dataesr/scientific-paragraphs-categorization huggingface dataset:
We finally used the dynamic acceleration of the three axes to derive the VeDBA  (Williams et al. 2015 , Wilson et al. 2020). We averaged the VeDBA values per second and applied a K-means clustering algorithm with k=2 to distinguish between active and passive flight. Average VeDBA values and activity classes were then associated to the GPS location matching in time. To segment the GPS data, we applied a running mean of 15 s on the vertical speed; we then applied K-means clustering with k=2 on the smoothed vertical speed to distinguish soaring from gliding behaviour. Vertical speed, horizontal speed and step length between consecutive GPS fixes were calculated for each flight session separately using the R package move  (Kranstauber et al. 2020). The results of the two K-means clusterings, the one based on the smoothed VeDBA and the one based on the smoothed vertical speed, were finally combined in one variable with four classes: passive soaring, passive gliding, active soaring and active gliding.
Datasets We analysed the effect of harness type on the flight parameters measured at two different levels. We first focused on the level of the behavioural segment: consecutive GPS fixes belonging to the same behavioural class were assigned to the same segment ID, and their flight parameters averaged across the segment. Therefore, each entry of the dataset used in the analysis corresponded to one behavioural segment with the following associated parameters: mean vertical speed, mean horizontal speed, glide ratio (ratio between the distance covered in the horizontal plane and the distance dropped in height during each gliding segment), maximum height above sea level (a. s. l. ) and mean VeDBA. The segments were highly variable in terms of their duration (number of consecutive fixes). To improve comparability of the flight parameters across segments of different duration we excluded segments longer than 733 fixes (>0.
01% percentile). This dataset included both the control (1 individual) and the treatment groups. We then worked at the level of the flight session. Each observation of this dataset corresponded to one flight session, whose performance was summarised in terms of: total flight duration, total distance covered during the flight, proportion of soaring flight along the track, proportion of active flight and cumulative VeDBA. The control individual was excluded from this dataset, as it was only tracked for two flight sessions.
Finally, we included the segment length (number of fixes in the segment) to account for the variability in the duration of the behavioural segments. Date of the flight session and individual identity were included as random terms in all models. The height a. s. l. and VeDBA models were run on a subset of the dataset, including one every second observation to reduce temporal auto-correlation. The variable horizontal speed during soaring was square-root transformed while the variables height a. s. l. and VeDBA were log transformed and all models were fitted with a Gaussian error distribution.
Acknowledgements We would like to thank the falconry centre  Le Rocher des Aigles , particularly directors  R. Arnaud  and  D. Maylin , for their welcome and financial support, and all bird trainers for their technical help. We also acknowledge  Camille Nouis  for her help during the data collection and the preprocessing of the data. We are also thankful to  Daniel Hegglin  for the discussions and advice on leg-loop attachment in large soaring birds.
Here, we study genome sequences generated from a largely complete ancient skull that was discovered alongside other skeletal elements in 1950 inside the Koněprusy cave system in present-day Czechia  6, 7  (Fig. 1 , Extended Data Fig. 1  and Supplementary Section 1). All skeletal elements were found to originate from one adult female individual called Zlatý kůň (Golden Horse) after the hill on top of the cave system. Archaeological investigations ascribed the stone and bone tools retrieved from the cave to the early Upper Palaeolithic. However, the artefacts in association with this individual could not be confidently assigned to any specific cultural technocomplex  6, 8. The remains were first thought to be at least 30,000 years old in accordance with morphological and stratigraphic information and the type of associated faunal remains  8, 9.
Around 4% of the mtDNA sequences were estimated to stem from human contamination (Supplementary Section 3). The reconstructed mtDNA belongs to haplogroup N and its branch length, measured as the number of accumulated substitutions, is similar to those of the currently oldest sequenced modern human mtDNA genomes (Fig. 2a  and Extended Data Fig. 3 ), including the recently published mtDNAs from Bacho Kiro, a cave in Bulgaria with remains dating to 43-47 ka 1. Bayesian tip dating suggests that Zlatý kůň lived ~43 ka (95% highest posterior density = 31. 5-52. 6 ka). To study the nuclear genome, we sequenced ~20 million DNA fragments after targeted enrichment with oligonucleotide probes for 1. 24 million single nucleotide polymorphisms (SNPs)  16. A total of ~678,000 targeted SNPs (54%) were covered at least once after genome-wide enrichment (capture dataset).
In addition, we sequenced ~4 billion random DNA fragments from the same DNA library of Zlatý kůň, resulting in ~3. 8-fold genomic coverage (shotgun dataset). In line with the sex assignment based on morphology  13  , the X chromosome and autosomes showed similar coverage, indicating that Zlatý kůň is female (Extended Data Fig. 4 ). The presence of Y chromosomal sequences suggested that up to 4% of the nuclear DNA sequences in the shotgun dataset originate from male contamination. Estimates based on linkage disequilibrium  17  suggest that nuclear contamination is <1% in the capture dataset and ~2% in the shotgun dataset (Supplementary Section 4). The majority of the Zlatý kůň shotgun sequences (~3. 2-fold out of ~3. 8-fold total) have been generated from a single-stranded DNA library that allows for the quantification of contamination with an explicit model of DNA damage in the DNA molecules  18.
This model yielded an estimate of contamination of 0. 1% (s. e. = ±2. 0%) (Supplementary Section 4). We used the non-human fraction of the shotgun data to further investigate whether the use of animal glue could have influenced our attempts at radiocarbon dating of the Zlatý kůň skull. Searching a metagenomic database, we found that the highest proportion of non-human mammalian shotgun sequences aligned to bovids (Supplementary Section 2). We were able to reconstruct ~95% of the bovid mtDNA from the shotgun sequences of the single-stranded library and found that it falls within the most common modern European cattle haplogroup  19  in a phylogenetic analysis (Extended Data Fig. 5 ). Low levels of substitutions that are indicative of ancient DNA damage suggest that the cattle sequences do not derive from present-day laboratory contaminants (Extended Data Fig.
5 ). Taken together, these results suggest that the Zlatý kůň skull has been preserved with glue from cattle that penetrated into the sequenced petrous bone. To gain insight into the genetic relationship of Zlatý kůň to present-day and ancient individuals, we calculated summary statistics based on the sharing of alleles (f 3 , f 4 and D statistics 20 ) with our capture and shotgun datasets. We first compared Zlatý kůň with present-day European and Asian individuals using an African population (Mbuti) as an outgroup and found that Zlatý kůň shares more alleles with Asians than with Europeans (Extended Data Fig. 6 ). A closer relationship to Asians has also been observed for other Upper Palaeolithic and Mesolithic European hunter-gatherers compared with present-day Europeans and can be explained by ancestry in present-day Europeans from a deeply divergent out-of-Africa lineage referred to as basal Eurasian  21.
Interestingly, we found that Ust'-Ishim shares more ancestry with later Eurasian individuals (Fig. 2b ). This suggests that Zlatý kůň was part of a population that split earlier from the population that later gave rise to Ust'-Ishim and other Eurasian populations (Fig. 2c ). Due to the limited data for Oase 1, we are unable to clarify whether Zlatý kůň and Oase 1 derive from the same or separate populations. Around 6-9% of the genome of Oase 1 is derived from Neanderthals, compared with 2-3% in present-day and ancient Eurasians  5, 27, 28. To test whether a higher contribution is also present in Zlatý kůň, we calculated Neanderthal ancestry on the shotgun dataset as the excess of shared alleles with a Neanderthal as opposed to an African and normalized this quantity by the expected sharing between two Neanderthals as opposed to an African (f 4 ratios  20  ; Supplementary Section 6).
9 ). We found that Zlatý kůň carries segments that are on average longer than those of all other Eurasian hunter-gatherers (Fig. 3c ). Assuming that recombination breaks Neanderthal ancestry into shorter segments every generation (see ref. 5  and Supplementary Section 8), we estimated that the last admixture with Neanderthals occurred ~70-80 generations before Zlatý kůň lived (AA map: 74 generations (95% confidence interval (CI) = 61-89); deCODE map: 78 generations (95% CI = 64-94)). In contrast, the genome of the currently oldest sequenced modern human, Ust'-Ishim, yielded significantly higher estimates of 94 generations (95% CI = 77-113) and 99 generations (95% CI = 81-119) using the AA map and deCODE map, respectively. Estimating ages using segments called by admixfrog (a software for inferring local Neanderthal ancestry  32  ) yielded comparable results (Supplementary Section 8).
To estimate the time of admixture independent of calling Neanderthal ancestry segments, we applied a previously published method that is based on the correlation of the state of Neanderthal informative sites over increasing distances  33. Applying this method to the Zlatý kůň shotgun dataset yielded an estimate of 63 generations since admixture (s. e. = ±0. 6), whereas Ust'-Ishim was estimated to have lived 84 generations after the admixture (s. e. = ±1. 3). Most of the Neanderthal ancestry in present-day and ancient humans probably originates from a common admixture event with a group of Neanderthals who were more closely related to European Neanderthals than to a Neanderthal from the Altai Mountains 28. To test whether the Neanderthal ancestry in Zlatý kůň shows the same relationship, we used D statistics to compare the sharing of alleles with the high-coverage genomes of a European and an Asian Neanderthal in addition to five published low-coverage genomes from late Neanderthals from Europe  34.
Target enrichment, sequencing and processing. The indexed library was further amplified to perform targeted enrichment of both the complete mtDNA (mtDNA capture)  5  and ~1. 24 million nuclear SNPs (1240K capture)  16  followed by HiSeq paired-end sequencing and index filtering, resulting in 600,000 and 20 million reads, respectively. MtDNA capture and 1240K capture data were mapped against the mtDNA reference sequence (the Revised Cambridge Reference Sequence (rCRS)) using CircularMapper  43  and the human reference genome (GRCh37/ hg19), respectively, with the same parameters as above and the mapping quality filter set to 30. A random allele was drawn from the 1240K capture data using PileupCaller in pseudohaploid mode (https://github. com/stschiff/sequenceTools). The calling of transversions among the 1.
24 million target SNPs considered full sequences, whereas 2 bp at both termini of sequences were trimmed before calling transition SNPs. Finally, the resulting calls were merged with a large genotype dataset of ancient and present-day individuals for the same set of ~1. 24 million SNPs ( ht tp s: // re ic h. hm s. ha rv ar d. ed u/ al le n-an ci en t-dn a-re so ur ce -a ad rdo wn lo ad ab le -g en ot ypes-present-day-and-ancient-dna-data; v er si on 37. 2). Reporting Summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Ust'-Ishim, Zlatý kun, X, Mbuti) D(Ust'-Ishim, Zlatý kun, X, Mbuti) Zlatý kun ˚ˇˇ˚F genetic relationship with present-day and ancient humans. a, mtDNA haplogroup N of a maximum-parsimony phylogenetic tree of mtDNA from Zlatý kůň (red font), Upper Palaeolithic individuals ~40 ka or older (blue) or between ~37 and ~24 ka (orange), and present-day individuals (black) (the entire tree is presented in Extended Data Fig. 3). b, Analysis of nuclear sequences showing that Ust'-Ishim shares more alleles with European and Asian hunter-gatherers and later Eurasians than does Zlatý kůň. The error bars represent two standard errors. c, Admixture graph of the relationship inferred from the nuclear capture dataset. Zlatý kůň diverges earlier than Ust'-Ishim and the ancestors of later Eurasian populations represented here by the Upper Palaeolithic Tianyuan and Sunghir genomes (highest outlier |Z| = 3).
Acknowledgements We thank the laboratory team at the  Max Planck Institute for the Science of Human History in Jena ,  M. Rivollat  and  K. Nägele  for preparing the genetic library;  V. Kuželka ,  J. Dašková  and  T. Přikryl  for preparing anthropological and geological background information; and  M. O'Reilly  for graphic design support. This work was funded by the  Max Planck Society. T. H. and T. D. are supported by  ERC  grant  324139  ( PalaeoChron ) and the  Natural Environment Research Council. P. V. is supported by the  Ministry of Culture of the Czech Republic  ( DKRVO 2019-2023/7. I. c, 00023272 ).
f 4 (ig. 2 | 42 Ust'-Ishim, Zlatý kun, X, Mbuti) D(Ust'-Ishim, Zlatý kun, X, Mbuti) Zlatý kun ˚ˇˇ˚F genetic relationship with present-day and ancient humans. a, mtDNA haplogroup N of a maximum-parsimony phylogenetic tree of mtDNA from Zlatý kůň (red font), Upper Palaeolithic individuals ~40 ka or older (blue) or between ~37 and ~24 ka (orange), and present-day individuals (black) (the entire tree is presented in Extended Data Fig. 3). b, Analysis of nuclear sequences showing that Ust'-Ishim shares more alleles with European and Asian hunter-gatherers and later Eurasians than does Zlatý kůň. The error bars represent two standard errors. c, Admixture graph of the relationship inferred from the nuclear capture dataset. Zlatý kůň diverges earlier than Ust'-Ishim and the ancestors of later Eurasian populations represented here by the Upper Palaeolithic Tianyuan and Sunghir genomes (highest outlier |Z| = 3).
" Estimation in the isotonic single index model has recently received increasing attention, see for example  Kakade et al. (2011) ;  Chen and Samworth (2016) ,  Foster et al. (2013)  for the high-dimensional setting, and the recent work of  Balabdaoui et al. (2019). The model is closely related to the semi-parametric single index model which was much popularized in econometrics  (Li and Racine, 2007). Unlike the isotonic single index model, no shape assumptions on the link function are made. However, it is necessary to impose some smoothness conditions on the unknown link function, which is typically estimated via kernel-based nonparametric methods. The most popular estimation methods are due to  Ichimura (1993)  and  Klein and Spady (1993) , which are implemented in the R package np. Despite their popularity, one of the main downsides of such approaches is the fact that they require bandwidth selection, known to be a hard problem to solve.
To the best of our knowledge, theoretical developments for the isotonic single index model are restricted to the setting of random and continuous design where it is assumed that the covariates are generated from a multivariate density. In the recent work of  Balabdaoui et al. (2019)  it is shown that the rate of convergence of the least squares estimator (LSE) of the isotonic link function in this setting is n 1/3 under some standard regularity assumptions. The methods used there cannot be adapted to datasets where repeated measurements of predictor values are observed. Such discrete design settings occur for example in drug-interaction studies, cf. Carter et al. (1986) ;  Wan et al. (2017). We note that although  Wan et al. (2017)  considered an estimation method which is not fully automatic like the one presented here, they stressed the benefit to be gained from fitting a monotone single index model.
We use a kernel-based estimator calculated with the R package np  (Hayfield and Racine, 2008). (f). A misspecified generalized linear model with inverse link function f 0 as in the true model but with µ 0 (x) = f 0 (α T 0 x) where x T = (d 1 , d 2 ). (g). A misspecified generalized linear model with link function equal to the identity and the predictor x as given in (  12 ). (h). A well-specified single index model with µ 0 (x) = f 0 (α T 0 x) where the predictor x is as given in (  12 ) but no assumptions are made on f 0. We use a kernel-based estimator calculated with the R package np  (Hayfield and Racine, 2008). (i). A misspecified generalized linear model with identity link function and x T = (d 1 , d 2 ). x T = (d 1 , d 2 ). Table  1  gives the mean of the estimated α 0 values for approaches (a), (b), (f), and (h) -as these are the only approaches which enable its estimation.
Proca-SAHA derivatives target HDACs but not DNMTs As we designed compound 4 to target DNMTs and HDACs, we assessed its activity in in-house DNMT  36  and HDAC enzymatic assays using P. falciparum extracts (Figure  1 ) and HeLa cells nuclear extracts (Figure  1b , Table  1 ). While compound 4 showed no activity against P. falciparum DNA methylation (Figure  1a ) nor human DNMT3A methylation (data not shown), it inhibited acetylation of histones in both human and P. falciparum extracts (Figure  1b ). The optimized compound 19 efficiently inhibited histone acetylation in P. falciparum extracts (Figure  1b , IC50 480nM comparable to compound 4, Table  1 ), but was less active on HeLa cell extracts than compound 4 (IC50 of 140nM and 60nM, respectively). Additionally, late stage cultures of P. falciparum were treated with 4 and 19 for 4h and the acetylation of lysine 16 of histone 4 (H4K16) was measured by western blot.
Both compounds and SAHA increased the parasite H4K16 acetylation 6-folds compared to the DMSO and chloroquine controls (Figure  1c ). Finally, we profiled the human HDAC inhibition of compounds 4 and 19 (Figure  2a , Table  1 ). Both of them have an HDAC inhibition profile similar to SAHA and showed a strong activity against hHDAC6 (IC50 of 14 and  19nM). In particular, 19 was less efficient in inhibiting hHDAC1 compared to compound 4 (Table  1 ). This was further confirmed by virtual Structure Activity Relationships (SAR) studies calculating the interaction score of compounds 4, 19 and SAHA in the catalytic pocket of hHDAC1 and hHDAC6 crystal structures (Figure  2b , PDB: 5EDU and 5ICN, and Supplementary Figures  1  and 2 ). While the three compounds have comparable affinity for hHDAC6, compound 19 interacts to a lesser extent with hHDAC1 (Figure  2b ).
Stage-Specificity during the Asexual Cell Cycle Stage-specificity was assessed using previously described methods  36. Briefly, asexual NF54 parasites were tightly synchronized (0-3hpi) and incubated for 6h with compound 4 at 3× and 10× IC50 value, at 0, 6, 12, 18, 24, 30, 36, or 42hpi. Following each treatment, cells were pelleted, washed with 10mL of RPMI, and put back into culture in a new plate. Parasitemia was assessed at 72h postsynchronization using Giemsa-stained thin blood smears. The percentage of survival was compared to DMSO-treated parasites. Data were obtained from three independent experiments (one well per condition).
falciparum isolates used in this study were collected in the framework of the therapeutic efficacy surveillance program in Cambodia between 2017 and 2019. PSA, RSA, AQSA and genotyping data have been performed in the Pasteur Institute in Cambodia. Parasites were chosen based on the pattern of multi-drug resistance they carry (10 isolates per type of resistance). IC50 values were obtained as previously described  41  with slight modifications. Ring-synchronized cultures (2% hematocrit, 1% starting parasitemia) were incubated for 96h before growth measurement using the SYBR-green I assay  36. A range of 11-point and 2-step serial dilutions, starting at 500nM were used to assess the activity of compounds 4 and 19. Mefloquine (MQ) and monodesethylamodiaquine (dAQ) were used as positive controls.
04% Triton X-100, 0. 5mM DTT) and 135ng/mL of hHDAC2 (Active Motif N°31909) or 90ng/mL of hHDAC3 (Active Motif N°31609) in Greiner Fluotrac 200 96-well plates. As controls, the substrate alone was incubated in the buffer (background) and DMSO was used to replace the compound for 100% activity. After incubating 45 minutes at 37°C, 20µL of trypsine (1mg/mL, SIGMA T1005) was added and incubated 15min at RT. Then 150µL of glacial ethanol was added and fluorescence was measured by Perkin Elmer Envision (Peptides AMC excitation at 380nm and emission at 460nm). The specificity of trypsin was measured in the same conditions on substrate Succinyl-Leu-Leu-Arg-AMC. Background was subtracted and data normalized to the DMSO value. Experiments were run in duplicates in at least two independent experiments.
ACKNOWLEDGMENT We thank  Bruno Vitorge  from the  Institut Pasteur Biological NMR Technological Platform  for help with NMR experiments,  Frédéric Bonhomme  of the  CNRS-Institut Pasteur UMR3523 Organic Chemistry Unit  for assisting with HRMS analysis and  SophieVichier-Guerre of the Chemistry and Biocatalysis Unit (Institut Pasteur-CNRS ) for HPLC analysis. We thank  Patty Chen  proof reading.
And b ij is the branching ratio of level i depopulating to j, v is the average recoil velocity and J hi , J ij are the relative intensities of the feeding and depopulating transitions, respectively. The derivative of Q ij (x) as a function of the distance, d dx Q ij (x), was extracted using the APATHIE software  [32]. The measured mean lifetimes of the 17/2 + and 21/2 + states were deduced from the statistical weighted average of the individual lifetimes at the different target-to-degrader distances within the sensitive region, as shown in Fig. 3  (a) and (b). In a singles RDDS analysis, the lifetime of the level of interest can be influenced by unobserved feeding. As has been discussed in Refs. [31, 33, 34] , in certain cases where the observed feeding times are not particularly long compared with the depopulating transition and the majority of the feeding is observed, it is reasonable to assume that the unobserved feeding does not significantly perturb the measured lifetime values.
Acknowledgements This work was supported by the  Swedish Research Council  under Grant No. 2019-04880 , the  United Kingdom Science and Technology Facilities Council (STFC) , the  EU   7th Framework Programme, Integrating Activities Transnational Access , project No. 262010  EN-SAR , and the  Academy of Finland  under the  Finnish Centre of Excellence Programme (Nuclear and Accelerator Based Physics Programme at JYFL ). E. I. , Y. D. F. , and M. K. R. acknowledge support from the  International Joint Research Promotion Program of Osaka University. W. Zhang acknowledges support from the  China Scholarship Council  under grant No. 201700260239. The authors acknowledge the support of the GAMMAPOOL for the JUROGAM detectors.
We then transferred this manually curated annotation to Kitaake, a cultivar that is closely related to Nipponbare, using an optimized strategy. Here, we discuss the breakthrough achieved by manual curation when comparing genomes and, in addition to 'functional' and 'structural' annotations, we propose that the community adopts this approach, which we call 'comprehensive' annotation. The resulting data are crucial for further studies on the natural variability and evolution of LRR-CR genes in order to promote their use in breeding future resilient varieties.