dataesr/scientific-paragraphs-categorization huggingface dataset:
Overlap between bound genes and differentially expressed genes The lists of the 312 genes downregulated in NEUROG3 À/À hESC cells and 3030 genes enriched in NEUROG3-HA-P2A-Venus hiPSC cells, differentiated to PEP, were intersected with the list of NEUROG3-bound genes by Venny 2. 1. 0. Expression of genes of interest in the human fetal pancreas and during in vitro differentiation of human embryonic stem cells into pancreatic endocrine cells was examined using https:// descartes. brotmanbaty. org [38]  and http://hiview. case. edu/public/ BetaCellHub/differentiation. php  [39] , respectively. 2. 11. 7. Overlap between NEUROG3 bound sites and cis-regulatory elements NEUROG3-bound regions were intersected using Bedtools 2. 29. 2 with: (1) human in vitroederived multipotent pancreatic progenitor enhancers (MPC Enhancers), cis-regulatory modules (MPC CRM), and transcription factor binding sites (ChIP-seq datasets)  [31] ; (2) in vitroe derived pancreatic endocrine progenitor enhancers (EN_enhancers)  [30] ; (3) adult islet regulatory elements (islet regulome)  [32] ; and (4) the 23,144 genetic variants associated with T2D and glycemic traits (T2D-FG) on 109 loci, compiled by Miguel-Escalada et al.
[32]. When necessary, coordinates of bed files were converted to hg19 or hg38 coordinates using the UCSC Liftover tool. Enrichment P-values of overlapping regions were calculated using Bedtools v2. 29. 2 Fish-erBedtool or, when indicated, using CEAS (one-sided binomial test)  [40]. 2. 11. 8. Data availability Raw data have been deposited in the GEO database under accession code GSE179264 for CUT&RUN data and RNA-seq data on NEUROG3-HA-P2A-Venusþ and VenusÀ PEP cells. hESC-derived NEUROG3 À/À  [25]  RNA-seq data are from E-MTAB-7185. hESC-derived endocrine progenitors (EN) data (enhancers, H3K27ac ChIP-seq and RNA-seq, Ref  [30] ) are from GSE139817. 2. 12. Luciferase assays Sequences encompassing NEUROG3-bound regions determined by CUT&RUN and assigned to MLXIPL, ETS2, and ISX genes were PCR amplified using primers listed in Suppl.
We also profiled active (H3K4me3) and repressive (H3K27me3) histone marks to map chromatin states. We identified 1873 and 1428 peaks using NEUROG3 and HA antibodies, respectively (Figure  1B ). To enhance the stringency of NEUROG3-bound regions, we intersected both datasets, defining NEUROG3 occupancy at 863 common sites (Figure  1B  and Suppl. Table  2 ). These highconfidence NEUROG3 binding sites were found at promoters (35%), introns (30%), and intergenic regions (31%) (Figure  1C ) and were assigned by GREAT to 1263 unique genes, with 573 peaks (66%) assigned to 2 or more genes (Suppl. Table  3 ). NEUROG3 binding to distal regions (located >5 kb from the TSS of their associated gene) was observed for 65% of sites (557 peaks for 1042 genes) (Figure  1D ). Remarkably, 90. 8% (506 peaks, P ¼ 4.
[31]. P-value of enrichment at NEUROG3 binding sites relative to genomic distribution is calculated by CEAS. In the left panel, the number of NEUROG3-bound and NEUROG3/FOXA2-bound CRMs is indicated. (E) Genome browser tracks showing NEUROG3, HA, and the CTRL CUT&RUN data at the FOXA2 loci. Coordinates are from hg19. The positions of NEUROG3 binding sites are highlighted in light blue (or in gray, when identified in a single dataset). Data of RNA-seq (EN_RNA), H3K27ac ChIP-seq (EN_K27AC), and the position of enhancers (EN Enhancers) from hESC-differentiated to endocrine progenitors were taken from Ref. [30]  and http://meltonlab. rc. fas. harvard. edu/data/UCSC/SCbetaCellDiff_ATAC_H3K4me1_H3K27ac_WGBS_tracks. txt. Position of hESC-derived multipotent progenitor cells (MPC) enhancers and cis-regulatory modules (CRM) are taken from Ref.
Of note, since the definition of CRCs relied on TF recognition motifs, NEUROG3, whose motif was not yet known, could not be integrated into the endocrine progenitor CRCs  [30]. Our data provide novel molecular mechanistic insights into the role of NEUROG3 as a possible direct regulator of many TFs of the endocrine CRCs. We further scrutinized the TFs dataset to examine whether NEUROG3 binds to genes known to control islet cell type development and unveil novel candidates. We focused on transcription factor genes for which NEUROG3 binding site(s) coincided with endocrine progenitor active enhancer regions  [30]  and enriched in developing alpha, beta, or delta cells based on recent transcriptomic profiling of the human fetal pancreas  [38]  (Figure  5A  and Suppl. Figure  7A ). An essential role of NEUROG3 in promoting the beta cell fate is supported by its direct regulation of Pax4 expression, a critical regulator of beta cell development  [52].
[30], P ¼ 4. 9e-324. (F) Normalized read density surrounding NEUROG3 peak summit AE5 Kb for H3K4me3 and H3K27me3 CUT&RUN datasets. (G) Genome browser tracks showing NEUROG3, HA, H3K4me3, H3K27me3, and the CTRL (Donkey anti-Sheep antibody) CUT&RUN data at the NEUROD1, NKX2-2, NEUROG3, PAX4, and INSM1/RALGAPA2 loci. Identified NEUROG3 peaks are highlighted in light blue. Peaks matching previously reported NEUROG3 binding sites are indicated by blue arrowheads, newly discovered peaks by purple arrowheads, and reported sites not confirmed here by gray arrowheads [12,13,15e17].
(D) Regions bound by NEUROG3 at PEP stage that are bound by FOXA2 and indicated TFs at the MPC stage. NEUROG3 cistrome was intersected with that of FOXA2, ONECUT1, TEAD1, GATA6, PDX1, and HNF1b from in vitroederived MPC and, as a control, with that of SOX2 from hESC cells, taken from Ref. [31]. P-value of enrichment at NEUROG3 binding sites relative to genomic distribution is calculated by CEAS. In the left panel, the number of NEUROG3-bound and NEUROG3/FOXA2-bound CRMs is indicated. (E) Genome browser tracks showing NEUROG3, HA, and the CTRL CUT&RUN data at the FOXA2 loci. Coordinates are from hg19. The positions of NEUROG3 binding sites are highlighted in light blue (or in gray, when identified in a single dataset). Data of RNA-seq (EN_RNA), H3K27ac ChIP-seq (EN_K27AC), and the position of enhancers (EN Enhancers) from hESC-differentiated to endocrine progenitors were taken from Ref.
ACKNOWLEDGMENTS The authors thank the members of the Gradwohl team and the Genomeast platform (particularly Christelle Thibault-Carpentier and David Rodriguez), Flow cytometry, and Cell culture facilities for the sequencing of the CUT&RUN samples, cell sorting, and hiPSC maintenance respectively. The authors are grateful to I. Cebola for providing ChIP-seq data and R. Scharfmann for helpful discussions. The Gradwohl lab is funded by the Novo Nordisk Foundation (Challenge Grant NNF14OC0013655). Sequencing was performed by the GenomEast platform, a member of the 'France Génomique' consortium (ANR-10-INBS-0009). This work used the Integrated Structural Biology platform of the Strasbourg Instruct-ERIC center IGBMC-CBI supported by FRISBI (ANR-10-INBS-0005-001). IGBMC is supported by the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.
, 2018)  record direct solar absorption spectra in the infrared region with a high spectral resolution (0. 0035-0. 005 cm -1 ) under clear-sky conditions. More than 20 atmospheric species can be retrieved from the FTIR observed spectra, and the data have been widely used to investigate the change in atmospheric composition to support satellite validation and model verification  (De Mazière et al. , 2018). However, until now, there are few studies focusing on FTIR NO retrieval. Notholt et al. (1995)  showed that the NO total columns can be retrieved from the FTIR spectra with a high spectral resolution of 0. 0035 cm -1 at Ny-Ålesund. Wiacek et al. (2006)  succeeded in retrieving NO in the stratosphere, mesosphere, and in the lower thermosphere from the ground-based FTIR measurements at Toronto and Eureka, but they found that there is almost no information in the troposphere for the FTIR NO retrievals at Eureka or even for measurements taken in the Toronto megacity.
The spectra at Xianghe used for the NO retrieval are operated with the NDACC Infrared Working Group (IRWG) optical filter no. 5  (Blumenstock et al. , 2021). The maximum optical path difference (MOPD) is 257 cm, which corresponds to a spectral resolution of 0. 0035 cm -1. There are normally one to five observed spectra on each measurement day. According to the Emissions Database for Global Atmospheric Research (EDGAR v4. 3. 2)  (Crippa et al. , 2018) , the NO x annual emission at Xianghe in 2012 is larger than 1000 t yr -1 (0. 1 • ) -2 , which is one of the largest NO x emission rates around the world. The NO concentration near the surface in this region was reported to be about 5-20 ppb  (Tang et al. , 2009). The high NO concentration provides an opportunity to study whether it is possible to retrieve tropospheric NO columns from the ground-based FTIR spectra.
The trace of the AVK matrix is the degree of freedom (DOF) for signal, indicating the number of individual pieces of information. The mean DOFs of the retrieved NO profiles over the entire datasets are 2. 3 ± 0. 4 (1σ ) at Xianghe and 1. 3 ± 0. 1 at Maïdo, respectively. Figure  4  shows the time series of the DOF in the troposphere, stratosphere, and above 60 km between July 2018 and June 2020 at Xianghe and between March 2013 and December 2019 at Maïdo. The DOF in the vertical range above 60 km is 0. 20 ± 0. 06 at Xianghe and 0. 10 ± 0. 02 at Maïdo. The DOF in the stratosphere is generally between 1. 0 and 1. 8 at both sites. The DOF in the troposphere is 0. 78 ± 0. 18, with 90 % of DOF from the layers below 2 km at Xianghe, reflecting that the tropospheric NO partial column is actually dominated by the NO partial column in the boundary layer.
Comparison with MIPAS measurements The MIPAS satellite observed the atmospheric NO concentrations globally between 2002 and 2012. There are two spectral resolutions for the MIPAS spectra: 0. 05 cm -1 before January 2005, named full-spectral-resolution (FR) mode, and 0. 121 cm -1 after January 2005, named reduced-resolution (RR) mode. In this study, we only use the NO MIPAS data after 2005, and the versions are V5r_NO_220 and V5r_NO_221. As MIPAS has a limb view, the MIPAS only provides the NO profile above ∼ 10 km. The NO profile is retrieved from MIPAS spectra at 5. 3 µm  (Bermejo-Pantaleón et al. , 2011). The vertical resolution of the NO profile is about 4-6 km, and the uncertainty in the NO profile in the altitude range of 20-60 km is 5 %-40 %  (Sheese et al. , 2016). There are no overlap MIPAS measurements with the FTIR measurements as Xianghe and Maïdo FTIR both started measuring after 2012.
36 % yr -1 observed by the FTIR measurements between 2013 and 2019. However, we observe a systematic difference of 0. 35 × 10 15 molecules cm -2 between MIPAS and FTIR measurements, which corresponds to 10. 6 % relative to MI-PAS data and 9. 6 % relative to FTIR measurements. The difference is within the uncertainties in both the MIPAS and FTIR measurements.
Acknowledgements. The authors would like to thank  Rebecca Buchholz  ( NCAR ) for providing the  CAM-Chem model data and Francois Hendrick (BIRA-IASB)  for providing the MAX-DOAS NO 2 measurements. EM is a senior research associate with the  F. R. S. -FNRS (Brussels, Belgium). Financial support. This FTIR measurements at Xianghe are funded by the  National Natural Science Foundation of China  (grant no. 41975035 ). The FTIR site at Reunion Island is operated by the  BIRA-IASB  and locally supported by LACy /UMR8105 ,  Université de La Réunion. Review statement. This paper was edited by  Frank Hase  and reviewed by two anonymous referees.
The Indian and East Asian Summer Monsoons shape the melt and accumulation patterns of glaciers in High Mountain Asia in complex ways due to the interaction of persistent cloud cover, large temperature amplitudes, high atmospheric water content and high precipitation rates. While the monsoons dominate the climate of the southern and eastern regions, they progressively lose strength westward towards the Karakoram, where the influence of Westerlies is predominant. Despite the major role of the monsoon in the Himalayas, a holistic understanding of their influence on the region's glaciers is lacking because previous applications of energy-and mass-balance models have been limited to single study sites. In this study, we use a full energy-and mass-balance model and seven on-glacier automatic weather station datasets from different parts of the Himalayas to investigate how monsoon conditions influence the glacier surface energy and mass balance.
Climatic and meteorological conditions Here, we use the monthly averaged ERA5-Land reanalysis data  (Muñoz Sabater, 2019)  to provide an overview of the long term climatic patterns, e. g. the average monsoonal regime from June through September, and evaluate the representativeness Table  1. Characteristics of the study sites. Planimetric glacier and debris surface areas, mean elevation, slope and aspect were calculated using the updated Randolph Glacier Inventory 6. 0 by  Herreid and Pellicciotti (2020)    of the AWS records in terms of seasonal variability (Figures A1 to A7), while acknowledging that the absolute values from the reanalysis dataset might be biased. Incoming shortwave radiation (Figure  3b ) shows a clear peak before monsoon onset at all sites. A smaller secondary peak is reached just after the monsoon in October at the Central Himalayan sites, but not at the Eastern Himalayan sites.
Precipitation partition Input precipitation is required to be partitioned into solid P r sno and liquid P r liq precipitation, because of the differing impacts of snow and rain on the energy and mass balance. For this study, the precipitation partition method described by Ding et al. (2014) was implemented into T&C. This parameterisation has been developed specifically for High Mountain Asia based on a large dataset of rain, sleet and snow observations, and does not require recalibration. It determines the precipitation partition based on the wet-bulb temperature, station elevation and relative humidity and allows for sleet events, as a mixture between liquid and solid precipitation. Ding et al. (2014)  found the wet-bulb (T wb ) to be a better predictor than T a of the precipitation type, that the temperature threshold between snow and rain increases at higher elevations, and that the probability of sleet is reduced in conditions of low relative humidity.
The parameters are determined by augmenting and refining the data obtained from XFoil Software, 1 in  Ducard and Hua (2014)  with wind-tunnel like practical tests  (Bauersfeld & Ducard, 2020; Spannagl & Ducard, 2020). Hereby, the wing is treated as a Clark-Y profile and the remaining passive sources as flat plates. The final values are presented in Table  3. The torque generated by the aerodynamic-control surfaces M δ ∈ R 3 is controlled by changing the angle of the ailerons around e x (δ Ar , δ A l ), the elevator around e y (δ E ) and the rudders around e z (δ Rr , δ R l ) as follows: M δ B = ρ 2 ⎛ ⎜ ⎜ ⎝ C A ( ⏐ ⏐ v a,Ar B ⏐ ⏐ 2 δ Ar + ⏐ ⏐ v a,A l B ⏐ ⏐ 2 δ A l ) C E ⏐ ⏐ v a,E B ⏐ ⏐ 2 δ E C R ( ⏐ ⏐ v a,Rr B ⏐ ⏐ 2 δ Rr + ⏐ ⏐ v a,R l B ⏐ ⏐ 2 δ R l ) ⎞ ⎟ ⎟ ⎠ (11) where the velocity vectors v a,Ar ,.
Control input synthesis In order to use the solutions from the optimization problem (12) as control inputs, a real-time nonlinear MPC solver is required. In this work, the code generation feature of the ACADO Toolkit 2  and its inherent real-time iteration (RTI) algorithm  (Houska, Ferreau, & Diehl, 2011)  are used to derive a nonlinear solver with solving times in the millisecond range, without the need for linearization or excessive simplifications. Once the solver has converged and an optimal and feasible solution is found, the following values will be extracted for further processing in the control structure: T mpc = 1 u n,1 (19a) χ mpc = 2 x n,4 (19b) M mpc B = ( 1 u n,3  19d)  where e. g. 2 x n,4 is the 4th element of the 2nd stage state vector in the MPC solution. Hereby, values from 2 x n are used, since the state 1 x n is given by the initialization constraint and not part of the computed MPC solution.
Experimental setup All flight experiments were conducted outdoors with wind disturbances between 5 km h -1 and 10 km h -1 , using the tiltrotor UAV shown in Section 2. The ACADO-generated MPC solver is setup with a sampling time of T MPC = 50 ms and prediction horizon N = 20. The resulting time horizon of 1 s allows coverage of a full transition maneuver at maximum propeller-tilt rate. The code runs on a Raspberry Pi 3 B with average convergence time of 40 ms, thereby allowing sufficient buffer for deviations in the solve time when running at 20 Hz. The controller weights used during the experiments are listed in Table  5. In case the solver fails to converge in time or throws an error, it is reinitialized with an attitude corresponding to level hover and the control commands are taken from the last valid solution instead.
The inner-control loop is embedded into the PX4 software 3 and runs, together with the native state and wind estimator, at 250 Hz on a Pixhawk R ⃝ 1 autopilot. Zero-order hold is used if no new MPC commands are available. Velocity and yaw rate references were directly provided through the sticks on the RC, according to the PX4 MC Position Mode. Pitch and roll angles and rates are set to zero and similarly all control input references. The respective MPC weights are tuned to allow appropriate attitudes for a given maneuver (e. g. acceleration, deceleration) and optimize for efficient control commands. 3 https://px4. io/.
Since the control allocation outputs match the desired forces and torques, the required yaw-torque magnitude must be too small to reject all disturbances, most likely due to the relation between the respective weights in the guidance MPC. Concerning efficiency, solutions of Eq. (  24 ) with minimum norm of squared rotor speeds were obtained using MATLAB's nonlinear fmincon optimizer. Mean and standard deviation of the relative error between these optimal ω opt , χ opt and the commanded ω cmd , χ cmd values are shown in Table  7.
1a )  47. Inspection of ATAC-seq  57  , H3K4me3, H3K4me1, and H3K27ac profiles  58  as well as of enhancer databases  59  revealed that the doc-TAD contains several putative CRMs, including four potential enhancers (CRM a , CRM b , CRM c and CRM d ) for the three doc promoters (Fig. 1c , and Supplementary Table  1 ,)  53, 54, 59, 60. We note that only CRM a displayed exhaustive binding by several chromatin insulators (Extended Data Fig. 1b ). Conventional Hi-M/Hi-C did not exhibit clear specific looping interactions within the doc-TAD, most likely due to insufficient genomic resolution and coverage (Fig. 1b ). To overcome these limitations and probe communications between CRMs and promoters within TADs in an unbiased manner, we improved the genomic resolution and coverage of Hi-M by 3-fold (from ~8-10 kb to ~3 kb) and painted the entire doc-TAD with contiguous barcodes (Extended Data Fig.
1d-f ). Remarkably, the improvement in genomic coverage in Hi-M now enabled the detection of specific looping interactions between genetic elements within the doc-TAD in intact embryos (Fig. 1f  and Extended Data Fig. 1g ). The strongest contacts represented in all cases interactions between CRMs (Fig. 1f , yellow arrows), but there was a considerable inter-nuclear variation (see single nucleus snapshots in Fig. 1f ). Contact frequencies did not vary considerably when only nuclei displaying the strongest doc1 RNA-FISH signals were used to construct the matrix (Extended Data Fig. 2d ), suggesting that stronger transcriptional activity did not involve different interactions. To quantify the strength of looping interactions, we calculated the intensity of the Hi-M map across an anchor to generate virtual interaction profiles (hereafter referred to as 4M plots).
We observed that CRM c predominantly interacts with CRM a and CRM b with similar probabilities (Extended Data Fig. 2e  vii). In contrast, we did not observe specific loops between CRMs and barcodes not containing early CRMs (e. g. ctrl barcode, Fig. 1c  and Extended Data Fig. 2e iv ). Interactions between CRMs and promoter regions (e. g. P1, P2 and P3) were present but displayed lower frequencies than interactions between CRMs (red arrow, Fig. 1f ). Interactions between CRMs and the doc1 promoter did not depend on the activation level of doc1 (Extended Data Fig. 2f ). Next, we investigated whether all putative CRMs displayed chromatin loops. Interestingly, a CRM predicted from epigenetic profiling but not present in enhancer databases (e. g. CRM a , see complete list of reported enhancers in Supplementary Table  1 ) displayed extensive interactions with reported enhancers (e.
For instance, upon Zld depletion, CRM c showed the largest drop in ATAC-seq signal amongst CRMs (Fig. 4f ), and a dramatic drop in its interactions with other CRMs (Fig. 4g ,h, yellow arrows). Finally, in Zld RNAi embryos formation of CRM hubs was also considerably impacted (Fig. 4i ) and topological reconstructions showed a loss of CRM clustering (Fig. 4j ). Altogether, these results suggest a model whereby the pioneering activity of Zld plays a key role in the activation of doc genes and participates in the formation of CRM-CRM and CRM-promoter loops during early embryogenesis, possibly through its ability to open chromatin at specific CRMs. To shed further light on the role of Zld in the formation of preferential interaction networks, we selected 5,038 genomic regions displaying Zld binding and calculated their pair-wise, intra-arm interaction frequencies 72 using publicly available datasets  41.
Hi-M libraries Oligopaint libraries, consisting of unique ~35/41-mer sequences with genome homology, were obtained from the Oligopaint public database (http://genetics. med. harvard. edu/oligopaints). We selected 20 barcodes in the doc locus (3L: 8882600. 9039000 Drosophila release 5 reference genome in all cases) for the low-resolution Hi-M library, 17 barcodes encompassing the doc-TAD (3L: 8974562. 9038920) for the high-resolution Hi-M library, and 65 barcodes (2L:15244500. 15630000) for the high-resolution sna locus library. For each barcode, we used 45-50 probes, covering ~3 kb. An additional fiducial barcode located at least ~1 Mb away was used for drift correction (see below). The coordinates of the targeted genomic regions are listed in Supplementary Table  3. Each oligonucleotide in the pool consisted of 5 regions: i-a 21-mer forward priming region, ii-a 32mer (low-res library) or two 20-mer separated by an AT sequence (high-res libraries) readout region unique for each barcode, iii-a 35/41-mer genome homology region, iv-a 32-mer (low-res library) or 20-mer (high-res libraries) readout region and v-a 21-mer reverse priming region.
A fluidic system was used for automated sequential hybridizations, by computer-controlling a combination of three eight-way valves (HVXM 8-5, Hamilton) and a negative pressure pump (MFCS-EZ, Fluigent) to deliver buffers and secondary readout probes onto a FCS2 flow chamber (Bioptechs). Software-controlled microscope components, including camera, stages, lasers, pump, and valves were run using a custom-made software package developed in LabView 2015 (National Instrument).
Acquisition of Hi-M datasets Embryos were attached to a poly-L-lysine coated coverslip and mounted into the FCS2 flow chamber. Fiducial readout probe (25 nM Rhodamine-labeled probe, 2× SSC, 40% v/v formamide) was flowed onto the sample and hybridized for 15 min, washed for 10 min with readout washing buffer (2× SSC, 40% v/v formamide) and for 5 min with 2× SSC before injecting 0. 5 mg/ml DAPI in PBS to stain nuclei. The imaging buffer (1x PBS, 5% w/v glucose, 0. 5 mg/ml glucose oxidase and 0. 05 mg/ml catalase) was injected. Subsequently, 10-15 embryos were selected according to developmental stage and orientation and segmented into a mosaic of multiple fields of view (FOV of 200 × 200 µm). After bright field image recording, z-stacks were taken with 405, 488 and 561 nm laser illuminations.