dataesr/scientific-paragraphs-categorization huggingface dataset:
We therefore searched for local variation in the mismatch within D1 and D2 blocks. For CP to call a block as Denisovan that has human sequence data incorporated into it, we expect that the human sequence data would need to be closely surrounded by real introgressing Denisovan sequence. If this were not the case, then CP is expected to simply bisect the Denisovan block. A Denisovan block containing human sequence is therefore expected to have a sharp increase in the mismatch against the Altai Denisovan and a sharp dip in the mismatch against a human genome toward the center of the block. We assessed this signal by dividing D1 and D2 blocks into thirds, and calculating the mismatch of each third against the Altai Denisovan and a West Eurasian sample. For this analysis, we used a minimally masked version of the dataset where the main phased data was combined with unphased data in which archaic heterozygous/low quality sites were not masked, and the call rate filter was not applied; heterozygote/homozygote mismatches in unphased regions were half-weighted to reflect the average 50% probability of applying to the block, and heterozygote-heterozygote mismatches were not counted.
For this analysis, we followed the phasing assessment (see STAR Methods S10e above) in using the Russian LP6005441-DNA_G10 to represent humans and a minimally masked dataset. We counted the number of mutations in the 16 possible sharing categories. For example, with notation 0 indicating the ancestral allele and 1 indicating the derived allele and in order [Human, Neanderthal, Denisovan, Block], a derived mutation that is unique to the block would contribute to mutation motif 0001, a derived mutation shared between the block and the Altai Denisovan would contribute to motif 0011, and a fixed derived mutation would contribute to mutation motif 1111. When counting, we downweighed the contribution of variants in unphased regions such that all possible mutation motifs represented were counted, in proportion to their probability given an equal chance of each variant being on each haplotype.
The downward bias in date estimation is limited to 10-15% for the times most likely corresponding to archaic introgression in humans, and is likely closer to 5%. Based on these simulations, we consider the dates we report to be probable lower bounds on introgression dates, with true dates up to 15% more ancient than our fitting suggest. We proceeded to perform exponential fittings on the lengths of blocks in the D1 and D2 sets (Fig. S4F  and S4G) using the Python package statsmodels v. 0. 8. 0  (Seabold and Perktold, 2010)  and maximum likelihood fitting, assuming either a constant recombination rate or the combined HapMap genetic map  (International HapMap Consortium et al. , 2007). We confirmed 95% confidence intervals through a block-bootstrap procedure, whereby the genome was divided into 2 Mb chunks, consecutive chunks were combined if blocks spanned boundaries, and artificial samples were generated by sampling the same chunks over all individuals with probability proportional to chunk length (usually 2 Mb, but sometimes 4 Mb or more) until the total observed number of blocks corresponded to that expected from the data.
Specifically, we simulated 5 Mb of sequence data for a sample of 144 Papuan haplotypes, two Altai Denisovan haplotypes and two Altai Neanderthal haplotypes. As in the  Malaspinas et al. (2016)  study, the mutation rate was set to 1. 4×10 -8 per base pair per generation. The recombination rate was constant and set to 1×10 -8 per base pair per year. To mimic our own data, we masked all sites that were heterozygote in either the two Altai Denisovan haplotypes or the two Altai Neanderthal haplotypes. We extracted introgressed blocks from each Papuan haplotype in the simulation (using custom scripts and the detailed migration tables recorded by msprime) and recorded the mismatch between these blocks and the Altai Denisovan. The process was repeated 240 times for each parameter set, yielding a total of 1200 Mb simulated data.
(2016)  study. As before, we masked archaic heterozygote sites, but this time calculated the mismatch over the entire dataset to obtain a population-average mismatch between simulated humans and Denisovan. When fitting the model, we expressed the mismatch distribution in introgressing Denisovan blocks found in simulated Papuans as a fraction of this simulated West Eurasian genome-wide average. We first explored the parameters on a coarse grid with  * ∈  [7000, 8000, 9000, 10000, 11000]  ,  C ∈  [10000, 11000, 12000, 13000, 14000]  ,  cdD5pD> ∈  [100, 200, 500, 1000, 2000, 3000, 4000]  and  * ∈ [0. 4, 0. 5, 0. 6]. For each parameter set, we retrieved the mismatch distribution based on the 2000 longest introgressing blocks in the real and simulated data and calculated the sum of squared difference.
No parameter value could fit both heterozygosity and weighted FST. We therefore conservatively chose to explore  Baining = 4500, which yields FST values considerably higher than observed FST (observed 0. 0934, 95% CI of simulated FST 0. 112-0. 115) and places the observed Baining heterozygosity in the 98. 9 th percentile of the simulated distribution (Fig. S6C ), such that our Model 3 used parameters tB = 800 and  Baining = 4500. As with Model 2, these parameter values are expected to generate simulated data with more drift in the Baining than is observed in our dataset. Model 3 also simulates substantially greater divergence between Papuans and the Baining than the observed FST , such that the model will tend to generate more variable D1 ratios and is extremely conservative. The results from these two additional models are shown in Main Text Fig.
We assigned genes from the Ensembl 91 (GRCh37) database to each intersected block, and report the top 1% frequency regions and the frequency of all introgressed regions in Table  S6A. We repeated this procedure for East ISEA individuals (Table  S6B ). A genome-wide map of Denisovan introgression in the Papuan and East ISEA samples (Main Text Fig. 6 ), based on Tables  S6A  and S6B , reveals several sharp peaks at known (e. g. , WARS2,  Racimo et al. (2017) ; TNFAIP3,  Gittelman et al. (2016); and FAM178B, Sankararaman et al. (2016)  and  Ilardo et al. (2018) ) and unreported (e. g. , WDFY2, the TMPO/IKBIP/APAF1 gene cluster) loci. The replication of several loci that have previously been proposed to be subject to adaptive introgression strongly supports our approach to detecting Denisovan introgression and potentially adaptively introgressed regions.
We observed an extreme nSL signal (nSL percentile 0. 2%) in the window containing CENPW in the Baining group, and note that the window containing ZNF280D (D1) also had a high nSL signal (see above). As we are only able to assign D1 and D2 ancestry to large blocks >180 Kb, we additionally explored a concept whereby confident D1 or D2 blocks might be used as local 'flags' for their respective ancestries. In this way, we can adopt an assumption -that short, <180 Kb, introgressing Denisovan blocks overlapping a >180 Kb D1 (or D2) chunk are also from the D1 (or D2) population -to leverage off our high confidence Denisovan ancestry dataset. We performed a bootstrapping analysis whereby we repeatedly sampled two Papuan individuals from our dataset and, using their >20 Kb high confidence introgressing Denisovan blocks, identified the overlap between them.
S13 -Rampasasa is not an introgression outlier Our dataset includes 19 samples from Rampasasa, Flores, a village that is home to some individuals of unusually short stature and close to the cave where Homo floresiensis  (Brown et al. , 2004 ) bones were found. The dataset also includes two other villages on Flores (Cibol and Bena) and samples representing many other islands in East ISEA. This offers the opportunity to test for anomalous signals of unusual archaic introgression into Rampasasa, as recently also assessed by  Tucci et al. (2018) , with the benefit of being able to include samples from nearby and regional populations. Compared to surrounding regions, we did not detect any unusual signs of Neanderthal or Denisovan introgression in the village -the total amount of the genome with evidence for Neanderthal introgression only (62.
) and East Asians (N = 293). The East Asian reference dataset included 293 geographically diverse samples, specifically 43 samples from the Simons Genome Diversity Project, and 50 random samples from each of the five East Asian populations in the 1000 Genomes Project (CDX -Chinese Dai, CHS -Southern Chinese Han, CHB -Northern Chinese Han, KHV -Vietnamese Kihn and JPT -Japan). Samples from ISEA were analyzed separately using the Papuan and East Asian reference datasets. To infer local ancestry tracts in Papuan samples, we created an individual Papuan reference dataset for every single target genome by excluding the individual sample from the full Papuan reference set to avoid self-copying. Results are reported in Main Text Fig. 1A and Table.
Acknowledgments We thank  Hie Lim Kim  ( Nanyang Technological University ),  Matthew Leavesley  ( University of Papua New Guinea ),  Irene Gallego-Romero  ( University of Melbourne ),  Nicolas Brucato  ( Université de Toulouse Paul Sabatier ),  Jeff Wall  ( University of California San Francisco ) and  Vitor Sousa  ( University of Bern ) for helpful comments. We also thank  Anto Aasa  ( University of Tartu ) for help with preparing the geographical map of the D1 distribution, and  Jonathan Friedlaender  for providing New Britain samples as part of the  GenomeAsia 100K  project. We especially thank all of our study participants. This study was supported by  National Science Foundation  Grant  SES 0725470  and  Singapore Ministry of Education Tier II  Grant  MOE2015-T2-1-127  to J. S. L. , an  NTU   Presidential Postdoctoral Fellowship  to G.
S. J. , an  NTU Complexity Institute Individual Fellowship  to P. K. , a  French ANR  grant  ANR-14-CE31-0013-01  to F. -X. R. , a  European Union  grant through the  European Regional Development Fund  (Project No. 2014-2020. 4. 01. 15-0012 ) to  M. Metspalu , and a  Royal Society of New Zealand  Marsden Grant  17-MAU-040  and a  German Alexander von Humboldt Foundation  fellowship to M. P. C. Computational resources were provided by a  Microsoft  research grant for Azure cloud computing and the  High Performance Computing Center, University of Tartu , Estonia. We used these SMC++ results to implement Model 1, with tB = 540 generations ago and the population sizes for Baining and mainland Papuans after tB as inferred by SMC++ (incorporating a recent population bottleneck among the Baining and recent population growth for mainland Papuans).
Ethical approvals and dataset description Here we report a new genomic dataset, the Indonesian Genome Diversity Project (IGDP), which includes 161 high coverage genomes from 14 islands and a wide variety of ethnic groups from Island Southeast Asia (ISEA), spanning the Indonesian archipelago and stretching into Island Melanesia (Table  S1 ). The samples used in this study were collected by J. Stephen Lansing, Herawati Sudoyo and an Indonesian team from the Eijkman Institute for Molecular Biology, with the assistance of Indonesian Public Health clinic staff, and also by Mark Stoneking (see  Stoneking et al. , 1990)  and Jonathan Friedlander (see  Friedlaender et al. , 2008) , in collaboration with the Institute for Medical Research of Papua New Guinea. A subset of the samples have also been incorporated into the Genome Asia 100K Project (http://www.
METHOD DETAILS A schematic overview of the analytical pipeline presented here is shown on Fig. S1A  (STAR Methods S1-5) and Fig. S1B  (STAR Methods S6-13). Datasets used are shaded in green; analyses and inferences in yellow; and key steps are outlined in bold. retains its sharp peak in Papua, while causing a reduction in the overall introgression signal of 75-80% in all populations when compared to S*. Repeating this process but instead removing S* windows inferred to be caused by Denisovan introgression leads to a slight dip in S*NoDeni signal in Papua (Fig. S7A , right pane), consistent with Denisovan introgression explaining the majority of the excess S* Papuan introgression signal. While the overall introgression signal drops by 84% in Papua compared to S*, there is still a fall of 54% in West Eurasia.
Extensive field notes were taken during the field study to record the investigation. Field notes were a means to reflect on the observations, conversations, concepts, and the challenges of the field study  (Strauss & Corbin, 1998). Regular Skype meetings between the researchers allowed us to develop, dispute, and elaborate our experiences and ongoing interpretations. Through them we developed and revised the themes and categories that helped us code and categorize the information collected  (Hammersley & Atkinson, 2007). We used the method of constant comparison to develop and elaborate our conceptual codes, saturating our conceptual scheme with examples. The notes enabled us to discuss and rethink our research strategy and our conceptual inclinations. In other words, the field notes were sketches, opportunities to experiment, in which the boundaries between concepts, the case, and authors are blurred and are transformed through the enquiry  (Alvesson & Kärreman, 2007).
A Toy Operating System. Consider the following definitions: Serv i := !r i (a). a OpSys := Serv 1 | • • • | Serv n R i (z) := νa(r i ⟨a⟩ | a. z⟨z⟩) U (z) := νs (s | !s. R 1 (z) | • • • | !s. R n (z) | !s. 0) User := νu (u⟨u⟩ | !u (z). U (z)) OpSys is the abstraction of a multi-user, concurrent operating system offering services Serv i for 1 ≤ i ≤ n. Each subprocess Serv i is the abstraction of a system call, which is invoked by sending a request on channel r i , together with a channel where the answer has to be sent. User is the abstraction of a user who is logged into the system. The real definition is in U , the rest is needed to encode recursion: the user requests an indefinite number of services, in random order, and may eventually decide to logout (this is represented by the subprocess !s.
The application of thermo-rheological models to forecast active lava flow emplacement and quantify important eruptive parameters of older flows has become a more common over the last decade. With the modification and adaption of these models to modular computing languages, they are now easier, quicker, and are being incorporated into studies of both terrestrial and planetary volcanism. These models rely on certain assumptions and input parameters, some of which such as emissivity are not well understood for molten materials. Without a well-grounded knowledge of how this parameter governs radiant cooling, remote measurements of temperature and models such as FLOWGO that rely on these temperatures to track cooling with time will be in error. Here, we perform a detailed FLOWGO-based modeling study of lava flows emplaced at Tolbachik volcano during the 2012-2013 and the 1975-1976 eruptions.
Specifically, we have modified the FLOWGO model to incorporate a two-component emissivity model linked to the fraction of molten lava and cooled crust. We focus first on the large Leningradskoye Flow emplaced at the start of the 2012 eruption, relying on data from numerous other orbital sensors including MODIS, ASTER and ALI to constrain some of the model input parameters. The twocomponent emissivity adaption produced better fits to the final flow length, directly related to the crust cover percentage. We then applied the constrained model to the large Cone II flow formed in 1975, for which no satellite-based data are available. Results revealed that a nearly identical model fit was achieved with initial effusion rate of 700 m 3 /s or 1250 m 3 /s. However, for the higher the effusion rate, a lower the crust cover is needed to fit the flow length and width.
It should be noted that the TADR values can have up to 30% error and represent a discharge rate averaged over the 12-24 hours prior to the image acquisition  [Coppola et al. , 2010]. TADR were also calculated throughout the eruption following each cloud-free high resolution satellite image. Ramsey and Harris [2016]  mapped the change in flow area using the high spatial resolution satellite data and a constant flow thickness of 5m (based on field reporting at the time) [e. g. ,  Gordeev at al. , 2013]. The volumes are reported in Table  1. Throughout 2013, the flow field expanded later being redirected to the eastern side of the vent chain, eventually building three flow fields (Vodopadnoye to the NW, Leningradskoye to the SW, and Toludskoye to the SE) that covered over 35 km 2  [Dvigalo et al.
High spatial resolution satellite data The Advanced Land Imager (ALI) was launched aboard the Earth Observing-1 (EO-1) spacecraft in November 2000, and was deactivated by NASA in 2017. ALI had ten spectral channels in the visible/short wave infrared (VSWIR) region (0. 43 -2. 35 µm) with nine having a spatial resolution of 30 m and one (the panchromatic channel) spanning 0. 48 -0. 69 µm with 10 m resolution. The data were collected in four swaths, which spanned 37 km in width, with each swath having a length that varied from 42-185 km  [Digenis et al. , 1998; Hearn et al. , 2001]. ALI acquired numerous observations of the flows throughout the 2012-13 eruptive phase, including the first clear higher resolution image on 1 Dec 2012. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) was launched on the Terra spacecraft in December 1999 and measures ground-leaving radiance in 14 spectral channels  [Yamaguchi et al.
, 1998]. With the failure of the shortwave infrared (SWIR) subsystem in 2009, only two subsystems remain, each with different spatial/spectral resolutions: 299 the visible/near infrared (VNIR) sensor with three channels (0. 56-0. 81 µm) at a 15 m spatial 300 resolution and the TIR sensor with five channels (8. 2-11. 3 µm) with 90 m spatial resolution. 301 ASTER was tasked to acquire data of the Tolbachik eruption at every observational opportunity 302 (both day and night) from early Dec 2012 until late June 2013 (Figure  5 ). 303 The ASTER science team also produced two versions of a global DEM (GDEM) dataset, which averages all the ASTER scenes acquired over a given region. This process greatly reduces instrument noise related errors and removes most cloud artifacts  [Fujisada et al. , 2005].
PyFLOWGO modeling The original one-dimensional FLOWGO model tracks the thermal and rheological evolution of a lava control volume using a series of heat loss equations coupled with the Jeffreys equation for Newtonian flow in an open channel, modified for a Bingham fluid  [Harris and Rowland, 2001; Harris and Rowland, 2014]. Lava is tracked down-flow with its cooling, crystallization, viscosity and yield strength re-calculated at each down-flow step to estimate the velocity  [Harris et al. , 2007b; Riker et al. , 2009; Robert et al. , 2014]. The model was applied to various cooling limited lava flows  [Harris and Rowland, 2001 , Harris et al. , 2005 , Harris and Rowland, 2012 , Rhéty et al. , 2017]. PyFLOWGO  [Chevrel et al. 2018 ] implements the model using the Python open source software  [van Rossum, 1995].
, MODIS) resolution satellite data. The MODIS time-averaged discharge rate and the ASTER-derived DEM data proved to be the most important as they provide the volume flux source term, and topographic underlay required by the model, respectively. The ASTER/ALI data also allowed the flow length, channel widths, advance rate, temperature and spectral radiance to be constrained to some degree. Two parameters measured from the high spatial resolution data (channel width and spectral radiance) proved more difficult to constrain. The excessive radiance from the wider, less crustcovered channels closer to the vent resulted in overestimations of the measured channel widths. Conversely, channelized regions with a modeled crust percentage greater than ~75% had emitted spectral radiance much lower than the PyFLOWGO predictions.
Acknowledgements. The research of MSR has focused on Kamchatka for over a decade supported by several  NASA  ( NNX08AJ91G ,  NNX11AL29G ,  NNX14AQ96G ) and  NSF  ( 1019558 ,  1524011 ) grants. This project evolved out of that past work and forms the basis for a new NASA planetary project funded under the  SSW Program. MSR would like to thank the collaborators on those past projects whose work helped facilitate this study ( A. Belousov ,  M. Belousova ,  E. Gordeev ,  J. Krippner  and  R. Wessels ). MOC and AH acknowledge the  Agence National de la Recherche  through the project  LAVA  (Program:  DS0902 2016 ; Project:  ANR-16 CE39-0009 , http://www. agence-nationale-recherche. fr/Projet- ANR-16-CE39-0009 ) for full support. This is ANR-LAVA contribution no. 9.
This paper tested the ability of machine learning techniques, namely artificial neural networks and random forests, to predict the individual trees within a forest most at risk of damage in storms. Models based on these techniques were developed individually for both a small forest area containing a set of 29 permanent sample plots that were damaged in Storm Martin in December 1999, and from a much larger set of 235 forest inventory data damaged in Storm Klaus in January 2009. Both data sets are within the Landes de Gascogne Forest in Nouvelle Aquitaine, France. The models were tested both against the data from which they were developed, and against the data set from the other storm. For comparison with an earlier study using the same data, logistic regression models were also developed. In addition, the ability of machine learning techniques to substitute for a mechanistic wind damage risk model by training them with previous mechanistic model predictions was tested.
All models were accurate at identifying whether trees would be damaged or not damaged but the random forests models were more accurate, had higher discriminatory power, and were almost totally unaffected by the removal of any individual input variable. However, if all information relating to a stand was removed the random forests model lost accuracy and discriminatory power. The other models were similarly affected by the removal of all site information but none of the models were affected by removal of all tree information, suggesting that damage in the Landes de Gascogne Forest occurs at stand scale and is not controlled by individual tree characteristics. The models developed with the large comprehensive database were also accurate in identifying damaged trees when applied to the small forest data damaged in the earlier storm.
However, none of the models developed with the smaller forest data set could successfully discriminate between damaged and undamaged trees when applied across the whole landscape. All models were very successful in replicating the predictions of the mechanistic wind risk model and using them as a substitute for the mechanistic model predictions of critical wind speed did not affect the damage model results. Overall the results suggest that random forests provide a significant advantage over other statistical modelling techniques and the random forest models were found to be more robust in their predictions if all input variables were not available. In addition, the ability to replace the mechanistic wind damage model suggests that random forests could provide a powerful tool for damage risk assessment at the stand or single tree level over large regions and provide rapid assessment of the impact of different management strategies or be used in the development of optimised forest management with multiple objectives and constraints including the risk of wind damage.