dataesr/scientific-paragraphs-categorization huggingface dataset:
sanbi. org/). All data and source references are listed in Appendix S3. Species distribution data were extracted from Coates Palgrave et al. (2002)  and  Van Wyk et al. (2011) , and overlaid on a 0. 5 • × 0. 5 • grid (approximately 50 km × 50 km). For each grid cell, we then extracted data on actual evapotranspiration, AET (data set GNV183; http://www. grid. unep. ch/data/), as a proxy for productivity, variance in elevation (data set GTOPO30; http://www1. gsi. go. jp/ geowww/globalmap-gsi/gtopo30/gtopo30. html), as a proxy for topographic heterogeneity, and human population density (data set GPWv3 http://sedac. ciesin. columbia. edu/data/collection/gpw-v3). We excluded population F I G U R E 1 Phylogeny of southern African tree species. Reconstruction based on DNA barcodes for land plants using maximum likelihood and transforming branch lengths to millions of years by enforcing a relaxed molecular clock and multiple fossil calibrations.
falciparum parasitized erythrocytes (3D7) with uninfected human whole blood. Then 5WBF was validated using 50 and 200 µL of whole blood from patients experiencing a P. falciparum malaria episode (ring-stage parasitaemia range: 0. 04-5. 5%). DNA extracts obtained after 5WBF were evaluated using the parasite:human DNA ratio assessed by qPCR and the performance of sequencing depth and percentages of coverage obtained through WGS data compared with sWGA.
Sequencing output analysis Sequence data obtained from each sample was subjected to standard Illumina QC procedures. Each sample was analysed independently by mapping sequence reads to the P. falciparum 3D7 reference genome v. 39 using the Burrows-Wheeler Aligner (BWA) software package  [14]. Samtools (http:// samto ols. sourc eforge. net/) was used to generate coverage statistics and depth estimates from the BWA mapping output. Qualimap v2. 2. 1 was also used to perform an analysis based on specific features derived from the alignment, including coverage, GC content and mapping quality  [15]. A home-made python script was developed to calculate the percentage of each P. falciparum gene covered at ≥ 10 × depth and the per-gene mean coverage depth (https:// github. com/ Rcopp ee/ Scan_ gene_ cover age).
The script required a reference genome file in fasta format, an annotation gff file indicating the location of each exon, intron and corresponding genes, and a per-base coverage depth file generated with Bedtools genomecov function using default parameters  [16]. This per-base coverage depth file was also used to plot the average read depth within 1-kb windows across the 14 P. falciparum chromosomes using the Circos software  [17]. Finally, per-gene copy number was assessed using PlasmoCNVScan, a custom read depth strategy specifically made for Plasmodium species  [18].
Here, the usefulness of 5WBF as a new leucodepletion protocol based on commercial 5 µm filters was shown. Other filtration approaches were already successfully developed but also have their own drawbacks-whether in terms of costs, blood volumes, or standardization  [7, 8]. In this work, 5WBF was used for WGS purposes, but this strategy may likely be useful for other sequencing applications, such as RNAseq, which often suffers from contaminant human DNA when applied to P. falciparum isolates and thus requiring additional costs. Sequencing data obtained with whole blood samples treated by 5WBF revealed that all the three P. falciparum genomes (nuclear, mitochondrial and apicoplast) were covered with high coverage depth. Almost all of the P. falciparum genes were fully covered at ≥ 10 × depth, except the highly variable var and rifin gene families.
We assumed that, before VFF, the distance between cell apices and the vitelline membrane is approximately 0. 2-0. 5 μm. This distance is allowed to vary locally  27, [49] [50] [51]  while the global volume of perivitelline fluid is kept constant. We assume that tangential forces, such as friction or specific adhesion between the apical elastic surface and the vitelline membrane, are negligible at this stage, consistent with experimental and theoretical results  5, 50, 51. We also hypothesize that viscous shear forces exerted on the surface are negligible. Using the finite element software Surface Evolver 52 , we model the mechanics of the apical surface of the embryo as a thin elastic shell of given elastic properties  53. In this continuous model, the tissue is not partitioned into cells, nevertheless regions of space can still be assigned different properties, reflecting distinct portions of the embryo showing different gene expression patterns and mechanical properties  5, 54.
For cross-sections of acellular mutant embryos showing MyoII localization, embryos were collected from Δ halo slam dunk 1 / CyO, SqhGFP fly stock. We also analysed movies of sqh AX3 ; sqh-GFP; GAP43-mCherry  70  , see below "Analysis of confocal movies" for more details. Actomyosin meshwork ablation. Apical actomyosin meshwork was ablated using a tunable femtosecond-pulsed infrared laser (IR fs, MaiTai) mounted on a Zeiss LSM 780 NLO confocal microscope tuned at 950 nm. Experiments were performed using a 40 × 1. 2 NA objective, and the bleach mode on the Zeiss Zen software (140 mW laser power at the focal plane, single iteration, 1 μs pixel dwell, 1 s frame rate). For segmented ablations and experiments to probe the necessity of apical actomyosin contractility for furrowing, only single ablations across the ventral tissue were performed.
Thus, for every single time point, four 3D stacks were recorded. Fusion of four stacks was obtained by using Matlab 4 resulting in a final isotropic pixel resolution of 0. 29 μm. Laser cauterization was performed by coupling a femtosecond 920 nm laser (Alcor2, SPARK LASERS) to MuVi SPIM and by following a similar protocol as presented in De Medeiros, G. et al. 63.
Quantifications in MuVi SPIM data Measuring furrow propagation along the AP axis. Cross-sections were digitally made 50 μm apart along the AP axis. Furrow depth at different AP positions was measured using a dedicated point-picker ImageJ macro. Measuring anterior pole distance. Mid-sagittal sections were digitally extracted from the time-lapses and the position of the vitelline membrane at the anterior pole and the apical position of the anterior-most blastoderm cell were recorded using the point-picker plugin. The relative apical position of the anterior-most blastoderm cell was calculated with respect to the position of the vitelline membrane at the anterior pole and this was plotted over time. Measuring tissue shortening. Digital mid-cross and sagittal sections were obtained using ImageJ to measure tissue shortening along the DV and AP axes.
Distance moved by the network after ablation was plotted against time and the maximum recoil velocity was measured by calculating the first derivative. Curvature analysis. An ImageJ macro was developed to measure the radius (R) of a circle generated from three consecutive points. The curvature was calculated as 1/R and was plotted against time. Curvature of the mesoderm tissue was measured both along the AP and DV axes. A convex curvature is given by a positive and a concave curvature is given by a negative value. 3D image segmentation and analysis. Mesodermal cells were segmented and tracked by inter-registration based on iterative projections of segmentations from one point to another using the ASTEC algorithm  71. Morphological data were extracted and analysed using Python. ImageJ dedicated macros were used for image treatment and the 3D Viewer plugin for rendering  72.
No data were excluded from the analyses, although only the confocal microscopy embryo movies analysed in ref. 70  which extended sufficiently early in time and had appropriate fluorescence signal level were included, as stated above. The experiments were not randomized and the Investigators were not blinded to allocation during experiments and outcome assessment. Time synchronization. Time 0 was set as the instant when cells within the 5 rows closest to the midline had their area reduced by 20% compared to pre-gastrulation, allowing to synchronize all embryos, whether imaged with SPIM or confocal. Interpolation between datasets and time derivatives were calculated by LOESS moving window technique with dedicated software  74. The dynamics of development was found to be different between MuVi SPIM and confocal experiments due to differences in temperature.
It was found that area reduction in confocal experiments was taking place at a twice slower rate than in MuVi SPIM, this factor was applied in Fig. 3a  and for all subsequent confocal experiments. This corresponds to T = 20 min in Eq. (  1 ). Numerical simulations. The geometry of the elastic surface used to construct the initial finite element mesh, as well as the volume constraint and constraint imposed by vitelline are decribed in Supplementary Information. A linear elastic model is then solved with the Surface Evolver software  52  with increasing pre-strain for the ventral region highlighted in Fig. 1f , as described in Supplementary Information. no. 614655, and are members of GDR 3570 MecaBio and GDR 2108 AQV of CNRS. J. E. was additionally supported by IRS "AnisoTiss" of Idex Univ. Grenoble Alpes.
Acknowledgements A. J. , J. E. and M. R. acknowledge that this research was supported in part by the  National Science Foundation  under Grant No. NSF   PHY-1748958  while in KITP at  UC Santa Barbara. M. R. thanks the company  Luxendo Bruker  and  SPARK LASERS  for fruitful collaborations. A. J. , B. D, A. P. and M. R. thank  PRISM imaging facility  for technical support. This work was supported by the  French government  through the  UCAJEDI Investments  for the  Future  project managed by the  National Research Agency  ( ANR-15-IDEX-01 ), the  Investments for the Future LABEX SIGNALIFE  ( ANR-11-LABX-0028-01 ), the  ATIP-Avenir  programme of the CNRS, the  Human Frontier Science Programme  ( CDA00027/2017-C ) and the  Region SUD PACA Research  and  ERC Booster programmes. G. B. , C. L. and B.
S. acknowledge financial support of a  Wellcome Trust   Investigator Awards   099234/Z/12/Z  and  207553/Z/17/Z. J. F. , A. T. , J. E. , C. Q. and P. M. acknowledge financial support from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 )  ERC  Grant Agreement Bubbleboost authors thank  Philippe Beys  who manages the cluster, and  K. Brakke  for developing and maintaining the  Surface Evolver  software as well as invaluable interactions during this work.
A Model for Analyzing Orchestration We conceive this project as being part of a global project of analysis based on "sonority," or, more precisely, on the concept of a compound sonic unit, a term that identifies, at a given moment, a certain sound state, a "instantaneous 16 Yann Maresz, "Pour un traité d'orchestration au XXIe siècle," L' Etincelle, no. 11 (2006). 17 Jinho Kim, "Représentation et analyse musicale assistée par base de données relationnelle de la partition des variations pour orchestre op. 30 d'Anton Webern: Vers un système d'analyse musicale assistée par base de données relationnelle" (Doctoral Dissertation, Université Paris IV, 2006), 426 and 434. synthesis of a certain number of components [of the compositional technique] that act and interact in complementarity. "  18  In other words, what we call at the bottom of this flowchart the "Orchestration Relative Complexity" will have to or will be able to interact with other aspects of the compositional technique that we have already modeled and implemented in the SOAL library, such as achronic and diachronic relative densities, harmonicity and sonance rates, periodicity, relative entropy, etc.
19  The objective of this data set is to systematize the qualification of a compound sound unit in all aspects relevant to sound, including that of the orchestration dimension, when it plays a structuring role. The orchestration is evaluated on two levels. First, we identify the prescriptions recorded by the composer in the score. We have formalized them within a hybrid component -LSS Relative Complexity in the flowchart -which counts the various instrumental combinations used during the work and qualifies them according to the way in which the instruments "agglomerate" or on the contrary "disperse" to generate more or less complex textures. In a second part -on the right side of the flowchart -the audio files of these same configurations are examined using ad hoc statistical and psychoacoustic descriptors in order to assess the concrete effect of the prescriptions, and to see to what extent their impact on the form reflects it.
26  This reading, which depends only on a careful reading of the score, 27  provides the initial database. Figure  1  illustrates the setup tracking process for some measures of the selected extract from the Webern's Variations. In this excerpt, the segmentation process uses 8 LSS, respectively: bars 68 and 69 (the thicker box indicates the most prominent sound unit in the sequence), 70. 3 (the segmentation is caused here by the break in the continuity of intensitychange of "nuance" in violas and cellos), 71. 2, 72, 73, 74 and 75. The reader will find this sequence in the table in Table  1. Some scores are more fluid, more progressive than others, manifesting a less clear-cut sonic structure. This means that the segmentation process may have to deal with sequences where the LSS interlock, link together in tiling, rather than by juxtaposition.
Implementation in OpenMusic and Procedure In the current state of our work, this process is assisted, initially, by a spreadsheet, in which information of the score is collected. These are then exported to an OpenMusic patch. We have implemented a function called soal-texture-complexity, which is located in the "Partitional Analysis" folder of SOAL. Equation T was also made available. The soal-texture-complexity function gathers all the algorithms and patches developed for this project. The SOAL library is implemented using the Common LISP language and a standalone, python version also exists. Figure  6  shows the compiled function, in a patch analyzing the Webern extract we have chosen for demonstration. The numbers and letters refer respectively to inputs and outputs, as described below.
Preparing the Audio Material The segmentation of the score into units that are coherent from the point of view of orchestration, explained above, must be reproduced identically for the audio file of the work: these segments are called Local Audio Units. LAUs are indeed replicas of LSS in the audio file. The aim is to analyse how and to what extent the written prescriptions are implemented, and to evaluate the convergences and divergences between them. 51  The segmentation task is automated thanks to the Generate Markers function available in the Audiosculpt software. 52  Proper programming of the generator parameters requires some testing and depends in any case on the work and its recording. In our experience, Positive Spectral Difference segmentation detects very finely the changes in energy, which, as a general rule, correspond to changes in sound, i.
This variable network of descriptors is standardized in our model by the LAU Relative Complexity component, acronym ARC. The advantage of this formatting is that it can produce a more synthetic, holistic vision of the behavior of sound configurations over time. Indeed, it is not so much a question of qualifying local audio units in absolute terms, but rather of evaluating the degree of difference, or distance, between one unit and the other in a given field. However, the development of ad hoc descriptors seems essential and is part of the specifications of our future steps. In the meantime, or in parallel, some of the statistical analytical tools offered in the SOAL library may be of some help, thanks to its ability to read both MIDI and SDIF files and extract data sets, all standardized on the simplicity-complexity vector.
Historical Flood Inventory Mapping Accurate FHZ mapping depends on different flood-causative factors suitable for the study area. Historical flood information is required to validate the flood hazard zones. The annual flood frequency of the study area is as high as 9-10, affecting around 40% of the state. The country has a flood-affected area of around 10%, which makes the region four times more prone to flooding in terms of the national average. The Assam region had major floods in  1954, 1962, 1966, 1972, 1974, 1978, 1983, 1986, 1988, 1996, 1998, 2000, 2004, 2008, 2015 , and 2018 (Government of Assam Water Resources) after independence. Historical flood point location data were collected from the flood annual layer of Web Map Service (WMS) maps from 1999 to 2010. The flooding hazard zone developed by the Bhuvan National Remote Sensing Centre (NRSC), India, field photographs, and damage site-.
Historical Flood Inventory Mapping Accurate FHZ mapping depends on different flood-causative factors suitable for the study area. Historical flood information is required to validate the flood hazard zones. The annual flood frequency of the study area is as high as 9-10, affecting around 40% of the state. The country has a flood-affected area of around 10%, which makes the region four times more prone to flooding in terms of the national average. The Assam region had major floods in  1954, 1962, 1966, 1972, 1974, 1978, 1983, 1986, 1988, 1996, 1998, 2000, 2004, 2008, 2015 , and 2018 (Government of Assam Water Resources) after independence. Historical flood point location data were collected from the flood annual layer of Web Map Service (WMS) maps from 1999 to 2010. The flooding hazard zone developed by the Bhuvan National Remote Sensing Centre (NRSC), India, field photographs, and damage site-based handheld global positioning system (GPS) points of 2020 and 2021 data were also collected for the flood inventory mapping in the study area.
Morphometric Factors The seven depicted flood conditioning factors, grouped under morphometric criteria, were derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) (GDEM) data of 30 m resolution. Elevation is the main criterion for predicting areas of flood occurrence. Flood occurrences are high in low-elevation zones in comparison to high-elevation areas  [33]. Slope also monitors subsurface runoff amount, sedimentation, infiltration rate, and flow accumulation level. Flood direction and duration are.
Morphometric Factors The seven depicted flood conditioning factors, grouped under morphometric criteria, were derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) (GDEM) data of 30 m resolution. Elevation is the main criterion for predicting areas of flood occurrence. Flood occurrences are high in low-elevation zones in comparison to high-elevation areas  [33]. Slope also monitors subsurface runoff amount, sedimentation, infiltration rate, and flow accumulation level. Flood direction and duration are also delineated by aspect and slope gradient factors. Degree of slope and aspect map were extracted through the Google Earth Engine code editor. Profile curvature represents the variation of convexity and concavity of relief features  [34]. Topographic position index (TPI), a component of terrain surface plane, represents deviation from the central cell to the surrounding cell.
Hydrologic Factors Topographic wetness index (TWI) and standardized precipitation index (SPI) factors were estimated from digital elevation model (DEM) data using the ArcGIS tool (Equations (  3 ) and (  4 )). Flood flow intensity is greatly controlled by the gravitational effect, which accounts for TWI distribution. TWI is a widely used geohydrological process influencing basin moisture level, soil depth, water table, and saturated zone management  [36]. SPI explains the erosive efficiency of flowing water within a particular point, reciprocal to flood occurrences  [37]. The drainage density (DD) map was computed through the line density tool in the ArcGIS software environment with the help of the HydroSHEDS free-flowing river network tool (Equation (  5 )). Duration and intensity of rainfall generally determine flood acceleration rate after a rainfall event.
Mean annual rainfall data for the years 2000-2020 were extracted from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS v2. 0, developed by USGS Earth Resources Observation and Science (EROS) Center, Santa Barbara, CA, USA) in ArcGIS 10. 7 software environment through the inbuilt inverse distance weighting (IDW) tool and were used for preparing the rainfall distribution map. Annual rainfall in the study region varied between 771 mm and 1321 mm (Figure  5d ). Drainage density monitors runoff rate, infiltration rate, and permeability, which employs the supplementary likelihoods of a flood event. Flood occurrence was directly proportional to the distance of the location from the rivers  [38]. The Euclidean distance tool was used for producing the distance stream map in the ArcGIS environment.
Soil Permeability Factors Soil type, a predisposing factor of floods, determines the soil permeability rate, soil percolation rate, and rate of runoff. The soil map of the study site was extracted from the Harmonized World Soil Database v1. 2 portal. The soil moisture active and passive (SMAP) satellite-derived L-band was used for developing the soil moisture distribution map through the GEE platform. Soil erosion could also trigger flood events due to their sediment load  [40]. The soil erosion map was developed from the Global Land Degradation as Debts database. Four soil types were defined in ascending order of their area coverage in percentages, such as clay (0. 06%), sandy loam (8. 68%), loam (39. 02%), and sandy clay loam (49. 88%) (Figure  6a ). Five soil moisture zones were demarcated for the study area (Figure  5b ).
Terrain Distribution Factors Land use/land cover (LULC) pattern directly impacts the occurrence of a flash flood. Land area covered with vegetation is less prone to flooding compared to barren land  [41]. The LULC map of 2020 was obtained from the European Space Agency (ESA/WorldCover datasets). Global Advanced Land Observing Satellite (ALOS) landform classes are very sensitive to the quantification of land cover zones, as they are ecologically relevant to the physiographic process  [42]. The landform layers were obtained from ALOS Polarimetric phased array L-band synthetic aperture radar (PALSAR) data. Twelve landform classes were utilized for FHZ, i. e. , peak/ridge (warm), peak/ridge, mountain/divider, cliff, upper slope (warm), upper slope, upper slope (flat), lower slope (warm), and lower slope.
Anthropogenic Inferences Factors Population density (PD), one of the major anthropogenic factors affecting all resources, monitors the recharge or discharge of water, infiltration rate, and water flow. The PD map was obtained from the Landsat v1-based GPWv4. 11 database through the GEE platform. The Global Human Modification of Terrestrial System (GHMTS) implies different human modification stress that could impact the degree of flood risk  [43]. The GHMTS layer was produced from the NASA Socioeconomic Data and Applications Centre database. The distance to the road factor was computed through the Euclidean distance tool in ArcGIS 10. 7 software. This current study has identified five classes of person/km 2 level e. g. , ≤471, ≤916, ≤2277, ≤4921, and ≤6675 in person/km 2 (Figure  8a ) for population density analysis.