dataesr/scientific-paragraphs-categorization huggingface dataset:
Secondary ACS mapping The coordinates of the predicted secondary ACSs are reported in Table  2. To map putative secondary ACS sequences, we considered a nucleotide frequency matrix for the ACS consensus sequence  (Coster and Diffley, 2017)  and produced a PWM (Position Weight Matrix) using the function PWM from the R Bioconductor package 'biostrings' using default options. We used the 'matchPWM' function from 'biostrings' to look for the best match for putative secondary ACSs in the range between the position +10 to+400 relative to the main ACS. We then calculated the distribution of distances between the main and the putative secondary ACSs and the distribution of matching scores (Figure 1-figure supplement 1). For the meta-analyses shown in Figure  1D-E , we restricted this analysis to a shorter range, considering that secondary ACSs located less than 70nt or more than 200nt might not be biologically significant.
Acknowledgements We thank  Etienne Schwob  ( IGMM, Montpellier ) for providing us the orc2-1, orc5-1 and cdc6-1 strains. Dirk Remus ( MSKCC, New-York ),  Philippe Pasero  ( IGH, Montpellier ),  Armelle Lengronne  and members of both  Pasero and Libri laboratories  for critical reading of the manuscript and fruitful discussions. Julien Soudet  and  Francoise Stutz  ( University of Geneva, Geneva ) for sharing results before publication. This work was supported by the  Centre National de la Recherche Scientifique (CNRS) , the  Fondation pour la Recherche Medicale  (FRM, programme equipes 2013), l' Agence National pour la Recherche (ANR , grant  ANR-16-CE12-0022-01 ), the  Labex Who Am I?  ( ANR-11-LABX-0071  and Idex  ANR-11-IDEX-0005-02 ). TC and JG were supported by fellowships from the  French Ministry of Research and the Ligue Nationale contre le Cancer  (allocation  GB/MA/CD/IQ -12031 ), respectively.
Thus, it seems that homomeric protein IDs are enriched toward the C termini of polypeptide chains across diverse proteomes and it has been suggested that this is the result of evolutionary constraints for folding to occur before assembly  38. In contrast, our bioinformatics analyses using a limited curated interaction database suggest that in heterodimeric proteins the N-terminal interaction regions are enriched (data not shown), further underlining the idea that co-translational protein assembly in heterodimeric proteins is beneficial for assembling cellular machineries. The role of chaperones in ribosome associated nascent protein folding is well studied. Hsp70 family of proteins (such as for example yeast Ssb) protect the nascent polypeptide from misfolding and aggregation in eukaryotes  39, 40.
0 ST arrays (Affymetrix) interrogating over 40000 RefSeq transcripts and ~11,000 LncRNAs represented by approximately 27 probes spread across the full length of the transcript. The chips were washed and stained in the GeneChip® Fluidics Station 450 (Affymetrix) and scanned with the GeneChip® Scanner 3000 7G (Affymetrix) at a resolution of 0. 7 µm. Raw data (. CEL Intensity files) were extracted from the scanned images using the Affymetrix GeneChip® Command Console (AGCC) version 4. 0. CEL files were further processed with Affymetrix Expression Console software version 1. 3. 1 to calculate probe set signal intensities using Robust Multi-array Average (RMA) algorithms with default settings (Sketch quantile normalization). Statistical analysis was performed using the FCROS package version 1. 5.
Imaging and image processing Confocal imaging of the smiFISH-IF samples was performed on an SP8UV microscope (Leica) equipped with a 633-nm HeNe laser, a 561-nm DPSS laser, a 488-nm argon laser and a 405-nm laser diode. A 63x oil immersion objective (NA 1. 4) was used and images were taken by using the hybrid detector photon-counting mode. The z-stacks were taken with a z-spacing of 300 nm for a total of 4-6 µm. Image processing was performed using the Fiji/Image J software. All images were processed the same way. In detail, one cell of an image was cropped and one representing z-slice per cell was chosen. Additionally, one single IF or smiFISH spot from the corresponding cells was cropped as well. Afterwards, the channels of the different images were split and grey values were adjusted to better visualize the spots in the cytoplasm.
Image analysis and data presentation To measure the degree of spatial overlap of smiFISH (mRNA) and IF (protein) signal, an enrichment ratio was calculated as described next. Such a quantification was opted in order to take into account the variability of IF signal between cells, making single object detection in this channel difficult. Cells and nuclei were outlined manually in 2D based on the GFP and DAPI image, respectively. Subsequent analyses were restricted to the cytoplasm. mRNAs were detected in 3D with FISH-quant  22. Identical detection settings were used when different experimental conditions were compared for the same gene. Each cell was post-processed separately. First, the median pixel intensity in the IF image at the identified RNA positions was calculated. Second, a normalization factor as the median IF intensity of the outlined cytoplasm within the z-range of the detected mRNAs was estimated.
ACKNOWLEDGEMENTS We would like to thank all members of the  Tora lab  for thoughtful discussions and suggestions throughout the course of the work, and  G. Caliskan  for help with the ATAC experiments. In addition, we are grateful to  E. Bertrand  and his group for smiRNA FISH training,  T. Sexton  for advice and carefully reading the manuscript,  V. Alunni  for microarray sample preparation,  S. Bour  for illustrations,  K. Gupta  and  I. Berger  for suggesting TAF deletions and mutations,  D. Van Essen  for providing constructs,  P. Jane Palli  and  G. Travé  for help with protein domain analyses, the IGBMC cell culture facility for cells and media.
FUNDING This work was supported by funds from  CNRS ,  INSERM , and  Strasbourg University. This study was also supported by the  European Research Council (ERC)   Advanced grant  ( ERC-2013-340551 ,  Birtoaction , to LT) and grant  ANR-10-LABX-0030-INRT  (to LT), a  French State  fund managed by the  Agence Nationale de la Recherche  under the frame program  Investissements d'Avenir   ANR-10-IDEX-0002-02  and a grant from the  Collaborative Center for X-linked Dystonia and Parkinsonism  (to HTMT).
Three satellite-derived parameters were determined to be most useful for determining HIWC probability: (1) the horizontal proximity of the aircraft to the nearest overshooting convective updraft or textured anvil cloud, (2) tropopause-relative infrared brightness temperature, and (3) daytime-only cloud optical depth. Statistical fits between collocated TWC and GEO satellite parameters were used to determine the membership functions for the fuzzy logic derivation of HIWC probability. The products were demonstrated using data from several campaign flights and validated using a subset of the satellite-aircraft collocation database. The daytime HIWC probability was found to agree quite well with TWC time trends and identified extreme TWC events with high probability. Discrimination of HIWC was more challenging at night with IRonly information.
The primary goals of the HAIC and HIWC campaigns were to provide statistics on the 99th percentile of in situ total water content (TWC), and characterize other relevant cloud parameters such as ice particle size for regulatory purposes. The high-frequency in situ TWC observations collected within the HAIC and HIWC campaigns are invaluable for development of satellite-based HIWC diagnostic products. Prior to these campaigns, a large sample of consistent in situ TWC data across diverse geographic regions had been lacking, which had prohibited HIWC diagnostic product development. GEO satellite imagers such as the Multifunction Transport Satellite (MTSAT) Japanese Meteorological Imager (JAMI), Geostationary Operational Environmental Satellite (GOES), and Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (SEVIRI) observed deep convective clouds throughout their lifecycles in 5 to 30 min intervals during the HAIC and HIWC campaigns, depending on the geographic region.
GOES-14 observations were also collected at up to 1 min intervals on two flight days during a Super Rapid Scan Operations for GOES-R (SRSOR,  Schmit et al. , 2014)  period. GEO imager ob-servations and associated derived products depict storm intensity, locations of intense updrafts, and cloud-top microphysical characteristics that have previously been analyzed for a small sample of HIWC events. A more comprehensive analysis of GEO imagery relative to aircraft TWC measurements enables the research community to better understand the characteristics and evolution of clouds that do and do not produce HIWC. This understanding is critical for the development of satellite-based HIWC detection products. This paper has two primary objectives, (1) to provide statistical analyses of cloud properties derived from GEO observations as a function of TWC using data from three recent flight campaigns and (2) to develop and demonstrate a prototype satellite-based probability of HIWC (PHIWC) diagnostic product.
Experiment and flight campaign descriptions In 2006, an industry working group tasked by the FAA to review issues related to mixed-phase and glaciated icing conditions recommended the collection of a new in situ data set to characterize the microphysical properties of deep convective clouds, the type that had been identified as causing engine events. The HIWC Study was then initiated within North America and initially worked on the modification and development of new instrumentation required to make accurate measurements in hostile convective conditions, while plans for a flight campaign were developed. In 2012, the HIWC team partnered with the HAIC Project to conduct the first HAIC-HIWC flight campaign (hereafter Darwin-2014) in Darwin, Australia using the SAFIRE Falcon 20 aircraft. The flight program was successfully conducted between 16 January and 18 February 2014, but fell short of its data collection goal due to an engine failure unrelated to engine icing.
Two aircraft collected cloud in situ data from this campaign: the Falcon 20 with approximately the same instrument suite as described above, and the National Research Council of Canada Convair-580 aircraft, also equipped for cloud in situ and remote sensing. The details of the many Convair instruments will not be provided here, but it carried a second copy of the IKP2 probe for primary TWC measurements, and the CDP-2, 2D-S, and PIP for particle size distribution measurements. A total of 17 Falcon 20 and 10 Convair-580 flights provided data from oceanic MCS and continental storms that were suitable for the HIWC cloud analysis. The NASA HIWC-RADAR flight campaign (hereafter Florida-2015) was conducted from 12 to 28 August 2015, with a base of operations in Fort Lauderdale, Florida. The NASA DC-8 aircraft was equipped with the IKP2 TWC probe, CDP-2, 2D-S, and PIP as primary cloud in situ instrumentation.
A total of 10 flights were conducted off the southeast coast of the USA, the Gulf of Mexico, and over the Caribbean. Although the primary objective of this campaign was to test pilot radar technologies, the project adopted the same cloud measurement strategies, focusing on oceanic MCS. The measurements included four flights in tropical storms Danny and Erika. The overall objective of the three flight campaigns was to collect data to support the aviation industry objectives, and thus the flights were designed and funded with the first priority to collect cloud characterization data in the types of clouds that cause engine events, and in regions of those clouds that would be similar to what a commercial aircraft pilot might traverse. These objectives were not necessarily compatible with collection of the best scientific data.
In this regard, the terms HIWC and high TWC are synonymous for the purposes of this paper. HIWC will be used as a generic term to denote the condition, and high TWC will be used when referring to the measurement. TWC values measured during the flight program reached a maximum of about 4. 1 g m -3 over a 0. 93 km distance scale, the shortest scale length that is provided for this dataset. Extended periods of HIWC, defined as TWC ≥ 1. 0 g m -3 , were not uncommon, with 92. 6 km averaged TWC values reaching as high as 2. 3 g m -3. An example of a flight pattern conducted during Darwin-2014 Flight 16 is shown in Fig. 1. The IR image is color-enhanced such that the broad white area with embedded purple is at -78 • C or colder, this temperature threshold being an overall informal estimate of the convective equilibrium level used during the project, and about 7 • C warmer than the value estimated for this day.
The OT detection algorithm offered a 69 % POD and 18 % FAR when high probability (≥ 0. 7) BTW-based OT detections were compared against a large sample of human OT identifications within 0. 25 km MODIS VIS imagery in  Bedka and Khlopenkov (2016). These accuracy statistics are based on automated detections using MODIS imagery sampled to a 4 km resolution, representative of MTSAT JAMI and GOES imager data analyzed in this paper. The POD and FAR changed to 51 and 2 %, respectively, when high OT Probability detections were collocated with VIS Rating, illustrating that VIS texture detection can be used during daytime to confine the BTW-based product almost exclusively to OT regions. Areas with a non-zero VIS Rating outside of human-identified OT regions in GOES-14 imagery often coincided with regions of > 30 dBZ radar echoes at a 10 km altitude for a case over the US, indicating the presence of strong updrafts  (Bedka and Khlopenkov, 2016).
The four nearest satellite pixels (i. e. , a 2 × 2-pixel box) were matched to the mid-point of each of the 45 s segments and the mean cloud properties were computed within this box to account for uncertainty in cloud position within the time window. Parallax corrections were based on the retrieved cloud top height and pixel location relative to GEO satellite nadir. It is important to consider only the matches where the aircraft was physically located within cloud, and not above cloud top or below cloud base. We consider a data point to be in-cloud when TWC ≥ 0. 1 g m -3. In total, 5371 satelliteaircraft matched 45 s data points within cloud were derived from the 50 flights during the three flight campaigns. 4598 of the matches occurred during daytime, defined by SZA ≤ 82 •. 67 % of the matches were used for PHIWC product training and the remaining 33 % were used for validation, described in Sect.
3. 5. We cluster our satellite datasets into three TWC categories with low, moderate, and high TWC defined as 0. 1 ≤ TWC < 0. 5, 0. 5 ≤ TWC < 1. 0, and TWC ≥ 1. 0 g m -3 , respectively, for discussion purposes. TWCs of 0. 5, 1. 0, and 2. 0 g m -3 correspond to the 50th, 75th, and 95th percentiles, respectively, for this specific satelliteaircraft matched dataset. Note that the matched aircraftsatellite dataset discussed in this article is different from the dataset used for the HAIC-HIWC and NASA HIWC-RADAR regulatory analysis. The latter will supersede the dataset of this article for any regulatory purposes.
Results We begin our presentation of the results by showing comparisons of satellite observations, GEO satellite-derived products, and in situ aircraft observations for a flight during the Darwin-2014 campaign to demonstrate the set of satellite products most relevant for incorporation into the PHIWC diagnostic product. We then show how select satellite products relate to aircraft TWC using the 5371 matched data pairs. We follow with a description of the PHIWC diagnostic and conclude this section with PHIWC examples and validation. ). TWC is not well correlated with BTW, BTW, or BTD during this flight, indicating that high TWC was present only in small regions within or beneath deep convective anvils (Fig. 4b-c , e-f). BTW was consistently less than -80 • C and BTW near 0 K but the TWC varied considerably with many periods of TWC near to or below 0.
PHIWC product examples Examples of the PHIWC night and PHIWC day products valid at 23:02 UTC (using the 22:59 UTC MTSAT JAMI scan) during Flight 22 of the Darwin campaign discussed in Sect. 3. 1 are shown in Fig. 2g-h. The aircraft-measured TWC within ±5 min of the image time are overlaid on these graphs, showing seven high TWC (magenta X symbols) and other moderate TWC observations nearly coincident with this image. The Falcon 20 was flying northwestward toward an area of very cold cloud ( BTW < 5 K). The highest PHIWC night is concentrated near the coldest clouds and IR-only OT detections (Fig. 2c ), as would be expected considering that dOT IR-only and BTW datasets are used to derive PHIWC night. Several high TWC observations were located in ∼ 20 K BTW and relatively far (∼ 80 km) from an IR OT detection, combining to generate low (∼ 0.
Although the aircraft was very near OTs (dOT < 20 km) at these times, very low TWCs, BTW > 30 K, and cloud top heights sometimes below flight level (Fig. 10a ) all indicate that the aircraft exited the cloud for brief periods in this region. PHIWC night was moderated by relatively high BTW > 25 K even very near to OTs (dOT < 20 km) and did not exceed 0. 70 throughout the entire flight. TWC of 1. 4 g m -3 is an extreme value for BTW > 25 K and thus it is impossible to achieve a high PHIWC night in these conditions. High TWCs observed at around 18:30 were associated with a different cloud system near Cayenne. Flight 5 on 19 August 2015 during the Florida-2015 campaign was observed by GOES-14 during an SRSOR period, which provided images at 1 min intervals for almost the entire flight. This high temporal resolution GOES-14 imagery enables precise matches between TWC and satellite products, virtually eliminating matching uncertainties, especially when compared to the 30 min match window used for the GOES-13 data from the Cayenne-2015 campaign.
The maximum AUC changes only slightly for all three PHIWC versions. A higher PHIWC threshold is required to discriminate the 1. 0 g m -3 TWC events; for example, the optimal PHIWC day threshold is 0. 75 (versus 0. 70 for 0. 5 g m -3 ) which yields a 75 % POD and 37 % FAR. A comparison of the PHIWC distribution for varying TWC intervals is shown in Fig. 13. In general, both PHIWC day and PHIWC night increase as a function of TWC up to a value of 1. 0 g m -3 but then level off at high PHIWC values in HIWC conditions. Very high TWC (> 2 g m -3 , 95th percentile in this 45 s dataset) seldom occurs when PHIWC day < 0. 7 and PHIWC night < 0. 4 during Florida-2015 and Darwin-2014 campaigns. Cayenne-2015 featured several cases of low PHIWC night in very high TWC conditions, perhaps driven by the coarse 30 min GOES sampling that cannot always capture rapidly evolving OTs or cold cloud tops signifying HIWC conditions.
The high TWC is then advected laterally within the anvil. These results are consistent with previous studies such as  Grzych et al. (2015)  and  Bravin et al. (2015)  who identified that engine icing events often occurred during long traverses through MCS clouds with cold tops, and in particular near to tops penetrating significantly above the surrounding anvil. An alternative HIWC diagnostic method from de Laat et al. (  2017 ) identifies any ice phase cloud top with moderate or greater COD (≥ 20) in the form of a binary HIWC mask. The goal of the de Laat et al method was to maximize the critical success index parameter which emphasizes POD. For example, based on a TWC threshold of 0. 5 g m -3 , the de Laat method would have offered a 100 % POD and 96 % FAR using the daytime satellite-aircraft match database from the three campaigns, indicating that this product is more useful for telling users where HIWC conditions cannot be present, rather than where it is present based on satellite-observed cloud conditions.
Low to mid-level cloud (flight level -10 to -30 • C) containing low TWC may be present beneath and de-coupled from the anvil above. This low TWC would agree poorly with the high PHIWC. Lightning detection data were used extensively in aircraft flight planning and routing during the campaigns. Active lightning-producing convective cores were intentionally avoided to ensure aircraft safety and preserve the science instrumentation, although the lightning measurements were also used to locate promising active cells for sampling shortly after the lightning had dissipated. In some flights, Flights 5 and 6 (19 and 21 August) in Florida-2015 for example, the aircraft could not sample freely due to the frequent combination of both lightning and high radar reflectivity at flight altitude. In such cases, the measurements were usually made along the edge or at an otherwise safe distance from the intense cell.
e. , HIWC) conditions. Flights through or beneath optically thick anvil cloud within 40 km of OTs or textured regions were most likely to experience HIWC conditions. In general, discrimination between moderate and high TWC conditions using satellite-derived signals at cloud top was found to be very challenging because (1) satellites observe signals and processes occurring at or near cloud top while the aircraft sampled cloud conditions typically several kilometers below the top and (2) the satellite products appear less sensitive to TWC beyond 0. 5 g m -3 (the 50th percentile of TWC for this satellite-aircraft matched dataset); in other words the same combination of satellite parameters can be present when either 0. 5 or 2. 0 g m -3 TWC are observed. PHIWC diagnostic products for both day and night application were developed using a fuzzy logic approach based on statistical fits between satellite data and aircraft TWC observations throughout the three campaigns.
This work demonstrates that the PHIWC products offer improved capability for identifying HIWC conditions in deep convection relative to other known diagnostic products. GEO imager data can now be acquired over a broad (i. e. , hemispheric) geographic domain from a remote server and processed with the OT and texture detection algorithms in ∼ 5 min. COD retrievals are more time consuming to produce but inclusion of this product increases accuracy by 8 % if some modest latency is not a concern to users. Rapid data access and processing enables real-time production, even with next-generation satellite imager data such as the GOES-16 Advanced Baseline Imager (ABI,  Schmit et al. , 2005)  and Himawari-8 Advanced Himawari Imager (AHI,  Bessho et al. , 2016)  that feature 4× higher spatial resolution than GOES-13 or MTSAT and have the capability to view hemispheric domains at 10-15 min intervals.
ucar. edu/master_list/?project=HAIC-HIWC but are available solely to core users of the HAIC-HIWC consortium until 14 July 2019. HAIC-HIWC data are available to all other individuals upon signing a data exchange protocol between 15 July 2019 and 14 July 2022. HAIC-HIWC data are fully publicly available on or after 15 July 2022. HIWC-Radar data are currently available upon request and will be available from the UCAR/NCAR Earth Observing Laboratory: https://data. eol. ucar. edu/project/545. The Supplement related to this article is available online at https://doi. org/10. 5194/amt-11-1615-2018-supplement.
3. 1 GEO satellite products and in situ TWC comparisons Flight 22 of the Darwin-2014 campaign, the Falcon 20 took off from Darwin around 21:45 UTC on 17 February 2014, sampled convective cells to the west over the Timor Sea, and returned to Darwin around 01:00 UTC on 18 February. A time series of observations and derived products using the satellite-aircraft matched dataset for Flight 22 is shown in Fig. 4. Cloud top heights were ∼ 16 km for the duration of the flight corresponding to BTW around -80 • C (Fig. 4ab). TWC measurements were collected at two distinct flight levels, the first at -30 • C from 22:10 to 23:45 UTC and the second at -40 • C from 23:50 to 00:35 UTC (Fig. 4b), which were approximately 5-6 km below our cloud top estimate. There were eight periods when TWC exceeded 1 g m -3 and two periods, 22:30-22:45 and 23:15-23:50 UTC, when TWC was relatively low (< 0.