dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions We measure the veiling in 1169 high-resolution spectra of TW Hya obtained over 25 years. These measurements are converted to accretion rates based on scalings obtained from 26 flux-calibrated low-resolution spectra. From analyzing this data set, we find the following results: 1. The veiling at 5000-5100 Å varies from 0. 13 to 2. 13, equivalent to mass accretion rates from 0. 47-9. 43 × 10 -9 M e yr -1 , with an average of 2. 51 × 10 -9 M e yr -1. These accretion rates are only slightly lower than the average accretion rate for accreting stars in nearby young clusters. With its current mass, the disk could survive for another 3. 8 Myr. The accretion rate would be 12% higher if we adopted the truncation radius of 3. 5 R * measured by Garcia  Lopez et al. (2020). 2. The distribution of accretion rates is well described by  = -M log 8.
Acknowledgments We thank the referee for a careful read and comments, which saved us from publishing a significant error and misinterpretation of possible periods in the veiling. We thank the many people who have contributed time and effort into obtaining this data at the telescope, reducing the data, and developing archives to make the data available. This list includes  Ilaria Pascucci ,  Feng Long , and  Claudio Melo. We also thank  Kevin France  for discussions of Lyα emission. This work benefited from discussions with the ODYSSEUS team ( HST AR-16129 ; Espaillat et al. 2022, https://sites. bu. edu/odysseus/). G. J. H. and J. T. are supported by theNational  Key R&D program  of China  2022YFA1603102  from the  Ministry of Science and Technology (MOST) of Chinaand  by general grant  12173003  from the  National Natural Science Foundation of China.
Z. G. acknowledges support from  FONDECYT Postdoctoral   3220029. Z. G. acknowledges support by  ANID Millennium Science Initiative Program   NCN19_171. Support for this HMG was provided by the  National Aeronautics and Space Administration  through Chandra Award Number  GO1-22007X  issued by the Chandra X-ray  Observatory Center , which is operated by the  Smithsonian Astrophysical Observatory  for and on behalf of the  National Aeronautics Space Administration  under contract  NAS8-03060. A. F. and J. A. acknowledge support by the  PRIN-INAF   2019 STRADE  ( Spectroscopically TRAcing the Disk dispersal Evolution ) and by the  Large Grant   INAF YODA  ( YSOs Outflow, Disks and Accretion ). This work has been funded by the  European Union  under the  European Union   Horizon Europe Research & Innovation Program   101039452  ( WANDA ).
Also, since nanopore sequencing does not require amplification of the isolated DNA and since we are using haploid cells, the fraction of methylated reads in the entire population of sequenced reads reflects the frequency of Sir3 contacts per cell. Given sufficient sequencing depth, this approach allows for the detection of rare contacts that occur only in a small fraction of the cell population and which would be too close to or even below the genome average read count in ChIP-seq datasets to be considered as bona fide Sir3 targets. We first mapped me A in saturated mid-log cultures of Sir3Dam cells, to minimize the number of cells undergoing replication since replication bubbles interfere with sequencing through nanopores. In addition to a wt Dam construct we used a Dam mutant with a K9A substitution that reduces its binding affinity for GATC motifs and consequently improves Sir3 specificity of Sir3DamK9A methylation  (Szczesnik et al.
2011) ). Likewise, the SN2 region at 7kbp downstream from the XCS contains PAU genes that show high me A density in the Sir3EcoG2 strain but no significant Sir3 enrichment in the wt Sir3 ChIP-seq dataset. Peaks of Sir3 enrichment at PAU genes located in the SN2 region only appear in ChIP-seq datasets if Sir3 is over expressed  (Figure 6B and (Radman-Livaja et al. 2011 )), thus demonstrating the high sensitivity and specificity of Nanopore-MetID. Indeed, the only Sir3 targets that are detectable by both ChIP and Nanopore-MetID when Sir3 is not overexpressed are located in the SN1 region that is immediately adjacent to SIR nucleation sites in the XCS region. The non-random distribution of SN1-4 is consistent with the folding of chromosome arms into a spiral with ~12kbp/gyrus that brings SN1-4 closer to the XCS, as illustrated in the bottom panel of Figure  2E.
An increase in mRNA instability is probably not the main cause of the more than 200 fold increase in insensitivity to α-factor in mid-log Sir3-3xHA|3xT7 cells compared to mid-log wt cells (Figure  4C ), since RNA-seq datasets normalized to an external S. pombe RNA "spike-in" (Figure  7 ) show that, during exponential growth, SIR3 mRNA levels are on average only 2 fold lower in the two tagged Sir3 strains than in wt strains (Figure  3D ). The slow recovery to mid-log SIR3 mRNA levels that takes the entire first cell cycle after exit from starvation is consistent with the increase in α-factor insensitivity relative to mid-log cells during this period in all three strains (Figure  3D ). Sir3 enrichment at nucleation sites in subtelomeric and silent mating type loci is 6 to 8 fold lower in the 3xHA strain compared to an untagged WT strain  (Radman-Livaja et al.
ChIP-seq datasets of old and new Sir3 showed that Sir3 dynamics are similar at all heterochromatic loci, including silent mating type loci and subtelomeric regions (Figure  5B-D  and replicate experiment in Supplementary Figure  S5 ). Our measurements of Sir3 ON and OFF rates (see Materials and Methods in the SI Appendix) show that the old Sir3 stays bound to chromatin in arrested cells even after the tag switch because of a slow OFF rate of 4%, 0. 6% and 0% decrease in Sir3-3xHA enrichment per hour and a slow ON rate of 3. 5%, 3% and 3. 5% increase in Sir3-3xT7 enrichment per hour for subtelomeres, HML and HMR, respectively (Figure  5C-E ). This results in very slow exchange rates of -1%/hr, +2. 1%/hr and +3. 5%/hr for subtelomeres, HML, and HMR respectively. Overall, Sir3 enrichment after 64 hrs of starvation is decreased ~2 to 4-fold relative to the genome average compared to the pre-arrest mid-log phase as shown in the replicate experiment in Supplementary Figure  S5B-C.
Third, the two fold decrease in global mRNA production during starvation, observed in the oeSir3 strain cannot be due to a pseudodiploid phenotype because HML and MAT loci are deleted in the oeSir3 strain. GO annotation analysis shows that genes that are the most affected by suboptimal SIR complex levels are involved in protein transport and localization, nucleotide/nucleoside binding, and Golgi apparatus function. The least affected genes are mostly involved in transcription, RNA processing, ribosome function, and mitochondrial function and respiration (Table  S2 ). We also looked at subtelomeric genes whose expression is more likely to be directly affected by changing Sir3 levels. Consistent with previous reports  (Ellahi et al. 2015) , wt levels of the SIR complex do not control the expression of most subtelomeric genes because we found only 16 genes out of 132 in our RNA-seq datasets that were located within 15 kbp of subtelomeric SIR nucleation sites, whose gene expression profiles in SIR mutants and Sir3 hypo-morphs were significantly different from WT cells (Δcorrelation(Sir3Δ-WT2) ≤ -0.
Conversely, Sir3 overexpression would cause a more efficient shutdown of transcription during starvation, resulting in lower global mRNA amounts as observed in Figure  7D  and shorter apparent mRNA half-lives than in wt cells. For simplicity sake, we assumed that the decay pattern for most mRNAs is exponential, even though it is probably more complex for a number of mRNAs  (Deneke et al. 2013). Of course, our prediction would only hold if the true mRNA decay rates are the same in all tested strains, which we assumed should be the case because decay rates are for the most part determined by mRNA sequence and secondary structure, which are identical in all strains  (Miller et al. 2011). We consequently determined apparent mRNA half-lives during growth arrest using the datasets from Figure  7.
ChIP-seq and RNA-seq data analysis Sequences were aligned to the S. cerevisiae genome with BLAT (Kent Informatics, http://hgdownload. soe. ucsc. edu/admin/). Read count distribution was determined in 1bp windows and then normalized to 1 by dividing each base pair count with the genome-wide average base-pair count. RNA-seq normalized read densities for each gene were aligned by the transcription start site and divided into sense and antisense transcripts. The median read density was determined for each transcript as above and normalized to the genome average read count of the S. pombe spike-in, as described in the SI Appendix and Figure  S8. Intron regions were excluded from the calculation.
Sir3 Nanopore-MetID nanopore sequencing data analysis Basecalling, demultiplexing and aligning to the S. cerevisiae genome was performed with the guppy software from Oxford Nanopore. Fastq files and modification probability tables were extracted from basecalled fast5 files using the ont-fast5-api package. Adenines with a methylation probability higher or equal to 0. 95 were considered as positive signals. > 0, and a non-zero probability of having at least 1 me A in the Sir3EcoG2 strain, either in their promoter or CDS or both. Additionally there are 723 genes with a non-zero probability of having at least 1 me A in the Sir3EcoG2 strain that do not have GATC motifs in their promoter, CDS or both that were included in the set of 1197 genes that are contacted by Sir3. ChIP-seq enrichments (y axis on the left) of genes located at the Sir3EcoG2 peaks located 1kbp (SN1), ~7kbps (SN2), ~13kbps (SN3) and ~22kbps (SN4) downstream of the ACS.
Acknowledgments We thank  Fred van Leeuwen ,  Kevin Struhl  and  Laure Crabbe  for yeast strains and plasmids. Thank you to  Marta Gut  and  Julie Blanc  from  CNAG (Barcelona, Spain ) and  Véronique Pantesco  ( IRMB, Montpellier, France ) for Illumina sequencing services. We thank  Virginie Georget  and  Leslie Bancel-Vallée  ( MRI, Biocampus, Montpellier ) for their assistance with the microscope set-up. Thanks to  Sylvie Fromont  and  Christelle Anguille  ( MGC platform, IGMM-CRBM, Montpellier ) for the LabChip experiments. Thank you to  Fabrice Caudron  for critical reading of the manuscript. Two previous versions of this article are part of the doctoral theses "Heterochromatin dynamics upon release from growth arrest in budding yeast" by  Hrvoje Galić  and "Functional analysis of heterochromatin dynamics after exit from growth arrest in budding yeast" by  Ana Hrgovčić.
Studies of Amazon carbon fluxes have concentrated mostly either on the roles of old-growth forests as a carbon sink  8  , on the emissions from deforestation and forest degradation  22, 23  , or on net biome productivity (NBP)  24, 25. They use different methodologies, study periods, and spatial domains of the Amazon area (e. g. , whole Amazon vs Brazilian Amazon). The Brazilian Amazon forests in 2010 was estimated to be a net carbon source of +0. 06 (-0. 01 to +0. 31) Pg C yr -1 based on a literature review and compilation of datasets  25. Estimates based on Earth observation data focusing on the carbon gains and losses in forest areas derived from the Global Forest change product  26  and their emissions and removal factors using the Intergovernmental Panel on Climate Change (IPCC) guidelines, indicated that the whole Amazon Forest region was a net carbon sink between 2001 and 2019, while, the Brazilian part of the Amazon forest acted as a net carbon source as a result of deforestation  23.
Spatiotemporal attribution of the land carbon fluxes in the whole biogeographical Amazon For the whole biogeographical Amazon, we relied on two global models to estimate the disturbance flux. A combination of net land use flux estimates from the Bookkeeping of Land Use Emissions (BLUE) added to the net wildfire flux from a fire bookkeeping model (FATE) (BLUE+FATE, see Methods), suggests the whole biogeographical Amazon released a net flux of 192 Tg C yr -1 over 2010-2020 to the atmosphere from land use and land cover changes and forest degradation fires. Over the same period, the Global Fire Emissions Database (GFED) suggested an average flux of 89 Tg C yr -1 from deforestation and degradation fires (Table  1 ). Both BLUE+FATE and GFED show similar interannual variability (Figure  1a ). However, the average flux simulated by BLUE+FATE is 116% higher than GFED, as the former includes more processes linked to land use and land cover changes, such as fluxes from transitions between different land uses, shifting cultivation, soil carbon and legacy fluxes, as well as the addition net legacy fluxes of forest degradation by fire from the FATE model, which include late tree mortality by fire.
Tg C to 76 Tg C) in the emissions estimated by the INPE emission model (INPE-EM) compared to 2019  35. Yet, the multi-model disturbance average (black line in Figure  2a ) did not reproduce higher emissions in 2020 compared to 2019, which is due to the BLUE and GFED models showing a decrease in emissions between these two years (blue and orange lines in Figure  2a ). The reason for the diverging results between The BLUE model uses as the Global Land Use Harmonization database to estimate the area impacted by land use changes, which rely on changes in agriculture to model the deforested area within old-growth and secondary forests in a calendar year (January-December). The GFED data used here is based on burned areas estimates associated to deforestation and degradation in tropical forests estimated from satellite data (MCD64 A1 product  36  ) in a calendar year (January-December).
This difference is present in all model estimates over both the whole Amazon (Figure  3a ) and Brazilian Amazon (Figure  4a ). The top-down inversion suggests a net carbon source in 2018, which is hypothesised to be related to reduced carbon uptake in the south-eastern Amazon  24. Our bottom-up attribution shows a net lower disturbance flux in 2018 compared to 2015-2017 and a larger sink from old-growth forests (i. e. , uptake), thus suggesting a net land carbon sink in 2018. We hypothesize that the large flux from the top-down inversion can be partly attributed to the difference in the spatial resolution of the datasets. For instance, the atmospheric inversion has a spatial resolution of 5. 6°, therefore it could be potentially accounting for surrounding fluxes within these large grid-cells, such as fluxes from savanna fires and additional fluxes coming from fossil fuel emissions; these large fluxes are mostly from locations in the south and east Amazon (see 2012 and 2018 maps in SI Figure  9 ).
We also used estimates of a fire bookkeeping model (FATE) that quantifies the long-term net carbon fluxes of burned forests in the Amazon based on inventory data from burned forests and upscales them to the Brazilian Amazon using burned area maps. Yet, this is a conservative estimate (i. e. , likely an underestimate) linked to limitations of mapping the extent of burned forests in the Amazon. Uncertainties are caused by 1) difficulties in mapping low intensity understory fires; 2) limited temporal availability of Landsat images using in FATE (i. e. , the satellite passes over the same region twice per month, but since the Amazon has high cloud cover it limits the number of images available for classification)  42. With the increasing availability of medium to high spatial resolution satellite images, such as the Sentinels from Copernicus program, as well as higher temporal availability by integrating a range of images of different satellites, this limitation might be overcome in the future.
The inclusion of additional edge effects would be possible by standardizing the same input dataset for the bottom-up models, such that we could overlay the edge and burned forest areas and separate each flux correctly. Moreover, currently there are still knowledge gaps on forest edge dynamics to produce estimate of its influence combined with wildfires on the Amazon carbon balance  18. For example, the few models that consider edge-effects only include gross carbon fluxes and not those associated with potential partial recovery from disturbance, e. g. , escaped agricultural fires. Therefore, the total disturbance flux estimate from this work can be considered conservative. Additionally, large uncertainties remain about the contemporary trends and magnitude of the old-growth Amazon forest sink.
Finally, we calculated the multi-model mean and standard deviation statistics. In order to assess the old-growth sink simulated by the DGVMs, we compared it against RAINFOR inventory based estimates  8, 9, 34  for the common period (2010-2015) by using a Welch's t-test to test whether the averages over the same period were significantly different. Additionally, we did a similar test comparing the aboveground carbon changes (AGC) in old-growth forests based on L-VOD  11  and the AGC changes based on the TRENDY-v11 multi-model mean between 2011 and 2019. We used the annual biomass data from each model of TRENDY-v11 to calculate the change between the years and compare to AGC based on L-VOD. Since the biomass variable from TRENDY-v11 accounts also for belowground biomass, we assumed that 20% of this biomass is belowground based on previous studies  63  and applied a factor of 0.
Spatial model outputs also differ in their spatial resolution and to avoid further error inclusion from spatial resampling, the annual values for the Brazilian Amazon and Biogeographical Amazon were extracted using each model's original spatial resolution. Then, to produce the net carbon flux maps we spatially resampled the bottom-up approaches to a standard spatial resolution of 0. 5 x 0. 5. For the whole biogeographical Amazon, we provide an additional estimate of the net land carbon fluxes from the CARbon Data Model fraMework (CARDAMOM)  31, 32. CARDAMOM uses a Bayesian approach within an Adaptative Proposal -Markov Chain Monte Carlo (AP-MCMC)  70  to retrieve parameters, at pixel scale, for the intermediate complexity C-cycle model DALEC  71. Observational constraints include earth observation datasets and databased information on soil C stocks.
Data availability INPE-EM deforestation and degradation estimates are freely available at http://inpeem. ccst. inpe. br/emissoes-liquidas-com-degracao-amz/. FATE dataset upon reasonable request to Camila Silva. Atmospheric inversion upon reasonable request to Luana S. Basso. GFED4 fire emissions is freely available at https://www. globalfiredata. org/. TRENDYv11 DGVMs used in the Global Carbon Budget 2022 and in this research can be requested at https://globalcarbonbudgetdata. org/closed-access-requests. html. BLUE land use and land use change emissions upon reasonable request to Julia Pongratz and Clemens Schwingshackl. CARDAMOM dataset upon reasonable request to Luke Smallman.
Uncertainty estimates are not available for this model because of the difficulties to estimate uncertainty of each input dataset and parameter. To produce the maps of net fluxes of deforestation and degradation, we used the gridded data from INPE-EM. The original resolution of the INPE-EM spatial output is 5-km and contains the aggregated emission of each grid-cell and comes in a shapefile format. This data was then converted to raster format and re-gridded to 0. 5 spatial resolution by aggregating the grid-cells with the sum of fluxes.
the state level. For wood harvest, LUH2 uses grazing areas using MapBiomas c6 areas at For Brazil it constrains the cropland and spatially explicit estimates. scale the in-country areas from FAO to global, Biome level carbon stocks (Hansis et al, 2015) based on literature 0. 25°x0. 25° Global NA gross source and gross sink flux based on Table 2) parameters details in SI literature (see response curves from carbon densities and shifting cultivation); degradation through logging. abandonment and agricultural natural vegetation after rangelands, regrowth of natural vegetation for logging or use of Hansis et al, 2015 Based on remote sensing observation; Burned *  † GFED area is derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). * † BLUE Based on the Land Use Harmonization 2 (LUH2) dataset.
deforestation for Brazil 12. To better represent and improve the magnitude of LULCC in Brazil and consequently in the Amazon, the cropland and grazing areas of LUH2/HYDE3. 3 (GCB 2022 version) dataset for the years 1700-2021 used by BLUE 30  was based on the areas derived from the remote sensing classification from MapBiomas (collection 6) maps at state level for the contemporary period (1985 until year 2020), and then spatially allocated by the HYDE 3. 3 algorithm. Due to challenges of estimating uncertainties of input parameters from the BLUE model, there is currently no regional uncertainty estimate available. In this study we provide estimates of the net land use and land cover change emissions (ELUC) for both study regions (Brazilian Amazon and Biogeographical Amazon) from the global BLUE model.
To produce the ELUC maps, BLUE output at 0. 25° spatial resolution was converted to raster format and re-gridded to 0. 5 of spatial resolution with the aggregated sum of the fluxes. This is a spatially explicit approach based on 20 which has been developed in partnership with the Brazilian Greenhouse Gas Emission and Removal Estimating. LUH2 System (SEEG) project and FATE network. The model is parametrized with a dataset incorporates multiple datasets at different spatial and temporal scales to produce a derived from field information of burned plots in the Amazon and includes estimates global gridded land use dataset. For example, it uses inputs from the History Database of combustion emissions, as well as post-fire temporal biomass changes and delayed of the Global Environment (HYDE 3. 3) 58 for cropland and grazing areas, which are mortality and recovery 20.
Objective: Accurate visual classification of bladder tissue during Trans-Urethral Resection of Bladder Tumor (TURBT) procedures is essential to improve early cancer diagnosis and treatment. During TURBT interventions, White Light Imaging (WLI) and Narrow Band Imaging (NBI) techniques are used for lesion detection. Each imaging technique provides diverse visual information that allows clinicians to identify and classify cancerous lesions. Computer vision methods that use both imaging techniques could improve endoscopic diagnosis. We address the challenge of tissue classification when annotations are available only in one domain, in our case WLI, and the endoscopic images correspond to an unpaired dataset, i. e. there is no exact equivalent for every image in both NBI and WLI domains. Method: We propose a semisurprised Generative Adversarial Network (GAN)-based method composed of three main components: a teacher network trained on the labeled WLI data; a cycle-consistency GAN to perform unpaired image-to-image translation, and a multi-input student network.
To ensure the quality of the synthetic images generated by the proposed GAN we perform a detailed quantitative, and qualitative analysis with the help of specialists. Conclusion: The overall average classification accuracy, precision, and recall obtained with the proposed method for tissue classification are 0. 90, 0. 88, and 0. 89 respectively, while the same metrics obtained in the unlabeled domain (NBI) are 0. 92, 0. 64, and 0. 94 respectively. The quality of the generated images is reliable enough to deceive specialists. Significance: This study shows the potential of using semi-supervised GAN-based bladder tissue classification when annotations are limited in multi-domain data. The dataset is available at https://zenodo. org/record/7741476#. ZBQUK7TMJ6k.