Leonardo da Vinci
- He was a painter, sculptor, architect, and engineer.
- Leonardo’s artistic inclinations must have appeared early, when he was about 15.
- His father, who enjoyed a high reputation in the Florence community, apprenticed him to artist Andrea del Verrocchio.
- In 1472 Leonardo was accepted into the painters’ guild of Florence, but he remained in his teacher’s workshop for five more years
- Leonardo did not apply himself to higher mathematics—advanced geometry and arithmetic—until he was 30 years old, when he began to study it with diligent tenacity.
- Leonardo spent 17 years in Milan, until Ludovico’s fall from power in 1499. He was listed in the register of the royal household as pictor et ingeniarius ducalis (“painter and engineer of the duke”).
- Leonardo painted at least 34 paintings.
- Leonardo considered his eyes to be his main avenue to knowledge.
For more info about Leonardo da Vinci


Train binary decision tree classifier. Highly optimized code for training decision trees over binary variables. Training a decision stump (depth=1) over 5000 features and 10000 training examples takes 70ms on a single core machine and *7ms* with 12 cores and OpenMP enabled (OpenMP is enabled by default, see toolboxCompile). This code shares similarities with forestTrain.m but is optimized for binary labels. Moreover, while forestTrain is meant for training random decision forests, this code is tuned for use with boosting (see adaBoostTrain.m). For more information on how to quickly boost decision trees see: [1] R. Appel, T. Fuchs, P. Dollár, P. Perona; "Quickly Boosting Decision Trees – Pruning Underachieving Features Early," ICML 2013. The code here implements a simple brute-force strategy with the option to sample features used for training each node for additional speedups. Further gains using the ideas from the ICML paper are possible. If you use this code please consider citing our ICML paper. During training each feature is quantized to lie between [0,nBins-1], where nBins<=256. Quantization is expensive and should be performed just once if training multiple trees. Note that the second output of the algorithm is the quantized data, this can be reused in future training. USAGE [tree,data,err] = binaryTreeTrain( data, [pTree] ) INPUTS data - data for training tree .X0 - [N0xF] negative feature vectors .X1 - [N1xF] positive feature vectors .wts0 - [N0x1] negative weights .wts1 - [N1x1] positive weights .xMin - [1xF] optional vals defining feature quantization .xStep - [1xF] optional vals defining feature quantization .xType - [] optional original data type for features pTree - additional params (struct or name/value pairs) .nBins - [256] maximum number of quanizaton bins (<=256) .maxDepth - [1] maximum depth of tree .minWeight - [.01] minimum sample weigth to allow split .fracFtrs - [1] fraction of features to sample for each node split .nThreads - [16] max number of computational threads to use OUTPUTS tree - learned decision tree model struct w the following fields .fids - [Kx1] feature ids for each node .thrs - [Kx1] threshold corresponding to each fid .child - [Kx1] index of child for each node (1-indexed) .hs - [Kx1] log ratio (.5*log(p/(1-p)) at each node .weights - [Kx1] total sample weight at each node .depth - [Kx1] depth of each node data - data used for training tree (quantized version of input) err - decision tree training error EXAMPLE See also binaryTreeApply, adaBoostTrain, forestTrain Piotr's Computer Vision Matlab Toolbox Version 3.40 Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] Licensed under the Simplified BSD License [see external/bsd.txt]


edtfy is a javascript tool that attempts to parse a user input and transforms it into the Extended Date/Time Format format.
The following forms of dates are currently supported:
date
,
interval
,
uncertain/approximate
,
season
,
one of a set
,
multiple dates
.
View code on github
Usage
Install:
npm install edtfy # npm
bowser install edtfy # bower
And use:
const edtfy = require('edtfy');
edtfy.locale('en');
edtfy('around march 1988'); // 1988-03~
Features
Date
A basic date can be either defined by
a year
,
a month and a year
, or
a day, a month, and a year
.
Recognized formats for days:
D
,
DD
,
Do
.
Recognized formats for months:
M
,
MM
,
MMM
,
MMMM
.
Recognized format for year:
YYYY
.
Interval
An interval is a period of time represented by a start date and an end date.
Start and end dates are dates can be either a
date
or any date format described above,
uncertain/approximate
,
unspecified
,
season
, or
various
.
Uncertain and approximate
The parser will look for terms as
around
or
about
that indicates the dates is approximate, or a
?
sign, for an uncertain date. A date can be both uncertain and approximate.
Unspecified
The characters
u
and
*
indicate that the value of the digit is unknown.
Season
The parser finds seasons, if they are used in combination with a year.
One of a set
The parser finds sets of dates, when these ones are separated by the
or
keyword. A set member can be as specified in
date
,
uncertain/approximate
,
unspecified
,
season
, or
various
.
Multiple dates
The same way as sets, the parser finds multiple dates, when these ones are separated by the
and
keyword. These dates can be as specified in
date
,
uncertain/approximate
,
unspecified
,
season
, or
various
.
Various
The parser handles centuries, as well as 5digits long numbers.


LSD.reporter¶
The different Reporter of superbubbles
-
class
LSD.reporter.
CompleteReporter
(g, path)[source]¶ Write a file with informations about the graph the detection time, the number of superbubbles and a list of superbubbles. Inspired by the output of SUPBUB. The superbubble <1,4> creates the output: <1,4>
-
class
LSD.reporter.
PrintReporter
[source]¶ Simple print the Superbubble: The superbubble <1,4> creates the output. [1, 2, 3, 4]


«
In order to remeber it for the next time I connect As a user I want to add a task with an name and an date in order to
Tags: @Todo


How Gilt’s Free R Class Set Me on the Path to Study Data Science
Written by Vivian Peng, Columbia University graduate (Public Health Informatics, 2014), professional designer and data science trainer
A year ago, “R” meant no more to me than any other letter of the English alphabet. Today, it represents the door that has opened a host of opportunities and a newly discovered passion into the data science world.
For those of you who are not familiar with R, it is an open-source programming language that’s widely used by statisticians and data scientists to explore data sets and conduct analyses. I first stumbled into R last fall, when I signed up for the Intro to Data Science course at Columbia University. As a second-year grad student at Columbia’s Mailman School of Public Health, I was lured in by all of the buzz about data science and interested in learning “enough about programming to be able to speak to developers.”
On the first day of lab, I received an email stating that our session was rescheduled to the following day because our professor had a scheduling conflict with the New York Open Statistical Programming Meetup. This presented a scheduling conflict for me—I had to work that day. Furious about having to miss my first Data Science session, I Googled our professor, Jared Lander, to see what his deal was.
Not only did I learn that Jared the organizer of the aforementioned meetup, but that he’s also the author of R for Everyone, a corporate trainer of R and data science, and the founder of his own consulting business. In conducting my Google search, I discovered that he was teaching a free, day-long R course at Gilt the next week, and immediately signed up to attend. In my application, I candidly wrote, “Jared is my professor, and I really need help on my homework."
In retrospect, I am incredibly grateful to Gilt for accepting me to the course for a couple of reasons. First, it saved my sanity that week, as I felt more prepared and better equipped to complete the assignment I was working on at the time. And most importantly, it was the first time I realized that I really enjoy coding. Little did I know that, one year out, I’d still be coding–and passionately helping others to code as well.
Since taking Gilt’s R class, I’ve continued to pursue my ambitions in data science by building data visualizations that raise awareness of public health issues. And just this week, I got the opportunity to assist Jared in his training course at a big investment-banking firm in NYC. It’s funny how life comes full-circle, and how the frustrations of our present can pave the way for possibilities in our futures.
Thank you Gilt and Jared Lander for setting me on this path!
Have you attended one of our free Gilt courses–and has it helped your career or started you on an unexpected path? Ping us on Twitter and let us know. :)


< root
TSLR - Components Guide
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Muhsin is a value driven IT Professional with a deep interest in technology and innovation. He has demonstrated direct sales experience and easily builds rapport with client prospects. Muhsin has completed a highly competitive NPower training program that provides him with the skills necessary to support today’s leading technologies. He takes pride in his Creativity, Teamwork, and maintains a strong passion for continuous learning! My social style is expressive. I'm someone who loves to learn, an innovative thinker, and I enjoy working with others . I have a strong interest in Technology, Business, and Innovation (I aspire to one day establish myself in all three fields). I've gone on many trips all around the globe, and an important lesson that I've learned is "Wherever you go, and whatever you do, life is what YOU strive for and make it ". With that being said, I give maximum effort when dealing with any type of task that comes my way in life, ultimately striving for success! Summary of Qualifications: • 2+ years’ experience programming while implementing standard practices (Organizing/Troubleshooting code, Nesting, & Commenting) • Easily able to communicate and collaborate with customers, coworkers, and supervisors • Experienced in assessing program/application performance and optimizing/improving it through design and best coding practices • 5+ years experience in customer service at marketing events, corporate security sites, and public/national/international events • Adept at investigating, examining, and solving technical programming problems Core Competencies: • Front End Development | • Testing, debugging, reporting, and documenting | • Software Lifecycle Development | • Pair Programming | Technical Competencies: HTML5 Certified(Proficient), JavaScript Certified(Intermediate), CSS3 Certified(Intermediate), Java(Intermediate), SQL Certfied(Intermediate), XML(Beginner), Python(In Development), Big Data Analytics: Hadoop Essentials Certified (Intermediate)
I'm an all around athlete, I enjoy playing basketball, football, and baseball everynow and then. However, Soccer is my main sport! Playing sports has definitely impacted my work ethic, in the sense that it has made me more of a team player/leader/motivator and someone that generally wants success for not only himself, but all his teammates
To learn new technical languages and concepts to further equip and enhance my IT skills
I'm aiming to become a top tier full-stack developer and to also get into the Big Data field of IT To eventually start my own innovative tech business
I'm currently on the market and looking for an exciting career in IT . Aiming to connect and collaborate with great minds to achieve great things!
647-687-6135


Before Change
msg = ("Graph was not connected, retained only the largest strongly "
"connected component ({:,} of {:,} total nodes)")
utils.log(msg.format(len(list(G.nodes())), original_len))
else:
// if the graph is not connected retain only the largest weakly connected component
if not nx.is_weakly_connected(G):
After Change
msg = (f"Graph was not connected, retained only the largest strongly "
f"connected component ({len(G)} of {original_len} total nodes)")
utils.log(msg)
else:
// if the graph is not connected retain only the largest weakly connected component
if not nx.is_weakly_connected(G):


I am a first year Master's student in the School of Electrical and Computer Engineering at Purdue University, West Lafayette. I'm presently working on Neural Speech Decoding, as a Research Assistant with Prof. Joseph Makin. In Spring 2021, I was a Teaching Assistant for CNIT 15501, an undergraduate course on Python Programming, with Prof. Tawfiq Salem.
I completed my undergradute degree from IIT Madras in Electrical Engineering with a minor in Data Science. I later worked as a Data and Applied Scientist at AI and Research, Microsoft, Bengaluru office for a year and half. At Microsoft, I build frameworks for detection, rejection and removal of adversarial attacks on multi-media advertising such as Product and Text Ads displayed anywhere by Microsoft that violates Editorial Policies or poses risks from Fraud, Account Compromise and Spoofing.
M.S. in Electrical & Computer Engineering (ongoing)
Purdue University, West Lafayette
B.Tech in Electrical Engineering
Indian Institute of Technology, Madras
AISSCE, CBSE (XII)
Chinmaya Vidyalaya, Vaduhala
AISEE, CBSE (X)
Talent Public School, Njarakkal
TA for CNIT 15501 Introduction to Software Development.
Responsibilities include:
Built frameworks for the detection of Editorial Policy violations and risks in Product and Text Ads.
Responsibilities include:


On approach, fly the 'Q' flag. Do not bother to call via VHF as the officials usually do not have working radios.
Officials do not come to the yacht and it is necessary to visit the relevant offices (Port Captain, Police, Immigration, Customs and Health) for clearance. Speaking French may be an advantage. However, some officials do know a little English.
On entry, the following information will be required: point of departure; itinerary; crew and passengers; a port of call authorization issued by nearest office of Service de la Marine Marchande; date of entry in and exit from Malagasy territorial waters.
It is necessary to both clear in and out at each port visited.
On leaving a port; ensure that you have an outward Clearance document and also a "Permis de Circulation".
Officials, apart from being slow and not always reliable, are normally not a problem. However, see the fees section on dealing with payment requests.
No agent is required.
Last updated November 2015.
Passports must be valid for 6 months beyond your departure date.
It is no longer possible to get a free 30 day visa on arrival.
For stays of up to 90 days, visas are available on arrival. Fees are as follows:
Ariary 80,000 for a stay not more than 30 days;
Ariary 100,000 for a stay between 30 days and 60 days; and Ariary 140,000 for a stay between 60 and 90 days.
After 90 days no visa extensions are possible. You will have to check out and do a run to Mayotte and on return can get 3 months again.
For those sailing to Madagascar from neighbouring countries and wanting to stay longer than 90 days, a visa in advance is required. The most convenient places to obtain a visa from a Malagasy consulate are the Seychelles, Mauritius, Réunion or Tanzania. The visa application form is available at http://www.madagascar-consulate.org/visainfo.html
Last updated July 2016.
Firearms must be declared and may be removed for the duration of the stay.
Fruit and vegetables will need to be declared.
Non-residents may take up to one kilogram of precious and semi-precious stones out of the country provided proper receipts are produced.
You may only take 100 grams of vanilla out of the country.
Last updated November 2015.
Update August 2016: An outbreak of Foot and Mouth disease in Reunion and Rodrigues has resulted in strict food import restrictions. See this Noonsite report
Any meat or dairy products may be destroyed.
If coming from continental Africa, yellow fever and cholera vaccination certificates will be required.
Immunisation against typhoid and poliomyelitis is often recommended.
Malaria risk, predominantly in the malignant falciparum form, exists all year throughout the country and is highest in coastal areas. Resistance to chloroquine has been reported. The recommended prophylaxis is mefloquine.
All water should be regarded as being potentially contaminated and should be boiled before its use in any form.
Hepatitis A, B, and E are endemic and precautions are advised.
It must also be noted that Madagascar has the highest number of cases of bubonic plague in the world (500 annually) including some fatalities.
Last updated September 2016.
If visiting another port in Madagascar, ensure that you have a "Permis de Circulation" as well as an outward clearance document.
Initial 30 day visa on arrival is free.
Official charge for a 2 month extension is 140,00 Ar (approx. $42)
The only other official charge is 35,000 Ar ($9 USD) for your exit paper (Maritime Clearance)
For advice on how to deal with other requests (bribes) for payment see the Noonsite report.
Last updated November 2015.
Military and police establishments must not be photographed.
There are random police vehicle checkpoints throughout Madagascar, so all visitors should carry photo identification in the event of police questioning. These check points are routine in nature, and should not result in vehicle and/or person searches as long as valid identification is shown.
Walking at night, whether alone or in a group is not considered safe in urban areas, including in tourist areas.
It is now possible to get authorisation to visit the Glorieuses Islands.
Last updated November 2015.
In some parts of Madagascar, aspects of daily life are regulated by taboos, known as fady. These vary from one region to another. Fady can range from forbidden foods to restrictions in clothing. Some areas subject to fady may be forbidden to foreigners, but these are mainly in remote parts of the country. If you intend to visit remote areas, seek advice first.
Animals are not allowed to land.


|
ThorVG
v0.1
|
A module managing the multiple paints as one group paint. More...
Functions | |
| TVG_EXPORT Tvg_Paint * | tvg_scene_new () |
| Creates a new scene object. More... | |
| TVG_EXPORT Tvg_Result | tvg_scene_reserve (Tvg_Paint *scene, uint32_t size) |
| Sets the size of the container, where all the paints pushed into the scene are stored. More... | |
| TVG_EXPORT Tvg_Result | tvg_scene_push (Tvg_Paint *scene, Tvg_Paint *paint) |
| Passes drawing elements to the scene using Tvg_Paint objects. More... | |
| TVG_EXPORT Tvg_Result | tvg_scene_clear (Tvg_Paint *scene) |
| Sets the total number of the paints pushed into the scene to be zero. (BETA version) More... | |
A module managing the multiple paints as one group paint.
As a group, scene can be transformed, translucent, composited with other target paints, its children will be affected by the scene world.
| TVG_EXPORT Tvg_Result tvg_scene_clear | ( | Tvg_Paint * | scene | ) |
Sets the total number of the paints pushed into the scene to be zero. (BETA version)
| TVG_EXPORT Tvg_Paint* tvg_scene_new | ( | ) |
Creates a new scene object.
A scene object is used to group many paints into one object, which can be manipulated using TVG APIs.
| TVG_EXPORT Tvg_Result tvg_scene_push | ( | Tvg_Paint * | scene, |
| Tvg_Paint * | paint | ||
| ) |
Passes drawing elements to the scene using Tvg_Paint objects.
Only the paints pushed into the scene will be drawing targets. If you know the number of pushed objects in the advance, please call tvg_scene_reserve().
| [in] | scene | A Tvg_Paint pointer to the scene object. |
| [in] | paint | A graphical object to be drawn. |
| TVG_RESULT_SUCCESS | Succeed. |
| TVG_RESULT_INVALID_ARGUMENT | An invalid pointer to the scene . |
| TVG_RESULT_MEMORY_CORRUPTION | An invalid pointer to the paint . |
| TVG_RESULT_INSUFFICIENT_CONDITION | An internal error. |
| TVG_EXPORT Tvg_Result tvg_scene_reserve | ( | Tvg_Paint * | scene, |
| uint32_t | size | ||
| ) |
Sets the size of the container, where all the paints pushed into the scene are stored.
If the number of objects pushed into the scene is known in advance, calling the function prevents multiple memory reallocation, thus improving the performance.
| [in] | scene | A Tvg_Paint pointer to the scene object. |
| [in] | size | The number of objects for which the memory is to be reserved. |
| TVG_RESULT_SUCCESS | Succeed. |
| TVG_RESULT_INVALID_ARGUMENT | An invalid Tvg_Paint pointer. |


About Page
About page helps you describe yourself or your company. This page contains such blocks: page title, company description, services that company provide, approach block and team members block. Body of this page must have id #about-page.
Company Description
This block contains title, lead paragraph and normal paragraph.
company description

The Studio

We have many years experience and many interesting problems which we solved. We are happy about collaboration with creative people and organizations to provide best design.

Suspendisse pellentesque nisi dui, sodales consequat dolor ullamcorper non. Integer malesuada elit sapien, sed volutpat justo maximus non.

Services
Each service block has next layout: title and description.
services items

Animation

Phasellus maximus felis purus, eget maximus ligula scelerisque id.

Objects & Installation

Etiam risus metus, vehicula gravida elementum in, mollis ut ipsum.

Identity

Quisque vulputate orci in ipsum ultrices, vel volutpat quam sagittis.

Videos

Nullam sit amet libero id augue tincidunt pretium pharetra eu lorem.

Approach
This part has image and text with links.
approach

Our Approach

We believe that design it's a first thing the user sees. So, we care about true solutions and we will focus only on the client's problem. We love our clients and make them happy.

Our Cases

Contact Us

Team
Each member block has next layout: photo, name, post and social links.
team member layout

Lydia Gyurina

Visual Artist

If you have any questions that are beyond the scope of this help file, please feel free to email via my user page contact form here. Thanks so much!


Vim documentation: os_unix
main help file*os_unix.txt* For Vim version 7.2. Last change: 2005 Mar 29 VIM REFERENCE MANUAL by Bram Moolenaar *unix* *Unix* This file contains the particularities for the Unix version of Vim. For compiling Vim on Unix see "INSTALL" and "Makefile" in the src directory. The default help file name is "/usr/local/lib/vim/help.txt" The files "$HOME/.vimrc" and "$HOME/.exrc" are used instead of "s:.vimrc" and "s:.exrc". Additionally "/usr/local/etc/vimrc" is used first. If "/usr/local/share" exists it is used instead of "/usr/local/lib". Temporary files (for filtering) are put in "/tmp". If you want to place them somewhere else, set the environment variable $TMPDIR to the directory you prefer. With wildcard expansion you can use '~' (home directory) and '$' (environment variable). *fork* *spoon* For executing external commands fork()/exec() is used when possible, otherwise system() is used, which is a bit slower. The output of ":version" includes |+fork| when fork()/exec() is used, |+system()| when system() is used. This can be changed at compile time. (For forking of the GUI version see |gui-fork|.) Because terminal updating under Unix is often slow (e.g. serial line terminal, shell window in suntools), the 'showcmd' and 'ruler' options are default off. If you have a fast terminal, try setting them on. You might also want to set 'ttyfast'. When using Vim in an xterm the mouse clicks can be used by Vim by setting 'mouse' to "a". If there is access to an X-server gui style copy/paste will be used and visual feedback will be provided while dragging with the mouse. If you then still want the xterm copy/paste with the mouse, press the shift key when using the mouse. See |mouse-using|. Visual feedback while dragging can also be achieved via the 'ttymouse' option if your xterm is new enough. *terminal-colors* To use colors in Vim you can use the following example (if your terminal supports colors, but "T_Co" is empty or zero): :set t_me=^[[0;1;36m " normal mode (undoes t_mr and t_md) :set t_mr=^[[0;1;33;44m " reverse (invert) mode :set t_md=^[[1;33;41m " bold mode :set t_se=^[[1;36;40m " standout end :set t_so=^[[1;32;45m " standout mode :set t_ue=^[[0;1;36m " underline end :set t_us=^[[1;32m " underline mode start [the ^[ is an <Esc>, type CTRL-V <Esc> to enter it] For real color terminals the ":highlight" command can be used. The file "tools/vim132" is a shell script that can be used to put Vim in 132 column mode on a vt100 and lookalikes. top - main help file


The audio offset controls where you are in the series of people. The larger your offset, the farther along you are in the chain, the more people will be ahead for you to hear, and the fewer people will be behind to hear you. If you're with a group of people, figure out now who will be at which position. A good place to start is people at 0, 10, 20, 30, 40, etc. Numbers over 100 generally won't work because the server buffer isn't large enough. Eventually, we're planning to tune this so delays can be much smaller.
Once everyone has an audio offset dialed in, if you're using a video call everyone should mute yourselves and switch to using chat. Press the "start" button and then the "run latency estimation" button. You should hear some clicks, once per second.
If you're not using headphones, the system should be able to hear the clicks it is producing and get a good estimate of your latency. If you're using headphones, try to position the earpiece as close as possible to the mouthpiece:
If that's working you should see "median of N samples" for progressively larger N:
If it doesn't work, try turning up the click volume:
You can also try turning up the overall volume on your computer.
If it still doesn't work, you can calibrate it manually. Each time you hear a click, clap your hands. This won't be as accurate, but it's still pretty good. Try to clap right on the click, and it should be able to make a good estimate for your latency.
Once you have a good latency estimate, scroll back up and click "Stop Latency Estimation". Post in chat that you're calibrated and ready.
At this point the system is running, and if someone with a smaller audio offset than you says something, you should hear it several seconds later. Try having a person at position zero saying something, and have everyone else sing along.
To stop the demo, press the Stop button.


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Team Green
This project is dedicated to the analysis of C++ files
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HowTo
To access your account, just click on its name. It will open the associated list of folders.
See Also:
What are the pre-requisites?


BXTR
Studio
Portfolio of artist and developer
James Baxter
Contact
Artist CV [PDF]
LinkedIn
Instagram


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|
breseq version 0.24 revision cd15a9d216e1
mutation predictions | marginal predictions | summary statistics | genome diff | command line log |
| Predicted mutations | ||||||
|---|---|---|---|---|---|---|
| evidence | position | mutation | freq | annotation | gene | description |
| RA | 649,391 | T→A | 100% | I471F (ATC→TTC) | mrdA ← | transpeptidase involved in peptidoglycan synthesis (penicillin‑binding protein 2) |
| RA | 683,496 | A→C | 100% | V65G (GTT→GGT) | nagC ← | DNA‑binding transcriptional dual regulator, repressor of N‑acetylglucosamine |
| RA | 751,788 | G→T | 21.3% | intergenic (+295/‑552) | ybgG → / → cydA | alpha‑mannosidase/cytochrome d terminal oxidase, subunit I |
| RA | 1,329,516 | C→T | 100% | H33Y (CAC→TAC) | topA → | DNA topoisomerase I |
| JC JC | 1,733,647 | IS150 (–) +3 bp | 98.1% | coding (683‑685/1413 nt) | pykF → | pyruvate kinase |
| RA | 2,082,685 | G→A | 100% | A494V (GCT→GTT) | yegI ← | hypothetical protein |
| RA | 3,762,741 | A→T | 100% | K662I (AAA→ATA) | spoT → | bifunctional (p)ppGpp synthetase II/ guanosine‑3',5'‑bis pyrophosphate 3'‑pyrophosphohydrolase |
| RA | 3,875,625 | +T | 100% | intergenic (‑59/+294) | glmU ← / ← atpC | bifunctional N‑acetylglucosamine‑1‑phosphate uridyltransferase/glucosamine‑1‑phosphate acetyltransferase/F0F1 ATP synthase subunit epsilon |
| MC JC | 3,894,997 | Δ6,934 bp | 100% | IS150‑mediated | rbsD–[yieO] | rbsD, rbsA, rbsC, rbsB, rbsK, rbsR, [yieO] |
| RA | 4,100,655 | C→T | 100% | M192I (ATG→ATA) | hslU ← | ATP‑dependent protease ATP‑binding subunit |
| RA | 4,192,978 | G→C | 100% | intergenic (+146/+147) | rrfE → / ← yjaB | 5S ribosomal RNA/predicted acetyltransferase |
| RA | 4,266,649 | C→A | 9.7% | intergenic (‑345/‑48) | acs ← / → nrfA | acetyl‑coenzyme A synthetase/nitrite reductase, formate‑dependent, cytochrome |
| Unassigned missing coverage evidence | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| seq id | start | end | size | ←reads | reads→ | gene | description | |||
| * | * | ÷ | REL606 | 558852 | 558935 | 84 | 18 [16] | [16] 18 | ECB_00517/appY | conserved hypothetical protein/DNA‑binding transcriptional activator |
| * | * | ÷ | REL606 | 2330774 | 2330873 | 100 | 18 [17] | [17] 18 | elaD/yfbL | predicted enzyme/putative aminopeptidase; b2271_1 |
| * | * | ÷ | REL606 | 3022856 | 3023168 | 313 | 18 [17] | [14] 20 | ECB_02821/ECB_02822 | hypothetical protein/bifunctional Ribulose 5‑phosphate reductase/CDP‑ribitol pyrophosphorylase |
| * | * | ÷ | REL606 | 3692587 | 3692708 | 122 | 19 [15] | [16] 18 | yiaY/selB | predicted Fe‑containing alcohol dehydrogenase/selenocysteinyl‑tRNA‑specific translation factor |
| Unassigned new junction evidence... | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| seq id | position | reads (cov) | reads (cov) | score | skew | freq | annotation | gene | product | ||
| * | ? | REL606 | = 122311 | 55 (1.100) | 9 (0.180) | 6/58 | NT | 15.1% | coding (227/855 nt) | ampE | predicted inner membrane protein |
| ? | REL606 | 1529589 = | 46 (0.770) | coding (19/1698 nt) | sfcA | malate dehydrogenase, (decarboxylating, NAD‑requiring) (malic enzyme) | |||||
| * | ? | REL606 | 553366 = | 10 (0.190) | 13 (0.250) | 6/60 | NT | 44.1% | coding (1526/2817 nt) | ECB_00512 | putative tail component of prophage |
| ? | REL606 | 4169224 = | 23 (0.380) | intergenic (+40/‑173) | rpoC/htrC | DNA‑directed RNA polymerase subunit beta'/heat shock protein | |||||
| * | ? | REL606 | 555924 = | 0 (0.000) | 20 (0.380) | 18/62 | NT | 100% | coding (1209/2346 nt) | ECB_00513 | conserved hypothetical protein |
| ? | REL606 | = 2035093 | NA (NA) | noncoding (1/768 nt) | IS1 | repeat region | |||||
| * | ? | REL606 | 632818 = | 39 (0.760) | 7 (0.140) | 4/60 | NT | 16.7% | coding (17/297 nt) | citD | citrate lyase, acyl carrier (gamma) subunit |
| ? | REL606 | 1241885 = | 31 (0.520) | coding (337/1737 nt) | ycgO | potassium/proton antiporter | |||||
| * | ? | REL606 | 640121 = | 40 (0.730) | 7 (0.130) | 6/64 | NT | 16.9% | intergenic (+39/+16) | cspE/ccrB | cold shock protein E/camphor resistance protein CrcB |
| ? | REL606 | 2994081 = | 29 (0.480) | coding (735/2136 nt) | speC | ornithine decarboxylase, constitutive | |||||
| * | ? | REL606 | = 1036073 | 27 (0.510) | 9 (0.170) | 7/62 | NT | 26.9% | intergenic (+45/+31) | ycbG/ompA | hypothetical protein/outer membrane protein A (3a;II*;G;d) |
| ? | REL606 | = 2540943 | 22 (0.370) | coding (38/1290 nt) | uraA | uracil transporter | |||||
| * | ? | REL606 | = 1081217 | 31 (0.520) | 7 (0.120) | 6/70 | NT | 17.9% | coding (892/1260 nt) | yccE | hypothetical protein |
| ? | REL606 | = 3014302 | 33 (0.550) | coding (883/1137 nt) | ECB_02815 | Capsule polysaccharide export inner‑membrane protein kpsE | |||||
| * | ? | REL606 | = 1106052 | 31 (0.600) | 4 (0.080) | 4/60 | NT | 8.5% | coding (91/2019 nt) | ycdR | predicted enzyme associated with biofilm formation |
| ? | REL606 | 3572096 = | 55 (0.920) | coding (545/1470 nt) | yhiP | predicted transporter | |||||
| * | ? | REL606 | 1241529 = | 22 (0.460) | 4 (0.080) | 3/56 | NT | 16.0% | coding (693/1737 nt) | ycgO | potassium/proton antiporter |
| ? | REL606 | = 2574504 | 20 (0.330) | intergenic (+346/+472) | sseA/sseB | 3‑mercaptopyruvate sulfurtransferase/rhodanase‑like enzyme, sulfur transfer from thiosulfate | |||||
| * | ? | REL606 | 1286705 = | 33 (0.690) | 8 (0.170) | 4/56 | NT | 20.8% | intergenic (+29/‑154) | narI/ECB_01206 | nitrate reductase 1, gamma (cytochrome b(NR)) subunit/hypothetical protein |
| ? | REL606 | 3092577 = | 28 (0.470) | coding (756/927 nt) | yqhG | hypothetical protein | |||||
| * | ? | REL606 | = 1854683 | 26 (0.560) | 5 (0.110) | 3/54 | NT | 14.5% | coding (79/255 nt) | yoaF | conserved outer membrane protein |
| ? | REL606 | = 4266654 | 33 (0.550) | intergenic (‑350/‑43) | acs/nrfA | acetyl‑coenzyme A synthetase/nitrite reductase, formate‑dependent, cytochrome | |||||
| * | ? | REL606 | 1946059 = | 37 (0.720) | 5 (0.100) | 4/60 | NT | 11.5% | coding (149/390 nt) | cheY | chemotaxis regulator transmitting signal to flagellar motor component |
| ? | REL606 | = 3240660 | 40 (0.670) | coding (392/1245 nt) | mtr | tryptophan transporter of high affinity | |||||
| * | ? | REL606 | = 2086451 | 49 (0.950) | 6 (0.120) | 4/60 | NT | 10.8% | intergenic (‑872/‑673) | yegL/yegM | hypothetical protein/multidrug efflux system, subunit A |
| ? | REL606 | 2752961 = | 50 (0.830) | coding (1009/2562 nt) | mutS | DNA mismatch repair protein | |||||
| * | ? | REL606 | = 2211885 | 48 (0.960) | 3 (0.060) | 3/58 | NT | 4.9% | coding (714/1692 nt) | fruA | fused fructose‑specific PTS enzymes: IIBcomponent/IIC components |
| ? | REL606 | = 2503593 | 69 (1.150) | coding (13/1044 nt) | ypfG | hypothetical protein | |||||
| * | ? | REL606 | 2353390 = | 27 (0.450) | 5 (0.080) | 4/70 | NT | 17.0% | coding (1554/1833 nt) | yfbS | predicted transporter |
| ? | REL606 | 3424865 = | 22 (0.370) | intergenic (+83/‑43) | nirD/nirC | nitrite reductase small subunit/nitrite transporter | |||||
| * | ? | REL606 | 2448493 = | 23 (0.420) | 15 (0.270) | 3/64 | NT | 39.5% | intergenic (+24/+26) | nupC/yfeA | nucleoside (except guanosine) transporter/predicted diguanylate cyclase |
| ? | REL606 | = 2774196 | NA (NA) | noncoding (1343/1343 nt) | IS186 | repeat region | |||||
| * | ? | REL606 | = 2574511 | 25 (0.520) | 6 (0.120) | 5/56 | NT | 15.6% | intergenic (+353/+465) | sseA/sseB | 3‑mercaptopyruvate sulfurtransferase/rhodanase‑like enzyme, sulfur transfer from thiosulfate |
| ? | REL606 | 4395051 = | 40 (0.670) | intergenic (‑126/‑56) | yjfO/yjfP | hypothetical protein/predicted hydrolase | |||||
| * | ? | REL606 | = 2574511 | 25 (0.540) | 11 (0.240) | 5/54 | NT | 27.5% | intergenic (+353/+465) | sseA/sseB | 3‑mercaptopyruvate sulfurtransferase/rhodanase‑like enzyme, sulfur transfer from thiosulfate |
| ? | REL606 | 4356096 = | 33 (0.550) | coding (338/537 nt) | blc | outer membrane lipoprotein (lipocalin) | |||||
| * | ? | REL606 | 2753982 = | 48 (0.930) | 4 (0.080) | 4/60 | NT | 8.7% | coding (2030/2562 nt) | mutS | DNA mismatch repair protein |
| ? | REL606 | = 4141016 | 36 (0.600) | coding (89/705 nt) | yijC | DNA‑binding transcriptional repressor | |||||
| * | ? | REL606 | = 3061117 | 31 (0.600) | 3 (0.060) | 3/60 | NT | 8.2% | coding (752/1173 nt) | ECB_02854 | conserved hypothetical protein |
| ? | REL606 | = 4141016 | 36 (0.600) | coding (89/705 nt) | yijC | DNA‑binding transcriptional repressor | |||||
| * | ? | REL606 | = 3203380 | 26 (0.520) | 6 (0.120) | 4/58 | NT | 16.2% | intergenic (+828/+174) | yhaC/garK | hypothetical protein/glycerate kinase I |
| ? | REL606 | = 4141016 | 36 (0.600) | coding (89/705 nt) | yijC | DNA‑binding transcriptional repressor | |||||
| * | ? | REL606 | = 3278116 | 37 (0.620) | 6 (0.100) | 6/70 | NT | 13.8% | coding (46/576 nt) | yrbK | hypothetical protein |
| ? | REL606 | 4541608 = | 38 (0.630) | intergenic (‑14/‑236) | yjiH/kptA | conserved inner membrane protein/RNA 2'‑phosphotransferase‑like protein | |||||
| * | ? | REL606 | 3289193 = | 52 (1.040) | 6 (0.120) | 4/58 | NT | 12.1% | coding (93/930 nt) | yhcC | predicted Fe‑S oxidoreductase |
| ? | REL606 | 3912273 = | 35 (0.580) | intergenic (+53/‑87) | ilvL/ilvG | ilvG operon leader peptide/acetolactate synthase II, valine insensitive, large subunit | |||||
| * | ? | REL606 | = 3814686 | 62 (1.130) | 6 (0.110) | 5/64 | NT | 8.0% | coding (834/1185 nt) | emrD | multidrug efflux system protein |
| ? | REL606 | = 4414859 | 77 (1.280) | intergenic (+305/‑4) | fklB/cycA | FKBP‑type peptidyl‑prolyl cis‑trans isomerase (rotamase)/D‑alanine/D‑serine/glycine transporter |


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