dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank the families for their participation and support. Sequencing was provided by the  University of Washington Center for Mendelian Genomics (UW-CMG ) and was funded by  NHGRI  and  NHLBI  grants  UM1 HG006493  and  U24 HG008956 , by the  Office of the Director, NIH  under Award Number  S10OD021553 , and by the  National Institute of Child Health and Human Development  ( 1R01HD048895  to M. J. B. ). We thank the  Ohio State Rightmire Hall zebrafish  staff for excellent animal care and husbandry and  Mark Nilan  and  Mika Gallati  for zebrafish care and mutant identification at the  University of Maine. We thank  Sarah Shepherd  for assistance during initial mylpfa oz43 construction. Zebrafish work was funded by  NIH  grants  GM088041  and  GM117964  (to S. L. A. ), an  NIH  T32 training grant  NS077984  and an  Ohio State Pelotonia postdoctoral fellowship  (to J.
10 10 Sangersequencing validated homozygosity of the c. 470G>T variant in all affected persons and that the parents in Family A were heterozygous carriers. These results suggested that homozygosity for c. 470G>T in MYLPF resulted in a pattern of multiple congenital contractures virtually indistinguishable from that observed in persons with DA, specifically DA1. In an effort to find additional families with a pathogenic variant in MYLPF and clinical characteristics of DA1, we submitted genetic data and phenotypic information to the MatchMaker Exchange (MME) via the MyGene2 node and identified two additional families (E and F) that had been submitted to the GeneMatcher node. Simultaneously, we queried commercial genetic testing companies and colleagues about families in which MYLPF had been identified as a candidate gene.
This is mainly due to oversimplifications that are introduced in the validation stage. In terms of frameworks and toolsets, a few approaches have emerged in the past few years, mainly from the co-simulation field. For example, in  [13]  DIgSILENT coupled with a custom JAVA-based multi-agent system is used for testing an adaptive power flow control strategy in transmission systems. Other approaches designed for analyzing the performance of wide area monitoring systems, as well as issues related to cyber-security, are shown in  [14, 15]. Mosaik  [16, 17]  is a Python based open source software package maintained and actively developed by OFFIS. Mosaik is a flexible co-simulation framework that enables the coupling of existing simulators and models in order to tackle complex smart-grid applications.
In  [18]  a Simulation Message Bus approach for co-simulation and rapid prototyping of networked systems was proposed. This approach was further developed into Lablink  [19] , an in-house tool used at the Austrian Institute of Technology. While Mosaik is mainly focused on simulator coupling, Lablink is used for coupling laboratory equipment with simulators and other software modules. Similar in nature to Lablink, OpSim is a co-simulation environment actively developed by Fraunhofer IEE  [20, 21]. It allows coupling of both software and hardware through a common message bus. Unlike Mosaik, both  Lablink and  OpSim are proprietary tools and are not available as open-source. VILLAS  [22, 23]  is an open source co-simulation platform developed at the Institute for Automation of Complex Power Systems at Aachen University.
Unlike XMPP and MQTT, HTTP implies direct point to point communication. Apache Thrift is another popular RPC framework that also has its origins at a large tech company, this time, Facebook. Designed for the development of scalable cross-platform and cross-language services, Apache Thrift supports several transport protocols (HTTP, TCP sockets, etc. ) as well as several compact encoding protocols (JSON, binary, etc. )  [35]. A great advantage of both gRPC and Apache Thrift is that they offer a language-agnostic interface definition language (IDL). In both these frameworks, a service is declared as an IDL file which is then processed by a code generator in order to produce language-specific bindings.
The main problem is that, as mentioned earlier, real-time simulators were designed mainly with PHIL or CIL experimentation in mind, therefore, the commercially available models are rather limited in their communication interfaces. In the type of large scale distributed applications that we consider there would be tens or even several hundreds of controllers that need to access data from different parts of the electrical network in a completely asynchronous manner. By default, RTSs do not offer an out-of-the-box solution for dealing with this sort of scenarios. Fig. 3  shows the method that we propose for mitigating this issue. Our implementation is based on OPAL-RT, as it is the RTS that our laboratory is equipped with. However, to our knowledge, the same approach can be used with other RTSs.
As shown in Fig. 3 , OPAL-RT is in charge of emulating the physical system. The figure shows our study case, which is a dc electrical network, however, any other dynamical system that fits in the computation limits of the real-time simulator could be used. In order to allow external controllers to interact with the physical system, we propose to run in parallel with the real-time process an additional process on the OPAL-RT in the form of a data server. The data server keeps two internal buffers, one for inputs and one for outputs, and acts as a gateway into the physical system. Any request from the controllers is being handled using  the information stored in these buffers, which are periodically synchronized via UDP with the information from the real-time process. While this approach allows for a large number of controllers to access information from the real-time process, it introduces an additional layer between the controller and the physical process.
Firstly, its Linuxbased operating system opens up the possibility of using high level programming languages such as Python, R, or Octave and provides easy access to a large collection of open-source scientific computing libraries. This greatly simplifies the process of porting ideas and concepts from other scientific communities, such as machine learning or artificial intelligence, to smart-grid applications. Performance wise, the model used in our setup is equipped with a 1. 2 GHz quadcore processor and 1GB of RAM, which makes it a suitable candidate for embedded applications of medium complexity. The controllers and the inter-controller communication, as well as the interface with OPAL-RT, were developed using Python. We selected Python because it is a powerful scientific programming language that allows fast prototyping and concept validation by providing access to a large collection of libraries and modules.
The first choice for covering this type of scenarios is to use a communication network simulator such as OMNET++  [42]  or gns3  [43]. However, since our framework involves real-time and physical connections between a relatively large number of hardware components, using a simulator is not feasible. Rather than a simulator, one would need a network emulator for this task. Specialized solutions such as CORE  [44]  exist, and even some simulators can be used in emulation mode. Setting up such a configuration requires additional hardware and software overhead. However, if a detailed representation of the ICT network is required then this is the only feasible solution. The method that we propose does not include a detailed representation of the ICT network. However, it has the great advantage that it introduces no overhead and it uses only the components that were already introduced as part of the system.
Mainly, since the RPIs used for the controllers are powered by a Linux based operating system, their kernel comes prepackaged with NetEm  [45] , a software package that provides network emulation functionality. Making use of NetEm we developed an approach for shaping the traffic between the controllers so that it emulates different network conditions. Our solution is based on the traffic control utility (TC)  [46]  and NetEm, and it was inspired by the methodology Facebook engineers use for testing services under various network conditions  [47]. By using TC at the level of each ethernet card in our fleet of controllers we can set different rules and filters (so called queuing disciplines or qdiscs) in order to enforce different latencies, packet losses, and data rates onto the communication channels.
As described in the previous section, the physical system together with the primary control layer were implemented in OPAL-RT. The system is initially modeled using Simulink/SimPowerSystems components then compiled and executed on the real-time target with a time step of 100 us. The data exchange between the data-server and the process of the physical model is triggered every 10 ms. Meanwhile, the secondary controllers were deployed on the RPIs as Python scripts. Since optimization is required at every iteration of the ADMM algorithm, every controller requires access to a local solver. In this regard we used the NLopt non-linear optimization package  [49]. While NLopt provides several solvers, the one that provided the best tradeoff between accuracy and performance for our problem was the COBYLA (constrained optimization by linear approximations) algorithm  [50].
General performance of the framework We start by providing some general performance metrics for the proposed framework. The data shown in Fig. 5  was compiled from several thousands samples, obtained from different operating scenarios. Firstly, the time it takes for the controllers to access data from the OPAL-RT (A2O), as can be seen from Fig. 5 , is smaller than 3. 5 ms. This is the delay between the controller and the data-server that runs on OPAL-RT. Secondly, there are two time metrics for the RPCs, i. e. , the time it takes for information to flow between controllers. In the first one, gRPC wired , the controllers communicate over an 100 Mbit ethernet network, i. e. , the RPIs communicate through their wired ethernet card, and the traffic is not shaped in any way through the TC utility.
Discussion and conclusions As mentioned in the introduction, at the moment there are no readily available tools for evaluating the performance and behavior of distributed control systems in real-life scenarios. While the proposed framework extends the available options in this regard, it does have its shortcomings. In the following paragraphs we discuss its applicability as well as possible bottlenecks and limitations. The first obvious limitation is imposed by the interface between the controllers and OPAL-RT. As mentioned earlier, the data server introduces a 14 ms delay between the physical signals and the measured signals at the controller level. Even with additional optimization, it is highly unlikely that this delay can be decreased below the ms range for large scale systems. Therefore, this solution cannot be applied for investigating fast primary controllers, such as current or voltage loops.
Ploidy Evaluation by Flow Cytometry Cell lines were resuspended in PBS 1X and fixed with 70% ethanol overnight at -20 • C. Then, cells were washed and a solution of propidium iodide (Sigma Aldrich, 50 µg/mL) and RNAse A (ThermoFisher Scientific, 10 µg/mL) was added for 30 min at room temperature. DNA content was quantified with propidium iodide fluorescence intensity by flow cytometry (MACS Quant VYB, Miltenyi) and analyzed using FlowJo software (BD Bioscience, Franklin Lakes, NJ, USA).
RNA Extraction, Sequencing and Analysis Total RNA from cell lines was extracted with TRIzol reagent (Life Technologies) and purified with the RNeasy MinElute Cleanup Kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. RNA concentration was evaluated with Clariostar and quality-checked with an Agilent 2100 Bioanalyzer (Agilent Technologies). RNA sequencing was performed by the Centro Nacional de Análisis Genómico (CNAG, Barcelona, Spain) to obtain more than 20 million paired-end reads with a length of 75 bp. Subsequent CNAG RNA sequencing steps, ERCC spike-in sequencing control quality check and bioinformatics analysis pipeline have already been described  [48]. Thereafter, genes with a FPKM mean across all cell lines superior to 1 were used to conduct a principal component analysis (PCA) using R package FactoMineR.
Gene expression was evaluated for the following groups: (1) A8 CFP and A8 Tomato, (2) H2 EP, H4 EP and H6 EP, (3) H2 LP, H4 LP and H6 LP, 4) H2 LP Tumor 1, H4 LP Tumor 3 and H6 LP Tumor 2. Then, Pearson's correlation was established from group 1 to group 4. Genes with a Pearson's coefficient ranges between 0. 9 to 1 and -0. 9 to -1 were selected to make a cluster analysis of z-score gene expression (https: //github. com/obigriffith/biostar-tutorials/blob/master/Heatmaps/heatmap. 3. R). Finally, six groups of genes were identified and a functional enrichment analysis of gene ontology was performed using Bioconductor packages goseq and org. Hs. eg. db. 4. 11. Genomic DNA Extraction, Cytoscan HD Array and Genomic Index Calculation DNA extractions were performed on cell lines and frozen tumor samples.
Genomic DNA was extracted using the standard phenol-chloroform method and quantified using Clariostar. DNA arrays were performed using Affymetrix CytoScan HD Arrays (Affymetrix, Santa Clara, CA, USA) according to the manufacturer's instructions. Analysis was performed using the Chromosome Analysis Suite software (Affymetrix) with genome version GRCH37 (hg19). Genomic index was calculated as follows: GI = A 2 /C where A corresponds to the total number of alterations and C to the number of chromosomes affected by these alterations. To compare genomic index of the different groups, a Mann-Whitney test was performed. Losses and gains of each sample were defined with an average log2(ratio) (Smooth signal) ≤ -0. 2 or ≥ 0. 2, respectively. Thus, penetrance plots were obtained for four subgroups: EP hybrids, LP hybrids, EP tumors and LP tumors.
Whole Genome Sequencing and Structural Variant Analysis DNA of H2 LP Tumor 1, H4 LP Tumor 1 and H4 LP Tumor 3 were sequenced at CNAG. Whole genome sequencing characteristics were paired-end sequencing with 150 bp reads and an expected coverage of 45×. Sequenced reads were aligned on Human GRCh38/hg38 genome with BWA mem v0. 7. 17  [49]  and PCR duplicates were removed with MarkDuplicates (PicardTools v2. 18. 2, http://broadinstitute. github. io/picard/). Thus, genomic rearrangements were established with GRIDSS  [50]  v2. 0. 0 and filtered to keep those occurring in one tumor sample, using bcftools (https://samtools. github. io/bcftools/bcftools. html) v1. 6 with RAS > 0, AS > 0 and QUAL > 0 criteria. A Circos plot  [51]  was performed with array-CGH average log2ratio and structural variants.
PCR For PCR, primers were designed using the Primer 3 program (http://frodo. wi. mit. edu. proxy. insermbiblio. inist. fr/primer3/). The touch-down (TD) 60 • C program was used (TD 60 • C; 2 cycles at a 60 • C, followed by 2 cycles at 59 • C, 2 cycles at 58 • C, 3 cycles at 57 • C, 3 cycles at 56 • C, 4 cycles at 55 • C, 4 cycles at 54 • C, 5 cycles at 53 • C, and finally 10 cycles at 52 • C). PCR was performed on 50 ng of DNA using AmpliTaqGold ® DNA polymerase (ThermoFisher Scientific).
Acknowledgments: The  Fondation ARC pour la Recherche Contre le Cancer  (T. L. ),  Fondation Recherche Médicale  (L. L. , L. D. ),  Association Pour Corentin  and  Phil'Anthrope  (C. M. ), supported this work. We acknowledge the personnel of  CREFRE US006  and  Animal Facility A2  for their technical assistance. We acknowledge  Bénédicte Chazaud  for cell lines of immortalized human myoblasts.
Since M2-polarized macrophages are known to relocate in the vicinity of blood vessels and contribute to blood vessel dysmorphia, we looked into whether surviving GFP + macrophages would associate with the blood vessels in the tumor. Tumor blood vessels were identified by CD31 staining and the distances from each macrophage to the nearest blood vessel were determined with the help of a customized Python script (for details, see Materials and methods). Consistently with the observations in Figure  5A , in 2 + 2 weeks secondary tumors GFP + surviving cells are located significantly closer to the blood vessels in comparison to their GFP -F4/80 + counterparts. A similar difference between surviving and host-derived TAMs is also observed in the 4 + 2 weeks secondary tumors. However, in 4 + 4 weeks tumors there is no significant difference anymore between the distance to blood vessels of GFP + surviving macrophages and GFP -host macrophages.
Acknowledgments We are grateful to  Dr Pavel Nedvetsky  for providing critical comments on the manuscript, to  Lisse Decraecker  for support with mouse work, to  Dr Fabio Stanchi  for the stably labeled CT-2A-BFP cells and tumor inoculation expertise, to  Magdalena Parys  for technical support with flow cytometry experiments, and to  Dr Joanna Kalucka  and  Dr Florian Rambow  for invaluable advice. Flow cytometry was performed by the  VIB-KU Leuven FACS Core Facility. CT-2A cells were kindly provided by  Dr Thomas N. Seyfried  ( Biology Department, Boston College, USA ). Cx3cr1 GFP/GFP breeders were kindly provided by  Dr Guy Boeckxtaens  ( KU Leuven ).
Immunofluorescence staining Formalin-fixed paraffin or snap frozen OCT-embedded skin sections (5-mm thickness) from TghKLK14 and WT mice were used. H&Esafranin staining and immunofluorescence staining were performed on paraffin-embedded skin sections using standard histologic procedures. Dsg3, Dsg4, Cd3, Cd4, Cd8, and NIMP-R14 stainings were performed on skin cryosections. Antibodies used in this study are listed in Supplementary Table  S2. Isotype controls were used for each experiment. Nuclei were stained in blue with 1 mg/ml DAPI. Image acquisition was performed using Leica TCS SP8 SMD confocal microscope and pictures were further processed using ImageJ software.
Transmission electron microscopy Skin samples were fixed in 4% paraformaldehyde and 2% glutaraldehyde, washed in Sorenesen buffer, post-fixed in 2% osmium tetroxide, dehydrated in graded ethanol solution, and embedded in epon. Skin sections were counterstained with 4% aqueous uranyl acetate and Reynolds lead citrate before imaging using a 1011 transmission electron microscope (Jeol, Tokyo, Japan), coupled to an Erlangshen CCD camera and a Digital Micrograph software (Gatan, Pleasanton, CA).
ACKNOWLEDGMENTS The authors are grateful to the  Mouse Clinical Institute (Department of Genetic Engineering  and  Model Validation ,  Institut Clinique de la Souris ,  MCI/ ICS, Illkirch, France ) for generating the TghKLK14 mice. The pH3700-pL2 plasmid containing the involucrin promoter was a gift from  Julia Segre  ( National Human Genome Research Institute, Bethesda, MD ). The authors thank  Alain Schmitt  for the access to the electronic microscopy platform at  Cochin Hospital. The authors acknowledge  Pierre Bonnesoeur  for his help with silver staining. This project received funding from the  Fondation des Maladies Rares  and the  European Union 's  Horizon 2020 research and innovation program  under the  ERA-NET Cofund action N 643578"  ( E-Rare-3 JTC 2015 ) ( KLKIN  project, coord.
Model Development. A patient-specific 3D geometric anatomical model was reconstructed based on medical images that were obtained by computed tomography angiography (CTA, Siemens). e CTA images of the patient, whose information was anonymized in this research, were provided by Anzhen Hospital with 512 × 512 pixels of each image, 450 slice images, and 1 mm gap between adjacent slice. is study has been approved by the Medical Ethics Committee of Beijing Anzhen Hospital and Tohoku University. e construction processing of the 3D model was done by using Mimics (Materialise NV, BE) through both manual and threshold segmentation. ree different diameters of CAA, including the diameters of two, three, and five times compared with the adjacent coronary artery, were applied to the left anterior descending artery by using Geomagic Freeform (3D system, US) software.
Computational Model and Properties of Fluid. e finite element analysis method will be adopted in which all models need to be preprocessed, including preprocessing of the inlets and outlets and the smooth processing of the area of interest. Preprocessing models need to be divided into meshes, which were hexahedral meshes that were controlled by means of size control with the software ANSYS Meshing (ANSYS, US), to generate the fluid computational models. e mesh of the area of interest is refined in order to get more accurate results in these areas. e mesh independency was analyzed to guarantee the reliability of the simulation results, which needed the number of elements and nodes to be large enough  [25]. Table  1  shows the results of the mesh independency test. e differences in WSS between the chosen mesh were less than 0.
Acknowledgments is research was supported by the  National Natural Science Foundation of China  (nos. 11832003  and  11772016 ), and  JSPS KAKENHI  (no. JP18K18355 ). is research was partially supported by the creation of a development platform for implantable/wearable medical devices by a novel physiological data integration system of the  Program on Open Innovation Platform with Enterprises, Research Institute  and  Academia (OPERA)  from the  Japan Science and Technology Agency (JST).
Timing of the study We decided to start our experiment in mid-June to exclude background seedling mortality from the dataset, since seedling establishment can vary considerably within and among sites  (Rothrock et al. 1993). Furthermore, A. artemisiifolia has a long germina-tion period  (Kazinczi et al. 2008 ) and possesses high variability in germination rate and onset even among individuals of a population  (Fogliatto et al. 2019). Thus, starting our experiment in mid-June allowed us to include plants that had germinated later and thus to cover a larger range in plant size.
Acknowledgements We are grateful to the land owner  Andrea Airoldi, the communes of Magenta and Magnago , and the  University of Torino  for allowing us to conduct experiments on their properties. Rudolf Rohr  and  Peter Stoll  provided valuable contributions to the statistical analyses. We thank  Yvonne Buckley  for reviewing the manuscript, which led to considerable improvements. The study was funded by the  EU   COST Action FA1203  ' Sustainable management of Ambrosia artemisiifolia in Europe (SMARTER )', the  Swiss State Secretariat for Education, Research and Innovation  (# C14. 0063  to U. S. and # C13. 0146  to H. M. S. ), the  Swiss National Science Foundation  (# 31003A_166448  to H. M. S), and the  Pool de Recherche of the University of Fribourg  (to H. M. S. and B. A. ). Suzanne Lommen  has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  786624.
METHOD DETAILS Live Imaging Embryos laid overnight at 25 C were collected in cell strainers (Falcon), dechorionated with bleach (Jangro), washed repeatedly with water and embryos at developmental stage 12 or 15 were mounted between a glass slide and a supported coverslip in droplets of VOLTALEF oil (VWR)  (Evans et al. , 2010b). Z stacks (20x 0. 5 mm slices) of the ventral macrophages were then acquired using a spinning disc confocal microscope (Perkin Elmer Ultraview) with a plan-apochromat 63x objective with a NA of 1. 4 and a Hamamatsu C9100-14 camera. The acquisition software used was Volocity (Perkin Elmer). Epithelial apoptosis was induced by targeted UV-irradiation using the FRAPPA unit (PhotoKinesis TM module) on the UltraVIEW spinning disc system. Global, Gal4-independent expression of an F-actin probe (GMA/mCherry-Moesin) delineated the epithelial boundaries and was used to target individual cells.