dataesr/scientific-paragraphs-categorization huggingface dataset:
The geometric correlation between track and energy cluster is then used for an alignment fit of CASTOR. From this alignment fit, we find that both detector halves are separated by 22 mm, a situation that was not exactly known prior to this measurement. Furthermore, minimum-bias collision data at √ s = 0. 9, 2. 76, and 7 TeV have been compared to corresponding detailed detector simulations. The energy distributions in depth and azimuth depend on the position and tilts of the two calorimeter halves. The position of CASTOR in simulations was therefore optimized to best describe the distribution found in data. There is agreement within the experimental uncertainties among the different approaches.
Simulation The detector simulation is fully integrated into the offline software package of the CMS experiment CMSSW  [83]. The particles produced by the event generator are transported to the CASTOR detector using the GEANT4 framework  [81]. Hereby, interactions with detector materials and the beam pipe in front of the calorimeter, as well as the magnetic field produced by the CMS solenoid that extends into the very forward region, are simulated. The geometry implementation of CASTOR is shown in Fig. 26. The two halves of the calorimeter are shown in the geometry corresponding to the 2013 pPb data-taking period. Electromagnetic and/or hadronic particle showers are detected in the sensitive volumes of the detector. The quartz and tungsten plates that define the volume of the CASTOR detector are accurately and separately modeled for each detector-half to allow the description of tilts and movements of the individual halves.
With these values for the factors g PMT and f electrons the simulated response of CASTOR is consistent with the measured energy of incoming individual electrons in beam tests. In Fig. 27 , the simulation of the CASTOR standalone detector is compared to test-beam data for electrons (left) and charged pions (right), illustrating the good agreement between data and simulations. In CMS detector simulations, the required time per generated event is an important factor, since the available computing resources are limited. For CASTOR, such computing constraints are important since particles and jets with very high energies, close to that of the beam, need to be simulated. The subsequent large and complex hadronic showers developing in the calorimeter require significant computing time to be simulated in full detail.
There is an additional scaling factor of 1. 25 on the uncertainties of the calibrated data, as discussed in the text. the shower shape. The uncertainties shown in the shape of the longitudinal projected data are scaled with the same factor as described in the previous paragraph. In the proton-lead data, there is some data-MC discrepancy in the rear part of the calorimeter, which is due to known alignment issues in 2013 relative to the LHC beam pipe, affecting the rear part of CASTOR. Thus, the data in modules 8 to 14 recorded in 2013 must be treated with special care. Dedicated detector simulation studies are needed to yield the required accuracy for physics analysis. It is also important to realize that the detailed shower development in the CASTOR calorimeter is very sensitive to the underlying physics processes under study and cannot be assumed to be well known at such high energies.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Moreover, the CASTOR detector project received important support and explicit funding from the  Helmholtz Association (Germany)  via grants  HN-NG-733  and the joint  Helmholtz-Russian  grant  HRJRG-002 , as well from the  "A. G. Leventis" Foundation (Greece). We are also grateful to our Col-leagues from the  TOTEM Collaboration  for the combined data taking and the fantastic work on the T2+CASTOR combined data analysis.
The parabolic fits have small curvatures (e. g. the one at 0. 88 μm in Fig. 3a ) and give large uncertainties in  0. Furthermore, the gratings under measurement are not perfectly rectangular in the top view and the sidewalls are not perfectly smooth. Different parts of the interacting surfaces have different crystal orientations, especially the rounded corners. Variations in the work function  49  could result in  0 not constant with displacement. The constants α for the comb actuator and k for the sensing beam are calibrated by fitting the frequency shift induced by the electrostatic force gradient to β(d) calculated using finiteelement simulations by the numerical package COMSOL. As discussed in Methods, the boundary conditions used in the calculations are obtained from the digitized top views of the sample.
Acknowledgements M. W. , L. T. , C. Y. N, C. T. C. and H. B. C. are supported by  AoE/P-02/12  from the  Research Grants Council of Hong Kong SAR. M. W. , L. T. and H. B. C. are also supported by  HKUST   16300414  from the  Research Grants Council of Hong Kong SAR. Research by M. A. , B. G. , and R. M. was carried out using computational resources of the group  Theory of Light-Matter and Quantum Phenomena of the Laboratoire Charles Coulomb. This is a preprint of an article published in Nature communications. The final authenticated version is available online at: https://doi. org/10. 1038/s41467-021-20891-4.
Panel design Primer design was performed using an in-house modified version of mprimer  (Qu et al. , 2012; Shen et al. , 2010). The program modifications vastly speed up the process of multiplex PCR design without any effect on the resultant primer design output itself. The initial list of 591 target sites was sorted to ensure that amplicons deemed highly informative were prioritized. Highly informative amplicons spanned three categories including those that were highly variable and supported discrimination across most lineages (n=22 available); those that were highly conserved, i. e. retained alignments in at least one of the three outgroup species (n=27 available); and those located on the An. gambiae X chromosome, while also being found in most other species (n=6 available). The remaining 536 amplicons were sequentially added for design prioritizing those that were likely to reveal the highest number of distinct sequences across species, and then by coordinates in the AgamP3 genome to spread targets across all chromosome arms.
Accepted Article This article is protected by copyright. All rights reserved from three continents. From Africa, we included 89 specimens representing 24 species of wildcaught mosquitoes from Gabon that were identified morphologically with further PCR-based species attribution where possible  (Ayala et al. , 2019). From Southeast Asia, we included 43 specimens representing 13 species of wild-caught mosquitoes from Cambodia  (St. Laurent et al. , in prep)  that were identified to species level using ITS2 sequencing  (Beebe & Saul, 1995). Finally, we obtained a single sample from three species of mosquitoes from Brazil, South America, which were identified morphologically. As a reference for species names, we used the NCBI Taxonomy database with some updates from the Mosquito Taxonomic Inventory  (Harbach, 2013b).
The details on each specimen together with its storage and extraction methods are summarised in Supplementary File 2 tab "sample metadata". In addition to sequencing wild caught specimens, we also generated the sequences that would have theoretically been amplified using these 62 primer pairs from each of the available 28 genome assemblies comprising 23 Anopheles species (Supplementary File 2 tab "sample metadata"). To do this, we used the SeekDeep v. 2. 6. 4  (Hathaway, Parobek, Juliano, & Bailey, 2018)  genTargetInfoFromGenomes command, which looks for primer matches in the genome and returns all of the potential amplification products. Additional mosquito samples were used to develop and validate non-destructive DNA extraction approaches, as well as to test the sensitivity of the Plasmodium primers to detect parasites in infected mosquitoes (both processes described in Supplementary Methods).
Single marker Sanger sequencing Details for species identity validation analysis are outlined in Supplementary Methods section "Molecular Species ID validation using COI and ITS2 single marker Sanger sequencing" and Figure  S17. Briefly, we amplified COI  (Folmer, Black, Hoeh, Lutz, & Vrijenhoek, 1994 ) and ITS2  (Beebe & Saul, 1995)  products and sequenced those with Sanger technology (Eurofins GATC SupremeRun 96). We performed a homology search in BOLD  (Ratnasingham and Hebert 2007)  and NCBI GenBank. We also aligned the sequences, reconstructed phylogenetic trees and obtained diversity estimates and compared those with amplicon sequencing results.
Distance-based species attribution The dataset used here included mosquitoes collected from Africa, Southeast Asia and South America, as well as target sequences extracted from reference genomes -a total of 161 samples belonging to 57 species from 4 subgenera (Cellia -119 samples, 42 species; Anopheles -36 samples, 9 species; Nyssorhynchus -4 samples, 4 species; Kerteszia -2 samples, 2 species). Amplicon sequences were aligned using MAFFT v. 7. 407  (Katoh, Misawa, Kuma, & Miyata, 2002). Sequence variation statistics were collected using BioPython v. 1. 74. Sequence clustering was performed for each amplicon independently with cd-hit-est v. 4. 8. 1  (Fu, Niu, Zhu, Wu, & Li, 2012) , which uses static sequence similarity threshold to delineate clusters. Two sets of thresholds were generated based on within-species diversity in either the sequenced dataset and reference genomes or in the continent-wide population sampling of An.
gambiae and An. coluzzii (1,142 individuals from the Ag1000g Phase2 dataset). The performance of both sets of thresholds was evaluated by excessive species splits between clusters and multiple species falling into a single cluster. The set of Ag1000g-derived thresholds proved to have higher species resolution with similar level of splits -presumably due to wider species sampling compared to sequenced mosquito dataset, so it was used in the downstream analysis (for details, see Supplementary Methods section "Distance-based species attribution" and Figs S19-S20). In order to predict the species of each sample based on clustering results, we generated a "species labelling dataset" comprising sequences with reliable species predictions. In this dataset, we included all predicted amplicon sequences generated from reference genomes as we consider their species and homology predictions to be robust.
For the mosquitoes we sequenced here, we introduced a filter to ensure that only consistent data were included in the species labelling reference dataset. In doing this, we did not question the original species labels that were given to samples based on morphology and, in some cases, a single molecular marker. Instead, we focused on internal coherence of the sequencing data -each species should be represented by a set of relatively similar sequences across the samples representing that species. We analysed within-species distances across all amplicons and identified eight samples that were too distant from other samples of that species. For one of the species, An. marshallii, all three.
Accepted Article This article is protected by copyright. All rights reserved samples were excluded due to this problem (due to suspected morphological misidentification or sample labeling mistake). The resulting species labelling dataset included 9,589 of 10,057 sequences available for both reference genomes and sequenced samples, and represented 56 of 57 species. For the purpose of species identification, the species labelling dataset was clustered together with the remaining sequences. Each cluster for each amplicon was labelled with the set of reference dataset species whose sequences fell within that cluster (Fig  S21 ). The species prediction procedure for any given sample consists of two steps: 1) for each sequence available for a sample identify the set of species labels for the corresponding cluster; 2) find the most frequent species label across these sets.
For closely related species that share many alleles, boundaries are not discrete. For example, a species label might be assigned as An. coluzzii because 75 of 80 alleles detected in the sample are found in the "species labelling dataset" assigned to An. coluzzii, yet 70 of those alleles are also found in the set of alleles assigned to An. gambiae. The final version of the species tree was reconstructed based on the samples that were included in the species labelling dataset only. Amplicon sequences for each sample were split into two pseudo-haplotypes. Haplotype 1 consisted of the most frequent allele, haplotype 2 consisted of the second most frequent allele or of a copy of haplotype 1 for homozygous genotype calls. For each amplicon, sequences for all haplotypes were aligned with MAFFT and maximum likelihood phylogenies were reconstructed with FastTree v.
2. 1. 10 (Price, Dehal, & Arkin, 2010). The species tree was reconstructed using ASTRAL v. 5. 6. 3  (Rabiee, Sayyari, & Mirarab, 2019) , tree visualisation and taxonomy manipulations were performed with ete v. 3. 1. 1 (Huerta-Cepas, Serra, & Bork, 2016). Population structure for An. gambiae and An. coluzzii High levels of sequence variation observed within species and even within samples across the targeted amplicons suggested to us that the panel might reveal sufficient variation to infer population structure. To test this, we examined the sequences from the amplicons targeted here in the whole-genome sequencing dataset of 1,142 individual An. coluzzii and An. gambiae mosquitoes (The Anopheles gambiae 1000 Genomes Consortium et al. , 2019), aiming to compare population structure based on just 62 amplicon sites reported here with that inferred using the whole genome of every sample.
Accepted Article This article is protected by copyright. All rights reserved The Ag1000g Phase 2 AR1 dataset of biallelic variants for 1,142 individuals of An. gambiae and An. coluzzii was subsetted to amplicon target sites, i. e. primer sequences were excluded from the analysis. Three available reference genomes were also synthetically analysed at the amplicon sites. We also included three An. coluzzii from Gabon and five An. gambiae from Gabon that were sequenced using the amplicon panel. For each amplicon, the FASTA sequence alignments were converted to genotype matrices using AgamP3 as a reference, then subsetted to Ag1000g Phase 2 AR1 biallelic sites. The overall population structure for the combined dataset including all An. gambiae and An.
Panel informativeness Using the panel of 62 targeted mosquito amplicons, we generated sequence data for 135 samples of 40 Anopheles species from three continents belonging to three subgenera (Cellia, Anopheles, and Kerteszia) and processed those using the DADA2 pipeline. The sequences of an additional 28 samples from 23 species (subgenera Cellia, Anopheles, and Nyssorhynchus) were extracted from published genome assemblies (Supplementary File 2 tab "sample metadata"), resulting in a total of 161 samples and 57 species in the combined dataset. For the 135 samples sequenced using the panel, a total of 6,130 loci were recovered (73% of 8,370 possible sampleamplicon combinations). At the sample level, this corresponds to 46. 1±14. 4 (median 50 of 62 possible) amplicons successfully sequenced. The success rate was negatively correlated with the divergence level from An.
gambiae, as this species was used as a baseline for primer design and much variation for Gambiae complex was captured in the genome alignment. This trend became noticeable at the subgenus level  (Fig 2A) , and such amplification failures can be used as phylogenetic signals in the future. For example, sequence data from amplicon number 60 are consistently missing from all An. funestus samples sequenced so far. The variation detected in the targeted amplicons was considerable. Out of 6,130 loci recovered in 135 sequenced samples, 2,104 (34%) were heterozygous and another 175 (3%) were multiallelic.
Species identification We anticipate that the panel described here will supplement and perhaps eventually replace morphological and single PCR marker based species diagnostics. Accordingly, we generated amplicon sequence data for about 10% of the described Anopheles species to assist with accurate identification of known species, and phylogenetic placement of previously undescribed species. To demonstrate that the panel based assignments surpass what is possible with single marker based assignments, we sequenced two conventional molecular species ID markers, COI and ITS2 (Supplementary Methods section "COI and ITS2 single marker Sanger sequencing") using Sanger sequencing for the majority of our samples. NCBI GenBank and BOLD database searches for COI and ITS2 sequences allowed us to confirm the species identities (or series/subgenus for species that were absent from the databases) and revealed a few mis-labelled samples (Supplementary File 2 tab "species identification").
Accepted Article This article is protected by copyright. All rights reserved option more informative, as it resolved more species and did not excessively split them into several clusters (Supplementary Methods section "Distance-based species attribution", Fig S20 ). Species prediction requires a reference dataset with trusted species labels. Here, we created one using a combination of reference genome sequences and quality-filtered amplicon sequence data for wild-caught specimens, retaining 9,589 of 10,057 (96%) sequences representing 56 of 57 sampled species. The species assignment for each of our samples was based on the most frequently encountered species label across all sequences available for a given sample. This approach resulted in reliable species resolution for most of the species groups and complexes demeilloni, An.
hancocki (Marshallii group). All of these groups showed evidence of introgression according to COI, ITS2 and amplicon sequencing. Moving forwards, both clustering and phylogenetic trees could be used to identify closest relatives for the new unidentified samples. To elucidate the power of our panel for resolution of higher level taxonomic relationships, we reconstructed the consensus species tree using ASTRAL based on maximum likelihood trees for individual amplicons for the reference dataset (Fig  3 ). As expected, the overall species tree topology is more similar to that derived from nuclear genomes  (Neafsey et al. , 2015)  compared to the mitochondrial  (Hao et al. , 2017)  and morphological  (Harbach & Kitching, 2016)  trees. The clade support is higher at both basal and terminal levels with all subgenera, as well as species groups and complexes showing monophyly.
Within the Cellia subgenus, the analysed series are all monophyletic with the exception of Neomyzomyia, which appear as a set of weakly supported sister clades at the base of Cellia. An. rhodesensis and An. jebudensis, members of the Neomyzomiya series, were actually found within Myzomyia series according to both amplicon sequencing, COI and ITS2. This also contradicts previous molecular identification results  (Ayala et al. , 2019) , and requires further investigation. It is interesting that the more stringently filtered dataset processed with SeekDeep yielded a more plausible species tree topology (Fig S18 ), where the Neomyzomyia series was monophyletic.
Population structure We next explored the power of the amplicon panel in resolving population structure within species. We generated an in silico targeted amplicon dataset based on the whole-genome sequencing data for two closely related and readily crossing species, An. gambiae and An. coluzzii (The Anopheles gambiae 1000 Genomes  Consortium et al. , 2019). Using the Ag1000g Phase 2 variation callset for 1,142 individuals we extracted biallelic SNP variants found in each amplicon. A total of 2,125 variants were found, and 1,417 of those were segregating (i. e. , not singletons). Variable sites were distributed across all amplicons and only amplicon 29 did not have any segregating sites among all samples. UMAP dimensionality reduction (Fig 4A ) readily split An. gambiae and An. coluzzii , and supported the differentiation of the most geographically distant populations (Kenya, Mayotte, Angola).
The separation of those three populations was also supported by F-statistics (Supplementary File 4 tab "Fsts"). Diversity estimates (nucleotide diversity, Watterson's θ, Tajima's D, see Supplementary File 4 tab "diversity") also correlated well with the values observed from the whole-genome dataset. Next, we combined the Ag1000g data with samples from our amplicon sequencing and reference genomes. UMAP allowed for clear species identification and even some level of population attribution  (Fig 4A) , despite only a fraction of sequence variation captured by amplicon sequencing was used in this analysis  (Fig 4B). To understand the within-species variation patterns outside of the Gambiae complex, we assessed the within-species sequence variation detected in amplicon sequencing and in Sanger sequencing of conventional markers (Supplementary Methods section "COI and ITS2 single marker Sanger sequencing", Supplementary File 2 tab "species summaries").
For most species, we had 1-5 individuals that were sequenced using these approaches. On average, amplicon sequencing yielded about 8,943±665 bp of sequence data per sample, and revealed 178±111 SNP sites within a species  (Fig S17). In comparison, conventional markers yielded far less sequence data per sample: 602±51 bp for COI and 537±143 bp for ITS2, with typically less than 12 variable sites within a species. The exceptions (listed in Supplementary Methods) were limited to a single marker, i. e. COI was diverged while ITS2 was conserved or vice versa, highlighting one problem with single marker approaches:these highly diverse outliers could be interpreted as a single species with one marker, and distinct species if a separate marker was used. Targeting multiple genomic regions helps to address this issue.
Accepted Article This article is protected by copyright. All rights reserved Amplicon sequencing informativeness was retained even when fewer amplicons were recovered and the combined amplicon length never dropped below 7kbp for any sequenced species, except An. paludis, An. nili s. s. , An. cruzii, An. oryzalimnetes and An. bellator -those mostly had less than 30 amplicons recovered in any sample  (Fig 2A)  and in some cases, we also failed to generate genome sequence data indicating a likely problem with the source DNA. Moreover, it was possible to discriminate species with higher and lower sampled genetic diversity. We examined a pair of species with lower amplicon recovery. In An. barbirostris from South-East Asia belonging to Anopheles subgenus, among 5 sequenced samples 44±3 amplicons recovered, and 21±1 of the recovered amplicons were heterozygous within any given sample.
Accepted Article This article is protected by copyright. All rights reserved the Marshalli and Coustani groups, highlighted in red in Fig 3 ), where larger sample sizes, finer scale analysis, and dimensionality reduction methods are also likely to help disentangle the relationships using this panel. When higher numbers of amplicon sequenced samples become available, missing data (e. g. , random amplification failures) will become common, and appropriate methods should be adopted to overcome this. Additional methods could be used for closely related species to discriminate amplicons with significant resolution power from those affected by introgression or incomplete lineage sorting. Obtaining a robust reference dataset for species labelling is another important challenge for future research.