dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledging that the graded Ph-dpp expression at this stage obscured the precise limits of its expression, this analysis suggested that the region of high/moderate Ph-dpp expression was localized to rows E4c, E4d and E5a that mostly contribute to the presumptive dorsal compartment, while low/no Ph-dpp expression could be detected in the prospective ventral rows E4b anteriorly and E5b posteriorly (Figure  7A'' ). Furthermore, the Ph-dpp concentration gradient faded towards the medial (prospective ventral) columns and the border between high/moderate and low/no expressing cells was located in descendent cells from column 4 as also predicted by our in silico cellular analysis (Figures  7A-7A'' ). During the next 12 hrs (S19, 108 hrs AEL), the domain of strong Ph-dpp expression was more localized in the proximal-distal row of anterior-dorsal cells abutting the AP compartment boundary (Figures  7C-7C '' and Figures  S10C-S10C' ).
MaMuT, a user-friendly tool for lineaging multi-dimensional image volumes MaMuT's unique feature that is currently not available in any other commercial or opensource software platforms for image data visualization and analysis is the capacity to track objects synergistically from all available views in multi-view LSFM recordings. This functionality has a number of very important advantages. Raw image stacks do not have to be fused into a single volume, which is computationally by far the most demanding step in multi-view LSFM image processing requiring access to high-end workstations and computer clusters or down-sampling of image volumes  (Preibisch et al. , 2014). The users also preserve the original redundancy of their data, which in many cases, including Parhyale, allows capturing each object of interest from two or more neighboring views that can be interpreted independently for a more accurate analysis.
Finally, MaMuT allows users to analyze sub-optimal datasets that cannot be fused properly or may create fusion artifacts. Of course, combining the raw views with a highquality fused volume is the best available option, especially when handling complex datasets with high cell densities. While offering multi-view tracking, MaMuT delivers also a number of other important functionalities for the exploration of multi-dimensional datasets. First, MaMuT is a userfriendly turnkey software with a convenient interface for interactively navigating, inspecting and curating the image and annotation data. Any time-lapse series acquired by any microscopy modality that can be opened in Fiji can be also imported into MaMuT. Second, MaMuT offers a highly responsive and interactive navigation through multiterabyte datasets fostered by the BigDataViewer architecture for image storage, loading and caching  (Pietzsch et al.
, 2015). Individual z-stacks representing different views and/or channels of a multi-dimensional image dataset can be displayed independently (or in any combination) in multiple Viewer windows. Each Viewer can be also adjusted independently for color and brightness, zoom, translation and rotation of image stacks and browsing through time points. Third, MaMuT relies on TrackMate for the annotation model, which is a Fiji plugin developed for tracking purposes in conventional imaging datasets  (Tinevez et al. , 2016). Objects of interest like cells and nuclei ("spots") can be selected synergistically from all available Viewers and followed over multiple time points to reconstruct their trajectories ("tracks"), division patterns and lineage information. Fourth, the created spots and tracks can be visualized and edited interactively in the Viewers and the TrackScheme lineage browser, and can be animated in the 3D Viewer window.
Lineage reconstruction with the Massive Multi-view Tracker (MaMuT) MaMuT is a Fiji plugin that can be installed through the Fiji Updater. The source code for MaMuT is available on GitHub (https://github. com/fiji/MaMuT) and detailed tutorials and training datasets can be found at http://imagej. net/MaMuT. In brief, for lineaging purposes, the Parhyale multi-view LSFM raw z-stacks were registered spatiotemporally and the image data together with the registration parameters were converted into the custom HDF5/XML file formats utilized by the BigDataViewer and MaMuT plugins. not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was this version posted February 28, 2017. ; https://doi. org/10.
29 A new MaMuT annotation project was created for each dataset and appendage of interest. The nuclei contributing to the T2 limb were identified in the first time-point and tracked manually every 5 time-points except during mitosis, in which case we also tracked one time-point before and one after segregation of the daughter chromosomes during anaphase/telophase. To guarantee the accuracy of our lineage reconstructions, the position of each tracked nucleus was verified in at least two neighboring views and by slicing the data orthogonally in separate Viewer windows. All color-coded cell animations were created with MaMuT's 3D Viewer, which is based on  (Schmid et al. , 2010). The graph data structure in MaMuT can handle efficiently up to about a hundred thousand annotations. This number is well within the realm of manually generated annotations, but is normally exceeded by large-scale fully automated lineaging engines like TGMM.
In situ hybridization In situ hybridizations were carried out as previously described  (Rehm et al. , 2009). The sequence accession number for Parhyale decapentaplegic (Ph-dpp) is XXX, for Dorsocross (Ph-Doc) XXX, and for engrailed-2 (Ph-en2) XXX. Stained samples were imaged on a Zeiss 880 confocal microscope using the Plan-Apochromat 10x/0. 45 and 20x/0. 8 objectives. Images were adjusted for brightness/contrast and processed using Fiji (http://fiji. sc) and Photoshop (Adobe Systems Inc). For color overlays, the brightfield image of the Ph-dpp or Ph-Doc BCIP/NBT staining was inverted, false-colored green and merged with the fluorescent signal of the Ph-en2 FastRed staining in magenta and the nuclear DAPI signal in blue. P. J. K. , P. T. and A. P. performed image analysis, E. S. carried out the in situ hybridizations, A.
Parhyale embryos with fluorescently labeled nuclei as described in Materials & Methods. Individual 3-day old embryos (stage S13) were mounted for LSFM in a column of low melting agarose with scattered red fluorescent beads. Embryos were imaged on a Zeiss Lightsheet Z. 1 microscope that offers dual-sided illumination with single-sided detection. Several parameters (detailed in Materials & Methods) were optimized to cover all stages of Parhyale appendage development over 4-5 days of embryogenesis at single-cell resolution with adequate temporal sampling and with minimal photo-bleaching and damaging. A typical 4 to 5-day long Parhyale embryo recording was composed of more than 1 million images resulting in >7 TB datasets. The slow tempo of Parhyale development enabled LSFM imaging of the entire embryo from multiple highly overlapping views (every 45˚, Figure 1A) without noticeable displacement of cell nuclei between all the views acquired in each time-point.
DISCUSSION In this article, we have established an integrated framework to study the cellular and genetic basis of developmental morphogenesis. By combining light-sheet microscopy with a newly developed versatile software for cell tracking and lineage reconstruction in large multi-view datasets, we have revealed the cellular architecture and cellular dynamics underlying organ morphogenesis in a non-conventional experimental model, and have provided new insights into the mechanisms underlying the three-dimensional epithelial remodeling during arthropod limb morphogenesis.
ACKNOWLEDGEMENTS We dedicate this article to the memory of  Hans Meinhardt , whose insightful theoretical work advanced our understanding of developmental pattering mechanisms. We are really grateful to  Michael Akam  and  Gerhard Scholtz  for hosting and actively supporting the early phases of this project,  Stephan Saalfeld  for his help with image 3D rendering,  Fernando Amat  for his help with importing TGMM annotations into MaMuT,  Angeliki-Myrto Farmaki  for designing the MaMuT logo, and  Michalis Averof  for comments on the manuscript. C. W. was supported by the  Einstein Foundation Berlin  grant  A-2012_114. J. Y. T. and S. S were supported by intramural funding from the  Pasteur Institute. T. P. and P. T. were supported by the core funding from  Max Planck Society  and the  European Research Council Community 's  Seventh Framework Program , grant agreement  260746.
A dedicated node in the network is considered as a gateway, called a command center node (CC) placed in the back gate of the office as shown in Figure  5a. The CC relays data from the deployed network to Internet and vice versa. Our emphasis of the evaluation mainly concerns the connectivity of the WBAN mobile nodes. To that end, the tactical nodes placements are selected as such to enhance the signal propagation and as a result increase the end-to-end connectivity between mobile WBANs and Internet (through the CC node). Experiment scenario map is depicted in Figure  5a. The experimental parameters are given in Table  3. Data collected from on-body sensors is routed from WBAN node through the other WBAN node and/or tactical deployed nodes. By reaching the CC node, data is pushed through Internet to the IoT platform.
This is because most of the current alternatives are typically hard to use, and difficult (or impossible) to configure at run-time. In 2015, Bagula and Erasmus have proposed an IoT emulation environment with COOJA  [6]. However, this environment is targeted only at Contiki systems and emulate motes such as Tmote Sky, Z1 mote, MicaZ mote, etc. It can not be used for Raspberry Pi emulation. More recently, Le-Trung has proposed an IoT testbed emulated over an OpenStack Cloud infrastructure  [7]. However, his testbed is based on the QOMET emulator which uses wireconf, a software only available on RHEL6 systems. For these reasons, this paper presents a method that leverages on NEMU to improve that process. NEMU is a tool to create (and manage) realistic virtual dynamic networks, allowing practitioners to test and evaluate prototypes of complex applications (such as IoT services) with a complete control over the characteristics of the emulated environment (e.
B. Testbed Generation The following steps are required to create a working emulated IoT environment: 1) Define the network topology. For this work, we focused on the widely-used tree topology  [9] ,  [10]  typically used to manage large scale networks. However, any topology can be built with NEMU. At the bottom of the tree structure, there are sensors constantly monitoring and reporting their measured values. On the next layer up there are a series of Raspberry Pis, each responsible for a cluster of sensors. Raspberry Pis are used as they are small, affordable computers, widely deployed in IoT environments. As we proceed up the tree, each higher level Raspberry Pi is responsible for a number of Raspberry Pis below it in the tree. Each Raspberry Pi will report both the sensor data it receives, as well as metrics about the performance of the device itself up the tree.
e. , Raspbian Wheezy). 5) Specify the parameters for the virtual Network Interface Cards (VNics). (VRouters) to simplify the management of the network. A VRouter provides services such as DHCP and IP forwarding. A VRouter can also be linked to a VSlirp. A Slirp is a particular virtual point-to-point link which enables a VHost to communicate with the Internet through NAT emulation inside QEMU. Slirp links also contain internal DHCP and DNS servers. This capability is needed to send information from the emulated IoT system to a machine outside of the virtual network (e. g. , on the Internet). 8) An optional step is to connect real hardware devices to the emulated network (as shown in Fig. 1 ). This is done by creating a Linux bridge and a TAP interface on the machine hosting the testbed (i. e.
, where the QEMU devices are being created). The TAP interface allows software in the Linux user space to be able to use a virtual Ethernet interface on the host machine. A real device can be connected to the host either straight into the host physical NIC or through a physical switch as shown on the figure. Both the host's NIC and the TAP are then connected to the Linux virtual bridge thus creating an hybrid virtual/real network. Finally, to complement the previous discussion, Fig. 2  presents an exemplary tree topology created using the above discussed method.
A. Setup Three types of experiments were done: 1) The first type used a real IoT system built with Raspberry Pi hardware. This real environment was composed of Raspberry Pi 3, model B, and connected by a 100 Mbps Ethernet switch. 2) The second type used an emulated IoT system built with ARM emulation software performed by the QEMU system emulator. The emulated environment was composed of the same number of equivalent emulated Raspberry Pis, using a QEMU specific kernel  1  and Raspbian Wheezy as the Operating System (OS). 3) The third one used a virtualized IoT system built with x86-64 virtualization performed by the QEMU-KVM virtualization software. The virtualized environment was composed of x86-64 virtual machines leveraging VTx hardware acceleration through the KVM module and running Debian Wheezy as OS.
As the Raspbian distribution used on Raspberry Pis is based on the Debian distribution, these VMs may run code compiled for the same major release without problems. Both emulated and virtualized environments were running on a Dell Precision T5500 workstation equipped with an Intel Xeon at 2. 40Ghz (4 cores/8 threads), 24 GB of RAM, under Linux Ubuntu 15. 10 64-bit. All experiments were done in isolation, so that all load was controlled. Also, we used LogEntries as real-time log analytics platform due to its strong analytic capabilities  [11]. Two representative anomaly detection scenarios were evaluated: sensor temperature warnings and processor and memory load warnings as already discussed in section III-C. The sensor temperature data was simulated by using a script which randomly generated temperature values by following a normal distribution centered at 20 C and a 2σ of 0.
2 C. It ran on the devices found on the leaf nodes of the network, transmitting this information along with the other metrics. To generate processor and memory loads, we introduced some controlled noise to the system by running on the devices a subset of the widely-used Java Dacapo benchmarks  [12]. As evaluation criteria, the main metrics were temperature measurements and anomaly detection, processor and memory usage (in %), and network throughput (in Mbps). These metrics were collected with the following tools: wireshark (for MQTT packets) and Logentries (for anomalies), htop and psutils (for processor and memory usage), iperf and the GNU/Linux ftp client (for network throughput). Monitored events (i. e. , detected anomalies) were retrieved from LogEntries' reports, which was fed by the NodeRED node.
Myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS) is a common and severe disease with a considerable social and economic impact. So far, the etiology is not known, and neither a diagnostic marker nor licensed treatments are available yet. The EUROMENE network of European researchers and clinicians aims to promote cooperation and advance research on ME/CFS. To improve diagnosis and facilitate the analysis of clinical trials surrogate markers are urgently needed. As a first step for developing such biomarkers for clinical use a database of active biomarker research in Europe was established called the ME/CFS EUROMENE Biomarker Landscape project and the results are presented in this review. Further we suggest strategies to improve biomarker development and encourage researchers to take these into consideration for designing and reporting biomarker studies.
The EUROMENE ME/CFS Biomarker Landscape project EUROMENE is a network of researchers and clinicians from 17 European countries and one COST (Cooperation in Science and Technology) near neighbor country on ME/CFS supported by the European COST program within Horizon 2020 (http://www. cost. eu/ COST_Actions/ca/CA15111). The aims of EUROMENE are to foster strategies for collaboration and harmonization of diagnosis and research, and to compile an inventory of clinical and scientific data in ME/CFS. The Biomarker working group will also try to develop guidelines for the usage of biomarkers and synchronization of biomarker research. As a first step, a database for active biomarker research in Europe was established called the EUROMENE ME/CFS Biomarker Landscape project. To achieve this, EUROMENE members performed a search for publications on biomarkers within their countries.
The search strategy used the medical subject headings (MeSH) term "chronic fatigue syndrome", which includes myalgic encephalomyelitis, and the respective country, and selected all publications from the last 5 years (2012-2016). The searches were reviewed by members of the Fig. 1  Biomarker studies were categorized as metabolic, immunological, neurological or infection-associated. The data was visualized as total numbers of studies (size of cake) per category (piece of cake) from each country, and the numbers of active biomarker research groups is indicated in the countries. EUROMENE countries are indicated by grey (dark grey countries with published studies, light grey those without studies) and non-EUROMENE by white biomarker working group. Studies not involving patients with ME/CFS, non-biomarker, and sole treatment studies were excluded, only one review article was included.
Motivation: Psychiatric disorders have a great impact on morbidity and mortality. Genotypephenotype resources for psychiatric diseases are key to enable the translation of research findings to a better care of patients. PsyGeNET is a knowledge resource on psychiatric diseases and their genes, developed by text mining and curated by domain experts. Results: We present psygenet2r, an R package that contains a variety of functions for leveraging PsyGeNET database and facilitating its analysis and interpretation. The package offers different types of queries to the database along with variety of analysis and visualization tools, including the study of the anatomical structures in which the genes are expressed and gaining insight of gene's molecular function. Psygenet2r is especially suited for network medicine analysis of psychiatric disorders.
Introduction Research in the genetics of psychiatric diseases generates a large amount of data dispersed in different repositories or only available in free text as publications. Knowledge platforms offering genotypephenotype information for psychiatric diseases are required to support translational research in psychiatry. PsyGeNET (Gutierrez-Sacristan et al. , 2015 , 2017 ) is a knowledge resource that collects and validates information of psychiatric diseases and their genes. The current release of PsyGeNET contains updated information on depression, alcohol and cocaine use disorders, and has been expanded to cover other psychiatric diseases, such as bipolar disorder and schizophrenia  (Gutierrez-Sacristan et al. , 2017). The PsyGeNET database is developed from information extracted from the literature by text mining tools  (Bravo et al.
, 2015) , followed validated by a team of experts (Gutierrez-Sacristan et al. , 2017). The information in PsyGeNET is standardized using community-driven standards, and the evidence supporting the association of a gene to a disease is recorded and available to the user, including contradictory or conflictive findings. By using PsyGeNET the user can gain insight on the molecular basis of psychiatric disorders and their comorbidities. The psygenet2r package has been developed to facilitate the query and analysis of PsyGeNET data and to allow its integration with other packages available in R to develop bioinformatic analysis workflows. Another advantage of using the psygenet2r package compared to the web interface is the variety of data visualization formats provided, such as networks, heatmaps and barplots.
Moreover, psygenet2r enables the user to: (i) retrieve the genes associated to PsyGeNET diseases, (ii) annotate a user's list of genes with PsyGeNET diseases, (iii) explore the molecular functions of the proteins encoded by the genes, (iv) analyse the tissues/anatomical structures in which the genes are expressed, (v) explore data provenance, such as number of publication and the Evidence Index and (vi) analyze the similarity between diseases based on shared genes.
The psygenet2r package 2. 1 Data input psygenet2r package allows retrieving and exploring PsyGeNET information using a specific gene or disease, or a list of them, using a variety of identifiers. The 'score' argument is based on the PsyGeNET Evidence Index (EI), which quantifies the level of supporting evidence for a gene-disease association (GDA). For instance, an EI of 1 indicates that all the evidence collected in PsyGeNET supports the association of the gene with the disease, while an EI of 0. 5 indicates that there are contradictory findings for a particular GDA.
S4 objects defined in psygenet2r To provide easy data storage and manipulation, we created two S4 class objects, DataGeNET. Psy and JaccardIndex. Psy. The DataGeNET. Psy object is obtained when using the query functions psygenetGene and psygenetDisease, and is used as input for the rest of psygenet2r functions, such as the plot function. The DataGeNET. Psy object (Fig. 1 ) contains a summary of the search and the results obtained. The JaccardIndex. Psy object is obtained when the jaccardEstimation function is applied, and computes the Jaccard Index (JI) as an estimation of the similarity of two diseases based on shared genes. JaccardIndex. Psy object contains a summary of the search, the parameters used for the estimation and the results.
Visualization of GDAs psygenet2r makes a special focus on visualizing the results, providing a variety of representation formats, such as networks, heatmaps and bar-plots. The results can be visualized in different ways by applying the plot and geneAttrPlot functions. Depending on the type of object used, the function and the type argument, the user can explore the different GDA attributes (Supplementary Table  S1 ). Visualizing the results according to the different attributes, such as the EI, the number of publications or the source database, is a functionality provided by the psygenet2r package that is not available in PsyGeNET web.
Enrichment analysis Two different psygenet2r functions-enrichedPD and topAnatEnrichment-can be used to characterize the list of genes. The function enrichedPD performs an enrichment analysis using the diseases from PsyGeNET, while the topAnatEnrichment function is based on the expression of genes in anatomical structures from Bgee database  (Bastian et al. , 2008; Komljenovic et al. , 2016). The result for both functions is a data frame containing the enrichment results with the associated p-value.
Conclusion psygenet2r is an R package for gaining insight on the molecular basis of psychiatric disorders and their comorbidities. psygenet2r imports data from PsyGeNET database, a knowledge resource on psychiatric disorders and their genes, and can be integrated with other R packages. psygenet2r also implements several functions to visualize and analyse the results in a clear and meaningful way. psy-genet2r is especially suited for network medicine analysis of psychiatric disorders.