dataesr/scientific-paragraphs-categorization huggingface dataset:
1F ). These findings demonstrate that the gene expression pattern obtained from PSC epidermal differentiation is consistent with that during epidermal development in vivo. p63 Predominantly Controls Genes during the Switch from the Multipotent State to the Epidermal Fate. To characterize the role of p63 during epidermal commitment, we searched for genes that were potential direct targets of p63. We firstly identified p63-bound enhancers that were dynamically changed during commitment. For this, we utilized p63 binding sites identified by p63 ChIP-seq in epidermal KCs (GEO: GSE119997)  (22)  and open chromatin regions identified by assay for transposase accessible chromatin followed by sequencing (ATAC-seq) generated during ESC epidermal commitment (GEO: GSE108248)  (23). We took this approach because the p63 protein level was low at the initiation stage of iPSC epidermal commitment, and p63 ChIP-seq using differentiated iPSCs was likely unreliable.
Deregulated Epithelial Gene Expression in Differentiating PSCs Carrying p63 EEC Mutations. To characterize EEC-iPSC commitment at the molecular level, we performed RNA-seq analysis of these cells during epidermal differentiation (SI Appendix, Fig. S4B  and Dataset S1). K-means clustering analysis of the top 500 DE genes identified 7 clusters (Fig. 3A , SI Appendix, Fig. S4A , and Dataset S4). Generally, the expression pattern of these 7 clusters of genes was consistent with that in control PSCs during differentiation, with down-regulated genes in clusters 1 and 2 playing roles in stem cell maintenance and genes in clusters 3 and 4 that were initially induced and subsequently down-regulated functioning in morphogenesis. Cluster 6 included genes that were up-regulated during differentiation, and many are known for a role in epidermal development.
S4C  and Dataset S5). Commonly down-regulated genes (241) in EEC-iPSCs were related to epithelial and epidermal development (e. g. , KRT5 and S100A9), whereas up-regulated genes (296) in both EEC-iPSCs were enriched in functions such as cell migration (IGFBP5) and nonepithelial functions such as immune response (IFIT2) (SI Appendix, Fig. S4D  and Dataset S5). We subsequently chose several deregulated typical epidermal and nonepidermal genes and validated their expression in control PSCs, EEC-iPSCs, and the isogenic iso-R304W PSCs by qRT-PCR analyses. Our analyses confirmed that epidermal genes KRT14, COLA17A1, and ITGB4 were consistently down-regulated in EEC-iPSCs and in isogenic iso-R304W PSCs, whereas nonepithelial genes VIM and MSX1 were up-regulated, as compared to control PSCs (Fig. 3D ).
Cell State Heterogeneity during Differentiation Identified by Single- Cell RNA-Seq. As differentiating PSCs are heterogeneous, to identify cell states associated with EEC-iPSCs that failed to commit to the epithelial fate, we performed single-cell RNA-seq using a modified single-cell tagged reverse-transcription sequencing (STRT-seq) protocol  (24). In total, we analyzed the transcriptome of 1,250 single cells from control PSCs and EEC-iPSCs at different differentiation stages, as well as from primary KCs of a control and of the same EEC patients carrying R204W and R304W. After filtering based on the data quality (SI Appendix, Fig. S5 ), we obtained the transcriptome of 964 single cells. Following normalizations to get rid of batch effects (25), we detected 500 DE genes and performed PCA analysis (Fig.
4A ). Upon differentiation, DE gene expression of all PSCs showed a triangle-shape transition in PCA. Genes associated with PC1 (14. 5%) showed an enrichment for functions in epidermal and epithelial development, whereas genes associated with PC2 (11. 4%) were more enriched for migration, defense, and extracellular matrix functions (Fig. 4B  and Dataset S6). On day 15, DE genes reached the highest level along PC2, with those of EEC-iPSCs higher than those of control PSCs. DE genes of day-30 iKCs moved down along PC2 toward primary KCs. The PCA analysis suggests that control PSCs went through an intermediated phase before committing to iKCs, and EEC-iPSCs did not switch to the route toward the epidermal fate. Furthermore, t-distributed stochastic neighbor embedding (t-SNE) analysis showed that control PSCs and EEC-iPSCs before differentiation clustered together, and upon differentiation induction when p63 expression became detectable, differentiating control PSCs and EEC-iPSCs clustered into distinct groups (SI Appendix, Fig.
S6B ). These findings are consistent with differentiation defects observed in differentiating EEC-iPSCs and with the data from the bulk RNA-seq analyses, suggesting that expression of mutant p63 gave rise to distinct cell states. For identifying cell fates, we deconvoluted bulk RNA-seq data of cells derived from different embryonic origins (the ENCODE data and GEO: GSE101661) (26) using our single-cell transcriptome. As expected, the starting PSCs showed high correlations with ESC-H9 bulk RNA-seq data, and the correlations decreased when PSCs were induced for differentiation (SI Appendix, Fig. S6C ). In addition, primary control KCs, EEC-KCs, and day-30 control iKCs showed high correlations with the 2 types of epithelial cells, normal human epidermal keratinocytes (NHEKs) and human mammary epithelial cells (HMECs) (SI Appendix, Fig.
5C ). GO analyses of cluster 1 genes showed that these genes were associated with epidermal development and keratinocyte differentiation, whereas that of cluster 2 genes showed an enrichment of genes involved in immune response (Fig. 5D  and Dataset S7). In agreement, correlation analyses to bulk RNA-seq data showed that genes enriched for branch 1 cells had a higher correlation to gene signatures of epithelial NHEK and HMEC cells, and genes enriched for branch 2 cells correlated better to HSMM and other nonepithelial cells (Fig. 5E  and SI Appendix, Fig. S6C ). Genes in other clusters were expressed earlier during differentiation (Fig. 5 B  and C ). These data demonstrated that the differentiation route of EEC-iPSCs largely deviated from the normal commitment route toward the epithelial fate.
PCA analysis (Fig. 6C  and Dataset S8) showed significant changes in the transcriptome for iso-R304W PSCs (both PC1 and PC2) and less significant changes for R304W-iPSCs (PC2). GO analysis of genes associated with PC1 showed enrichment of genes associated with immune and defense response, whereas genes associated with PC2 that were rescued in all R304W cells were enriched for genes important for epidermal development, such as KRT5 and EDAR2 (Fig. 6E  and SI Appendix, Fig. S8 ). To better examine whether suramin-treated R304W PSCs were indeed more similar to controls cells, we mapped the transcriptome of suramin-treated PSCs into the differentiation trajectory of niPSC1 between differentiation days 7 and 15 (Fig. 6D ), using initial PCA of all PSCs (SI Appendix, Fig. S4B ). This analysis showed that, at the global level, the gene expression of untreated EEC PSCs including EEC-iPSCs and iso-R304W PSCs was between niPSC1 on day 7 and day 15, consistent with our previous bulk RNA-seq experiment (SI Appendix, Fig.
S4B ). The suramin treatment indeed pushed EEC PSCs closer to niPSC1 on day 15, with more significant effect on iso-R304W PSCs. Among 241 down-regulated genes in both EEC-iPSCs on differentiation day 15 (SI Appendix, Fig. S4C ), 114 genes were rescued by suramin (Fig. 6F ). Many of these genes play roles in epidermal development (e. g. , KRT5) and in adhesion and extracellular matrix (e. g. , DSG3) (Dataset S8). Among 296 up-regulated genes in EEC-iPSCs (SI Appendix, Fig. S3F ), only 68 genes were rescued (Fig. 6F ), and many are known in neural and vascular endothelial function (Dataset S8). Furthermore, potential p63 direct targets mapped by dynamic p63 enhancers were highly enriched among rescued 114 genes that were down-regulated in EEC-iPSCs (P value = 2. 2e-16), but not among 68 genes that were up-regulated in EEC-iPSCs (Fig.
This interpretation is consistent with a recent report on p63 using mouse embryonic epidermis  (21). To address the question whether p63 represses mesodermal genes directly or the repression is an indirect effect, we showed that the percentage of p63 direct targets among these mesodermal genes was significantly lower than epithelial and epidermal genes that are normally activated by p63, and even lower than the random expectation (Fig. 3C ). This indicates that the predominant role of p63 is to activate epithelial and epidermal genes, and repressing mesodermal induction is probably indirect. However, a small number of mesodermal genes activated in EEC PSCs (cluster 5) or in mouse p63 KO cells did contain a dynamic p63 binding site nearby (Fig. 3C  and Dataset S5), suggesting that they might be p63 direct targets.
ACKNOWLEDGMENTS. We thank  Ellen van den Bogaard  and  Matthijs Gerritse  for discussion and for providing technical support,  Ewart Kuijk  and  Edwin Cuppen  for providing the ESC H9 line, and  Eva Janssen-Megens ,  Siebe van Genesen , and  Rita Bylsma  for operating the Illumina analyzer and initial data output. We thank the  Studer Lab  ( Sloan Kettering Institute, New York ) and the  ENCODE Consortium  for sharing their data. This research was supported by  Royal Dutch Academy of Science  ( KNAW/CEP 530-5CDP08 ),  Netherlands Organisation for Scientific Research  ( NWO/ALW/MEERVOUD/ 836. 12. 010 ,H. Z. ); a  Radboud University  fellowship (H. Z. );  Brazilian Science without Borders program  (E. S. ); and  Chinese Scholarship Council grants 201606230213  (Q. X. ) and 201406330059 (J. Q.
OCT4 Methylation Serves as a Predictor of Cancer Patient Survival in Multiple Human Cancers To investigate the predictive value of OCT4A mRNA expression, next, we turned to the analysis of patient survival in 32 human cancers available in The Cancer Genome Atlas (TCGA) datasets. The Andersen modified Peto-Peto survival estimate identified only one cancer type that showed a strong correlation between OCT4A mRNA levels and the patient survival, brain low-grade glioma (LGG) (Figure  6A ). These results were not unexpected, as our model predicts that OCT4A could only be transiently induced and, as such, its levels are not necessarily increased in cancer. In contrast, the methylation status of OCT4 locus could be a stable epigenetic mark and, as such, could serve a better predictor of cancer patient survival.
To verify that, first we determined the list of OCT4 methylation probes that show a strong negative correlation with OCT4A mRNA levels across all cancer samples found in TCGA dataset. We further mapped MYC-binding sites based on the results of more than 200 ChIP sequencing (ChIP-seq) data deposited in the University of California, Santa Cruz (UCSC) database (Figure  6B ). Among 38 identified probes, 13 probes were present directly in the area of a MYC-binding site 1 (mbs1) located 10 kBp upstream of TSS. Other 4 probes were in the close vicinity of mbs2 and mbs3 located 5 kBp from TSS and in the first intron, respectively. Importantly, the methylation status of all three MYC-binding sites was reduced in WIP-1-depleted HCT116 cells in an ATMdependent manner (Figure  6C ). We further found that, in HCT116 cells, MYC binding on MBS3 is regulated in a WIP1dependent manner (Figure  6D ).
ACKNOWLEDGMENTS The research in BD laboratory is funded by the  Foundation ARC  ('' Accueil de nouveaux talents pour une recherche innovante en cancerologie '' and  Programme Labellise). We are very grateful to  Canceropole PACA (France)  for providing additional funding during the revision. We are grateful to  Anna Brichkina  and  Hui Mun Low  ( IMCB, Singapore ) for initial mouse breeding and initial analysis of tracing experiments. We thank  Ludovic Cervera  ( Cytometry Platform, IRCAN, France ) for his help with cell sorting and analysis. We also acknowledge the  IRCAN's Molecular and Cellular Core Imaging (PICMI)  and  Animal Facilities.
The samples show no significant differences in terms of their characteristics, although tests have low statistical power because of the limited sample sizes. Data in the Netherlands were collected from 2013-2015 and in Russia from 2015-2016. The samples consist of business owner/founders with sizeable variation in the age of the participant, age of the business, business experience, firm size, sector, and current financial performance. This allowed us to analyze the experience of autonomy across a variety of settings and conditions. Furthermore, the business owners had to own at least one independent business that employed at least one employee. For business owners who are self-employed without employees (freelancers), autonomy considerations may also be of concern, but they are likely of a qualitatively different nature and are therefore not considered in this study.
Synthetic Hydrometeorological Ensemble Members Current and future potential daily rainfall and river flows are required to determine when restrictions on irrigation abstraction might be implemented. The hydrometeorological data set used in this study was produced within the MaRIUS project (http://www. mariusdroughtproject. org/; Managing the Risks, Impacts and Uncertainties of droughts and water Scarcity), using the weather@home2 modeling framework  (Guillod et al. , 2017; Massey et al. , 2015) , in which the HadAM3P atmosphere-global climate model is dynamically downscaled by the HadRM3P regional climate model. Accounting for uncertainty through application of small perturbations to the temperature field in the initial conditions  (Massey et al. , 2015)  and sampling from the CMIP5 uncertainty range in sea surface temperature and sea ice extent allowed a large number (100) of spatial-temporally consistent ensemble members to be generated for the recent past and for the future  (Guillod et al.
Acknowledgments This research forms part of the  Natural Environment Research Council  ( NERC ) program on Droughts and  Water Scarcity , funded through the  Historic Droughts  ( NE/L010070/1 ) and  MaRIUS  ( NE/L010186/1  and  NE/L010208/1 ) research projects. The authors acknowledge access to the MaRIUS weather@home2 event sets ( NE/L010364/1 ,  NE/L010208/1 ,  NE/L010399/1 , and  NE/L010186/1 ), which are available on the Centre for.
Environmental Data Analysis (CEDA) platform (see https://doi. org/10. 5285/ 0cea8d7aca57427fae92241348ae9b03). The Grid-to-Grid model estimates of daily mean river flow for gauged catchments in Great Britain driven by the weather@home2 (climate model) driving data (MaRIUS-G2G-WAH2daily) are available at the CEH website (https://doi. org/10. 5285/f6cac471-7d92-4e6d-be8a-9f7887143058). All additional data supporting this study are openly available at the website (https://doi. org/10. 17862/cranfield. rd. 7613192). We would like to thank the editor and two anonymous reviewers for the comments they provided to improve the quality of this paper.
| Patients We retrospectively reviewed all the medical files extracted from our institutional database and found 402 patients with anal canal cancer treated between January 2000 and January 2017. Inclusion criteria were as follows: Histological confirmed SCCA, inoperable locally recurrent or metastatic disease, measurable disease by Response Evaluation Criteria in Solid Tumors (RECIST) criteria, and first-line metastatic treatment combining cisplatin and 5-FU. Patients refusing the use their personal data to be used for scientific research, patients with a previous history of another cancer less than 5 years with the exception of basal cell or squamous cell skin carcinomas, and patients lost to follow-up were excluded from the analysis. The medical files of each patient were reviewed. The starting date of treatment and date of progression or, when relevant, the date of the last delivery of the treatment regimen were collected.
Ancillary products In order to determine the history of air masses prior aircraft sampling, backward trajectories and satellite images were used. The trajectories were computed using the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) and the National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) data with 0. 5° horizontal resolution for sequences and times of interest. We compared the backward trajectory heights with information of fire burning times (e. g. MODIS Burnt Area Product) and dust release periods (e. g. Meteosat Second Generation (MSG) dust RGB composite images) to assess the aerosol source regions of the investigated air masses. The air masses represented by the trajectory are assumed to obtain their aerosol loading from source regions when the trajectory passes over regions with significant dust activation and/or fire activity at an altitude close to the surface.
SSA values of anthropogenic pollution aerosols were generally intermediate in magnitude with median values of 0. 81 at 440 nm, 0. 82 at 550 nm and 0. 82 at 660 nm. Our data show that the value of SSA varied significantly for the different plumes. Some pollution aerosols absorb almost as strongly as biomass burning aerosols with SSA(550nm) values as low as 0. 72, whereas the highest SSA(550nm) value observed was 0. 86. In addition, the absorption properties of urban aerosol varied greatly between the sampled plumes for smoke of apparent same geographic origin. For example, we measured SSA(550nm) values from 0. 72 to 0. 82 in the Accra pollution outflow. The variability in SSA values may be due to the possible contribution of emissions from different cities to the sampled pollution plumes  (Deroubaix et al.
Datasets We use an ensemble of 17 regional climate simulations taken from the most up-to-date ensemble of highresolution RCM projections produced in the recent years for Africa: CORDEX-AFRICA  [21] [22] [23]. In this ensemble, five RCMs are used to downscale 10 GCMs under the climate scenario RCP8. 5 (table  1 ). The simulations span the period 1976-2099 at a daily time step, and cover Africa (24. 64°W-60. 28°E; 45. 76°S-42. 24 °N) with a spatial resolution of 0. 5°(∼50 km) in latitude and longitude. For each simulation and each grid cell, daily time series of surface downwelling shortwave radiation (RSDS), surface air temperature (TAS), and 10 meters Wind speed (W10) are retrieved. As explained in the Methodology section, TAS and W10 are used to compute the solar potential, as they affect the temperature of the PV cells.
iop. org/ERL/14/124039/mmedia). Higher (lower) values of mean resource are found near the mid-latitudes (along the equator) in both datasets. Larger biases (>10%) are found over the southern subtropics in December-January-February (DJF), the tropics in DJF (including West Sahel and Guinea Coast), and the Horn of Africa in March-April-May (MAM; supplementary figure  1 ). In all four seasons, both datasets show a daily variability (20%) that is a factor 10 higher than the interannual variability (2%), with the models slightly overestimating the observed interannual variability. Overall, the general agreement between the two datasets encourages the use of the CORDEX-AFRICA ensemble for investigating the projections of future solar potential in Africa.
Acknowledgments The research leading to this publication received funding from the  French public research institution IRD , and was supported by the  French National program LEFE (Les Enveloppes Fluides  et l'Environnement). We acknowledge the usage of the CORDEX-AFRICA dataset from the  World Climate Research Program 's Working Group on Regional Climate (http://cordex. org/data-access/esgf/),and the usage of the SARAH2 dataset (https://wui. cmsaf. eu/safira/action/viewDoiDetails?acronym=SARAH_V002). This work is a contribution to CORDEX-AFRICA initiative. The research leading to this publication is co-funded by the  NERC/DFID  ' Future Climate for Africa' program  under the  AMMA-2050 project , Grant No. NE/M019969/1  and by  IRD (Institut de Recherche pour le Développement; France). We also thank the staff at the  IGE computation center (Patrick Juen and Wajdi Nechba)  for their technical support, as well as  Nicolas Plain  for his role in accessing the SARAH2 data and his insights into the dataset.
Data availability statements The data that support the findings of this study are openly available at https://doi. org/10. 5676/EUM_ SAF_CM/SARAH/V002 ([44]; SARAH2); and https://doi. org/10. 1594/WDCC/CXAF44CLCL (  [45] ; CORDEX-AFRICA CLM). The remaining experiments from CORDEX-AFRICA (SMHI 2017, DMI 2017, GERICS 2017, and KNMI 2017) data will soon be openly available at DOI: https://cera-www. dkrz. de/WDCC/ui/cerasearch/q? page=0&query=cordex+AFR-44&rows=15.
INTRODUCTION Soil moisture plays a key role in different hydrological processes (floods, runoff, evapotranspiration, infiltration, soil erosion, and imbalances in the water and carbon cycles). The arrival of Sentinel-1 SAR data (10 m spatial resolution and a 6-day revisit period) and Sentinel-2 optical data (10 m spatial resolution and a 5-day revisit period) have encouraged the development of operational algorithm for soil moisture mapping over agricultural areas with high revisit time and high spatial resolution (up to plot scale). Several studies tend to use the neural networks technique to invert backscattering models and estimate the soil moisture. To estimate soil moisture and roughness from the C-band polarimetric radar data, Baghdadi et al. [1]  first generated a database of backscattering coefficients for a wide range of bare soil conditions (soil moisture and surface roughness) using the Integral Equation Model IEM  [2].
Then, the neural networks (NNs) were trained using the synthetic database. A priori information on the soil moisture and roughness were considered to improve the accuracy of neural networks. Finally, the trained neural networks were validated using a real database and show an accuracy on the soil moisture estimates of approximately 7 vol. % with the use of a priori information. El Hajj et al. [3]  inverted the Water Cloud Model (WCM)  [4]  combined with the Integral Equation model (IEM)  [2, 5]. In their study, they used the WCM parameterized by Baghdadi et al. [6]  for the C-band. The total backscattering coefficient in the WCM is considered as the sum of the direct vegetation contribution and the soil contribution multiplied by the attenuation factor. Using a vegetation descriptor derived from optical images (Sentinel-2), the direct contribution of vegetation can be calculated.
S1-SSM ALGORITHM The approach for the operational soil moisture mapping uses the neural networks (NN) technique to invert the radar backscattering signal  [3]. The development of the inversion method consists of the following steps:  Generate a synthetic database of backscattering coefficients using a parameterized WCM  [4]  combined with the modified IEM  [2, 5] ;  Noise the synthetic SAR C-band database and NDVI values to obtain synthetic database close to the real SAR and NDVI data;  Train and validate the neural networks using the synthetic dataset;  Finally, apply the trained neural networks to S1 and S2 images to map the soil moisture. Incidence angle, noisy radar signal at VV polarization, and noisy NDVI are the inputs of the NN. The soil roughness (root mean square surface height "Hrms") was not used as an input parameter for the neural networks training and validation.
However, the synthetic database was simulated using Hrms values between 0. 5 and 3. 8 cm. The NN was trained using the Levenberg-Marquardt algorithm with two hidden layers. The number of neurons for each hidden layer is 20. Linear and tangent-sigmoid transfer functions were associated with the first and second hidden layers, respectively. To improve the soil moisture estimates, a priori knowledge on the soil moisture state is introduced. Indeed, from weather forecasts (precipitation and temperature) it is easy to define if the soil is is either dry to slightly wet (no precipitation for many days before SAR acquisition) or very wet (heavy rainfall preceding SAR acquisition). The integration of a priori information constrains the range of possible soil moisture parameter values estimated by the neural networks and thus leads to a better estimation of the soil moisture:  Case 1: A priori information is available for mv.