dataesr/scientific-paragraphs-categorization huggingface dataset:
(2011). Planktic foraminiferal taxonomy follows  Pearson et al. (2006) , and species range charts are presented in Tables  S3  and S4. Representative specimens of selected biostratigraphically important planktic foraminifera were imaged either under reflected light or using scanning electron microscopy (SEM). Specimens were gold coated prior to SEM analysis, which was conducted on a Philips XL-30 Environmental SEM at the University of Birmingham. We also define calcareous nannofossil datums (from  Martini, 1971; Agnini et al. , 2014)  based on semi-quantitative counts by  Bralower and Mutterlose (1995)  at one sample per core section, supplemented with new nannofossil data based on analysis of an additional 20 samples from Sections 865B-4H-3 to -5H-1 and Sections 865C-4H-5 to -5H-5 (Tables  S5  and S6 ).
New calcareous nannofossil data are derived from variable sampling intervals as they were selected to test and refine the published datum events occurring within the focal sampling interval of this study. Calcareous nannofossils were analysed using simple smear slides and standard light microscope techniques  (Bown and Young, 1998). Data were collected semi-quantitatively using a Zeiss Axiophot photomicroscope at ×1000 magnification. Species abundance is estimated per field of view (FOV) after viewing at least five slide transects (> 500 FOV), with slides studied for at least 40 min. Species range charts are presented in Tables  S5  and S6. Taxonomy typically follows Nannotax  (Young et al. , 2017). For defining both planktic foraminiferal and calcareous nannofossil datums, we use Top (T) and Base (B) to describe the highest and lowest occurrences of taxa and Top and Base common (Tc and Bc) for the highest and lowest common occurrences, respectively (following  Backman et al.
Stable carbon and oxygen isotope measurements Benthic foraminiferal stable isotope (δ 13 C and δ 18 O) data were generated from the epifaunal taxon Nuttallides truempyi following the taxonomic concept of  Holbourn et al. (2013). Foraminifers were picked from the 250-300 µm sieve size fraction and cleaned by ultrasonication to remove any loose fine material prior to stable isotope analysis. Benthic foraminifera used for stable isotope analysis were partially recrystallized (with a "frosty" texture) but were not infilled or heavily overgrown (also see  Arreguín-Rodríguez et al. , 2016). All stable isotope measurements were determined using a Thermo Scientific DELTA V Advantage mass spectrometer coupled to a Gas Bench II in the School of Earth and Ocean Sciences at Cardiff University and are reported relative to the Vienna Pee Dee Belemnite (VPDB) standard.
XRF data and composite splice Despite initial concerns about coring disturbance and the lack of clear signals in the shipboard physical property datasets  (Shipboard Scientific Party, 1993b) , distinct features and cyclicity are evident in the high-resolution XRF records throughout the study interval aiding correlation between holes (Fig. 2 ; Table  S1 ). Sr/Ca and Ba/Sr ratios are the most useful parameters for correlation because of the higher signal-to-noise ratio compared to other elemental ratios, e. g. Fe/Sr. For the first time at Site 865 these datasets enabled the identification of several unambiguous tie points between holes and construction of a composite section spanning a ∼ 25 m interval (Tables  1  and 2 ). This is the first composite depth scale available for any interval at Site 865.
Integrated biostratigraphic schemes and relationship to the MECO Whilst both calcareous nannofossil and planktic foraminiferal zonation schemes can be applied to ODP Site 865 and are in generally good agreement, there is significant disagreement in the post-MECO interval between ∼ 35 and 30 amcd (Fig. 6 ). Taken at face value, planktic foraminifera indicate constant sedimentation rates of ∼ 0. 5 cm kyr -1 throughout this interval, whereas calcareous nannofossil datums suggest a 3. 5 Myr hiatus between 38. 6 and 42. 1 Ma. However, XRF-derived elemental and stable isotope datasets and cyclostratigraphic analysis do not indicate any large shifts at this level that might indicate an abrupt shift in environmental conditions  (Figs. 2, 3, and 5). The identified presence of the MECO event itself, which falls within this potential gap, also implies that a hiatus is unlikely.
Conclusions In this study, we have developed new biostratigraphic, stable isotope, and XRF records that span the MECO event at ODP Site 865 in the tropical Pacific Ocean. This site possesses a surprisingly coherent signal in XRF-derived elemental counts and ratios permitting us to construct a reliable composite section for Holes 865B and 865C spanning the late middle Eocene time interval  (38) (39) (40) (41) (42) (43). Cyclicity observed in XRF datasets is likely orbitally paced, and a tentative astronomical tuning of the study section indicates consistently low but steady sedimentation rates of ∼ 0. 4-0. 6 cm kyr -1 , in agreement with bio-and chemostratigraphic age calibrations and correlations to other sites. Benthic foraminiferal stable isotope data indicate that the MECO and the C19r events are present at Site 865, albeit relatively condensed.
Acknowledgements. This research used samples provided by the  International Ocean Discovery Program (IODP). The authors would like to thank  Sandra Nederbragt  for technical assistance with δ 18 O and δ 13 C analyses and  Cardiff University  undergraduate students  Bo Fan Peng ,  Jonathon Gamble , and  Holly Welsby  for help picking benthic foraminifera. Financial support. This research has been supported by the  Natural Environment Research Council  (grant nos. NE/H016457/1  and  NE/P013112/1 ) and the  Leverhulme Trust  (grant no. ECF-2013-608 ). Review statement. This paper was edited by  Laia Alegret  and reviewed by two anonymous referees.
In addition, we noticed that mt-aFtsZ induced a zipper-like phenotype with regular, closely apposed cristae, which we also observed by high pressure freezing electron microscopy. Next, we investigated the spatiotemporal relationship between mt-aFtsZ and Toghether, these data indicate that matrix-localized mt-aFtsZ is a bona fide marker for mitochondrial fission sites and supports a fission model in which matrix constriction precedes Drp1 recruitment and mitochondrial abscission.
As mislocalized mt-aFtsZ colocalizes with microtubules (suppl Fig 1A/B), we were not surprised to detect numerous proteins associated with microtubules among our hits. Interestingly, previous experiments have shown that E. coli FtsZ expressed in the cytosol of mammalian cells does not spontaneously colocalize with tubulin  42  , suggesting important structural differences between alpha-and gammaproteobacterial FtsZ. Among the overall 1060 interactants of mt-aFtsZ, 31. 7% were organellar proteins and 25% (i. e. 269) mitochondrial according to the mitochondrial protein database IMPI (Integrated Mitochondrial Protein Index, v2018_Q2), despite the fact that we did not purify mitochondria prior to immunoprecipitation, reflecting an ~3 fold enrichment in mitochondrial proteins if compared with ~8% mitochondrial proteins in the human genome  43, 44.
Consistently, Gene Ontology term analysis showed significant enrichment of organellar proteins, and in particular mitochondrial proteins (Fig 4B  and  suppl fig 4). Our dataset contained several inner and outer mitochondrial membrane proteins that have been linked to mitochondrial morphology or division (e. g. MICOS complex proteins Mic60, Mic27 and Mic19  45, 46  , Prohibitin 2 and SPY complex members Yme1L and Stomatin-like protein 2  47  , mitochondrial fission protein 1 (Fis1 48 ), mitochondrial fission process protein 1 (MTFP1/MTP18 49 ), ATAD3B  50  , SLC25A46A 1 and AFG3L2  51  , reinforcing our hypothesis that mt-aFtsZ can interact with an endogenous mitochondrial matrix fission machinery. Gene ontology analysis with the DAVID software highlighted an enrichment in mitochondrial inner membrane proteins (suppl fig 4A).
Statistical analysis Results are expressed as means of at least three independent experiments, error bars represent the standard error of the mean. For multiple comparisons, data were analyzed with the Prism software (Graphpad) by one-way ANOVA, followed by Dunnett's multiple comparisons test to obtain the adjusted P value. Significance is indicated as p<0. 05 (*), p<0. 01 (**) and p<0. 005 (***), ns for p>0. 05. N refers to the number of independent experiments, n refers to the number of counted events (cells, individual mitochondria, fissions).
For each sample, 1µg of peptides was injected onto a 50 -cm homemade C18 column (1. 9µm particles, 100 Å pore size, ReproSil-Pur Basic C18, Dr. Maisch GmbH) and separated with a multi-step gradient from 2% to 45% ACN at a flow rate of 250 nl/min over 180 min. The column temperature was set to 60°C. The data were acquired as previously described  77. Data processing: Raw data were analyzed using MaxQuant software version 1. 5. 3. 8  78  with database search parameters as described in  77. The MS/MS spectra were searched against Uniprot proteome database of Human (January 13, 2015, 20,432 entries) and mt-aFtsZ-Flag protein, and usual MS contaminants. Data were quantified with the MaxLFQ algorithm by requiring a minimum peptide ratio count of 2. The parameter "match between run" was checked. Raw data have been deposited to the ProteomeXchange Consortium via the PRIDE  79  repository with the dataset identifier PXD016722.
Statistical and functional analysis: For the statistical analysis of one condition versus another, proteins exhibiting fewer than 2 intensities in at least one condition were first discarded. After log2 transformation, intensities were normalized by median centering within conditions (normalizeD function of the R package DAPAR  80  ). Proteins without any intensity in one condition (quantitatively present in a condition, absent in another) were considered as differentially abundant. Next, missing values were imputed using the imp. norm function of the R package norm. Proteins with a log2(fold-change) inferior to 1 have been considered as proteins with no significant difference in abundance. Statistical testing of the remaining proteins was conducted using a limma t-test  81. An adaptive Benjamini-Hochberg procedure was applied on the resulting p-values to select a set of significantly differentially abundant proteins with a false discovery rate of 1%  82.
The proteins of interest are therefore the proteins that emerge from this statistical test supplemented by those being quantitatively absent from one condition and present in another. Gene ontology analysis of mass spectrometry results was performed with the online softwares Panther (http://pantherdb. org/) and DAVID (https://david. ncifcrf. gov/), using standard settings. Transmembrane proteins were predicted using the online software TMHMM (http://www. cbs. dtu. dk/services/TMHMM/).
Acknowledgements We would like to thank  Véronique Hourdel  and  Quentin Giai Gianetto  for initial analysis of mass spectrometry data and  Ludmila Bonnand  for help with live cell imaging analysis. Alessandro Pagliuso,  Jan Riemer  and  Tim Wai  are thanked for discussion and  Simonetta Gribaldo ,  Bastian Huelsmann ,  Nika Pende , and  Hans Spelbrink  for critical reading of the manuscript. This study was supported by the  European Research Council  ( H2020-ERC-2014-ADG 670823-BacCellEpi  to P. C. ) and  Institut Pasteur. A. S. was supported by a  BioSPC doctoral fellowship  from the  Université Paris Diderot. P. C. is a  Senior International Research Scholar  of the  Howard Hughes Medical Institute. F. S. is a CNRS permanent researcher.
Results Before analysing the model, we tested our manipulation of branded employee behaviour. We used the natural fit scale by  Simmons and Beckel-Olsen (2006)  to measure the fit of the employee's appearance and behaviour with the focal brand. An ANOVA confirmed that branded service employee behaviour had a significantly greater fit than non-branded service employee behaviour (M branded = 6. 26, SD =. 93; M non-branded = 5. 26, SD = 1. 40; F = 27. 88; p <. 001). In addition, the branded service employee was perceived as more sophisticated than the non-branded employee (M branded = 6. 25, SD =. 93; M non-branded = 5. 28, SD = 1. 43; F = 27. 93; p <. 001). We used structural equation modelling (SEM) analysis with a maximum likelihood (ML) estimator to analyse the data from our experiment. Our conceptual model fit the data very well (see Table  3 ), with a comparative fit index (CFI) of.
I. INTRODUCTION Satellite images time series analysis algorithms have been used widely these days. Among them, there are numerous ecological applications such as the analysis and preservation of the stability of ecosystems  [1] , the detection and the analysis of such phenomena as deforestation and droughts  [2] ,  [3] ,  [4] ,  [5] , real-time monitoring of natural disasters  [6] ,  [7] , study of the evolution of urbanization  [8] , crop changes follow up  [9] ,  [10] , etc. While for some applications we can create a training database with the objects presented in SITS  [11] ,  [12] , for others it may be a challenging task as the nature of the researched spatio-temporal phenomena is often not known, unique or, on the contrary, has a lot of variations. For this reason, unsupervised and semi-supervised change detection and time series analysis have been a hot research topic for the past years.
To correctly model "normal" behavior and detect seasonal trends, the SITS data should be long enough and have regular and frequent temporal resolution for the study area. At the moment, this type of freely-available time series can be provided only by long existing missions such as MODIS (250 m-1 km resolution) and Landsat (30 m resolution) that are mostly used for vegetation analysis and, hence, have a low spatial resolution in favor of a high frequency of image acquisition. As the proposed techniques are adapted for low resolution images, they are not suitable to perform SITS analysis at the local level or to distinguish different land-cover subclasses. Moreover, the aforementioned methods are adapted for univariate data (usually, NDVI index) that sometimes can not properly characterize all the data present in SITS.
Then we use object-based techniques to model temporal behavior of the detected change phenomena in the form of evolution graphs. Finally, we obtain a summarized representation of the extracted evolution graphs and apply Gated Recurrent Units (GRU)  [22]  AE combined with hierarchical agglomerative clustering  [23]  to regroup them in different types of spatio-temporal change phenomena. Our algorithm framework does not demand any training data and gives us promising results on two real-life datasets of cities of Montpellier, France and Rostov-on-Don, Russia. Our proposed method can be used for projects studying the urban evolution of cities: changes in density of residential areas, major urban structure constructions (such as transportation systems or stadiums), trends in the evolution of green areas and parks, etc.
, CM S-1,S for the whole dataset. • We extract spatio-temporal change areas by applying the change masks to corresponding images of SITS. Then we perform image segmentation within these change areas to obtain changed objects. • Afterwards, the change objects located in the same geographic area are grouped in temporal evolution graphs  [18] ,  [17]. • Finally, we cluster the obtained graphs using the features extracted from the change areas. We use summarized representation of graph structure -synopsis -as input sequences of clustering approach. B. Change detection 1) Bi-temporal change detection: For the proposed framework, we have developed a bi-temporal non-trivial change detection algorithm  [24] ,  [25]  that is applied to the whole SITS. Contrary to multi-temporal approaches that demands regular temporal resolution of SITS to get correct results, the bi-temporal approach does not assume the temporal regularity of the SITS.
In the previous work  [24] , we have proposed two different AE models for bi-temporal change detection (see Table  I , where p is the patch size and B is the number of spectral bands). The choice of model depends mostly on the patch size p. Note that for all convolutional layers the kernel size is 3. We equally apply batch normalization to all convolutional layers, except the one with 2 -norm. In the proposed method, the change detection is performed in a patch-wise manner for every i, j-th pixel of every couple of images (i ∈ [1, H], j ∈ [1, W ], where H and W are images height and width respectively). To pre-train the model and avoid over-fitting, we randomly select H × W/S patches from each image to form a pre-training dataset. During the pre-training phase each patch is encoded in some abstract presentation and then decoded back with a chosen AE model.
Every object of a graph represents a node and overlapping values between two objects at two consecutive timestamps represent an edge. Objects at timestamp T n can be connected only to objects from T n-1 or T n+1 , a timestamp that contains a BB can have only one object that corresponds to this BB. 1) Bounding box selection: For BB selection, we use the same strategy as in the initial articles  [18] ,  [17] , except that we do not work with the whole dataset, but only with change areas. The methodology is the following: 1) We create an empty 2D grid G that covers all changed areas. If a segment is chosen as a BB, the grid is filled with its footprint. 2) We create an empty list of candidate BB L cand BB. 3) We sort all the segments presented in SITS change areas by their size in the descending order.
In our work, each object is characterized by the feature values extracted with deep convolutional denoising autoencoder. We add a Gaussian noise to the initial patches of the dataset during the training to extract more robust features. The noise is added only to the input of the first convolutional layer. To calculate the MSE for model optimization, we use the initial patches without noise as targets. For every pixel i, j, n its features are extracted in a patch-wise manner. The choice of feature extraction and its efficiency is described in the next section, where we compare the clustering performance for our proposed framework using a feature extraction step and also using raw pixel values for graph descriptors. All the images are normalized using dataset mean and standard deviation before texture extraction.
In our proposed framework, the feature extraction steps are the following: 1) We extract patches of size p for every pixel of every image of SITS to train convolutional AE model. 2) We divide the extracted patch dataset in training and validation parts (67% and 33%). Validation part is used for early stopping algorithm  [29]  that prevents our model from over-fitting and works in an unsupervised manner. The more the model is generalized-the more robust features we get. 3) We train the AE model in such manner that every patch from the training dataset is firstly encoded in a feature vector and then is decoded back to the initial patch. We use mean square error of the patch reconstruction to optimize the model at each iteration. II , where f is the size of the encoded feature vector. Note that for all convolutional layers the kernel size is 3.
Data preparation is performed for every training batch individually, after the input GRU dataset has been created. For every batch B i , we perform the following steps (see Figure  7 ): 1) We define the maximum sequence length d of B i. 2) We zero-pad the end of all the sequences of B i , so they have the same length d. 3) The padded sequences are passed to the encoder, for each sequence its final hidden state h S is obtained. 4) As indicated before, we use the cloned h S as the input of GRU layer in the decoding part, where h S is repeated S times. 5) We apply the inverted padding mask to the cloned h S sequence that is fed to GRU layers of the decoder. 6) The output of the decoder should resemble to the inverted padded input sequence. While the padding of the encoder input sequence allows us to proceed batches with varying length sequences, the padding of the decoder input improves the model quality, especially, it lowers the influence of sequence lengths on the extracted encoded features.
We do not divide the sequence data into training and validation datasets as the nature of some change sequences may be unique. For these reason, we control the training loss changes between two consecutive epochs to prevent the model over-fitting. We have used MSE as the loss function. The model configuration is presented in Table  III , where f is number of features of the input sequences, hidden size is the length of the hidden state vector, d is the maximum sequence length per batch, f hidden is the size of the encoded hidden state vector.
III. DATA AND DATA PRE-PROCESSING We test the proposed approach on two real-life publicly available time series -SPOT-5 and Sentinel-2. While the first SITS contains 12 images that are irregularly sampled over 6 years, the second one contains 17 images taken over 2 years with more regular temporal resolution. A. Montpellier SPOT-5 dataset was taken over the Montpellier area, France between 2002 and 2008 and belongs to the archive Spot World Heritage 1. This particular dataset was chosen due to its high ratio of agricultural lands and progressive construction of new areas. We have filtered out the cloudy and flawed images from all the images acquired by SPOT-5 mission over the considered geographic area and obtained 12 exploitable images with irregular temporal resolution (minimum temporal distance between two consecutive images is 2 months, maximum -14 months, average -6 months).
Distribution of dataset images is presented in Table  IV. All SPOT-5 images provide green, red, NIR and SWIR bands with 10 meters resolution. The original images are clipped to rectangular shapes of 1600×1700 pixels and transformed to UTM zone 31N: EPSG Projection. The clipped image extent corresponds respectively to the following latitude and longitude in WGS-84 system: • bottom left corner: 43°30 6. 0444 N , 3°47 30. 066 E • top right corner: 43°39 22. 4856 N , 3°59 31. 596 E.
B. Rostov-on-Don Sentinel-2 dataset was taken over the city of Rostov-on-Don 2  (the dataset called later Rostov), Russia between 2015 and 2018. This dataset was chosen as the city of Rostov-on-Don underwent the constructions coincided with FIFA world cup 2018 and equally because it has waste agricultural areas. We have filtered out cloudy, snow-covered and flawed images  Sentinel-2 provides multi-spectral images with different spatial resolution spectral bands. It was decided to keep only 10 meter resolution green, red and NIR bands as they contain all the essential information for general change detection and analysis. The images were provided in WGS84/UTM zone 37N projection and clipped to 2200×2400 pixels areas. The clipped image extent corresponds respectively to the following latitude and longitude in WGS-84 system: • bottom left corner: 47°11 3.
5664 N , 39°34 3. 2628 E • top right corner: 47°24 7. 3332 N , 39°51 41. 328 E The pre-processing level of both datasets is 1C (orthorectified images, reflectance at the top of atmosphere). For this reason, both SITS were radiometrically normalized with the aim to obtain homogeneous and comparable spectral values over each dataset. For the image normalization, we have used an algorithm introduced in  [34]  that is based on histogram analysis of pixel distributions.