dataesr/scientific-paragraphs-categorization huggingface dataset:
We found that under external mixing assumption the models tend to overestimate ω 0,440 by 0. 03-0. 04 (3-5 %), while they tend to underestimate it under internal mixing assumptions. It should be noted that, although the relative bias is apparently low, it is comparable in magnitude to the dispersion of the data (the standard deviation is 0. 06 and 0. 12 over Europe and North America, respectively). The CSBC case has a negative bias of the same order of magnitude as the EXT case, while the HOM and CSBCV cases have a relative bias a factor of 3 higher (-12/ -15 %). This is consistent with previous findings that the homogeneous internal mixing is unphysical, because perfect stirring of black carbon inside a particle is impossible, and exaggerates the BC absorption enhancement  (Bond et al. , 2006 (Bond et al.
440. However, experimental data suggest that AAE 675 440 should decrease with wavelength (a fact that is confirmed by most models in the CSBC case), and thus HOM and CSBCV tests might be predicting a correct spectral dependence of the aerosol absorption for the wrong reason. In conclusion, this work suggests that the combination of external and core-shell mixing state has the potential for a realistic representation of atmospheric aerosol absorption and its spectral dependence. However, the validation of model calculations using only sunphotometer retrievals as the term of comparison is not exhaustive. Further evaluations against more comprehensive campaign data that include a full characterization of the aerosol profile in terms of chemical speciation, mixing state, and related optical properties (such as in the study recently reported by  Wang et al.
Acknowledgments Acknowledgements. The group from the  University of L'Aquila  kindly thanks the  EuroMediterranean Centre on Climate Change (CMCC)  for the computational resources. Paolo Tuccella  is the beneficiary of an  AXA Research Fund postdoctoral  grant. The contribution from CIEMAT was kindly supported by the  Spanish Ministry of Agriculture and Fisheries, Food and Environment. The views expressed in this article are those of the authors and do not necessarily represent the views or policies of the  U. S. Environmental Protection Agency. We thank two anonymous referees whose comments helped improve the robustness and clarity of the presented results. Two anonymous referees from the  U. S. EPA  provided suggestions on the first submitted version of the manuscript.
Crucially, we varied independently three parameters -actin network density, ADF/Cofilin concentration and network width -and measured their effect on the network length. Mathematical modeling was very successful in deciphering the data from in vitro experiments on the actin disassembly  (Roland et al. , 2008; Berro et al. , 2010; Michalski and Carlsson, 2010, Michalski and Carlsson, 2011; Reymann et al. , 2011; Stuhrmann et al. , 2011; Tania et al. , 2013). Most theoretical studies either considered the disassembly of individual filaments  (Roland et al. , 2008) , or a first-order reaction of a continuous network density decrease  (Ofer et al. , 2011; Reymann et al. , 2011; Stuhrmann et al. , 2011) , or treated the disassembly as a boundary condition  (Raz-Ben Aroush et al. , 2017). Pioneering theory of  Michalski and Carlsson (2010) ,  Michalski and Carlsson (2011)  demonstrated how fragmentation of the network at the trailing edge resulted from stochastic accumulation of discrete disassembly events in the network.
Image acquisition Image acquisition was performed using an upright Axioimager M2 Zeiss microscope equipped with an EC Plan-Neofluar dry objective (x20, NA 0. 75), a computer controlled fluorescence microscope light source X-Cite 120PC Q (Lumen Dynamics), a motorized XY stage (Marzhauser) and an ORCA-ER camera (Hamamatsu). The station was driven by MetaMorph software (Universal Imaging Corporation). The growth rates were calculated using ImageJ software.
We represented the network as an undirected graph using Matlab routines, allowing to quickly determine connected components, which can be a time-consuming step. Edges that are connected to only one node can be represented as a loop, that is we formally connect both edge ends to the same node. Finally, if a node has two or three edges that are connected to only this node, this can be accounted for by assigning a weight of two or three to that edge. This is necessary to keep edges unique in the graph-based description. For example, a weight of two means that this edge counts twice when determining actin densities.
Acknowledgements This work was supported by grants from  European Research council  ( 741773 (AAA) ) awarded to LB,  Agence Nationale de la recherche  ( MaxForce ,  ANR-14-CE11 ) awarded to LB and MT. AI was supported by a fellowship from  Universitede Grenoble Alpes. AM and AM are supported by  US Army Research Office  grant  W911NF-17-1-0417.
In Section 3 we formalise our proposal in the general setting of event/state linear temporal logic. Section 4 presents a prototype implementation of the proposed technique in the Electrum Analyzer  [2] , the model checker for the Electrum language  [8] , an extension of Alloy  [7]  with linear time temporal logic. The goal of this prototype is mainly to show the viability of the approach, namely in terms of user-experience and efficiency. Section 5 wraps-up the paper and presents some ideas for future work.
Implementation Electrum is an extension of the popular Alloy formal specification language, developed for the analysis of dynamic systems. An Alloy model consists of a set of static signatures and relations (of arbitrary arity). Properties can be specified in an extension of first-order logic: apart from the standard connectives and quantifiers, Alloy supports closures and some derived relational logic connectives, such as composition ( ) or Cartesian product (→). To make the verification decidable, the user must specify a scope setting the maximum size of all signatures. Counter-examples are depicted graphically with user-customisable themes. In Electrum, signatures and relations can be declared mutable (with keyword var) and properties can be specified using linear temporal logic connectives (including past ones) and primed expressions (denoting their value in the next state) in addition to Alloy connectives.
In Fig. 2  we can see that after check-in the only options are for the first guest to check out or enter the room. Unlike in the previous version of the Analyzer, it is now easy to understand that, for the given scope, the check-in of the second guest must necessarily be followed by an entry or reentry of the first guest, and there are no other possibilities to breach safety. To assess the efficiency of the proposed technique, we measured the required time to determine which operations are enabled in the different states of the first counter-example returned by the Analyzer. Table  1  shows the results of this preliminary evaluation for different scopes. The first column (C) shows the configuration (number of guests and a list with the number of keys per room), the second the time (in seconds) to compute the first counter-example (T ), and then, for each state i, the total time (in seconds) to compute which event types are enabled (T i ), and the set of enabled events (a i ), with the subscript of each event type identifying the guest involved, and also highlighting the event chosen to be executed in bold.
Acknowledgements This work is financed by the  ERDF -European Regional Development Fund  -through the  Operational Programme for Competitiveness and Internationalisation -COMPETE 2020  -and by  National Funds through the Portuguese funding agency, FCT -Fundação para a Ciência e a Tecnologia , within project  POCI-01-0145-FEDER-016826 , and the  French Research Agency  project  FORMEDICIS ANR-16-CE25-0007. The third author was also supported by the  FCT  sabbatical grant with reference  SFRH/BSAB/143106/2018.
Results From October 2014 to September 2016, 2024 individuals attended to in the six hospitals were screened for strongyloidiasis and 77 of them were excluded from the study because either the screening had not been done with a serological test or it could not be assured that a systematic approach had been used in several units of one hospital. Therefore, 1948 individuals were finally included. Basic demographic data are summarized in Table  1. The median age was 38 years (Interquartile range 31-47) and 57. 96% were male; 804 (41. 27%) were screened at infectious disease or tropical disease units, 709 (36. 40%) in HIV-units, 150 (7. 70%) in transplant units, 101 (5. 18%) in oncology or hematology units, and 86 (4. 41%) in rheumatology or autoimmune disease departments or other units specifically addressing potentially immunosuppressed patients.
stercoralis prevalence in people coming from Asian countries. The seroprevalence in women was found to be significantly higher than that of men, which differs from other studies, where the prevalence was more frequent in males  [31]. One possible explanation for this is the large contribution of Bolivian women to the population tested in our dataset, which have a high seroprevalence. In our study, the prevalence in HIV-infected individuals and patient from transplant units, but not necessarily immunosuppressed individuals, was much lower compared with other general out /in-patient unit services of the hospitals. One possible explanation that could have contributed to the lower prevalence found in potentially immunosuppressed patients is that the serology had a lower sensitivity in immunosuppressed patients, as has been reported in other studies  [18].
Statistical Analysis Categorical variables were described using a frequency distribution and the median and interquartile range were used to describe age. Countries of origin were subsequently grouped into different geographic areas according to the criteria that could more precisely identify the migration patterns. The GeoSentinel geographic area distribution was selected  [33]. Prevalence point estimates and their 95% confidence intervals (95%CI) were obtained. We used Fisher's exact test to compare percentages. Data were managed and analyzed using STATA 13 (StataCorp LLC, Lakeaway, Texas USA). We built a world map with country of acquisition's prevalence using the free software QGIS, version 2. 18. 22. Hospital units identified as attending to potentially immunosuppressed patients were: hematology, oncology, HIV, autoimmune diseases, rheumatology, transplant units and oncology.
Targeted Sanger-based screening of relatives following the diagnosis of probands with NPC1/NPC2 variants confirmed a high prevalence of NP-C carriers in some regions. Based on a Turkish National Registration Database, Topcu et al. screened 510 family members of four NP-C probands with data suggestive of consanguinity. Two new NP-C patients (0. 4%) from two families were identified  [34]. Notably, the overall frequency of heterozygous NPC1/NPC2 carriers in this cohort was 22. 7%. Cohort studies have also been published in which no patients were diagnosed with NP-C using Sanger sequencing. Among 50 adults with early-onset neurodegenerative dementia and atypical symptoms ('dementia plus syndrome'), Cupidi et al. only observed four individuals with single NPC1 or NPC2 variants  [35] , and suggested a possible contributing role for NPC1/NPC2 variants in these cases.
3%)  [54] , and the lifetime risk of NP-C as a cause of PIND among children was estimated at 0. 38 per 100,000 live births. Notably, both of these studies highlighted high rates of PIND in areas with higher rates of consanguinitya recognised predisposing factor in NP-C  [15]. Data mining studies, where pre-existing databases are examined to generate new data, represent another form of retrospective, file-based patient screening. A UK study compiled information from regional and national patient registries, reporting a higher prevalence of autosomal recessive conditions (including NP-C) in an ethnic subpopulation (N = 13,000) versus the general population  [55]. Similar to the BPSU health surveillance studies  [53, 54] , this finding served to highlight the influence of consanguinity/endogamy on the prevalence of autosomal recessive diseases in some UK communities.
Study design Key factors that should be addressed in designing a screening study include: 1) identification and selection of an appropriate patient cohort based on available data (e. g. , from living patients, biobank samples, medical charts); 2) the use of optimal and accepted diagnostic method(s) (see Diagnostic methods); and 3) relevant local factors (e. g. , available expertise, funding, regulations). Consider which at-risk patient populations might include 'hidden' ultra-rare IEM patients. Define simple, concise screening objectives addressing appropriate clinical disease phenotypes. Involve the lay-community through medical education on IEM natural history (as for NP-C).
The potential application of NGS methods as the initial (first-line) diagnostic test in an ultra-rare IEM depends on available resources, genetic mutation types and complexity, disease awareness, and the nature of the disease and patients/cohorts in question. For instance, genetic analysis of FMR1 variants would not work as a screening method for Fragile X syndrome. Nevertheless, based on experience to date in NP-C, the potential inclusion of ultra-rare IEM genes in large NGS gene panels holds great promise for future screening protocols. The use of WES and WGS databases is growing, and ultra-rare IEM gene databases are increasingly becoming interconnected and/or made public. Where possible, an 'exome-first' approach, where WES is conducted as a first step to identify potential new cases in at-risk cohorts, may provide a more direct route to NP-C diagnosis  [63].
Acknowledgements Matthew Reilly PhD  provided medical writing support in the preparation of this manuscript, paid for by  Actelion Pharmaceuticals Ltd. Matthis Synofzik  is supported by the  Else-Kröner Fresenius Stiftung  and the grant  779257  " Solve-RD " from the  EU   Horizon 2020 research and innovation programme. Maria-Jesús Sobrido  receives research funding from the  Instituto de Salud Carlos III  (grant  PI17/01582 ).
2015 [27] Adolescents/adults with unexplained EOA 96 Germany NPC1/NPC2 (NGS gene panel b ) Marelli et al. 2016 [40] Adolescents/adults with probable EOA 33 France NPC1/NPC2 (NGS gene panel) Pyle et al. 2015 [41] Adult patients with inherited and sporadic 35 UK NPC1/NPC2 sequencing (WES) ataxias McKay et al. 2014 [42] Infants with jaundice/cholestasis 228 UK NPC1/NPC2 sequencing (WES) Herbst et al. 2015 [43] Infants with jaundice/cholestasis 6 Germany NPC1/NPC2 sequencing (NGS gene panel) Mavridou et al. 2014 [70] Family members of NP-C patients 153 Greece NPC1/NPC2 sequencing a , RFLP analysis Wassif et al. 2016 [14] Subjects from 4 WES sequencing projects 17,754 c International Historical WES + WES from public databases Blood biomarker screening Reunert et al. 2016 [44] Patients with suspected NP-C 1800 Germany Oxysterol level (C-triol) Ribas et al.
The phylogenies of the components of the type IV filament super-family The presence of homologs of the major functional components of the TFF-SF in most types of systems raises the question of how their functional diversification took place from a common ancestor. To study this, we added to the models described above a very simple generic model to identify all systems that have a minimal number of essential components (the ATPase, the integral membrane platform, and a major pilin) (Fig. S1 ). The search for systems using the MacSyFinder models resulted in the identification of 6652 systems in 3700 genomes (1486 species) (Fig S2 ), of which 1584 were classed as generic systems, reflecting the conservative character of the initial models. This dataset was too large to analyse using sophisticated phylogenetic methods and included many systems that were very similar, e.
g. from different strains of the same species. We reduced this redundancy by clustering very similar systems. We then picked one representative per cluster, thus preserving most of the diversity of the dataset. In this process, we prioritized the inclusion of experimentally validated systems, including MSH (1), and T4bP (5) for which models were not available (see Methods). This non-redundant set contains 309 representative systems  (33 T4aP, 47  Archaeal-T4P, 29 T2SS, 5 T4bP, 1 MSH, 31 ComM, 72 Tad, 101 generic) (Table  S2 ). Hence, the systems used in the subsequent analyses are associated with a (sometimes large) number of other very similar systems that are from the same cluster. We inferred the phylogeny of each of the five key protein components (AAA+ ATPase, IM platform, major pilin, secretin and prepilin peptidase) by maximum likelihood with IQ-Tree  [51].
Apart from these duplications that span large numbers of systems, there were other duplicates in the systems that were rare (present in 8% of the representatives' dataset) and dealt with a de-replication procedure (see Methods). The prepilin peptidase tree is poorly supported and shows scattered distribution of the different types of systems. Since prepilin peptidases from one type of systems can be used by different systems  [52] [53] [54] , we have excluded them from further analyses. The secretin and major pilin trees have some poorly supported branches, but they separate well the different systems. Overall, the protein components' trees show that the ATPase, the IM platform and the major pilin are phylogenetic markers that provide significant information about the evolution of the super-family.
DISCUSSION We used comparative genomics and phylogenetics to produce models and protein profiles that identify TFF-related systems in genomic data of all Prokaryotes. They are publicly available and provide a significant advance relative to our previous work, since they are more sensitive and cover more types of systems (Archaeal-T4P, ComM, MSH and T4bP). We used them to quantify the frequency and taxonomic distribution of the different systems. Strikingly, every inspected phylum of Prokaryotes has some type, and often several types, of systems from the TFF-SF. Some types are more widespread (e. g. T4aP, Tad) than others within the limitations of the taxonomic coverage provided by current genome databases. Notably, the MSH and T4bP, for which few experimentally validated systems are known, are abundant in Proteobacteria, but absent from most other phyla.
Detection of the TFF super-family All the systems of the family were detected using MacSyFinder v1. 0. 2  [46]. This program uses a model to identify a type of system in a DNA sequence (typically a replicon). The model specifies the components of the system, each represented by a HMM profile, and how their systems are organized in the sequence. A full description of the program and the models can be found in: http://macsyfinder. readthedocs. io/en/latest/index. html. Briefly, the components can be mandatory, accessory, or forbidden. A system is only validated if it fulfils a quorum of mandatory (MMGR: minimum mandatory genes required) and/or mandatory + accessory genes (MGR: minimum genes required). A locus is excluded if it contains a forbidden gene (these are useful to discriminate between closely related systems with a few specific components).
This resulted in a list of systems in which we had strong confidence. However, it also missed many systems. To identify these systems, we built a model called generic that had only the basic building blocks of these systems, with all the homologous proteins set as "exchangeable". Following the comparative and phylogenetic analyses we re-defined all the models to make less-initial models that could identify a larger number of systems. Both sets of models are made available in Dataset S1. The table with all protein profiles is given in Table  S4. Generic. We defined the model called generic to search for variants of the TFF-SF that include the key components but do not fit the strict definitions of the more specific models (T4aP, T2SS, etc). This model assumes that all the HMM profiles of the same connected component in the profile-profile graph of similarity can fill in for the function.
The final model of T4P includes pilW, pilX and pilY as new accessory components. We decreased MMGR to 4 and MGR to 5, which fits better the data. We set fimT, pilM, pilP and pilA as accessory, to help MacSyFinder to search more complete T4P in the genome. We also removed the forbidden genes gspN, tadZ and gspC. T2SS. The initial model of T2SS Tad followed closely the definitions proposed in  [41] , where we increased the MGR to 8 and set the prepilin peptidase gspO as mandatory, loner and exchangeable with a number of homologous components from the T4aP (pilD). The final model was relaxed to identify a larger fraction of the systems. We reduced the MMGR to 4 and the MGR to 5. To fit the data better, we added the prepilin peptidase of ComM (comC) as another exchangeable gene of gspO. We set gspC gene as mandatory, gspM as accessory and gspD was set as loner to fit better the data.
Retrieval and construction of protein profiles We retrieved 37 profiles for T2SS, T4aP and Tad from TXSScan  [41]. For two HMM profiles of T4aP that combine the detection of two proteins paralogs (T4P_pilT_pilU and T4P_pilAE), we decided to separate from the original alignment of this profile the sequence of the different proteins to generate five separate HMM profiles (T4P_pilT, T4P_pilU, T4P_pilA, T4P_fimT and T4P_pilE). To create the HMM profiles, we used the following methodology. For the genes that had few representatives in the experimentally validated dataset, we used BLASTP v 2. 5. 0+ (default settings, e-value < 10 -6 )  [103]  to search for homologs among complete genomes. To remove very closely related proteins, we performed an allagainst-all BLASTP v2. 5. 0+ analysis and clustered the proteins with at least 80% sequence similarity using SiLiX v1.
2. 10-p1 (default settings)  [104]. We selected the one sequence from each family as a representative. We aligned all the representatives using MAFFT v7. 273 (--auto, automatic selection of the parameters depending of the size of the alignment, default values for the other parameters)  [100]. With SEAVIEW  [105] , the poorly aligned regions at the extremities were manually trimmed in the alignment. The trimmed alignment was used to build the HMM profile using hmmbuild (default parameters) from HMMER package v3. 1b2  [106]. For the HMM profiles of the final model, we used the sequences of the profiles describe above. Using the information of the phylogeny of the systems, we added sequence of systems that was annotated as generic but that clustered in a group of experimentally validated systems.
We aligned all the representatives using MAFFT v7. 273 (--auto, automatic selection of the parameters depending of the size of the alignment, default values for the other parameters)  [100]. With SEAVIEW  [105] , the poorly aligned regions at the extremities were manually trimmed in the alignment. The trimmed alignment was used to build the HMM profile using hmmbuild (default parameters) from HMMER package v3. 1b2  [106].
Phylogenetic inference Phylogenetic analyses based on protein sequences involved an initial alignment of the sequences using MAFFT v7. 273 ("linsi" algorithm)  [100]. Multiple alignments were analysed using Noisy v1. 5. 12 (default parameters)  [107]  to select the informative sites. We inferred maximum likelihood trees from the curated alignments, or their concatenates, using IQ-TREE v 1. 6. 7. 2  [51]  (options -allnni, -nstop 1000, -nm 100000). We evaluated the node supports using the options -bb 1000 for ultra-fast bootstraps and -alrt 1000 for SH-aLRT  [108]. The best evolutionary model was selected with ModelFinder (option -MF, BIC criterion)  [109]. We used the option -wbtl to conserve all optimal trees and their branch lengths. The phylogenetic trees of 16S rRNA sequences were built from a dataset including one sequence per genome of 5776 genomes.