dataesr/scientific-paragraphs-categorization huggingface dataset:
Only pixels with at least a 5 % FLC occurrence frequency in the climatology (as in  Andersen et al. , 2019)  are used. A specified averaging time period is needed to avoid statistically mixing two separate FLC events occurring on successive nights, which would be the case in a daily average FLC occurrence data set. The specific time period is chosen to include all periods of the diurnal cycle, with FLC occurrence rising, peaking, and starting to dissipate  (Andersen and Cermak, 2018)  during this time. The spatial and temporal averaging is illustrated for an exemplary day in Fig. 1a. While the specific day shown here is arbitrary, the general feature of maximum FLC cover in the early morning hours and rapid decline shortly after sunrise is typical of the region  (Andersen and Cermak, 2018).
For further analyses, the data set is divided into "FLC days" with mean regional FLC cover exceeding 50 % between 03:00 and 09:00 UTC and "clear days" with mean FLC cover below 3 %. These thresholds are chosen to represent two clearly separated parts of the FLC cover distribution that occur with similar frequencies. The resulting distribution of daily average FLC cover and the number of cases in each class are shown in Fig. 1b. As the time of sunrise varies by season, the constructed data set is likely to feature a seasonal bias in FLC occurrence. It should be noted that this has no effect on the separation of FLC days and clear days within seasons, the analysis of which is the main purpose of this data set. The resulting monthly average FLC cover in the central Namib (Fig. 1c ) should not be used in a quantitative sense but rather to illustrate the general seasonal cycle of FLCs in this region.
ERA5 reanalysis To investigate the large-scale meteorological conditions associated with FLCs in the central Namib, ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) are used. ERA5 is the new generation of reanalysis and follow-up of ERA-Interim  (Dee et al. , 2011). In comparison to ERA-Interim, it features higher spatial (0. 25 • ) and temporal (hourly) resolutions, along with other improvements  (Hersbach, 2016). In the context of this study, 14 years (2004-2017) of meteorological fields are analyzed. To characterize large-scale dynamic and thermodynamic conditions, fields of mean sea level pressure (MSLP), geopotential height at 500, 700, 850, and 925 hPa (Z500, Z700, Z850, and Z925), 2 m air temperature (T2m), sea surface temperature (SST), total columnar water vapor (TCWV), specific humidity (Q), as well as winds at 10 m and at all ERA5 pressure levels between 1000 and 500 hPa, and lower tropospheric stability (LTS; computed as the difference between potential temperature at 700 hPa and T2m,  Klein and Hartmann, 1993)  are used.
By doing so, the back trajectories represent air masses for the levels where fog and low clouds in the region are typically observed  (Andersen et al. , 2019). In order to obtain insights about the physical properties of the air masses, the temperature, potential temperature, specific humidity, and relative humidity are tracked along the trajectories. The location is chosen, as it is the main site of both historic and present-day scientific activity in the region  (Lancaster et al. , 1984; Seely and Henschel, 1998; Kaseke et al. , 2017; Spirig et al. , 2019). (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017). Observations are separated in two classes: clear days (< 3 % mean FLC cover) and FLC days (mean FLC cover > 50 %). Days with mean FLC cover between 3 % and 50 % are not considered in analyses based on this classification (2418 cases for 404 d of FLC cover could not be computed due to missing data or complete coverage with higher-level clouds).
The wind fields are first remapped onto a 1 • regular grid (PCAs are computationally expensive;  Pham-Thanh et al. , 2019). Then daily 06:00 UTC anomalies are computed by subtracting the 14-year climatological average wind components at each grid point. The PCA is applied to the covariance matrix of both components in the domain. Remapping to 1 • resolution allows not only an accurate description of the atmospheric variability at synoptic scale but also a smoothing out of the variability associated with small-scale effects. The sensitivity of the PCA to the spatial resolution is tested by conducting the analysis based on wind fields remapped to a 2 • resolution. The re-sults of the two PCAs at the different resolutions are very similar, demonstrating their robustness. Daily anomalies are computed with respect to the 14-year sampling of the FLC data set, in order to compare wind and FLC variability over a homogeneous climatology.
001; Fig. 3a ), in agreement with the distinct niches observed in hospital environments (Fig. 2a-c ), and indicated that enrichment cultures could capture a diverse set of species. We exploited this observation to use a culturing, antibiotic selection (five antibiotics) and metagenomic nanopore sequencing approach (Fig. 1b ) to obtain a large database of high-contiguity assemblies (n = 2,347) from the hospital environment (median N50 > 1 Mb; Fig. 3b , Supplementary Data 3 and Methods), expanding substantially on genomic resources established by previous studies  10, 11. Overall, a large percentage of sites led to viable cultures (>95%), with antibiotic selection resulting in growth in >80% of plates (1,495 of 1,790) and >42% of sites resulting in cultures for all five antibiotics. Control swabs led to no.
Articles NATure MeDiCiNe cultures (0 of 10), confirming that cultures were not likely due to contamination (Methods), and further testing of isolates confirmed that the vast majority of strains in the cultures were likely to be antibiotic resistant (99%; Supplementary Note 2). DNA was extracted from 1,661 plates and sequenced on a GridION to provide 535 Mb of data on average per sample (median read length > 2. 5 kb). Long-read metagenomic assembly enabled the reconstruction of megabase-pair sized contigs (versus average N50 < 5 kb for Illumina assemblies) as the communities were largely simple (Supplementary Fig. 8 , Fig. 3b  and Methods ). Evaluation of these draft genomes based on conserved single-copy genes confirmed that they were of high quality (completeness > 99%, contamination < 0.
5%; Methods). In total, we obtained genomes for 69 species from the hospital environment, 40% of which belonged to common pathogens (Methods). Our results confirm the viability of these species in different hospital environments and the ability to enrich them for sequencing and genome reconstruction despite their low abundances in hospital microbiomes (median relative abundance = 0. 68%, averaged across species; Fig. 3c ). Large-scale homology analysis with public databases  [31] [32] [33]  also helped to identify 13 (out of >80) species-level clusters (11 different genera including Bacillus, Pseudomonas and Staphylococcus) with no representatives from known species, highlighting recovery of high-quality genomes for novel species using this approach (Methods). Rarefaction analysis showed that >90% of the species and resistance gene diversity (>50% richness) that could be sampled from sites in this study was captured by our sample size (Supplementary Fig.
5 times the interquartile range (outlier points are not included in the visualization). g, Heat map depicting the frequency of detection for beta-lactamases at different sites in hospital wards. Multiple carbapenemases and the mecA gene were detected as part of the resistomes that were primarily defined by the community types (CTA and CTB). As plasmids and phages serve as an important medium for the evolution and spread of ARGs and emergence of multidrug resistance  34, 35  , we characterized corresponding sequences in our genomic database (Methods). In total, we recovered 696 Mb of plasmid sequences (n = 5,910 closed and 493 Mb of linear fragments) and 63 Mb of phage sequences (n = 1,693, of which 277 are circular), most of which are not present in existing databases for plasmids  36  or phages  37  (>90%; 1,505 of 1,588 plasmid clusters and 501 of 557 phage clusters; Methods) despite being commonly distributed in the hospital (Supplementary Fig.
10 ), highlighting its underexplored genetic diversity. Many closed plasmids were >100 kb long (>9%, n = 536), rich in repeats and present at low abundance, impeding characterization using Illumina metagenomics. We noted the presence of several large mecA-carrying plasmids that contained antiseptic or disinfectant resistance genes (qacA or qacC), a combination that is not represented in existing databases  36  but is in agreement with high biocide resistance for MRSA in clinical settings  38. One of the plasmids had genes from several additional ARG classes that have not been seen in combination (for example, dfrC, lnuA and aac6-Aph2), highlighting the value of closed sequences for characterization of novel ARG combinations. The availability of a large collection of highly contiguous plasmid (closed) and chromosomal (megabase-pair contigs) assemblies allowed us to perform genomic relatedness (with environmental and patient strains) and structural (common gene cassettes and exchange across cassettes) analysis.
Comparing our database of 2,347 high-contiguity genomes and 5,910 closed plasmids against existing databases, we found that 34% of the ARG combinations observed were novel (255 of 752; Supplementary Data 4). Certain ARG combinations have obvious clinical importance, for example, the co-occurrence of mecA with fosB (fosfomycin resistance) in several environmental S. aureus strains, an observation that is concerning given the potential utility of fosfomycin for treating MRSA infections  42. Notably, we detected Enterobacteriaceae-associated genes that can confer resistance to gentamicin (aac3-IIa), fosfomycin (fosA, fosA2) and colistin (mcr1), all last-resort antibiotics for CRE infections. Additionally, two Enterobacteriaceae-associated plasmids, one carrying fosA and the other carrying mcr1, were obtained from the same bedside locker, highlighting the potential reservoir for emergence of co-resistance to colistin and fosfomycin.
Notably, genes that are widely distributed across plasmids (for example, tetK, far1 and blaZ) can come together in a novel, clinically relevant backbone (Fig. 4b ; Slu3, with 38 sequences in our database), as described for a cytotoxin-producing MRSA strain  48. While the previously isolated strain was resistant to fusidic acid and tetracycline, but susceptible to erythromycin and clindamycin, we noted the presence of a common plasmid backbone in our database (Sha2 with 88 sequences) that carried a new combination of resistance genes for all four antibiotics (Fig. 4b  and Supplementary Data 4). Similarly, we observed that ARGs found in phages, such as aac6-Aph2 and far1, tended to be more widely present (Fig. 4  and Supplementary Fig. 13a ), with evidence for recent phage-mediated dissemination of far1 across Staphylococcus species (Supplementary Fig.
Hospital-environment strains overlapping with patient isolates are globally disseminated and enriched for multidrug resistance. The availability of a large database of genomes from many species in the hospital environment, an obvious hub for patient colonization, prompted us to ask how environmental strains are related to patient-colonizing strains. To examine this, we constructed phylogenetic trees for environmental strains and patient isolates across species (Fig. 5 ). We started with Singaporean E. anophelis isolates from a 2012 outbreak  49  (n = 10) and an additional set of patient isolates from 2009-2012 (n = 52; Fig. 5a  and Methods ). Despite sampling from different Singaporean hospitals after a span of 5-8 years, patient-associated genomes matched environmental genomes with just 16 SNPs (s1; 99.
29  ). We confirmed detection of these rifampicin-resistant  29  lineages in our data (Fig. 5d ), with 80 SNPs (99. 997% ANI) from our hospital-environment genomes. One other lineage (ST16) not known to be globally disseminated (isolated from a patient sample in the United States  29  ) was represented by a genome in our database with similarity at the derivative threshold (99. 991% ANI). Finally, we found that the overlap between S. epidermidis patient isolates (surveillance samples from Austin Health in Australia  29  ) and environmental genomes from this study was enriched for multidrug resistance in derivative clusters (binomial test P value < 4 × 10 -12 ). Together with the observation that multiantibiotic-resistant strains are persistent and widely distributed across the hospital environment (Fig.
Discussion While the importance of hospital design for preventing infections is known  52  , the utility of metagenomic surveys in medical facilities remains underexplored  12. A detailed survey helps provide a reference map (with three-dimensional (3D) visualization; https://github. com/csb5/hospital_microbiome_explorer) that can be updated based on periodic scans whose frequency and locations can be informed by the initial survey. For example, the turnover score and specificity of a site can determine whether and how frequently it should be sampled. Variations in human influence scores could fine-tune cleaning practices, and distribution of specific pathogens could inform infection control in outbreak settings. As genomics-guided infection control advances, this knowledge could feed back into better hospital designs.
With further improvements in the cost and ease of short-read sequencing, hospital-wide Genes are colored according to their respective antibiotic classes. b, Circles represent different plasmid clusters (95% identity), and their corresponding ARGs are connected by edges and indicated by diamonds. Plasmid nodes were labeled based on a three-letter short form for the host species and assigned a number (for example, Kpn1 for a K. pneumoniae plasmid); the number after the colon indicates how many representatives of the plasmid family were observed in the database. edges are weighted by the frequency at which a gene is present in a plasmid, and frequencies >80% are indicated with red solid lines. Genes and backbones are color coded according to their respective ARG classes and inferred host species for ease of reference.
While most studies have focused on patient isolates  58  , relatedness between environmental and patient-colonizing strains is important for understanding the risk that environmental strains pose  15, 17. For contemporary and co-located strains, high relatedness between a subset is expected. Despite samples being separated by >8 years, obtaining highly similar genomes suggests that large reservoirs of multidrug-resistant strains are maintained with limited diversification. The identification and elimination of these reservoirs may reduce the incidence of corresponding infections and the risk from maintenance of ARGs. Another interesting observation is the high genomic similarity between MDROs in Singaporean hospitals and those from patients globally. The consistency of these patterns across species emphasizes the global dissemination of newly emerging MDRO lineages; thus, the role of hospital environments deserves investigation, leveraging multinational metagenomic datasets  26.
In total, 1,752 swabs were collected from 179 sites in the hospital at three time points, representing 438 unique samples. Swabs were also collected from an office environment (Genome Institute of Singapore) with sites selected to approximately match those from which samples were collected in the hospital (aerator, sink trap, chair handle, office desk, keyboard and door handle; n = 30; Supplementary Data 1). MetaSUB Singapore samples were collected from high-touch surfaces in different parts of the city and analyzed based on MetaSUB protocols as described in Danko et al. 26  (n = 99; Supplementary Data 1). DNA extraction from swabs. DNA was extracted from swabs using a bead-beating and automated DNA purification system. Briefly, 300 µl of lysis buffer was added to Lysing Matrix E tubes (MP Biomedicals, 116914500).
Antibiotic susceptibility testing for E. anophelis isolates was performed with 13 antimicrobial agents (cefotaxime, ceftazadime, cefepime, imipenem, meropenem, ampicillin-sulbactam, piperacillin/ tazobactam, tigecycline, gentamicin, nalidixic acid, ciprofloxacin, levofloxacin and trimethoprim/sulfamethoxazole) using Etest strips (bioMérieux). Minimum inhibitory concentrations (MICs) were interpreted according to the Clinical and Laboratory Standards Institute (CLSI) guidelines for non-Enterobacteriaceae Gram-negative bacilli (performance standards for antimicrobial susceptibility testing, M100-S22 and CLSI 2012; Supplementary Data 6). Antibiotics to which all strains were resistant were excluded from statistical analysis. Antibiotic susceptibility testing for A. baumannii complex isolates was conducted with 11 antimicrobial agents (ampicillin/sulbactam, piperacillin/tazobactam, cefepime, imipenem, gentamicin, amikacin, ciprofloxacin, levofloxacin, trimethoprim/ sulfamethoxazole, minocycline and polymixin B).
Samples were multiplexed (9-12 samples for each pool for culture-enriched samples and 24 samples for each pool for isolates) and sequenced with MIN106, MIN106D or MIN107 flowcells on a GridION machine. Taxonomic and resistome profiling with Illumina shotgun metagenomic data. Illumina shotgun metagenomic sequencing reads were processed using a Snakemake pipeline (https://github. com/gis-rpd/pipelines/tree/master/ metagenomics/shotgun-metagenomics). Briefly, raw reads were filtered to remove low-quality bases using skewer (v0. 2. 2; -q 3 -l 30 -n) and human reads were removed by mapping to the hg19 reference using BWA-MEM (v0. 7. 10-r789).
Acknowledgements Funding for this work was provided by  A*STAR  (N. N. ), and we are grateful for support from  NMRC  ( NMRC CGAug16C005 : O. T. N. and K. M. ). C. E. M. acknowledges support from the  WorldQuant Foundation , the  Bill and Melinda Gates Foundation  ( OPP1151054 ) and the  Alfred P. Sloan Foundation  ( G-2015-13964 ). The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript. We would like to thank  J. Gilbert  for insightful comments and feedback on this work.
First, we use a 3 days old transgenic zebrafish larva (Tg(mpeg1:Gal4/UAS:Kaede)) in which green fluorescent protein Kaede is expressed under the control of macrophage-specific promoter mpeg1, so that macrophages produce the green fluorescent protein in their cytoplasm. After wounding of its caudal fin, migrating macrophages are imaged for 5 hours with a time step of 4 minutes and a z step of 4 μm using a Spinning disk Confocal microscope. Afterwards the three-dimensional (3D) images are projected onto a plane with the maximum intensity of 3D dataset overlap selected. The videos of the data are available on Zenodo (https://doi. org/10. 5281/zenodo. 3267228). We focus on the segmentation of a single macrophage in the resulting 2D image data, which is implemented by using two different approaches: automatic and semi-automatic (driven by an expert user).
As it is seen in Figure  7 , several values of the Hausdorff distance in the results from the E-SUBSURF method are high. The reason is that those final results contain boundaries of the background noise, and the noise is relatively far from the macrophages. Whereas, in case of the O-SUBSURF method, the large values of the Hausdorff distance are due to missing parts of the macrophages. For example, the largest Hausdorff distance from the O-SUBSURF method is 35 th image (the bottom images in Figure  1  and Figure  6 ), and its perimeter and area also show the biggest difference from those of the semi-automatic method. Contrarily, data with big Hausdorff distances in the E-SUBSURF method do not show striking differences from the perimeter and area of the semi-automatic method (Figure  8 ). To indicate how big the Hausdorff distances are in terms of macrophage size, the ratio of the Hausdorff distances and the perimeters is measured.
Conclusion The segmentation of a single macrophage from 2D data was achieved via automatic and semi-automatic methods. In the former, two types of thresholding techniques were carried out and used as the initial functions of the classical SUBSURF method, while in the latter, the Lagrangian method was utilized as the gold standard for examining the automatic segmentation method. The final segmentation functions from the classical SUBSURF method were scaled into interval [0, 1] with the selected middle value. The performance of automatic segmentation was measured with the Hausdorff distances in a view of matching the boundary shapes with those from the semi-automatic method. In addition, geometric information (perimeter, area) and its difference between the two methods was calculated. Based on these measurements, the combination of the level-lines from O-SUBSURF and E-SUBSURF gave reasonable results when compared to the semi-automatic segmentation.
Measurements Publicly available measurements of PM 2. 5 , where they exist, are generally of background levels in selected areas within cities  [10]. The reason for this is usually just economics; the reference grade equipment for measuring air quality is expensive  [11] , so that installations are rare and end up being far apart. Data from the instruments are used to give an averaged level of pollution across wide areas  [10]. These networks are usually operated by government agencies and are the main source of information about urban air quality for the public. Concentrations of pollutants from traffic are, of course, greatest near busy roads  [12, 13]. This is where many people in cities spend much of their time: commuting, at work, or shopping. Measurements of air quality in these places are rare and usually intermittent  [13].
Air quality and IoT The recent increase in the availability of cheap, low-powered sensors, combined with free, open radio networks (such as The Things Network, TTN) has led to a boom in measurements of many environmental parameters, including air quality. This technology, commonly called the Internet of Things (IoT), makes possible the deployment of sensors in many more locations than in the past. The technology offers the potential for non-scientists to make their own devices and measurements and to make the data publicly available. However, there can be significant problems with the reliability and accuracy of data from low-cost air quality instruments  [13]. The sensors are of variable quality and the resulting low-cost instruments are often not subject to testing, calibration or oversight by experts in the measurement.
• mobile, power-battery (5 V), data-SD card, geolocation & timekeeping-GPS The first versions of the sensors sent data over free and long-range wireless networks in urban settings. The devices were designed to be situated outdoors in a busy city centre and data were collected via The Things Network. We deployed free LoRaWAN networks, well suited for IoT applications, in Wollongong and Liverpool, New South Wales. Liverpool is a rapidly growing centre in the south-west of Sydney, Australia  [25]. The municipality includes the construction site for Sydney's new second airport. Wollongong is a coastal city of just over 302,000 people  [25, 26] , 68 km south of Sydney. Mains AC power (240 V) was available for the sensors that were installed in city centres (Fig  1 ). Portable sensors were built to be powered with batteries, for mobile roadside measurements.
Timestamping of the collected data is managed by The Things Network. Sensors for measuring smoke in remote areas had to run on batteries (5 V) for 48 h. Since no network was available, data were written to SD memory cards. The sensors operated independently, making measurements at a distance around the perimeter of the fire, so the readings needed accurate timestamps. Stationary units had an onboard clock with a drift of +/-30 sec over 48 h. Portable devices kept time with satellite data from their Global Positioning System (GPS) integrated module, also used for geolocation.