Taxonomic information for New Zealand mosquito species is predominantly morphological with very few molecular data available to date. In this study, the 5’ end of COI (775 nt) and ITS1 of the nuclear ribosomal DNA internal transcribed spacer regions (388 – 638 nt) were amplified and sequenced from DNA templates representing 17 species in total; the 15 previously known New Zealand mosquito species, a newly discovered undescribed Aedes sp. nov. from the Chatham Islands and a recently eradicated invader, Ae. camptorhynchus. This paper provides DNA barcoding sequences for the entire New Zealand mosquito biota, the first for the majority of these species. Phylogenetic analysis of COI and ITS1 indicated that the endemic species are all genetically distinct from the exotic species examined including disease vector species. The genus Opifex is distant from the genus Aedes but Ae. chathamicus is not thereby refuting the proposed move of this species to the genus Opifex by Reinert et. al. (2004). Culex asteliae results show it to be a valid species but Cx. rotoruae not necessarily so. The Aedes sp. nov. appears to be a valid new species closely related to Ae. subalbirostris. No evidence of population variation based on geographic location was detected.
This zip-file contains
(1) the daily root scanner images of three flat-bed scanners during the growing season of 2018 in SMEAR II, Hyytiälä.
The images were brightness adjusted by the Authors. The images were taken between 25.04.2018-01.10.2018. The missing images for scanner 2 due to connection errors: 27.05-28.05.2018 and 04.08.2018. Missing dates for scanner 3: 29.08-02.09.2018 and 14.10.2018.
We used 'Winrhizotron 2015a' software to analyze the daily root elongation data, divided by pioneer and fibrous roots, including daily active root number, and average daily elongation rate
We estimated the growth phenology of aboveground tree organs, such as shoots, needles, buds and secondary xylem using the ‘Carbon Allocation Sink Source Interaction’ (CASSIA) model (Schiestl-Aalto et al. 2015). Simulated aboveground organs' data and our root data were combined in the same dataset.
Root surface area increase rate ( analyzed by 'Winrhizotron 2015a' software), divided by pioneer and fibrous roots
Contributors:Gabriela Montejo-Kovacevich, Letitia Cookson, Eva van der Heijden, Ian Warren, David P. Edwards, Chris Jiggins
Cambridge Butterfly Collection. Loreto, Peru Part 1
EN: This upload contains photographs taken by Eva van der Heijden at the Butterfly Genetics Group at the University of Cambridge, from a butterfly wing collection from Loreto, Peru, in collaboration with Green Gold Forestry. Individual sample names can be found in the information sheet. Further Information on individual samples from the Butterfly Genetics Group Collection can be found on the public database Earthcape (click here for the database, and here for FAQ). Please contact Chris Jiggins (c.jiggins[at]zoo.cam.ac.uk) or Gabriela Montejo-Kovacevich (gmontejokovacevich[at]gmail.com) for further information.
ES: Este repositorio contiene fotografías tomadas por Eva van der Heijden en el Butterfly Genetics Group de la Universidad de Cambridge, de mariposas de Loreto (Peru), en colaboración con la compañía Green Gold Forestry. Puede encontrar información sobre muestras individuales de Butterfly Genetics Group Collection en la base de datos pública Earthcape (haga clic aquí para la base de datos, y aquí para preguntas frecuentes) Por favor, póngase en contacto con Chris Jiggins (c.jiggins [arroba] zoo.cam.ac.uk) o Gabriela Montejo-Kovacevich (gmontejokovacevich[at]gmail.com) con sus preguntas o peticiones.
Contributors:Daisuke Komura, Shumpei Ishikawa
This is a set of 1,608,060 image patches of hematoxylin & eosin stained histological samples of various human cancers.
Whole Slide Images of TCGA dataset from 32 solid cancer types were downloaded from GDC legacy database during December 1, 2016 to June 19, 2017. 9,662 diagnostic slides (the filename contains ’DXn’, where n stands for the slide number) from 7,951 patients in SVS format were then processed to annotate.
For each slide, at least three representative tumor regions were selected as polygons by two trained pathologists using a Web browser-based software developed for this purpose. The pathologists selected uniform tumor regions and avoided the regions with noncancerous structures as much as possible. 926 slides were removed due to poor staining, low resolution, out of focus across a slide, no cancerous regions, or incorrect cancer types. Finally 8,736 diagnostic slides from 7175 patients were remained.
Next, 10 patches with 6 magnification levels from 128 x 128 to 256 x 256 μm were randomly cropped with random angle from each annotated region using keras-OpenSlideGenerator (https://github.com/quolc/keras-OpenSlideGenerator). Each patch was selected so as not to include the region outside the annotated region. The selected region was resized to 256 x 256 pixels. Consequently, the number of patches subjected to the analysis ranged from 264,110 to 271,700.
filename: [cancer_type]/[resolution]/[TCGA Barcode]/[region]-[number]-[pixel resolution in original WSI image].jpg
- 0-> 0.5 μm/pixel
- 1-> 0.6 μm/pixel
- 2-> 0.7 μm/pixel
- 3-> 0.8 μm/pixel
- 4-> 0.9 μm/pixel
- 5-> 1.0 μm/pixel
TCGA-XX-XXXX represents patient ID.
Please see https://docs.gdc.cancer.gov/Encyclopedia/pages/TCGA_Barcode/ for detail.
Contributors:Wanford, Joseph J, Holmes, Jonathan C, Bayliss, Christopher D, Green, Luke R
Neisseria meningitidis are Gram-negative human commensal-pathogens, with extensive phenotypic plasticity afforded by phase variable gene expression. Phase variation is a stochastic switch in gene expression from an ON to an OFF state, mediated by localised hypermutation of simple sequence repeats. Circulating N. meningitidis clones vary in propensity to cause disease with some clonal complexes classified as hypervirulent and others as carriage-associated. We examined the phase-variable gene repertoires, or phasome, of these lineages in order to determine if phase variation contributes to disease propensity. We analysed 3,328 genomes representative of nine circulating meningococcal clonal complexes with PhasomeIt, a tool which identifies phase-variable genes by the presence of simple sequence repeats (SSR) and homologous gene clusters. Presence, absence and functions of all identified phase-variable gene clusters were confirmed by annotation or BLAST searches within the Neisseria pubMLST database. While no significant differences were detected in the number of phase-variable genes or the core, conserved phasome content between hypervirulent and carriage lineages, individual clonal complexes exhibited major variations in phase-variable gene numbers. Phylogenetic clusters produced by phasome or core genome analyses were similar indicating co-evolution of phase-variable genes with the core genome. While conservation of phase-variable clusters is high, with 76% present in all meningococcal isolates, maintenance of an SSR is variable ranging from conserved in all isolates to present only in single clonal complexes, indicating differing evolutionary trajectories for each lineage. Diverse functional groups of PV genes were present across the meningococcal lineages, however, the majority directly or indirectly influence bacterial surface antigens and could impact on future vaccine development. Finally, we observe that meningococci have open pan phasomes, indicating on-going evolution of PV gene content and a significant potential for adaptive changes in this clinically relevant genus.
KEGG Orthology groups predicted for bacteria of different groups
Contributors:Usman Ashraf, Christoph Mayr-Dorn, Alexander Egyed, Sebastiano Panichella
# Realtional Dataset
- Downlaod Dump.sql
- Before importing Dump.sql icrease your max_allowed_packet=4M to 16M in your MySQL initialization usually located at C:\ProgramData\MySQL\MySQL Server 8.0\my.ini
- Import Dump.sql to your local MySQL server by MySQL Workbech->Server->Data Import -> Import from Self-Contained File -> Start Import
# Graph Dataset via graphs_cypher
# Download Neo4j Desktop
Download link can be found here https://neo4j.com/
# Add Graph in Neo4j Desktop
Run Neo4j Desktop application and follow instructions:
- Click on Add Graph
- Set desired name and password
- Click on Create
- Start the Graph database
# Loading Cypher scripts to Graph database
Once the graph database is running, start Neo4j browser and execute cypher script for each project.
as show in the figure below
The script will insert the data of the graph in the database.
# Loading from graph.db
- Download the graph.db
- graph.db contains subfolder DeveloperInteractionGraph.db and SubsystemInteractionGraph.db
- To visualize any desired graph from the graph.db/DeveloperInteractionGraph.db or SubsystemInteractionGraph.db
- Create a graph in Neo4j
- Click on Manage
- Click on Open Folder
- Paste the desired (project).db inside the data/databases folder
- Restart DB
- Neo4j credentials:
userid = neo4j
password = admin
## Visualize graph
The resultant graph can be visualized using following query:
Show only 25 nodes
$ MATCH (n) RETURN n LIMIT 25
Show all nodes
$ MATCH (n) RETURN n