dataesr/scientific-paragraphs-categorization huggingface dataset:
As a compromise between the number of birds that we could include in our study and the number of images per bird (i. e. to avoid generating an excessively imbalanced dataset), we aimed to use 1,000 images per bird-900 images for the training dataset and 100 images for the validation dataset. Training a deep learning model with an imbalanced training dataset (i. e. when the different classes, here the individuals, have different number of training pictures) can result in the over-generalization for the classes in majority due to its increased prior probability. For instance, a naïve classifier for a binary classification task for a dataset in which the ratio of the minority class to the majority class is 1:100 will have 99% accuracy if it simply learns to always output one result-the majority class.
tures in the training and validation datasets, we used images from different days in our training and validation datasets. In total, we constructed a dataset of sociable weavers containing 27,038 unique images of 30 individuals, or 901 ± 173 (M ± SD) per bird and a dataset of great tits containing 7,605 unique images of 10 individuals, 761 ± 223 (M ± SD) per bird.
Application of transformations to the sociable weaver training data to facilitate individual identification across different contexts. Comparison of the images' quality in (a) the testing dataset (see Section 2. 4 below) with (b) the training dataset. (c) The same training image after applying a transformation to simulate the low quality of the testing dataset training dataset did not use this new feeding spot. In all, 94 pictures were taken but the number of pictures collected at this setup varied greatly between birds (from 2 to 38 pictures, M: 15. 70 ± 11. 30.
chance that a new bird will be erroneously classified as one of the birds of the training dataset. A value of 17% should be acceptable if new individuals are not common (both in number of different new individuals and in the frequency of appearance). In order to reduce the probability of identifying a new sociable weaver as a bird present in the training dataset to <5%, a confidence threshold for the entropies would have to be set to 0. 018. However, this would result in discarding 36% of the images of the sociable weavers present in the training dataset. TA B L E 1 Rate of positive identification when testing in all contexts for the sociable weavers. Right column gives the identification success rate when noise and blurs were artificially added to training images to match the quality of testing images (see Section 2.
4) Distribution of the entropies of softmax probabilities when predicting the identity of birds from the training dataset or of new birds. Distributions are given for (a) sociable weavers and (b) great titsFor the great tits scenario, in which the appearance of new birds is frequent, defining a simple threshold that differentiates new birds from the birds already present in the training dataset would not be enough as there is a too much overlap between the birds in the training and the new birds' entropy. For example, 90% of the entropies are below 0. 8 when predicting the identity of birds that are present in the training dataset. However 62% of the entropies for the birds not present in the training dataset are also below this value. Under this scenario, reducing the probability of identifying a new individual as a bird present in the training dataset to <5%, would require to set a confidence threshold for the entropies of 0.
Photographing the animals across different times of the day and in different days provides the CNN with a very diverse training dataset making the CNN invariant to such variations. Furthermore, we show here that if the conditions for training are slightly different from the recording conditions in which the CNN is going to be applied, it is possible to artificially modify the pictures used for training in order to simulate the conditions under which the pictures of the context of interest will be taken. Specifically, we used blur and noise transformations in the sociable weaver dataset to improve the generalization capability of our model, as the testing images had a lower quality than the training images. This confirms that using artificially degraded training pictures can be used to improve CNN generalization capability (e.
g. Vasiljevic, Chakrabarti, & Shakhnarovich, 2016). Other transformations could potentially be applied on the training dataset. Such transformations should consider the type of images on which the model will be used. For example, if illumination conditions of the training pictures are different from the context of interest, brightness and contrasts transformations could be applied to the training data in order to make the CNN light invariant. This generalization capability is an important novelty of this study compared to previous work on small-animal tracking using computer vision, which has been restricted to standardized conditions (e. g. Pérez-Escudero et al. , 2014)  that are not easily satisfied when working with wild animal populations. Besides the recording conditions, it is also important to consider how tags used for human identification could artificially increase the accuracy of the models.
For example, here the sociable weavers had three coloured bands and a metal ring in their legs (two in each leg) that form a unique colour combo code. The Mask R-CNN trained on Microsoft COCO dataset used here to extract the birds from the pictures resulted in a dataset with 36% of the pictures containing at least one of the three colour bands partially visible, whereas the full colour code was almost never visible (fewer than 1% of the pictures). Since the majority of the pictures did not have any colour band visible, and three colour rings are needed to.
has been used numerous times to create algorithms for object recognition. The Labelled Faces in the Wilde (LFW) dataset (Huang, Ramesh, Berg, & Learned-Miller, 2007)  contains thousands of pictures of human faces to development algorithms for human face recognition and identification. The nordland dataset (Sünderhauf, Neubert, & Protzel, 2013)  contains footage of more than 700 km of northern Norway railroad recorded in different seasons (summer, winter, spring and fall) and has been used to address the problem of place recognition under severe environmental changes. Biologists aiming at taking advantage of the potential of deep learning will also benefit from assembling large datasets of labelled pictures containing many individuals, taken across different contexts and across different life stages.
By making our dataset freely available, we provide the foundations for continued development of more reliable algorithms that are able to cope with the challenges presented here, among others. Having large datasets will allow optimizing performance of CNNs as well as identifying the relative performance of alternative solutions. Other network architectures (e. g. ResNet; He, Zhang, Ren, & Sun, 2016) and different hyper-parameters settings (e. g. learning rate) than the ones used here can yield different, and potentially improved, results. Other deep learning methods approaches could also be explored and applied not only to closed-set identification problems (as we did here) but also to verification and open-set identification. For example Siamese neural networks (Varior, Haloi, & Wang, 2016)  and triplet loss-based methods (Schroff, Kalenichenko, & Philbin, 2015)  are able to make pairwise comparison of two different images and output if the different images belong to the same individual or not, which could help solve the issue of the introduction of new individuals to the population and obtain higher overall performance.
There are also other pre-processing steps that can greatly improve the model training and reduce the number of images needed. For example, image alignment (e. g. Deb et al. , 2018; Lopes, de Aguiar, De Souza, & Oliveira-Santos, 2017)  can be used to decrease variation in the birds' pose. Training an algorithm for individual recognition not only encompasses a great deal of trial and error, and different systems will present different challenges, but also opens up many new opportunities. Comparison of the performance of different methods for individual recognition in birds should therefore be the scope of intense research once sufficient individually labelled dataset becomes available.
Funding information  Max-Planck-Gesellschaft ; Fundação para a Ciência e a  Tecnologia, Grant/ Award Number :  CEECIND/01970/2017 ,  IF/01411/2014/CP1256/CT0007 ,  PTDC/BIA-EVF/5249/201  and  SFRH/ BD/122106/2016 ;  Agence Nationale de la Recherche , Grant/Award Number:  ANR-15-CE32-0012-02  and  ANR 19-CE02-0014-02 ;  Deutsche Forschungsgemeinschaft , Grant/Award Number:  FA 1402/4-1  and  422037984 ;  University of Cape Town ; European Union's  Horizon 2020 , Grant/ Award Number:  850859 ;  Max Planck Society.
F Application of transformations to the sociable weaver training data to facilitate individual identification across different contexts. Comparison of the images' quality in (a) the testing dataset (see Section 2. 4 below) with (b) the training dataset. (c) The same training image after applying a transformation to simulate the low quality of the testing dataset training dataset did not use this new feeding spot. In all, 94 pictures were taken but the number of pictures collected at this setup varied greatly between birds (from 2 to 38 pictures, M: 15. 70 ± 11. 30.
To examine whether there are other proteins cleaving PR-Ub from serine, we generated a Legionella strain without DUPs and mixed lysates with PR-ubiquitinated Rab33b. Depletion of both DUPs, but not SidJ, which has been previously suggested to serve as a PR-ubiquitin specific deubiquitinase, failed to hydrolyze PR-ubiquitin from Rab33b (Figure  1F ). Collectively, our data establish a new class of deubiquitinases specific for PR ubiquitination that includes DupA and DupB.
Structural and Functional Analyses of DupA PDE Domain To elucidate the molecular mechanism of DUPs, we determined the crystal structure of DupA 4-345 (PDB: 6RYB, Figure  2A ). The overall structure of DupA resembled the PDE domains of the SidE family ligases SdeA  (Kalayil et al. , 2018)  and SidE  (Wang et al. , 2018)  as well as the PDE domain of DupB  (Akturk et al. , 2018). The three catalytic residues from SdeA and DupB PDE  domains were also highly conserved in the core of DupA (H67, E126, and H189), while several loop regions differed (Figure  2A ). and S1G ). This suggests that, similar to the SdeA PDE domain, DupA utilizes a histidine-based intermediate reaction to catalyze the hydrolysis of ADPR-Ub  (Kalayil et al. , 2018). Thus, the PDE domains of SidEs and DupA/B may share the same catalytic residues to mediate opposite reactions: PR-Ub transfer to substrates and removal of PR-Ub from substrates (deubiquitination), respectively.
DupA Interactions with Ub Define Its Catalytic Activity To reveal the atomic basis underlying the bias of DupA toward deubiquitinase activity, we sought to determine the structure of enzymatically inactive DupA (H67A) in complex with a PRubiquitinated substrate. A Rtn4 peptide, acting as a minimal ubiquitination substrate of SdeA  (Kalayil et al. , 2018) , was modified to harbor only one target serine residue  (Figures S2A and S2B). Crystals of DupA H67A and PR-ubiquitinated Rtn4 peptide diffracted up to 2. 0 A ˚, and molecular replacement revealed the densities for both Ub and DupA but not for the Rtn4 peptide, likely due to the flexibility of the peptide (PDB: 6RYA, Figure  S2C ). S2F ; Table  1 ). Moreover, the SdeA PDE domain did not bind to the PR-ubiquitinated serine peptide, whereas DupA maintained a strong binding affinity.
Based on these observations, we postulated that the differences in binding dynamics and affinities of DupA and SdeA to Ub might help explain how two similar PDE domains elicit two counteracting reactions. To explore this, we introduced multiple mutations in the DupA PDE domain (Figure  3F ). Only the DupA E242R mutant affected the hydrolase ability and displayed reduced binding affinity to PR-ubiquitinated substrates (Figure  3G ). More importantly, the same mutant was now able to promote stable PR ubiquitination (Figure  3H ). These data reveal that the extent of interaction between PDE domains and Ub governs the directionality of their enzymatic activity. The hydrolase (PR deubiquitinase) activity is favored by high affinity and longer residence time of the PR-ubiquitinated substrate to DupA/B, while the transferase (PR-Ub ligase) activity is dictated by lower-affinity interactions of the SidE family PDE domain with ADPR-Ub.
Initial datasets were processed using XDS  (Kabsch, 2010) , and phases were determined by Phaser molecular replacement in ccp4 module with SdeD, SdeD:Ub as template model  (McCoy et al. , 2007) , PDB:6B7P, 6B7O, respectively). Structure refinement and manual model building were performed with Coot and Phenix. Refine  (Afonine et al. , 2012; Emsley et al. , 2010). During data analysis, twinning is indicated from DupA:PR-Rtn4-peptide dataset, and twinning operators were applied during refinement. In vitro ubiquitination/deubiquitination assays SdeA mediated PR ubiquitination assay was done as previously described  (Kalayil et al. , 2018). Briefly, 2 mg of purified Rab33b, ADPR-ubiquitin, 200 mM of NAD + were incubated with 1 mg of GST-PDEs at 37 C for 1 h in 30 ml of reaction buffer (50 mM Tris-HCl pH 7.
The prevalent species had in all cases a fraction of more than 99. 5%. Binding kinetics determination by biolayer interferometry (BLI) Binding kinetics were determined with OctetRed system (Fortebio). His-Ub (12. 5 mg/mL), His-Ub-ADPR (12. 5 mg/mL), His-Rtn4 peptide-PR-Ub (12. 5 mg/mL), His-Rtn4 peptide (50 mg/mL) were loaded onto Ni-NTA biosensor and equilibrated with binding buffer for the baseline. To examine the association rate, equilibrated sensors were transferred into solutions containing various concentration of GST-DupA H67A or GST-SdeA PDE H277A (0. 15 -10 mM, 1. 5 -100 mM, respectively). Dissociation of PDEs were initiated by placing sensor into in reaction buffer again. Association rate constant (kon), dissociation rate constant (koff), dissociation constant (Kd) values were calculated by Octet Data analysis software (ForteBio).
The adenosine monophosphate was removed, obtaining an Ub-PR fragment. As peptide structure (Sub), a sterically suitable structure from the peptide production run was placed into the active side of DupA and covalently attached to the oxygen of the phosphoribose. Small steric clashes were manually removed. For DupA with Ub-PR-Sub in SidE position, the initial structure was generated by aligning the structure of DupA with Ub-PR-Sub bound in DupA position to SidE PDE:Ub complex (PDB: 5ZQ3  (Wang et al. , 2018) ). The alignment was performed using the Multiseq tool  (Roberts et al. , 2006)  in VMD 1. 9. 2  (Humphrey et al. , 1996). Coordinates of Ub and DupA were saved. Ub-PR-Sub was aligned onto the DupA-Ub structure. Ub-PR-Sub and DupA coordinates were saved, combined, and steric clashes manually removed.
For SdeA PDE with Ub-PR-Sub in DupA position, the initial structure was generated based on the SdeA PDE structure (2. 8 A ˚resolution, PDB: 6G0C  (Kalayil et al. , 2018) ). Unresolved regions were modeled with the program MODELER  (Webb and Sali, 2017). The modeled SdeA PDE structure was aligned to the DupA structure with Ub-PR-Sub in DupA position using the Multiseq tool in VMD 1. 9. 2. Coordinates of Ub-PR-Sub and SdeA PDE were saved, combined, and steric clashes manually removed.
, 2010; Zgarbovaet al. , 2011) $ The initial ARG-PR-SER structure was based on the DupA structure with a bound ADP-ribosylated Ub (Ub-ADPR). The adenosine monophosphate was removed, obtaining an Ub-PR fragment. The ARG from Ub was truncated between the Cg and Cd position, i. e, a methylated guanidine bound to the C1' of the phosphoribose. The phosphate at the other side was methylated, mimicking the Cb of serine. The partial charges of the remaining neutral fragment were determined as follows: The geometry was energy minimized and restraint electrostatic potential (R. E. S. P)  (Bayly et al. , 1993)  charges were calculated using the R. E. D. program  (Dupradeau et al. , 2010)  (v. III. 52) with Gaussian09[g09D] (RESP-A1: HF/6-31G* Connolly surface algorithm, 2 stage RESP fit qwt = 0. 0005/.
Molecular dynamic simulation All MD simulations were run with Amber16. Systems were prepared with the tleap program from AmberTools14. Proteins were set into a periodic, truncated octahedral box and solvated with TIP3P water  (Maier et al. , 2015) , with a minimum 15-A ˚water layer to the box edges. The systems were neutralized and additional NaCl was added to mimic a salt concentration of 150 mM. Proteins and peptides were described with the Amber FF14SB force field  (Maier et al. , 2015). The force field parameters for the ARG-PR-SER linker are described in the preceding paragraph. HIS407 in SdeA PDE and HIS189 in DupA were doubly protonated. Other amino acid protonation states were assigned according to the PROPKA3. 0 as part of the PDB2PQR web server  (Vinet and Zhedanov, 2011 ) (pH = 7, http://nbcr-222.
1% formic acid) on a 20 cm column packed with 1. 9 mm C18 material (Dr. Maisch) and injected online into the mass-spectrometer. Survey scans were recorded with a resolution of 60,000 and the 15 most abundant precursor ions were subjected to HCD fragmentation. Data analysis was performed with MaxQuant 1. 6. 11 against the uniport human reference proteome database (December 2017) and the Legionella pneumophila reference proteome (December 2017). Label-free quantification was performed by MaxLFQ quantification with activated match-between-runs. Statistical testing and GO annotation were done with Perseus (1. 6. 1. 1). Missing values in the control samples (Not infected and DSidE) were amputated and proteins, that were significantly enriched in WT and/or DDupA2 samples were identified by 5% FDR corrected T-Tests.
Western Blotting and Immunoprecipitation Tris-Glycine gels were used for SDS-PAGE followed by western blotting. Quantification of western blots was done using Image Lab software of Bio-rad. At least 3 independent experiments were performed and band intensities were normalized to loading control. p values were determined using Student's t test. For immunoprecipitation, cells were lysed in immunoprecipitation buffer (50 mM Tris-Hcl, pH 7. 5, 150 mM NaCl, 1% Triton X-100, 1 mM PMSF, protease inhibitor cocktail (Sigma Aldrich)), mixed with 10ul agarose conjugated Myc beads (SantaCruz Biotechnology), and incubated for 4 h at 4 C with end to end rotation. Beads were washed twice in IP buffer containing 400 mM NaCl. Proteins were eluted by boiling with 2X gel loading dye followed by western blotting.
Fluorescence signal of Star 580 and Star 635P was excited at 580 and 650 nm and detected at 590 -610 nm and 660 to 740 nm, respectively using Hybrid Detectors (HyD) in photon counting mode. Images were recorded with a pixel size of 27 nm, a dwell time of 1,5 ms and 10 line accumulations. For 2D STED imaging a pulsed 775 nm laser was used and time gated detection from 0,5 -6,0 ns was applied. Data were post processed using the Leica's Lighting image information extraction software. The image is a Maximum Intensity projection of 9 optical sections.
ACKNOWLEDGMENTS We thank  Mohit Misra  and  Marta Campus Alonso  for their contribution in protein purification;  Anshu Khatri  and  Frank Lo  ¨hr for their technical assistance in NMR titration assay;  Apirat Chaikuad  and  Stefan Knapp  for advices in structure determination and sharing synchrotron time; and  Sissy Kalayil ,  Daniella Hoeller , and  Hadir Marei  for discussion and critical reading of the manuscript. The authors also thank staff at  SLS  for their support during crystallographic X-ray diffraction test and data collection and  Ulf Schwarz  from  Leica  for STED image microscope image collection. The data collection at SLS has been supported by the funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement number  730872 , project  CALIPSOplus.
Software and Algorithms HHpred  (Alva et al. , 2016)  https://toolkit. tuebingen. mpg. de/hhpred XDS (Kabsch, 2010) http://xds. mpimf-heidelberg. mpg. de/ CCP4 (Winn et al. , 2011) https://www. ccp4. ac. uk/ Phenix (Adams et al. , 2010) https://www. phenix-online. org/ Coot (Emsley and Cowtan, 2004) https://www2. mrc-lmb. cam. ac. uk/ personal/ pemsley/coot/.
Sample Collection and Selection S. noctilio samples were collected from native European, non-Chinese invaders, and Chinese populations, comprising 44, 127, and 607 specimens, respectively. A summary of sampling sites and common parameters of the genetic diversity of S. noctilio in this study is provided in Table  1. GenBank/DRYAD provided 23 of the 44 native European specimens; others were dry or anhydrous ethanol-preserved individuals borrowed from museums or donated by local institutions. Non-Chinese invader populations were represented by 22, 39, 50 and 16 GenBank/DRYAD samples from Africa, North America, South America, and Oceania, respectively. All Genbank/DRYAD samples are retrieved from DRYAD: https://datadryad. org/stash/dataset/doi:10. 5061/dryad. 37mm8; from Genebank: https: //www.
Sample Collection and Selection S. noctilio samples were collected from native European, non-Chinese invaders, and Chinese populations, comprising 44, 127, and 607 specimens, respectively. A summary of sampling sites and common parameters of the genetic diversity of S. noctilio in this study is provided in Table  1. GenBank/DRYAD provided 23 of the 44 native European specimens; others were dry or anhydrous ethanol-preserved individuals borrowed from museums or donated by local institutions. Non-Chinese invader populations were represented by 22, 39, 50 and 16 GenBank/DRYAD samples from Africa, North America, South America, and Oceania, respectively. All Genbank/DRYAD samples are retrieved from DRYAD: https://datadryad. org/stash/dataset/doi:10. 5061/dryad. 37mm8; from Genebank: https://www.
DNA Sequence Analysis For the native European samples not supplied as sequences directly from GenBank, DNA extraction, PCR amplification and sequencing of the barcode fragment were performed for 15 specimens at the Laboratory of Forest Zoology URZF, INRAE (Orléans, Paris, France). DNA extracts were prepared from one adult hind leg or thorax muscle using NucleoSpin tissue XS kits (Macherey Nagel, Dylan, Germany) according to the manufacturer's protocol. The COI barcoding fragment was amplified via PCR using the forward primer LCO1490 (5 -GGT CAA CAA ATC ATA AAG ATA TTG G-3') and the reverse primer HC02198, (5'-TAA ACT TCA GGG TGA CCA AAA AAT CA-3') (Vrijenhoek, Rijswijk, Netherlands, 1994). PCR reagents were used and reactions performed following Sun  [24]. PCR products were purified using the QIAquick PCR Purification Kit (Qiagen, Hilden, Germany).
After electrophoresis at 130 V for 20 min, the PCR products were visualized using ethidium bromide and ultraviolet light. Finally, products clearly visible after electrophoresis were sent to a commercial company (SinoGenoMax, China) for sequencing in both directions. All electropherograms were checked manually in CodonCode Aligner v3. 7. 1. (CodonCode Corporation, Centerville, MA, USA) to assess their quality. The alignment was done independently using Clustal W implemented in MEGA v6. 06 with default parameters, and no pseudogenes or stop codons were detected in the sequences  [28, 29]. All COI sequences were trimmed to the same length (322 bp) for final alignment. Details on the collecting data for each specimen, as well as photographs, sequence records, trace files, and primer sequences used for PCR amplification, together with GenBank accession numbers, are available through the dataset in BOLD (www.