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CHANNEL UPDATE AND 300 SUBSCRIBERS! - Duration: 5:23.
Subtitles...
Intro, blah blah blah
awkward studdering
more blah
stating the obvious ʕ•ᴥ•ʔ
Weird "YAY"
The video starts randomly zooming in on random things on the screen while Cman says stuff.
Cman jokes about the face revial.
AHH
What was that?
AHHHHHH!! WHAT IS THAT THING?!?!
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Gamusa | New Assamese Bihu video song|Siddarth Sinha & Madhushmita|Singer:Aveenab & Bornali kalita| - Duration: 4:25.
For more infomation >> Gamusa | New Assamese Bihu video song|Siddarth Sinha & Madhushmita|Singer:Aveenab & Bornali kalita| - Duration: 4:25. -------------------------------------------
The 6th Key: Video - Duration: 1:34.
Today, marketing isn't just a creative discipline —
it's also a technical one.
Just look at the explosion of marketing technology startups in the last five years.
As CMOs adapt to this major shift,
we expect their overall technology spend to increase ten times over the next ten years.
Money alone won't do it though —
navigating the complexity of a changing industry demands a great framework.
That's why last year we published the 5 keys to unlocking the decade of the CMO,
organizing principles meant to help you achieve great things.
But today it's time we add a sixth key:
video.
Because it's the digital video revolution that will be marketing's next defining moment.
The amount of time Americans 18- to 24-years-old spend watching traditional TV
has gone down by 38% in the past five years,
while 70% of Generation Z prefers streaming over broadcast or cable.
Soon, VR will be as widespread as smartphones are today.
What once seemed simple is now nearly infinitely complex.
CMOs are responsible for delivering authentic, customized content across
an ever-growing number of devices, channels, platforms, and media —
and they have to do it every single day.
We know, it's an enormous challenge.
But for those companies willing to take the lead,
this era will offer dazzling opportunity.
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Why is video good? - Duration: 0:41.
For more infomation >> Why is video good? - Duration: 0:41. -------------------------------------------
NOKIA 6 LAUNCHED!! MY WORDS || FIRST VIEW - Duration: 2:43.
For more infomation >> NOKIA 6 LAUNCHED!! MY WORDS || FIRST VIEW - Duration: 2:43. -------------------------------------------
My Edited Video - Duration: 12:42.
Its Najeeb. I'm student of TU Delft in the
in the department of electrical
engineering mathematics and computer
science today in this video I'm going to
show you where using office simple Excel
formulas to make our excel sheets more
interesting and more effective to deal
with in the course of programming and
data science for 99 person we have a
learning numerius very basic formulas to
make our complex data analysis tasks
very easy so in this video i will use
these same basic formulas to
implement a searchable list. As you know
when we deal with the with the excel
formulas and Excel sheets we normally
deal with a huge list of numbers for
example the list of names and thousands
of the names so if we want to search
something out of the names It's really hard
to search the some text from the
huge list of names or
anything and it's become even more
harder if we don't know that exactly
what we are looking for example if we
only know that maybe the second name of
a person or maybe even a few charcters
in the middle name of person, so
it's really effective if we can
implement a searchable list in our
excel sheet. so i will implement this.
searchable list without using any arrayList or
any activex component i
will use all the basic formulas that we
have learned.
we can implement so let's start our
implementation so this is that this is
the the text box where I will put my text to be searched. but i will put my text research
and i will start from the first function
with the search formula so the search
formula is very basic to search something
out of the string so in search formulas
I will use
as the target and list from this list
we're from this list so maybe you so
Because I will always put my text here
put my text here so I will lock
it with $ sign or F4.
Now I can type anything
in this textbook so i will type AB.
example
and It giving me
the the #value. it's giving me
the value because it it couldn't find
anything in this if I will drag it to
rest of cells so in most of the cases I found
value i couldn't find anything but you
see in one of the column i found one
so this is the position of the text
I found my targeted tax so i can i can
generate for example I you put the I so
it gave me gave me another cell that is
well that is wrong number nine and it
giving me seven so it's again the
starting position of of the text that I
want to search
ok so it's fine and about actually don't
want to see a value with the combination
of numbers of the values from and also i
will wrap this function and with another
function that is his number-two to just
see that if ya if it could find from the
tax included display and the number
otherwise it will be displayed nothing
so it is displayed to this fall's very
we found something it will return me
true and otherwise it began tested again
we are going to find it like TS or it
it gave me through somewhere here you
can see true look so so but I actually i
will change it a bit further
I don't want to see true or false i will
wrap it further with the if condition so
if it is if it is true it will
give me one and if it is false it will
we give me 0 so it will let me go
further closer
what I want to do it so I will drag it
to rest of the cell and i will see how
to hear you see it is working fine and
but i will write some other text for
example i will type for their half our
double and our Father and yeah it
it gave me something it to pull their
own number six
slow starts working fine so up to now
it's it's fine but my goal is not just
to c 0 1 10000 to see if it is brandon
my goal is to to search all the cells
that contain the target text that I have
put it in this set so for the before
this the preferably is to use the max
function so the what the max function do i will
explain it later, first I will a Implement
that max function. I will use max from the
first cell to the rest of cells so here
you see, lets drag it down and hear
something out
and so this is the example I want to
such en okay you're so it's working fine
what actually the the max function does
max function start from the beginning
here for example i gave it start from
the beginning and right from the
beginning to fill their cell it counts
the maximum number that if we get and
before that before to put its own
value in the cell it will increment it
with one to put the number
example if I found here one occurence in the
next occurrence it we find the maximum
til that cell here the maximum till
that so you can see all these
green and green area on the
maximum number is 1 and then before to
put here it will increase here it will
implement it with one input we can test
it with some other number for them well
we can just put a hour you see it
started from the beginning of the first
occurrence second third 456 still here
and hit found the 16 and then and 17 and
18 column it had nothing so I i can see
here because i have not dragged it
till here i can further drag it to all
the cells to see the effect more clearly
here you see
it counted till 16 and next next two cells
it doesn't contain 'A' and then and for
the further cell it
it found a but it is counted from the
beginning you you can
you can see from here you see it counted
from the beginning till that cell and
put the maximum ok now i am moving this
this row to the left of the names list
to get further closer what I actually
want to do I got it and I'm going to cut
this and going to paste in this so here
I i have the same implementation and
I can type anything in it and you see
If I type, I have no occurrence, so i will type
sage and you see here it gave
me the exact match
ok so the next function i'm going to use
is Row function row is a very useful
function to get list of incremented
numbers so here it is
row function and i will give this cell
and i will start from the first of the cell
drag it down so here give me an
increment function so i will combine and
this Row function with the vlookup and
in the Conjunction of these two these two
cells to get the dynamic list of names
of all the person containing the text that
i will put in this box
so let's wrap it with the vlookup
so wrapping it with vlookup
function we look at something like that
in Vlookup function we we are using
we're rapidly using row and in
there
in the next to the road we are using
this table at least the list of
occurrences that we implemented using
the search function and then and then
actually using the name of the list and
we get the second column and 0 as a full
match so here you see we got siege
wisiter that contain the text that it is
in the box it is in the sage in the 10th row
we can drag it down
to get the list also so something went
wrong. so the problem is because it
found the this text inside this and if
it couldn't find it returns NA values
so we get rid of this by wrapping this
function further in ifError, we already have
used iferror function in our course
if error and it will return the
empty string if it will get an error so
we dragged it to see the effect here you
see we got only the the string that
contain the name that contain this text
we can type something else for example a
a and get nothing. we can type a to give a
a list of names that contain a so
here we have a very simple such search
box maybe we can something and it will
narrow down over such criteria by giving
all the names that contains this text further
we can also implement something to
get the total list of names that contain
this because here we don't know how many
names contain these texts so we can use
countif functions. the count
count if function and in countIf function
will give the Ranger ,Ranger starting
from i1 and range starting from I one
to I don't know, lets say I30 and the criteria
is anything anything means if there is
something in the less than it should
counted so for anything we can use ?
followed by a * inside in inverted commas
so here it is we can find
see that there are total 17 matches that
contains a so for example we can type it
again
so again Y U, it's one then we
can type anything our it give one and
anything for example here ai give to so
here this is box maybe type
anything and it's quite dynamic will
generate a dynamic list of all the names
containing this text and we also
getting the total count in this column
that's all
so this is how we can implement such
using all the very simple formulas
that we implemented in our course thank
you very much for watching this video
hope it will be useful to for the further
Excel exploration
thank you
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What is in here? - Duration: 3:07.
For more infomation >> What is in here? - Duration: 3:07. -------------------------------------------
Clone DB FTP Files: Content and Uses for Genome Assemblies - Duration: 9:32.
The NCBI Clone database integrates information about clones and libraries, including sequence
data, map positions and distributor information.
This video focuses on the Clone DB FTP files, which can be a great help in improving genome
assemblies, detecting potential structural variation, and placing clones based on clone
end sequences.
Also see the companion video about using clone placements to interpret and improve assemblies.
A link to that video is provided at the end of this video.
Let's start on the Clone DB home page.
For general information and Help, use the links under Getting Started.
I'll go to the FTP site using the link under Tools.
You see two top-level directories here, reports and utility.
The utility directory contains scripts that you can use for bulk download of FASTA sequences
for clone ends.
This README file explains how to use these scripts.
The reports directory is organized alphabetically by organism scientific name, and the README
files provide detailed descriptions of each file, including column identifiers.
I'll use chicken, Gallus gallus as the example for this video.
The files here provide information about the sequences and genomic placements for the clones
in Clone DB.
You find library-specific files, such as CH261 and J_AC, that begin with their Clone DB genomic
library abbreviations, then at the bottom are files general to the organism; Gallus
gallus has taxid 9031.
Let's review these files to see how they can help you.
We'll first look at files with information about all libraries for this organism.
At the bottom of the page, library_9031.out provides general data such as the total number
of clones with end sequences, with inserts, and that have both ends sequenced.
These numbers are given for each library and vector type.
The clone_acstate.out report provides sequence identifiers and metadata for the inserts.
FASTA for these insert sequences can be downloaded using the E-Utilities, our API for accessing
Entrez Nucleotide.
Likewise, endinfo_9031.out provides lists of sequence identifiers and metadata for the
end sequences.
To download the FASTA for these sequences, use the identifiers as input to the Perl script
in the utility directory we saw earlier.
Notice that both the clone_acstate and endinfo.out reports are available in *library*-specific
and across library versions.
The remaining files are associated with clone placements, a valuable tool for assembly assessment,
but many researchers lack the resources to generate them in-house.
Here, the data are immediately available for your use and interpretation.
Clone DB generates clone placements by aligning end and insert sequences to NCBI-annotated
genome assemblies and assigning the clone one or more, or possibly zero, genomic locations.
In order for a clone to receive a placement based on its end sequences, both ends must
be placed on the same assembly molecule.
For additional details about the clone placement process, see the Clone Placements FAQ.
All of these library-specific .gff files follow the same naming scheme.
The library abbreviation is followed by the accession.version of the assembly on which
the clones are placed.
This is followed by the Clone DB placement release ID, which defines the set of clone
sequences and software versions used for the placements.
And finally, the type of placements contained in the file, such as, both ends not uniquely
placed or both ends uniquely placed.
See the README_GFF.txt in the reports directory.
Note, you may find some legacy files that pre-date the use of the release id, as show
here.
Clone DB suggests always using the most recent files, unless your analysis specifically calls
for an older set of placement data.
Now let's take a closer look at how these data can help you.
I'll cover the four general tasks shown here: - Generate basic assembly QA stats
- Fill in assembly gaps - Detect mis-assembly and structural variation
- Place unlocalized or unplaced assembly molecules
And in some cases I'll use screenshots from our Genome Data Viewer to graphiclly illustrate
these tasks.
Be aware that in most cases, the task is best completed by parsing and analyzing the clone
placement files.
For the first task, generating assembly QA statistics, clone placement data can provide,
- the percentage of clones that are placed - concordant vs. discordant placements, and
- unique vs. non-unique placements
For these analyses, the clone end placement summary report, as well as the 3 clone placement
GFF files, unique_concordant, unique_discordant and multiple, are easily parsed to provide
this information.
To identify clones that contain sequence missing from an assembly, you can identify assembly
gap locations from the organism's Assembly AGP files in the /genomes/genbank FTP site.
To get these files for chicken, go to latest_assembly_versions, on to assembly_structure, Primary_Assembly,
assembled_chromosomes/, and finally, the AGP directory.
Again, use the AGP files to get assembly gap locations.
With those locations, you can parse the unique_concordant and unique_discordant placement files to identify
clones that span the gap locations.
This graphic shows that clone CH261-52K22, and several other unique_concordant placements,
spans this assembly gap in chicken chromosome 1.
Likewise, end placements near and properly oriented towards gaps may identify clones
that project into, but do not span gaps.
Use the single_end_placed files for this.
The third task is to detect regions with mis-assembly or structural variation.
These are typically revealed by analysis of discordant clone placements, that is, placements
which are larger, smaller or oriented contrary to the corresponding assembly region.
One example of using discordant placements to identify a potential region of mis-assembly
is illustrated on chicken chromosome 6.
Here we see a tight cluster, by location, of 3 discordant placements from the J_AC and
J_AD libraries in which the ends align in an inverted orientation relative to this assembly
component.
Library tracks like these present data from the unique_concordant, unique_discordant and
multiple GFF files.
This pattern suggests an issue with the orientation of this component in the assembly, consistent
with the report of an assembly error at this location to the Genome Reference Consortium
(GRC), the group that maintains the assembly for this organism.
Another example is illustrated on chicken chromosome Z.
Here you see a dramatic shift from unique concordant (blue) to mostly discordant placements,
the red clones, in three different libraries.
This is another likely region of mis-assembly.
Switching over to chromosome 21, here we see a region with both concordant and discordant
placements.
The top library is derived from the same source DNA as the assembly, but the other two libraries
come from a different source.
This heterogeneity, and the mix of concordant and discordant placements, may reflect a structurally
variant region.
Other useful files for detecting mis-assembly include the multiple and both_ends_not_uniquely_placed
files.
These can help identify genomic regions with repetitive or segmental duplications.
And the both_ends_uniquely_placed file can help detect chimerism, a hallmark of a misassembly.
The final task is to place unlocalized or unplaced assembly molecules.
Unlocalized scaffolds can be assigned to a chromosome, but can not be ordered or oriented
on that chromosome, whereas unplaced scaffolds can not be assigned to any chromosome.
Clone sequences in the both_ends_uniquely_placed GFF file can help assign these scaffolds to
chromosomal locations.
The assembly_report.txt file, which is in the same FTP location we saw earlier for the
Gallus gallus assembly_structure directory, lists the roles, that is, unlocalized or unplaced,
for all assembly sequences.
That concludes this tutorial.
If you would like help using these files, ask questions via the "Support Center" link
on the Clone DB home page.
And if you want more detail about the Clone DB placement process, see the NCBI Bookshelf
chapter and the January 2013 Database issue of Nucleic Acids Research.
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