dataesr/scientific-paragraphs-categorization huggingface dataset:
'Introduced' has only been used for ticks and invasive mosquitos. Ticks are likely to be carried to locations by a host but are not recorded throughout the year. For mosquitoes, the first record of an invasive species is categorised as introduced if there is no evidence of overwintering, unless the record consists of multiple records throughout a year. An 'introduced' record reverts to a display of 'absent' after 5 years if searches have found no further presence records. VectorNet aims to depict current vector distribution, therefore only data after 1980 are collated. For vector species complexes (sibling or cryptic species), data are reported at complex level (e. g. Culicoides obsoletus s. l. , Anopheles maculipennis s. l. ) unless complex members are reported based on reliable identification methods (i.
What did you want to address in this study? We wished to present the VectorNet surveillance data and map archive, which has been compiled since 2010, to provide public health professionals, academics and the public with state-of-the-art spatial distribution maps of the mosquito, tick, sandfly and biting midge species that transmit a wide range of human and animal diseases. An important objective of the paper is to provide practitioners with details of the methodology used and broad interpretations of its results.
KEY PUBLIC HEALTH MESSAGE and any suggested status changes according to the hierarchical dominance of the reported status, which is as follows: present, introduced, absent, anticipated absent, no data and unknown. These changes are then validated by the VGLs based on their expert knowledge, and, together with any corrections, incorporated into the ECDC VectorNet database. To facilitate change assessment, all data records are retained. This updated database is subsequently used to map distributions in Feature Manipulation Engine (FME) software. Because polygon sizes vary considerably and, for some countries, are sometimes difficult to visualise at pan-European scale, they are aggregated to mapping polygons, assigning the dominant distribution status to any aggregated polygon. The generated maps are then re-circulated to the VGLs for approval before posting online.
Coverage and completeness of the database A preliminary assessment of VectorNet record numbers, coverage and accuracy in relation to other continental databases such as GBIF, VectorBase and EID2 was undertaken. Of these other databases, either GBIF or VectorBase was found to have the most records for each of the species mapped by VectorNet. The March 2021 VectorNet distribution maps were therefore compared (and overlaid) with point records from the other database with the most records per species (downloaded in July 2021), and the number of VectorNet polygons with VectorBase or GBIF records were counted. Comparisons with modelled distributions was not made as many, if not most, continental scale models incorporate VectorNet data in their training datasets  [25, 26].
Presence or absence records By 2013, 46% (730/1,587) of the polygons had presence or absence records but that dropped to 17% (862/5,070) as European surrounding areas (some with many small polygons) were incorporated, then increased to 31% when administrative unit size was adjusted in some countries and data collection increased (Table  2 ). The invasive mosquito and sandfly maps have fewer gaps (< 50%) than other groups as the absence entries are more complete, while maps of other groups are ca 20% (native mosquitoes) to 37% (biting midges) complete. These summary data hide significant interspecific variation. For example for ticks, there are very few records for the Ornithodoros species, but many for Ixodes ricinus. Many of the missing data are beyond the vectors' environmental ranges.
Surveillance and distribution maps The maps of surveillance activity per vector group are shown and described in Supplementary Figures  S1-5. Data were submitted for between 68% (biting midges) and 86% (native mosquitoes) of the polygon-vector group combinations. The surveillance effort maps are patchy, rarely national in scope and vary intra-nationally for all groups. The maps highlight stark differences between vector groups: active surveillance of native mosquitoes is implemented for more months of the year than for the other vector groups and is the most diverse and widespread. Surveillance and distribution maps are compared in Supplementary Figure  S6  and by tabulating the numbers of administrative unit polygons into three categories: (i) agreeing with distribution data and recorded surveillance or agreeing with no surveillance and no distribution data; (ii) disagreeing where data are present but no surveillance reported; and (iii) disagreeing where surveillance is reported but distribution data are incomplete (Table  3 ).
Discussion The VectorNet project series has continuously collated and integrated vector distribution data provided by a large and diverse network of volunteer professionals and academic source material, posting its maps online regularly since 2013. A wide range of formal and informal sources, rigorous validation through error trapping, iterative data entry and correction and expert opinion assures the quality of the maps made available. In addition to formal accesses and citations, which the non-exhaustive nature of the search is likely to underestimate, the scientific, public and veterinary health impact of the maps is probably most evident from their use in professional presentations, grey literature reports and project proposals. Here, the data they provide are used as reference and background information to illustrate the extent of disease risk at continental level, support investment in surveillance and control and feed media outlets.
On a more technical level, the polygon and point data on which the maps are built have also underpinned a large number of spatial distribution models for many species or risk assessment of specific diseases  [29] [30] [31] [32]. Both models and maps are also useful to target regional, national and even subnational surveillance, although detailed surveillance recommendations are likely to require higher resolution than NUTS3. National normative bodies can also use the continental maps to define measures or even legal restrictions such as storage regulations for used tyres in the Netherlands that are informed by the VectorNet invasive mosquito maps. A less concrete but nevertheless valuable impact of the maps is as a 'glue' to encourage a large and active international entomological volunteer network to collaborate and, through an expert-led technical framework, to adopt a set of data sampling and reporting standards, enhancing compatibility of datasets across the continent.
The maps can be used by planners to justify surveillance funding and encourage reporting at subnational scale, and the spatial models derived from the point data can be used to target surveillance more precisely. Some caveats need to be mentioned. The maps are released as 'current known distribution', but the availability of a series of updated maps risks confusing additional or new spatial distribution data with spreading of the vector, which may not be the case. Also, as vector infection rates are often low in Europe, their presence only represents potential not active risk of disease. The move to recording point location abundance and sample effort data are not yet reflected in the published maps, partly because the body of published sampling methods are inconsistent and thus difficult to combine and partly because publications often contain only summarised data and records of high-profile species, with incomplete descriptions of sampling methodology.
This last point is especially important as sampling methods are different for each vector group, and indeed for different species within each group. Improved standardisation of published data (including, for example, full sampling effort details and absence reporting, as illustrated in Aedes Invasive Mosquito Cost Action (AIMCOST) continental survey outputs  [33] ) will enable the acquisition of more consistent quantitative distribution data and so the production of density as well as presence/absence maps. A key feature of the recent phases of the VectorNet projects is a focus on acquiring and maintaining standardised data records, including specimen numbers, sampling dates and locations, sampling effort and methodologies and validated species identifications. This provides the potential for detailed analysis of changes in distribution and abundance and a wide range of other spatial analyses.
Conclusions The VectorNet datasets compare favourably with other datasets in terms of coverage and record numbers because they record absences and are validated by experts. They are, however, less accessible in terms of interactive querying and download. The datasets and many maps are (still) patchy and incomplete, mostly because of missing absence data in areas outside known vector ranges, or because absence is not recorded when suitable trapping efforts have resulted in zero records. Planned improvements, such as producing density and seasonality maps and including inferred absences, would not only make the maps more informative but would also facilitate tracking expansion and change more effectively. Such changes would enhance what is already a valuable dataset providing validated vector distribution information to a wide range of professional and lay audiences.
However, GBIF and VectorBase have some presence records in areas where VectorNet has none, most noticeably for Aedes vexans, Aedes caspius, Ae. detritus, An. maculipennis complex, An. superpictus, Coquillettidia richardii and Dermacentor reticulatus. Note that these comparisons do not include VectorNet's many thousand (> 120,000) absence records of which the other databases have very few.
Plasmid Construction The L. monocytogenes AcrIIA4-coding sequence followed by a NLS at the N-terminus side, under the control of the vasa2 promoter  25  , was ampli ed from C77 plasmid 20 using primers containing overhangs for Gibson assembly (RG964-RG969; Supplementary Table  7 ). A plasmid backbone containing the piggyBac inverted repeats and two ϕC31 attP recombination sites, as well as a fragment containing eGFP marker under the control of the 3xP3 promoter using primers also adapted for Gibson assembly (RG970-RG971 and RG968-RG967, respectively; Supplementary Table  7 ). The nal plasmid was named C119 (Genbank accession code XXXXXXX) and was assembled using the standard Gibson assembly protocol  26.
gRNA probes were designed using CHOPCHOP (sequences provided in Suppl. Table  7 ) and synthesized using synthetic CRISPR RNA (crRNA) and trans-activating crRNAs (tracrRNAs) to assemble a duplex. The resulting reads were mapped against a hybrid AgamP4-Ag(Vasa:A4)2 reference genome, in which the sequence of the Ag(Vasa:A4)2 transgene is appended to the latest AgamP4 genome le. BLASTn analysis of the reads aligning to the construct sequence was used to identify the insertion locus of the construct. Raw sequencing read are available in the EBI-ENA database (accession codes PRJEB61434).
Measuring Life-history Parameters Life-history parameters were measured for Ag(Vasa:A4)2 and wild-type G3 in small cages (BugDorm-4) as described in  5  assessing egg deposition, hatching rate, larval and pupal mortality, time of pupation and mating competition. Adult mortality assessment was conducted in both medium and large cages. To determine eggs number and hatching rate en masse, three replicate crosses were performed with 150 females and 120 males of the following genotypes: Ag(Vasa:A4)2 +/-males with G3 females; Ag(Vasa:A4)2 +/-females to G3 males; Ag(Vasa:A4)2 +/+ males to Ag(Vasa:A4)2 +/+ females; and wild type males to females. Females were blood-fed after four days, and the egg progeny was counted using EggCounter v1. 0 software  30. The hatching rate was estimated three days post oviposition, visually checking 200 eggs under a stereomicroscope (Stereo Microscope M60, Leica Microsystems, Germany).
Large Cage Environment In preparation for an anti-drive release study in large cages, overlapping generation populations of wildtype An. gambiae mosquitoes were maintained in a climatic chamber with two 6. 4 m 3 cages of 2. 9 m x 0. 96 m x 2. 30 m (length, width, and height), with a temperature of 28ºC ± 0. 5ºC and relative humidity of 80% ±5%. Two sets of three LEDs (3000, 4000, and 6500 K correlated colour temperatures) were used to illuminate the climatic chamber, controlled by Winkratos software (ANGELANTONI Industries S. p. A. , Massa Martana, Italy), permitting to simulate of the dawn, dusk, and sunset. Full light conditions were adjusted to last 11 h and 15 min. The dusk lasted for 1 hr, to encourage the swarming behaviour of male mosquitoes; dawn lasted for 45 min from dark (11 h) to full light, and sunset lasted for 1 hr, to encourage the swarming behaviour of male mosquitoes.
Establishment, Maintenance, And Monitoring Of Age-structured Large Cage (ASL) Populations A stable age-structured large (ASL) cage population of An. gambiae (G3 strain) was established to investigate the potential of Ag(Vasa:A4)2 to inhibit the spread of the Ag(QFS)1 gene drive. The mean adult population size was estimated based on the mean adult daily survival and the biweekly restocking size (Suppl. Data 1). Based on the weekly release of 1200 pupae (600 twice a week), we estimated a mean adult population of 912 mosquitoes. 600 G3 pupae (300 males and 300 females) were introduced twice a week (1200 total per week) in each large cage for 70 days (establishment) until the populations were producing enough eggs to self-maintain. Then, the only progeny of these populations was used to repopulate the cage (restocking) for 56 days (pre-release, 126 days total), with the introduction of G3 reared separately when progeny numbers were too low.
01 (strong effect) to 1 (no effect). The modi er was applied to the base mating probability, regardless of the genotype at locus 1. Base parameters and genotype speci c modi ers for both environments are reported in the Suppl. Tables  8  and 9. The model is implemented in the python programming language, using the numpy  31  , scipy  32  and pandas  33  libraries. The model code and parameter les for each presented run are available on github at https://github. com/khalillab/largecage, together with a snakemake 34 pipeline for reproducibility.
Amplicon Sequencing And Analysis Analysis of gene drive target site resistance by deep-amplicon sequencing was performed as previously described in  7  , with the difference of using exuviae from the restocked samples instead of L1 larvae. Samples were collected in duplicate from both experimental large-sized cages at 4, 21, 49, 84, 105, 203, 231 and 273 days post-release, and genomic DNA extracted in pool to investigate presence of mutations at the target site of Ag(QFS)1. The libraries were prepared following the Illumina 16S Metagenomic Sequencing Library Preparation protocol and the Nextera XT index kit. The raw sequencing data were analysed using CRISPResso2  35  , setting the minimum average read quality score (phred33) to 30 and a window of 20 bp surrounding the cleavage site was preferred to evaluate indels and substitutions as previously described  22.
3A, C ). In the 'gene drive + anti-drive' cage, the genotype frequency of the Ag(QFS)1 did not exceed the 56% (day 14), then progressively declined (as the anti-drive frequency increased), oscillating between 20% and 5% in the last 60 days of the experiment (Fig. 3B ). The frequency of the anti-drive individuals increased from 19% at the time of release, up to 70% after 60 days, and above 88% from day 140 onwards. These data support the e cacy of the anti-drive construct to block the spread of a highly invasive gene drive and to prevent the reduction in population size and eventual collapse. We performed pooled amplicon sequencing of the gRNA target site on samples collected at early and late time points after the gene drive release to monitor for gene drive resistance. The frequency of all indels observed around the target site did not exceed 0.
Acknowledgements thank  Jarek Krzywinski  for useful comments on the manuscript. This work was supported by the  Defense Advanced Research Projects Agency  ( HR0011-17-2-0042 ) for this research to C. T. , M. G. , R. L. M. , A. GT. S. , R. M. , A. S. K. , A. S. , R. G. and A. C. The views, opinions and/or ndings expressed should not be.
Here we show that a simple iBAQ analysis performed from trypsin digest data can provide a good understanding if proteins of interest are abundant enough to achieve successful cross-linking data. Comparing our findings to large-scale data on diverse system from several other groups, we show that proteins of interest should be at least in the top 20 % abundance range to expect more than one cross-link found per protein. We foresee that this guideline is a good starting point for researchers who would like to use XL-MS to study their protein of interest and help ensure a successful cross-linking experiment from the beginning. Data are available via ProteomeXchange with identifier PXD045792.
Rather, information was evaluated on the entire proteome. It is no doubt that these in vivo studies have provided good cross-link proteome coverage from high to low abundance proteins, achieving cross-links from all cellular compartments. However, if the goal of the experiment is to study the structure and interaction partners of a specific protein assembly, the interference of other higher abundant proteins may result in insufficient cross-linking information for that system. A good balance must be reached where enough cross-linking information is captured for structural analysis and creating informative PPI networks, while still preserving a native environment and as many protein interactors as possible. It is clearly not optimal, and rather frustrating, to launch into an in vivo cross-linking experiment expecting to study a particular system, to finally achieve poor XL data on proteins of interest.
1 µg of peptides were analyzed using a nanoLC system coupled to an Orbitrap Eclipse™ Tribrid (Thermo-Scientific) mass spectrometer and separated on a 50 cm C18 column with a 2-hour linear gradient from 7 to 30 % solvent B (80 % acetonitrile, 0. 1 % FA) mixing with solvent A (0. 1 % FA) followed by a 30 min ramp up to 60 % solvent B. The data was acquired using data dependent mode with a 3 s cycle time. A survey scan was collected from 400-1700 in the Orbitrap (resolution 60k at m/z 200) with a maximum injection time of 50 ms and an AGC target of 4e5. Dynamic exclusion was set to 25 s targeting charge stated 2 -7. Peptides were fragmented using HCD at 27 % collision energy and acquired at a 30 K resolution. Raw files were analyzed using MaxQuant. XL. We used the raw files generated by Rey et al.
in 2021 23 (PXD021553) that were obtained from a biological triplicate on an Orbitrap Q-Exactive HF mass spectrometer (Thermo-Scientific) mass spectrometer. For consistency, we reprocessed these raw files with the latest version of MassSpec Studio (see below). Neisseria meningitidis membrane pellet analysis (Figure  1B ). Eight plates of Nm8013 ∆siaD were prepared as described above, harvested the following day, and washed in PBS. The sample was centrifugated for 20 minutes at 3,000 rpm and 4 °C and the supernatant was carefully discarded. The pellet was resuspended and incubated with Lysis buffer (HEPES 50 mM, NaCl 300 Mm, Lysozyme 100 µg/ml, Glycerol 5 %, Benzonase Nuclease ® 2. 5 µl, EDTA 0. 1 mM, MgCl2 1 mM, pH 8. 0) for 3 hours at 4 °C with a rotational shaker, then followed by two steps of sonication for two minutes each at 4 °C, 10 second intervals and 30 % amplitude.
Released peptides were pipetted and transferred into the injection vial. Eluted peptides from PCAB were analyzed by nanoLC-MS/MS using a Vanquish Neo UHPLC system (Thermo Scientific) coupled to an Orbitrap Eclipse™ Tribrid mass spectrometer fitted with an EASY-Spray™ Source (Thermo Scientific). Peptides were separated on an EASY-Spray TM PepMap TM Neo (75 µm × 500 mm, 2 μm particle size, 100 Å pore size, Thermo Scientific, San Jose, CA) heated at 60 °C with a 3-hour linear gradient from 8 to 30 % solvent B followed by a 35 min ramp up to 60 % solvent B. The data was acquired using data dependent mode with a 3 s cycle time. A survey scan was collected from 300-1500 in the Orbitrap (resolution 60k at m/z 200) with a maximum injection time of 100 ms and an AGC target of 4e5. Dynamic exclusion was set to 30 s targeting charge stated 3 -8.
Peptides were fragmented using HCD at 27 % collision energy and MS/MS spectra acquired at a 30 K resolution. Identification of cross-linked peptides. All raw files were analyzed using MassSpec Studio 29 (v2. 4) including those from Rey et al. 2021  23  for consistency. Processed files were exported as spectrum matches and processed using in-house scripts to accept all identifications within a 1 % FDR and remove duplicates within and between replicates. PPI networks were created using Cytoscape (v3. 9. 1). The number of cross-links per protein were calculated by taking a unique list of all proteins found (Protein 1 and Protein 2) when filtering labeled peptides with the type: "cross-linked" and counting the frequency of their appearance in the cross-link data set. Whole cell and membrane cross-link data can be found in Table  S1.
iBAQ and average cross-link analysis. Proteins were ranked by decreasing order of abundance calculated by the iBAQ (intensity-based absolute quantification) value from MaxQuant. The iBAQ is calculated by dividing the total sum of all peptide peak intensities by the total number of theoretical observable peptides for a given protein  30. Their percent abundance was calculated by their ranking from the total proteins identified and grouped into 5 % protein abundance intervals. Cross-linked proteins and their calculated cross-linked frequencies were matched to those found in the bottom-up data. If a protein was not found to be cross-linked, the cross-link frequency was assigned to 0. An average per protein group was then calculated and plotted alongside the decreasing iBAQ values. Data availability.
With a well-enhanced membrane proteome, we repeated a triplicate Figure  3. Comparison of three membrane assembly abundances and cross-link information obtained on their networks. Proteins from the ATPase (pink), BAM (orange), and T4P (green) are highlighted in both the membrane pellet (A) and whole cell (B) conditions. The networks on these three assemblies obtained from cross-linking experiments are given to the right of the graphs. Average crosslink per 5 % abundance percentile range is given as a bar graph for the membrane pellet cross-link data set (A) and coverage of cross-linked proteins is shown in blue. cross-linking experiment this time on the membrane pellets to see if it changed the number and nature of the cross-links seen and how much further into the membrane proteome we could probe.
As in Figure  2 , we used our cross-linking dataset from the membrane pellet to re-calculate the average cross-links per abundance percentile (Figure  3A ). In blue are all the proteins we found cross-linked from the iBAQ curve our membrane digest showing good coverage of cross-linking data on the range of proteins present in the cell membrane pellet. Plotting the average cross-links obtained per 5 % abundance increments, we again see that most of the cross-links occur in the top 5 % of proteins with a drastic decrease in the following interval ranges. Interestingly, the point at which an average of less than one cross-link per protein is reached is maintained around 20 %. The minimum abundance range here is pushed slightly to 25 % but only with a mere increase of an average of 2 cross-links per protein despite almost 4x more identifications.