dataesr/scientific-paragraphs-categorization huggingface dataset:
The Parisian group (I. A. ) was supported by  LabEx LifeSenses, IHU FOReSIGHT , and a  Fondation Fighting Blindness grant  ( BR-GE-0619-0761-INSERM ). DNA samples collected by the Parisian group originate from NeuroSensCol, a biobank for research in neuroscience (Principal Investigator:  J. A. Sahel ; co-Principal Investigator: I. A. , partner with  Centre Hospitalier National d'Ophtalmologie des Quinze-Vingts, INSERM and CNRS). Katarina Stingl ,  Isabelle Audo ,  Isabelle Meunier ,  Michel Michaelides ,  Helene Dollfus ,  Birgit Lorenz ,  Markus Preising ,  Eberhart Zrenner , and  Susanne Kohl  are members of ERN-EYE (European Reference Network for Rare Eye Diseases). PNAS 2022 Vol. 119 No. 27 e2115538119 https://doi. org/10. 1073/pnas. 2115538119 9 of 10 Downloaded from https://www. pnas.
DNA/RNA mobility shift assay In a 10ml reaction, the indicated nucleic acid substrate (10nM TERRA or Oligo T (Figure  1C ), 2nM TERRA R-loop (Figure  1D ) was incubated for 5min at 37 C with RAD51AP1 or the mutant in RB buffer (35mM Tris-HCl, pH7. 5, 1mM MgCl 2 , 6% glycerol, 100ng/ml BSA, 60mM KCl). Figure  1C   : RAD51AP1 25, 50, 75, 100nM; Figure 1D: RAD51AP1 WT (20, 40, 60, 80, 120, 240nM)  or RAD51AP1 K6K7 (80, 120, 240nM) were tested. The reaction mixtures were run in 6% polyacrylamide gels in TB buffer (90mM Tris-borate, pH 8. 3). The gels were imaged with ChemiDoc imaging system (Biorad) and analyzed by Image Lab software.
D-loop and R-loop assays In a 12. 5ml reaction, 2nM Cy5-labeled 90-mer ssDNA (Oligo-D, 2. 4 mM nucleotides) was incubated with 0. 8mM RAD51 in RD buffer (35mM Tris-HCl, pH7. 5, 1mM MgCl 2 , 2mM ATP, 100ng/ml BSA) for 10min at 37 C. 400nM RAD51AP1 was added, with or without 27nM RNA (RNA 1 to RNA7) followed by a 5min incubation at 37 C, and then an addition of 12nM pBluescript (3mM base pairs) to initiate strand invasion reaction. (Bower and Griffith, 2014). The reaction mixtures were incubated for 10 min at 37 C and the reaction was terminated by adding 1ml each of 5% SDS, 1 mg/ml proteinase K, and a 5min incubation at 37 C. The reaction products were resolved by electrophoresis in 0. 9% agarose gels in TAE buffer (40mM Tris-acetate, pH 7. 5 0. 5mM EDTA). The gels were imaged with ChemiDoc imaging system (Biorad) and analyzed with Image Lab software.
Proteomics analysis Raw spectral files were converted to mzML format using Proteowizard. The mzML files were searched using X!Tandem search engine  (Craig and Beavis, 2004). The searches were performed using the following parameters: trypsin digestion, allowing up to two missed tryptic cleavages, parent ion tolerance window of 10 ppm, and fragment ion tolerance of 0. 02 Da. Oxidation of Methionine (+15. 9949@M) was specified as variable modification and carbamidomethylation of Cysteine (+57. 0215@C) as fixed. All protein searches were performed using the Human UniProt protein database appended with common contaminants and reversed protein sequences to serve as decoys. The X!Tandem results were then post-processed with PeptideProphet  (Keller et al. , 2002) and ProteinProphet  (Nesvizhskii et al.
, 2003). The data was filtered to 1% protein-level FDR using the target decoy strategy (PMID: 20816881). Protein abundance in each sample was estimated using spectral counts. Spectral counts were extracted from the search results for all reported proteins across all 12 experiments (4 replicates for each of TRF1 and RAD51AP1, and the negative controls experiments) using Abacus  (Fermin et al. , 2011). The resulting table of spectral counts was processed using SAINT (run using reprintapms. org resource) to calculate fold change (FC) scores and probabilities of true interactions using default settings  (Choi et al. , 2011; Mellacheruvu et al. , 2013). Using spectral counts as measures of protein abundance in each sample, SAINT calculates the probability for each identified protein that it is a true interactor of the bait protein.
QUANTIFICATION AND STATISTICAL ANALYSIS All data in this study were analyzed in GraphPad Prism, ImageJ, and Microsoft Excel. Detection, colocalization, and quantification of cells were performed using the ComDet v. 0. 5. 3 plugin for ImageJ. Statistical tests used are indicated in the figure legend accompanying each figure. In most cases, one-way analysis of variance (ANOVA) was used. Typically, unless otherwise stated n refers to the # of independent experiments and SEM refers to the standard error of means. The sample size was not pre-determined.
ACKNOWLEDGMENTS We are indebted to  Joachim Lingner  ( EPFL, Lausanne ) for sharing detailed RNA/RNA-DNA immunoprecipitations protocols. We thank members of the  Bakkenist lab  for sharing reagents and equipment. We thank  Nicolas Paquet  ( UTHSA ) for RAD51AP1 preparation. We thank  Stephen West  ( Crick Institute, London ),  Ana Losada  ( CNIO, Madrid ),  Nabieh Ayoub  ( Technion, Israel), Eric Greene (Columbia University ), and  Jan-Michael Peters  ( IMP, Vienna ) for sharing plasmids. We thank  Lee Zou  ( Harvard ) for sharing reagents and communicating data before publication. We thank  Dr. Richard Jones  at MS.
, 2014). Many catchments in Nepal are experiencing negative impacts to the hydrological regime through land-use changes, such as terrace abandonment  (Gardner & Gerrard, 2003; Paudel et al. , 2014) , deforestation  (Chaudhary et al. , 2016; Gilmour et al. , 1987; Ives & Messerli, 1989 ) and urbanisation  (Rimal et al. , 2019) , with potential consequent impacts on flooding. This study applies SCIMAP-Flood  (Reaney, 2022) , a catchment-scale decision support framework that uses geospatial data to identify CSAs of floodwater and informs the spatial targeting of flood management measures. SCIMAP-Flood can be used in both data-sparse and data-rich catchments. Implementation in data-sparse catchments is achieved using openly available elevation and land-cover datasets in tandem with regional resolution data, such as gauged rainfall data.
Local runoff generation potential is based on a combination of land cover, land management, soil properties and slope gradient  (Bracken & Croke, 2007; Kirkby et al. , 2002; Reaney et al. , 2014). With the minimal information requirement of SCIMAP-Flood, these interrelated factors are simplified such that land cover is taken as the dominant factor and is scaled with slope gradient; a flood risk generation weighting is then assigned to the catchment land covers based on either expert definition or fitted to an observed pattern of flood magnitude. The hydrological connectivity is calculated from elevation data and is determined using the network index  (Lane et al. , 2004). Overland flow travel times are used within SCIMAP-Flood to determine the magnitude of a flood event due to the synchronisation of flows from sub-catchments.
A combination of gauged rainfall data from the Government of Nepal's Department of Hydrology and Meteorology (DHM) and the Tropical Rainfall Measuring Mission (TRMM_3B42_Daily) satellite rainfall data  (Huffman et al. , 2007)  was used to determine the spatial rainfall pattern. Daily rainfall data was acquired for the F I G U R E 2 An overview of the SCIMAP-Flood approach including the SCIMAP-Flood Fitted extension to determine land cover risk weightings. The spatial data used by SCIMAP-Flood are a digital elevation model, set of rainfall maps, land cover information and observations of peak river flow for a set of point across the catchment period 1998-2016 for eight DHM rainfall gauges around the catchment. A combination of gauged rainfall estimates, and bias-corrected satellite rainfall estimates was deemed necessary to compensate for areas poorly represented by the gauge network.
To create the combined daily rainfall grid, a monthly bias correction approach using TRMM data has been adopted. The rainfall totals for each month were calculated for each rain gauge and compared against the monthly total rainfall estimate of the corresponding TRMM tile. This process produces a bias adjustment factor for each month. To spatially distribute the bias adjustment factor across the catchment, a Theissen polygon method using the DHM rain gauge locations was used. TRMM rainfall data in areas poorly captured by the DHM gauge network was adjusted at a monthly resolution, using the bias adjustment factor based on the closest DHM rainfall gauge using the Theissen polygon output. With the rainfall input data for SCIMAP-Flood at a sub-monthly resolution, the distribution of monthly to daily rainfall was calculated by attaining the percentage of total monthly rainfall for a given day in that month from the TRMM data and applying this percentage to the bias-adjusted monthly rainfall totals.
The rainfall total for 5 days preceding the flow was considered part of the event rainfall total to account for the antecedent conditions. The 10 highest magnitude flow events were selected based on daily mean flow data within the catchment ranking each day between 1998 and 2016. To achieve this selection, the daily river flow for the three DHM flow gauges in the catchment (DHM station numbers 460, 465 and 470) were normalised to between 0 and 1. These three flow values were multiplied to indicate flow magnitude for each day across the catchment. The East Rapti land cover data was created through supervised classification using maximum likelihood classification  (Otukei & Blaschke, 2010) , derived from a combination of Landsat 8 satellite imagery, slope and aspect data, generated from the AW3D30 elevation data.
With a range of suitable geospatial data that can meet the requirements to run SCIMAP-Flood, it is necessary to assess the sensitivity of the SCIMAP-Flood output to rainfall and overland flow travel time data. To achieve this, three different sources of rainfall data were tested: (i) storm selection variation using only the five largest storm events; (ii) a spatially coarse but less time-intensive approach using only TRMM satellite data for the 10 largest storm events; and (iii) temporal resolution variation using monsoonal rainfall patterns with the TRMM satellite rainfall data. For these simulations, the overland flow travel time data, and the top 10 land cover weighting sets (Section 2. 4) remained constant. The monsoonal rainfall patterns covered the total rainfall between May and September for the years 1998 to 2016.
This was based on a uniform distribution, with no a priori likelihood assigned. For each simulation, an objective function was determined to quantify the level of association between the estimated risk indicator and the spatially corresponding SCIMAP-Flood risk estimate. The objective function used here was the Pearson correlation coefficient. This was selected as it offers an appropriate solution for the continuous nature of the SCIMAP-Flood Fitted outputs. The estimated risk indicators used were the peak discharges associated with the August 2017 floods in the East Rapti (Section 2. 5). This event was chosen as it was a high magnitude flood event and there was the opportunity to collect post-event data to help ascertain appropriate land cover risk weightings. The Pearson correlation coefficient is then used to detail the level of association between the August 2017 peak flow estimates and the corresponding SCIMAP-Flood risk estimates for each of the 15,000 model simulations.
| East Rapti catchment data for SCIMAP-Flood Fitted Estimation of the August 2017 peak flows was achieved at 10 locations (Figure  1 ) distributed across the East Rapti catchment. The locations were selected to estimate the peak flow contribution of the main tributaries at their confluence with the main East Rapti channel. The flow estimation was undertaken using Manning's equation  (Manning, 1891)  (Equations 1 and 2): Q ¼ VA, ð1Þ V ¼ 1 n A P 2 3 ffiffiffi S p , ð2Þ where Q is flow rate (m 3 /s), V is flow velocity (m/s), n is Manning's roughness coefficient, A is flow area (m 2 ), P is the wetted perimeter (m) and S is the channel slope (m/m). The AW3D30 elevation data were not of sufficient resolution or from a contemporaneous date for accurate characterisation of the channel conditions being modelled.
An example of this imagery is provided in Figure  4  where the peak inundation extent can be identified from extensive sediment deposits. Through the integration of these two datasets, the cross-sectional area, wetted perimeter and local slope through the cross-section could be extracted. Crosssection averaged velocities were empirically derived from flow gauging results undertaken throughout the East Rapti catchment in May 2018 (Table  2 ). Analysis indicated three classes of channel roughness in the East Rapti River. A Manning's n value of 0. 0422 was calculated from the field data for the Terai plains in the downstream part of the catchment, a value of 0. 0484 was calculated for the main East Rapti channel and large tributary channels and a value of 0. 0663 was calculated in the steeper upstream locations in the northern part of the catchment.
Incorporation of this data enables estimation of peak discharge for the observed inundation extents at each of the 10 locations within the East Rapti catchment. Flow estimates were then normalised by the catchment area to give runoff depth (mm). F I G U R E 4 A comparison of the (a) pre-and (b) post-event Planet (Planet Team, 2017) satellite imagery for a section of the East Rapti catchment. The pre-event imagery was taken on 31 July 2017 and the post-event imagery taken on 26 August 2017. The sediment deposition used to help estimate flood extent is evident in (b) with an indicative inundation extent marked in red. Grid projection: WGS84 UTM Zone 45 N. Base imagery: Planet Team.
| Identifying flood source areas using SCIMAP-Flood SCIMAP-Flood was used to identify areas within the East Rapti catchment that are more likely to generate floodwater during a high-flow event (Figure  7 ). Using the rainfall patterns associated with the 10 largest recent flow events, the top 10 land cover weighting sets from the SCIMAP-Flood Fitted inverse modelling approach and the six flood impact points overland flow travel time data, it is evident from this dataset that the eastern part of the East Rapti catchment near Hetauda is most likely to be the source of floodwater to the respective flood impact points. There are also areas along the main East Rapti channel and in the northern sub-catchments that were identified as having a higher floodwater generation potential than the catchment on average.
Regardless of the land cover risk weightings, the The spatial distribution of rainfall was another dominant factor when identifying areas that generated floodwater during high flows in the East Rapti catchment. The spatial rainfall patterns developed using the DHM gauged data and scaled TRMM satellite rainfall show the highest rainfall totals associated with the top 10 high-flow events recorded principally in the southeast of the catchment around Hetauda. This is one of the areas with a high potential to generate floodwaters (Figure  8 ). The variation in land cover weightings can act as a further driver of the flood regime. The identified high potential flood generating areas around Hetauda in the southeast of the catchment are dominated by irrigated agriculture. The dominance of land cover with a high relative flood generation weighting will increase the likelihood of floodwaters generated from the area.
There is no maximum catchment size for using SCIMAP-Flood and it can be used for rapid assessments for very large catchments (e. g. , the Ganges) or when investigating the potential for nationwide flood management approaches. SCIMAP-Flood can be deployed using available global elevation models, remotely sensed and hence globally available rainfall, and land cover datasets for catchments with sparsely available local data. However, the sensitivity analysis of the East Rapti inputs highlighted the potential influence of variation in the required SCIMAP-Flood input data. Identifying areas with a low sensitivity to different SCIMAP-Flood inputs could help avoid placing interventions in areas only identified as having a high floodwater generating potential under specific input combinations. Within the East Rapti catchment it is evident that the rainfall patterns associated with high-flow events are a key factor in the generation of floodwaters and, as a result, both the higher relief topography of the northeastern part of the catchment and the highly connected agricultural area in the southeastern part of the catchment have a high relative potential to generate floodwaters.
ACKNOWLEDGEMENTS This research was funded by the  Natural Environment Research Council  as part of the  IAPETUS Doctoral Training Programme  ( NE/L002590/1 ). The authors acknowledge the financial support donated by  Charles Wilson  through the  Institute of Hazard, Risk and Resilience at Durham University , which funded fieldwork conducted by  S. M. Reaney  and  C. J. Pearson. The authors acknowledge the financial support provided by the  Faculty of Humanities and Social Sciences Bid Preparation Fund at the University of Newcastle , which funded the fieldwork conducted by  M. T. Perks & B. Hortobagyi.
This work focuses on the study of the drilling performance of bio composites reinforced with date palm fibers (CDPF). It is a new fiber that is characterized in terms of piercing behavior for the first time. The experimental study was carried out with three types of drills, with a diameter of 10 mm and feed and spindle speeds of the order of (40, 80 and 200 mm/min) and (560, 1120 and 2240 rpm), respectively. The drilling performance was assessed in terms of delamination and surface quality, such as circularity and cylindricity of the drilled holes. Using the CMM machine, the value of the delamination factor was determined using Image J software. The results showed that the value of the latter increased with the increase in feed rate and decreased with the increase in spindle speed. The delamination factor has maximum and minimum values of approximately 1.
4 and 13. 8%, 6. 4 and 10. 9%, and 7. 1 and 17. 5% in L, M, and H-JFRP, respectively). As in the previous study, it was observed that the value of the delamination factor increases with both spindle speed and feed rate. Pramod et al. [20]  expressed their agreement with the results of the above work in their study in which they addressed the evaluation of drilling-induced delamination of nanoparticle-reinforced polymer matrix composites. All samples were drilled with 5, 6, 8 and 10 mm diameter Brad and Spur type carbide tools at different feed rates of 0. 5, 1. 0, 1. 5 and 2 mm/rev and at different spindle speeds of 1000, 1500,200 and 2500 RPM under dry conditions. The delamination factor and circularity ratio were analyzed at using image processing technique in MATLAB and Image J. The results revealed that minimum delamination damage and better circularity ratio were achieved at higher spindle speed, lower drill diameter and a lower forward speed.
From the literature review, it can be stated that few research studies have discussed the area of drilling of natural fiber bio composites. In other words, there is still an important field of research in the field of machining bio composites. In this context, the objective of the current research is to study  the evaluation of drilling by delamination and surface quality of lignocellulosic date palm fiber reinforced bio composites. In this study, we focused on the machinability of date palm tree reinforced bio composites (CDPF) with a content of 30 wt% by changing the machining parameters. The first novelty lies in the selected fiber used in this study. From literature review, its clear that it is a new fiber that is characterized in terms of piercing for the first time. The second novelty is to provide readers with more knowledge on the delamination phenomenon and the circularity and cylindricity properties of the holes of the bio composites involved in this work under different machining conditions with the use of Image J software and Coordinate Measuring Machine (CMM).
The choice of cutting parameters was selected after a literature study summarized in Table  2. The composite samples were drilled under dry cutting conditions. The depth of the holes can reach 8 mm in the thickness of the sample. To prevent the holes from being affected by each other, a distance of 25 mm on the X axis and a distance of 15 mm on the Y axis separated each hole from its center, as shown in the illustration (Fig. 2 ). The drilling process was programmed in the CNC machine using a G-code program generated by MASTERCAM software, version 2021.
Acknowledgements This research is supported by  PRFU  Project-N  A11N01UN280120220001  organized by the  Algerian Ministry of Higher Education and Scientific Research (MESRS). The authors would like to thank  Mr. Belkacem Aouifi  Assistant Ingenious university of M'sila, Faculty of technology,  Mr Laifa FAkerdien ,  Mr. Tarek Bidi  and  Mr. Hassan Mokrani  company manager Alger precision. LGP2 is part of the  LabEx Tec 21  ( Investissements d'Avenir -grant  agreement n  ANR-11-LABX-0030 ) and of the  PolyNat Carnot Institut  ( Investissements d'Avenir -grant  agreement n  ANR-11-CARN-030-01 ). The authors also like to extend their gratitude to  King Saud University (Riyadh ,  Saudi Arabia)  for funding this research through Researchers Supporting Project number ( RSP-2021/ 241 ). r e f e r e n c e s.
In this article, we describe the free, open-source Python-based Space Physics Environment Data Analysis System (PySPEDAS), a platform for multi-mission, multi-instrument retrieval, analysis, and visualization of Heliophysics data. PySPEDAS currently contains load routines for data from 23 space missions, as well as a variety of data from ground-based observatories. The load routines are built from a common set of general routines that provide access to datasets in different ways (e. g. , downloading and caching CDF files or accessing data hosted on web services), making the process of adding additional datasets simple. In addition to load routines, PySPEDAS contains numerous analysis tools for working with the dataset once it is loaded. We describe how these load routines and analysis tools are built by utilizing other free, open-source Python projects (e.
Introduction Dynamically typed, interpreted languages, such as Interactive Data Language (IDL) and Matlab, have exploded in usage for day-to-day data analysis due their ease of use, curated libraries of scientific routines, simplified debugging, and interactive plotting. There is a need for tools tailored toward the Heliophysics community; for example, to provide access to the scientific data from multiple missions in a generic and easy-to-use way, as well as general analysis tools to work with these data. Releasing generic analysis tools has numerous benefits, such as limiting duplication of effort and reducing potential errors made by researchers. In IDL, this need led to the development of the SolarSoft (SSW) 1 package for solar physicists and the Space Physics Environment Data Analysis System [SPEDAS;  Angelopoulos et al.
, 2019)  2 package for space physicists. The SPEDAS package supports loading, plotting, analysis, and integration of data from several space-based and ground-based observatories, providing a comprehensive environment for space physics data analysis and visualization. While powerful, IDL has numerous limitations, including the high cost of licensing, limited support, difficulty in bridging among different programming languages, as well as conflicts with other libraries caused by the single namespace limitation. The single namespace limitation requires that all IDL functions have unique names, making it nearly impossible to use SPEDAS and other large packages (e. g. , SolarSoft) in the same environment without naming conflicts. Due to these limitations and the increasing popularity of the Python programming language  (Burrell et al.