dataesr/scientific-paragraphs-categorization huggingface dataset:
3, "Meteorological forcing": it should be mentioned (probably in the discussion part) that the EWEMBI dataset (Lange, 2016) used here has now been superseded by WFDE5(Cucchi et al. , 2020 2) produced for phase 3 of ISIMIP (https://www. isimip. org/protocol/3/). ○Table,entry"firstyear":→"earliestpossible year". ○YesCompetingInterests:SebastianSterl,Vrije Universiteit Brussel, Brussels, Belgium This data note describes an atlas of hydropower in Africa. This atlas, compiling existing databases on existing and planned hydropower plants, and continental-scale hydrological modelling, provides information on the hydropower generation profiles at all these plant/reservoir locations. It is therefore of high interest for continental-scale energy modelling. The revised version of the manuscript has been much improved following the first-round comments of reviewers, and future projections are welcome.
However, there are still places in the text and specific points that lack clarity, and some methodological issues would also require clarifications and additions to the text and the database. They are detailed below. Author Response: We thank the reviewer for agreeing to provide feedback on our revised manuscript, and for their positive comments and constructive criticism. We provide pointby-point replies to the issues raised by the reviewer below. Section 2. 3, quality of SWAT+ simulations:The responses to comments by Benoît Hingray on this specific issues are in my view not satisfying. Hydrological modelling is in this study used to get reasonable estimates of seasonal and interannual variability of streamflow across the continent. I therefore strongly believe that an appropriate assessment of such properties is definitely required for one to be able to gain confidence in the AHA results.
) Section 2. 4, "First, the flow profile…": here, in spite of modifications made after the first round of comments, I am still unclear on how the final monthly regimes of normal, dry and wet are obtained. More precisely it is not clear what is the "monthly median of the dataset". I would tend to understand that you took the median interannual value for each month of the year and use it to build a "normal" 12-value regime. And in parallel, you considered the distribution of annualaveraged values and computed the ratio between the median value and the 5/95 percentiles to get the correction factors. And that you subsequently applied these correction factors to all 12 values of the "normal" regime. Am I right? But then the following paragraph with the scaling made me once more hesitating on what is really done.
Using Primer3Plus we compute a primer set for each of the 10 features, using sequence EPI_ISL_601443 as the reference sequence. Only the two '21-bps' features that include mutation T16176C are suitable for a forward primer. The two features are ACCTCAAGGTATTGGGAACCT and CACCTCAAGGTATTGGGAACC: it is easy to notice that the two features are actually part of the same sequence, just displaced by a bps, and therefore generate the same reverse primer CATCACAACCTGGAGCATTG. For further analysis, we check the presence of the signature mutations of B. 1. 1. 7; T1001I, A1708D, I2230T, SGF 3675-3677 deletion, HV 69-70 deletion, Y144 deletion, N501Y, A570D, P681H, T716I, S982A, D1118H, Q27stop, R52I, Y73C, D3L and S235F of variant B. 1. 1. 7  96. To verify the presence of mutations, we generate 21-bps sequences, with 10 bps before and after the mutation, and search for their presence: e.
The resulting sequence is CTCCAGGGCAAACTGGAAATA, with a reverse primer TGCTACCGGCCTGATAGATT. We check whether 21-bps sequences containing the signature mutations T265I, K1655N, K3353R, SGF 3675-3677 deletion, L18F, D80A, D215G, R246I, K417N, E484K, N501Y, A701V, 242-244 del, Q57H, S171L, P71L, and T205I of variant B. 1. 351  96  could function as a forward primer. From the Primer3Plus results using EPI_ISL_678597, mutations D215G (TTAGTGCGTGGTCTCCCTCAG) and Q57H (CTGTTTTTCATAGCGCTTCCA) can be used as forwards primers (Fig. 2 ).
1  53  could function as a forward primers. We put mutation T20N and L18F into the same '21-bps' sequence, given their proximity. From the Primer3Plus results using EPI_ISL_792683 , 3 of the generated sequences using mutations can be forward primers candidates; K417T (CAAACTGGAACGATTGCTGAT), H665Y (AGGGGCTGAATATGTCAACAA) and P80R (AATAGCAGTCGAGATGACCAA) (Fig. 3 ). Using the sequences of variant B. 1. 617. 2 downloaded from the GISAID repository we ran an evolutionary algorithm 10 times to find the most important sequences to separate the B. 1. 617. 2 from the rest of the sequences. From the results 4 out of 10 point to mutation D63G, 4 to mutation I82T and 2 to mutation T120I. The forward primer for mutation I82T is (CTACCGCAATGGCTTGTCTT) and for mutation D63G is (ATGGCAAGGAAGGCCTTAAA) using sequence EPI_ISL_1337507.
In a first step, we train a convolution neural network (CNN) using the training and testing sequences obtained from GISAID. The architecture of the network is shown in Fig  7 , and is the same as the one previously reported in  94. Next, if the classification accuracy of the CNN is satisfying (> 99%), using a subset of the training sequences we translate the CNN weights into '21-bps' features, necessary to differentiate between the targeted variant samples and all the others. The length of the features is set as 21 bps, as a normal length for primers to be used in PCR tests is usually 18-22 bps. Then, we apply recursive ensemble feature selection (REFS)  104, 105  to obtain a reduced set of the most meaningful features that separate the two classes. Finally, we simulate the outcome of treating the most promising features obtained in the previous step as primers, using Primer3Plus 103 in the canonical sequences EPI_ISL_601443 for B.
Experimental evaluation of B. 1. 1. 7 Primers Amplification efficiency of the designed primer sets were evaluated using viral RNA extracts from two sequenced SARS-CoV-2 strains: the original Wuhan strain 210207 (GISAID N°EPI_ISL_437689) and VOC B. 1. 1. 7 strain (GISAID N°E PI_ISL_683466). Viral RNA were extracted from infected cell culture supernatants using the NucleoSpin Dx Virus kit (Macherey-Nagel), following the manufacturers' protocol. Viral RNA extracts (5 µL) were analyzed either using the IP2/IP4 dualplex real-time reverse-transcriptase (RT)-PCR assay, developed by following the Pasteur Institute and targeting conserved regions of the SARS-CoV-2 RdRP gene  98  , or primer set B. 1. 1. 7-1 (Table  1 ), using the LightCycler EvoScript RNA SYBR Green I Master kit (Roche). Both RT-PCR assays were conducted on a LightCycler® 480 System (Roche), using the thermal cycling program described in the Pasteur Institute protocol  98.
A function that is conserved in mammals by the Wg homolog, Wingless-type (Wnt)  (Ahmad-Annuar et al. , 2006; Gross et al. , 2012; Hooper et al. , 2012; Koles et al. , 2012; Ekström et al. , 2014). The transmembrane protein Evenness interrupted (Evi/Wls) transports Wg on exosomes to the postsynaptic site where Wg binds to its receptor Frizzled2. Several publications report also quantifications of exosome release at the Drosophila NMJ  (Korkut et al. , 2009; Koles et al. , 2012; Korkut et al. , 2013; Lauwers et al. , 2018) , but the manual methods used here are not extensively described. Manual quantification is timeconsuming and involves several individual decisions (i. e. , set up of the threshold, area of interest, etc. ) which make replication challenging. In this paper, we described and report two different macros built with FIJI (an image processing package of ImageJ: an open-source image processing program) that serves as a powerful toolbox to quantify autophagosomes (protein clusters) and exosomes (extrasynaptic protein intensity) at the Drosophila NMJ.
Likewise, we use Hsp83 mutation, which has already been shown to impact exosome release, to validate our automatic quantification method to analyze exosome release. This study is the first that completely automatized the task without the researchers decision-making intervention, making it highly reproducible over time and providing new opportunities to compare the data in an objective/unbiased and reproducible way. Drosophila adults (F0) with the correct genotype are cross using a 3:1 female to male ratio at 21 °C. F1 Drosophila larvae with the desired genotype are dissected in L3 stage. A critical process in the dissection step is to maintain the nerves intact. Dissected larvae are fixed with PFA before immunohistochemistry using the appropriate antibodies. After the immunohistochemistry, larvae are mounted and imaged using confocal microscopy.
PROCEDURE Image Acquisition The macros Exoquant and Autophagoquant work on confocal microscope images. However, these macros could potentially quantify other types of images with similar characteristics just by making some adjustments. The images should have been optimized to have a high signal-to-noise ratio. The ideal confocal images should not contain pixel saturation as this will not allow quantification. However, saturation is not a problem in areas that are not the object of the quantification. If you want to compare between different images, it is highly recommended -if not mandatory-to maintain identical acquisition settings for all the images and conditions. In particular, the settings used to obtain the data were: image size 1024x1024 pixels 12 bits. The pinhole size was fixed at 1 AU.
Function of the Macros Exoquant 1) Open a confocal image using Fiji (or ImageJ) software from which you want to quantify the amount of fluorescence surrounding the presynaptic NMJ bouton. 2) Adjust the extracellular distance from the outer membrane of the bouton you want to measure in lines 38 and 42. Evi-GFP positives extracellular vesicles ranging between 30 and 150 nm (hereby "exosomes") are traveling inside the subsynaptic reticulum (SSR) of the postsynaptic side. Therefore, the default extracellular distance to quantify the exosomes is 1 μm (corresponding to the SSR diameter). 3) Launch Exoquant Macro from Plugins > Macro > Exoquant. 4) The macro opens four windows [Figure  2A, A', A'', channel 1 and 2 in the same window)] corresponding to (Figures  2A'''-A'''' ) your confocal image, (Figure  2A ), the raw image measurements in both channels (row 1 for the membrane marker and row 2 for the protein of interest) (Figure  2A ") One image is a duplicate from the membrane marker channel in which the Otsu threshold method is applied to highlight the shape of the bouton (the mask) in black.
(Figure  2A' ). A pop-up window that requires an action indicating "Choose the perfect bouton" will appear. This action initiates the auto-Threshold (v1. 17. 2) using the Otsu method  (Otsu, 1979). We empirically tested 16 auto-threshold methods in FIJI such as Default, Huang, Huang2, Intermodes, IsoData, Li, MaxEntropy, Mean, MinError(I), Minimum, Moments, Percentile, RenyiEntropy, Shanbhag, Triangle and Yen (https://imagej. net/Auto_Threshold) and the Otsu method obtained the best results in our datasets. The Otsu method optimizes the threshold for each image. 5) To "Choose the perfect bouton" for the action window, click on the bouton within the threshold window to define the region of interest (ROI, in yellow) using the wand tool (see specification in macro code (line 23) (Figure  2A" ) and then click "ok" in the action required window to finish the task.
Rows 7 and 8 contain the measurements of the full image after subtraction of the data from row 3 in (7) the mask-channel and row 4 in (8) the protein-of-interest-channel. Autophagoquant 1) Open a confocal image using Fiji (or ImageJ) software in which you want to quantify the protein clusters (like Atg8 positive dots/autophagosomes) inside the bouton. The confocal image is automatically transformed into an 8-bit image to allow protein cluster quantification. 2) The results table must be empty before running the macro. The macro will automatically clean the results table before each new image is quantified. The reason is that the macro will always use the data from raw 2 in the results table to determine the mean and the standard deviation of the current image. 3) Personalization of Parameters: a) Particle size.
Autophagoquant Amino-acid starvation is the most widely used paradigm to induce autophagy. Upon 3-4 h of amino acid starvation in third instar Drosophila larvae, the number of autophagosomes increases in the presynaptic compartment of the NMJ  (Soukup et al. , 2016). We completely automatized the task and reached  The precise lines of the code (in blue) and a brief description of the underlying algorithm is described for each step of the macros (Exoquant: A-E; Autophagoquant: F-J). The "Exoquant" workflow shows the two channels from the original image obtained in the confocal microscope, in magenta the membrane marker (HRP) and in green the protein channel (A). Fluorescence intensity is measured and stored in lines 1 and 2 of the results table. The membrane channel was thresholded with the Otsu method and highlighted in yellow after the selection with the wand tool (B).
ACKNOWLEDGMENTS We thank  T. Neufeld , the  Bloomington Drosophila Stock Center, Kyoto Stock Center (DGRC) , the  Developmental Studies Hybridoma  bank for reagents. We acknowledge  Antonio Martin-Ruiz  for advice in the code and  Dr. Fabrice Cordelières  ( Bordeaux Imaging Center ) and  Dr Michel Thiebaut  ( IMN ) for critical reading of the manuscript. Research in the laboratory is supported by the  Initiative d'Excellence de l'Université de Bordeaux (IDEX)  and the  Region Nouvelle-Aquitaine.
Biological datasets NeuN-stained 40-µm-thick serial coronal sections of a healthy macaque brain were digitized using an AxioScan. Z1 (Zeiss) with an in-plane resolution of 0. 22 µm/pixel (×20 magnification). Three datasets from  You et al. (2019)  were used in this study: 1) segmentation dataset, 2) individualization dataset, and 3) stereology dataset. The segmentation dataset is used to obtain neuron mask that can stand up to staining intensity difference existing in various brain sections. The individualization dataset is used to learn to detect neuron centroids and perform neuron individualization. In order to use deep learning methods, each image of size 5000 × 5000 pixels in the individualization dataset was cropped to 144 small images of size 512 × 512 pixels as shown in Figure  3. There are 104 overlapped pixels between each two adjacent images in both horizontal and vertical directions.
Fifty images of the individualization dataset were then cropped to a total of 7200 images. In this work, a first training data of 152 representative images (i. e. about 2% of the individualization dataset) from different anatomical regions were used to train the deep learning network and 50 images reconstructed from the 7200 predicted images were used to validate the network. Each neuron was identified by an expert by manually marking a disk (5 pixels in radius) in practice in its center location whose intensity is the lowest (darkest) among its neighbors, as shown in Figure  1  b1 & b2. The stereology dataset is used to generate cartography of features which reflect neuron morphology and distribution in different anatomical regions from mesoscopic level. 2019 ) was cropped to 144 small images of size 512×512 pixels adapted for deep learning.
Neuron individualization Based on the segmentation dataset, the U-net model was generated (the learning rate was set to 1e-4, Adam optimizer was selected, cross entropy was chosen for the loss function and 300 epochs was performed for training). Neuron mask of the individualization dataset noted Im (Figure  5  b) was then obtained by using this U-net model. On the probability map (Figure  5 c ) predicted by the proposed network, pixels with intensity greater than 0 were extracted as neuron centroid candidates, noted Ip (Figure  5 d ). Since numerous small receptive fields were applied in the proposed network, mathematical operation should be used to reduce the noise. Ip was first eroded by using a structuring element with 5 pixels in radius. Followed by erosion, reconstruction and closing operation, the sparse pixels were removed (Figure  5  e).
Comparison experiments of manual and automated neuron counting Neuron counting is important information in the field of biomedicine, so we count the number of neurons on the individualization dataset using unsupervised methods proposed by  You et al. (2019)  and deep learning methods of the proposed network, U-net  (Ronneberger et al. , 2015) , U-net++  (Zhou et al. , 2020) , FCRN  (Xie et al. , 2016)  and multiscale CNN  (You et al. , 2020). They respectively detect 111542, 106076, 85532, 94842, 101819, and 87320 neurons while a total of 111971 neurons were marked by expert (Table  1 ). The total number of neurons detected by the unsupervised method  (You et al. , 2019)  and the proposed method are respectively the first and second method close to the expert's counting.
Comparison of automated neuron individualization Besides the number of neurons, the quality of neuron individualization is important. Therefore, an evaluation by using different methods based on F-score (equation  = Figure  8  shows the F-scores obtained by different automated methods applied on the individualization dataset. We observe that in the regions of caudate and subiculum, all the automated methods have good performance, almost all the F-scores are greater than 0. 8. In the regions of claustrum, putamen, thalamus and cortex, the performance of different automated methods differs because both light-and dark-stained neurons, size-varying and touching neurons and neurons of different shapes are present which increase the difficulty in correctly detecting neurons. As for the challenging heterogeneous hippocampus region in neuroscience, the performance of all the methods are decreased.
Individualization results with different number of images in the training set The influence of different number of images in the training set to the individualization performance of automated methods is studied. The average F-score of all the images of the individualization dataset is presented in Table  3. The number of training set is respectively 152, 304, 456 and 608, i. e. 2%, 4%, 6% and 8% of the dataset size. Since  You et al. (2019)  proposed unsupervised method to individualize neurons, their F-score has no correlation with the number of training set. For the remaining deep learning methods except multiscale CNN  (You et al. , 2020) , the average F-scores are increased with the number of training set. The F-score of multiscale CNN increased first and then decreased, that is because this method is specially designed for the detection of highly aggregated neurons.
It cannot correctly extract features of neurons of different sizes. The individualization dataset is composed of the major anatomical regions of macaque brain data which concerns not only highly aggregated neurons but also size-varying neurons. Therefore, it leads to a performance decrease when the number of training set increases. Overall, we observe that among all the methods in Table  3 , the proposed network consistently provides the best (the largest average F-score) and the most robust (the smallest standard deviation) individualization performance.
Representation of neuron features in anatomical region Based on "neuron vector" computed at neuron level, several types of cartography can be generated. The number of neurons in anatomical regions of the brain is a major focus of interest for biologists. Figure  9  b1-b8 show the cartographies of the number of neurons in 6 regions of interest (stereology dataset shown in Figure  9  a1-a8) reflecting the density of neurons. The pixel aggregation has been fixed at 512 × 512 pixels which produces a new resolution of 0. 11264 mm/pixel in both horizontal and vertical directions. Visually, the regions from higher densities to lower densities are in order: dentate gyrus, cortex, subiculum, caudate, claustrum, and thalamus. In the cortex, especially the Figure  9  b1, we observe very dense bands of neurons that correspond to different known layers of the cerebral cortex  (Kolb et al.
, 2015). f1-f9) Individualization results obtained by applying U-net++  (Zhou et al. , 2020). g1-g9) Individualization results obtained by applying FCRN  (Xie et al. , 2016). h1-h9) Individualization results obtained based on  You et al. (2020). Figure  8  F-score calculated using the proposed method on 50 images of the individualization dataset. L (respectively, R) means that this image is extracted from the left (respectively, right) side of the section. Figure 9 Representation of cartography of two descriptors (the number of neurons and the average of orientation of neurons) in 6 anatomical regions of interest. The aggregation faction is 512 pixels in both horizontal and vertical directions. The resolution at mesoscopic level is 0. 11264 mm/pixel. a1) S1_cortex. a2) S2_caudate. a3) S3_thalamus.
Foreword The "Quaternary Vegetation Dynamics", and the 35th volume of Palaeoecology of Africa (PoA) was born out of two workshops organised to relaunch the African Pollen Database initiative in Bondy near Paris (October 2019) and Amsterdam (January 2020). These workshops brought together researchers from around the world to showcase their work from across the African continent and to train them in data management. The training at the workshop was led by the late Eric Grimm who sadly passed away in November 2020. We hope that Eric's enthusiasm for collating, standardising, and disseminating palaeoecologicial data is captured in this volume. In total this volume of PoA, that for the first time is available as an OPEN ACCESS publication, contains twenty-four papers and is organised regionally following the format of the first meeting: western Africa (chapters 3-6), eastern Africa and Arabia (chapters  [7] [8] [9] [10] [11] , central Africa (chapters 12-13), southern Africa (chapters  [14] [15] [16] [17] [18] [19] , islands in the vicinity of Africa (chapter 20), and pan-African syntheses (chapters  [21] [22] [23] [24].
Finally, we would like to thank all the participants in the meetings, the authors and the reviewers whose contributions have made this volume possible. We would like to also thank the French Agence Nationale pour la Recherche and the Belmont-Forum (VULPES: ANR-15-MASC-0003 and ACCEDE: 18 BELM 0001 05) who have provided financial support for the meetings and publication of this volume. We also take this opportunity to place on record our gratitude to Senior Publisher Janjaap Blom and his team from Routledge/Taylor & Francis/CRC Press for the continuous support to PoA. Guest Editors: William D. Gosling (Amsterdam), Anne-Marie Lézine (Paris), and Louis Scott (Bloemfontein) Series Editor: Jürgen  Runge (Frankfurt)  February 2021.
MAIN ACHIEVEMENTS TheAfrican Pollen Database (APD) was first developed in 1996 in close cooperation with its European counterpart (EPD) (European Pollen Database; http://www. europeanpollendatabase. net) and the Global Pollen Database hosted at the National Oceanic and Atmospheric Administration (NOAA) Paleoclimatology Database (USA) (https://www. ncdc. noaa. gov/dataaccess/paleoclimatology-data). The initial workshop and subsequent work, funded by the French National Centre for Scientific Research (CNRS), the European Union (International Cooperation for development (INCO-DEV) and European network of research and innovation centres (ENRICH) programmes and the UNESCO International Geoscience Programme (PICG), established methods of collating pollen data, developed a standardized pollen nomenclature  (Vincens et al.