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9,152 results for “Networking”
Integrated analysis of miRNA landscape and cellular networking pathways in stage-specific prostate cancer
<p><strong>Figure S1.</strong> Heat map of miRNA-microarray. Expression of miRNAs differentially expressed and assessed in microarray analysis of RNA isolated from four different cell lines of prostate cancer (LNCaP, PC3, DU145, 22Rv1) compared with control cell line of prostate cancer (PrEc). The red color depicts high and green color showed a lower level of expression at p value <0.01.</p> <p><strong>Figure S2A.</strong> Drug resistance by drug efflux. The internetworking relationship with miRNAs and plasma membrane protein P-glycoprotein (Pgp-plasma membrane protein). The miRNA-130a and miR-181a are downregulated in this pathway (green color) and linked with Pg and BCRP (breast cancer resistant protein). miR-133a and miR379 are involved in regulating the expression of MRP2. While miR-298, miR27a, miR331-5p and miR-130a are involved in regulating the expression of poly-glycoprotein (P-gp).</p> <p><strong>Figure S2B.</strong> Epithelial mesenchymal transition pathway. The miR-200b was downregulated during early-stage prostate cancer and was involved in inhibiting Jagged-2 (JAG2), one of the NOTCH ligands.</p> <p><strong>Figure S2C.</strong> Adipogenesis pathway. The expression of miR-326 was upregulated during the metastatic stage of prostate cancer, (red color) and involved in regulating the expression of CCAT/enhancer binding protein β (C/EBPβ), directly linked with the nuclear hormone receptor peroxisome proliferator-activated receptor-gamma (PPAR-γ).</p> <p><strong>Figure S2D.</strong> Bone metamorphosis signaling pathway. The expression of miR-140, miR-145, and miR-155 was upregulated, along with miR-140 and miR-145 were associated with modulation of gene SOX9, and miR-155 was involved in regulating the gene FOXO3A.</p> <p><strong>Figure S2E.</strong> Th1 pathway. In this pathway, the expression of miR-146a showed a higher level of expression (red color) and modulated the expression of NF-κB signaling.</p> <p><strong>Figure S2F.</strong> Th1 and Th2 pathway. In this pathway, the expression of miR-146a showed a lower level of expression (green color) and may modulate the expression of NF-κB signaling.</p> <p><strong>Table S1.</strong> List of differentially expressed miRNAs derived from LNCaP cells lines statistically significant as P < 0.001.</p> <p><strong>Table S2.</strong> List of differentially expressed miRNAs derived from PC3 cells lines statistically significant as P < 0.001.</p> <p><strong>Table S3.</strong> List of differentially expressed miRNAs derived from DU145 cells lines statistically significant as P < 0.001.</p> <p><strong>Table S4.</strong> List of differentially expressed miRNAs derived from 22Rv1 cells lines statistically significant as P < 0.001.</p>
Freeway Inductive Loop Detector Dataset for Network-wide Traffic Speed Prediction
<p>The data is collected by the inductive loop detectors deployed on freeways in Seattle area. The freeways contain I-5, I-405, I-90, and SR-520. This data set contains spatiotemporal speed information of the freeway system. At each milepost, the speed information collected from main lane loop detectors in the same direction are averaged and integrated into 5 minutes interval speed data. The raw data is provided by Washington Start Department of Transportation (WSDOT) and processed by the <a href="http://www.uwstarlab.org/">STAR Lab</a> in the University of Washington according to data quality control and data imputation procedures [1][2]. </p> <p>The data file is a pickle file that can be easily read using the read_pickle() function in the Pandas package. The data forms as a matrix and each cell of the matrix is speed value for the specific milepost and time period. The horizontal header of the data set denotes the milepost and the vertical header indicates the timestamps. For more information on the definition of milepost, please refer to this <a href="http://data.wsdot.wa.gov/traffic/">website</a>.</p> <p>This data set been used for traffic prediction tasks in several research studies [3][4]. For more detailed information about the data set, you can also refer to this <a href="https://github.com/zhiyongc/Seattle-Loop-Data">link</a>.</p> <p><strong>References</strong>:</p> <p>[1]. Henrickson, K., Zou, Y., & Wang, Y. (2015). Flexible and robust method for missing loop detector data imputation. <em>Transportation Research Record</em>, <em>2527</em>(1), 29-36.</p> <p>[2]. Wang, Y., Zhang, W., Henrickson, K., Ke, R., & Cui, Z. (2016). <em>Digital roadway interactive visualization and evaluation network applications to WSDOT operational data usage</em> (No. WA-RD 854.1). Washington (State). Dept. of Transportation.</p> <p>[3]. Cui, Z., Ke, R., & Wang, Y. (2018). Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction. <em>arXiv preprint arXiv:1801.02143</em>.</p> <p>[4]. Cui, Z., Henrickson, K., Ke, R., & Wang, Y. (2018). Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting. <em>arXiv preprint arXiv:1802.07007</em>.</p>
Ozone and Carbon Monoxide Dataset Collected by the OpenSense Zurich Mobile Sensor Network
<p><strong>Ozone and Carbon Monoxide Dataset Collected by the OpenSense Zurich Mobile Sensor Network</strong></p> <p>This dataset contains ozone (O3) and carbon monoxide (CO) concentration measurements collected by the OpenSense (<a href="http://www.opensense.ethz.ch">http://www.opensense.ethz.ch</a>) mobile senor network over the course of 4.5 years (2012/02-2016/09). The sensors are mounted on top of 10 streetcars in the city of Zurich, Switzerland.</p> <p><br> In particular, the dataset contains: </p> <ol> <li>Ozone (O3) data: 2012/02 - 2016/09 (19.9 Mio samples)</li> <li>Carbonmonoxide (CO) data: 2014/03 - 2016/09 (49.7Mio samples)</li> </ol> <p><strong>Hardware:</strong><br> --------------</p> <ol> <li>Ozone sensor: SGX (former e2V) MiCS-OZ-47 Ozone Sensing Head with Smart Transmitter PCB</li> <li>Carbon monoxide sensor: Alphasense CO-B4</li> <li>GPS receiver: u-blox EVK-6p</li> </ol> <p><strong>Data files format: </strong><br> -------------------------<br> co_data_*: </p> <ol> <li>Time of day: yyyy.mm.dd HH:MM</li> <li>Latitude WGS84</li> <li>Longitude WGS84</li> <li>HDOP: horizontal dilution of precision, uncertainty of the GPS position</li> <li>Tram ID</li> <li>WE_CHANNEL_SENSOR_1_MV: The voltage [in mV] at the working electrode of the electrochemical sensor (see Alphasense CO-B4 datasheet for more details)</li> </ol> <p>o3_data_*: </p> <ol> <li>Time of day: yyyy.mm.dd HH:MM</li> <li>Latitude WGS84</li> <li>Longitude WGS84</li> <li>HDOP: horizontal dilution of precision, uncertainty of the GPS position</li> <li>Tram ID</li> <li>Ozone [ppb]: On-device calibrated (according to manufacturer) ozone measurement [in parts-per-billion] </li> <li>Temperature [in °C]</li> <li>Relative Humidity [in %]</li> </ol> <p><strong>Data quality:</strong><br> ------------------<br> The data has NOT been post-processed!<br> In order to achieve high data quality, the data needs to be cleaned (e.g. outlier filtering) and, most importantly, the sensors need to be individually calibrated.<br> Reference data can be obtained from <a href="http://www.ostluft.ch">www.ostluft.ch</a>, the official air quality monitoring network in eastern Switzerland, which operates multiple monitoring stations in the city of Zurich.</p> <p><strong>Plot Coverage Map (MATLAB):</strong><br> --------------------------------------------<br> The provided MATLAB script plot_data_coverage.m plots the locations of the collected samples onto the map of Zurich (map_zurich.png). </p> <p><strong>References:</strong><br> -----------------<br> The dataset (and related aspects) has partly been used and is described in more detail in the following publications:</p> <ol> <li>Balz Maag et al. <strong>SCAN: Multi-Hop Calibration for Mobile Sensor Arrays</strong>. In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol.1, No.2 (IMWUT), 2017.</li> <li>Olga Saukh et al. <strong>Reducing Multi-Hop Calibration Errors in Mobile Sensor Networks</strong>. In IEEE/ACM International Conference on Information Processing in Sensor Networks (IPSN), 2015. Best Paper Award!</li> <li>Olga Saukh et al. <strong>Route Selection for Mobile Sensor Nodes on Public Transport Networks</strong>. In Journal of Ambient Intelligence and Humanized Computing, 5(3), Springer, 2014.</li> <li>Olga Saukh et al. <strong>On Rendezvous in Mobile Sensing Networks</strong>. In Proceedings of the 5th Workshop on Real-World Wireless Sensor Networks (RealWSN), 2013.</li> <li>Jason Jingshi Li et al. <strong>Sensing the Air we Breathe – The OpenSense Zurich Dataset</strong>. In Proceedings of the 26th International Conference on Artificial Intelligence (AAAI), 2012.</li> <li>Olga Saukh et al. <strong>Route Selection for Mobile Sensors with Checkpointing Constraints</strong>. In Proceedings of the 8th International Workshop on Sensor Networks and Systems for Pervasive Computing (PerSeNS, in conjunction with IEEE PerCom), March 2012.</li> <li>David Hasenfratz et al. <strong>On-the-fly Calibration of Low-Cost Gas Sensors</strong>. In Proceedings of the 9th European Conference on Wireless Sensor Networks (EWSN), 2012. </li> </ol> <p><br> For further information, visit: <a href="http://www.opensense.ethz.ch">http://www.opensense.ethz.ch</a></p>
Visualizations of the Network Analysis of Königsfelden Abbey
<p>Visualizations produced from networks regarding the production of charters in and for Königsfelden Abbey.</p> <p>A commented version of all networks is added as PDF file: modularity-and-networks.pdf.</p> <p>The referenced numbers of people can be found as PDF file: kf-personenliste.pdf.</p> <p>The data used, is published online: https://doi.org/10.5281/zenodo.632560</p>
Network data for the paper: Intellectual and social similarity among scholarly journals.
<p>Network data used for the analysis contained in Baccini A, Barabesi L, Gingras Y, Kalfaoui M (2019) Intellectual and social similarity among scholarly journals. An exploratory comparison of the networks of editors, authors and co-citations.</p> <p>Data are in .net format for Pajek software</p> <p>CC indicates co-citation network.</p> <p>IA indicated Interlocking authorship network.</p> <p>IE indicates interlocking editorship network.</p> <p>Stat is for statistics; Econ is for economics; ILS is for information and library science.</p> <p> </p> <p> </p>
Handschriftencensus data for Network of Shared Manuscript Transmission
<p>List of nodes and edges to create networks of shared manuscripts transmission with the data from <em>Handschriftencensus</em> (http://handschriftencensus.de)</p> <p>The files can be uploaded directly to <em>Gephi</em> and can be easily adapted to use in other softwares for network analysis. </p> <p>Reading the<em> Gephi</em> documentation should be enough to understand the fields used.</p>
Map of Màndén, the Ghana and Mali empires, and Jùlá trade network
<p>Map of Màndén, the Ghana and Mali empires, and Jùlá trade network. Includes original color and an adapted black and white version.</p> <p>Originally appeared in the following:</p> <p>Donaldson, Coleman. 2017. “Clear Language: Script, Register and the N’ko Movement of Manding-Speaking West Africa.” Doctoral Dissertation, Philadelphia, PA: University of Pennsylvania. Philadelphia, PA. <a href="https://repository.upenn.edu/dissertations/AAI10681364/">https://repository.upenn.edu/dissertations/AAI10681364/</a>.</p> <p>Created with data from the following sources:</p> <p>Dalby, David. 1971. “Introduction: Distribution and Nomenclature of the Manding People and Their Language.” In <em>Papers on the Manding</em>, edited by Carleton Hodge, 3:1–13. African Series. Bloomington, IN: Indiana University Publications.</p> <p>Launay, Robert. 1983. <em>Traders Without Trade: Responses to Change in Two Dyula Communities</em>. Cambridge University Press.</p> <p>Simonis, Francis. 2010. <em>L’Afrique soudanaise au Moyen âge: le temps des grands empires (Ghana, Mali, Songhaï)</em>. Marseille: CRDP de l’académie d’Aix-Marseille.</p> <p>--</p> <p>Blog: <a href="https://ajami.hypotheses.org/">https://ajami.hypotheses.org/</a><br> Project: <a href="https://www.manuscript-cultures.uni-hamburg.de/ajami/index_e.html">https://www.manuscript-cultures.uni-hamburg.de/ajami/index_e.html</a></p>
Dataset for IJCAI 2019 paper, Scribble-to-Painting Transformation with Multi-Task Generative Adversarial Networks
<p>The dataset and pre-trained model for IJCAI 2019 paper "Scribble-to-Painting Transformation with Multi-Task Generative Adversarial Networks"</p> <ul> <li>Pre-trained model(Pytorch): DSPNet_G_200_epochs.pth</li> <li>Dataset (generated from coco dataset): starry_night_coco.zip</li> <li>Github: https://github.com/jinningli/DSP-Net</li> </ul> <p>Please cite our paper if you are using this dataset:</p> <p><em>Jinning Li, and Yexiang Xue. Scribble-to-Painting Transformation with Multi-Task Generative Adversarial Networks. In International Joint Conference on Artificial Intelligence (IJCAI) 2019</em></p> <p>Abstract:</p> <p><em>We propose the Dual Scribble-to-Painting Network (DSP-Net), which is able to produce artistic paintings based on user-generated scribbles. In scribble-to-painting transformation, a neural net has to infer additional details of the image, given relatively sparse information contained in the outlines of the scribble. Therefore, it is more challenging than classical image style transfer, in which the information content is reduced from photos to paintings. Inspired by the human cognitive process, we propose a multi-task generative adversarial network, which consists of two jointly trained neural nets -- one for generating artistic images and the other one for semantic segmentation. We demonstrate that joint training on these two tasks brings in additional benefit. Experimental result shows that DSP-Net outperforms state-of-the-art models both visually and quantitatively. In addition, we publish a large dataset for scribble-to-painting transformation.</em></p>
Proxies' response times measured by clients in an emulated community network
<p>13 virtual nodes were deployed in Planetlab testbed (https://www.planet-lab.org) to emulate a small community network with 8 clients and 5 proxies. Each client probed all proxies every 10 seconds during two days. The same file (http://ovh.net/files/1Mb.dat) was requested in all probes. A probe was considered successful if the file was completely downloaded by the client. In this case, the response time was registered by the client, considering the time elapsed from the moment the client sent the request until the last byte of the response was received.</p> <p>This dataset contains the proxies' response times that were measured by clients in sucessful probes.</p>
A global network of biomedical relationships derived from text
<p>This repository contains labeled, weighted networks of chemical-gene, gene-gene, gene-disease, and chemical-disease relationships based on single sentences in PubMed abstracts. All raw dependency paths are provided in addition to the labeled relationships.</p> <p>PART I: Connects dependency paths to labels, or "themes". Each record contains a dependency path followed by its score for each theme, and indicators of whether or not the path is part of the flagship path set for each theme (meaning that it was manually reviewed and determined to reflect that theme). The themes themselves are listed below and are in our paper (reference below).</p> <p>PART II: Connects sentences to dependency paths. It consists of sentences and associated metadata, entity pairs found in the sentences, and dependency paths connecting those entity pairs. Each record contains the following information:</p> <ul> <li>PubMed ID</li> <li>Sentence number (0 = title)</li> <li>First entity name, formatted</li> <li>First entity name, location (characters from start of abstract)</li> <li>Second entity name, formatted</li> <li>Second entity name, location</li> <li>First entity name, raw string</li> <li>Second entity name, raw string</li> <li>First entity name, database ID(s)</li> <li>Second entity name, database ID(s)</li> <li>First entity type (Chemical, Gene, Disease)</li> <li>Second entity type (Chemical, Gene, Disease)</li> <li>Dependency path</li> <li>Sentence, tokenized</li> </ul> <p>The "with-themes.txt" files only contain dependency paths with corresponding theme assignments from Part I. The plain ".txt" files contain all dependency paths.</p> <p>This release contains the annotated network for the <strong>September 15, 2019 version of PubTator</strong>. The version discussed in our paper, below, is an older one - from April 30, 2016. If you're interested in that network, it can be found in Version 1 of this repository. We will be releasing updated networks periodically, as the PubTator community continues to release new versions of named entity annotations for Medline each month or so.</p> <p>------------------------------------------------------------------------------------<br> REFERENCES</p> <p>Percha B, Altman RBA (2017) A global network of biomedical relationships derived from text. <em>Bioinformatics, </em>34(15): 2614-2624.<br> Percha B, Altman RBA (2015) Learning the structure of biomedical relationships from unstructured text. <em>PLoS Computational Biology,</em> 11(7): e1004216.</p> <p>This project depends on named entity annotations from the PubTator project:<br> https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator/</p> <p>Reference:<br> Wei CH et. al., PubTator: a Web-based text mining tool for assisting Biocuration, Nucleic acids research, 2013, 41 (W1): W518-W522.</p> <p>Dependency parsing was provided by the Stanford CoreNLP toolkit (<strong>version 3.9.1</strong>):<br> https://stanfordnlp.github.io/CoreNLP/index.html</p> <p>Reference:<br> Manning, Christopher D., Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014. The Stanford CoreNLP Natural Language Processing Toolkit In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55-60.</p> <p>------------------------------------------------------------------------------------<br> THEMES</p> <p><strong>chemical-gene</strong><br> (A+) agonism, activation<br> (A-) antagonism, blocking<br> (B) binding, ligand (esp. receptors)<br> (E+) increases expression/production<br> (E-) decreases expression/production<br> (E) affects expression/production (neutral)<br> (N) inhibits</p> <p><strong>gene-chemical</strong><br> (O) transport, channels<br> (K) metabolism, pharmacokinetics<br> (Z) enzyme activity</p> <p><strong>chemical-disease</strong><br> (T) treatment/therapy (including investigatory)<br> (C) inhibits cell growth (esp. cancers)<br> (Sa) side effect/adverse event<br> (Pr) prevents, suppresses<br> (Pa) alleviates, reduces<br> (J) role in disease pathogenesis</p> <p><strong>disease-chemical</strong><br> (Mp) biomarkers (of disease progression)</p> <p><strong>gene-disease</strong><br> (U) causal mutations<br> (Ud) mutations affecting disease course<br> (D) drug targets<br> (J) role in pathogenesis<br> (Te) possible therapeutic effect<br> (Y) polymorphisms alter risk<br> (G) promotes progression</p> <p><strong>disease-gene</strong><br> (Md) biomarkers (diagnostic)<br> (X) overexpression in disease<br> (L) improper regulation linked to disease</p> <p><strong>gene-gene</strong><br> (B) binding, ligand (esp. receptors)<br> (W) enhances response<br> (V+) activates, stimulates<br> (E+) increases expression/production<br> (E) affects expression/production (neutral)<br> (I) signaling pathway<br> (H) same protein or complex<br> (Rg) regulation<br> (Q) production by cell population</p> <p>------------------------------------------------------------------------------------<br> FORMATTING NOTE</p> <p>A few users have mentioned that the dependency paths in the "part-i" files are all lowercase text, whereas those in the "part-ii" files maintain the case of the original sentence. This complicates mapping between the two sets of files.</p> <p>We kept the part-ii files in the same case as the original sentence to facilitate downstream debugging - it's easier to tell which words in a particular sentence are contributing to the dependency path if their original case is maintained. When working with the part-ii "with-themes" files, if you simply convert the dependency path to lowercase, it is guaranteed to match to one of the paths in the corresponding part-i file and you'll be able to get the theme scores.</p> <p>Apologies for the additional complexity, and please reach out to us if you have any questions (see correspondence information in the <em>Bioinformatics</em> manuscript, above).</p>
JoseBSL/Geonet: Climate mediates pollinator species roles in plant-pollinator networks
<p>Code and data from the article "Climate mediates pollinator species roles in plant-pollinator networks".</p>
A Convolutional Neural Network classifier identifies tree species in mixed-conifer forest from hyperspectral imagery
<p>Published online: <a href="https://www.mdpi.com/2072-4292/11/19/2326">https://www.mdpi.com/2072-4292/11/19/2326</a></p> <p>DOI: 10.3390/rs11192326</p> <p><strong>Abstract:</strong></p> <p>In this study, we automate tree species classification and mapping using field-based training data, high spatial resolution airborne hyperspectral imagery, and a convolutional neural network classifier (CNN). We tested our methods by identifying seven dominant trees species as well as dead standing trees in a mixed-conifer forest in the Southern Sierra Nevada Mountains, CA (USA) using training, validation, and testing datasets composed of spatially-explicit transects and plots sampled across a single strip of imaging spectroscopy. We also used a three-band ‘Red-Green-Blue’ pseudo true-color subset of the hyperspectral imagery strip to test the classification accuracy of a CNN model without the additional non-visible spectral data provided in the hyperspectral imagery. Our classifier is pixel-based rather than object based, although we use three-dimensional structural information from airborne Light Detection and Ranging (LiDAR) to identify trees (points > 5 m above the ground) and the classifier was applied to image pixels that were thus identified as tree crowns. By training a CNN classifier using field data and hyperspectral imagery, we were able to accurately identify tree species and predict their distribution, as well as the distribution of tree mortality, across the landscape. Using a window size of 15 pixels and eight hidden convolutional layers, a CNN model classified the correct species of 713 individual trees from hyperspectral imagery with an average F-score of 0.87 and F-scores ranging from 0.67–0.95 depending on species. The CNN classification model performance increased from a combined F-score of 0.64 for the Red-Green-Blue model to a combined F-score of 0.87 for the hyperspectral model. The hyperspectral CNN model captures the species composition changes across ~700 meters (1935 to 2630 m) of elevation from a lower-elevation mixed oak conifer forest to a higher-elevation fir-dominated coniferous forest. High resolution tree species maps can support forest ecosystem monitoring and management, and identifying dead trees aids landscape assessment of forest mortality resulting from drought, insects and pathogens. We publicly provide our code to apply deep learning classifiers to tree species identification from geospatial imagery and field training data</p> <p>Digital Publication of the training data polygons and hyperspectral imagery used in the manuscript "A Convolutional Neural Network classifier identifies tree species in mixed-conifer forest from hyperspectral imagery".</p> <p>Code is available in a Jupyter Notebook and can be found here: <a href="https://github.com/jonathanventura/canopy">https://github.com/jonathanventura/canopy</a></p> <p>National Ecological Observatory Network. 2018. Provisional data downloaded from <a href="http://data.neonscience.org/">http://data.neonscience.org</a> on 22 June 2018. Battelle, Boulder, CO, USA</p>
Network analysis highlights increased generalisation and evenness of plant-pollinator interactions after conservation measures
<p><strong>DATASET used in the article entitled</strong> “Network analysis highlights increased generalisation and evenness of plant-pollinator interactions after conservation measures”.</p> <p>We supply weighted and binary matrices used for plant-pollinator network analyses, before and after the implementation of conservation measures.</p> <p>We also supply the list of plant and pollinator species recorded in this study.</p>
Bayesian network analysis of plasma microRNA sequencing data in patients with venous thrombosis
<p>This dataset contains the results of 2 related analyses, described in "Bayesian network analysis of plasma microRNA sequencing data in patients with venous thrombosis" (European Heart Journal Supplements, OUP). Link to the article: https://www.hal.inserm.fr/inserm-02310241</p> <p>1) In the directory "miRNAs_MARTHA_GWAS" : GWAS summary statistics for 162 circulating miRNAs in 344 VTE patients from the MARTHA cohort.</p> <p>Header for each summary file:</p> <p>Trait: miRNA id<br> chr: Chromosome<br> pos.hg19: Position of the variant in hg19/GRCh37 coordinates<br> SNP: rsid<br> A1: Reference allele on the forward strand<br> A2: Alternate allele on the forward strand<br> freq_A1: Frequency of reference allele<br> rsqr: Imputation quality defined by MACH<br> beta_A1: Estimated effect size (beta regression coefficient) of reference allele<br> se_A1: Estimated standard error of beta<br> p: p-value (significance of estimated beta)<br> z.score: Z-score</p> <p> </p> <p>2) In the directory "meta_analysis": Random effect meta-analysis combining the results of our GWAS on the MARTHA cohort, and the results from a similar analysis conducted by Nikpay et al. (doi: 10.1093/cvr/cvz030). Summary statistics of 142 microRNAs, common to both datasets, were processed (and combine 1054 samples).</p> <p>Header for each summary file:</p> <p>chr: Chromosome<br> pos.hg19: Position of the variant in hg19/GRCh37 coordinates<br> SNP: rsid<br> A1: Reference allele on the forward strand<br> A2: Alternate allele on the forward strand<br> N: Sample size<br> Q: Cochran's heterogeneity statistic<br> Q.p: p-value of Cochran's Q<br> beta_A1: Estimated effect size (beta regression coefficient) of reference allele<br> se_A1: Estimated standard error of beta<br> p: p-value (significance of estimated beta)</p> <p> </p>
The North American Monsoon GPS Hydrometeorological Network 2017: Flux and Precipitation Data
<p>Water, energy and carbon fluxes and ancillary meteorological measurements and precipitation data taken during The North American Monsoon GPS-Hydrometeorological Network 2017. The experiment was carried out during the summer of 2017 in the state of Sonora in northwestern Mexico.</p>
Plant health data from Belgian Plant Sentinel Network
<p>The enclosed files contain the questions (in Dutch, English & French) and the responses from tree surveys conducted under the Belgian Plant Sentinel Network.</p> <p>Background to the project</p> <p>Botanic gardens and arboreta possess outstanding scientific collections of a wide diversity of plants, frequently growing outside their natural geographical range. These can be used as sentinels for both early detection of emerging pests and for potentially invasive pests. This is why the International Plant Sentinel Network was launched by EUPHRESCO (EUropean PHytosanitary RESearch COordination), an international network of organisations funding research projects and coordinating national research programmes in the phytosanitary area. International Network developed a transnational network consisting of gardens, diagnostic laboratories and National Plant Protection Organisations, working together in order to provide an early warning system for new and emerging plant pests and diseases. It focused on developing tools and took place in a limited number of countries.</p> <p>Belgium did not participate in the first phase of International Plant Sentinel Network, even though there are many botanic gardens and arboreta in the country. The largest of them is Meise Botanic Garden, which is one of the largest botanic gardens in the world (92 ha). Besides Meise there are several gardens with diverse and unique collections spread all over the country. Among these gardens many have staff members with good knowledge of plant protection, which assure the control of pests and diseases in the collections of the gardens. On the other hand, up to now there have been only limited interactions between the gardens and the National Reference Laboratories for plant pests and diseases. It would be interesting to strengthen these interactions, both for the gardens and arboreta, for a better management and protection of their living collections, and for the Reference Laboratories, in order to gather more data on the presence of pests and diseases in the country.</p> <p>That is why we have created a Belgian network similar to international network, aiming at supporting national plant health policy by early warning of new pest threats, and meanwhile operating in the new transnational initiative of the international network. Together they can form a dense Belgian network to gather data and expand the surveillance of emerging pests over the entire national territory. They also hold most of the plant diversity present in the whole country.</p> <p>Standardized methods and tools for making the plant health surveys have been developed by the International Plant Sentinel Network during its first phase from 2013 to 2016. One of these tools is the Plant Health Checker, a form for making standardized surveys of trees. Two versions are available, one for surveying deciduous trees and one for conifers, as the symptoms to watch for differ between these two groups in some cases. Each form is available in English as a paper form with two sides. Moreover, a reference guide with instructions on how to use the checker had also been made with it, as well as a guide to taking photographs for diagnostic purposes.</p> <p>For the Belgian project we adapt these forms in order to facilitate their use. We translated them to French and Dutch, the two main languages of the country, so that all gardeners could use them. A second goal was to adapt them to the test cases selected for the project. Indeed, some symptoms that are important for these organisms are not included in the original plant heath checker, mainly concerning the symptoms on roots for the root-knot nematode and <em>Phytoplasma</em> case. A third aim was to investigate whether the forms could be simplified for making the surveys in the field. For the latter, however, it was decided to await the user experience of the first year, so as to make an evaluation and suggest eventual amendments.</p> <p>It is also planned to develop an electronic version of the checker form, so as to be able to input the survey data directly in digital format and thus skipping a second and tedious step of entering the data from paper forms into the computer. Moreover, it is preferable to have a system which can easily make the data available to a central data system. That is why we chose to use Google Forms within Google Drive, because it can be utilized as a central data system and flexible, and has many functionalities such as sharing the data. This system also allows the direct input of data with an internet connection.</p>
Fig. 1 in Trophic network of aquatic macroinvertebrates along an altitudinal gradient in a Neotropical mountain river
Fig. 1. Static models of trophic networks at three sites in the Gaira River representing basal resources (red), intermediate consumer (orange) and top predators (yellow) and the interactions among them. SL, San Lorenzo, upper sector; LV, La Victoria, middle sector; PM, Puerto Mosquito, lower sector; D, dry season; R, rainy season.
Fig. 2 in Trophic network of aquatic macroinvertebrates along an altitudinal gradient in a Neotropical mountain river
Fig. 2. Representation of the standard deviation of vulnerability (SD-V) of the main food sources (FPOM, fine particulate organic matter; CPOM, coarse particulate organic matter; PT, plant tissue) calculated from the ratios recorded in the guts of the macroinvertebrates sampled in the elevational gradient of Gaira River during the dry and rainy seasons. Arrow thickness indicates the vulnerability importance.
Fig. 5. Parsimony splits network constructed from a per and ITS2 concatenated sequence data set. Heterozygous specimens are indicated with A and B in Ecological and geographical speciation in Lucilia bufonivora: The evolution of amphibian obligate parasitism
Fig. 5. Parsimony splits network constructed from a per and ITS2 concatenated sequence data set. Heterozygous specimens are indicated with A and B. 'bufonivora_EUROPE_A' represents a consistent haplotype present in all 12 samples from Europe (Table 1), of which just two were heterozygous ('bufonivora_frog' and 'bufonivora_NLWi'). 'bufonivora_CAN' and 'elongata_CAN' are represented by two samples each, none of which were heterozygous. Scale bar represents expected changes per site.
Figure 3. Median-joining networks for J in Mitochondrial evidence indicates a shallow phylogeographic structure for Jaculus blanfordi (Murray, 1884) populations (Rodentia: Dipodidae)
Figure 3. Median-joining networks for J. blanfordi mitochondrial DNA haplotypes in the different datasets for cyt b (1110 bp), COI (618 bp) and COI + cyt b (313 bp + 284 bp). The numbers of mutations (greater than 1) between the haplotypes are indicated near the branches and circle sizes are proportional to the number of similar haplotypes. See Table 1 for the haplotype designations.
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