The Spry.Data.NestedXMLDataSet class allows users to create data sets from more complex data structures. The Spry.Data.NestedXMLDataSet class provides the same basic functionality for maintaining an array of row objects, destructive and non-destructive filtering, and sorting, with the addition of controlling the flattening of nested repeating nodes.
The Spry.Data.NestedXMLDataSet class derives from Spry.Utils.Notifier so it can also dispatch notifications.
Notifier --> DataSet
Spry.Data.NestedXMLDataSet is the constructor function for the Nested XML Data Set.
Spry.Data.NestedXMLDataSet(dsName, xpath)
The constructor takes two arguments:
Options - An optional argument which if specified, must be an object with the option names as properties with values. Properties that can be specified are:
N/A
//define a regular XML Data Set. var ds1 = new Spry.Data.XMLDataSet(); //create a Nested XML Data Set based on the above. var nds2 = new Spry.Data.NestedXMLDataSet(ds1,"products/product/feature",{distinctOnLoad:true});
This function is used to remove duplicate rows from the Nested XML Data Set. The distinct function will evaluate all the columns and if it finds 2 or more rows where every value is the same, it will throw away the duplicate rows and keep only one. This function can only be called *after* the data has been loaded into the Nested XML Data Set.
Distinct can also be run automatically whenever new data is loaded into the Data Set. This is done by specifying the distinctOnLoad constructor option. In case the distinct functionality is need only for specific column(s) they may be defined through the additional parameter distinctFieldsOnLoad.
distinct(columnNames)
columnNames - String or Array. The column(s) of the Nested XML Data Set to use when determining if a row is distinct. This parameter is optional. If ommited, all columns are used to determine if a row is distinct or not. If specified, must be a string that is the name of the column to use for the distinct process, or, must be an array of column names to used for the distinct process.
N/A
/* After a Nested XML Data Set is defined and loaded with data, you can call * distinct on it. */ varn ds1 = Spry.Data.NestedXMLDataSet(ds1,"xpath"); ... ds1.distinct();
This function is used to non-destructively filter the Nested XML Data Set based on a filter function provided by the caller. The filter function passed in must have the following interface:
function myFilterFunc(ds, row, rowIndex) { ... }
The arguments to this function are as follows:
The caller's filter function is called once for each row in the Nested XML Data Set. If the caller's filter function wishes to add the row to the filtered view of the Nested XML Data Set, then it must return the row object passed into the function. If it does not want the row to be in the filtered view of the Nested XML Data Set, then it must return null.
As previously mentioned, this filtering is done non-destructively so internally, the Nested XML Data Set is using the function to determine if it should add a row to a copy of the row array.
The caller's filter function remains active until it is removed. The filter function can be removed by calling filter with a null value instead of a function reference.
After filtering, a call to the getData function will return the filtered array of rows. To get the unfiltered array of rows, you must call getData, passing in a true for the unfiltered argument.
FilterData is the destructive filter, which actually throws out the filtered data from memory.
datasetname.filter(filterFunction);
filterFunction - Function Reference. The function to use to determine if a row should be filtered out or not.
N/A
var myFilterFunc = function(dataSet, row, rowNumber) { // Filter out all rows with paths that begin // with the letter 's'. if (row["@path"].search(/^s/) != -1) return row; // Return the row to keep it in the Nested XML Data Set. return null; // Return null to remove the row from the Nested XML Data Set. } // Filter the data. dsPhotos.filter(myFilterFunc); ... // Remove the filter. dsPhotos.filter(null);
This function is used to filter the Nested XML Data Set based on a filter function provided by the caller. filterData is a destructive method, which means that all rows that are discarded by the filtering is lost. The data can only be retrieved again by loading the data back into the Nested XML Data Set again.
The filter function passed in must have the following interface:
function myFilterFunc(ds, row, rowIndex) { ... }
The arguments to this function are as follows:
The caller's filter function is called once for each row in the Nested XML Data Set. If the caller's filter function wishes to keep the row, then it must return the row object passed into the function. If it does not want to keep the row, then it must return null.
As previously mentioned, this filtering is destructive so internally, the Nested XML Data Set is modifying its only copy of the row array.
The caller's filter function remains active until it is removed, so if the Nested XML Data Set is reloaded, the filtering will occur. The filter function can be removed by calling filter with a null value instead of a function reference.
filter is the non-destructive version of this function.
datasetname.filterData(filterFunction);
filterFunction - Function Reference. The function to use to determine if a row should be kept or discarded.
N/A
var myFilterFunc = function(dataSet, row, rowNumber) { // Filter out all rows with paths that begin // with the letter 's'. if (row["@path"].search(/^s/) != -1) return row; // Return the row to keep it in the Nested XML Data Set. return null; // Return null to remove the row from the Nested XML Data Set. } // Filter the data. dsPhotos.filterData(myFilterFunc); ... // Remove the filter. dsPhotos.filterData(null);
N/A
Finds all rows within the Nested XML Data Set that have the same column values contained in the specified object.
findRowsWithColumnValues(valueObj, firstMatchOnly, unfiltered)
valueObj - Object. Object containing the names of the columns and their values which each row must contain to be considered a match.
firstMatchOnly - Boolean. Optional argument. If true, causes the function to return the row object that first matched the values specified in valueObj. If not match was found, returns null.
unfiltered - Boolean. Optional argument. If true the function does its find through the unfiltered set of rows within the Nested XML Data Set. If false or undefined, the find is done through the filtered set of rows.
The value returned by this function depends on the value of the firstMatchOnly argument. If firstMatchOnly is true, the function will return either the first row object that matches, or null. If firstMatchOnly is false or undefined, the function will return an array of all matches. This array may be empty (length == 0) if no matches were found.
var ds = new Spry.Data.NestedXMLDataSet(); .. // Look for all rows that have a column called "color" with a value of "red" // and a column called "type" with a value of "sports". var matchingRows = ds.findRowsWithColumnValues({ color: "red", type: "sports" alert("Found " + matchingRows.length + "match(es)."); //Look for the first row that matches: var firstMatchingRow = ds.findRowsWithColumnValues({ color: "red", type: "sports" }, true); if (firstMatchingRow != null) alert("Found a match!"); else alert("Failed to find a match!");
This function returns the column type of the specified column.
getColumnType(columnName);
columnName - String. The name of the column.
The column type of the specified column, as a string:
var ds = new Spry.Data.NestedXMLDataSet(); ... var colType = ds.getColumnType("firstname");
This function returns the object that is the 'currentRow' in the Nested XML Data Set. By default, the current row is the first row of the Nested XML Data Set, but this can be changed by calling the function setCurrentRow() or setCurrentRowNumber().
getCurrentRow()
N/A
The row object or null.
Developers should note that the row object returned is the Nested XML Data Set's internal row object. Any changes to the properties of this object results in a change to the data within the Nested XML Data Set.
var row = ds1.getCurrentRow(); if (row["@title"] == "Me, Myself and I") ...
This function returns the row ID of the current row.
Developers should not confuse the ID of a row with the index of the row. The ID is a column that contains a unique identifier for the row. This identifier does not change if the rows of the Nested XML Data Set are sorted, where as the row index for a given row will change if the rows are sorted.
getCurrentRowID()
N/A
Integer or String that represents the row ID.
The actual type of the row ID is left up to the implementor, but it must be an integer or string that can be used as a key into an associative array.
var rowID = ds.getCurrentRowID(); alert("Current row is: " + rowID);
This function returns the row index for the current row.
Developers should not confuse the row number for a row with its ID. The ID is a column that contains a unique identifier for the row. This identifier does not change if the rows of the Nested XML Data Set are sorted, where as the row index for a given row may change if the rows are sorted.
getCurrentRowNumber()
N/A
Returns an integer index (row number) of the row within the Nested XML Data Set.
var rowNumber = ds.getCurrentRowNumber(); alert("Current row number is: " + rowNumber);
This function returns an array of the internal row objects that store the data in the Nested XML Data Set. Modifying any property of a row object results in a modification of the data in the Nested XML Data Set. Users should keep in mind that some Nested XML Data Sets load their data asynchronously, so they should check if it is ok to access the data, with a call to getDataWasLoaded(), before attempting to call getData().
getData(unfiltered)
unfiltered - Boolean. Optional argument. If true, getData() returns the unfiltered data array. If false or undefined, it returns the filtered version of the rows array.
Returns an array of row objects.
// Run through all of the rows in the Nested XML Data Set. For each row, // get the value in the "name" column and check to see if it // is equal to the string "Spry". If it is, return the ID of // the matching row. var rows = ds.getData(); for (var i = 0; i < rows.length; i++) { if (rows[i]["name"] == "Spry") return rows[i]["ds_RowID"]; }
This method returns the row object associated with the specified row ID.
getRowByID(rowID)
rowID - Integer or String. The ID of the row.
Returns the row object with the given rowID or null if it is not found.
// Get the row with the ID of 10. var row = ds.getRowByID(10); // If we have a match, extract out the value // of the firstname column. if (row) var firstname = row.firstname;
This method returns the row object at the specified row number within the Nested XML Data Set.
getRowByRowNumber(rowNumber, unfiltered)
rowNumber - Integer. The row number of the row to fetch.
unfiltered - Boolean. If true, the Nested XML Data Set will return the row object at the given row number in the internal unfiltered data array. Otherwise, it returns the row object at the given row number in the filtered data array.
Returns the row object at the given row number or null if it is not found.
// Get row 10 of the unfiltered data. var row = ds.getRowByRowNumber(10, true); // If we have a match, extract out the value // of the firstname column. if (row) var firstname = row.firstname;
This function returns the number of rows in the Nested XML Data Set. If the Nested XML Data Set has a non-destructive filter, this function returns the number of filtered rows. Users can pass an optional boolean argument to this function. If true, it will return the number of unfiltered rows. If false it will return the number of filtered rows.
getRowCount(unfiltered);
unfiltered - Boolean. Optional argument. If true, getRowCount the number of rows in the unfiltered data array. Otherwise, it returns the number of rows in the filtered data array.
An integer that is the number of rows in the Nested XML Data Set.
var filteredRowCount = ds.getRowCount(); var unfilteredRowCount = ds.getRowCount(true);
This function returns the row number for the specified row object in the Nested XML Data Set. If the Nested XML Data Set has an active non-destructive filter, the row number returned is of the row within the filtered data array. An optional boolean argument can be passed to the function which tells the function to return the row number for the row within the unfiltered data array.
getRowNumber(rowObject, unfiltered)
rowObject - Object. The row to look for.
unfiltered - Boolean. Optional argument. If true the row number returned for the specified row is its row index within the unfiltered data array. Otherwise, it returns the index of the row within the filtered data array.
An integer that is the number of the specified row in the Nested XML Data Set. Returns -1 if the row is not found within the Nested XML Data Set.
This function returns the name of the column that was used when the Nested XML Data Set was last sorted. This function will return an empty string if the Nested XML Data Set was never sorted.
getSortColumn()
N/A
A string that is the name of the column last used for a primary sort. If the Nested XML Data Set has never been sorted, an empty it returns an empty string.
var columnName = ds.getSortColumn();
This function returns the name of the sort order that was used when the Nested XML Data Set was last sorted. This function will return an empty string if the Nested XML Data Set was never sorted.
getSortOrder()
N/A
The current sort order of the Nested XML Data Set:
var sortOrderName = ds1.getSortOrder();
The default implementation for this function fires off asynchronous notifications for "onPostLoad" and "onDataChanged" events. Classes that derive from Spry.Data.NestedXMLDataSet are expected to override this function to trigger async loading of the data for the Nested XML Data Set.
loadData()
N/A
NA
ds.setURL("newXML.xml"); ds.loadData();"
This function is used to set a column type for a data column. This is used to tell the Nested XML Data Set the type of data it is dealing with. A common application is for setting numeric data as 'number'. This ensures that the column will sort numerically, and not alpha-numerically.
The valid column types are:
The 'html' column type results in the automatic entity decoding of values stored in a column of that type when the data is first loaded.
setColumnType(columnName, type)
columnName - String or Array of Strings. The name(s) of the column(s) to set.
type - String. One of the following values:
N/A
var ds1 = new Spry.Data.XMLDataSet("myxml.xml","races/standings"); // Example of setting the type of one column: ds1.setColumnType("start", "date"); // Example of setting the type of multiple columns // with one call: ds1.setColumnType(["rank","place"],"number");
Sets the current row of the Nested XML Data Set to the row with the specified row ID.
This method will cause an "onCurrentRowChanged" notification to be sent to all observers.
setCurrentRow(rowID)
rowID - Integer or String. The row ID of the row that is to become the current row.
N/A
// Set the current row of the Nested XML Data Set // to the row with the integer ID of 13:
ds.setCurrentRow(13);
This function makes the row, at the specified row number (index), the current row of the Nested XML Data Set. Spry uses a zero based counting system for row numbers.
Developers should not confuse the ID of a row with the index of the row. A row ID is a unique identifier that is stored within the row in a column called "ds_RowID". This identifier does not change even if the rows of the Nested XML Data Set are sorted, where as the row index for a given row can change if the rows are sorted.
setCurrentRowNumber(rowNumber)
rowNumber - Integer. The row number of the row that will become the current row.
N/A
// Make the 3rd row of the Nested XML Data Set the current row. Remember // that the row number is zero based. ds.setCurrentRowNumber(2);
This function changes the path used by the data set for calculating the nested repeating nodes.
Calling this function will trigger an immediate reloading of the nested data set. If the parent data set of the nested data set has not loaded any data yet, it will be asked to load its data so that the load request of the nested data set can be satisfied.
setXPath(path)
path - String. An XPath string, to the nested repeating node, that is relative to the XPath used in the parent data set.
N/A
// Create the parent XML data set. var dsDonuts = new Spry.Data.XMLDataSet("donuts.xml", "/items/item"); // Create the nested data set, and specifiy <batter> as the repeating node. var dsNested = new Spry.Data.NestedXMLDataSet(dsDonuts, "batters/batter"); ... // Change the xpath so that the repeating node is now <topping>. dsNested.setXPath("topping");
Sorts the Nested XML Data Set using the specified column(s) and sort order.
Developers specify what columns to sort by passing in a single column name string, or an array of column name strings as the first argument to the function. By specifying more than one column name, developers are able to perform secondary, tertiary, etc, sorts on the data.
Valid sort orders are:
The "toggle" sort order will sort the Nested XML Data Set in "ascending" order if it has not been previously sorted, otherwise, it causes the Nested XML Data Set to sort in the opposite direction from the last sort.
sort(column, order)
column - String or Array of Strings. The column names to sort.
order - The sort order to be used during the sort. Values for order must be one of the following:
N/A
// An example of sorting a Nested XML Data Set in ascending order // based on the values in a single column called "lastname":
ds.sort("lastname", "ascending"); // An example of toggling the sort of a Nested XML Data Set based // on the values in multiple columns. Since 3 columns are // specified, the Nested XML Data Set will perform a tertiary sort: ds.sort(["firstname", "lastname", "@id"], "toggle");
Copyright © 2007. Adobe Systems Incorporated.
All rights reserved.


KNIME
A demo of buiding a KNIME workflow
Yi Hsiao
2015/09/15
Introduction
- You can review today's demo materials through this presentation and document here
- I will show you how to build a workflow related to k-means clustering in KNIME
Iris Dataset
- A multivariate data set introduced by Ronald Fisher in 1936
- The dataset consists of 50 samples from each of three species of Iris flowers (Iris setosa, Iris virginica and Iris versicolor)
- Four attributes were measured from each sample. These attributes are the length and the width of sepal and petal, in centimeters.
- There are no missing values
- Based on the combination of the four features most Machine Learning methods can learn to distinguish among the irises
k-means Clustering
- Clustering
- Use an iterative refinement technique
- Given an initial set of k means, the algorithm proceeds by alternating between two steps:
- Assignment step
- Update step
k-means Clustering
Let's Demo!
- The finished workflow would look like this
Thank you for your listening
Appendix
- There are traffic light signal under every node:
- Red: need configuration
- Yellow: ready to be executed
- Green: finished


I am a Junior Software Developer and I love making customers satisfied and pleased with my work. I recently graduated Computer Science profile from an elite high school in my hometown. Even if I'm just a Junior Developer, that doesn't mean I don't like facing new challenges. Always I bring a touch of originality to every project I do and I love to communicate while carrying out the project, precisely to satisfy the needs and desires of each client.
I am a Junior Software Developer and I love making customers satisfied and pleased with my work. I recently graduated Computer Science profile from an elite high school in my hometown. Even if I'm just a Junior Developer, that doesn't mean I don't like facing new challenges. Always I bring a touch of originality to every project I do and I love to communicate while carrying out the project, precisely to satisfy the needs and desires of each client.


Join the numbers and get the highest score in this page!>
How to play: Use the keys W, E, A, D, Z, X to move the tiles in the six possible directions. When two tiles with the same number touch, they merge into one!
Click here to go back to<PHONE_NUMBER>.


bips: reusable, queriable and identifiable workflows
We wanted to make brain imaging easier. Run workflows on the server or download and install bips locally on your machines.
BIPS collects provenance and results into a database. You can compare across sites and studies and provide dynamic views of results.
How do you find the workflow you need? Every workflow is tagged with keywords. This makes them easily searchable.


The purpose of this problem is to verify whether the method you are using to read input data is sufficiently fast to handle problems branded with the enormous Input/Output warning. You are expected to be able to process at least 2.5MB of input data per second at runtime.
The input begins with two positive integers n k (n, k<=107). The next n lines of input contain one positive integer ti, not greater than 109, each.
Write a single integer to output, denoting how many integers ti are divisible by k.
Input: 7 3 1 51 966369 7 9 999996 11 Output: 4


Keypairs¶
Setup¶
The first thing to do is pass in your credentials and instantiate a Rackspace client:
<?php
require 'vendor/autoload.php';
use OpenCloud\Rackspace;
$client = new Rackspace(Rackspace::US_IDENTITY_ENDPOINT, array(
'username' => '{username}',
'apiKey' => '{apiKey}',
));
Now, set up the Auto Scale service:
$service = $client->computeService('{catalogType}', '{region}', '{urlType}');
{catalogType} is the name of the service, as it appears in the service catalog. For Rackspace users, this will default to cloudServersOpenStack; for OpenStack users, you must set your own value since it can depend on your environment setup.
{region} is the Compute region the service will operate in. For Rackspace users, you can select one of the following from the supported regions page.
{urlType} is the type of URL to use, depending on what endpoints your catalog provides. For Rackspace, you may use either internalURL or publicURL. The former will execute HTTP transactions over the internal Rackspace network, reducing latency and the overall bandwidth cost - the caveat is that all of your resources must be in same region. publicURL, however, which is the default, will operate over the public Internet and is to be used for multi-region installations.
Generate a new keypair¶
This operation creates a new keypair under a provided name; the public key value is automatically generated for you.
// Instantiate empty object
$keypair = $service->keypair();
// Send to API
$keypair->create(array(
'name' => 'jamie_keypair_1'
));
// Save these!
$pubKey = $keypair->getPublicKey();
$priKey = $keypair->getPrivateKey();
Upload existing keypair¶
This operation creates a new keypair according to a provided name and public key value. This is useful when the public key already exists on your local filesystem.
$keypair = $service->keypair();
// $key needs to be the string content of the key file, not the filename
$content = file_get_contents('~/.ssh/id_rsa.pub');
$keypair->create(array(
'name' => 'main_key',
'publicKey' => $content
));
List keypairs¶
To list all existing keypairs:
$keys = $service->listKeypairs();
foreach ($keys as $key) {
}


#include <DSIGTransformBase64.hpp>
Inheritance diagram for DSIGTransformBase64:
The DSIGTransformbase64 class is used to hold base64 <Transform> elements within a document.
This is a very simple transform with no real options.
Public Member Functions | |
Constructors and Destructors | |
| DSIGTransformBase64 (const XSECEnv *env, XERCES_CPP_NAMESPACE_QUALIFIER DOMNode *node) | |
| Contructor used for existing XML signatures. | |
| DSIGTransformBase64 (const XSECEnv *env) | |
| Contructor used for new signatures. | |
| virtual | ~DSIGTransformBase64 () |
| Destructor. | |
Interface Methods | |
| virtual transformType | getTransformType () |
| Determine the transform type. | |
| virtual void | appendTransformer (TXFMChain *input) |
| Create the Base 64transformer element. | |
| virtual XERCES_CPP_NAMESPACE_QUALIFIER DOMElement * | createBlankTransform (XERCES_CPP_NAMESPACE_QUALIFIER DOMDocument *parentDoc) |
| Construct blank Base64 Transform element. | |
| virtual void | load (void) |
| Load a DOM structure. |
|
|
Contructor used for existing XML signatures. The Node structure already exists, so read the nodes in.
|
|
|
Contructor used for new signatures. The Node structure will have to be created.
|
|
|
Destructor. Destroy the DSIGSignature elements. Does not destroy any associated DOM Nodes |
|
|
Create the Base 64transformer element. Implemented by each Transform class and used by the DSIGSignature to construct a complete Transform list. Implements DSIGTransform. |
|
|
Construct blank Base64 Transform element. Instruct the implementation to create the required transform and return the newly constructed DOMNode structure Implements DSIGTransform. |
|
|
Determine the transform type. Used to determine what the type of the transform is. Implements DSIGTransform. |
|
|
Load a DOM structure. For a Base64 transform this does almost nothing. Implements DSIGTransform. |


Clustered Data Warehouse
Data warehouse for Bloomberg to analyze FX deals. Importing deals details from files into DB. The requested performance is to be able to import the file containing 100,000 records in less than 5 seconds.


Access services offered by the county government
This is just place holder text, someone with content please get in touch and provide whatever is supposed to appear here.
Pay from anywhere
Keep your records
No waiting, queues.
It's great


FLASH FLOOD BY RISK
{{ row.name }}
{{ row.value | number }}
RIVER FLOOD BY RISK
{{ row.name }}
{{ row.value | number }}


Several methods are provided for customizing the underlying HTTP client and client builder classes:
- {@link oajrc.RestClient.Builder}
- {@link oajrc.RestClient.Builder#httpClientBuilder(HttpClientBuilder) httpClientBuilder(HttpClientBuilder)} - Set the client builder yourself.
- {@link oajrc.RestClient.Builder#createHttpClientBuilder() createHttpClientBuilder()} - Override to create the client builder.
- {@link oajrc.RestClient.Builder#createHttpClient() createHttpClient()} - Override to create the client.
- {@link oajrc.RestClient.Builder#createConnectionManager() createConnectionManager()} - Override to create the connection management.
Additionally, all methods on the HttpClientBuilder class have been extended with fluent setters.
| // Create a client with customized HttpClient settings.
| MyBean bean = RestClient
| .create()
| .disableRedirectHandling()
| .connectionManager(myConnectionManager)
| .addInterceptorFirst(myHttpRequestInterceptor)
| .build();
Refer to the {@code org.apache.http.client.impl.HttpClientBuilder} docs for more information.


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Hi, I'm Rosemary. This is my learning journey through Enspiral Dev Academy. This blog contains some examples of my work and interesting things I've learnt along the way.


Blocking Updates
- Turn on your Switch and go to the Settings application
- Go to Network settings and scroll down to DNS settings
-
Change DNS to Manual and change your Primary DNS to
<IP_ADDRESS>
- Leave the Secondary DNS as it was previously
<IP_ADDRESS>


Traditionally performance improvements in computer architecture have come from cramming ever more functional units onto silicon, increasing clock speeds and transistors number. Moores law [@mooreLaw1965] states that the number of transistors that can be placed inexpensively on an integrated circuit will double approximately every two years.
Coupled with increasing clock speeds CPU performance has until recently scaled likewise. But this trend cannot be sustained indefinitely or forever. Increased clock speed and transistor number require more power and generate more heat. Although the trend for transistor densities has continued to steadily increase, clock speeds began slowing circa 2003 at 3 GHz. If we apply Moore’s law type thinking to clock-speed performance, we should be able to buy at least 10 GHz CPUs. However, the fastest CPU available today is 3.80 GHz At same point the performance increase fails to increase proportionally with the added effort in terms of transistors or clock speed because efficient heat dissipation and increasing transistor resolution on a wafer becomes more important and challenging (there will be still the physical limit of dimension for each transistor, the atom). The heat emitted from the modern processor, measured in power density
()
rivals the heat of a nuclear reactor core [@Gelsinger2004].
But the power demand did not stop in these year, here the necessity of switching on parallel architectures, so today the dominating trend in commodity CPU architectures is multiple processing cores mounted on a single die operating at reduced clock speeds and sharing some resources. Today is normal to use the so-called multi-core (2,4,8,12) CPUs on a desktop PC at home.


{% extends "base.html" %} {% block body %}
Got lost? Whatever happened the page you were looking for isn't here :(
Try another castle!


Data check: As Gavaskar offers Kohli a reminder, a look at India’s Test record over the decades
by Scroll StaffAnother decade of Test cricket comes to a close for India. Fittingly, perhaps, the curtain comes down on 2010s with a dominant series win at home: a world record-extending 12th on the trot.
India finished their Test match assignments for 2019 with a dominant win over Bangladesh and extended their reign at the top of the rankings.
In the post-match presentation, a jubilant India captain Virat Kohli spoke about his side’s rise over the past few seasons and was full of praise for his fast bowlers. Talking about how the bowling attack has become a fearless unit helping his side stand up to the best of challenges, Kohli said:
“Test cricket is a mental battle. We’ve learned to stand up – and it all started from Dada’s [Sourav Ganguly] team, we are carrying it forward. The belief is key and to be honest we’ve worked hard, and are reaping the rewards of it now.”
With Ganguly present at the presentation ceremony, it was perhaps understandable that Kohli, who grew up watching Test cricket in the 90s, spoke fondly of ‘Dada’s’ leadership.
But it seemed to elicit a defensive response from former Indian captain Sunil Gavaskar who took exception to Kohli’s comment, arguing that India won Tests abroad when the current captain was not even born.
“The Indian captain said that this thing started in 2000 with Dada’s [Ganguly’s] team. I know Dada is the BCCI president, so maybe Kohli wanted to say nice things about him. But India were also winning in the ‘70s and ‘80s. He wasn’t born then,” Gavaskar said.
“A lot of people still think that cricket started only in the 2000s. But the Indian team won overseas in the ‘70s. The Indian team also won away in 1986. India also drew series overseas. They lost as other teams did,” he added.
Now, it’s impossible and irresponsible to compare results because the eras are like chalk and cheese in Indian cricket and it is likely that Kohli did not mean to forget the achievements of the teams in the 60s and 70s.
But Gavaskar’s comment is a chance to look back at India’s record over the years in Tests – especially away from home– and see how the evolution picked up in his era, suffered a serious dip in the 90s, and has gathered pace once again since the turn of the century.
India's overall Test record by decade
| Decade | Matches | Won | Lost | Draw (tie) | W/L |
|---|---|---|---|---|---|
| 2010s | 107 | 56 | 29 | 22 | 1.931 |
| 2000s | 103 | 40 | 27 | 36 | 1.481 |
| 1990s | 69 | 18 | 20 | 31 | 0.900 |
| 1980s | 81 | 11 | 21 | 48 (1) | 0.523 |
| 1970s | 64 | 17 | 19 | 28 | 0.894 |
| 1960s | 52 | 9 | 21 | 22 | 0.428 |
| 1950s | 44 | 6 | 17 | 21 | 0.352 |
| 1930s | 7 | 0 | 5 | 2 | 0.000 |
| 1940s | 13 | 0 | 6 | 7 | 0.000 |
The numbers clearly indicate that under Kohli, India are producing results in Test cricket like no previous team in the history of the game.
Best away Win-Loss ratio in Tests as captains
| Captain | Span | Matches | Won | Lost | Draw | W/L |
|---|---|---|---|---|---|---|
| Kohli | 2014-2019 | 27 | 13 | 9 | 5 | 1.444 |
| Dravid | 2004-2007 | 17 | 5 | 4 | 8 | 1.250 |
| Ganguly | 2000-2005 | 28 | 11 | 10 | 7 | 1.100 |
| Kapil Dev | 1983-1986 | 14 | 2 | 3 | 9 | 0.666 |
| Wadekar | 1971-1974 | 11 | 2 | 3 | 6 | 0.666 |
| Kumble | 2007-2008 | 7 | 2 | 4 | 1 | 0.500 |
| Dhoni | 2009-2014 | 30 | 6 | 15 | 9 | 0.400 |
| Bedi | 1976-1978 | 14 | 3 | 8 | 3 | 0.375 |
| Gavaskar | 1976-1984 | 18 | 2 | 6 | 10 | 0.333 |
| Pataudi | 1962-1968 | 13 | 3 | 10 | 0 | 0.300 |
| Azharuddin | 1990-1999 | 27 | 1 | 10 | 16 | 0.100 |
Every record has a starting point and and there is no doubt that Gavaskar was part of the era that finally saw India be recognised as a worthy opponent in the Test arena wherever they travelled. After the first overseas win under Ajit Wadekar’s captaincy in New Zealand back in 1968, India turned a corner. The team started to register wins away from home more often.
India's away wins in Gavaskar's era
| Series | Season | Matches won by India | Series winner |
|---|---|---|---|
| India in West Indies | 1970/71 | 1 | India |
| India in England | 1971 | 1 | India |
| India in New Zealand | 1975/76 | 1 | drawn |
| India in West Indies | 1975/76 | 1 | West Indies |
| India in Australia | 1977/78 | 2 | Australia |
| India in Australia | 1980/81 | 1 | drawn |
| India in England Test | 1986 | 2 | India |
As the tables above show it’s been an up-and-down curve for India’s Test record over the decades but the upsurge restarted, without a doubt, in the 2000s under Ganguly.
Best away win % in a decade
| Decade | Teams | Matches | Won | Lost | Tied | Draw | W/L | Win% |
|---|---|---|---|---|---|---|---|---|
| 2000s | 1 | 56 | 19 | 19 | 0 | 18 | 1.000 | 33.92% |
| 2010s | 1 | 57 | 19 | 25 | 0 | 13 | 0.760 | 33.33% |
| 1970s | 1 | 30 | 6 | 12 | 0 | 12 | 0.500 | 20.00% |
| 1960s | 1 | 16 | 3 | 13 | 0 | 0 | 0.230 | 18.75% |
| 1980s | 1 | 39 | 3 | 12 | 0 | 24 | 0.250 | 7.69% |
| 1990s | 1 | 39 | 1 | 15 | 0 | 23 | 0.066 | 2.56% |
| 1930s | 1 | 4 | 0 | 3 | 0 | 1 | 0.000 | N/A |
| 1940s | 1 | 8 | 0 | 5 | 0 | 3 | 0.000 | N/A |
| 1950s | 1 | 19 | 0 | 9 | 0 | 10 | 0.000 | N/A |
In conclusion, Gavaskar is absolutely right in saying India started winning in Tests much before before Gangulys and Kohlis arrived on the scene. But for Kohli’s generation, it was Ganguly’s team that showed them how to compete at the highest of levels.
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Ranking results in a subquery using ROW_NUMBER()
December 16th, 2018
I was recently looking at some data that I wanted to rank. The problem was I did not want to see all the data in the table ranked. I just wanted to see a subset of the data, but with each row ranked among the entire dataset. Let me layout an example to illustrate the situation.
Let us imagine a table that contains the average wait time for attractions at Magic Kingdom in Walt Disney World. Our data might look like this:
| Row | Attraction | Zone | WaitTime |
|---|---|---|---|
| 1 | Astro Orbiter | Tomorrowland | 20 |
| 2 | The Barnstormer | Fantasyland | 30 |
| 3 | Big Thunder Mountain Railroad | Frontierland | 40 |
| 4 | Country Bear Jamboree | Frontierland | 15 |
| 5 | Dumbo the Flying Elephant | Fantasyland | 20 |
| 6 | Enchanted Tales with Belle | Fantasyland | 25 |
| 7 | Haunted Mansion | Liberty Square | 90 |
| 8 | Jungle Cruise | Adventureland | 120 |
| 9 | Pirate of the Caribbean | Adventureland | 90 |
| 10 | Seven Dwarfs Mine Train | Fantasyland | 110 |
| 11 | Space Mountain | Tomorrowland | 55 |
| 12 | Splash Mountain | Frontierland | 60 |
Now let us say we want to rank these attractions by WaitTime. We might execute this query:
SELECT ATTRACTION, ZONE, WAITTIME, ROWNUM
FROM ATTRACTIONS ORDER BY WAITTIME DESC;
We could expect to get these results back:
| Row | Attraction | Zone | WaitTime | Rownum |
|---|---|---|---|---|
| 1 | Jungle Cruise | Adventureland | 120 | 8 |
| 2 | Seven Dwarfs Mine Train | Fantasyland | 110 | 10 |
| 3 | Haunted Mansion | Liberty Square | 90 | 7 |
| 4 | Pirate of the Caribbean | Adventureland | 90 | 9 |
| 5 | Splash Mountain | Frontierland | 60 | 12 |
| 6 | Space Mountain | Tomorrowland | 55 | 11 |
| 7 | Big Thunder Mountain Railroad | Frontierland | 40 | 3 |
| 8 | The Barnstormer | Fantasyland | 30 | 2 |
| 9 | Enchanted Tales with Belle | Fantasyland | 25 | 6 |
| 10 | Astro Orbiter | Tomorrowland | 20 | 1 |
| 11 | Dumbo the Flying Elephant | Fantasyland | 20 | 5 |
| 12 | Country Bear Jamboree | Frontierland | 15 | 4 |
Rownum represents the order that the row was selected in the query, so lets assume that each row was selected in alphabetical order.
Now instead of ranking all the attractions across the entire park lets rank just the ones in Fantasyland. So now our query is:
SELECT ATTRACTION, ZONE, WAITTIME, ROWNUM
FROM ATTRACTIONS WHERE ZONE = 'Fantasyland'
ORDER BY WAITTIME DESC;
We could expect to get these results back:
| Row | Attraction | Zone | WaitTime | Rownum |
|---|---|---|---|---|
| 1 | Seven Dwarfs Mine Train | Fantasyland | 110 | 10 |
| 2 | The Barnstormer | Fantasyland | 30 | 2 |
| 3 | Enchanted Tales with Belle | Fantasyland | 25 | 6 |
| 4 | Dumbo the Flying Elephant | Fantasyland | 20 | 5 |
Great! We have the attractions in Fantasyland ranked, but we have lost the information indicated how they rank against other rides in the other zones. This is where the window function ROW_NUMBER() comes in. Briefly, window functions are are SQL methods that calculate a single value for each row inside the “window” or a subset of data.
So if we try this query:
SELECT * FROM
SELECT ATTRACTION, ZONE, WAITTIME, ROW_NUMBER()
OVER(ORDER BY WAITTIME DESC)
AS RANK FROM ATTRACTIONS)
WHERE ZONE = 'Fantasyland';
We should get:
| Row | Attraction | Zone | WaitTime | RANK |
|---|---|---|---|---|
| 1 | Seven Dwarfs Mine Train | Fantasyland | 110 | 2 |
| 2 | The Barnstormer | Fantasyland | 30 | 8 |
| 3 | Enchanted Tales with Belle | Fantasyland | 25 | 9 |
| 4 | Dumbo the Flying Elephant | Fantasyland | 20 | 11 |
Now we can see that the Seven Dwarfs Mine Train ride has the second highest wait time in the park. Thanks ROW_NUMBER()!
Window functions are really powerful when you are trying to do data analysis.
Check out this page SQL Window Functions Introduction to read more about them!


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I am also employed in a concession stand in Páirc Uí Chaoimh on a number of occasions since the summer of 2018. This involves serving customers, inputting transactions at a till and stocking shelves in the shop.
In 2017 I did my Work Experience in Logitech in Cork. I was involved in a number of projects including 3D scanning software, project planning and performing research for upcoming projects. I gained a lot of experience in Logitech and thoroughly enjoyed my time there
I volunteer as a Venturer Scout Leader in the 43rd Scout Troop Bishopstown I have been a member of Scouting Ireland since I was 12 and I wanted to give something back so I decided to become a Venturer Scout Leader


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An analysis of tweets from the Biology of Genomes 2019 conference. A total of 2398 tweets from 677 users were collected using the rtweet
R package.
Data were last collected on 2019-05-13 11:54:37
Filtered for dates 2019-05-07 - 2019-05-11 in the America/New_York timezone.
The “replies network”, composed from users who reply directly to one another, coloured by page rank.
The “mentions network”, where users mention other users in their tweets. Node colour, label size and alpha adjusted according to PageRank score.
| screen_name | text | retweet_count |
|---|---|---|
| ewanbirney | Peak east coast techno-hipster cryptocurrency Americana cannabis genome #bog19 https://t.co/x4hOYHdeGe | 61 |
| StevenSalzberg1 | Just in time for #bog19, @genomeresearch posts early access version (today) of our paper where we found 2250 bacterial genomes that had human contamination, plus 3,437 “proteins” that are erroneous translations of these human contaminants https://t.co/koQRkmLRPw | 42 |
| MollyGasp | Like this year’s #BoG19 cover illustration? You can thank the multi-talented @Anna_Minkina, a talented artist and an even-more-talented grad student in the @JShendure lab! https://t.co/1KGplQaqSL | 30 |
| athmapai | Recent or soon to be grads: looking for a postdoc in gene regulation, RNA, and genomics? I’d love to chat! Come find me at #BoG19! I’ll be presenting poster #185 on Thursday https://t.co/FBsPTVH0Tn | 26 |
| NatalieTelis | Do you know how many questions have been asked since the last one asked by a woman? I do. It’s 32. #BoG19 | 25 |
| JedMSP | Oxford Nanopour #bog19 https://t.co/QeSusqSoaz | 20 |
| hagentilgner | Wow: ScISOr-Seq type work is catching on (https://t.co/JVU0YLcLLQ). Happy to see this and absolutely have to read in detail. BTW, if you like single-cell isoform work (see https://t.co/OFPcZDaxMt), come and see me at poster 271. We are hiring! #BoG19 | 18 |
| StevenSalzberg1 | Anonymous questions from the audience (moderated) are a terrific way to include more audience members. Otherwise the questions tend to be from a small cadre of frequent-askers. Plus it allows people to phrase their questions more carefully #BoG19 https://t.co/TOFIHMVlog | 17 |
| NatalieTelis | For those of you interested in the experiment we’re doing at #BoG19, here’s some preliminary results (right before the last talk): Participation, in person, has changed drastically. For the first time ever, we’ve accidentally created overrepresentation. https://t.co/3Zn0JSDFFS | 16 |
| aaronquinlan | Really fun talk from @BeEngelhardt on a Bayesian approach for optimizing experimental design to maximize cell type discovery from single-cell sequencing experiments. Calling it GT-TS (Good-Toulmin like estimator via Thompson sampling). Paper here: https://t.co/uWm7ySP4lG #bog19 | 14 |
| screen_name | text | favorite_count |
|---|---|---|
| ewanbirney | Peak east coast techno-hipster cryptocurrency Americana cannabis genome #bog19 https://t.co/x4hOYHdeGe | 231 |
| MollyGasp | Like this year’s #BoG19 cover illustration? You can thank the multi-talented @Anna_Minkina, a talented artist and an even-more-talented grad student in the @JShendure lab! https://t.co/1KGplQaqSL | 203 |
| JedMSP | Oxford Nanopour #bog19 https://t.co/QeSusqSoaz | 152 |
| jtung5 | At the #BoG19 closing banquet and the big news is that @blekhman just received tenure!! Big congrats Ran, so well deserved!! https://t.co/9s8c9bMgdX | 117 |
| StevenSalzberg1 | Just in time for #bog19, @genomeresearch posts early access version (today) of our paper where we found 2250 bacterial genomes that had human contamination, plus 3,437 “proteins” that are erroneous translations of these human contaminants https://t.co/koQRkmLRPw | 82 |
| StevenSalzberg1 | Anonymous questions from the audience (moderated) are a terrific way to include more audience members. Otherwise the questions tend to be from a small cadre of frequent-askers. Plus it allows people to phrase their questions more carefully #BoG19 https://t.co/TOFIHMVlog | 79 |
| NatalieTelis | Do you know how many questions have been asked since the last one asked by a woman? I do. It’s 32. #BoG19 | 76 |
| carninci | #bog19 the first BOG without the portrait of Watson. Finally. https://t.co/i42jfyMKdt | 63 |
| NatalieTelis | For those of you interested in the experiment we’re doing at #BoG19, here’s some preliminary results (right before the last talk): Participation, in person, has changed drastically. For the first time ever, we’ve accidentally created overrepresentation. https://t.co/3Zn0JSDFFS | 61 |
| StevenSalzberg1 | Love it that the genome scaffolders used by @mike_schatz for the tomato genome are called SALSA2 and RaGOO. Coincidence? #BoG19 | 60 |
| screen_name | text | quote_count |
|---|---|---|
| AnnaCuomo92 | Great Illustration and great meeting so far! #BoG19 https://t.co/MbRJgVUS5E | 2 |
| Awesomics | The Biology of Genomes (#BoG19) now underway https://t.co/uJpygePI9e. Access live streamed talks, abstract PDF, and video archive (for 6 weeks) at https://t.co/L8JiHY0BFH and enter “99” in the ‘Payment Authorized’ field. Cost=$99. Thanks @cshlmeetings! https://t.co/a4zUxtCduV | 2 |
| deboramarks | Club #BoG19 ever wants me as a ‘member’ I’d pretend I didn’t want to join but then I’d say yes within 30 seconds-sounds like a great conf https://t.co/8hW8w9NCcf | 2 |
| mammothbiosci | Exciting to hear about everything going on at #BoG19 - work in #genome #biology helps drive the creation of DNA-based diagnostic tools. https://t.co/9kf9P9T051 | 2 |
| DevilsAdvoMax | Big, big thanks @NatalieTelis and Friday chairs for pushing towards participatory diversity in the QAs. Clear improvements over the course of #BoG19 sessions - a more inviting spirit for young trainees, first-time attendees or simply more hesitant folks. Exemplary activism! 💕🙂 https://t.co/G1rrlNS8bB | 2 |
| girlscientist | Interesting – here’s what offering the chance to ask questions online during a talk did to in-person questions at #BoG19 (thread)…. https://t.co/zzr6KPoOer | 2 |
| screen_name | text | favorite_count |
|---|---|---|
| ewanbirney | Peak east coast techno-hipster cryptocurrency Americana cannabis genome #bog19 https://t.co/x4hOYHdeGe | 231 |
| MollyGasp | Like this year’s #BoG19 cover illustration? You can thank the multi-talented @Anna_Minkina, a talented artist and an even-more-talented grad student in the @JShendure lab! https://t.co/1KGplQaqSL | 203 |
| JedMSP | Oxford Nanopour #bog19 https://t.co/QeSusqSoaz | 152 |
| jtung5 | At the #BoG19 closing banquet and the big news is that @blekhman just received tenure!! Big congrats Ran, so well deserved!! https://t.co/9s8c9bMgdX | 117 |
| carninci | #bog19 the first BOG without the portrait of Watson. Finally. https://t.co/i42jfyMKdt | 63 |
| NatalieTelis | For those of you interested in the experiment we’re doing at #BoG19, here’s some preliminary results (right before the last talk): Participation, in person, has changed drastically. For the first time ever, we’ve accidentally created overrepresentation. https://t.co/3Zn0JSDFFS | 61 |
| aaronquinlan | Are you interested in what can be learned about germline mutation dynamics from WGS of large, 3-generation pedigrees? Check out @tomsasani’s poster (240) today. Tom is exceptional and is looking for postdoc opps in the coming year. Preprint: https://t.co/CwpBnGP0f1 #bog19 https://t.co/DkbX1KqHPh | 58 |
| kajawasik | Whenever I’m back at @CSHL I realize how much I miss this place. It really made me a scientist and taught me to enjoy the small things in life like wine and cheese on President’s lawn.. #bog19 https://t.co/efXS121Mh9 | 46 |
| NeBanovich | Fish mafia #bog19 #GenomesSparkJoy https://t.co/yEgLgcQlAx | 40 |
| notSoJunkDNA | #bog19 “Germline and somatic variant calling with NovaSeq™ 6000 2x250bp reads” @nygenome poster with @mjs2225 @gnarzisi et al. To be presented on Friday but available at https://t.co/tETp7Brcmz https://t.co/Xky5B00oXy | 34 |
The top 100 words used 3 or more times.
Words that were tweeted next to each other at least 5 times.
Top 10 words associated with 6 topics identified by LDA.
Most representative tweets for each topic
| screen_name | text | gamma |
|---|---|---|
| JavierHerrero7 | .@nonfiniteloop — Can we detect post zygotic variations. Re-purpose GTEx data. Develop method to detect mutations from bulk RNA-seq and validate in Sun exposed/unexposed skin. More mutations in UV exposed skin, increase in UV-induced mut. signature #BoG19 | 0.9936772 |
| fellgernon |
Elise @EliseScience thinks that the + and - correlated eQTLs with aFC could be due to transcription factor binding levels Correlated aFC with TF expression in each tissue (used ENCODE data) 5.6% of tested TF-eQTLs pairs are corr with 1 TF 41% of are corr with > 1TF #BoG19 |
0.9929538 |
| carninci | Julie Jerber, mapping neuronal development eQTLs. eQTL limitations: tissue, time points, stimulations not feasible (in vivo). Instead use hiPS cells. Go beyond primary blood cells! using iPS can stimulate and then analyze at single cell level (capture different cells). #BoG19 | 0.9929538 |
| carninci | Calvert/2: single cell analysis enables detection of infiltrating cells in various pathological conditions of human brains. 27,696 patients genes tested across 96 patients, analyzed with Rasqual, -> 4382 eQTL genes (Including known disease candidate genes). #BoG19 | 0.9929538 |
| CharlestonCWKC | SM: acquired genomic mosaicism most apparent in blood-derived DNA (small number of SNPs that have unusual small allelic fraction in exome sequencing). Clonal hematopoiesis rarely detected in young individual, process takes decades to make up sig fraction of cell pop. #BoG19 | 0.9929538 |
| carninci | Wu/2 capitalize on exome sequencing, predict candidate new antigens. POC: Melanoma, predicting antigens, manufacturing antigens, given to patients with adjuvant to stimulate immune memory. Other study-glioblastoma. Combination with anti- PD-1. Successful promising so far. #BoG19 | 0.9926744 |
| carninci | Rockweiler/2: measuring skin exposed/not to UVs showing more skin mutation in caucasians if compared to African American donors. identify 79 mutations/donor, 4/tissues, 7% overlap, >82-88% validation rate. Mutation rate very variable across tissues and samples. #bog19 | 0.9926744 |
| carninci | Jerber/2: inducing dopaminergic neurons (3 time points, 11-30-52 days) and perturbations (toxicity drugs: rotenone); from 129 donors; Capturing nicely the cell types. Rotenone treatment shows clear gene expression differences -> discovers eQTLs for all cell types. #BoG19 | 0.9926744 |
| carninci | Rockweiler/3. Mutations are tissue specific (example: C->T in skin). Brain less mutations. Age/timing? Shared mutation is more ancient -> Surprise: 80% of mutations happen between zygote and gastrula! (surprise: in so few cell cycles!) Do not miss cell specific ones? #bog19 | 0.9926744 |
| carninci | Fiona Calvert; regulatory map of human primary microglia (~resident macrophages of CNS). Why important? -> genetics data points at association with Alzheimers. Single cell sequencing (15 patients) -> association with neurological disorders. Here 141 neurosurgery patients. #BoG19 | 0.9926744 |
| screen_name | text | gamma |
|---|---|---|
| carninci | Farren Isaacs; Multiple automated genome engineering (MAGE) works without double strand brake (big advantage). Eukaryotic MAGE. Using a ssODN annealing DNA replication fork enhances gene editing 100x. Multiplexing: msh2- allow multiple editing events. Cycling adds variants #bog19 | 0.9938864 |
| fellgernon |
EG is using 12 pcw (post conception weeks) human neural stem cells with a CRISPR/Cas9 deletion screen to learn more about enhancer function (2,233 of them) across 26,260 non-coding regions and 10,656 coding regions expressed in this cell line, + 2,608 bkg regs via ROADMAP #BoG19 |
0.9936772 |
| carninci | Sanders: Structural variations in single cells. Developed a method for single cell genome sequencing which maintains strand information, taking advantage of BdrU incorporation -> digestion of one strand -> sequencing the remaining strand. Measures depth, strand, phase. #BoG19 | 0.9936772 |
| fellgernon |
Ashley Sanders developed Strand-seq which takes advantage of the paternal homologues to track cells across divisions https://t.co/K3xs3OcG5I abstract: “resolves the individual homologs within a cell” scTRIP: single cell Tri-channel processing is the follow up method #BoG19 https://t.co/jKG5JgFcfW |
0.9932127 |
| fellgernon |
YQ from @MarthGabor wants to study subclone-specific mutations, #scRNAseq fits the task very well Challenge: cell coverage is low, roughly 3 MB of “real-estate” for determining the presence of somatic variants, so only ~20 somatic variants with coverage
|
0.9929538 |
| carninci | Luca Pinello @lucapinello CRISPResso (cool name!): analysis of CRISPR-Cas genome editing outcomes from deep sequencing! Now CRISPResso2! Which will take into account base modification, batch mode, allele specific quantification, biology informed alignment, fast processing! #bog19 | 0.9929538 |
| carninci | Ines Cebola, gene regulation in pancreas islets releated to type2 diabetes (T2D). GWAS previously identified; genomics (many experiments) identifies “regulome”, mapping enhancers etc. Next: how to assign enhancers to true targets; Can we understand molecular mechanisms. #bog19 | 0.9929538 |
| carninci | Hannah Currant, on UK Biobank, 500K individuals, 96M variants: focus on retinal phenotype. Retina inspected for physical differences (example: thickness, as “endophenotype”), which is connected with diseases/biology. ~31K GWAS- identify loci associated with retina problems #bog19 | 0.9926744 |
| cshlmeetings | Unable to make it to next week’s #BoG19? Live stream it! Each $99 subscription includes live stream of talks, PDF of the abstract book, and six-week access to #BoG19’s video archive. Register online: https://t.co/fMcgEzbvys and enter “99” in the ‘Payment Authorized’ field. https://t.co/AHcQxt1Sg6 | 0.9923720 |
| carninci | Evan Geller with the fascinating question of what makes human brains unique. Mutations of regulatory elements (enhancers) a better candidate than mutations in coding regions. Then experiments to mutagenize regulatory elements in human neural stem cells. #BoG19 | 0.9923720 |
| screen_name | text | gamma |
|---|---|---|
| CharlestonCWKC | JMR: Settlement of Near / Remote Oceania - modern humans arrived in Near Oceania ~40-65kya, but did not extend beyond Soloman island. Austronesian expansion from Taiwan ~ 5.5 kya; Lapita people arrived in Vanuatu ~3kya; mirgration from Bismarck Archipelago ~2.5kya. #BoG19 | 0.9929538 |
| CharlestonCWKC | JX: patients hospitalized for bone marrow transplantation provide longitudinal data for host-microbiome studies. For ~2 weeks patients are immunocompromised, hence carefully monitored. Has a lot of clinical variable/meta-data, and can collect stool sample for microbiome. #BoG19 | 0.9929538 |
| fellgernon |
Peter @genemodeller on types of joint analysis:
You could also cluster GWAS results. Did so using a bayesian approach (would make @BeEngelhardt proud ^^) based on a Dirichlet prior Can analyze simultaneously >1k GWAS #BoG19 |
0.9926744 |
| obahcall |
Appreciation for our wonderful #BoG19 organizers, who are also illustrious scientists celebrating recent well deserved recognitions! Matt Hurles @mehurles elected FRS @royalsociety Elaine Ostrander elected @theNASciences Jay Shendure @JShendure Lounsbery award @theNASciences |
0.9923720 |
| saketkc | My poster with @WenzhengLi and Andrew Smith for #bog19! We developed a simple method for finding actively-translating ORFs from Ribo-seq data. Performs well at both short and long ORFs. Code: https://t.co/s9Z77yGD2T (also on pip and bioconda) Poster: https://t.co/rKwfLQQVbI. https://t.co/gXdXAPqpEC | 0.9923720 |
| ewanbirney | MS: Good final chromosome level leveraging reference and HiC for validation, polishing with short reads using FreeBayes as the polisher. Shows an example of the “easy to break free” tomato (jointless) and “long stem” tomato which has a negative epistatic interaction #BoG19 | 0.9920435 |
| sorrywm | JXavier finds that gut microbial diversity is predictive of recovery from bone marrow transplants, uses modeling in model systems to identify inter-microbial interactions, and tries out recovering a patient’s microbiome using their own pre-operation fecal sample. #BoG19 | 0.9920435 |
| NatureEcoEvo |
A taste of recent pop gen papers published in NEE: Admixture with a ghost lineage in bonobos https://t.co/YGP2Ci0P9I Using selection at linked sites to estimate adaptation https://t.co/RRn3kmVmju Ancient genomes of Neolithic Britain https://t.co/OXJwkj7hbn #BoG19 |
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| fellgernon |
Next up Anat Kreimer @anatkreimer from Nir Yosef’s lab on using massively parallel reporter assays (MPRAs) for high-throughput enhancer validation (uses a lentivirus that is transducted, then followed by RNA and DNA seq) Related pre-print: https://t.co/UeaaDYWm6U #bog19 |
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| NeBanovich | RB: Describing why it is so difficult to quantify the heritability of the gut microbiome in humans - very plastic trait that is strongly impacted by dietary choices - decided to use the Amboseli Baboons - near-daily observation and fecal samples collected since 2000. #bog19 | 0.9916855 |
| screen_name | text | gamma |
|---|---|---|
| fellgernon |
SMAC-seq by Georgi Marinov and team re-captures the peaks by DNA-seq peak but also overlaps H4S37C chemical nucleosome mapping peaks Their profiles are nicely phased with Cse4 ChIP-seq as well (I don’t know what Cse4 nor H4S37C do! Tweet and later revisit? :P) #bog19 |
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| nanopore | Will you be at Biology of Genomes next week? Register for our free lunch & seminar on Thursday 9th May. Steven Salzberg is giving a talk on ‘The Redwood Genome Project: sequencing and assembling the largest trees in the world’ join us here: https://t.co/gq8U4SXRh8 #bog19 https://t.co/GTbHxxK06n | 0.9923720 |
| CharlestonCWKC | MS: greedily identify the most diverse species from short-read data to identify candidates for long-read sequencing. PromethION improved sequencing throughput dramatically. Read length ~10-100kb length, rarily > 1 Mb. #BoG19 | 0.9923720 |
| ewanbirney | MS: Need to bring some new technology to capture SVs. there are 50 varieties with short reads from 10 main clades. Long Reads from @nanopore - start at MinION+GridION - 5/10 flowcells per genome - but then PromethION really changed the game. Best run 140GB flow cell #BoG19 | 0.9920435 |
| PLOSBiology | John Wolters @HittingerLab: Engineering yeast mtDNA: deplete mtDNA in recipient, put manipulated mtDNA back with gene gun, prime translation, go! Use small, less AT-rich Naumovozyme castellii backbone with S. cerevisiae genes patched in. Ongoing… #BoG19 | 0.9920435 |
| JavierHerrero7 | Ready for #BoG19. Poster tube to present the work of Cecilia (bright BSc student) and Garima (bright postdoc) on using vg for somatic variant detection. All started while chatting to @erikgarrison last year at #BoG18. At the airport, free coffee refills. Will need them! 😎 | 0.9916855 |
| CharlestonCWKC | KL: Otters diverged from terrestrial mustelids ~14 Mya, Sea otter diverged from giant otter ~10 Mya. Giant otter genome sequenced at Broad already, here sequence the Sea otter genome. #BoG19 | 0.9912937 |
| obahcall | So glad to see #BoG19 continues to lead in the genome way. (Nearly) all talks have been tweetable. And most speakers encourage: showing twitter handle, relevant preprints, software & QR code. Discussion in room & online goes much deeper from this- esp when we can read preprint. | 0.9912937 |
| ewanbirney | RF: Talks us about spermatid biology which form syncetium between gametes (!) - freely sharing the mRNA between spermatids but some genes can be selfish - this is the explanation for the Mouse T complex genetics - lead to extreme transmission distortion. #bog19 | 0.9912937 |
| carninci | Cristoph Bock on CISRP-single cell sequencing. High throughput functional biology! To establish causality in epigenetics- without using ONE GENE - ONE POSTDOC, which is very risky on postdoc career, unless you’re conservative!! -> arguments for the need high throughput! #bog19 | 0.9912937 |
| screen_name | text | gamma |
|---|---|---|
| fellgernon |
Joseph Thornton used an ordinal logistic regression model to study epistasis effects and main effects A joint model includes 2 extra sum of coefficients (sum of i to j) Misclassification rate is < 0.6% overall Presented a heatmap of main vs epistasis significant terms #BoG19 |
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| ewanbirney | AH: Performed matched unrelated-GWAS vs sib-GWAS to look at this “three headed dragon”. Can plot sib-GWAS vs unrelated. Thankfully physical traits line up pretty well, but social traits (educational attainment, smoking, age at first sex) show strong shifts #bog19 | 0.9932127 |
| CharlestonCWKC | DV: PRS by either GW SNPs, conditionally independent SNPs, fine-mapped SNPs, etc. Effect size from UKB, validated in INTERVAL study (N ~ 43K). Measure of prediction accuracy is Pearson’s R - actually observed improvement with decreasing number of variants from 5M to 300) #BoG19 | 0.9929538 |
| CharlestonCWKC | RJ: Causal effect size distribution parameterized by effect size (beta) and number of causal SNPs (k) (Zhang et al. NG 2018). Want to know how is heritability divided - centered on 1 SNP or distributed among multiple SNPs, has implication to effect distr at regional level. #BoG19 | 0.9929538 |
| CharlestonCWKC | HM: male-bias not explained by de novo variants found on X chr. Haldane proposed a theory of de novo vs. inherited pathogenic variants in X-linked recessive genes, based on mutation-sel balanec. Expect 18%, but observed 32% #BoG19 | 0.9929538 |
| CharlestonCWKC | DV: BCX consortium (N > 560K EUropean ancestry meta-analysis) found > 10,000 variants associated with at least 1 of 29 blood count phenotypes, 510 rare (< 1% MAF). Statistical fine-mapping assign 10% of the loci to a single variant. #BoG19 | 0.9929538 |
| CharlestonCWKC | BP: observation “SNP with lower levels of LD have significantly larger per-SNP heritability” - MAF/LD-dependent architecture biases SNP heritability estimation. Today will discuss assumptions of estimation, and how to drop them to estimate SNP h2. #BoG19 | 0.9926744 |
| CharlestonCWKC | AH: under null, prediction accuracy should be equal between GWAS and sib-GWAS. If there are indirect effects, GWAS-based PS should outperform sib-GWAS-based. Find for most traits standard GWAS outperforms (matched sample size between GWASes) #BoG19 | 0.9926744 |
| CharlestonCWKC | AH: what to investigate if other factors beyond ancestry impacts PS. Used the “White British” of UKB. Performed GWAS in 120K men + 120K women, and then tested prediction accuracy in 20K men and 20K women, separately. Prediction accuracy differed by sex. #BoG19 | 0.9926744 |
| fellgernon |
Now Hanna Ollila @hannamollila from @jkpritch’s lab (his lab meetings must feel like today’s morning session hehe) on USF1 as a pleiotropic meta regular for sleep and circadian rhythms Story started back in her PhD in Helsinki #BoG19 |
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| screen_name | text | gamma |
|---|---|---|
| majbvc | El @centrodemocratico buscará un candidato distinto a Ángela Garzón para enfrentar las elecciones en Bogotá. Será…??? O será otra salida en falso…?? #losleo @AlvaroUribeVel @diegomolanovega @SamuelHoyosM #FelizJueves #BoG19 #filbo2019 #video @ChurchillBogota @MiguelUribeT https://t.co/GUJ8jaznOy | 0.9936772 |
| CharlestonCWKC | JH: chromatin-modifying genes (CMBs) - diverse set of genes that bind chromatin and regulate cellular processes (remodelling or modification). In DDD project (13.6k patient with undiagnosed DD) de novo variants explain ~50% of risk, enriched in CMG set. #BoG19 | 0.9936772 |
| fellgernon |
Marina Ruiz-Romero (cc @GuigoLab) has ~50 million Illumina TruSeq 75 bp reads from 9 fly samples across 3 developmental stages She found that tissue specificity increases along differentiation from larva 3 to white pupa to fly (?) matches modEncode data based on PC1/2 #BoG19 |
0.9932127 |
| fellgernon |
Coronary artery disease is the leading global cause of mortality Current risk assessment of heart attack reviewed by Khera et al https://t.co/7BJCbpN2xv based on non-🧬metrics but @skathire has now shown that polygenic risk score is the first risk factor for this disease #BoG19 https://t.co/UuHPODF7cq |
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| CharlestonCWKC | JX: can we explain sudden shifts in microbial composition? In mice, single dose of clindamycin lead to reduction of diversity, highly variable over time, but eventually by day 21 or so stabilize to a lower diversity composition. Replicated in multiple animals #BoG19 | 0.9926744 |
| fellgernon |
Mikkel @mikkelschierup is talking about Skov et al, bioRxiv, 2018 https://t.co/modrYeXWWz They can use this to derive mean archaic segmenting lengths (SGDP) & the total amount of archaic sequence Then they explored how the segments are shared by West Eurasia & East Asia #BoG19 https://t.co/XSh7Ah1kmI |
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| CharlestonCWKC | JMR: exploratory PCA using archaic samples show Denisovan cline for Oceanian population. Neandertal anc ~uniformly distributed (~2-3%), but Denisovan anc more variable (0-3%), and scale well with Papuan ancestry (except some outlier population like Agta HG in Phillipines. #BoG19 | 0.9923720 |
| GenomeNathan |
@adamauton @jkpritch @molly_przew Don’t worry, you didn’t miss anything tonight – save two keynotes that anchored fresh insights on basic biological processes (mutation, development) in deep histories of the subfields in question. Basically, giants now…lucidly extending the footsteps of giants past. #bog19 |
0.9923720 |
| fellgernon |
Peter @genemodeller wants to build polygenic risk scores for many traits using their fine mapping approach He looked at the top 3, lowest 3 and middle 20% risk scores with the weights applied to @uk_biobank data. The results can inform policies for when to do screenings #BoG19 |
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| CharlestonCWKC | JX: Typical profile - diverse microbiome initially, then antibiotic dosed -> reduction of diversity and expansion of E. Coli -> body temperature increase -> switch of antibiotic -> solves problem, different microbe expanded. #BoG19 | 0.9923720 |
## R version 3.5.1 (2018-07-02)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS 10.14.4
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] bindrcpp_0.2.2 here_0.1 knitr_1.20
## [4] viridis_0.5.1 viridisLite_0.3.0 ggraph_1.0.2
## [7] wordcloud_2.6 RColorBrewer_1.1-2 ggrepel_0.8.0
## [10] ggplot2_3.0.0 igraph_1.2.2 topicmodels_0.2-8
## [13] tidytext_0.2.0 forcats_0.3.0 lubridate_1.7.4
## [16] tidyr_0.8.1 dplyr_0.7.6 rtweet_0.6.8
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.19 lattice_0.20-35 utf8_1.1.4
## [4] assertthat_0.2.0 rprojroot_1.3-2 digest_0.6.18
## [7] ggforce_0.1.3 slam_0.1-45 R6_2.3.0
## [10] plyr_1.8.4 backports_1.1.2 stats4_3.5.1
## [13] evaluate_0.12 highr_0.7 httr_1.3.1
## [16] pillar_1.3.0 rlang_0.2.2 lazyeval_0.2.1
## [19] Matrix_1.2-14 rmarkdown_1.10 labeling_0.3
## [22] stringr_1.3.1 munsell_0.5.0 broom_0.5.0
## [25] compiler_3.5.1 janeaustenr_0.1.5 pkgconfig_2.0.2
## [28] htmltools_0.3.6 tidyselect_0.2.5 tibble_1.4.2
## [31] gridExtra_2.3 fansi_0.4.0 crayon_1.3.4
## [34] withr_2.1.2 MASS_7.3-50 SnowballC_0.5.1
## [37] grid_3.5.1 nlme_3.1-137 jsonlite_1.5
## [40] gtable_0.2.0 magrittr_1.5 units_0.6-1
## [43] scales_1.0.0 tokenizers_0.2.1 cli_1.0.1
## [46] stringi_1.2.4 farver_1.0 reshape2_1.4.3
## [49] NLP_0.2-0 xml2_1.2.0 tools_3.5.1
## [52] glue_1.3.0 tweenr_1.0.0 purrr_0.2.5
## [55] parallel_3.5.1 yaml_2.2.0 tm_0.7-6
## [58] colorspace_1.3-2 bindr_0.1.1 modeltools_0.2-22