dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical evaluation was performed using IBM SPSS Statistics software version 20. 0 (SPSS, Inc, Chicago, IL). To derive optimal cut-off values of gene expression levels (SASH1, 0. 29; CRKL, 0. 44), maximally selected log-rank statistics performed by R Software version 2. 13. 0 (R Foundation for Statistical Computing, Vienna, Austria) were used. To consider multiple test issues within these analyses, the R-function maxstat. test was used. Survival analysis was performed using Kaplan-Meier estimates. All statistical tests were performed 2-sided, and the significance level was set at 0. 05. No correction of P values was applied to adjust for multiple test issues. However, results of all statistical tests being conducted were thoroughly reported so that an informal adjustment of P values could be performed while reviewing the data.
Acknowledgments The authors wish to thank  Alexandra Gnann  and  Widya Johannes  for excellent technical assistance,  Dr Julia Slotta-Huspenina  for histopathologic evaluation of tissue samples, and  Maximilian Ehrenfeld ,  Dr Bernhard Holzmann , and  Dr Melanie Laschinger  for critical discussion. The authors thank the  3P5 proteomic facility  ( Institut Cochin, Paris, France ) for liquid chromatography-MS/MS data acquisition. Current address of H. W. :  Institute of Clinical Chemistry and Laboratory Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. Current address of J. M. :  Max Planck Institute of Biochemistry, Research Dept. Proteomics and Signal Transduction, Martinsried, Germany.
In many cases, it takes several minutes after an earthquake to publish online a seismic location with confidence. Via monitoring for specific types of increased website, app, or Twitter usage, crowdsourced detection of seismic activity can be used to "seed" the search in the seismic data for an earthquake and reduce the risk of false detections, thereby accelerating the publication of locations for felt earthquakes. We demonstrate that this low-cost approach can work at the global scale to produce reliable and rapid results. The system was retroactively tested on a set of real crowdsourced detections of earthquakes made during 2016 and 2017, with 50% of successful locations found within 103 s, 76 s faster than GEOFON and 271 s faster than the European-Mediterranean Seismological Centre's publication times, and 90% of successful locations found within 54 km of the final accepted epicenter.
The CsLoc procedure is illustrated in Fig. 1. Upon notification of a crowdsourced detection, CsLoc searches for phases from stations within 2000 km of the crowdsourced barycenter and close in time to the crowdsourced detection time. A "phase" or "arrival time" is the measured time that a seismic wavefront arrives at a seismic station. A simple phase association procedure is then used. The chosen phases are located in a window centered on a regression to the ak135 seismic propagation model (Fig. 2 )  (14). These phases are then analyzed by the location software iLoc  (15). If a location is found, then the result must also satisfy publication criteria defined in terms of standard seismological parameters. Until the criteria are met, the process runs iteratively at 15-s intervals; each iteration may add newly received arrival times and starts from the solution obtained in the previous iteration (see Fig.
2A ). For this study, P-wave arrival times have been provided by the GEOFON program from more than 800 stations affiliated to 73 seismic networks distributed worldwide from the International Federation of Digital Seismograph Networks (FDSN)  (16). In real time, data are received via the httpmsgbus (HMB) protocol  (17) , which is essential for the system's response time. The transmission of data from GEOFON to the EMSC has been measured to take less than 1 s. Details of the entire procedure are available in the Supplementary Materials.
As the seismic network was not homogeneously distributed, successful locations occurred predominantly in well-instrumented re-gions (Fig. 3A ). Of the 1536 earthquakes, CsLoc located 735 earthquakes well enough to satisfy our publication criteria, and 97% of these were located at less than 100 km from the published EMSC location and 89% were located at less than 50 km. Successful location was not found to depend on earthquake magnitude (fig. S5 ). For the rest, 231 events were poorly located and 520 had insufficient data. Although it had been considered a possibility, for this study, CsLoc did not locate any earthquakes that were not already recorded in the EMSC databases, although it did detect events that were not 1. Earthquake crowdsourced detection. Eyewitnesses feel ground shaking and generate a fast reaction on the internet and social media.
Fusion with seismic data. The crowdsourced detection defines a reduced area to search for seismic data compatible with the earthquake's propagation (red circle/red triangles). LastQuake  officially published by GEOFON while using the same seismic stations (discussed later). For the 735 CsLoc-located earthquakes in our test dataset, 50% of locations were found within 103 s from the origin time and 90% were found within 3 min. The speed of the CsLoc system depends largely on two factors: the seismic network coverage and the density of internetenabled users in the felt area. Closer stations have shorter seismic wave travel times, making the phases available quickly. Equivalently, dense populations generally lead to faster crowdsourced detections (independently of earthquake magnitude). When both criteria are fulfilled, the location can be computed in some cases in less than a minute.
The delay of the CsLoc system for the selection of phases and analysis by the iLoc software was less than 5. 3 s in 90% of cases. As shown in Fig. 4A , the system was actually often constrained by the time taken to receive enough phases to produce a publishable result. For comparison with a purely seismic system, the current system of publishing earthquake locations at GEOFON relies on strict criteria for the automatic analysis to guarantee high-quality solutions. To avoid false locations, 30 associated phases are necessary to produce an automatic published solution. CsLoc located more events than GEOFON within the first 10 min following each event (735 versus 406). CsLoc located more small or moderate events usually since these events failed to have the 30 phases necessary for GEOFON to publish them.
DISCUSSION In summary, fusion of crowdsourced detections and seismic data yields timely, accurate earthquake locations for precisely those earthquakes that are felt by and that most affect the public. It accelerates the publication of locations by over a minute compared to seismic analysis alone, and it can confirm a seismic event more reliably than purely crowdsourced detections (with 97% accuracy compared to 85%). This can be used to raise situational awareness and engage with eyewitnesses. CsLoc is also multiscale, locating events independently of magnitude as long as there is regional station coverage. CsLoc does not compete with the rapidity of early warning systems, but advantageously, CsLoc works on a global scale and is a low-cost approach, since it takes advantage of existing seismic networks and the crowdsourced detection methods require relatively low investment [compared to the installation of early warning detector networks  (2) ].
Crowdsourced detection of earthquakes In this study, three systems of crowdsourced earthquake detections were used: the detection of increased traffic on the www. emsc-csem. org or m. emsc-csem. org website, the detection of increased launches of the EMSC's LastQuake app, and the detection of peaks in the rate of tweets with less than seven words and containing "earthquake" in 1 of 59 languages [Twitter Earthquake Detection (TED)]. Each peak was associated either automatically or manually to a published earthquake record with an 85% success rate. Table  S1  shows the detections and success rates for each system during the test period of 2016-2017. The EMSC's detection systems have been developed over several years to their current state  (5, 12, 18, 19). The TED system is a collaboration between the EMSC and P.
The LastQuake app asked its users to enable access to their mobile phone's location, which could be sourced from Global Positioning System readings or the location of the local cell masts. Hence, the positions could be accurate to less than a kilometer. At the time of writing, more than 80% of users allowed LastQuake to use their location. The Twitter detection system had to perform an analysis of the user-written location string found in the profile of the author of each tweet. A process called "geocoding" attempted to extract a location from the natural language text (i. e. , a city, town, or village name)  (21). Naturally, there were many missing or humorous location entries in users' profiles, but nevertheless, useful information could be extracted. Once a detection was made, the set of users within 2 min of the trigger time (and within the same country as the detection for the app and website peaks) was collected and geolocated.
A hierarchical bottom-up clustering algorithm was applied to this dataset using linkage based on the unweighted pair group method with arithmetic mean (UPGMA) and a Euclidean distance metric between latitude and longitude coordinates. This procedure eliminated outliers and found the largest cluster of activity. The barycenter (the average coordinate of this cluster) was taken to be the location of the crowdsourced detection. For the crowdsourced detections during 2016-2017, fig. S1  shows the distribution of the locations with respect to the EMSCpublished epicenters (for those detections that could be associated with EMSC earthquakes) and table S2 presents some summary percentiles. A single earthquake could have multiple crowdsourced detections from each of the systems, as well as from neighboring countries within the same system.
CsLoc procedure Upon notification of a crowdsourced detection, CsLoc searched for phases from stations within 1000 to 2000 km of the crowdsourced barycenter (regional scale), with the maximum radius varying to ensure that at least seven stations were present. The search was limited to between 210 s before and 120 s after the crowdsourced detection time. This was simply a Structured Query Language request performed on the phase data, which were stored in a database table. A phase is the earliest time that a particular type of seismic wave is measured as arriving at a seismic station. The P waves arrive the earliest and so have the cleanest signal for analysis. A phase is equivalently known as an "arrival time" or a "pick. " For this study, P-wave arrival times were provided by GEOFON from more than 800 stations affiliated to 73 FDSN seismic networks distributed worldwide  (16).
In real time, data are received via the HMB protocol  (17) , which is essential for the system's response time. We note that the analysis of P-wave arrival times from the waveform data was found to take approximately 30 s for GEOFON, and the transmission of data from GEOFON to EMSC has been measured to take less than 1 s. Once the phases were collected, the phase association procedure was applied to filter away outlying events (see below). The chosen phases, along with the current epicenter estimate (which is initially the crowdsourced location), were given to the iLoc locator for analysis. The association and location process ran iteratively at 15-s intervals; each iteration might add newly received arrival times and started from the solution obtained in the previous iteration. The iteration process stopped when the seismic location satisfied the publication criteria.
The final chosen phases were those within the bounds of three times the median absolute deviation (MAD) of the fitted ak135 Pn line (sloping dashed black lines in Fig. 2 graphs ). This algorithm naturally becomes more selective as a more accurate solution is used as the starting location and has proven effective at rejecting the phases of aftershocks as seen in Fig. 2C  for the 10th iteration of the CsLoc analysis. The seismic inversion was then computed with the selected phases by the location software iLoc developed for the International Seismological Centre (ISC)  (15).
It can use any three-dimensional crustal and upper mantle model compliant with the RSTT parameterization, and it also supports the use of local, one-dimensional velocity models. One of the major strengths of the locator is that it accounts for correlated travel-time prediction errors due to unmodeled velocity structures along similar ray paths that allow it to obtain reliable locations and uncertainty estimates even with unfavorable station distributions. It obtains the initial hypocenter guess from the neighborhood algorithm search  (26, 27). Once close to the global minimum, it switches to an iterative linearized inversion using an a priori estimate of data covariance matrix whose offdiagonal elements represent the correlation structure in the dataset  (28). Another important feature is that it attempts free-depth solution, only if there is depth resolution either from local networks or from teleseismic depth phases, thus avoiding the pitfall of depth-origin time trade-off.
2 ) from the ak135 fit of phases selected for iLoc but recalculated using the iLoc solution's epicenter <4. 4. These parameters were optimized empirically using the test dataset, trading off between obtaining the most reliable results and obtaining the fastest results. Figure  S3  attempts to show the effect of applying the criteria to the located results for the 10th iteration of the analyses. Each parameter eliminates a different set of outliers in the results to improve the quality of the published locations.
Test dataset To validate the CsLoc system, we analyzed 2590 crowdsourced detections recorded between 1 January 2016 and 31 December 2017 at the EMSC, of which 2200 were associated to 1536 distinct earthquakes with published locations (table  S1 ). Some duplicate detections had occurred because of multiple detection methods (fig. S2 ) and also since countries had been monitored individually to increase the signal-to-noise ratio (table  S1 ). The full dataset of crowdsourced detections and the results of testing the CsLoc system can be found at  (29).
Acknowledgments: Twitter earthquake detections were provided by  P. Earle  and  M. Guy  from  USGS  as part of a long-running collaboration with the EMSC. P-phase arrival times were obtained from the  GEOFON program  of the  GFZ German Research Centre for Geosciences. We thank network operators for making data available in real time and  M. Landes ,  E. Matrullo ,  L. Fallou ,  F. Roussel , and  R. Madariaga  for feedback and advice during both the research and the composition of this article. We also thank  S. Hough  and an anonymous reviewer for.
It uses profile hidden Markov models (pHMM) to identify sequence segments that poorly fit their MSA and selectively removes them. We assessed its performances using > 700 amino-acid MSAs from prokaryotes and eukaryotes, in which we introduced several types of simulated primary sequence errors. The sensitivity of HmmCleaner towards simulated primary sequence errors was > 95%. In a second step, we compared the impact of segment filtering software (HmmCleaner and PREQUAL) relative to commonly used block-filtering software (BMGE and TrimAI) on evolutionary analyses. Using real data from vertebrates, we observed that segment-filtering methods improve the quality of evolutionary inference more than the currently used block-filtering methods. The formers were especially effective at improving branch length inferences, and at reducing false positive rate during detection of positive selection.
Besides, primary sequence errors provide a strong non-historical signal that is more likely to bias evolutionary estimates (e. g. , by lengthening the corresponding terminal branches in a phylogeny). Accordingly, a few studies have shown that they can be a source of erroneous signal  [3]  or even drive alignment errors  [5]. Yet, this aspect is generally not taken into account while analyzing MSAs. In fact, nothing is known about the relative importance of primary sequence errors versus alignment errors in evolutionary analysis of real MSAs. Here, we present HmmCleaner, a program dedicated to the detection and removal of primary sequence errors in multiple alignments of protein sequences. It implements an approach looking for low similarity segments specific to one sequence using a profile hidden Markov model (pHMM) built from the whole alignment with HMMER  [19].
In the following sections, we first introduce the HmmCleaner principle. Then we explain the optimization of its parameters, characterize its performance by simulating primary sequence errors and compare it to PREQUAL performance  [20] , a recently released software package with a similar approach based on pairHMM. Then, we address the effect of filtering software on evolutionary analysis. First, we determine whether the use of HmmCleaner avoids the erroneous detection of positive selection when frameshift errors have been voluntarily introduced. Second, using empirical datasets, we compare the effect of segment-and block-filtering methods on evolutionary inferences (single-gene phylogenetic reconstruction and branch length estimation) as a first insight into the relative impact of alignment errors and primary sequence errors.
The principle is to take a genuine protein-coding alignment of nucleotide (nt) sequences and to randomly introduce a unique error in a specified number of sequences. The resulting sequences are then translated into amino acids (aa) before realignment. Here, we chose to generate frameshift errors, each one followed by the opposite (compensatory) mutation after a predefined number of out-of-frame codons. This approach allowed us to use multiple alignments of true protein sequences resulting from real evolutionary processes whereas primary sequence errors are simulated, contrary to Whelan et al. [20] , who started from simulated sequences. Two empirical datasets of 100 MSAs composed of prokaryotic genes (either Euryarchaeota or Cyanobacteria) were submitted to our simulator in four variants of species sampling (5, 10, 25 and 50 species), so as to generate 100 replicates of these 800 combinations containing 1 to 5 primary sequence errors of length 10 to 100 aa.
The random sampling of species allowed us to test a large variety of tree shape, given that HMMER implicitly assumes that a star tree topology has generated the alignment. The 80,000 simulated MSAs were then used to explore the effect of the four parameter values on both the sensitivity (detection of truly non-homologous segments) and the specificity (non-detection of genuinely homologous segments) of HmmCleaner. We chose to work only on unambiguously aligned regions (UARs), reasoning that it is more difficult to differentiate non-homologous segments from homologous but highly divergent ones in ambiguously aligned regions (AARs). Indeed, over a grid of 2835 quartets of parameter values (9*c1, 7*c2, 9*c3, 5*c4, see Materials and Methods), HmmCleaner only reached a limited specificity (93%) in AARs (Additional file 1 Figure  S1 ), and this came at the expense of a low sensitivity (22%).
8. However, the usual trade-off between specificity and sensitivity, and the negative effect of smaller numbers of species on sensitivity, led us to define three new additional scoring matrices. First, we created a parameter set to use when the number of sequences in the MSA is large, optimized only from simulations performed with 50 species. This scoring matrix achieved a global sensitivity of 97% and a global specificity of 96% in UARs of species-rich MSAs. Second, for users wishing a low false positive rate, we built a high-specificity matrix reaching a global specificity of 97% while keeping a sensitivity of 88%. Finally, we also generated a matrix that simultaneously addresses these two requirements (see Table  1 ). Since the default scoring matrix had been optimized using MSAs aligned with MAFFT using the L-INS-i algorithm and by building a single pHMM per MSA (complete strategy), we checked that parameter optimization was robust to a change of the aligner software and the HmmCleaner strategy.
While both sensitivity and specificity revealed virtually insensitive to the aligner software (MAFFT  [21]  with two different algorithms, MUSCLE  [22]  and Clustal Omega  [23] , Additional file 1 Figure  S3 ), the leave-one-out strategy showed a slightly higher sensitivity (98% versus 97%), but a lower specificity (79% versus 86%), with respect to the complete strategy (Additional file 1 Figure  S4 ). Given these results and the computational burden implied by the leave-one-out strategy, we decided to stick to the complete strategy in the remaining of this article.
Impact of error length, number and conservation context on HmmCleaner performance To investigate the impact of the length and number of primary sequence errors on the sensitivity and specificity of HmmCleaner, 640,000 simulations introducing a total of 4,960,000 individual errors were run on MSAs from 4 different prokaryotic lineages (Alphaproteobacteria and Crenarchaeota in addition to the Cyanobacteria and Euryarchaeota used so far). Introduced errors were 10 to 100 aa in length and 1 to 15 in number per MSA of 25 randomly selected sequences. Neither error length or number, nor MSA lineage substantially impacted specificity and sensitivity of HmmCleaner, except in two cases (Fig. 3 ). First, specificity decreased with evolutionary depth and diversity of the clade (Cyanobacteria < Alphaproteobacteria < Crenarchaeota < Euryarchaeota, Fig.
3f ), which is in agreement with the idea that HmmCleaner wrongly detects some homologous low similarity segments (see below). Second, sensitivity was severely  The four new scoring matrices provided with HmmCleaner v2 and the scoring matrix equivalent to HmmCleaner v1. 8 for comparison. c1-c4: values of the elemental scores for the four levels of residue conservation provided by HMMER. Global sensitivity and specificity were computed across all conditions of simulation, whereas the last two columns only used the most species-rich MSAs impacted by short error lengths (Fig. 3a ), owing to the limited statistical power provided by such short primary sequence errors. The same kind of simulations were used to compare HmmCleaner and its different parameter sets to PREQUAL (Additional file 1 Table  S1 , Additional file 1 Figure  S5 ).
Similarly, scrambled segments were detected as efficiently as frameshifts (~96%) while arbitrary insertions were more easily recognized (99%). PREQUAL yielded similar results (Table  2 ), except that its sensitivity was lower for frameshifts in eukaryotes (only 85. 64%). Regarding specificity, error type had no effect, except for Fig. 4  High-resolution analysis of the impact of the length of primary sequence errors on the sensitivity of HmmCleaner used with the default scoring matrix. The plain line represents the improvement in mean sensitivity with increasing error length, while error bars show the variability across 400,000 simulated primary sequence errors in 400 MSAs arbitrary insertions in prokaryotes, which slightly decreased HmmCleaner specificity (85. 64% versus ~87%), possibly because insertions disturbed the alignment of correct sequences  [5].