dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction The transcription factor p53 is usually kept at low levels in normal, unstressed cells, but it is stabilised and activated in response to certain stresses (e. g. , DNA damage)  [1]. This activation leads to a variety of outcomes such as cell cycle arrest, senescence or apoptosis depending on the severity of the damage. By preventing the growth of stressed and damaged cells, p53 plays a vital role in tumour suppression  [2]. The function of p53 can be altered by mutations in the TP53 gene that encodes for p53. TP53 is the most commonly mutated gene in cancer with around 50% of all human tumours harbouring a mutation in TP53. These are mostly missense mutations occurring in the DNA binding domain encoded by exons 5-8  [3]. The International Agency for Research on Cancer (IARC) curates a database (www.
p53. iarc. fr) of all mutations found in the TP53 gene published in the scientific literature. tumours harbouring a mutation in TP53. These are mostly missense mutations occurring in the DNA binding domain encoded by exons 5-8  [3]. The International Agency for Research on Cancer (IARC) curates a database (www. p53. iarc. fr) of all mutations found in the TP53 gene published in the scientific literature. This database currently lists around 30,000 mutations in human cancers. Most missense mutations in TP53 cause a loss of function such that tumour suppressor capability is lost. However, some TP53 mutations can lead to a gain of function, whereby the mutant p53 acquires a new activity  [4]. A unique tool to study carcinogen-induced human TP53 mutations in a mammalian cell context uses Hupki mouse embryo fibroblasts (HUFs) to perform the HUF immortalisation assay (HIMA).
Results of TP53 Mutation Analysis in HUFs After amplifying exons 4-9 by PCR, an agarose gel should be run to ensure that the PCR was successful and that the amount of DNA is enough for Sanger sequencing. A DNA ladder is run alongside the samples, which helps to estimate the sizes of the PCR products and the DNA amount. The size and intensity of each PCR product is compared to the ladder, where the 500 bp band is equivalent to 100 ng DNA and the other bands are equivalent to 50 ng DNA. An example of a representative agarose gel is shown in Figure  15. Once the sequencing data are available and FASTA sequences have been analysed by alignment against the human TP53 reference sequence, variations (e. g. , single base substitutions, deletions) are assessed using the mutation validation tool of the IARC TP53 mutation database.
TP53 mutations can be classified as either homo-/hemizygous or heterozygous. Typical results and a way to display them are illustrated in Table  10. Features that should be listed in the table are: codon number where the mutation is located, mutation type, strand on which the mutation is harboured, WT-and MUT-codon bases, zygosity, coding change, and the functional activity as assessed by Kato et al. [37]  in a yeast promotor transactivation assay. Based on this yeast assay the impact of the mutation can be categorised as functional, non-functional or partially functional. Once the sequencing data are available and FASTA sequences have been analysed by alignment against the human TP53 reference sequence, variations (e. g. , single base substitutions, deletions) are assessed using the mutation validation tool of the IARC TP53 mutation database.
Investigating Human Cancer Aetiology Using the HIMA As shown in Table  11  and Figure  16  the HIMA has been used successfully to analyse the mutagenic potential and the TP53 mutation pattern for a variety of environmental carcinogens. In several cases, the TP53 mutation pattern generated corresponded to the pattern found in human tumours where exposure to these agents has been documented: (a) UV light, which is linked to human skin cancer, leads to the preferential induction of C→T and CC→TT mutations; (b) BaP or its reactive metabolite BPDE, which are associated with tobacco smoke-induced lung cancer, predominantly induce G→T mutations; and (c) AA, which is linked to urothelial cancer, and leads to a prevalence of A→T mutations. These data have been recorded in the IARC TP53 Database (www.
As shown in Figure  18B , these mutational hotspots frequently observed in smokers' lung tumours are less common in non-smokers' lung cancer. In BaP/BPDE-treated HUFs, codons 157, 158, 248, and 273 are also recurrent sites of mutation, with a significant proportion being G→T indicating that data collected in the HIMA are consistent with the hypothesis that PAHs such as BaP have a direct role in causing TP53 mutations in smokers' lung tumours  [24]. [11, 15, 25]  and the spectrum found in lung cancer of (B) smokers, (C) non-smokers and (D) all cancers in exons 4-9. Reference for human cancer mutation distribution acquired from IARC TP53 mutation database, R20 July 2019  [48]. Exclusions for lung cancer mutation distribution: radon, asbestos, mustard gas, and coal  [49, 50]. Mutation hotspots are indicated in grey.
Future Perspectives: Investigating Mutational Signatures by Whole Genome Sequencing in Immortalised HUFs after Carcinogen Exposure Several recent publications have reported using the HIMA immortalisation assay in conjunction with massively parallel sequencing (next-generation sequencing), conducted at either the wholeexome or whole-genome levels. Proof-of-principle reports have revealed successful identification of genome-wide mutational signatures of AAI, BaP, MNNG, UV light class C (UVC), as well as of the ectopically expressed activation-induced cytidine deaminase (AID). These signatures correspond   [11, 15, 25]  and the spectrum found in lung cancer of (B) smokers, (C) non-smokers and (D) all cancers in exons 4-9. Reference for human cancer mutation distribution acquired from IARC TP53 mutation database, R20 July 2019  [48].
For MS/MS, the 10 most abundant multiple-charged ions were selected for fragmentation on the high-energy collision dissociation cell at a target value of 100,000 charges or maximum acquisition time of 100 ms. The MS/MS scans were collected at a resolution of 17,500. Target ions already selected for MS/MS were dynamically excluded for 30 s. The resulting MS raw files were submitted to the MaxQuant software version 1. 6. 1. 0 for protein identification using the Andromeda search engine. The UniProt human database (October 2017) was used for database searches. Carbamidomethyl (C) was set as a fixed modification, and protein N-acetylation and methionine oxidation were set as variable modifications. First search peptide tolerance of 20 ppm and main search error 4. 5 ppm were used. Trypsin without proline restriction enzyme option was used, with two allowed miscleavages.
The minimal unique + razor peptides number was set to 1, and the allowed false discovery rate was 0. 01 (1%) for peptide and protein identification. Label-free quantitation () was used with default settings. Known contaminants as provided by MaxQuant and identified in the samples were excluded from further analysis. Statistical analysis was performed with Perseus software version 1. 5. 6. 0. The label-free quantitation data were log10 transformed, filtered to include only proteins, which were quantified in at least in three of five replicates in at least one group, and missing values were imputed with values representing a normal distribution with default settings in Perseus. To find statistically significant differences between the groups, t test was performed using permutation-based false discovery rate with 0.
Electron tomography and modelling Samples were prepared as described for TEM. Dual axis tilt series  (Mastronarde, 1997)  were recorded from one or consecutive semi-thick (250 nm) sections using Tecnai FEG 20 (FEI Company) microscope operating at 200 kV. The sections were tilted at onedegree intervals using a high-tilt specimen holder (model 2020; E. A. Fischione Instruments) between ±62°. Images were acquired with SerialEM software  (Mastronarde, 2005)  using a 4 k × 4 k Ultrascan 4000 CCD camera (Gatan Corporation) at nominal magnification of 9,600× or 11,500×. The alignment of the tilt series and reconstructions were done with IMOD software package  (Kremer et al, 1996)  using 10-nm colloidal gold particles as fiducial markers. Tomographic reconstructions were segmented with MIB software  (Belevich et al, 2016)  and visualization was performed using Amira (VSG, FEI Company).
Acknowledgements We thank  LS Churchman ;  E Dufour ,  C Dunn ,  K Holmstr  öm,  H Jacobs ,  T Langer ,  V Paavilainen , and  E Rugarli  for valuable discussion;  EA Shoubridge ,  T Langer , and  CL Otín  for generously sharing reagents; and the  Max Planck-Genome-Centre Cologne , the  University of Helsinki Electron Microscopy Unit and Genome Biology Unit. This work was supported by the  Academy of Finland Centre of Excellence on Mitochondria  to BJ Battersby; an  Academy of Finland postdoctoral award  (to  U Richter ); the  Ella and Georg Ehrnrooth Foundation  (to F Suomi). RW Taylor is supported by the  Wellcome Centre for Mitochondrial Research  ( 203105/Z/16/Z ), the  Medical Research Council Centre for Translational Research in Neuromuscular Disease, Mitochondrial Disease Patient Cohort (UK)  ( G0800674 ), the  Lily Foundation , and the  UK NHS Highly Specialised Service for Rare Mitochondrial Disorders of Adults and Children.
Results Characterizing zonal convective clustering and ITCZ properties. We start by investigating the relationship between the zonal clustering of convection and the observed meridional precipitation distribution using the Global Precipitation Climatology Project (GPCP) dataset  37, 38. We characterize the degree of zonal convective clustering by the spatial concentration of convection in the zonal direction along the equator, with strong (weak) clustering corresponding to a small (large) fractional area covered by convection. Several metrics can be used for this purpose (see Methods section). We will focus here on the monthly mean of the zonal standard deviation of the meridional average of precipitation from 6 S to 6 N, normalized by the mean precipitation over the equatorial band from 6 S to 6 N, a metric referred to as S λ ðPÞ.
4 ). A decrease in equatorial-mean meridional moisture convergence is also found in the daily ERA-Interim reanalysis data (Fig. 4 ). While the meridional convergence decreases, the contribution of the anomalous zonal moisture convergence to the equatorial precipitation increases in both the reanalysis data and simulations. This suggests that in more clustered states, a larger fraction of the moisture convergence into the convective regions occurs along the zonal direction. The increased zonal moisture convergence may thus contribute to a decrease in meridional moisture convergence as sketched in Fig. 5. An energetic perspective of convective clustering. Several studies have shown that the behavior of the ITCZ could be explained to a large extent by energetic arguments  [10] [11] [12] [13] [15] [16] [17] [18].
Consis ently, the widening of the ITCZ with the convective clustering in our simulations can be interpreted by looking at the atmospheric energy budget. The reduction of areas covered by deep convective clouds with stronger convective clustering leads to a reduction of the equatorial atmospheric cloud-radiative effect. Since the changes in atmospheric clear-sky radiative cooling and turbulent The red line on the right-hand side panels shows the vertical pressure velocity at the 500 hPa level (ω 500hPa ) inferred from the ERA-interim reanalysis dataset for the same months. Note that even in the strong clustering case with a distinct precipitation-inferred double-peak structure of the intertropical convergence zone (ITCZ) there is just one upwelling region (with negative ω 500hPa ), and thus the dynamical width inferred with this quantity comprises both peaks.
The correlation coefficients are calculated using monthly mean data from observational (GPCP data for precipitation-inferred quantities and CERES-EBAF for energy fluxes) or from the ERA-interim reanalysis datasets (for all other quantities) and statistically steady-state mean data for the aquaplanet simulations (last row). P E denotes the equatorial mean precipitation, W P the precipitation-inferred and W ω the dynamically inferred width of the intertropical convergence zone (ITCZ), ϕ S denotes the distance between the two peaks in zonal-mean precipitation following ref. 56 , T 2m denotes the average 2m temperature from 6S to 6N, Δ λ T 2m the zonal standard deviation in 2m temperature, Δ ϕ T 2m the difference in average 2m temperature between the zonal band from 6 S to 6 N and the two zonal bands from 10 to 16 degrees latitude, ∇ λ Á qu the zonal standard deviation of zonal moisture convergence at the equator normalized by the equatorial precipitation and ∇ ϕ Á qv the mean meridional moisture convergence.
H rad denotes the vertically integrated atmospheric radiative cooling between 6 S and 6 N calculated from the CERES-EBAF data set and H ACRE its cloud-radiative contribution (only from March 2000 to December 2016). Values in the first row were calculated taking all months into account, whereas values in the second row only took the values for the months with a symmetric precipitation distribution about the equator energy uptake (H a ½0;ϕ;t ) between the equator and latitude ϕ, the area AðϕÞ from the equator to latitude ϕ and the gross moist stability Δh (derived in the Methods section) Ψ max ðϕÞ ¼ AðϕÞH a ½0;ϕ;t ΔhðϕÞ : ð1Þ Because the gross moist stability is positive and because we keep the considered AðϕÞ fixed between experiments, this equation implies that a reduction of the net atmospheric energy uptake in the equatorial region leads to a weakening of the meridional circulation for fixed latitude ϕ.
Does the zonal convective clustering have the same influence on the cloud-radiative effect in observations? With the observational energy balanced and filled (EBAF) dataset of the Clouds and the Earth's Radiant Energy System (CERES)  [45] [46] [47]  , we find indeed that a stronger zonal convective clustering is associated with a weaker atmospheric cloud-radiative effect and an enhanced equatorial atmospheric radiative cooling (Table  1 , Supplementary Fig. 2 ). However, the uncertainties in the radiative fluxes from CERES-EBAF are currently too large to assess whether these relations are indeed robust (see Methods). So while these observations in principle tend to support the energetic framework outlined above, a rigorous observational verification is not yet possible. It is left to future investigations to determine whether the change in net atmospheric energy uptake with convective clustering indeed scales with the atmospheric cloudradiative effect.
Methods Indicators and observational and reanalysis data. We use the Global Precipitation Climatology Project (GPCP) daily data with a resolution of 0. 75°× 0. 75°f or the period from October 1997 to December 2016  37, 38  interpolated on a 1°× 1°l at-lon grid. We use this data to calculate the zonal-mean precipitation, the relative width of the precipitation-inferred ITCZ (W P ) and the different metrics for the zonal clustering of convection. The relative width of the precipitation is defined as )) and c for the average 2m-temperature (T 2m ) between 6 S and 6 N as a function of the corresponding period. The largest peak corresponds to a period of one year and the second largest to half a year. The maximum at around a 1000 days corresponds approximately to the period of the El Niño Southern Oscillation (ENSO).
The pressure velocity is obtained from the European Center for Medium-Range Weather Forecasts Interim Reanalysis (ERA-interim)  41  dataset from October 1996 to December 2016, by interpolating onto a lat-lon grid with 1 o 1 o resolution and taking daily means of the 6 hourly data. Note that since there is only one tropical ascent region in the monthly and zonal mean ERA-interim data (even if there are two peaks in precipitation), the value calculated for W ω is not dependent on the definition used to infer the ITCZ position. In order to assess whether the precipitation is symmetric about the equator we use the tropical precipitation asymmetry index  8  : A P ¼ P 0SÀ15N À P 15SÀ0S P 15SÀ15N ; ð3Þ and define the precipitation distribution to be hemispherically symmetric if jA P j < 0:4. With this definition around a third of all considered months are symmetric and all symmetric months lie between November and May.
The vertically integrated atmospheric radiative cooling is derived from the EBAF dataset of the CERES  [45] [46] [47]  from March 2000 to December 2016. It is calculated as the difference between the monthly averaged net longwave plus shortwave fluxes at the top-of-the-atmosphere and the surface. The clear-sky atmospheric heating and the atmospheric cloud radiative heating are computed consistently by using either the clear-sky or the cloud radiative effect components of the top-of-the-atmosphere and surface components. The monthly CERES-EBAF data set at 1 o 1 o resolution is estimated to have a regional uncertainty of 2:5 Wm À2 for the top-of-the-atmosphere radiative fluxes over the period from July 2002 to June 2015  46. The uncertainty of the CERES-EBAF global annual-mean surface radiative fluxes was estimated to be 8 Wm  À2 47.
In the other experiments, we apply different values of ν and α to Eqs. (  5 ) and (  6 ) to create a large range of evaporation fields as explained in the previous subsection. We use the values 1 through 10 for ν and 2, 4 and 8 for α which leads to 30 ¼ 10 Á 3 experiments as well as two experiments with ν ¼ 20 and α ¼ 2; 4. Model diagnostics. We use the same diagnostics to analyze the model simulations as for the observational and reanalysis datasets, except that we consider the daily values for the entire statistically steady-state period, instead of single months. Hence, whenever a scatter plot with model output is shown, one marker corresponds to one of the simulations. Intensity of meridional circulation and energy transport. In order to link the intensity of the circulation to the meridional energy transport, we start from the energy balance equation in flux form  10, 11, 18  : F where ϕ denotes the latitude, λ the longitude, t time, a the radius of the Earth, H a the net atmospheric energy uptake, that is the sum of the vertically integrated radiative heating and the turbulent surface energy fluxes and F H ðϕ; tÞ the total meridional energy flux across latitude ϕ.
Acknowledgements We thank  Nicholas J. Lutsko  and  Ángel F. Adames  for constructive discussions and valuable suggestions. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  694768 ). This work was granted access to the  HPC resources of IDRIS  under the allocation 0292 made by  GENCI.
Data availability The processed GPCP data is available on the website of the Observations for Model Intercomparisons Project (obs4MIPs, https://esgf-node. llnl. gov/projects/obs4mips/). The ERA-interim data is available upon registration on the homepage of ECMWF (https:// www. ecmwf. int/en/forecasts/datasets/reanalysis-datasets/era-interim). The processed data used here is available on IPSL's ClimServ http://climserv. ipsl. polytechnique. fr/fr/lesdonnees/era-interim. html upon request. The ERA-5 data is available through Copernicus at https://cds. climate. copernicus. eu. The amplitude of the MJO is available at Columbia University at https://iridl. ldeo. columbia. edu/SOURCES/. BoM/. MJO/. RMM/. amplitude/. The monthly Niño 3. 4 SST index inferred from the HadISST1 data set is available at.
Code availability The atmospheric component of the IPSL-5A-MR climate model LMDZ (to be run in version 5A) used in this study is freely available at http://svn. lmd. jussieu. fr/LMDZ/ LMDZ6/trunk. The specific version of the model code that allows the prescription of the evaporation patterns can be obtained from the authors along with all necessary boundary conditions for the specific simulations and all scripts used to process the simulation results.
S 3 SFig. 1 31 Fig. 1Examples of observed weak and strong convective clustering. a The mean precipitation of the December month with the weakest convective clustering and b the December month with the strongest convective clustering. In the case of strong convective clustering the zonal-mean precipitation distribution (shown on the right) is wider than in the case of weak clustering. The red line on the right-hand side panels shows the vertical pressure velocity at the 500 hPa level (ω 500hPa ) inferred from the ERA-interim reanalysis dataset for the same months. Note that even in the strong clustering case with a distinct precipitation-inferred double-peak structure of the intertropical convergence zone (ITCZ) there is just one upwelling region (with negative ω 500hPa ), and thus the dynamical width inferred with this quantity comprises both peaks.
MATERIALS AND METHODS We follow the taxonomy of the International Union for Conservation of Nature (IUCN) Red List of threatened species  (BirdLife International, 2018a). We based this review on the 124 petrel species from three families of Order Procellariiformes: Procellariidae (including diving petrels Pelecanoides), Oceanitidae, and Hydrobatidae  (BirdLife International, 2018a)  and on the expertise of 38 petrel researchers from 34 institutions from 10 countries. We also used data from the BirdLife International Data Zone 1 compiled and regularly updated by BirdLife Partners, scientists, ornithologists, conservationists and birdwatchers. Species are rated into the seven IUCN threat categories: Data Deficient (DD), Least Concern (LC), Near Threatened (NT), Vulnerable (VU), Endangered (EN), Critically Endangered (CR), and Extinct (EX).
Scope/ severity (1) invasive species, (2) light pollution, (3) bycatch, (4) human exploitation, (5) problematic native species, (6) climate change and severe weather, (7) residential and commercial development, (8) pollution, (9) disturbance, (10) energy production and mining, (11) overfishing, and (12) diseases. These threats were ordered in the text by the number of species affected according to the BirdLife International database (Figure  2 ). Some sections may overlap slightly given the multiple impacts of some activities. BirdLife International staff (led by MPD and RM) assessed (1) the timing of each threat (i. e. , ongoing; past, likely to return; past, unlikely to return; future); (2) threat extent or scope (i. e. , the proportion of the total population affected: minority; majority; whole); and (3) threat severity (i.
As a result, strong local partnerships are required to understand social acceptability and alignment with community goals  (Glen et al. , 2013; Russell et al. , 2018). Continued research and application of other restoration tools, including predator-proof fencing and reintroductions, invasive plant management, habitat restoration, and artificial nest construction, are required to protect remnant colonies on islands, and to achieve gains in efficacy and efficiency of pest control  (Kappes and Jones, 2014). Improved reporting of conservation outcomes and knowledge transfer among seabird practitioners remains a key recommendation, including workshops, exchange programs, online databases and reporting in open access media. Investment in transformative innovations are also required  (Campbell et al.