dataesr/scientific-paragraphs-categorization huggingface dataset:
FrontiersFIGURE 5 | 5 FIGURE 5 | Estimated SOC content in different vegetation types by different methods. Model performance at 0-30, 30-100, 100-200 cm depth intervals by different methods (i. e. , neural networking, one-batch, and random-sampling methods) was presented. The observation bars were plotted from the results of the site-by-site method considering its very good linear agreement between observed and modeled SOC content. The number in the parentheses after the names of vegetation types suggests the number of sites at corresponding vegetation type in the dataset we used in this study. The error bars indicate the ±0. 5 standard deviation. The distribution of different vegetation types on the conterminous United States is shown in Figure S7.
LSDV field isolate Neethling Warmbaths LW sequence (AF409137)  (7, 8) , and discrepancies were confirmed by Sanger sequencing. A total of 25 nucleotide mutations and 12 single-or 3-nucleotide indels were identified. The localization and the impact of these nucleotide modifications are shown in Table  1. Contemporary LSDV field strain genomes differ by only a couple of mutations. Therefore, additional (nearly) complete genome sequences of circulating LSDV strains are needed to identify a possible source for the outbreak in Kazakhstan. M IR LD029 to LD030 M LD035 M LD042 M I ¡ N LD046 M LD059 M K ¡ F LD062 M LD071 M E ¡ K LD075 M LD087 M K ¡ E IR LD089 to LD090 I LD091 M LD094 M S ¡ L LD096 D E deletion LD098 M IR LD103 to LD104 I IR lD125 to LD126 M LD126 M E ¡ K LD127 M LD128 M S ¡ F LD133 M IR LD133 to LD134 D LD140 M IR LD143 to LD144 I IR LD146 to LD147 D LD148 M R ¡ K a GenBank accession number AF409137.
Earth and Space Science The FD telescopes are located at four sites. Six telescopes at each site are arranged for a total FoV of 180 • (azimuth) × 30 • (elevation). Due to the geometrical orientation of the FD sites, the physical aperture of the detector for the observation of elves is broken in three overlap regions: 8% seen by one site, 74% seen by 10. 1029/2019EA000582 two sites, and 18% seen by three sites. Detection probabilities due to variability in coverage are discussed in section 5. The data readout of the Auger FD includes three trigger levels to select events of interest. The analog signals for each pixel are digitized every 100 ns and pass the first level trigger (FLT) if the analog-to-digital conversion (ADC) threshold requirement is satisfied. The second level trigger (SLT) is a pattern recognition designed to select UHECR signals; it requires at least four adjacent pixels passing the FLT.
Collected Data and Reconstruction of Lightning Location The Pierre Auger Observatory started taking data in 2004. The fourth FD site, at Loma Amarilla, started operations in 2007. The first elve was observed in 2005, and two more events, which occurred in 2007, were discovered in a search for exotic events performed in 2009. A thorough search for elves in randomly saved events with loose trigger requirements, harvested in the period from 2007 to 2011, was exploited to design a modified TLT algorithm. The search yielded 58 more candidates  (Aab et al. , 2013b). In 2013, the observatory started acquiring elve candidates with the standard trace length (100 μs), and in 2014, we improved the TLT to acquire up to 300 μs of signal. In what follows, we present the data acquired during the 2014-2016 time period, for which we can now provide a more accurate reconstructed location and time.
The South Atlantic Anomaly may be a factor affecting the altitude of the ionosphere base. Ultimately, we may have observed an event at more than one FD site but reconstructed it solely once. Hence, we define a confirmed elve event as one that passed the verification stage and that was reconstructed at least once. In this 3-year data set, we found 1,598 confirmed elve flashes. In addition, the coverage of WWLLN in Argentina is such that three antennas are within the observational footprint for elves of the Auger FD  (Hutchins et al. , 2012; Jacobson et al. , 2006). Our correlation with the network was 72%: 1,158 Auger elves correlated within 5 ms of a WWLLN reconstructed lightning strike. A finer time correlation study will be presented in the next section. We summarize all event counts in Table  2.
Possible sources of error to explore are the differentiation between IC and CG sources in the WWLLN data set, as well as in the elve data set. Two additional parameters in the Auger reconstruction will be released for multipeaked events to improve the timing resolution. In addition, elves are created from an EMP with a wider frequency band and a greater energy density than the EMP observed by WWLLN; hence, we expect our photo traces to differ from the direct observation of that network. By applying a cut on the distance from the Auger Observatory on both the Auger elves and the WWLLN lightning strike (250 to 1,000 km still selecting >95% of observed elves) and requiring all FD sites to be active (in data taking mode), we compare the energy of lightning which created elves to that of all lightning observed by WWLLN within this time and footprint (Figure  4b ).
This distance cut is chosen to optimize the comparison between events of both data sets happening within the FoV of the Auger FD. WWLLN records the far-field radiated electromagnetic energy in the 6-to 18-kHz frequency band. The peak radiated energy is known to be in the 10-to 15-kHz range. The 474 confirmed elve events satisfying the above correlation requirements are correlated to WWLLN events at the high end of the energy spectrum. We omitted elves with more than one WWLLN event correlated within the 5-ms coincidence. Adding those events to the analysis uniformly increases the counts in the last four bins. To obtain the median energy of both data sets, we calculate the mean of the log-normal distributions to obtain 16±2 kJ for the matched elves and 1. 3±0. 1 kJ for all lightning. Using an empirical equation for peak current  (Hutchins et al.
Multiple FD sites observing in the same region have a higher chance of an elve observation. We aim at disentangling our high detection probability in the north-east from the high occurrence of elves in that region. Each data point in the maps is the location of the lightning strike reconstructed from an elve. The inhomogeneous strike density, as a function of distance from the center of the Auger Observatory, reveals unavoidable cutoffs for data acquisition in the observational footprint of the FD. When too close to the horizon, the light from the top of thunderstorm systems may reach the pixel array before the light emission from the ionosphere. The discarded lightning events induce a natural inner cutoff at ≈230 km. We color the overlap regions of the detector FoV in green for mono, blue for stereo, and orange for triplet (Figure  6a ).
At closer distances, the FoV overlap located east of the Auger Observatory increases the observation probability, and the light from the emission region travels through less atmosphere to reach the telescopes. Hence, the Auger FD triggers on numerous, dimmer events at near distances. By cross-checking the on-time of the Auger FD with the WWLLN data set, we created a density map of WWLLN lightning events displayed on a log scale with quarter geodetic degree bin size (Figure  6c ). From this heat map of WWLLN events acquired from 2014 to 2016, uncorrected for relative detection inefficiencies  (Hutchins et al. , 2012) , we confirm the high density of lightning strikes present in the north-east of Argentina. The low density of lightning strikes over the ocean coincides with the low elve count observed by the Auger Observatory, consistent with the lightning climatology study of  Virts et al.
10. 1029/2019EA000582 To confirm the anisotropic elve distribution, we investigate the increased probability of observation in the surrounding overlap regions. Assuming a hypothetical flat elve at 92-km altitude, with an averaged radius of 250 km and an equal detection probability at all FD sites, we calculate the percentage of that elve in the FoV of each sites. The value for the elve radius is representative of the Auger data set; it is much larger than what was previously reported by the PIPER experiment  (Blaes et al. , 2014). If at least 15% of the elve is in the FoV of an FD site, then we flag the center of that elve as a geodetic location with elve-inducing lightning, detectable by the Auger Observatory. From the number of FD sites which can detect at least 15% of the same elve, we infer coverage regions which differed from the overlap regions mentioned previously (Figure  6d ): one-site, two-site, three-site, and four-site coverage.
Summary After adding a new trigger channel to target a class of atmospheric TLEs known as elves, the Pierre Auger Observatory has recorded almost 1,600 of these events over the 3-year period from 2014 to 2016. This cosmic-ray observatory, located in the Mendoza province of Argentina, includes 24 fixed-direction UV fluorescence photometric telescopes distributed over four different sites. These telescopes operate every night when the weather is reasonably clear and the moonlight is sufficiently low. The total FoV of the FD spans in azimuth the entire horizon, and 92% of it is covered by two FD sites. Several hundred photomultiplier pixels, digitized at 10 MHz, participate in a typical elve measurement. The data set reported here demonstrates that the observatory acceptance for elves extends over 3 • 10 6 km 2.
We developed an algorithm to reconstruct the latitude and longitude of the lightning from the measured light-time distributions of the recorded elves. A list of the coordinates, and UTC times, of 1,598 elves is available on the website of the Pierre Auger Observatory. When the height of the UV emission is constrained to 92 km above sea level, the current state of the resolution analysis shows that we agree with a WWLLN estimate of the FD trigger time. This analysis also shows that we slightly overestimate the distance and time of our reconstructed events; 72% of the observed elves correlate with independent radio-frequency measurements of lightning by WWLLN. For a quality subset of these correlated events (474), the lightning energy as measured by WWLLN had a median of 16 kJ, while the median energy of all lightning measured by WWLLN that occurred inside the elve footprint while the telescopes were taking data was 1.
3 kJ. Using this particular lightning data set and lightning energies, the turn-on threshold for elve detection by the Auger Observatory is about an order of magnitude higher than the turn-on threshold for lightning detection by WWLLN. The observed elve locations exhibited seasonal and geographical patterns: 44% of the elves observed occurred during the southern-summer months, and just 2. 5% occurred during winter months. Nearly all of the 10. 1029/2019EA000582 observed elves appeared east of the Andes, and just two were observed and reconstructed over the Pacific Ocean, confirming a study by  Virts et al. From the multiplicity of peaks in the traces, we conclude that 18% of our data set was related to IC lightning activity (at least two peaks in the photo trace) while the rest shows simpler structure.
The Pierre Auger Observatory is scheduled to operate until at least 2025. In 2017, we implemented a deeper readout window of 900 μs for elves, to increase the quality of our current reconstruction. We are planning refinements of the on-line TLE-trigger algorithm. To our knowledge, the Auger Observatory is the first and only ground-based facility that measures elves with year-round operation with full horizon coverage, controlled photon counting, and 100-ns resolution. We look forward to possible correlation studies between Auger data and various ongoing experiments: the RELAMPAGO ground-based lightning detection campaign  (Nesbitt et al. , 2017) , the GLM instrument aboard the GOES-16 satellite  (Goodman et al. , 2013) , the ASIM TLE detector  (Neubert et al. , 2009)  and the Mini-EUSO cosmic-ray detector  (Capel et al.
Data collection Demographic data included age, gender, comorbidities including chronic respiratory disease and standard laboratory test were obtained. Moreover, airway sample (nasopharyngeal swab, throat sample) and 2. 5 ml peripheral blood sample in PAXgene tubes were collected. For those admitted in intensive care unit (ICU) and were under mechanical ventilation, additional respiratory samples were obtained. Virus testing consisted of nucleic acid PCR in order to detect influenza A, influenza B, respiratory syncytial virus, rhinovirus, parainfluenza virus and metapneumovirus.
RNA extraction, normalisation and microarray analysis In each sample, whole blood RNA was extracted from PAXgene tubes as per manufacturer's protocol (QIA-GEN PreAnalytiX -Blood RNA version 2; 2015). After checking RNA integrity on Bioanalyzer (Agilent Technologies; Waldbronn, Germany), 100 ng of total RNA was applied for Cy3-labelling reaction using the one color Quick Amp labelling protocol (Agilent Technologies; Waldbronn, Germany). Labelled cRNA was hybridized to Agilent 8x60k Human V3 (Design ID: 072363) microarrays for 16 h at 68 °C and scanned using the Agilent DNA Microarray Scanner. Results were then analysed using the R software package (version 3. 1. 3). Pre-processing steps included background correction, adding an offset of 50, quantile normalization and annotation using the limma package and Agi4x44PreProcess packages.
Multi-group comparisons and identification of differentially expressed probe sets were performed using limma with Benjamini and Hochberg correction for multiple testing. Differentially expressed probesets (some genes are represented by several probesets) were identified based on an adjusted p-value of < 0. 05 and exhibiting more than a two-fold difference in expression levels ([log 2 ] > 1). Full dataset of the expression data is available at the National Centre for Biotechnology Information Gene Expression Omnibus (GEO accession number GSE101702).
Pathway analysis Three lists of differentially expressed genes were generated by comparing three different phenotypes (Fig. 1 ): I. Moderate influenza vs. healthy controls II. Severe influenza vs. healthy controls III. Severe influenza vs. moderate influenza Differentially expressed (DE) probesets between these comparison groups were identified based on an adjusted p-value of < 0. 05 and exhibiting more than a two-fold difference in expression levels ([log 2 ] > 1). Changes in expression levels were presented as fold changes for probesets of a given gene. For pathway analysis, probeset lists were collapsed to gene lists (some genes are represented by multiple probesets that may be differentially expressed) and these three DE gene lists were used as input for the pathway analysis software, MetaCore.
MetaCore is a topologybased analysis software (Clarivate Analytics, Philadelphia, U. S. A. ) designed for functional analysis of transcriptomic data. Pathway analysis consists in examining the intersection between our lists of differentially expressed genes and prebuilt canonical pathways. A first statistical analysis is performed that take into account the number of genes differentially expressed in each comparison, the number of genes that intersect the prebuilt maps and the number of genes in the database. A False Discovery Rate (FDR) adjustment is apply for multiple testing. An FDR of 5% was used as the cut-off to determine whether a pathway was statistically overrepresented in the gene list. Adjusted p-value are expressed in -log(p-value) and ranked by statistical significance. Finally, these statistically overrepresented pathways were re-organized under broad biological themes (e.
Clinical data One hundred seven patients were recruited with laboratory-confirmed influenza infection (either nasopharyngeal swab or bronchoalveolar lavage (BAL)). There was no viral coinfection observed in the cohort. Microarray analysis was performed for all 107 samples. The moderate group and the severe group, as defined by clinical criteria (see Methods), consisted of 63 and 44 patients respectively. Patient assignment matches perfectly between the 2 experts. A group of 52 healthy control subjects without any medical illnesses had also been enrolled (Fig. 1 ). Baseline characteristics are described in Table  1. Age, gender, cell counts did not differ significantly between the groups. Eighty five percent (n = 91) of the patients had at least one comorbidity and the Fig. 1  Flow chart and study scheme.
Interferon-related pathway genes As expected, the most statistically significant pathways (in both moderate and severe groups) were those involved in antiviral response and interferon signalling. Notably, the interferon stimulated gene (ISG) IFI27 was the most up-regulated transcript in almost all infected patients. Other transcripts from interferon-stimulated genes, with anti-viral functions, were also up-regulated in both moderate and severe groups compared to controls including IFI44 (inhibits viral replication, p < 0. 0001), IFIT1 and IFIT2 (inhibits viral mRNA translation initiation, p < 0. 0001), MX1 (anti-influenza, p < 0. 0001), OAS3 (degrades viral RNA, p < 0. 0001). Conversely, we found only a small number of interferon stimulated genes (ISGs from the INTERFEROM database  [20] ) for the comparison between severe and moderate illness (Additional file 1: Figure  S2 ).
External validation We also performed a gene-based comparison with an external dataset (GEO 111368)  [12]. This external dataset has a similar study design to ours, which included 109 adult patients with laboratory-confirmed influenza infection and 130 healthy participants. We found strong similarity in the upregulated genes between the two studies: 17 upregulated genes were shared between two studies (70%) (Although only 3 (15%) downregulated genes were shared between the two studies). Notably, the upregulated genes encode proteins involved in neutrophil functions and the downregulated genes encode proteins involved in the immune response. Among the most differentially expressed genes, neutrophil activation and reduced immune response were the dominant biological themes found in both dataset (Additional file 1: Table  S5 ).
In 2013 in Senegal, when the last census was collected, the average electrification rate in rural areas was as low as 24%. Paradoxically, mobile phones have still found their way into the homes of about 75% of the population in these same rural areas. In fact, some studies praise the large coverage achieved by mobile phones in the entire African region  [16, 17]. Our aim is to use the resulting data to guide efficient manual data collection and therefore reduce the logistic costs of gathering information for development and infrastructure planning in developing countries. We both test the possibility of estimating census data from mobile phone data and evaluate the potential of better predicting electricity demand from mobile phone data rather than from the population count. We first describe our method in details in the materials and methods section, then use a bulk of data that we have gathered for Senegal in 2013, including the aggregated call detail records from Sonatel, the leading mobile phone operator in Senegal (with 65% market share), to validate our proposed approach.
Materials and methods The population density for each commune in Senegal is given by the 2013 census. There are 552 communes of irregular sizes according to the division provided (created in December 2013), including urban communes (communes de ville and communes d'arrondissement) and rural communes (communautés rurales). This information was collected using a door to door approach over the entire country, rather than by estimation. The population densities' distribution is close to a narrow Poisson distribution, with an average of 2162 inh. /km 2 and a maximum of 54325 inh. /km 2. The distribution is mapped over a rather precise shapefile that includes in particular the boundaries of all big and medium towns. We use mobile phone data provided by the largest Senegalese telecommunication operator, with about 65% market share in 2013, Sonatel.
They contain the number of text messages, number of calls and total length of calls made during each hour between each of the operator's 1666 communication towers during the year 2013. Out of these, 54 towers were inactive and have been removed from the analysis, leaving 1612 towers. To estimate the electricity consumption, we used NOAA's average nighttime lights intensity for the year 2013. The intensity is given as a number between 0 and 63 for each cell of a 30 second arc grid. Since Senegal is close to the equator, this grid is regular and its cells measure about 1km per 1km. This data has been cleaned by NOAA from the interference of moonlight, clouds, etc. to the best of their ability. We have produced two different levels of aggregation. The first one is the commune level. It is aimed at preserving the population counts as accurate as possible.
To obtain the dataset over the entire year, one would need to contact Sonatel directly and present the research project that would require the data (contact: Mr El Hadji Birahim Gueye, Direction des Systèmes d'information Sonatel, ebgueye@orange-sonatel. com or post mail: Orange-Sonatel, 46 Boulevard de la Republique, BP 69 Dakar, Senegal). pixel is only partially included in the cell, its intensity is weighted in the average by the area actually included in the cell. The end result is two tables containing the population count per square kilometre, the average number of texts, average number of calls and average total call length per hour per square kilometre, and the nighttime light intensity per pixel, inside each of the 552 communes and each of the 1612 Voronoi cells. For the record, we also computed the total value of each variable inside the cells instead of their density per square kilometre.
As a reference, the hourly curves for the number of calls aggregated at national level for each day of the year are represented in Fig  1(a). There is one colour per month ranging from reds to yellows to greens to blues. The yearly average of number of texts per hour of the day sent from each tower is shown in random colours in Fig  1(b). The network structure in January limited to edges corresponding to at least 2000 text messages sent is represented in Fig  1(c). Our proposed method consists in trying to rebuild the original dataset from a sample as small as possible using hierarchical clustering of some of the 33 previously created matrices. The working hypothesis is that similar locations will share in particular similar phone activity habits and have a similar place in the communication network.
Examples are given in Fig  2(c ). With a depth of 3 (dark blue), we obtain 0. 5% of all towers, with a depth of 7 (medium blue), we obtain 3. 3% of all towers, and with a depth of 19 (teal), 44. 4% of all towers. Naturally, in the event of a branch being reduced to only one leave before the chosen depth is reached, this leave is kept in the sample and the branch is not divided further. There may therefore be fewer elements in the sample than the power of two of the chosen depth. The census data for the year 2013 in Senegal can be directly accessed through the official website. The nighttime lights data can be accessed through NOAA's open database. The mobile phone data at Voronoi and Commune level and aggregated over the year are available as part of the supplementary material (S1 and S2 Files).