dataesr/scientific-paragraphs-categorization huggingface dataset:
We also see a low count for the number of events at the beginning of the time window, due to the actual low frequency of events from 2011-2014, as described earlier. The difference between these two low counts at the beginning compared to the end is due to observed low frequencies vs lack of data availability in the time window, respectively. We see a decrease and then increase in conflict in the middle of the dataset. Next, we plot the estimate of the intensity function over 12 month intervals for our 8 years of data. We note that although time is continuous, we discretize time into years in order to visualize the change in the kernel density estimate over time. In Fig. 5 , we see that our spatial kernel density estimate changes over the year-long periods. In Fig. 5a , all of the kernel density estimates use the same scale so we are able to see that there is really only one time period, in 2016, where the conflict events are quite concentrated in space at high levels in that concentration.
We see that the estimate of the covariance function does change over time in our dataset, but not drastically. We include the effective range over time in Table  3. We see that the effective range decreases from 2011 to 2014 and then increases from  2014-2016 but this increasing trend does not hold through 2018. This implies that between 2011 and 2014, events started to exhibit a weaker dependence structure, where events had a smaller effect on surrounding events than previously. This makes intuitive sense as conflict events were relatively infrequent in the region throughout this time period. Between 2014-2015, the effective range increased, meaning that each event had a stronger effect for more distance, or a larger radius, r, around that given event. This also makes intuitive sense as there were large increases in conflict events during this time period.
This suggests that the diffusion mechanism might be strongest when there are state actors and civilians involved in the conflict but other actors do not have a large effect on diffusion. This would illustrate that when state actors and civilians are involved, this may spur additional events around it more-so than other conflict events, perhaps due to the contentious nature of these kinds of events. This makes intuitive sense as these longer events are likely to be larger-scale events in severity, and therefore might spur other events. Through this analysis, we show that it is important to analyze diffusion mechanisms, such as the effective range of any given event, over time. We have shown that for our dataset, the dependence between events changes over time, and the effective range for conflicts in South Sudan decreased from 2011-2014, suggesting a weaker diffusion or dependence mechanism, but then increased from 2014-2016, as conflict escalated in the region.
Our analysis shows that conflicts with longer duration exhibit stronger spatial dependence. Throughout the process of being an independent country, conflict events in South Sudan are most spatially concentrated in 2016, when compared to the other years in our dataset. This finding reveals that the long-term unsolved ethnic tension between the government and rebel groups led to the outbreak of large-scale violence against civilians in 2016. Both the government and the rebel groups committed abuses against civilians in and around Juba and other areas. According to the Global Conflict Tracker and Human Rights Watch, the government and the rebel group targeted civilians along ethnic lines, and millions of people were displaced or forced to flee their homes. Military groups committed rape and sexual violence, destroyed property and looted villages, and recruited children into their ranks.
Introduction How do rebel groups control territory and engage with the local economy during civil war? Charles Tilly's seminal War and State Making as Organized Crime  (Tilly, 1985)  posits that the process of waging war and providing governance resembles that of a protection racket, in which aspiring governing groups extort local populations to gain power, and civilians or businesses pay to ensure their own protection. We present an agent-based simulation model that attempts to capture the decisionmaking and behavior of the actors involved: rebel groups and enterprises. The model is available online at https://gnardin. github. io/RebelGroups as a web-based simulation and can be run in any modern web browser. The use of agent-based modeling in civil war research is not a new endeavor. The groundwork is laid by  Epstein (2002) , who analyzes the conditions under which individuals may mobilize and protest.
Implementation We implement our model using the JavaScript-based Object Event Simulation framework OESjs,  4  which is an open-source web-based simulation platform that allows publishing simulation models on the Web and running them in any modern web browser  (Wagner, 2017). The Rebel Group Protection Rackets simulation is available online at https://gnardin. github. io/RebelGroups and the source-code is available at https://github. com/gnardin/RebelGroups/releases v1. 0.
The inclusion of probabilistic functions allows us to account for random variation or error that could capture the unpredictability of real-world events. We encourage interested readers to explore various initial settings to develop an understanding of civil war dynamics based on the assumptions laid out in this study (executable at https://gnardin. github. io/RebelGroups). Future adjustments to the model could include incorporating external influences such as foreign military intervention or outside sources of rebel funding. The difference in results between scenarios SO3 and SO3* in the Somalia experiment demonstrates the potential significance of this addition. Another potential improvement could be the inclusion of spatial data. The model currently does not account for "space" or "territory," which are instead represented through the allocation of Enterprises.
Chapter 2: Inventory of Conflict Data A list of datasets of conflict events and links to the websites where they can be obtained can be found at the end of the chapter "Advances in Data on Conflict and Dissent. " Chapter 4: R-Package Relational Event Models Laurence Brandenberger's R-package for relational event models (REM) can be used to calculate endogenous network effects in event sequences and fit REMs. Using network event sequences (where each tie between a sender and a target in a network is time-stamped), REMs can measure how networks form and evolve over time. Endogenous patterns such as popularity effects, inertia, similarities, cycles, or triads can be calculated and analyzed with the package. Link: https://github. com/brandenberger/rem [170] [171] [172] 180 Granovetter, M. , 114, 120, 123, 143, 151 Grid models, 205 Group formation pattern, 68 Group size, 118, 120, 121, [126] [127] [128] [129] 133, 135 H Hawkes process, [172] [173] [174] [175] 118, 120, 122, 126, 127, 129, 132, 134 Homophily, [77] [78] 118, [120] [121] [122] [126] [127] [128] [129] [131] [132] 134 Human rights, 94, 102, 106   Granovetter's, 114, 120, 123, 126, 143, 144, 151.
This overview is not an exhaustive list but rather a selection of text mining examples in the field of conflict research Author(s) Thematic content analysis using supervised machine learning: An empirical evaluation using German online news. Quality and Quantity, 47(2), 761-773. Schwemmer, C. (2018). Stminsights: A shiny application for inspecting structural topic models [Software-Handbuch]. https://github. com/cschwem2er/stminsights (R package version 0. 3. 0). Stewart, B. M. & Zhukov, Y. M. (2009). Use of force and civil-military relations in Russia: An automated content analysis. Small Wars and Insurgencies, 20(2), 319-343. Stone, P. , Dunphy, D. , Smith, M. , & Ogilvie, D. (1966). The general inquirer: A computer approach to content analysis. Cambridge: MIT Press. Tausczik, Y. R. & Pennebaker, J.
Acknowledgements Acknowledgements Most of the contributors of this book met at the  BIGSSS Summer School  in Acknowledgments The authors would like to acknowledge and thank the  BIGSSS Computational Social Science Summer School on Conflict, Jacobs University , and the  Volkswagen Foundation , Acknowledgements The authors thank the organizers and participants of the BIGSSS summer school for fruitful discussions and feedback,  James A. Kitts  and the  Social Network Group  at the  Sociology Department at the University of Groningen  for insightful comments and feedback, and the  Regional Computing Center of the University of Cologne (RRZK)  for providing computing time on the DFG-funded High Performance Computing (HPC) system CHEOPS as well as support. Rocco Paolillo  completed this work while on the programme  EU COFUND BIGSSS-departs ,  Marie Sklodowska-Curie  grant agreement no.
Acknowledgments The work was initiated on the  BIGSSS Summer School in Computational Social Science 2018 at Jacobs University Bremen  funded by  Volkswagen Foundation  (Grant Number  92145 ). We thank all experts and participants for their feedback. The work is also part of  Jan Lorenz ' project on  Opinion Dynamics  and  Collective Decision  funded by the  German Research Foundation  (DFG grant number  265108307 ) which covered the travel cost for Asgharpourmasouleh and Fattahzadeh.
Results Candidate biomarkers and assay establishment. The markers to be quantified were selected from the list of differentially expressed proteins in plasma of haemodialysis (HD) patients with CKD stage 5 versus CKD stages 2-3 in Glorieux et al. 37  , further considering availability of proteotypic peptides for MS quantification assays, as described in Methods. To avoid extensive pre-fractionation steps potentially compromising assay reproducibility and applicability, only markers with expected relatively high (>100 ng/ml) plasma abundance levels reported in the literature and existing proteomic databases were considered (Supplementary Table  S1 ). The selected marker candidates were B2M, SERPINF1, AMBP, LYZ, HBB, and IGHA1. Following a development phase, involving testing multiple peptides per marker, as described in Methods, standard curves per selected peptide were generated to ensure reproducibility, minimal or lack of matrix effects and linearity in measurements in the range of expected quantified values.
Differential expression analysis for baseline data. Using the established assays, quantification of the selected markers was performed on all baseline samples from 72 patients that were included in the original study. These included patients with CKD stage 5 (n = 33), CKD stage 4 (n = 21), CKD stage 3 (n = 12), and CKD stage 2 (n = 6) at baseline. Thirty-five events (deaths) were observed (mean follow-up time 3. 61 ± 2. 96 years), with n = 34 patients being alive at the last contact date (mean follow-up time 5. 21 ± 2. 77 years). For 3 patients, no follow-up information was available. The main clinical data are summarized in Table  1  and more details can be found in Supplementary Table  S3. Using the established assays, high quality spectra were obtained for all but one sample, where the data for SERPINF1, AMBP and B2M did not pass quality control (representative spectra of SIS and NAT peptides per marker are presented in Supplementary Fig.
A prominent example is the multi-peptide urinary classifier CKD273 being used for the detection of nephropathy at early stages  26, 29. We employed a multiplex MRM mass spectrometry assay for the quantification of six previously described, exploratory CKD markers. MRM, in contrast to the classically employed highly sensitive ELISA assays, does not require the use of specific antibodies, thus facilitating multiplexing and increasing specificity via eliminating antibody cross-reactivity problems  32, 33. MRM has been widely applied in plasma for proteomic biomarker validation  60  and for various plasma proteins (such as apolipoproteins). It has been demonstrated to provide assay performance equal to well characterized ELISA assays  61. In fact, the observed correlations between our MRM data and respective independent immunoassays (Rho = 0.
Selection of peptides for LC-MRM-MS. For the establishment of the MRM assays, proteotypic peptides were selected considering the following criteria: (i) the peptides uniquely represented the target protein, (ii) lysine or arginine was allowed only at the carboxy terminus (no missed tryptic cleavage sites are allowed), (iii) peptide sequence included 6-25 amino acid residues to ensure acceptable ionization and gas-phase fragmentation, and (iv) amino acids that are susceptible to chemical modifications such as cysteine, methionine should be absent. To perform the selection, multiple tools were employed in combination: a spectral library from the National Institute of Standards and Technology (NIST) ["human consensus final true lib", downloaded from http://www. nist. gov/] on 05062014] was imported into Skyline software.
A mixture of the SIS peptides was then added in each sample after drying the peptides as follows: IGHA1: 8000 ng/mL, B2M: 800 ng/mL, HBB: 770 ng/mL, AMBP: 2240 ng/mL, LYZ: 800 ng/mL, SERPINF1: 668 ng/mL. Liquid chromatography was performed using an Eksigent nano-HPLC system, coupled with a C18 analytical column (75 μm × 150 mm, particle size 5 μm, pore size 100 Å) (Thermo Scientific). Peptide separation and elution were performed with a 60 min gradient of 5-90% mobile phase B (80% acetonitrile v/v, 0. 1% FA, 19. 9% H 2 O) at a flow rate of 300 nL/min. Samples were injected into the LC system and loaded on the C18 column. Tryptic peptides were analyzed on an AB Sciex 4000QTRAP with a nanoelectrospray ionization source controlled by Analyst 1. 5 software (AB Sciex). The mass spectrometer was operated in MRM mode, with the first (Q1) and third quadrupole (Q3) at 0.
Standard curve. To define the range and ensure linearity in measurements, a calibration curve for each peptide was generated spiking SIS peptide at different amounts in a CKD pooled plasma sample (CKD stages 2-5, n = 4), in order to maintain the same background matrix to the analyzed samples. Dilution points were selected to cover reported concentration ranges per marker based on the literature and/or reported MS data and each dilution point was analyzed in triplicate. Samples were run in LC-MRM-MS, as described above. Data analysis and quantification. Data analysis was performed using the Skyline software  91  and all chromatograms were manually inspected to ensure the good quality and accurate peak picking. In particular, manual inspection ensured that the extracted ion chromatograms of the stable isotope-labeled standard (SIS) and endogenous (or natural-NAT) peptides chromatographically co-eluted and exhibited identical peak symmetry, shape, and width.
Statistical analysis (correlation-survival analysis). Visualization of the stage analysis was performed by GraphPad Prism version 8. 0. 0 for Windows (GraphPad Software, San Diego, California USA, www. graphpad. com), whereas statistical and survival analysis were conducted in the R (version 3. 6. 1) environment for Windows utilizing base functions from the packages stats, survival and survminer. Significant changes at the levels of continuous variables across groups were determined with the non-parametric Kruskal-Wallis and Mann-Whitney tests. Kaplan-Meier plots were constructed to compare survival between Low and High protein concentration (ng/mL) groups, defined by a median cut-off point per protein, across samples. Survival curves were compared for significant differences (p < 0. 05), from baseline diagnosis to 7 years follow-up time with the Log-rank test.
Similarly, correlation analysis between eGFR and MRM quantifications were performed. Linear relationships between the continuous variables were assessed using Spearman's Rank correlation coefficient and significance was defined at p ≤ 0. 05. Predictive value of the MRM panel. The prognostic value of the quantified proteins was assessed in distinguishing disease status at 7 years follow up time. The k-nearest neighbor classifier was developed with the packages DMwR, and pROC, in the R environment for Windows. In particular, the complete MRM data of 4 proteins (HBB, AMBP, B2M and LYZ) quantified across 69 subjects with CKD were utilized to build the model and parameter optimization was assessed in a 3-fold cross validation setting. In brief, patients were divided into three random partitions (adjusted for a balanced ratio of deceased/censored, stage and class cases), each time using two out of the three partitions for training, and the remaining third as a test set.
On certain datasets, initial contig N50 may be increased up to 4. 5-fold. For smaller vertebrate genomes, chromosome-scale assemblies can be achieved within 12 h using low-cost, high-end desktop computers. Mammalian genomes can be processed within 16 h on compute-servers. Using diverged reference genomes for fish, birds, and mammals, we demonstrate that CSA calculates chromosome-scale assemblies from long-read data and genome comparisons alone. Even contig-level draft assemblies of diverged genomes are helpful for reconstructing chromosome-scale sequences. CSA is also capable of assembling ultra-long reads. Conclusions: CSA can speed up and simplify chromosome-level assembly and significantly lower costs of large-scale family-level vertebrate genome projects.
Relatively simple procedures split primary de novo assemblies at regions with low support (e. g. , non-continuously covered regions in comparisons of 2 de novo assemblies or regions of low read coverage), which is relatively fast, even when dealing with very large genomes. For assembly reconciliation, more sophisticated tools have been developed, e. g. , SMSC  [54]  or BIGMAC  [55] , but these have been rarely tested on large genomes and, according to published benchmarks on small-sized genomes (bacteria, yeast), these modules might take longer than the entire CSA pipeline. Nevertheless, improved faster detection of misassemblies by read re-mapping could further improve assembly quality in future versions of CSA. Another well-tuned example seems our choice of RAGOUT, which can be easily adapted to the maf format output of the fast and sensitive LAST aligner, while similar tools like MeDuSa  [56]  and RACA  [44]  use computationally more expensive (LASTZ in RACA) or less sensitive aligners that do not work well on diverged reference genomes (MUMMER  [57]  in MeDuSa).
The detailed results of these benchmarks are shown in Supplementary Table  S1. In terms of chromosomal assembly completeness, we measured how much of the consensus sequence is contained in the top n largest scaffolds, where n is the haploid chromosome number. All CSA assemblies placed >94% of the consensus into the top n scaffolds (Hs = 97. 5%; Tg = 94. 2%; Sc = 99. 3%). The contig N50 length was 25. 9, 27. 7, and 16. 5 Mb for Hs, Tg, and Sc, respectively. These values outperform the current reference contig N50 for Tg (VGP assembly) and Sc (our own results), which are based on the same long-read input data but included different genome maps and curation steps to improve the assembly. For Hs we compared contig N50 to the so far best assembly from PacBio (Accession: GCA 003634875. 1) data and found that CSA produced similar values, although we used an older dataset (P4C6 chemistry from RSII sequencer) for our tests.
Benchmark Scenario 2: CSA using divergent genomes as reference allows chromosomal-scale assemblies from long reads only Although this approach can be limited by complex evolutionary scenarios involving major rearrangements of genomes, in principle, the mapping steps in CSA have been designed to allow for incorporation of highly diverged genomes as references. Nowadays (and in the future even more) one will find suitable, perhaps distantly related reference genomes for most vertebrate species in databases, and this gives us the opportunity to obtain high-quality chromosome-scale assemblies from long-read data alone-potentially even without having other mapping data at hand (e. g. , Hi-C, optical maps, linkage maps). We tested CSA on the long-read data from above using high-quality reference genomes of species that diverged between 10 and 240 Mya as references.
Benchmark Scenario 3: CSA on a fish genome, integrating ONT reads, 10X Genomics scaffolds, and diverged reference genomes The previous benchmarks used SMRT long-read data and a single reference genome. In the following, we ran CSA using long reads generated by ONT sequencing from genomic DNA of Perca fluviatilis and supported the assembly by a 10X Genomics assembly of the same species and 2 diverged reference genomes. A high-quality chromosomal-scale genome assembly of P. fluviatilis assembled from the same ONT long reads, and Hi-C sequencing has recently become available (BioProject Accession: PRJNA549142, the assembly has been updated recently, note that the reference used here was an earlier version called PFLU1. 1 and is available from the supporting data  [71] ). The highquality draft genome assembled from 10X Genomics sequence data was published earlier (68× Illumina short-read coverage, N50 contig/scaffold length 18.
3 kb/6. 3 Mb according to  [63] ). A chromosomal-scale reference genome for P. flavescens, a close relative (genus-level) of P. fluviatilis, is available  [64] ; both Perca diverged ∼8. 2-17. 5 Mya. As a more distantly related reference (divergence time ∼65 Mya), we used the chromosomal-scale Siniperca chuatsi genome (BioProject Accession: PRJNA513951). To test whether CSA could reach a similar assembly quality as for the P. fluviatilis reference genome, we ran CSA on P. fluviatilis ONT long reads, supported by 10X Genomics scaffolds and P. flavescens and S. chuatsi genomes (see Supplementary Table  S3 ). CSA managed to place 94. 3% of the assembled sequence into 24 large scaffolds, which corresponded to the reference chromosomes. Only a few differences (f: 0; t: 12; i: 13) in chromosomal structure were apparent in the dot plot (Fig.
3 ). CSA increased the contig N50 nearly 3. 1-fold compared to the reference genome. The total time to complete the chromosomal assembly was 5h:30 min, when using 80 CPU threads on a highperformance computing server or 12 h on a high-end desktop computer (12 CPU threads, 128 GB RAM). We polished the CSA assembly using MEDAKA (by ONT  [60] ) and PILON (RRID:SCR 0 14731)  [65]  and performed BUSCO (RRID:SCR 015008)  [66] , which confirmed that the assembly was highly complete on the gene level (Actinopterygii dataset, complete genes: 95. 9%, fragmented genes: 2. 1%, missing genes: 2. 0%, number of tested genes: 4,584). Next, we performed the same runs and omitted the 10X Genomics scaffold data. This only resulted in a slight loss of sequences placed in chromosomes (now 93. 8%; loss 0. 5%) and a few more intrachromosmal translocations and inversions (f: 0; t: 19; i: 15).
Finally, using only the most diverged reference still resulted in 86. 3% of sequence placed in the top 24 scaffolds, but more intra-chromosomal translocations (f: 0; t: 57; i: 16). Yet, improvement of contig N50 was still 2. 7-fold and structural errors in contigs were low (f: 0; t: 0; i: 4). Thus, CSA was able to compute chromosome-scale assemblies by sequentially using species-, genus-and order-level references together with ONT long-read data. The species-level reference (10X Genomics, Supernova assembly) was only slightly contributing to the final assembly owing to its lower N50 scaffold length of 6. 3 Mb. We have observed that ONT long-read datasets of comparable N50 read length and coverage produce less contiguous assemblies than SMRT datasets, possibly due to coverage bias of genomic sequences that interfere with ONT sequencing.