dataesr/scientific-paragraphs-categorization huggingface dataset:
Results and discussion The 10×5-fold CV DAP has been applied on instances of the synthetic datasets and on the IBD datasets, comparing the performance with standard (and shallow) learning algorithms such as linear Support Vector Machines (SVM) and Random Forest (RF), and with a standard Multi Layer Perceptron (MLPNN)  [48]. As expected  [49] , no classification task can be reliably tackled by Ph-CNN using the IBD dataset alone: the very small sample size causes the neural network to overfit after just a couple of epochs. To overcome this issue we explore the potentialities of transfer learning, as described in what follows. As a first experiment, we apply the DAP on D. In this case, the SELU activation function is used for every layer. The results of the Ph-CNN DAP on D are listed in Tab. 2 (internal validation) and Tab.
3 (external validation) on the six classification tasks Healthy vs. {UCf, UCr, CDf, CDr, iCDf and iCDr}; MCC on DAP internal validation is shown with 95% studentized bootstrap confidence intervals  [50]. The second experiment instead is based on a domain adaptation strategy. The Ph-CNN is first trained on the synthetic dataset D, then all layer but the last one are freezed, the last layer is substituted by a 2neurons Dense layer and then retrained on the IBD dataset. Since only the last layer is trained in the second step, the term domain adaptation is best describing the methodology rather than the more generic transfer learning. Here, the activation function is the ReLU for every layer. The results of the Ph-CNN DAP together with the comparing classifiers are listed in Tab. ?? (internal validation) and Tab.
5 (external validation). As an observation, Ph-CNN tends to misclassify more the samples in class Healthy, rather than those in the other class, for each classification task. In Fig. 7  we show the embeddings of the original features at 6 different levels (after initial input and after 5 PhyloConv filters) for the iCDf task (IBD dataset) by projecting them in two dimensions via t-distributed Stochastic Neighbor Embedding (t-SNE)  [51]  with perplexity = 5 and 5,000 iterations. While at input level the problem seems hardly separable, the classes tend to form distinct clusters during the flow through convolutional filters applied on OTUs close in the taxonomy.
Conclusions We introduced here Ph-CNN, a novel DL approach for the classification of metagenomics data exploiting the hierarchical structure of the OTUs inherited by the corresponding phylogenetic tree. In particular, the tree structure is used throughout the prediction phase to define the concept of OTU neighbours, used in the convolution process by the CNN. Results are promising, both in terms of learning performance and biomarkers detection Extensions of the Ph-CNN architecture are addressing the testing of different tree distances, optimization of neighbours detection and of the number of Phylo-Conv layers. Further, different feature selection algorithms, either generic or DLspecific can be adopted  [53, 54, 55]. Improvements are expected on the transfer learning and domain adaptation procedures, such as learning on synthetic data and testing on metagenomics, and applying to larger datasets.
Dataset D: classification performances of Ph-CNN compared to other classifiers on six classification tasks. Performance measure is MCC, with 95% studentized bootstrap confidence intervals (min CI, max CI). Models are computed for p = {25%, 50%, 75% and 100%} of total number of features for each task. Comparing algorithms are linear Support Vector Machines (LSVM), Random Forest (RF) and MultiLayer Perceptron (MLPNN).
Dataset IBD: classification performances of Ph-CNN (in transfer learning from D) compared to other classifiers on six classification tasks. Performance measure is MCC, with 95% studentized bootstrap confidence intervals (min CI, max CI). Models are computed for p = {25%, 50%, 75% and 100%} of total number of features for each task. Comparing algorithms are linear Support Vector Machines (LSVM), Random Forest (RF) and MultiLayer Perceptron (MLPNN). Principal component analysis of the four datasets D 0 , D 1 , D 2 , D 3 with same sample size as IBD dataset is displayed in Fig.
742 0. 791 0. 741 0. 836 iCDf 247 0. 681 0. 614 0. 739 0. 561 0. 507 0. 621 0. 708 0. 652 0. 764 0. 775 0. 730 0. 820 iCDr 65 0. 537 0. 480 0. 601 0. 338 0. 277 0. 409 0. 526 0. 475 0. 581 0. 552 0. 492 0. 612 iCDr 129 0. 522 0. 453 0. 595 0. 319 0. 254 0. 385 0. 558 0. 493 0. 623 0. 563 0. 516 0. 609 iCDr 193 0. 556 0. 492 0. 617 0. 377 0. 315 0. 437 0. 459 0. 388 0. 527 0. 566 0. 516 0. 616 iCDr 257 0. 477 0. 411 0. 544 0. 438 0. 378 0. 492 0. 529 0. 462 0. 598 0. 539 0. 482 0. 596 Table 5 Dataset D on IBD: classification performances of Ph-CNN compared to other classifiers on the external validation dataset. Task Ph-CNN LSVM MLPNN RF UCf 0. 741 0. 740 0. 666 0. 699 UCr 0. 583 0. 497 0. 608 0. 678 CDf 0. 858 0. 642 0. 705 0. 707 CDr 0. 853 0. 654 0. 654 0. 597 iCDf 0. 842 0. 418 0. 401 0.
, 2017a). While the transcriptional events orchestrating leaf growth upon mild stress have been studied extensively, the early proteome and phosphoproteome changes are not yet fully understood. In this study, we performed full proteome and phosphoproteome analyses on growing leaf tissue exposed to mannitol-induced stress and captured the rapid (within the first half hour) events associated with this stress. We demonstrate differences in proteome changes in the early (30 min) and later (4 h) mannitol-triggered proteome, such as the translational machinery and oxidation-reduction processes. Next, we evaluated the phoshoproteome and found several connections with the GA-DELLA pathway, which form interesting candidates for follow-up studies. We compared four phosphoproteome datasets after mild and severe stress highlighting their distinct signalling pathways.
qPCR analyses Samples were immediately frozen in liquid nitrogen and ground with a Retsch machine and 3-mm metal beads. Subsequently, RNA was extracted with TriZol (Invitrogen) and further purified with the RNeasy plant mini kit (Qiagen). For cDNA synthesis, the iScript cDNASynthesis Kit (Bio-Rad) was used with 1 µg of RNA as starting material. qRT-PCR was performed with the LightCycler 480 Real-Time SYBR Green PCR System (Roche). The data were normalised against the average of housekeeping genes AT1G13320 and AT2G28390  (Czechowski, 2005) , as follows: dCt = Ct (gene) -Ct (average [housekeeping genes]) and ddCt = dCt (Control) -dCt  (Treatment). Ct represents the number of cycles at which the SYBR Green fluorescence reached a threshold during the exponential phase of amplification. Primers were designed with Primer-BLAST (https://www.
(Phospho)proteome workflow Plant material was flash-frozen in liquid nitrogen and ground into a fine powder. Subsequent proteome and phosphoproteome analyses were performed as previously described  (Vu et al. , 2016; Nikonorova et al. , 2018). For details, we refer to the Supplementary Information. LC-MS/MS analysis was performed as previously described  (Vu et al. , 2016). Both the proteome and phosphoproteome samples were analysed using 3 h gradients on a quadrupole Orbitrap instrument (Q Exactive). MS/MS spectra were searched against the Arabidopsis proteome database (TAIR10, containing 35,386 entries; http://www. arabidopsis. org/) using the MaxQuant software (version 1. 5. 4. 1). Details on settings can be found in Supplementary Information. All MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD008900.
For the quantitative proteome and phosphoproteome analyses, the "ProteinGroups" and 'Phospho(STY)sites' output files, respectively, generated by the MaxQuant search were loaded into Perseus software. For phosphoproteome data only high-confidence hits with phosphorylation localisation probabillity > 0. 75 were included in analysis. Data analysis was performed as described previously  (Vu et al. , 2016) , and modifications are added in the main text.
In silico analyses, data visualisation and statistics To generate networks for known and predicted protein-protein interactions, the datasets were loaded to the STRING database (https://string-db. org; version 10. 5) using high confidence interaction score (> 0. 7). As active interaction sources text mining, experiments, databases, co-expression, neighbourhood, gene fusion and co-occurrence were selected. (Phospho)proteins that did not have any interaction were removed from the network. Biological Process terms were retrieved from the TAIR portal (www. arabidopsis. org, Bulk Data Retrieval). Further data visualisation was performed in Cytoscape (version 3. 5. 1) on the extracted interaction network and GO annotations. GO-enrichment analysis was performed in PLAZA 4. 0 workbench using entire species as background model and 0.
05 as a p-value cutoff. The HMMER web server was used for the prediction of kinase domains. For Motif-X analyses  (Chou and Schwartz, 2011) , the sequences (limited to 13 amino acids) of up-and downregulated phosphosites were pre-aligned with the phosphosite centered. The IPI Arabidopsis Proteome was used as the background database. The occurrence threshold was set at the minimum of 5 peptides, and the p-value threshold was set at <10 -6. Venn diagrams were created using the Venny 2. 1 online tool (http://bioinfogp. cnb. csic. es/tools/venny). Barcharts, boxplots and statistical analyses for qPCR and phenotyping data were performed using R software (https://www. R-project. org) and all figures were generated with Inkscape or Photoshop. Details on ANOVA results can be found in Supplementary Information.
To date, post-Mass Spectrometry (MS) data analysis remains a highly debated field with numerous imputation techniques and sophisticated statistical approaches that aim to deal with a complete dataset with missing values  (Koopmans et al. , 2014; Lazar et al. , 2016; Wang et al. , 2017b). Here, to overcome the problem of missing values without imputation or complex statistical analysis, we applied a hybrid approach for data analysis that treats intensity-based and presence/absence data separately (Figures  1  and 2  and Supplementary     Figure 1 ). The original, complete dataset containing log2-transformed intensities was split in three subsets (Supplementary Figure  1 ; see Supplementary Information for details). Dividing the dataset in three subsets allowed us to minimise the number of missing values in the input for regression analysis (subset 1), eliminate unreliable detections or quantifications (subset 2) and include unique proteins or phosphopeptides (subset 3) (Figures  1  and 2 , Supplementary Figure  1  and Supplementary Tables  S1-3 ).
Using the PLAZA 4. 0 platform, GO enrichment analysis was conducted on the dataset in the context of biological process (Supplementary Table  S4 ). GO enrichment on biological processes showed that differentially regulated proteins (including unique ones) were involved in various processes, such as response to stress and abiotic stimulus. To better understand the relationships between the 167 up-and down-regulated proteins, we constructed a protein-protein interaction network consisting of 105 interacting proteins with 242 (potential) interactions (see Material and Methods for details). To get insight into the early biological processes affected by mild osmotic stress, GO annotations of up-and down-regulated proteins were superimposed on the network and nodes were grouped accordingly (Figure  3 ).
A proteinprotein interaction network was built for up-and downregulated proteins (see Material and Methods for details), and similar to 30 min mannitol treatment, the vast majority of interacting proteins was involved in translation and ribosome biogenesis (Figure  5 ). To unravel dynamic changes of proteins that were up-and downregulated at 30 min, we traced these proteins in the 4 h mannitol dataset. Despite that we cannot compare the exact values from both experiments, as they were processed separately, we can make some assumptions based on the protein fold changes. Thus, 167 up-and downregulated proteins of the 30 min dataset were mapped on the total 4 h proteome data and 120 proteins were retained (Figure  6  and Supplementary Table  S5 ). However, only 6 of these proteins were significantly differentially abundant after 4 h (Supplementary Table  S3 ).
Upon mild osmotic stress, the DELLA proteins play an important role in the growth inhibition in leaves upon mild osmotic stress  (Claeys et al. , 2012). GA binds to its receptor GA INSENSITIVE DWARF1 (GID1) (Ueguchi-Tanaka et al. , 2005) and forms a complex with DELLA proteins, leading to its degradation  (Nakajima et al. , 2006). Under mild stress, the gene encoding a GA-degradation enzyme, GA2-OX6, is upregulated by several mannitolresponsive transcription factors  (Van den Broeck et al. , 2017) , which leads to a decrease in GA levels and subsequently to the stabilisation of DELLA proteins. Such stabilisation of DELLA proteins leads to changes in the activity of transcription factors and expression of GA-regulated genes. In our phosphoproteomic dataset, we found a few differentially phosphorylated proteins that are linked to DELLAs.
Comparative phosphoproteome analysis identifies bZIP30 and RBB1 as general players in osmotic stress response To assess whether the mannitol-regulated phosphorylated proteins in growing leaf tissue are part of a more general stress response or are specific for the stress-induced growth-regulating response, we compared our data set (30 min 25 mM mannitol) with 3 previously published osmotic stress-related phosphoproteome datasets  (Xue et al. , 2013; Stecker et al. , 2014; Bhaskara et al. , 2017b). Given the differences in experimental set-up (long-term versus shortterm or mild versus severe stress) (see Table  2  for details), we found little overlap between all four datasets; only two proteins, BASIC LEUCINE-ZIPPER 30 (BZIP30)/DRINK ME (DKM) and REGULATOR OF BULB BIOGENESIS1 (RBB1)  (Han et al.
, 2015) , were detected in all datasets, possibly indicating a general role in osmotic stress responses (Figure  9  and Supplementary Table  S8 ). Interestingly, the same phosphorylation site, Ser 176 , of bZIP30 was found to be upregulated in the 3 datasets where mannitol was used. It should be noted that bZIP30 (Ser 176 , >1. 5-fold upregulated, Table  1 ) was also identified in our network as a central phosphoprotein in the longest interaction chain (Figure  7 ). This transcription factor influences the expression of cell cycle and cell expansion genes, two processes that are affected by mild mannitol treatment  (Skirycz et al. , 2011b; Lozano-Sotomayor et al. , 2016). In addition, we found 4 common mannitol-responsive phosphoproteins (based on overlap between  (Xue et al. , 2013) ,  (Stecker et al.
(Phospho) proteome profiling identifies AHA2 and CRRSP38 as growth regulators under mannitol stress To assess the involvement of proteins with a differential abundance or phosphorylation status in shoot growth and/or mannitol response, and thus the quality of our data set, we selected two candidates for phenotypic analysis. From the mannitol-regulated proteome data set (Supplementary Table  S1 ), we selected the uncharacterized CYSTEINE-RICH REPEAT SECRETORY PROTEIN 38 (CRRSP38, AT3G22060). Interestingly this protein was identified as down-regulated after 30 min of mannitol application and was not detected after 4 h of mannitol stress treatment. This is in contrast to the absence of any mannitol-induced transcriptional change at 20 and 40 min, and the increasing expression from 2 h on (Supplementary Figure  S4 ).
Quantitative Dot Blots Yeast lysates were prepared as described above, serially diluted, and blotted onto nitrocellulose membranes, along with a series of ten 2-fold serial dilutions of purified 25QP-GFP at a known concentration (from a starting concentration of 2 mM). The resulting blots were probed and imaged as described above and analyzed in Fiji. First, the images were inverted and background subtracted using a rolling ball radius of 10. Equally sized circles, the sizes of dots, were centered on each dot and used to measure the raw integrated density of the fluorescence signal from each dot. The dots made by purified 25QP-GFP were used to generate calibration curves by plotting the raw integrated density against the known concentration of the protein (Figure  5B ). The calibration curves were generated by fitting the data to a hyperbolic curve, Raw integrated density = a 3 ½protein b + ½protein with a and b being free parameters  (Janes, 2015).
Single Cell Tracking Computational cell tracking was performed as previously described  (Bove et al. , 2017) , using the time-lapse movies that were collected on the incubator microscope described above. Briefly, we trained a deep convolutional neural network (CNN) to segment cells from movie frames based on a training dataset of $500 manually segmented cells. The only input to the network was the brightfield transmission channel. To increase the number of training examples, we augmented the examples by introducing random transformations, rotations, and flips. Training was performed using a momentum optimizer with an exponentially decaying learning rate until convergence. We used dropout (50% while training) to prevent over-fitting. The CNN was implemented using Tensorflow (https://www. tensorflow.
org) (Abadi et al. , 2016). Next, the centroids of each segmented cell were tracked using a Bayesian tracking method (https://github. com/quantumjot/BayesianTracker). The tracking procedure resulted in trajectories of single cells over time. Well-tracked cells were then selected and a custom Python (https://www. python. org) script was used to generate the time-resolved histograms of fluorescence intensity, using the segmentation output as a mask. These were further analyzed and plotted using R (https://www. r-project. org).
FRAP Methods FRAP was performed using a Leica TCS SP5 microscope equipped with an HCX Plan-Apochromat lambda blue 63x oil-immersion objective (NA 1. 4). Intracellular assemblies were bleached in a circular 0. 28 m 2 region of interest using a 0. 2 s pulse of the 488 nm laser line at full power. Recovery was monitored every 0. 116 s for 400 frames. The recovery curves were analyzed using the jython script, http://fiji. sc/Analyze_FRAP_movies_with_a_Jython_script, in Fiji. Plotting and error calculations were done in R, and curve fitting was carried out in MATLAB. For yeast FRAP, analysis was performed using the FRAP Profiler plugin in Fiji (http://worms. zoology. wisc. edu/research/4d/4d. html#frap), which normalizes for overall bleaching and therefore avoids inaccurate estimation of mobile/immobile fractions in small cells, where bleaching can result in a significant reduction in total cellular fluorescence.
Electron Tomography Data Collection and Processing Grids were mounted on a Model 2040 dual-axis tomography holder (Fischione Instruments; Export, PA) and imaged using a Tecnai T12 microscope (FEI; Hillsboro, OR) operated at full voltage and equipped with a Ultrascan US4000 4k CCD camera (Gatan; Abingdon, UK). Acquisition of dual-axis tilt series was controlled using SerialEM software  (Mastronarde, 2005). Tilt-series were typically collected over a tilt range of ±60 with 2 increments, at calibrated unbinned pixel sizes of 0. 456 nm (26kx magnification) or 0. 514 nm (21kx magnification), with a defocus of 0. 8 mm. Tomograms were reconstructed from tilt series by weighted back-projection using the IMOD package, version 4. 9  (Kremer et al. , 1996).
ACKNOWLEDGMENTS We thank  Anthony Roberts  and  Jernej Ule  for critical feedback on the manuscript;  Jernej Ule  for hosting FR;  Natasha Lukoyanova ,  Shu Chen , and  Luchun Wang  for EM support;  Dave Houldershaw ,  Yanni Goudetsidis , and  Richard Westlake  for computing support;  Anne Bertolotti  and  Joe Grove  for plasmids;  Claire Bagneris  for laboratory support;  Anna Bove  for advice on the incubator microscope; members of the Saibil and Lowe groups for helpful discussions. This work was supported by a  Wellcome Trust   Clinician Scientist Fellowship  ( 101149/ Z/13/A ) to R. P. , a  Medical Research Council award  ( MR/K015826/1 ) to A. R. L. , and  Wellcome Trust  awards  106249/Z/14/Z ,  101488/Z/13/Z , and  079605/Z/ 06/Z  to H. R. S. T. R. P. was supported by a  Wellcome Trust  studentship ( 102401/Z/13/Z ).
1) Background: Echinococcus multilocularis (Em), a highly pathogenic parasitic tapeworm, is responsible for a significant burden of human disease. In this study, optical and time-series Synthetic Aperture Radar (SAR) data is used synergistically to model key land cover characteristics driving the spatial distributions of two small mammal intermediate host species, Ellobius tancrei and Microtus gregalis, which facilitate Em transmission in a highly endemic area of Kyrgyzstan. (2) Methods: A series of land cover maps are derived from (a) single-date Landsat Operational Land Imager (OLI) imagery, (b) time-series Sentinel-1 SAR data, and (c) Landsat OLI and time-series Sentinel-1 SAR data in combination. Small mammal distributions are analyzed in relation to the surrounding land cover class coverage using random forests, before being applied predictively over broader areas.
Cloud cover inhibiting data collection is a persistent problem in many parts of the world, which significantly impacts data availability. Often, this results in limited optical satellite data availability for the study areas and time-periods pertinent to the studies being conducted. Where a single image is available, this can also be problematic. While a single image characterises the landscape at a snap-shot in time, that snap-shot may not necessarily be representative of the broader seasonal phenological variability in the landscape and vegetation characteristics of that location over the course of a year  [28]. This can be problematic when investigating the relationships between the landscape and vegetation characteristics, and small mammal distributions for species where behaviour is highly responsive to the vegetation condition (and, consequentially, food and shelter availability) over relatively short time periods.
This could result in inappropriate conclusions being drawn on the influences of the landscape on small mammal distributions. For example, in the case of M. arvalis and A. terrestris in France, absence is typically determined by bare ground in winter as a result of tilling, where populations cannot be sustained due to the lack of vegetation  [16, 29]. If single-date remote sensing data acquired in the spring or the summer is used to characterise vegetation in these locations, then these areas will, misleadingly, be characterised by cereal crops, which are considered a favourable habitat for these species. The only way to map the permanent presence of favourable habitats and vegetation conditions such as the permanent grassland shown to be key factors for species such as M. arvalis and A. terrestris  [16, 29]  is to use a time-series of data.
The importance of seasonal differences in vegetation condition for small mammal studies has also been illustrated by Reference  [28] , which determined relationships between Ochotona spp. small mammal presence and Enhanced Vegetation Index values to be the strongest in specific periods in the spring and autumn, and weaker in the summer and winter. Similarly, Reference  [30]  observed that correlations between vegetation index values and deer mouse (a primary host of sin nombre virus) density, and the number of infected deer mice, typically peaked in May or June. This again indicates that biomass levels at certain periods of year are key, and illustrates the importance of incorporating multi-temporal data capturing phenological variability, rather than just single-date imagery, for small mammal-landscape modelling.