dataesr/scientific-paragraphs-categorization huggingface dataset:
Second we have calculated the common subset of nodes for all layers. Some layers have been discarded from the analysis since they do not have enough observations. A more detailed discussion of our two steps strategy is provided in the Appendix, section Data selection. As a result we selected a multiplex of 19 layers with the same set of 112 nodes in each of them, sampled at two time periods around 2003 and 2010. Table  1  shows the features of our final selection and in "Summary statistics" section we describe the content of each layer. By looking at the distribution of weights and at the density of each layer we can see that, even though balancing our two cross sections on the same set of nodes and layers ensures that we can make meaningful comparisons before and after the Crisis, some further steps are required to normalize the content of the dataset, across multiple and heterogeneous dimensions.
Summary statistics Table  1  reports the list of selected networks in our multiplex, with the short name of each layer used in our dataset in the first column and some summary statistics for each layer of the multiplex: number of nodes and edges in each of the cross sections analyzed, symmetry and weight checks on the edges and actual years used to construct the database. Layers can be classified in six categories according to the type of international relationship they represent: • trade: trade agreements among countries (fta_wto), commodity and services exchanges (expv, serv_exp) and arms transfers (arms) • investment: foreign direct investments (FDI, FDI_Greenfield), total portfolio investments (TPI) (invest), value of mergers and acquisitions (M&A) (value), international aid (aid) and international bank loans (BIS_flow_claims) • human mobility: movement of individuals between states as measured by permanent migration in flows and stocks (migration_flow, migration_stock) and temporary mobility, i.
They come from separate sources collected in the the SIPRI database. Financial layers too have edges representing monetary flows, such as FDI, TPI and M&A. It is worth noticing that FDI flows are reported twice, since we integrate OECD data with a separate source (FDI_Greenfield) collected in  Kirkegaard (2013)  focusing on specific type of firms which are difficult to measure in the official FDI statistic. Given the extent of this second source and the relevance of its content (which differs significantly from OECD) we added it as a separate source. As for BIS flows, which represent data collected from the Bank of International Settlements on loans among international banks, we selected only the claims layer, which is also automatically adjusted for changes in exchange rates. Finally, flows of international aid constitute another exception.
Both measures are collected by the United Nations statistical division. A third measure, flows of migrants among countries, has been produced for every five years from 1990 to 2010 by  Abel and Sander (2014)  through estimates based on the UN stock data 6. Knowledge flows are represented by citations of patents and papers by countries. Patent citations come from the NBER database and refer to the country of patent inventors. Paper citations instead have been collected by  Pan et al. (2012)  and represent an average over a five year period. These layers have a common unit of measurement: each edge between two nodes represents the number of papers/patents of the destination country cited by the source one. The last two layers are cable route capacity and diplomatic alliances. The first is a measure of the common telecommunication infrastructure linking two countries: each edge represents how many terabyte of data can travel between two countries via cable connection.
Preliminary results Our dataset contains 19 different layers representing heterogeneous relations among countries. Our objective is to show how different information sources can be integrated in order to obtain synthetic measures of importance of countries and which are the possible differences among these measures. Hence we have focused on using the full set of layers at our disposal, discarding unsuitable or repeated layers before constructing the multiplex (more details on this in "Data selection" in the Appendix). However another possible question would be to check how much of the final results is correlated with the single layer composing our network, i. e. to investigate the redundancy of some layers in determining the multilayer centrality of the nodes. The intuition would be that if layers are heavily correlated among themselves the analysis could be carried on by removing some of them without loosing explanatory power.
We can see that there is some redundancy in the dataset and that the number of required is almost half the one we are analyzing in our work for all the centrality measures. Moreover we have regressed the multilayer centrality measures against the principal components of their single layer versions, added progressively one by one. The blue line in Fig. 4  show how the Mean Squared Error of the regression behaves. We can see that it roughly agrees with the explained variance result and reaches a minimum for a number of component near half the ones we are using (even though with more variability). All in all, we find that it exists some level of redundancy among the layers in our dataset and that a more parsimonious choice of layers could deliver almost similar results than Fig. 4  In red: number of principal components sufficient to explain 90% of the variance of the measures of centrality calculated on the single layers (scale reported on the right y-axis).
Conclusions In this work we have collected a new dataset of international network measures, measuring the connections between countries over different financial and non-financial dimensions and using multilayer network analysis for the first time. We have computed different centrality measures and we have shown how different aspects of the data can be highlighted with little change in the parameters of the algorithms and how the resulting change in ranking can be interpreted. Next, we have compared the ranking of countries obtained by network measures and a common measure of macroeconomic performance, such as pcGDP. We have found a satisfying correlation for some of the algorithms, the MultiHub score especially, both at the static cross section level and for the change in pcGDP. To show the differences between network measures and pcGDP, we have analyzed how each country development trajectory compares to changes in network centrality.
Data selection Reporting agencies collect data on a fixed set of countries in different years. Hence an empty report would not imply the absence of the country from the dataset but for a given year only. To translate in network theory terms: there may be some nodes in a network whose edge weights with respect to all other nodes are 0 at a certain time t i , but are still present in the network with positive edge weights at other t j with j = i. These nodes belong to the network and we call them isolated nodes at time t i. On the other hand, missing nodes don't have link to other nodes at any t. To tell if the absence of a certain node from a specific year means its absence in the overall layer we needed to define when a node is part of our dataset. To do that we have made the following assumption: if a node is present as origin or destination at least once in the whole reported set of years for a certain layer, that node has to be considered as existing in the dataset.
13 To calculate the set of existing nodes in each layer we have used the whole span of its observations in our dataset, hence not only years 2003 and 2010. Finally, to get a constant set of nodes across all the multiplex, the existing nodes of each layer must match across all the others. The result of this process is a set of 112 nodes common to all our layers. By referring to the nodes column in Table  1  we can get a good approximation of this process: we define the set of nodes for each layer as the union of the existing nodes in the years 2003 and 2010. Hence their size corresponds to the maximum number between t0 and t1 in Table  1  and ranges from 211 in the free trade agreement layer to 124 in the alliances layer. This leaves us with 19 different sets of nodes, one for each layer, partially overlapping.
Hence we have reversed negative flows when possible, i. e when the whole layer showed a consistent pattern of reversed flows and there were no duplicates after reversion. If instead most negative weights seemed randomly distributed or reverse flows were already recorded in the dataset we have dismissed the negative values as computational errors. 14 nother type of requirements we imposed is to have edge weights not smaller than one, in order to avoid problems with eventual filtering operations. As one can see from the minimum weight column in Table  A1  several layers have fractional values. Most of the time this was due to the unit of scale employed, hence it has been sufficient to rescale them with the right multiplier. This is the case for FDI, FDI_Greenfield, BIS_flow_claims, aid, value, serv_exp and totIC layers.
Background: Patient-reported outcomes (PROs) can be of great value for both research and chronic disease management. We developed a new module of the ANRS CO3 Aquitaine cohort study's Web-based data capture and visualization solution (APPEGE 2. 0) for the collection of electronic PROs among people living with HIV cared for in Nouvelle Aquitaine, France. Objective: This study aimed to evaluate the usability of 2 successively developed prototypes of ARPEGE 2. 0's electronic PROs module before launching a pilot study, owing to the novelty of the proposed data collection method for our setting and specific characteristics of the target population. Methods: A total of 2 sequential rounds of empirical, task-based usability evaluations were conducted, involving 8 research staff and then 7 people living with HIV.
0) was developed in 2017  [15]. As the overall usefulness of interactive health care applications or their usability is likely to affect their acceptability and adoption, usability evaluations of 2 successively developed prototypes of the ARPEGE 2. 0 solution were conducted in preparation for a pilot study  [15]. The content of ARPEGE 2. 0's initial electronic PRO module is based on current treatment guidelines for people being treated for HIV and associated comorbidities  [16]. Prototyping was carried out over 2017 with the support and regular feedback from a working group comprising research staff, local stakeholders, and end users (clinicians and patient representatives). The questionnaires were evaluated individually according to their psychometric properties, administration method, and length.
Acknowledgments Alain Volny-Anne  (European AIDS Treatment Group) and  Eugenie Destandau  provided valuable feedback on the design and content and the QuAliV website and electronic PRO module. The authors would like to thank all of the evaluators, both research staff and patients, who contributed to this study. They thank the scientific committee, clinical sites and their investigators, and clinical research associates, further cited, who have been involved in the successful implementation of the subsequent phase of the study.
Therefore, we developed a special software module (called exposure visualization and distance emission (EVADE)) for this purpose. 15 The software augments image guided drilling during AP with 'distance control' and 'virtual drilling' feedback. Distance control continuously computes the distance between the drill tip and important structures (eg, the cochlea, internal acoustic meatus, and internal carotid artery) and emits audiovisual warnings when the drill tip comes within a protective perimeter set at a certain distance around these structures. 16, 17 he system can supply audio feedback through 1 of 2 modes. Mode 1 is a beep followed by a human voice stating the name of the structure approached. Mode 2 consists of a beep, with different tones for each structure (similar to a neuromonitoring device).
The surgeon can adjust the system's sensitivity per individual structure (by adjusting that structure's protective perimeter distance, which can be set to any value ≥ 1. 0 mm), the warning mode and the audio volume, at any time during surgery (Video). VIDEO. This video showcases the 2 different distance control warning modes of the EVADE system. Mode 1 is a beep followed by a human voice stating the name of the structure approached. Mode 2 consists of a beep, with different tones for each structure. Furthermore, it shows the employed experimental and clinical setups. Virtual drilling updates the preoperative computed tomography (CT) scan in (near) real time to show virtual bone drilling, enabling the surgeon to see the current extent of the drill cavity during surgery (see Figure  1 ). Our hypothesis was that usage of the presented software improves the anatomical orientation of surgeons, thus reducing the risk of drill damage to anatomical structures during AP, while maintaining, or even improving surgical performance.
The aim of this article was to investigate this hypothesis, and furthermore evaluate the accuracy and clinical feasibility of the software during AP. Efficacy was evaluated in a trial comparing surgical performance in a population of skull base surgeons performing AP either with standard image guidance and EVADE, or with just standard image guidance, in a cadaveric simulated surgical setting. Second, we researched whether virtual drilling feedback was accurate. Third, we investigated the clinical feasibility of the software during AP in a case of petroclival meningeoma.
Instructions and Task All surgeons were introduced to the feedback functions of the EVADE image guidance software module via a standardized presentation before the start of the experiment. The presentation instructed the surgeons about the experimental task. Their task was to make the largest possible bony exposure through the petrous apex, drilling as close as possible to the cochlea and meatus without injuring these structures. Surgeons were free to expand their exposures laterally. However, they were instructed to maintain the same kind of expansion for both approaches (on both sides). Moreover, surgeons were allowed to vary the protective perimeter value during the experiment. Completion of the task was defined as follows: the experiment ended when the surgeon believed that no more bone in the petrous apex could be drilled without injuring the cochlea or meatus.
AP Trial Outcome Measure Definitions Postoperatively, the cadaver heads were rescanned using high resolution temporal bone CT. Subsequently, the postoperatively acquired temporal bone scans were registered to the corresponding preoperative temporal bone scans. The drilled cavities were then delineated semiautomatically by an inhouse algorithm developed and validated (data not shown) for this purpose. The relevant anatomical structures, being the cochlea, the internal acoustic meatus, the carotid artery, and the semicircular canals were delineated manually slice-by-slice on axial scans, and subsequently checked in 3 orthogonal directions and through 3dimensional (3D) rendering. The drill cavity and anatomical structure segmentations were converted to 3D surface meshes without loss of resolution.
AP Trial Results Three right-sided APs and 2 left-sided APs were performed with EVADE. Vice versa for the control group. The total time needed to complete the AP task was on average 7 min longer (on a total of 74 min) in the group using EVADE compared with the control group (P =. 69). The drill-on time, the drill cavity volumes, and the drill speed were similar in both groups. Surgeons using EVADE approached the cochlea on average 1 mm closer, as average minimum distances were 1. 1 ± 0. 7 mm in the feedback group compared with 2. 2 ± 1. 4 mm in the control group (P =. 19). The meatus was approached on average 0. 8 mm closer in the group using EVADE (minimum distances of 0. 2 ± 0. 4 mm vs 1. 0 ± 1. 2 mm P =. 21). The data for the carotid artery and semicircular canals were calculated but are not reported here, because they were similar between groups.
The 'underused area' (see Methods: AP Trial Outcome Measure Definitions) was 0. 1 ± 0. 1 mm 2 in the EVADE group and 0. 9 ± 1. 2 mm 2 in the control group (P =. 22). The basal turn of the cochlea was inadvertently opened by the drill on 1 occasion in the control group. The cochlea and meatus were not injured in the feedback group. See Table  1  for a group-wise comparison of outcome measures. Surgeons rated EVADE feedback as very safe (4. 6/5). They rated the usefulness of the module's visual feedback in locating anatomical structures as average (3. 2/5), and the audio distance feedback as excellent (5. 0/5). Surgeons rated their intraoperative anatomical orientation statistically significantly better with the feedback system as compared to standard image guidance (4. 6/5 vs 3. 4/5 P =. 03).
Acknowledgments First and foremost, we express our thanks to the skull base surgeons who volunteered to participate in our research. Furthermore, we kindly acknowledge  Prof. Dr Ronald Bleys  from the department of anatomy for providing the cadaver heads necessary to complete this research. We also like to thank  Glenn Bronkers  and  Saskia Redegeld  for their help in setting up the experiments in our cadaver lab. Additionally, we acknowledge  Rick Mansvelt Beck ,  André van Dieren ,  Sander van Thoor ,  Harry Teirlinck , and  Claartje Beks-Ypma  from the  Brain Technology Institute  for their technical assistance and advice.
Background: Accurate structural annotation of genomes is still a challenge, despite the progress made over the past decade. The prediction of gene structure remains difficult, especially for eukaryotic species, and is often erroneous and incomplete. We used a proteogenomics strategy, taking advantage of the combination of proteomics datasets and bioinformatics tools, to identify novel protein coding-genes and splice isoforms, assign correct start sites, and validate predicted exons and genes. Results: Our proteogenomics workflow, Peptimapper, was applied to the genome annotation of Ectocarpus sp. , a key reference genome for both the brown algal lineage and stramenopiles. We generated proteomics data from various life cycle stages of Ectocarpus sp. strains and sub-cellular fractions using a shotgun approach.
Background Proteomics and genomics data combined with bioinformatics tools, known as proteogenomics  [1] [2] [3] , is a valuable strategy to improve genome annotation  [4] [5] [6]. Proteomics methods and applications have been reviewed by Nesvizhskii  [7]  and more recently by Menschaert & Fenyö and by Ruggles and collaborators  [8, 9]. Proteomics data provides direct access to amino-acid sequences that can be mapped onto translated genomic sequences  [10, 11]. The combined use of experimental proteomics data and genomic sequences is a powerful way to: i) confirm gene-model predictions, ii) correct possible intron/exon boundary errors or wrong start/stop codons, and iii) find new CDSs that have not been computationally predicted by machine learningbased approaches or homology searches. Many studies have demonstrated the use proteomics datasets to provide protein-level evidence of gene expression and refine gene models  [3, 12].
We developed Peptimapper using the genome of Ectocarpus sp. , a key reference genome for both the brown algal lineage and stramenopiles. For each biological sample, we generated the MS/MS spectra file in Mascot Generic File (MGF) format using conventional proteomics software (i. e. , Mascot Distiller, Proteome Dis-coverer™, etc. ). We used the "sequence tagging" approach  [10] , in which a PST is defined by a small sequence tag (usually three or four amino acids) and the two flanking (N-and C-terminal) masses (Fig. 1b ). The bioinformatics steps are shown in Fig. 2. PSTs were generated de novo from the MS/MS spectra information of the MGF. After testing several PSTs generation tools (see Step 1: From MGF files to PSTs), we decided to adapt an existing tool: PepNovo + 3. 1 beta  [26]  (LXRunPepNovo).
PSTs were then mapped on the six-frame translations of the genome sequence, resulting in a list of hits. A hit is defined as the location of a PST on the genome sequence. Finally, closely located hits were clustered to identify regions potentially associated with genes or, at least exons. This was achieved by testing (see Step 2: PST mapping and clustering below) and bundling three modules of the PepLine software (PMTrans, PMMatch, PMClust) into one script: LXPepMatch. By cross-checking results using a classical database-driven proteomics approach, we tested and optimized step 1 and step 2 modules by varying settings according to known Ectocarpus sp. genome features (downloaded from ORCAE, a public database: http:// bioinformatics. psb. ugent. be/orcae/overview/Ectsi) and using as input a subset of reference spectra (see MS/MS reference datasets building).
The workflow construction is detailed below (see section Workflow construction: step-by-step). The last step consisted of classifying clusters generated in step 2, according to their annotation and confidence level using the script we developed for this purpose: LXQualify. Clusters on the genome were visualized by implementing another specific script, LXClust2Gff. It wrote cluster locations generated by LXPepMatch into GFF files for further integration into a genome viewer (i. e. , Artemis (Sanger Institute, England)  [27] ). Results were validated by manually comparing various clusters of interest with EST and RNA-Seq data (see workflow building step by step, step 3 section, below). From biological samples to MS/MS spectra files (MGF files) We extracted various subcellular samples (cell walls, cytoplasm, nuclei, membranes) from various life cycle stages of Ectocarpus sp.
The mass spectrometer was operated in the data-dependent mode by automatic switching between full-survey scan MS and consecutive MS/MS acquisition. Survey full scan MS spectra (mass range 400-2000) were acquired in the Orbitrap section of the instrument with a resolution of r = 60,000 at 400 m/z; ion injection times were calculated for each spectrum to allow the accumulation of 10 6 ions in the Orbitrap. The seven most intense peptide ions in each survey scan with an intensity above 2000 were sequentially isolated and fragmented in the linear ion trap by collision-induced dissociation. For Orbitrap measurements, an external calibration was used before each injection series to ensure an overall error mass accuracy below 5 ppm for the detected peptides. MS data were Fig. 2  Steps of the bioinformatics workflow saved in RAW file format (Thermo Fisher Scientific) using XCalibur 2.
Building the MS/MS reference dataset For each step of Peptimapper, we developed specific tools or used or adapted existing tools. We built three reference datasets for their assessment. These were obtained by a classical database-driven proteomics approach. Peptides were identified using Proteome Dis-coverer™ 1. 2 software supported by the Mascot search engine (Mascot server v2. 2. 07; http://www. matrixscience. com), using its decoy strategy. This software matches each MS/MS experimental spectrum (RAW file) against a database comprising all theoretical MS/MS spectra calculated for every possible peptide from an in silico digestion of Ectocarpus sp. gene model proteins (downloaded from ORCAE, https://bioinformatics. psb. ugent. be/gdb/ ectocarpus/, Ectsi_prot, 2010, 16,533 sequences). Mass tolerance was set to 10 ppm and 0.