dataesr/scientific-paragraphs-categorization huggingface dataset:
Greedy optimisation In the Markovian dynamic stochastic block model described, we seek a clustering solution Ẑ maximising the ICL ex value defined in (16). A complete exploration of all possible allocations is not feasible, even for small datasets. However, similar combinatorial problems have been recently tackled successfully using heuristic greedy routines, resembling the well known Iterated Conditional Modes of  Besag (1986)   et al. (2016)  and for Gaussian finite mixtures in  Bertoletti et al. (2015). Here, we propose an extension of the same ideas to our dynamic network context. The algorithm repeatedly sweeps over the network's nodes and attempts to reallocate them using a greedy behaviour. In each step, a random time frame t and a random node i are chosen, and the variation of ICL ex is evaluated for all the possible reallocations of the corresponding node.
We consider 9 different scenarios, each corresponding to a different choice of θ 0 , ranging from 0. 1 to 0. 9. The perturbation created by the random variable u is necessary to ensure identifiability of the model, as explained in  Matias and Miele (2016). The experiment is executed as follows: for every choice of π and θ 0 , 100 random dynamic undirected networks are generated, each corresponding to a different random realisation of the connection probability matrix. In each network, the variational algorithm of  Matias and Miele (2016) , implemented in the R package dynsbm (version 0. 3), and our implementation of the greedy algorithm are executed. As concerns the variational approach, dynsbm is run 4 times for every value of K in the set {1, 2,. , 6}, and the best run is retained as optimal solution according to the approximate ICL criterion advocated in  Matias and Miele (2016).
Time Proportion Oct '01 q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q Employees Managers Figure  6 : Enron dataset. On the left panel, the evolution of the number of non-empty groups is shown. This quantity appears to increase until the collapse, signalling an increase in heterogeneity in the network. On the right panel, the proportions of managers and employees allocated to the first group are shown. These proportions correspond to the nodes that are not particularly active. Temporal dynamics. Our model allows for groups to become inactive, whenever their sizes decrease to zero. This means that the connection profile associated to a particular group is not exhibited by any of the nodes in the time frame considered. Hence, the number of non-empty groups can be used as a measure of heterogeneity in the network, and the temporal dynamics of this quantity can be used to assess how heterogeneity evolves over time.
Number of edges Wed Thu Fri Sat Sun Mon Tue Wed q q q q q q q q Figure 7: The number of edges in each time frame for the London bikes dataset are shown on the left panel. The right panel shows instead the total number of edges in each day. hires data can be easily rearranged and visualised as a dynamic network structure, where edges correspond to hires and nodes to stations. Similar approaches have been recently proposed in a number of works, such as:  Guigourès et al. (2015) ,  Randriamanamihaga et al. (2014)  and  Matias et al. (2016). In a similar fashion, here we propose an application of our methodology to a dataset of cycle hires in London. The data. The cycle hire data for the London bike sharing system is publicly available from Transport for London (2016). We use here the data from Wednesday, 5 June 2013 to Wednesday, 12 June 2013 (included).
Number of non-empty groups Wed Thu Fri Sat Sun Mon Tue Wed q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q Figure 8: This plot shows the number of non-empty groups at each time frame for the London bikes dataset. The total number of unique groups found is 43. The level of heterogeneity in the network has a noticeable temporal dynamic, which mimics the evolution of the number of links shown in Figure  7. activity is observed throughout the weekend, with a single activity peak appearing in the late mornings. Results. We run our algorithm GreedyIcl once for each of the initialisation methods, with each run taking about six hours long. The optimal clustering found has a total of K = 43 groups. Heterogeneity. The temporal dynamic of the activity level is also exhibited in Figure  8  by the number of active groups at each time frame: as in the Enron dataset, we use this as a measure for the level of heterogeneity in the network.
Proportion of nodes changing allocation Aggregated group size Proportion q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q Figure 10: Stability level in the London bikes dataset. The left panel shows that although a good portion of nodes are very stable, there are some stations changing their allocations in more than 45 out of 64 time frames. The plot on the right hand side studies instead the stability of groups. It appears that larger groups are very stable, and viceversa smaller groups are not. The size of the circles is proportional to the size of the group aggregated over time. side reinforces the idea that larger groups are also very stable whereas smaller groups exhibits high instability. As shown in Figure  11  the stability does not seem to be particularly related to the geographical position of stations.
Most unstable stations (more than 35 swaps) Longitude Latitude q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q Figure 11: On the left panel, the spatial distribution of the stations not changing allocations is shown for the London bikes dataset.
Expected average degrees of groups Average in-degree Average out-degree q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q Figure 12: Expected out-degree versus expected in-degree for the groups found in the London bikes dataset. The size of circles is proportional to the size of groups aggregated over time. All of the large groups of inactive nodes have balanced degrees, in that they tend to send and receive the same number of edges. Smaller groups, instead, can exhibit very unbalanced degrees, either in favour of the out-degree or in-degree. of view, due to the inherent computational difficulties. In this paper we have focused on an extension of the stochastic block model to dynamic networks, where the temporal evolution of the nodal information is captured by a Markovian process.
One appealing feature of our approach is that model-choice is carried out automatically, since the number of groups can be deduced at each time frame from the optimal clustering solution. Through a simulation study, we have validated both our optimality criterion and the greedy algorithm used to estimate the optimal partition. Also, we have compared our method to one based on a variational Expectation-Maximisation algorithm, showing that with careful initialisation our approach achieves better results when the underlying number of groups is unknown. We have applied our methodology to the Enron email dataset, and found 17 underlying groups. Each of the groups found appears to have a specific role within the network and the original status of the members seem to be particularly related with the nodes' allocations.
Also, we have analysed some relevant summaries from our optimal clustering underlining the dynamic evolution of the heterogeneity within the network and of the overall activity level. We have also applied our methodology to a London Bikes dataset, to study the flows and connections between bike stations. Our method has returned an optimal clustering made of 43 groups. The analysis of such a complex structure has been particularly challenging but we have managed to extract some interesting information regarding the dynamics captured by the model. We note that, while the practice of integrating out the likelihood parameters has been exploited in a number of recent papers, the collapsing of the transition probabilities in the Markov process is a rather new original technique. Not only this creates an ideal setup for our method, but it may also be generalised and exploited in arbitrary discrete hidden Markov models and their extensions.
Matias and Miele 2016, as shown in Figure3. 7 Enron datasetThe data. The Enron Corporation filed for bankruptcy in late 2001, leading to an unprecedented scandal and to dire consequences for the US stock market. The data we use in this paper consists of all the emails exchanged from January 2000 to March 2002 between the Enron members. This data was originally made public, and posted to the web, by the Federal Energy Regulatory Commission during its investigation.
N-glycosylation site multiplicity and primary sequence underlies the selective protein glycosylation defect To further elucidate the N-glycosylation deficiency, we included a lectin-affinity based (ConA, WGA, RCA 120 ) enrichment step at the peptide level, followed by deglycosylation  (Zielinska et al. , 2010) , prior proteomic analysis (Figure  4 -figure supplement 1A, data are available via ProteomeXchange with identifier PXD009906). This enrichment can identify N-glycosylation sites and quantify the abundance of each site when they are glycosylated. Using this dataset, we identified 140 likely new glycosylation sites ( » 8% of the total, see Materials and method, Supplementary file 2) with high recurrence of non-canonical sequons ( » 40% of the new sites, Supplementary file 2). Most proteins carrying these sites are intrinsic to membrane as observed for proteins with previously identified glycosylation sites (Gene Ontology analysis in Supplementary file 2).
Among these peptides, 13 were never detected in control but only in mutant samples in total proteomics. These data indicate that these 13 peptides were not glycosylated in the mutant. We conclude that most differences we detected in the glycoproteomic experiment reflect reduced glycosylation site occupancy. We also detected an extremely significant correlation between N-glycan multiplicity and N-glycopeptide levels (Figure  4B , Mann-Whitney test, p-value<0. 0001). This result is consistent with our larger glycoprotein dataset. To delineate the origin of reduced occupancy of individual N-glycosylation sites, we analyzed their primary sequences (Figure  4C ). We identified a mild effect of the presence of a non-aromatic amino-acid (other than Phe, Tyr, His or Trp) at position À2 and a more significant effect of the Serine at position +2 (Figure  4D ), as previously described  (Gavel and Heijne, 1990; Murray et al.
, 2013; Stoeckli, 2010). We speculate that this particular sensitivity of IgSF-CAMs to a N-glycosylation defect arises from their higher N-glycosylation site multiplicity with an average of 3. 3 sites versus 2. 3 in non IgSF-CAM proteins (Calculated based on the reference glycoproteomic dataset, Supplementary file 2, Mann Whitney test, p-value<0. 0001). So, we next hypothesized that impaired IgSF-CAMs function derived from sub-optimal N-glycosylation and that defective IgSF-CAMs contributed to our observed histological defect. We confirmed the enrichment for IgSF-CAMs found in our over-representation analysis at the N-glycopeptide level (18. 4% of the decreased N-glycopeptides belong to an IgSF-CAM protein, Fisher exact test, p-value=0. 0058, Figure  5B ). We selected two highly N-glycosylated IgSF-CAMs relevant for the granule cell histological phenotype for validation by WB.
We analyzed the parallel fibers (PFs) organization in the cerebellar ML using electronic microscopy. In control samples, PFs are consistently oriented perpendicular to PCs branches. However, Srd5a3 mutant mice exhibit an aberrant orientation of groups of PFs in the outer ML, likely due to defective axonal guidance (Figure  7F, G ). Taken together, our data suggest that IgSF-CAMs hypoglycosylation may underlie many neurological defects in CDG.
0 (MTA 1. 0. , Affymetrix) at 45 ˚C for 17 hr. After O/N hybridization, chips were washed on the fluidic station FS450 following specific protocols (Affymetrix) and scanned using the GCS3000 7G. The scanned images were then analyzed with Expression Console software (Affymetrix) to obtain raw data (cel files) and metrics for Quality Controls. The observations of these metrics and the study of the distribution of raw data showed no outlier experiment. Robust multi-array average (RMA) normalization was obtained using R, and normalized data were subjected to statistical tests. For RT-PCR/RT-qPCR 1 mg of RNA was retrotranscribed into cDNA with SuperScript II reverse transcriptase (18064014, Thermo Fisher). qPCR was performed with PowerUp SYBR Green Master Mix (A25777, Thermo Fisher). The following primers were used: mbActin (F 5'-TACAGC TTCACCACCACAGC-3'; R 5'-AAGGAAGGCTGGAAAAGAGC-3') and mSrd5a3 (F 5'-CCGGGCTA TGGCTGGGTGG-3' and R 5'-CTGTCTCAGTGCCTCTAGGAATGG-3').
LC-MS/MS analysis For each run, estimated 0. 5 mg were injected in a nanoRSLC-Q Exactive PLUS (Dionex RSLC Ultimate 3000, Thermo Scientific, Waltham, MA, USA). Peptides were separated on a 50 cm reversed-phase liquid chromatographic column (Pepmap C18, Dionex). Chromatography solvents were (A) 0. 1% formic acid in water and (B) 80% acetonitrile, 0. 08% formic acid. Peptides were eluted from the column with a linear gradient of 120 min from 5% A to 80% B followed by 27 min of column re-equilibration in 5% A. Two blanks, each with two 25 min-linear gradient, were run between samples to prevent carryover. Peptides eluting from the column were analyzed by data dependent MS/MS, using top-10 acquisition method. Briefly, the instrument settings were as follows: resolution was set to 70,000 for MS scans and 17,500 for the data dependent MS/MS scans in order to increase speed.
Total proteomics and glycoproteomics data analysis The MS files were processed with MaxQuant software version 1. 5. 8. 3 and searched with Andromeda search engine against the mouse subset from the UniProtKB/Swiss-Prot complete proteome database (release 2016_06). Statistical analysis and logo extractions were performed using Perseus version 1. 5. 5. 3. Different thresholds were applied to total proteomics and glycoproteomics analysis given that the intensity of several peptides in total proteomics is used for determining protein intensity, while a single peptide in glycoproteomics is analysed at the time. For total proteomics, only proteins detected in all eight samples (4 controls and four mutants) were retained for statistical analysis, avoiding all data imputation. For comparative glycoproteomics, we retained glycosites detected in at least 3 out of 4 control samples.
Additionally, we selected proteins that were specifically detected in control or mutant samples by retaining proteins detected solely in at least 3 samples of one group. Both FDR and p-value (q-value <0,05, paired student t-test) was used for total proteomics, whereas the p-value (<0,05, unpaired student t-test) was used for N-glycopeptides. As a database for N-glycoproteins and number of N-glycosylation sites per protein (qualitative dataset, reference glycoproteomic dataset), the data obtained by glycoproteomics was used: any glycopeptide detected in at least two control samples was considered as potentially N-glycosylated (Supplementary File 2). Sites that were not previously described in Zielinska et al. or not predicted by uniprot were classified as likely novel N-glycosylation sites.
Volcano plots were generated using the VolcanoShiny app (https://hardingnj. shinyapps. io/volcanoshiny/). PCA and variance analysis were done with the Partek Genomics Suite software. For homologous IgSF-CAM proteins (CNTNs, L1CAMs and NCAMs), HomoloGene and the study from Chen et al. (Chen and Zhou, 2010)  were used.
Cell and neurite dynamics were measured every 3 hr by live cell imaging (Incucyte ZOOM with Neurotracker module, Essen Bioscience). Neurite branching points and neurite length were normalized to the total number of cell body clusters. As long as GCs do not arborize, the neurite branching points parameter provided by the Incucyte software was translated as neurite number. For GCs re-aggregates, cells were isolated and plated for 24 hr in un-coated surfaces (2 Â 10 6 cells/cm 2 ) to promote aggregation. The aggregates were then collected and seeded on the coated surface for 24 hr. Sholl analysis (using the ImageJ plugin) was performed to quantify the aggregates' neurite number as the number of neurites crossing each circle. For migration analyses, the cells within the same circles used in Sholl analyses but the first circle, were counted  (Tanaka et al.
, 2004). Five different aggregates were quantified per mouse and coating condition. Human-induced pluripotent stem cells (hiPSCs) SRD5A3 -/-generation and culture iPSCs were derived from hPBMCs from a control male donor via Cyto-Tune Sendai virus reprogramming. Cells were cultured at 37 ˚C on vitronectin-coated (10 mg/mL; 07180, Stem Cell) dishes with mTeSR media (Stem Cell). SRD5A3 KO hiPSCs clones were generated by CRISPR/Cas9. sgRNA (inserted into a GFP-containing PX458 plasmid, Addgene) targeting the first exon of the gene were generated via the CRISPOR website and validated in T293 HEK cells by Sanger sequencing combined with tides analysis (https://tide-calculator. nki. nl/; data not shown). hiPSCs were transfected by nucleofection (Amaxa 4D, Lonza) and transfected cells (GFP+) were isolated by FACs (BD FACSAria II SORP, BD Biosciences) to generate single-cell-of-origin colonies.
DNA was extracted from a piece of each colony by ZR-Duet DNA MiniPrep (D7003, Zymo) and sequenced. After selection, SOX2 (1:2000; AB5603, Millipore) and OCT4 (1:100; SC5279, Santa Cruz) immunostaining was used to confirm pluripotency (Figure  5 -figure supplement 2). No major chromosomal abnormalities were detected by CGH array (60K, data not shown).
hiPSCs differentiation towards late cortical progenitors (LCPs) Differentiation of hiPSCs towards LCPs and cortical projection neurons (CPNs) was carried as described by Benchoua and colleagues  (Boissart et al. , 2013). Briefly, iPSCs colonies were transferred to a non-coated dish with neural induction media (N2B27 with FGF2 and double SMAD inhibition by SB431542 and LDN193189, Stem Cell) for 6 hr and were afterwards transferred onto polyornithine and laminincoated dishes. Following neural rosette formation (12 -15 days), the cells were passaged onto a geltrex-coated (A1413301, Life Tech) flask (LCPs P1) and were further cultured with N2B27 supplemented with FGF2, EGF and BDNF (Stem Cell). SOX2 and Nestin (1:250; N5413, Sigma) staining confirmed LCPs multipotency. Neuroectodermal origin of the emerging neural progenitor-like cells was assessed by HNK1/P75 FACs staining (data not shown).
Acknowledgements We thank  JG Gleeson  for the support to generate Srd5a3 conditional mouse and helpful advices. We are grateful to  L Goutebroze ,  F Francis ,  N Altin ,  K Siquier ,  K Radjamani  for valuable discussions or technical help. The project is funded by the  French National Research Agency   ANR-16-CE12-0005-01  (VC), la  Fondation pour la Recherche Medicale FRM-DEQ20160334938 (LC) , the association Connaı ˆtre les Syndromes Cerebelleux  CSC (VC) , the program  ERA-NET 643578-EURO-CDG2  and  E-Rare -3 (FF and CT),  Paris Descartes University BioSPC program  ( DM-C ),  Imagine Foundation (DM-C) ,  Imagine International PhD program  with Bettencourt-Schueller-foundation (EU). We thank  Cochin genomic  (GENOM'IC), electron microscopy core facilities and the  Imagine animal facilities (LEAT)  for services and support.
Discussion We show that self-noise PSDs of the iGravs at sub-seismic frequencies are at the level of detection of the Slichter mode and hence what limits our capabilities to detect this inner core signal is not instrumental. The slight increase of self-noise towards longer periods is quite overwhelmed by geophysical signals. Differences of noise levels between the self-noises and the observed PSD levels after removing tides and atmospheric pressure effects are hence due to geophysical processes that have not been reduced from gravimetric records. Using least-squares adjustment software like ETERNA  44  better reduces the observed noise level than subtracting a local tidal model (Fig. 3 ) but the PSD is still 20 dB larger than the self-noise. We computed PSDs for the available atmospheric and hydrological surface loading products using a modified Welch periodogram computed on 15-day segments overlapped with 75% and the median was taken.
Acknowledgements We are grateful to  R. Warburton  and  R. Reineman  from  GWR Instruments  for the installation of the instruments at J9 and the determination of the oscillator parameters. We thank  F. Littel  for the maintenance of the instruments. The Micro-g LaCoste Inc. gPhone # 54  is owned by the  Instituto Geografico Nacional (IGN) of Madrid in Spain. We thank  J. -P. Boy  for providing surface loading computation of MERRA2 and ECMWF + TUGO-m products at J9 gravimetric site through the EOST loading service http://loading. u-strasbg. fr/. The iOSG #23 gravimeter at J9 gravimetric observatory was funded by the  EQUIPEX RESIF-CORE. The iGravs # 29 , # 30  and # 31  were funded by the  EQUIPEX CRITEX. iGrav # 15  belongs to GWR and was in test in Strasbourg before being sent to Iceland.
Proper species identification from ancient DNA samples is a difficult task that sheds light on the evolutionary history of pathogenic microorganisms. The field of palaeomicrobiology has undoubtedly benefited from the advent of untargeted metagenomic approaches that use next-generation sequencing methodologies. Nevertheless, assigning ancient DNA at the species level is a challenging process. Recently, the gut microbiome analysis of three pre-Columbian Andean mummies [1](Santiago-Rodriguez et al. 2016) has called into question the identification of Leishmania in South America. Here, the metagenomic data filed in MG-RAST (Metagenomics RAST server) were used for a further attempt to identify members of the Trypanosomatidae family infecting these ancient remains. For this purpose, we used two metagenomic analysis tools.
Until recently, BLAST (Basic Local Alignment Search Tool) alignments, which rely on finding the best alignment to a panel of genomic sequences, were the traditional approach to assign a taxonomic label to an unknown sequence. However, unambiguous assignment at the species level is very hard and this tool is very expensive in CPU time for NGS data analysis. To shed light on the causative agent of leishmaniasis infection (and more broadly on parasites belonging to the Trypanosomatidae family) in these ancient remains, we decided to use new software dedicated to metagenomic data analysis. Kraken, a bioinformatic program  [5] , presents numerous advantages over other programs, including its speed of performing analysis on metagenomes. The identification at the species level by Kraken is based on the use of exact-match database queries of k-mers, rather than on alignment similarity.