dataesr/scientific-paragraphs-categorization huggingface dataset:
Nowadays, due to the recent establishment of the National Tsunami Warning System in Spain, run by the National Geographic Institute (IGN: Instituto Geográfico Nacional) in collaboration with Spanish Civil Protection, real time sea level data and the mentioned alert messages from the REDMAR network are also received by the IGN. However, this algorithm for tsunami detection has also proven useful for identifying other more frequent highfrequency harbor oscillations such as "meteotsunamis" (related to atmospheric pressure) or infragravity waves (generated by wind waves), which present strong similarities to seismically generated tsunamis (same periods and physical properties), in such a way that it can be difficult to recognize one from another. Testing the software with data from a seismic tsunami is of course difficult because these are fortunately rare events in our region.
At the same time, as new tide gauge technology allows us to measure other sea level processes apart from the tide and storm surge contribution, the study extends our existing knowledge on the physical sources of these higher frequency oscillations and their associated coastal risks. Finally, the paper provides also an assessment of the response of the upgraded REDMAR network and PdE multi-parameter alert system during those dates. The paper is structured as follows: after description of the data and methods, the results of the analysis of the two events are presented, followed by the discussion and conclusions on the main findings about these particular events and the consequences for future detection and interpretation of sea level oscillations data.
DATA AND METHODS The 1 min sampled sea level time series available today in the region are the main source of information used in the analysis of these two events, most of them from PdE REDMAR sea level network, composed of 37 tide gauge stations at the main harbors of Spain. Wind wave, atmospheric pressure and wind data from wave buoys deployed near the Spanish coast are also used to determine the influence of these parameters on the observed sea level oscillations. These buoys belong to PdE deep water buoy network  (Álvarez-Fanjul et al. , 2002) , and are located at around 400 m depth and 10-20 miles away from the coast. Outputs from the wave and sea level forecasting systems of PdE  (Álvarez-Fanjul et al. , 2001; Gómez-Lahoz and Carretero-Albiach, 2005)  were used to understand the physical conditions relating to wind waves, the tide and storm surges during the dates of the events.
, 2016). From the 37 stations in REDMAR, 17 already have these new meteorological sensors transmitting their data in real time to PdE system (13 of these were installed during the last year within the national project SAMOA: Sistema de Apoyo Meteorológico y Oceanográfico a las Autoridades Portuarias: System of Oceanographic and Meteorological Support to the Harbour Authorities). Unfortunately for the events analyzed here, 1-min atmospheric pressure was only available for the Vigo tide gauge, on the Galician coast. Total 1-min sea level data from the REDMAR are automatically filtered before being passed through the tsunami detection algorithm and the multi-parameter alert system of waves, sea level, and high frequency oscillations (including tsunamis) implemented by PdE. A detailed description of this system can be found in  Pérez-Gómez et al.
The algorithm has been in operation since 2008, having been tested and validated using the tsunami of May 2003 in the Balearic Islands and other tsunamis in the Indian Ocean. The filtered signal and the alert "level" are stored in the PdE data bank. To date, the performance of the algorithm has been very good, which is in part due to the near-real time automatic quality control of sea level that is undertaken every 15 min. This quality control in real time is a complex task: a tide gauge malfunction will almost always generate an initial false alert when the first 1-min observation is received, and this is automatically canceled when detailed analysis and quality control running a few minutes later flags the suspect data. For this reason, the alerts are received solely by experienced operators at PdE and the IGN who make informed decisions based upon comparisons with other parameters and stations in the system (the IGN will only pay attention to these messages if an earthquake has been detected by their seismic network).
Data from non-REDMAR tide gauges were downloaded from the data portals of the IOC Sea Level Station Monitoring Facility (SLSMF: http://www. ioc-sealevelmonitoring. org/) or the IBIROOS In-Situ Tac (http://www. ibi-roos. eu/Accessto-data/The-IBI-Portal). As high-passed filtered data were not available from these sites, we applied the same filter used in REDMAR to the 1-min sea level data from other institutions. In order to characterize the 1-min high-pass filtered oscillations in this study, we have implemented a scheme of data processing for a specific period, which consists of the following steps: • Interpolation of short gaps (<4 min) • Estimation of seiche wave amplitude and period by means of the zero-up crossing procedure • Temporal evolution of the spectra: spectrogram The zero-up crossing procedure is usually adopted for wind waves, where a wave is defined as the portion of a record between two successive zero-up crossings.
In order to identify clearly the periods of the infragravity waves and the presence of wind waves or swell, we also performed spectral analysis of the 2 Hz data during the events at some of the stations. After applying a high-pass filter to eliminate periods larger than 45 min, the power spectral density is obtained by calculating the auto-covariance function smoothed with a Parzen lag window  (Jenkins and Watts, 1968 ). In the future, additional data processing scripts will be needed to improve the characterization in near-real time of these highfrequency sea level oscillations from 2 Hz data, especially for those periods of the infragravity waves (30 s to 5 min), which are not well-resolved with 1 min sampling. To address this, we plan to analyze original 2 Hz raw data from the tide gauges in the REDMAR network, that are currently being transmitted hourly to PdE.
Observational Data As can be seen in Figure  4  (left) the high-pass filtered 1min time series from the tide gauges along the Spanish coast exhibit significant sea level oscillations simultaneously with this sequence of storms along almost all the coastline. A detailed study of the sea level data and a description of the atmospheric and oceanographic conditions during these days are presented below. The REDMAR tide gauges also provide information on wind waves recorded locally in the harbors and the significant wave heights (H m0 ) from these are also displayed in Figure  4  (right). We can see that during these storms there were also important wind waves (0. 4-1 m height is considered important inside a harbor) even at the quays where the tide gauge station is located. These wind waves (mostly swell) are usually a problem for harbor operations and confirm the critical situation at the coast and harbors these days: the local wind waves along with sea level oscillations and their associated currents may increase the damage to infrastructures and the possibility of flooding.
During the first one, maximum significant wave heights of 12. 4 m and 12. 0 m were recorded by Vilano-Sisargas and Estaca de Bares buoys respectively (PdE, 2014). On February 2nd, however, the latter recorded an even larger significant wave height: 12. 8 m, very close to its historical record (12. 9 m). Maximum wave heights may be estimated roughly by multiplying these values by 1. 6, which would result in individual wind waves of 20 m or more in open waters. This was confirmed by the delayed data processing of raw data from Estaca de Bares buoy, where an individual freak wave of 29 m height was found, becoming the highest wave height ever measured along the Spanish coast (M. I. Ruiz, personal communication). An alert level "4" for wind waves was issued by PdE alert system. As already mentioned these waves presented as well extremely long periods with peak periods reaching 22.
Contribution to Extreme Sea Levels A better understanding of the contribution of these oscillations to total sea level is possible from the data presented in Table  1. Column 2 shows the maximum sea levels recorded by the tide gauges of Bilbao, Santander, Gijón, and Coruña during the storm of February 2nd, when the coastal inundation affected a largest area of the coast. These extreme sea levels are derived from the total 1-min sea level data. Column 3 contains the height of the tide at that instant, computed with the harmonic constants derived from harmonic analysis of more than one year of hourly sea level data in each harbor. The magnitude of the storm surge forecasted by the Nivmar system in operation at PdE is presented in column 4 for the point closer to each tide gauge, and column 5 contains the contribution from the "seiche" oscillation, estimated as half the significant wave height H s (of the "seiche") obtained in the way described in Section Data and Methods.
5. In spite of the relatively high magnitude of the earthquake the focal mechanism was a strike-slip one in a transform fault (with centroid depth: 25. 2 km, strike: 277, dip: 88, slip: -170, according to the Global Centroid Moment Tensor Catalog:  Dziewonski et al. , 1981; Ekström et al. , 2012) ; a tsunami wave is not expected for this kind of earthquakes due to the lack of significant vertical movement of the ocean bottom. For this reason, NEAMTWS warning center provided just an information message for the region 12 min after the earthquake. Interestingly, about 3 h after, a small increase of variability was observed in the 1-min filtered data from Langosteira tide gauge, a station located on the Northwest corner of the Spanish coast, at the external harbor of La Coruña (Figure  1 ). This signal could have been interpreted by mistake as the record of a small tsunami by a technician on duty on a tsunami warning center.
In fact, this information was leaked out to press by an expert external to NEAMTWS and the National Tsunami Warning Center in Spain. The automatic algorithm for tsunami detection in PdE triggered an alert level "2" message for the station of Langosteira; the first alert was issued at 22:54:00 UTC, and several times thereafter until the morning of the 14th of February. It is important to stress that the thresholds of the algorithm are fixed according to the local variability of sea level at each harbor. As described in the previous section, oscillations similar to these ones are common along the North Spanish coast and are usually caused by wind waves in this region. Looking for more tsunami signals after the earthquake of February 13th, 2015, a review was made through the IOC Sea Level Station Monitoring Facility (IOC/SLSMF) and other data portals such as the IBIROOS In-Situ Tac (that includes all in-situ marine observational platforms, including tide gauges, in the Atlantic European coast).
These stations were: Bilbao, Santander, Gijón, Coruña, Ferrol, and Langosteira, from the REDMAR network, and Peniche, Nazare, and Leixoes in the Portuguese coast, north of Lisbon. A possible signal could also be perceived at Reykjavik (Iceland) and Palmeira (Cape Vert). However, there were not significant oscillations in the filtered time series at the Canary Islands stations. The arrival time of the potential tsunami wave was obtained by IGN for each tide gauge station from the numerical model TTT (Travel Time Software) SDK v3. 3 (Table  2 ). The program calculates propagation velocities based on an input bathymetry grid and uses Huygens constructions to propagate the wave front from the epicenter to all nodes on the grid  (Wessel, 2009). A map of this theoretical tsunami propagation time is shown in Figure  10  (right), with contour lines of 1 h.
Although there were not extreme waves and storms during February 2015, their influence cannot be completely disregarded for Langosteira. Figure  14  shows the increase in significant wave height (up to 6. 5 m) and mean period of the wind waves recorded at the nearby buoy of Estaca de Bares (offshore, north of Langosteira), during the appearance of the oscillations in this station. Based on the experience of other events, therefore, this sudden change in amplitude and period of the wind waves would be most likely the origin of the oscillations (infragravity waves) observed in this tide gauge coincidentally when a potential small tsunami could have reached the station. The 1 min meteorological data recorded at Vigo tide gauge during the same period also simply reflect an increase in the wind velocity at that time (related to wind waves on the other hand); the atmospheric pressure data does not show any relevant feature before February 13th, confirming the origin of the observed sea level oscillations in Langosteira in the infragravity waves.
A prototype is in fact already in operation for one tide gauge from the REDMAR network. The Atlantic Ridge earthquake on 13th February 2015 was not expected to generate a tsunami signal; however, the occurrence of small oscillations in the North and West coast of the Iberian Peninsula revealed the complexity of interpretation of tide gauge data and the need for analysis of sea level in combination with other environmental variables. Although the signal observed in a few tide gauges is coherent with the expected arrival time of a small tsunami, and the wind waves at that time were not extreme, a detailed analysis has shown that these oscillations could be most likely infragravity waves and not a tsunami signal (spectral analysis of the time series reveals similar behavior of this event and the previous one).
Although we point to the wind waves as the main mechanism of the oscillations observed in these two examples, we cannot discard completely the possibility of a combination of effects and the occurrence as well of a meteotsunami, especially during one of the events of 2014. Considerable research has been done about these oscillations associated with atmospheric pressure disturbances in the last decades; the availability of microbarographs like those already in operation in the REDMAR network is expected to be of help in the establishment of future meteotsunami warning systems. These data could be integrated into these systems as the sea level data are already included in the tsunami warning systems. Although ideally offshore bottom pressure sensors would be more useful in both cases, the reality is that nowadays the only data available in real time for most of the countries in Europe are provided by tide gauges at the harbors, so it makes sense that we should improve the tools to use these data in near-real time and to interpret them correctly.
ACKNOWLEDGMENTS We wish to thank first all the operators and maintenance technicians of Puertos del Estado networks of tide gauges and buoys (Sidmar and Oceanor personnel,  Marta de Alfonso  and  María Isabel Ruiz ). We thank as well the  Sea Level Station Monitoring Facility of the IOC (UNESCO) , the  IBIROOS In-Situ Tac  and the different national operators for the provision of tide gauge data of non-Spanish stations for the event of February 13th 2015. The Tsunami Travel Time software (TTT SDK v 3. 3) was developed by  Dr. Paul Wessel  (Geoware, http://www. geoware-online. com),and is used by the  NOAA Pacific Tsunami Warning Center  and licensed to  NOAA/ITIC  for redistribution. Finally, we would like to express our gratitude to  Dr. Angela Hibert , who kindly accepted to read the manuscript and make suggestions to improve the English writing.
RESULTS An Experimental and Computational Approach to Understand Immune Response Variation Population variation in immune responses was characterized in primary monocytes, as a model of an innate immune cell type, from 200 healthy individuals of self-reported African and European ancestry (100 individuals of each population) (see STAR Methods; Figure  S1 ). Cells were exposed, for 6 hr, to ligands activating TLR4 (bacterial lipopolysaccharide  [LPS] ) and TLR1/2 (Pam 3 CSK 4 , a synthetic triacylated lipopeptide), responsible principally for sensing bacterial components, TLR7/8 (R848, an imidazoquinoline compound), responsible predominantly for sensing viral nucleic acids, and to a human seasonal influenza A virus (IAV). RNA-seq data were collected from unstimulated and stimulated monocytes, yielding a final dataset of 970 transcriptional profiles with $34 million single-end reads per sample.
Context-Specific Transcriptional Signatures of Monocyte Activation To assess the transcriptional responses of CD14 + monocytes, we first processed and normalized the RNA-seq data and accounted for potential batch effects and confounding factors that could impact gene expression variation (Figures  S2  and S3 ; see STAR Methods). We obtained a final set of 12,578 expressed genes, 6,752 of which were differentially expressed upon stimulation in at least one condition (jlog 2 (fold change  [FC] )j > 1) at a false discovery rate (FDR) < 0. 05 (Table  S1A ). Using adjusted expression data, principal component (PC) analysis revealed that PC1 and PC2, accounting for 65% of total variation, corresponded primarily to IAV infection and TLR activation, respectively (Figure  1A ). We next used weighted correlation network analysis (WGCNA)  (Langfelder and Horvath, 2008)  to define modules of genes that showed similar behaviors (up-/downregulation) upon immune stimulation and identified ten modules, each comprising 257-4,070 genes (Figures  1B  and S4 ).
RNA-Sequencing Analysis Reads were assessed for multiple quality metrics, including number of reads, nucleotide distribution and sequencing quality, and the last base of all reads was trimmed due to a fall in sequencing quality. RNA reads were then mapped onto the human GRCh37 genome with TopHat  (Kim et al. , 2013) , resulting in the successful mapping of 89. 9% of reads per sample on average (minimum 67. 3%; maximum 93. 7%). We used the RSeQC package to assess the alignment of reads with various genomic features, GC content, and gene body coverage (Figures  S2B-S2E ). Samples with uneven gene body coverage were found to be more likely to be outliers. We used gene body coverage regularity as an indicator of library quality, removing eight samples due to irregular gene body coverage. The remaining 970 samples were used for subsequent analyses and consisted of 200 non-stimulated (EUB: 100, AFB: 100), 184 LPS (EUB: 96, AFB: 88), 196 Pam 3 CSK 4 (EUB: 100, AFB: 96), 191 R848 (EUB: 98, AFB: 93), and 199 IAV samples (EUB: 99, AFB: 100).
Cufflinks/CuffDiff (v2. 0. 2)  (Trapnell et al. , 2012)  was used to quantify expression levels in FPKM (fragments per kilobase of transcript per million mapped reads) for each annotated transcript of the genome in Ensembl (v. 70), and FPKM values for which Cufflinks returned FAIL status (< 0. 5% of quantified transcripts) were set to missing values. Gene expression data were filtered to remove genes with low levels of expression (mean FPKM < 1 in all conditions) and their quality was checked by principal component analysis  (PCA). PCA captured differences between conditions and populations on the first two axes, but we tested for additional causes of technical variability, by fitting, for each gene, a mixed model of gene expression as a function of condition, population, and technical covariates, including total RNA concentration, RIN, percentage of high-quality bases (Q30), mean GC content, library concentration, 5 0 /3 0 coverage bias (measured as the mean difference in coverage between the 5 0 and 3 0 ends of the gene) as continuous covariates, and date of experiment, library preparation batch, sequencing batch, sequencer used, sequencing index, and sequencing lane as putative batch effects.
Modules of Correlated Genes Modules of genes presenting correlated expression patterns, extracted from log-transformed FPKM data, were defined by weighted correlation network analysis (WGCNA)  (Langfelder and Horvath, 2008). In our setting of immune response activation, this analysis detects modules of correlated genes that can reflect either shared regulation by common transcription factors, or regulation by independent transcription factors with similar patterns of activation upon stimulation. Tukey's biweight correlation was used as a measure of gene relatedness to reduce the influence of outliers, and correlations were measured across all 970 samples. The scale-free topology of the networks was assessed for various values of the b shrinkage parameter, according to WGCNA user manual, and the default value of b = 6 appeared to give a satisfactory fit to scale-free topology.
Signed clustering of genes (grouping only positively correlated genes) was used to simplify the interpretation of the extracted modules. We also found that varying the level of shrinkage (b = 5 or 6) or the depth of the clustering (deepsplit parameter set to 3 or 4) had only a mild impact on the number of clusters or the enrichments obtained, confirming the robustness of these analyses. For each module, we used PASTAA  (Roider et al. , 2009)  to identify transcription factor binding site motifs overrepresented within the annotated proximal promoters of the genes within each module. We first defined the proximal promoter region for each gene as the region extending 200 bp on either side of the transcription start site (TSS) of the most abundant transcript on the basis of Cufflinks FPKM estimates.
We then used the transcription factor affinity prediction (TRAP) method  (Thomas-Chollier et al. , 2011)  to measure the binding affinities of each transcription factor present in the Jaspar core vertebrate database  (Mathelier et al. , 2014)  with the proximal promoters of the 12,578 expressed genes, and these affinities were then used as the input for PASTAA enrichment analysis. We reported only enrichments significant at a false discovery rate (FDR) of 0. 05 with a fold-change (i. e. observed/expected) greater than 1. 2. For each module, we represent the transcription factor binding sites with the highest value for the lower limit of the odds ratio confidence interval.
Differential Expression Analysis Differential expression was assessed directly from log-transformed FPKM, using t tests for each condition. FDR was then calculated jointly for all conditions, with the R package fdrtool, and genes differentially expressed between populations (popDEGs) were defined as genes presenting an absolute log 2 fold change between populations -log 2 FC pop -greater than 0. 2 and at FDR < 0. 05. We then calculated the fold-change in expression after stimulation relative to the basal state, and used t tests to determine whether there was a differential response. Population differential response genes (popDRGs) were then defined as popDEGs for which there was a differential response between populations under stimulated conditions, at FDR < 0. 05 (i. e. the transcriptional response to treatment, relative to the basal state, differed between populations), resulting in a larger difference in expression after stimulation.
log 2 FC stimulated pop > log 2 FC basal pop e4 Cell 167, 643-656. e1-e10, October 20, 2016 Gene Ontology Enrichment Analysis All Gene Ontology (GO) enrichment analyses were performed with GOSeq package  (Ashburner et al. , 2000) , using the default settings, with the 12,578 expressed genes as the background set. Only enrichments significant at FDR of 0. 05 and with a fold-change (i. e. observed/expected) greater than 1. 2 are reported.