated. Any deep-well plates where mutants did not grow as expected were also discarded and reinoculated. Because the deep-well plates do not fit in standard plate readers, optical density/absorbance was not measured. We recognize that as a limitation of this protocol, as that data could be important for identifying mutants with reduced overnight growth that is not detectable by eye.