Uses of Package
com.opengamma.strata.data
-
Packages that use com.opengamma.strata.data Package Description com.opengamma.strata.calc Calculates risk measures on trades, applies scenarios and manages market data.com.opengamma.strata.calc.marketdata Provides the ability to obtain market data and perform calibrations and scenario perturbations.com.opengamma.strata.calc.runner The calculation runner.com.opengamma.strata.data Basic types to model market data.com.opengamma.strata.data.scenario Basic types to model market data across scenarios.com.opengamma.strata.loader.csv Loader that reads market data from CSV files.com.opengamma.strata.market Data structures for market data.com.opengamma.strata.market.curve Definitions of curves.com.opengamma.strata.market.curve.node Curve nodes.com.opengamma.strata.market.observable Market data for quotes.com.opengamma.strata.market.param Market data based on parameters.com.opengamma.strata.market.sensitivity Entity objects for sensitivities.com.opengamma.strata.market.surface Definitions of surfaces.com.opengamma.strata.measure.bond Base package for calculation functions.com.opengamma.strata.measure.capfloor Calculation functions for Ibor cap/floor products.com.opengamma.strata.measure.credit Calculation functions for credit products.com.opengamma.strata.measure.fx Calculation functions for FX products.com.opengamma.strata.measure.fxopt Calculation functions for FX option products.com.opengamma.strata.measure.index Calculation functions for index products.com.opengamma.strata.measure.rate Base package for calculation functions.com.opengamma.strata.measure.swaption Calculation functions for swaption products.com.opengamma.strata.pricer Calculators for financial instruments.com.opengamma.strata.pricer.bond Calculators for bonds.com.opengamma.strata.pricer.capfloor Calculators for Ibor cap-floor.com.opengamma.strata.pricer.credit Calculators for credit instruments, such as Credit Default Swap (CDS).com.opengamma.strata.pricer.curve Provides the ability to calibrate curves.com.opengamma.strata.pricer.fx Calculators for FX instruments, such as FX forward and FX swap.com.opengamma.strata.pricer.fxopt Calculators for FX options.com.opengamma.strata.pricer.index Calculators for products based on rate indices, such as Short Term Interest Rate futures (STIRs).com.opengamma.strata.pricer.rate Calculators for rates instruments, such as Forward Rate Agreement (FRA) and interest rate swap.com.opengamma.strata.pricer.swaption Calculators for swaptions. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.calc Class Description MarketData Provides access to market data, such as curves, surfaces and time-series. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.calc.marketdata Class Description MarketData Provides access to market data, such as curves, surfaces and time-series.MarketDataId An identifier for a unique item of market data.MarketDataName A name for an item of market data.NamedMarketDataId An identifier for a unique item of market data that can has a non-unique name.ObservableId A market data identifier that identifies observable data. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.calc.runner Class Description FxMatrixId Identifies the market data for an FX matrix.MarketData Provides access to market data, such as curves, surfaces and time-series.MarketDataId An identifier for a unique item of market data.ObservableId A market data identifier that identifies observable data.ObservableSource Identifies the source of observable market data, for example Bloomberg or Reuters. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.data Class Description FieldName The name of a field in a market data record.FxMatrixId Identifies the market data for an FX matrix.FxRateId Identifies the market data for an FX rate.ImmutableMarketData An immutable set of market dataImmutableMarketData.Meta The meta-bean forImmutableMarketData
.ImmutableMarketDataBuilder A mutable builder for instances ofImmutableMarketData
.MarketData Provides access to market data, such as curves, surfaces and time-series.MarketDataFxRateProvider Provides FX rates from market data.MarketDataId An identifier for a unique item of market data.MarketDataName A name for an item of market data.ObservableId A market data identifier that identifies observable data.ObservableSource Identifies the source of observable market data, for example Bloomberg or Reuters. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.data.scenario Class Description MarketData Provides access to market data, such as curves, surfaces and time-series.MarketDataId An identifier for a unique item of market data.MarketDataName A name for an item of market data.ObservableId A market data identifier that identifies observable data.ObservableSource Identifies the source of observable market data, for example Bloomberg or Reuters. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.loader.csv Class Description FxRateId Identifies the market data for an FX rate.ObservableId A market data identifier that identifies observable data. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.market Class Description MarketDataName A name for an item of market data. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.market.curve Class Description MarketData Provides access to market data, such as curves, surfaces and time-series.MarketDataId An identifier for a unique item of market data.MarketDataName A name for an item of market data.NamedMarketDataId An identifier for a unique item of market data that can has a non-unique name.ObservableId A market data identifier that identifies observable data.ObservableSource Identifies the source of observable market data, for example Bloomberg or Reuters. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.market.curve.node Class Description FxRateId Identifies the market data for an FX rate.MarketData Provides access to market data, such as curves, surfaces and time-series.MarketDataId An identifier for a unique item of market data.ObservableId A market data identifier that identifies observable data. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.market.observable Class Description FieldName The name of a field in a market data record.MarketDataId An identifier for a unique item of market data.ObservableId A market data identifier that identifies observable data.ObservableSource Identifies the source of observable market data, for example Bloomberg or Reuters. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.market.param Class Description MarketDataName A name for an item of market data. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.market.sensitivity Class Description MarketDataName A name for an item of market data. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.market.surface Class Description MarketDataName A name for an item of market data. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.measure.bond Class Description MarketData Provides access to market data, such as curves, surfaces and time-series.MarketDataId An identifier for a unique item of market data.ObservableSource Identifies the source of observable market data, for example Bloomberg or Reuters. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.measure.capfloor Class Description MarketData Provides access to market data, such as curves, surfaces and time-series.MarketDataId An identifier for a unique item of market data. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.measure.credit Class Description MarketData Provides access to market data, such as curves, surfaces and time-series.MarketDataId An identifier for a unique item of market data.ObservableSource Identifies the source of observable market data, for example Bloomberg or Reuters. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.measure.fx Class Description FxRateId Identifies the market data for an FX rate. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.measure.fxopt Class Description MarketData Provides access to market data, such as curves, surfaces and time-series.MarketDataId An identifier for a unique item of market data. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.measure.index Class Description MarketData Provides access to market data, such as curves, surfaces and time-series.MarketDataId An identifier for a unique item of market data. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.measure.rate Class Description MarketData Provides access to market data, such as curves, surfaces and time-series.MarketDataId An identifier for a unique item of market data.ObservableSource Identifies the source of observable market data, for example Bloomberg or Reuters. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.measure.swaption Class Description MarketData Provides access to market data, such as curves, surfaces and time-series.MarketDataId An identifier for a unique item of market data. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.pricer Class Description MarketDataId An identifier for a unique item of market data.MarketDataName A name for an item of market data. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.pricer.bond Class Description MarketDataId An identifier for a unique item of market data.MarketDataName A name for an item of market data.NamedMarketDataId An identifier for a unique item of market data that can has a non-unique name. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.pricer.capfloor Class Description MarketDataId An identifier for a unique item of market data.MarketDataName A name for an item of market data.NamedMarketDataId An identifier for a unique item of market data that can has a non-unique name. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.pricer.credit Class Description MarketData Provides access to market data, such as curves, surfaces and time-series.MarketDataName A name for an item of market data. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.pricer.curve Class Description ImmutableMarketData An immutable set of market dataMarketData Provides access to market data, such as curves, surfaces and time-series. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.pricer.fx Class Description MarketDataName A name for an item of market data. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.pricer.fxopt Class Description MarketDataId An identifier for a unique item of market data.MarketDataName A name for an item of market data.NamedMarketDataId An identifier for a unique item of market data that can has a non-unique name. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.pricer.index Class Description MarketDataId An identifier for a unique item of market data.MarketDataName A name for an item of market data.NamedMarketDataId An identifier for a unique item of market data that can has a non-unique name. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.pricer.rate Class Description MarketDataId An identifier for a unique item of market data.MarketDataName A name for an item of market data. -
Classes in com.opengamma.strata.data used by com.opengamma.strata.pricer.swaption Class Description MarketDataId An identifier for a unique item of market data.MarketDataName A name for an item of market data.NamedMarketDataId An identifier for a unique item of market data that can has a non-unique name.


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[Gecode] Equality, variable aliasing and AllDistinct
Guido Tack
tack at ps.uni-sb.de
Thu May 27 10:30:44 CEST 2004
Hi!
In a discussion with Thorsten and Didier, a technical question came up:
In Mozart, {FD.distinct [A B C]} will immediately detect failure if A=B. This
is not the case for Gecode, where A=B just posts a propagator instead of
identifying the two variables. I know from a discussion with Christian that
he thinks that identifying variables in the store is a bad thing because it
makes the system much more complicated. So the question was whether there is
either a simple solution that enables distinct to test for aliasing, or
whether this is a border case that is not interesting in practical
applications.
Guido
--
Guido Tack
Programming Systems Lab
http://www.ps.uni-sb.de/~tack
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I am hoping to use the skills I gain during this course to successfully gain employment in the IT industry. The destination in mind for me is to eventually run my own business and support other businesses. This is the impact I intend to make when I leave Dev Academy.
These have not changed over the course of foundations and there are not any new ones that will help and hinder me. The experience of foundations has given me some insights for what I understand Bootcamp might be like.
My biggest non-technical challenge is still speaking in front of a group and I hope to work on this as well as giving and receiving feedback, leadership skills and improving my reflective skills to continue with my self-awareness growth.
My expectations of the boot camp team are the same as foundations - be available to assist, pushback when the answer should be obvious, but also support or indicate if I am heading way off track.
My expectations of myself are to give it a go, make the most of the opportunity to succeed, focus on what I need to get done and do it even if it may not go as I expect, intend or plan.
My commitment to how and where I will seek help in a timely way if assistance is needed throughout the day then I think time-boxing the period to self troubleshoot needs to be agreed (no more than 10 - 15 mins and then approach someone else.
My commitment to how I will manage my workload - I think my strengths really help with my personal learning development, especially when it's in an independent hands-on environment. I like to expand my learning experience and stretch the topics the best I can.
The boot camp has certainly taught me how not to manage myself during the foundation part of the programme. I will be looking to fully understand what needs to be achieved each week, work out how much time I need to commit to that and have a daily plan of tasks to track and then execute towards completion.
Where there are dependencies on others I think having scheduled daily stand-ups will help assist us with monitoring progress with each other, seeing what is blocking someone from progressing and determining how we best support them to move forward.
I believe one of my greatest strengths is being a problem solver, I should be able to find ways to overcome barriers along the way to becoming a great developer.
My scheduling information (i.e. block out times at this stage are only 10th Jan - all day and 29th Jan - am only). Any others will be planned into each week to ensure impact to learning is not affected.
My long term goal or career pathway is to attain employment in the IT industry and eventually maybe running my own business to support others in business too.
The skills I would like to see developed in myself while at EDA is the belief in my capability to deliver on what is needed. By communicating, listening, asking questions and gaining an understanding of exactly that I think this can be achieved.
My strengths would include curiousity, problem solving, dedication and persistance.
To me problem solving and curiousity run hand in hand. They have helped me overcome a number of challenges and situations in life. I have always been curious when it comes to how things work and what makes them tick. I also believe dedication and persistance play a big part and work along side these as well. I have a strong internal battle when it comes to problem solving and hate walking away from anything I put my mind to.
The struggle is real being a perfectionist - my key limitation!! And I also have a problem with public speaking or even group speaking. I'm not exactly sure what it is but I tend to go into shutdown mode, forget what I'm talking about and lose all direction, go bright red and feel like the walls are closing in on me.
My description of what I expect from the facilitation team is to be available to assist, pushback when the answer should be obvious, but also support or indicate if I am heading way off track.
I think my strengths really help with my personal learning development, especially when its in an independant hands-on environment. I like to expand my learning experience and stretch the topics the best I can.
The foundations study has certainly taught me how not to manage myself during the bootcamp part of the programme. I will be looking to fully understand what needs to be achieved each week, work out how much time I need to committ to that and have a daily plan of tasks to track and then execute towards completion.
Where there are dependencies on others I think having scheduled daily stand-ups will help assist us with monitoring progress with each other, seeing what is blocking someone from progressing and determining how we best support them to move forward.
My commitment of how and where I will seek help in a timely way if assistance is needed throughout the day then I think time-boxing the period to self trouble shoot needs to be agreed (no more than 10 - 15 mins and then approach someone else.
I believe one of my greatest strengths is being a problem solver, I should be able to find ways to overcome barriers along the way to becoming a great developer.


Challenge 1:
Translate:
- When is the time for a drink?
- Whenever you are thirsty!
Dictionary:
- ut = when
- beba = drink
- oc ut = whenever
- siro = thirsty
- tepa = time
Maj:
- Ut sonu la tepa fo una beba?
- Oc ut tu sonu siro!
Challenge 2:
Translate:
- What can a man drink at a party?
- Usually I drink a beer but sometimes wine!
Dictionary:
- ke posu = what can
- un xoma = a man
- sh bebu = to drink
- at una feta = at a party
- bo algo-tepa = but sometimes
- vino = wine
Maj:
- Ke posu un xoma sh bebu at una feta?
- Iujo ea bebu una bera bo algo-tepa vino!
Note: In some languages like Romanian and Russian "da" means "yes". In Maj, "bo" is using the meaning of "but". This is a conjunction. A preposition that connects arguments in a logical way.
Challenge 3:
Translate:
- How many beers can a man drink before he is drunk?
- Just a single beer of course!
Dictionary:
- vi fil = how many
- ate = before
- ivro = drunk
- ena = a (one)
- solo = single
- zogo = for sure / of course
Maj:
- Vi fil bere posu un xoma sh bebu ate sonu ivro?
- Djo ena solo bera bo zogo!
Challenge 4:
Translate:
- What can a boy drink at his birthday party?
- We serve to our children only water, milk or natural juice!
Dictionary:
- noi = we
- nose = ours
- gatsa = boy
- akva = water
- leca = milk
- nato = natural
- soka = juice
- kide = children
- djo = only
Maj:
Ke posu un gatsa sh bebu la sua naco-feta?
Noi sevu zu nose kide djo akva, leca od nato-suka!
Note: In this example I have used pattern O-A to make a composite word: naco-feta = "birthday party" equivalent to expression: "feta de nacu". Both can be used having the same meaning.
Challenge 5:
Translate:
- Who can drink alcoholic beverages?
- Only healthy adults, which have nothing better to do!
Dictionary:
- ki = who
- buja = booz (drink)
- sano = healthy
- dute = adults
- xaru = to have
- vexa = nothing
- beta = better
- sh faru = to do
Maj:
- Ki poso sh bebu buja?
- Djo dute sano, uk xaru nika beta sh faru!
Challenge 6:
Translate:
- What is your favorite drink?
- The best drink is sparkling water!
Dictionary:
- uk = which
- beta = best
- miro = mineral
- akva = water
Maj:
- Uk sonu al ti fevo beba?
- Lo beta beba sonu miro-akva!
Challenge 7:
Translate:
- Hello girl how are you?
- Do you have money?
Dictionary:
- tcao = hello
- Tcika = girl
- faru = to do
- gete = money
- tenu = to hold
Maj:
- Tcao Tcika ke faru? = Hello girl how are you?
- Bine, tenu gete? = Okay, do you have money?
- No lo tenu = I don't have.
- Poi, vale! = Then good bye!
Challenge 8:
Translate:
- hey boy quit drinking;
- hey boys you must quit drinking;
Dictionary:
- alo = hey
- gatsa = boy
- gatse = boys
- lasu = to leave
- ivru = drinking
- dovu = must
Maj:
- alo gatsa lasu-te de ivru;
- alo gatse dovu sh vh lasu de ivru;


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public interface DpDbCallback
We may also optionally have a set of callback methods which span over several transactions.
If the system is configured in such a way so that the external database owns the candidate data store we must implement callback methods to do this. If ConfD owns the candidate the candidate callbacks can be skipped. Additionally, the lock() callback should be implemented (see more below).
If ConfD owns the candidate, and ConfD has been configured to support confirmed-commit, then three checkpointing methods must be implemented. When confirmed-commit is enabled, the user can commit the candidate with a timeout. Unless a confirming commit is given by the user before the timer expires, the system must rollback to the previous running configuration. This mechanism is controlled by the checkpoint callbacks. See further below.
An external database may also (optionally) support the lock/unlock and lockPartial/unlockPartial operations. This is only interesting if there exists additional locking mechanisms towards the database - such as an external CLI which can lock the database, or if the external database owns the candidate.
Finally, the external database may optionally validate a configuration. Configuration validation is preferably done through ConfD - however if a system already has implemented extensive configuration validation - the validate() callback can be used.
| Modifier and Type | Method and Description |
|---|---|
void |
activateCheckpointRunning(DpDbContext dbx)
This method should rollback running to the checkpoint created by
addCheckpointRunning().
|
void |
addCheckpointRunning(DpDbContext dbx)
This method should be implemented only when ConfD owns the candidate, and
confirmed-commit is enabled.
|
void |
candidateChkNotModified(DpDbContext dbx)
This method should check to see if the candidate has been modified or
not.
|
void |
candidateCommit(DpDbContext dbx,
int timeout)
This method should copy the candidate DB into the running DB.
|
void |
candidateConfirmingCommit(DpDbContext dbx)
If the timeout in the candidate_commit() method is != 0, we will be
either invoked here or in the candidateRollbackRunning() method within
timeout seconds.
|
void |
candidateReset(DpDbContext dbx)
This method is intended to copy the current running configuration into
the candidate.
|
void |
candidateRollbackRunning(DpDbContext dbx)
If for some reason, apart from a timeout, something goes wrong, we get
invoked in the candidateRollbackRunning() method.
|
void |
candidateValidate(DpDbContext dbx)
This callback is optional.
|
void |
copyRunningToStartup(DpDbContext dbx)
Copies the 'running' database to 'startup'.
|
void |
delCheckpointRunning(DpDbContext dbx)
This method should delete a checkpoint created by addCheckPointRunning().
|
void |
deleteConfig(DpDbContext dbx,
int dbname)
Will be called for 'startup' or 'candidate' only.
|
void |
lock(DpDbContext dbx,
int dbname)
This should only be implemented if our database supports locking from
other sources than through ConfD.
|
void |
lockPartial(DpDbContext dbx,
int dbname,
int lockid,
ConfObject[][] paths)
This should only be implemented if our database supports locking from
other sources than through ConfD, see
lock(DpDbContext,int)
above. |
int |
mask()
Mask of flags for methods that are supported by this callback:
M_CANDIDATE_COMMIT
M_CANDIDATE_CONFIRMING_COMMIT
M_CANDIDATE_RESET
M_CANDIDATE_CHK_NOT_MODIFIED
M_CANDIDATE_ROLLBACK_RUNNING
M_CANDIDATE_VALIDATE
M_ADD_CHECKPOINT_RUNNING
M_DEL_CHECKPOINT_RUNNING
M_ACTIVATE_CHECKPOINT_RUNNING
M_COPY_RUNNING_TO_STARTUP
M_LOCK
M_UNLOCK
M_DELETE_CONFIG
M_LOCK_PARTIAL
M_UNLOCK_PARTIAL
M_RUNNING_CHK_NOT_MODIFIED
|
void |
runningChkNotModified(DpDbContext dbx)
This function should check to see if running has been modified or not.
|
void |
unlock(DpDbContext dbx,
int dbname)
Unlocks the database.
|
void |
unlockPartial(DpDbContext dbx,
int dbname,
int lockid)
Unlocks the partial locks that where previously locked with
lockPartial(DpDbContext,int,int,ConfObject[][]) . |
static final int M_ACTIVATE_CHECKPOINT_RUNNING
activateCheckpointRunning(DpDbContext)
method.static final int M_ADD_CHECKPOINT_RUNNING
addCheckpointRunning(DpDbContext)
method.static final int M_ALL
static final int M_CANDIDATE_CHK_NOT_MODIFIED
candidateChkNotModified(DpDbContext)
method.static final int M_CANDIDATE_COMMIT
candidateCommit(DpDbContext,int)
method.static final int M_CANDIDATE_CONFIRMING_COMMIT
candidateConfirmingCommit(DpDbContext)
method.static final int M_CANDIDATE_RESET
candidateReset(DpDbContext)
method.static final int M_CANDIDATE_ROLLBACK_RUNNING
candidateRollbackRunning(DpDbContext)
method.static final int M_CANDIDATE_VALIDATE
candidateValidate(DpDbContext)
method.static final int M_COPY_RUNNING_TO_STARTUP
copyRunningToStartup(DpDbContext)
method.static final int M_DEL_CHECKPOINT_RUNNING
delCheckpointRunning(DpDbContext)
method.static final int M_DELETE_CONFIG
deleteConfig(DpDbContext,int)
method.static final int M_LOCK
lock(DpDbContext,int)
method.static final int M_LOCK_PARTIAL
lockPartial(DpDbContext,int,int,ConfObject[][])
method.static final int M_RUNNING_CHK_NOT_MODIFIED
runningChkNotModified(DpDbContext)
method.static final int M_UNLOCK
unlock(DpDbContext,int)
method.static final int M_UNLOCK_PARTIAL
unlockPartial(DpDbContext,int,int)
method.void activateCheckpointRunning(DpDbContext dbx) throws DpCallbackException
dbx
- The database contextDpCallbackException
- Callback method failed.void addCheckpointRunning(DpDbContext dbx) throws DpCallbackException
It is responsible for creating a checkpoint of the current running configuration and storing the checkpoint in non-volatile memory. When the system restarts this method should check if there is a checkpoint available, and use the checkpoint instead of running.
dbx
- The database contextDpCallbackException
- Callback method failed.void candidateChkNotModified(DpDbContext dbx) throws DpCallbackException
DpCallbackException
(error) if any
uncommitted modifications exist.dbx
- The database contextDpCallbackException
- Callback method failed.void candidateCommit(DpDbContext dbx, int timeout) throws DpCallbackException
dbx
- The database contexttimeout
- Timeout valueDpCallbackException
- Callback method failed.void candidateConfirmingCommit(DpDbContext dbx) throws DpCallbackException
dbx
- The database contextDpCallbackException
- Callback method failed.void candidateReset(DpDbContext dbx) throws DpCallbackException
dbx
- The database contextDpCallbackException
- Callback method failed.void candidateRollbackRunning(DpDbContext dbx) throws DpCallbackException
dbx
- The database contextDpCallbackException
- Callback method failed.void candidateValidate(DpDbContext dbx) throws DpCallbackException
dbx
- The database contextDpCallbackException
- Callback method failed.void copyRunningToStartup(DpDbContext dbx) throws DpCallbackException
dbx
- The database contextDpCallbackException
- Callback method failed.void delCheckpointRunning(DpDbContext dbx) throws DpCallbackException
dbx
- The database contextDpCallbackException
- Callback method failed.void deleteConfig(DpDbContext dbx, int dbname) throws DpCallbackException
dbx
- The database contextdbname
- The database typeDpCallbackException
- Callback method failed.void lock(DpDbContext dbx, int dbname) throws DpCallbackException
DpCallbackException
(error) if the lock was already held by
an external entity.
The dbname constants:
dbx
- The database contextdbname
- The database typeDpCallbackException
- Callback method failed.void lockPartial(DpDbContext dbx, int dbname, int lockid, ConfObject[][] paths) throws DpCallbackException
lock(DpDbContext,int)
above. This callback is invoked if a northbound agent requests a partial
lock. The paths[] argument is an array of keypaths that identify the
leafs and/or subtrees that are to be locked. The lockid is a reference
that will be used on a subsequent corresponding unlockPartial()
invocation. Throws a DpCallbackException
(error) if the lock
was already held by an external entity.
The dbname constants:
dbx
- The database contextdbname
- the databaselockid
- The lock referencepaths
- Paths to lockDpCallbackException
- Callback method failedint mask()
M_CANDIDATE_COMMIT
M_CANDIDATE_CONFIRMING_COMMIT
M_CANDIDATE_RESET
M_CANDIDATE_CHK_NOT_MODIFIED
M_CANDIDATE_ROLLBACK_RUNNING
M_CANDIDATE_VALIDATE
M_ADD_CHECKPOINT_RUNNING
M_DEL_CHECKPOINT_RUNNING
M_ACTIVATE_CHECKPOINT_RUNNING
M_COPY_RUNNING_TO_STARTUP
M_LOCK
M_UNLOCK
M_DELETE_CONFIG
M_LOCK_PARTIAL
M_UNLOCK_PARTIAL
M_RUNNING_CHK_NOT_MODIFIED
void runningChkNotModified(DpDbContext dbx) throws DpCallbackException
dbx
- The database contextDpCallbackException
- Callback method failedvoid unlock(DpDbContext dbx, int dbname) throws DpCallbackException
dbx
- The database contextdbname
- The database typeDpCallbackException
- Callback method failed.void unlockPartial(DpDbContext dbx, int dbname, int lockid) throws DpCallbackException
lockPartial(DpDbContext,int,int,ConfObject[][])
. The dbname
constants:
dbx
- The database contextdbname
- The database typelockid
- The lock referenceDpCallbackException
- Callback method failed.


Opus.js Encoder / Decoder Sample
Select RIFF PCM Wave file
Commands
play wav
opus encoder/decoder benchmark
(8000, 12000, 16000, 24000, or 48000 Hz only)
resample>encode>decode>resample>play
opus sampling rate:
8 kHz
12 kHz
16 kHz
24 kHz
48 kHz
opus frame duration:
2.5 ms
5 ms
10 ms
20 ms
40 ms
60 ms
opus application:
VoIP
Audio
Restricted Low Delay
info
self testing...


"How long should a man's legs be in proportion to his body?" Lincoln replied: "I have not given the matter much consideration, but on first blush I should judge they ought to be long enough to reach from his body to the ground."
Hi, I am Cameron Houser and I am a budding computer programmer. I graduated from Eleven Fifty Academy March 2017 with over 750 hundred hours of intensive training in JavaScript and JavaScript related libraries. As a career changer, I chose to move to programing because I have had a passion for the industry and I love the lifelong learning and team aspect to coding. I am confident that combining my years of experience in management and customer engagement with programing will be rewarding for myself and the organization(s) I have the privilege working for. I am currently looking for employment in Indianapolis. If you have an opportunity that you want me to engage in, please feel free to contact me below or through LinkedIn.
It did not take me long to fall in love with Angular 2 with its clear structure and data movement
As the backbone to most of the languages I have learned JavaScript has proven to be a great starting point in my programming journey
At Eleven Fifty I built the server side, connected to Mongoose and used JWT for Authentication
React-Redux proved to be fun to learn and as you can see below fun to use


{% extends 'transfers/base.html' %} {% block h1_title %}{{page_title}} {{organization}}{% endblock %} {% block content %}
Use these fields for basis start and end dates which should be calculated based on creation dates of records in a transfer. Enter only whole numbers, for example: 10.
Use these fields if this basis has hardcoded start and end dates, for example the date a donor agreement was signed. These fields are seldom used for copyright.
Use these fields if this basis has hardcoded start and end dates, for example the date a donor agreement was signed.
Use these fields for start and end dates which should be calculated based on creation dates of records in a transfer. Enter only whole numbers, for example: 10.
Use these fields when permissions or restrictions have hardcoded start and end date, for example the date a donor agreement was signed .


Last updated: 2021-12-07
Checks: 7 0
Knit directory: MC015_Persister_public/
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| Rmd | 8cbdba4 | Mark J Chen | 2021-12-07 | workflowr::wflow_publish(all = TRUE, force = TRUE, update = TRUE, |
| html | 8cbdba4 | Mark J Chen | 2021-12-07 | workflowr::wflow_publish(all = TRUE, force = TRUE, update = TRUE, |
| html | cfff17a | Mark J Chen | 2021-12-07 | Build site. |
| html | 5ccd486 | Mark J Chen | 2021-12-06 | chore: build website. |
| Rmd | b80ba35 | Mark J Chen | 2021-12-06 | feat: add PC9 time course analysis. |
You can download filtered feature-barcode matrices in HDF5 format from GEO database (GSE189639). The script here suppose an organization of files as below:
2214004_JCC287_PC9_MOCK2_GEX/outs/filtered_feature_bc_matrix.h5
2214005_JCC287_PC9_1d_rel_GEX/outs/filtered_feature_bc_matrix.h5
2214006_JCC287_PC9_3d_rel_GEX/outs/filtered_feature_bc_matrix.h5
2214007_JCC287_PC9_1w_rel_GEX/outs/filtered_feature_bc_matrix.h5
source("code/param_pkg_func.scRNA.R")
Basic steps:
# Path to raw data
# Change to your folder if needed
SRM229845_LOCAL_CACHE_DIR <- "/Users/jchen4/Cache/MC015_Persister/result/SRM229845/"
X2214004_JCC287_PC9_MOCK2_GEX.CR_OUT <- "2214004_JCC287_PC9_MOCK2_GEX"
X2214005_JCC287_PC9_1d_rel_GEX.CR_OUT <- "2214005_JCC287_PC9_1d_rel_GEX"
X2214006_JCC287_PC9_3d_rel_GEX.CR_OUT <- "2214006_JCC287_PC9_3d_rel_GEX"
X2214007_JCC287_PC9_1w_rel_GEX.CR_OUT <- "2214007_JCC287_PC9_1w_rel_GEX"
PC9_SET6.RES <- here::here("output/PC9_set6")
if (!dir.exists(PC9_SET6.RES)) dir.create(PC9_SET6.RES, recursive = TRUE)
X2214005_JCC287_PC9_1d_rel_GEX.srt <-
Read10X_h5(filename = file.path(
SRM229845_LOCAL_CACHE_DIR,
X2214005_JCC287_PC9_1d_rel_GEX.CR_OUT,
"outs",
"filtered_feature_bc_matrix.h5"
)) %>%
CreateSeuratObject(counts = .,
project = X2214005_JCC287_PC9_1d_rel_GEX.CR_OUT)
# Add mito ratio, ribo ratio and alias
X2214005_JCC287_PC9_1d_rel_GEX.srt <- add_alias_meta(X2214005_JCC287_PC9_1d_rel_GEX.srt)
# After tasting the thresholds
nFeature_min <- 1000
nFeature_max <- 10000
mito_ratio <- 10
ribo_ratio <- 0
# Filtering
min_gene_cell_frac <- 1.5e-3
ncol(X2214005_JCC287_PC9_1d_rel_GEX.srt) * min_gene_cell_frac
selected_c <-
WhichCells(
X2214005_JCC287_PC9_1d_rel_GEX.srt,
expression = nFeature_RNA > nFeature_min &
nFeature_RNA < nFeature_max
)
selected_f <-
rownames(X2214005_JCC287_PC9_1d_rel_GEX.srt)[Matrix::rowSums(X2214005_JCC287_PC9_1d_rel_GEX.srt) > floor(ncol(X2214005_JCC287_PC9_1d_rel_GEX.srt) * min_gene_cell_frac)]
X2214005_JCC287_PC9_1d_rel_GEX.flt.srt <-
subset(X2214005_JCC287_PC9_1d_rel_GEX.srt,
features = selected_f,
cells = selected_c)
X2214005_JCC287_PC9_1d_rel_GEX.flt.srt <-
subset(X2214005_JCC287_PC9_1d_rel_GEX.flt.srt,
subset = mitoRatio < mito_ratio & riboRatio > ribo_ratio)
X2214005_JCC287_PC9_1d_rel_GEX.flt.srt[!grepl("MALAT1", rownames(X2214005_JCC287_PC9_1d_rel_GEX.flt.srt)), ]
X2214005_JCC287_PC9_1d_rel_GEX.flt.srt <- X2214005_JCC287_PC9_1d_rel_GEX.flt.srt[!grepl("^MT-", rownames(X2214005_JCC287_PC9_1d_rel_GEX.flt.srt)), ]
X2214005_JCC287_PC9_1d_rel_GEX.flt.srt <- X2214005_JCC287_PC9_1d_rel_GEX.flt.srt[!grepl("^RP[SL]", rownames(X2214005_JCC287_PC9_1d_rel_GEX.flt.srt)), ]
X2214005_JCC287_PC9_1d_rel_GEX.flt.srt <- X2214005_JCC287_PC9_1d_rel_GEX.flt.srt[!grepl("FTH1", rownames(X2214005_JCC287_PC9_1d_rel_GEX.flt.srt)), ]
X2214005_JCC287_PC9_1d_rel_GEX.flt.srt <- X2214005_JCC287_PC9_1d_rel_GEX.flt.srt[!grepl("FTL", rownames(X2214005_JCC287_PC9_1d_rel_GEX.flt.srt)), ]
X2214005_JCC287_PC9_1d_rel_GEX.std.srt <-
standard_preprocess(
X2214005_JCC287_PC9_1d_rel_GEX.flt.srt,
prefix = "X2214005_JCC287_PC9_1d_rel_GEX.std"
)
X2214006_JCC287_PC9_3d_rel_GEX.srt <-
Read10X_h5(filename = file.path(
SRM229845_LOCAL_CACHE_DIR,
X2214006_JCC287_PC9_3d_rel_GEX.CR_OUT,
"outs",
"filtered_feature_bc_matrix.h5"
)) %>%
CreateSeuratObject(counts = .,
project = X2214006_JCC287_PC9_3d_rel_GEX.CR_OUT)
# Add mito ratio, ribo ratio and alias
X2214006_JCC287_PC9_3d_rel_GEX.srt <- add_alias_meta(X2214006_JCC287_PC9_3d_rel_GEX.srt)
# After tasting the thresholds
nFeature_min <- 1000
nFeature_max <- 10000
mito_ratio <- 20
ribo_ratio <- 0
# Filtering
min_gene_cell_frac <- 1.5e-3
ncol(X2214006_JCC287_PC9_3d_rel_GEX.srt) * min_gene_cell_frac
selected_c <-
WhichCells(
X2214006_JCC287_PC9_3d_rel_GEX.srt,
expression = nFeature_RNA > nFeature_min &
nFeature_RNA < nFeature_max
)
selected_f <-
rownames(X2214006_JCC287_PC9_3d_rel_GEX.srt)[Matrix::rowSums(X2214006_JCC287_PC9_3d_rel_GEX.srt) > floor(ncol(X2214006_JCC287_PC9_3d_rel_GEX.srt) * min_gene_cell_frac)]
X2214006_JCC287_PC9_3d_rel_GEX.flt.srt <-
subset(X2214006_JCC287_PC9_3d_rel_GEX.srt,
features = selected_f,
cells = selected_c)
X2214006_JCC287_PC9_3d_rel_GEX.flt.srt <-
subset(X2214006_JCC287_PC9_3d_rel_GEX.flt.srt,
subset = mitoRatio < mito_ratio & riboRatio > ribo_ratio)
X2214006_JCC287_PC9_3d_rel_GEX.flt.srt[!grepl("MALAT1", rownames(X2214006_JCC287_PC9_3d_rel_GEX.flt.srt)), ]
X2214006_JCC287_PC9_3d_rel_GEX.flt.srt <- X2214006_JCC287_PC9_3d_rel_GEX.flt.srt[!grepl("^MT-", rownames(X2214006_JCC287_PC9_3d_rel_GEX.flt.srt)), ]
X2214006_JCC287_PC9_3d_rel_GEX.flt.srt <- X2214006_JCC287_PC9_3d_rel_GEX.flt.srt[!grepl("^RP[SL]", rownames(X2214006_JCC287_PC9_3d_rel_GEX.flt.srt)), ]
X2214006_JCC287_PC9_3d_rel_GEX.flt.srt <- X2214006_JCC287_PC9_3d_rel_GEX.flt.srt[!grepl("FTH1", rownames(X2214006_JCC287_PC9_3d_rel_GEX.flt.srt)), ]
X2214006_JCC287_PC9_3d_rel_GEX.flt.srt <- X2214006_JCC287_PC9_3d_rel_GEX.flt.srt[!grepl("FTL", rownames(X2214006_JCC287_PC9_3d_rel_GEX.flt.srt)), ]
X2214006_JCC287_PC9_3d_rel_GEX.std.srt <-
standard_preprocess(
X2214006_JCC287_PC9_3d_rel_GEX.flt.srt,
prefix = "X2214006_JCC287_PC9_3d_rel_GEX.std"
)
X2214007_JCC287_PC9_1w_rel_GEX.srt <-
Read10X_h5(filename = file.path(
SRM229845_LOCAL_CACHE_DIR,
X2214007_JCC287_PC9_1w_rel_GEX.CR_OUT,
"outs",
"filtered_feature_bc_matrix.h5"
)) %>%
CreateSeuratObject(counts = .,
project = X2214007_JCC287_PC9_1w_rel_GEX.CR_OUT)
# Add mito ratio, ribo ratio and alias
X2214007_JCC287_PC9_1w_rel_GEX.srt <- add_alias_meta(X2214007_JCC287_PC9_1w_rel_GEX.srt)
# After tasting the thresholds
nFeature_min <- 1500
nFeature_max <- 9500
mito_ratio <- 20
ribo_ratio <- 0
# Filtering
min_gene_cell_frac <- 1.5e-3
ncol(X2214007_JCC287_PC9_1w_rel_GEX.srt) * min_gene_cell_frac
selected_c <-
WhichCells(
X2214007_JCC287_PC9_1w_rel_GEX.srt,
expression = nFeature_RNA > nFeature_min &
nFeature_RNA < nFeature_max
)
selected_f <-
rownames(X2214007_JCC287_PC9_1w_rel_GEX.srt)[Matrix::rowSums(X2214007_JCC287_PC9_1w_rel_GEX.srt) > floor(ncol(X2214007_JCC287_PC9_1w_rel_GEX.srt) * min_gene_cell_frac)]
X2214007_JCC287_PC9_1w_rel_GEX.flt.srt <-
subset(X2214007_JCC287_PC9_1w_rel_GEX.srt,
features = selected_f,
cells = selected_c)
X2214007_JCC287_PC9_1w_rel_GEX.flt.srt <-
subset(X2214007_JCC287_PC9_1w_rel_GEX.flt.srt,
subset = mitoRatio < mito_ratio & riboRatio > ribo_ratio)
X2214007_JCC287_PC9_1w_rel_GEX.flt.srt[!grepl("MALAT1", rownames(X2214007_JCC287_PC9_1w_rel_GEX.flt.srt)), ]
X2214007_JCC287_PC9_1w_rel_GEX.flt.srt <- X2214007_JCC287_PC9_1w_rel_GEX.flt.srt[!grepl("^MT-", rownames(X2214007_JCC287_PC9_1w_rel_GEX.flt.srt)), ]
X2214007_JCC287_PC9_1w_rel_GEX.flt.srt <- X2214007_JCC287_PC9_1w_rel_GEX.flt.srt[!grepl("^RP[SL]", rownames(X2214007_JCC287_PC9_1w_rel_GEX.flt.srt)), ]
X2214007_JCC287_PC9_1w_rel_GEX.flt.srt <- X2214007_JCC287_PC9_1w_rel_GEX.flt.srt[!grepl("FTH1", rownames(X2214007_JCC287_PC9_1w_rel_GEX.flt.srt)), ]
X2214007_JCC287_PC9_1w_rel_GEX.flt.srt <- X2214007_JCC287_PC9_1w_rel_GEX.flt.srt[!grepl("FTL", rownames(X2214007_JCC287_PC9_1w_rel_GEX.flt.srt)), ]
X2214007_JCC287_PC9_1w_rel_GEX.std.srt <-
standard_preprocess(
X2214007_JCC287_PC9_1w_rel_GEX.flt.srt,
prefix = "X2214007_JCC287_PC9_1w_rel_GEX.std"
)
X2214004_JCC287_PC9_MOCK2_GEX.srt <-
Read10X_h5(filename = file.path(
SRM229845_LOCAL_CACHE_DIR,
X2214004_JCC287_PC9_MOCK2_GEX.CR_OUT,
"outs",
"filtered_feature_bc_matrix.h5"
)) %>%
CreateSeuratObject(counts = .,
project = X2214004_JCC287_PC9_MOCK2_GEX.CR_OUT)
# Add mito ratio, ribo ratio and alias
X2214004_JCC287_PC9_MOCK2_GEX.srt <- add_alias_meta(X2214004_JCC287_PC9_MOCK2_GEX.srt)
# After tasting the thresholds
nFeature_min <- 3500
nFeature_max <- 10000
mito_ratio <- 25
ribo_ratio <- 10
# Filtering
min_gene_cell_frac <- 1.5e-3
ncol(X2214004_JCC287_PC9_MOCK2_GEX.srt) * min_gene_cell_frac
selected_c <-
WhichCells(
X2214004_JCC287_PC9_MOCK2_GEX.srt,
expression = nFeature_RNA > nFeature_min &
nFeature_RNA < nFeature_max
)
selected_f <-
rownames(X2214004_JCC287_PC9_MOCK2_GEX.srt)[Matrix::rowSums(X2214004_JCC287_PC9_MOCK2_GEX.srt) > floor(ncol(X2214004_JCC287_PC9_MOCK2_GEX.srt) * min_gene_cell_frac)]
X2214004_JCC287_PC9_MOCK2_GEX.flt.srt <-
subset(X2214004_JCC287_PC9_MOCK2_GEX.srt,
features = selected_f,
cells = selected_c)
X2214004_JCC287_PC9_MOCK2_GEX.flt.srt <-
subset(X2214004_JCC287_PC9_MOCK2_GEX.flt.srt,
subset = mitoRatio < mito_ratio & riboRatio > ribo_ratio)
X2214004_JCC287_PC9_MOCK2_GEX.flt.srt[!grepl("MALAT1", rownames(X2214004_JCC287_PC9_MOCK2_GEX.flt.srt)), ]
X2214004_JCC287_PC9_MOCK2_GEX.flt.srt <- X2214004_JCC287_PC9_MOCK2_GEX.flt.srt[!grepl("^MT-", rownames(X2214004_JCC287_PC9_MOCK2_GEX.flt.srt)), ]
X2214004_JCC287_PC9_MOCK2_GEX.flt.srt <- X2214004_JCC287_PC9_MOCK2_GEX.flt.srt[!grepl("^RP[SL]", rownames(X2214004_JCC287_PC9_MOCK2_GEX.flt.srt)), ]
X2214004_JCC287_PC9_MOCK2_GEX.flt.srt <- X2214004_JCC287_PC9_MOCK2_GEX.flt.srt[!grepl("FTH1", rownames(X2214004_JCC287_PC9_MOCK2_GEX.flt.srt)), ]
X2214004_JCC287_PC9_MOCK2_GEX.flt.srt <- X2214004_JCC287_PC9_MOCK2_GEX.flt.srt[!grepl("FTL", rownames(X2214004_JCC287_PC9_MOCK2_GEX.flt.srt)), ]
X2214004_JCC287_PC9_MOCK2_GEX.std.srt <-
standard_preprocess(
X2214004_JCC287_PC9_MOCK2_GEX.flt.srt,
prefix = "X2214004_JCC287_PC9_MOCK2_GEX.std"
)
The samples at different time points have different number of cells. I down-sample the cells so that all time points have the same number.
# Common genes
lapply(list(
X2214004_JCC287_PC9_MOCK2_GEX.std.srt,
X2214005_JCC287_PC9_1d_rel_GEX.std.srt,
X2214006_JCC287_PC9_3d_rel_GEX.std.srt,
X2214007_JCC287_PC9_1w_rel_GEX.std.srt
), rownames) %>%
Reduce(f = intersect, x = .) -> PC9_set6.common_genes
save(PC9_set6.common_genes, file = file.path(PC9_SET6.RES, "PC9_set6.common_genes.Rda"))
# Project name
sapply(list(
X2214004_JCC287_PC9_MOCK2_GEX.std.srt,
X2214005_JCC287_PC9_1d_rel_GEX.std.srt,
X2214006_JCC287_PC9_3d_rel_GEX.std.srt,
X2214007_JCC287_PC9_1w_rel_GEX.std.srt
), function(x) {
<EMAIL_ADDRESS>}) -> PC9_set6.proj_name
# Min cell number
lapply(list(
X2214004_JCC287_PC9_MOCK2_GEX.std.srt,
X2214005_JCC287_PC9_1d_rel_GEX.std.srt,
X2214006_JCC287_PC9_3d_rel_GEX.std.srt,
X2214007_JCC287_PC9_1w_rel_GEX.std.srt
), ncol) %>%
unlist() %>%
min() -> min_cell_counts
# Cell barcode index
set.seed(42)
lapply(list(
X2214004_JCC287_PC9_MOCK2_GEX.std.srt,
X2214005_JCC287_PC9_1d_rel_GEX.std.srt,
X2214006_JCC287_PC9_3d_rel_GEX.std.srt,
X2214007_JCC287_PC9_1w_rel_GEX.std.srt
), function(x) {
cell_num <- ncol(x)
cell_idx <- sample(1:cell_num, min_cell_counts, replace = FALSE)
cell_idx
}) -> PC9_set6.down_sample
# Check
lapply(PC9_set6.down_sample, length)
save(PC9_set6.down_sample, file = file.path(PC9_SET6.RES, "PC9_set6.down_sample.Rda"))
# Down-sampled and select common genes
set.seed(42)
lapply(list(
X2214004_JCC287_PC9_MOCK2_GEX.std.srt,
X2214005_JCC287_PC9_1d_rel_GEX.std.srt,
X2214006_JCC287_PC9_3d_rel_GEX.std.srt,
X2214007_JCC287_PC9_1w_rel_GEX.std.srt
), function(x) {
cell_num <- ncol(x)
cell_idx <- sample(1:cell_num, min_cell_counts, replace = FALSE)
# Select common genes
x[PC9_set6.common_genes, cell_idx]
}) -> PC9_set6.srtLst
lapply(PC9_set6.srtLst, colnames) -> PC9_set6.down_sample.barcode
names(PC9_set6.down_sample.barcode) <- PC9_set6.proj_name
# Check
lapply(PC9_set6.srtLst, ncol)
lapply(PC9_set6.srtLst, nrow)
lapply(PC9_set6.down_sample.barcode, length)
save(PC9_set6.down_sample.barcode, file = file.path(PC9_SET6.RES, "PC9_set6.down_sample.barcode.Rda"))
# Clean
save(
X2214004_JCC287_PC9_MOCK2_GEX.std.srt,
X2214005_JCC287_PC9_1d_rel_GEX.std.srt,
X2214006_JCC287_PC9_3d_rel_GEX.std.srt,
X2214007_JCC287_PC9_1w_rel_GEX.std.srt,
file = file.path(PC9_SET6.RES, "samples4.std.srt.Rda")
)
rm(
X2214004_JCC287_PC9_MOCK2_GEX.std.srt,
X2214005_JCC287_PC9_1d_rel_GEX.std.srt,
X2214006_JCC287_PC9_3d_rel_GEX.std.srt,
X2214007_JCC287_PC9_1w_rel_GEX.std.srt
)
gc()
I merge and follow the standard processing regarding to Seurat documentation, assuming there is no batch effect since the experiments were carried out by the same person and on the same day.
PC9_set6.merge.srt <- merge(
x = PC9_set6.srtLst[[1]],
y = c(
PC9_set6.srtLst[[2]],
PC9_set6.srtLst[[3]],
PC9_set6.srtLst[[4]]
),
add.cell.ids = c("PT", "PS1D", "PS3D", "PS7D"),
project = "PC9_set6_merge"
)
# Add group
PC9_set6.merge.srt %>%
colnames() %>%
stringr::str_remove("_[ACGT]+-[1-9]+$") %>%
forcats::fct_relevel(., "PS1D", "PS3D", "PS7D", "PT") -> Group
PC9_set6.merge.srt[["Group"]] <- Group
save(PC9_set6.merge.srt, file = file.path(PC9_SET6.RES, "PC9_set6.merge.srt.Rda"))
# Clean
rm(PC9_set6.srtLst)
gc()
# Standard preprocessing
PC9_set6.merge.std.srt <-
standard_preprocess.merge(
PC9_set6.merge.srt,
use_pre_cal_cell_cycle_scores = TRUE,
prefix = "PC9_set6.merge.std",
Rda_file = file.path(PC9_SET6.RES, "PC9_set6.merge.std.srt.Rda")
)
PC9_set6.merge.std.meta <-<EMAIL_ADDRESS>save(
PC9_set6.merge.std.meta,
file = file.path(PC9_SET6.RES, "PC9_set6.merge.std.meta.Rda")
)
UMAP with the default parameters do not have obvious trajectories, I tune the parameters as the author of Slingshot suggested. Alternatively, other dimensional reduction method can be used.
# Choose a = 200, b = 0.4
PC9_set6.merge.std.srt <-
RunUMAP(PC9_set6.merge.std.srt,
dims = RunUMAP.DIMS,
a = 200,
b = 0.4,
verbose = TRUE)
gc()
PC9_set6.merge.std.a200.b0.4.umap <- PC9_set6.merge.std.srt@reductions$umap
save(PC9_set6.merge.std.a200.b0.4.umap, file = file.path(PC9_SET6.RES, "PC9_set6.merge.std.a200.b0.4.umap.Rda"))
forcats::fct_relevel(PC9_set6.merge.std.srt$Group, "PS1D", "PS3D", "PS7D", "PT") -> PC9_set6.merge.std.group.fct
levels(PC9_set6.merge.std.group.fct)
PC9_set6.merge.std.srt$Group <- PC9_set6.merge.std.group.fct
Idents(PC9_set6.merge.std.srt) <- "Group"
PC9_set6.merge.std.group_size <- unique(PC9_set6.merge.std.srt$Group) %>%
length()
PC9_set6.merge.std.group.pal <- ggsci_pal_d(ggsci_pal_name = "jco", pal_size = PC9_set6.merge.std.group_size)
save(PC9_set6.merge.std.group.pal, PC9_set6.merge.std.group_size, file = file.path(PC9_SET6.RES, "PC9_set6.merge.std.group.pal.Rda"))
gc()
DefaultAssay(PC9_set6.merge.std.srt) <- "RNA"
<EMAIL_ADDRESS>%>%
colnames() %>%
grep(pattern = "^sling", x = ., value = TRUE)
PC9_set6.merge.std.sce <-
as.SingleCellExperiment(x = PC9_set6.merge.std.srt, assay = "RNA")
PC9_set6.merge.std.sce@colData %>%
colnames() %>%
grep(pattern = "^sling", x = ., value = TRUE)
rd_method <- "UMAP"
approx_pt <- 150
library(slingshot)
library(SingleCellExperiment)
# Run
PC9_set6.merge.std.sce <-
slingshot(
PC9_set6.merge.std.sce,
reducedDim = rd_method,
clusterLabels = PC9_set6.merge.std.sce$Group,
start.clus = "PS1D",
# end.clus = "PS7D",
approx_points = approx_pt
)
gc()
# Add to Seurat object
PC9_set6.merge.std.sce@colData %>%
colnames() %>%
grep(pattern = "^sling", x = ., value = TRUE) -> slingshot_res_colnames
slingshot_res_colnames
for (cn in slingshot_res_colnames) {
message(cn)
PC9_set6.merge.std.srt[[cn]] <- PC9_set6.merge.std.sce@colData[, cn]
}
# Plot
## UMAP by group
table(PC9_set6.merge.std.srt$Group == "PT")
PT_barcode <- colnames(PC9_set6.merge.std.srt)[PC9_set6.merge.std.srt$Group == "PT"]
DimPlot(
PC9_set6.merge.std.srt,
reduction = "umap",
group.by = "Group",
label = FALSE,
# order = PT_barcode,
shuffle = TRUE,
cols = PC9_set6.merge.std.group.pal
) +
# scale_size_manual(values = c(0.5, 0.5, 0.5, 4), breaks = levels(group.fct)) +
ggtitle("UMAP") +
labs(x = "UMAP 1", y = "UMAP 2") +
theme(title = element_text(size = 10)) -> PC9_set6.merge.std.groupByGroup.umap
# Add trajectories
PC9_set6.merge.std.traj_num <- length(slingshot_res_colnames) - 1
PC9_set6.merge.std.traj.pal <-
ggsci_pal_d(ggsci_pal_name = "aaas", pal_size = PC9_set6.merge.std.traj_num)
lapply(1:PC9_set6.merge.std.traj_num, function(i) {
message(glue("Trajectory {i}"))
PC9_set6.RNA.slingshot_curvei_coord.df <-
slingCurves(PC9_set6.merge.std.sce)[[i]]$s[slingCurves(PC9_set6.merge.std.sce)[[i]]$ord,] %>%
as.data.frame()
PC9_set6.merge.std.groupByGroup.umap +
ggplot2::geom_path(
data = PC9_set6.RNA.slingshot_curvei_coord.df,
aes(x = UMAP_1, y = UMAP_2),
size = 1.5,
color = PC9_set6.merge.std.traj.pal[i],
arrow = arrow(angle = 30)
) +
labs(x = "UMAP 1", y = "UMAP 2") +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()
) ->> PC9_set6.merge.std.groupByGroup.umap
}) %>%
invisible()
# Add caption and save
PC9_set6.merge.std.groupByGroup.umap +
plot_annotation(
title = "Trajectory",
subtitle = glue("Slingshot trajectories"),
caption = glue(
"Regress out cell cycle (S and G2M scores), proportion of reads mapped to mitochondrial,
gene number per cell.
Dim=40 without specific mention.
Trajectory: Slingshot, start from PS, approx_points = {approx_pt}, integrated assay, {rd_method} project."
),
theme = theme(
title = element_text(size = 12, face = "bold"),
plot.caption = element_text(size = 9,
hjust = 0,
face = "plain")
)
) -> PC9_set6.merge.std.groupByGroup.umap.comb
ggsave(
plot = PC9_set6.merge.std.groupByGroup.umap.comb,
filename = file.path(
PC9_SET6.RES,
glue("PC9_set6.merge.std.Slingshot.groupByGroup.umap.comb.pdf")
),
height = 5.6,
width = 5
)
# plot(PC9_set6.merge.std.srt$slingPseudotime_1, PC9_set6.merge.std.srt$slingPseudotime_2)
## Combine UMAPs by trajectory
PC9_set6.merge.std.traj_num <- length(slingshot_res_colnames) - 1
lapply(1:PC9_set6.merge.std.traj_num, function(i) {
message(glue("Trajectory {i}"))
feat_name <- paste0("slingPseudotime_", i)
PC9_set6.RNA.slingshot_curvei_coord.df <-
slingCurves(PC9_set6.merge.std.sce)[[i]]$s[slingCurves(PC9_set6.merge.std.sce)[[i]]$ord, ] %>%
as.data.frame()
FeaturePlot(
PC9_set6.merge.std.srt,
reduction = "umap",
features = feat_name,
cols = viridis_pal(begin = 0.1, end = 0.9)(2),
combine = FALSE
) %>%
lapply(., function(p) {
p +
lims(x = c(-0.25, 0.25), y = c(-0.25, 0.25)) +
ggplot2::geom_path(
data = PC9_set6.RNA.slingshot_curvei_coord.df,
aes(x = UMAP_1, y = UMAP_2),
size = 1.5,
color = PC9_set6.merge.std.traj.pal[i],
arrow = arrow(angle = 30)
) +
labs(x = "UMAP 1", y = "UMAP 2") +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()
)
}) -> PC9_set6.merge.std.pseudotime.umap
## Pseudotime UMAP by group
FeaturePlot(
PC9_set6.merge.std.srt,
reduction = "umap",
features = feat_name,
cols = viridis_pal(begin = 0.1, end = 0.9)(2),
split.by = "Group",
combine = FALSE
) %>%
lapply(., function(p) {
p +
lims(x = c(-0.25, 0.25), y = c(-0.25, 0.25)) +
ggplot2::geom_path(
data = PC9_set6.RNA.slingshot_curvei_coord.df,
aes(x = UMAP_1, y = UMAP_2),
size = 1.5,
color = PC9_set6.merge.std.traj.pal[i],
arrow = arrow(angle = 30)
) +
labs(x = "UMAP 1", y = "UMAP 2") +
theme_bw() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()
)
}) -> umapLst
umapLst$Pseudotime <- PC9_set6.merge.std.pseudotime.umap[[1]]
umapLst2 <- list(
Group = PC9_set6.merge.std.groupByGroup.umap,
Pseudotime = umapLst$Pseudotime + ggtitle("Pseudotime"),
PS1D = umapLst[[1]] + ggtitle("PS1D"),
PS3D = umapLst[[2]] + ggtitle("PS3D"),
PS7D = umapLst[[3]] + ggtitle("PS7D"),
PT = umapLst[[4]] + ggtitle("PT")
)
wrap_plots(umapLst2, nrow = 2) +
plot_annotation(
title = "Trajectory",
subtitle = glue("Slingshot trajectory {i}"),
caption = glue(
"Regress out cell cycle (S and G2M scores), proportion of reads mapped to mitochondrial,
gene number per cell.
Dim=40 without specific mention.
Trajectory: Slingshot, start clusters PS, end cluster not specified, approx_points = {approx_pt}, integrated assay, {rd_method} project."
),
theme = theme(
title = element_text(size = 12, face = "bold"),
plot.caption = element_text(
size = 9,
hjust = 0,
face = "plain"
)
)
) -> PC9_set6.Slingshot.umaps
ggsave(
plot = PC9_set6.Slingshot.umaps,
filename = file.path(
PC9_SET6.RES,
glue("PC9_set6.Slingshot_trajectory{i}.umaps.pdf")
),
height = 4.0 * 2,
width = 4.5 * 3
)
}) -> PC9_set6.merge.std.umaps
# Pseudotime density
PC9_set6.merge.std.meta.df <-<EMAIL_ADDRESS>PC9_set6.merge.std.traj_num <- length(slingshot_res_colnames) - 1
lapply(1:PC9_set6.merge.std.traj_num, function(i) {
message(glue("Trajectory {i}"))
feat_name <- paste0("slingPseudotime_", i)
df <- PC9_set6.merge.std.meta.df[, c("Group", feat_name)]
colnames(df) <- c("Group", "Pseudotime")
ggplot(df,
aes(x = Pseudotime, group = Group)) +
geom_density(aes(fill = Group, color = Group), alpha = 0.5) +
scale_fill_manual(values = PC9_set6.merge.std.group.pal) +
scale_color_manual(values = PC9_set6.merge.std.group.pal) +
labs(x = "Pseudotime", y = "Density") +
facet_grid(Group ~ ., scales = "fixed") +
ggtitle(glue("Trajectory {i}")) +
theme_bw() +
ggplot2::theme(
legend.position = "none",
axis.title.x = element_text(size = 7, face = "bold"),
# axis.ticks.x = element_blank(),
axis.title.y = element_text(size = 7, face = "bold"),
axis.text.y = element_text(size = 6),
strip.background.y = element_rect(fill = "grey20"),
strip.text.y = element_text(
angle = 0,
face = "bold",
size = 7,
color = "grey90"
),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank()
) -> PC9_set6.Slingshot.track
}) -> PC9_set6.Slingshot.tracks
wrap_plots(PC9_set6.Slingshot.tracks, nrow = 1) +
plot_annotation(
title = "Trajectory analysis",
subtitle = glue("Slingshot"),
caption = glue(
"Regress out cell cycle (S and G2M scores), proportion of reads mapped to mitochondrial,
gene number per cell.
Dim=40 without specific mention.
Trajectory: Slingshot, start clusters PS, end cluster not specified, approx_points = {approx_pt}, integrated assay, {rd_method} project."


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Programmers and problem solvers will compete in teams of four to solve problems from Project Euler, using their own choice of programming language. Beginners and experienced programmers alike will find problems at their level, and peers to team up with. Prizes will be awarded in three categories: Beginner, Intermediate, and Open, according to the rules.
| 11 a.m. | Doors open |
|---|---|
| 12 noon | Lunch (provided) and formation of teams |
| 1 p.m. | Competition begins |
| 4 p.m. | Winners and prizes announced |
The event is hosted by ThoughtWorks and will be held at their offices on the 25th floor of the Aon Center, 200 E Randolph St, Chicago, IL 60601 (map).


{% extends "base.html" %} {% load static %} {% load widget_tweaks %} {% block content %}
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Created by Maksim Yashchykoucki
div {+ filter: grayscale (100%) sepia (100%);}
/ * Prefixes * /
-webkit-filter: ...
-moz-filter: ...
-ms-filter: ...
-o-filter: ...
filter: ...
1. Grayscale filter - grayscale (controlled). Converts colors in an image to shades of gray
2. Sepia filter - sepia (controlled). Sepia filter change colors with sepia tones, like in old photos.
3. Filter saturation - saturation (controlled). This effect will add color saturation, and make them brighter.
4. Filter hue lighting - hue-rotate (angle). The effect (angle) changes the angle of illumination so that the original image looks with a different color.
5. Filter invert - invert (controlled)
6. This effect flips colors - the amount of effect is controlled
7. Filter opacity - opacity (controlled)
8. Filter brightness - brightness (controlled)
9. Filter contrast - contrast (controlled)
10. Filter blur - blur (radius)
11. Shadow filter drop-shadow (shadow)
#shaded {filter: grayscale (1); transition: filter ease-in 0.3s; }
#shaded: hover {filter: grayscale (0); }


--- layout: default-en title: Contact class: contact lang: en ref: contact --- {% include navigation.html %} Create your Geotrek ? Ours experts are available to answer yours questions Contact You are invited to let us know about your project's features and we will be happy to contact you to understand your needs. Tell us about your project {% include contact_form_support.html %}
You are invited to let us know about your project's features and we will be happy to contact you to understand your needs.


{# Licensed to the Oregon State University Open Source Lab (OSL) under one or more contributor license agreements. See the LICENSE.txt filedistributed with this work for additional information regarding copyright ownership. The OSL licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. #} {# If you've come here, you must be curious and willing to dig deeper. This is not just plain html. We are using something called jinja Jinja is an html templating language. It will write your html for you based off of variables you passed it when you called 'render_template' Things in curly braces are jinja. Things in angle brackets are html. Don't worry, it's not too complicated! #} {# This is a comment, in case you haven't figured that out already #} {# The html generated from this file will get knitted together with the html generated from layout.html #} {% extends "layout.html" %} {% block body %} {# This creates an html list from the python list named space #}
Filesystem Size Used Avail Use% Mounted on
{{ line|escape }}


City of Barcelona, Spain
Barcelona (/bɑːrsəˈloʊnə/, Catalan: [bəɾsəˈlonə], Spanish: [barθeˈlona]) is the capital city of the autonomous community of Catalonia in Spain and Spain's second most populated city, with a population of 1.6 million within its administrative limits. Its urban area extends beyond the administrative city limits with a population of around 4.7 million people, being the sixth-most populous urban area in the European Union after Paris, London, Madrid, the Ruhr area, and Milan. It is the largest metropolis on the Mediterranean Sea, located on the coast between the mouths of the rivers Llobregat and Besòs, and bounded to the west by the Serra de Collserola mountain range, the tallest peak of which is 512 metres (1,680 ft) high. View Barcelona.


Element.innerHTML
returns the HTML as its name indicates. Quite often, in
order to retrieve or write text within an element, people use innerHTML. However, textContent often has
better performance because the text is not parsed as HTML. Moreover, using textContent can prevent XSS
attacks.
Element.innerHTML - Web APIs | MDN


MIT Center for Airborne Organics
EPA Grant Number: R824970Center: EERC - Center for Airborne Organics (MIT)
Center Director: Seinfeld, John
Title: MIT Center for Airborne Organics
Investigators: Howard, Jack B. , Molina, Mario J. , Pfeffer, Robert , Seinfeld, John
Institution: Massachusetts Institute of Technology , California Institute of Technology , New Jersey Institute of Technology
Current Institution: Massachusetts Institute of Technology
EPA Project Officer: Shapiro, Paul
Project Period: October 1, 1992 through September 30, 2002
Project Amount: $5,745,000
RFA: Center on Airborne Organics (1993) Recipients Lists
Research Category: Air Quality and Air Toxics , Targeted Research
Objective:
Abstract Currently Not AvailableJournal Articles: 76 Displayed | Download in RIS Format
| Other center views: | All 102 publications | 77 publications in selected types | All 76 journal articles |
|---|
| Type | Citation | ||
|---|---|---|---|
|
|
Allen JO, Fergenson DP, Gard EE, Hughes LS, Morrical BD, Kleeman MJ, Gross DS, Gallo ME, Prather KA, Cass GR. Particle detection efficiencies of aerosol time of flight mass spectrometers under ambient sampling conditions. Environmental Science & Technology 2000; 34(1): 211-217. |
R824970 (Final) |
not available |
|
|
Andino JM, Smith JN, Flagan RC, Goddard III WA, Seinfeld JH. Mechanism of atmospheric photooxidation of aromatics: a theoretical study. Journal of Physical Chemistry 1996; 100(26): 10967-10980. |
R824970 (Final) |
not available |
|
|
Bowman FM, Odum JR, Seinfeld JH, Pandis SN. Mathematical model for gas-particle partitioning of secondary organic aerosols. Atmospheric Environment 1997; 31(23): 3921-3931. |
R824970 (Final) R823514 (Final) |
not available |
|
|
Bozzelli JW, Dean AM. Hydrocarbon Radical Reactions with O-2 - Comparison of Allyl, Formyl, and Vinyl to Ethyl. Journal of Physical Chemistry 1993; 97(17): 4427-4441. |
R824970 (Final) |
not available |
|
|
Bozzelli JW, Sheng C. Thermochemistry, reaction paths, and kinetics on the hydroperoxy-ethyl radical reaction with O2: new chain branching reactions in hydrocarbon oxidation. Journal of Physical Chemistry A 2002;106(7):1113-1121. |
R824970 (Final) |
not available |
|
|
Bozzelli JW, Chang AY, Dean AM. Molecular density of states from estimated vapor phase heat capacities. International Journal of Chemical Kinetics 1997; 29(3): 161-170. |
R824970 (Final) |
not available |
|
|
Chen CJ, Bozzelli JW, Wong D. Standard chemical thermodynamic properties of multichloro alkanes and alkenes: a modified group additivity scheme. Journal of Physical Chemistry 1998; 102(24): 4551-4558. |
R824970 (Final) |
not available |
|
|
Chen CJ, Bozzelli JW.  Journal of Physical Chemistry A 1999; 103(48): 9731-9769. |
R824970 (Final) |
not available |
|
|
Chen CJ, Bozzelli JW. Kinetic analysis for HO2 addition to ethylene, propene, and isobutene, and thermochemical parameters of alkyl hydroperoxides and hydroperoxide alkyl radicals. Journal of Physical Chemistry A 2000; 104(21): 4997-5012. |
R824970 (Final) |
not available |
|
|
Chen CJ Bozzelli JW. Thermochemical property, pathway and kinetic analysis on the reactions of allylic isobutenyl radical with O-2: an elementary reaction mechanism for isobutene oxidation. Journal of Physical Chemistry A 2000; 104(43): 9715-9732. |
R824970 (Final) |
not available |
|
|
Christoforou CS, Salmon LG, Hannigan MP, Solomon PA, Cass GR. Trends in fine particle concentration and chemical composition in Southern California. Journal of the Air & Waste Management Association 2000;50(1):43-53. |
R824970 (Final) |
|
|
|
DiNaro JL, Howard JB, Green WH, Tester JW, Bozzelli JW. Elementary reaction mechanism for benzene oxidation in supercritical water. Journal of Physical Chemistry A 2000; 104(45): 10576-10586. |
R824970 (Final) |
not available |
|
|
Forstner HJL, Flagan RC, Seinfeld JH. Molecular speciation of secondary organic aerosol from photooxidation of the higher alkenes: 1-octene and 1-decene. Atmospheric Environment 1997;31(13):1953-1964 |
R824970 (Final) |
not available |
|
|
Forstner HJL, Seinfeld JH, Flagan RC. Secondary organic aerosol from the photooxidation of aromatic hydrocarbons: molecular composition. Environmental Science & Technology 1997; 31(5): 1345-1358. |
R824970 (Final) |
not available |
|
|
Gard EE, Kleeman MJ, Gross DS, Hughes LS, Allen JO, Morrical BD, Fergenson DP, Dienes T, Galli ME, Johnson RJ, Cass GR, Prather KA. Direct Observation of Heterogeneous Chemistry in the Atmosphere. Science 1998; 279(5354): 1184-1187 |
R824970 (Final) |
not available |
|
|
Glarborg P, Kubel D, DamJohansen K, Chiang HM, Bozzelli JW. Impact of SO2 and NO on CO oxidation under post-flame conditions. International Journal of Chemical Kinetics 1996; 28(10): 773-790. |
R824970 (Final) |
not available |
|
|
Green WH, Barton PI, Bhattacharjee B, Matheu DM, Schwer DA, Song J, Sumathi R, Carstensen H-H, Dean AM, Grenda JM. Computer construction of detailed chemical kinetic models for gas-phase reactors. Industrial & Engineering Chemistry Research 2001; 40(23): 5362-5370. |
R824970 (Final) |
not available |
|
|
Griffin RJ, Dabdub D, Cocker III DR, Seinfeld JH. Estimate of global atmospheric organic aerosol from oxidation of biogenic hydrocarbons. Geophysical Research Letters 1999; 26(17): 2721-2724. |
R824970 (Final) |
not available |
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Griffin RJ, Cocker III DR, Flagan RC, Seinfeld JH. Organic aerosol formation from the oxidation of biogenic hydrocarbons. Journal of Geophysical Research 1999; 104(D3): 3555-3568. |
R824970 (Final) |
not available |
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Grosjean E, Grosjean D, Seinfeld JH. Atmospheric chemistry of 1-octene, 1-decene, and cyclohexene: gas-phase carbonyl and peroxyacyl nitrate products. Environmental Science and Technology 1996; 30(3): 1038-1047. |
R824970 (Final) |
not available |
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Grosjean E, Grosjean D, Seinfeld JH. Gas-phase reaction of ozone with trans-2-hexenal, trans-2-hexenyl acetate, ethylvinyl ketone, and 6-methyl-5-hepten-2-one. International Journal of Chemical Kinetics 1996; 28(5): 373-382. |
R824970 (Final) |
not available |
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Hannigan MP, Cass GR, Lafleur AL, Longwell JP, Thilly WG. Bacterial mutagenicity of urban organic aerosol sources in comparison to atmospheric samples. Environmental Science & Technology 1994;28(12):2014-2024. |
R824970 (Final) |
Exit |
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Hannigan MP, Cass GR, Lafleur AL, Busby Jr WF, Thilly WG. Seasonal and spatial variation of the bacterial mutagenicity of fine organic aerosol in Southern California. Environmental Health Perspectives 1996; 104(4): 428-436. |
R824970 (Final) |
not available |
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Hoffmann,T, Odum JR, Bowman F, Collins D, Klockow D, Flagan RC, Seinfeld JH. Formation of organic aerosols from the oxidation of biogenic hydrocarbons. Journal of Atmospheric Chemistry 1997; 26(2): 189-222. |
R824970 (Final) |
not available |
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Hughes LS, Allen JO, Kleeman MJ, Johnson RJ, Cass GR. Size and composition distribution of atmospheric particles in Southern California. Environmental Science & Technology 1999; 33(20): 3506-3515. |
R824970 (Final) |
not available |
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Hughes LS, Allen JO, Bhave P, Kleeman MJ, Cass GR, Liu D-Y, Fergenson DP, Morrical BD, Prather KA. Evolution of Atmospheric Particles along Trajectories Crossing the Los Angeles Basin. Environmental Science and Technology 2000;34(15):3058-3068. |
R824970 (Final) |
Exit |
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Jung D, Chen C-J, Bozzelli JW. Structures, rotation barrier, and thermodynamic properties ΔHf°298, S°298, and Cp(T) of chloromethyl hypochlorites CH3OCl, CH2ClOCl, CHCl2OCl, and CCl3OCl. Journal of Physical Chemistry A 2000;104(42):9581-9590. |
R824970 (Final) R826371 (Final) R826371C009 (Final) |
Exit |
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Jungkamp TPW, Seinfeld JH. The enthalpy of formation of trioxy radicals ROOO (R=H, CH3, C2H5). Journal of Chemical Physics 1996; 257(1-2): 15-22. |
R824970 (Final) |
not available |
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Jungkamp TPW, Seinfeld JH. The mechanism of methoxy radical oxidation: hydrogen abstraction versus trioxy radical formation. Journal of Chemical Physics 1996; 263(3-4): 371-378. |
R824970 (Final) |
not available |
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Jungkamp TPW, Smith JN, Seinfeld JH. Atmospheric oxidation mechanism of n-butane: the fate of alkoxy radicals. Journal of Physical Chemistry 1997; 101(24): 4392-4401. |
R824970 (Final) |
not available |
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Jungkamp TPW, Seinfeld JH. Prediction of bond dissociation energies and transition state barriers by a modified complete basis set model chemistry. Journal of Physical Chemistry 1997; 107(5): 1513-1521. |
R824970 (Final) |
not available |
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Kronholm DF, Howard JB. Analysis of Soot Surface Growth Pathways using Published Plug-Flow Reactor Data with New Particle Size Distribution Measurements and Published Premixed Flame Data. Proceedings of the Combustion Institute 2000; 28: 2555-2561. |
R824970 (Final) |
not available |
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Lay TH, Bozzelli JW, Dean AM, Ritter ER. Hydrogen-atom bond increments for calculation of thermodynamic properties of hydrocarbon radical species. Journal of Physical Chemistry 1995; 99(39): 14514-14527. |
R824970 (Final) |
not available |
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Lay TH. Krasnoperov LN, Venanzi CA, Bozzelli JW, Shokhirev NV. Ab initio study of alpha-chlorinated ethyl hydroperoxides CH3CH2OOH, CH3CHClOOH, and CH3CCl2OOH: Conformational analysis, internal rotation barriers, vibrational frequencies, and thermodynamic properties. Journal of Physical Chemistry 1996; 100(20): 8240-8249. |
R824970 (Final) |
not available |
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Lay TH, Bozzelli JW, Seinfeld JH. Atmospheric photochemical oxidation of benzene: Benzene plus OH and the benzene-OH adduct (hydroxyl-2,4-cyclohexadienyl) plus O-2. Journal of Physical Chemistry 1996; 100(16): 6543-6554. |
R824970 (Final) |
not available |
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Lay TH; Bozzelli JW. Enthalpies of formation and group additivity of alkyl peroxides and trioxides. Journal of Physical Chemistry A 1997;101(49):9505-9510. |
R824970 (Final) |
not available |
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Lay TH, Bozzelli JW. Enthalpies of formation of cyclic alkyl peroxides: Dioxane, 1,2-dioxetane, 1,2-dioxolane, and 1,2-dioxane. Chemical Physics Letters 1997; 268(1-2): 175-179. |
R824970 (Final) |
not available |
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Lay TH, Tsai P-L, Yamada T, Bozzelli JW. Thermodynamic parameters and group additivity ring corrections for three-to-six-membered oxygen heterocyclic hydrocarbons. Journal of Physical Chemistry A 1997; 101(13): 2471-2477. |
R824970 (Final) |
not available |
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Liang CK, Pankow JF, Odum JR, Seinfeld JH. Gas/particle partitioning of semivolatile organic compounds to model inorganic, organic, and ambient smog aerosols. Environmental Science & Technology 1997;31(11):3086-3092 |
R824970 (Final) |
not available |
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Meng Z, Dabdub D, Seinfeld JH. Chemical coupling between atmospheric ozone and particulate matter. Science 1997; 277(5322): 116-119. |
R824970 (Final) |
not available |
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Mitra S, Lai A. A Sequential Valve-Microtrap Injection System for Continuous, Online Gas-Chromatographic Analysis at Trace Levels. Journal of Chromatographic Science 1995; 33(6): 285-289. |
R824970 (Final) |
not available |
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Mitra S, Li WF, Kebbekus B. Evaluation of a thermionic ionization detector for selective detection of oxygenated volatile organic compounds. Journal of Chromatographic Science 1995; 33(7): 405-409. |
R824970 (Final) |
not available |
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Molina MJ, Zhang R, Broekhuizen K, Lei W, Navarro R, Molina LT. Experimental study of intermediates from OH-initiated reactions of toluene. Journal of the American Chemical Society 1999; 121(43): 10225-10226. |
R824970 (Final) |
not available |
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Odum JR, Hoffman, Thorsten, Bownman F, Collins D, Flagan RC, Seinfeld JH. Gas/particle partitioning and secondary organic aerosol yields. Environmental Science & Technology 1996; 30(8): 2580-2585. |
R824970 (Final) |
not available |
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Odum JR, Jungkamp TPW, Griffin RJ, Flagan RC, Seinfeld JH. The atmospheric aerosol-forming potential of whole gasoline vapor. Science April 4, 1997; 276(5309): 96-99. |
R824970 (Final) |
not available |
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Odum JR, Jungkamp TPW, Griffin RJ, Forstner HJL, Flagan RC, Seinfeld JH. Aromatics, reformulated gasoline, and atmospheric organic aerosol formation. Enviornmental Science & Technology 1997;31(7):1890-1897. |
R824970 (Final) |
Exit Exit |
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Palotas AB, Rainey LC, Sarofim AF,VanderSande JB, Ciambelli P. Effect of oxidation on the microstructure of carbon blacks. Energy & Fuels 1996; 10(1): 254-259. |
R824970 (Final) |
not available |
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Palotas AB, Rainey LC, Feldermann CJ, Sarofim AF, Vander Sande JB. Soot morphology: An application of image analysis in high-resolution transmission electron microscopy. Microscopy Research and Technique 1996; 33(3): 266-278. |
R824970 (Final) |
not available |
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Palotas AB, Rainey LC, Sarofim AF, Vander Sande JB, Flagan RC. Where Did That Soot Come From?. Chemtech 1998; 28(7): 24-30. |
R824970 (Final) |
not available |
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Park BI, Bozzelli JW, Booty MR. Polymer pyrolysis and oxidation studies in a continuous feed and flow reactor: Cellulose and polystyrene. Environmental Science & Technology 1999;33(15):2584-2592 |
R824970 (Final) |
not available |
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Pope CJ, Howard JB. Simultaneous particle and molecule modeling (SPAMM): an approach for combining sectional aerosol equations and elementary gas-phase reactions. Aerosol Science and Technology 1997; 27(1): 73-94. |
R824970 (Final) |
not available |
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Procaccini C, Bozzelli JW, Longwell JP, Smith KA, Sarofim AF. Presence of chlorine radicals and formation of molecular chlorine in the post-flame region of chlorocarbon combustion. Environmental Science & Technology 2000; 34(21): 4565-4570. |
R824970 (Final) |
not available |
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Rainey L, Palotas A, Bolsaitis P, Vander Sande JB, Sarofilm AF. Application of high resolution electron microscopy for the characterization and source assignment of diesel particulates. Applied Occupational and Environmental Hygiene 1996; 11(7): 777-781. |
R824970 (Final) |
not available |
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Richter H, Howard JB. Formation of polycyclic aromatic hydrocarbons and their growth to soot -a review of chemical reaction pathways. Progress in Energy and Combustion Science 2000; 26(4-6): 565-608. |
R824970 (Final) |
not available |
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Richter H, Mazyar OA, Sumathy R, Green WH, Howard JB, Bozzelli JW. Detailed kinetic study of the growth of small polycyclic aromatic hydrocarbons 1. 1-naphthyl plus ethyne. Journal of Physical Chemistry A 2001; 105(9): 1561-1573. |
R824970 (Final) |
not available |
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Schwer DA, Tolsma JE, Green WH, Barton PI. On upgrading the numerics in combustion chemistry codes. Combustion and Flame 2002; 128(3): 270-291. |
R824970 (Final) |
not available |
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Sheng C, Bozzelli JW. ab initio molecular orbital and density functional analysis of acetylene + O2 reactions with CHEMKIN evaluation. International Journal of Chemical Kinetics 2000;32(10):263-641 |
R824970 (Final) |
not available |
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Sun H, Chen CJ, Bozzelli JW. Structures, intramolecular rotation barriers, and thermodynamic properties (enthalpies, entropies and heat capacities) of chlorinated methyl hydroperoxides (CH2ClOOH, CHCl2OOH, and CCl3OOH). Journal of Physical Chemistry A 2000;104(35):8270-8282. |
R824970 (Final) R826371C009 (Final) |
Exit |
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Thomson MJ, Lucas D, Koshland CP, Sawyer RF, Wu Y-P, Bozzelli JW. An experimental and numerical study of the high-temperature oxidation of 1,1,1-C2H3. Combustion and Flame 1994;98(1-2):155-169. |
R824970 (Final) |
not available |
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Tolsma JE, Barton PI. DAEPACK: an open modeling environment for legacy models. Industrial Engineering & Chemical Research 2000; 39(6): 1826-1839. |
R824970 (Final) |
not available |
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Tolsma JE, Barton PI, Clabaugh JA. Symbolic incorporation of external procedures into process modeling environments. Industrial Engineering & Chemical Research August 7, 2002; 41(16): 3867-3876. |
R824970 (Final) |
not available |
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Tolsma JE, Barton PI. Hidden discontinuities and parametric sensitivity calculations. SIAM Journal on Scientific Computing 2002; 23(6): 1861-1874. |
R824970 (Final) |
not available |
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Tse CW, Flagan RC, Seinfeld JH. Rate constants for the gas-phase reaction of the hydroxyl radical with a series of dimethylbenzaldehydes and trimethylphenols at atmospheric pressure. International Journal of Chemical Kinetics 1997; 29(7): 523-525. |
R824970 (Final) |
not available |
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Wu KC, Hochgreb S. The roles of chemistry and diffusion on hydrocarbon post-flame oxidation. Combustion Science and Technology 1997;130(1-6):365-398 |
R824970 (Final) |
not available |
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Yamada T, Bozzelli JW. Ab initio calculations and internal rotor: contribution for thermodynamics properties SO298 and Cp(T) (300 < T/K < 1500: group additivity for fluoroethanes. Journal of Physical Chemistry A 1998; 102(37): 7286-7293. |
R824970 (Final) |
not available |
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Yamada T, Bozzelli JW, Lay T. Kinetic and thermodynamic analysis on OH addition to ethylene: adduct formation, isomerization, and isomer dissociations. Journal of Physical Chemistry A 1999; 103(38): 7646-7655. |
R824970 (Final) |
not available |
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Yamada T, Bozzelli JW, Berry RJ. Thermodynamic properties (Delta H-f(298), S(298), and C-p(T) (300 <= T <= 1500)) of fluorinated propanes. Journal of Physical Chemistry A 1999; 103(28): 5602- 5610. |
R824970 (Final) |
not available |
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Yamada,T.; Bozzelli,J.W.. Thermodynamic properties Delta H-f degrees(298), S degrees(298), and C-p(T) for 2-fluoro-2-methylpropane, Delta H-f degrees(298) of fluorinated ethanes, and group additivity for fluoroalkanes. Journal of Physical Chemistry A. 1999; 103(36): 7373-7379. |
R824970 (Final) |
not available |
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Yamada T, Bozzelli JW, Lay TH. Comparisons of CBS-q and G2 calculations on thermodynamic properties, transition states, and kinetics of dimethyl-ether plus O-2 reaction system. International Journal of Chemical Kinetics 2000; 32(7): 435-452. |
R824970 (Final) |
not available |
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Yu JZ, Flagan RC, Seinfeld JH. Identification of products containing -COOH, -OH, and -C=O in atmospheric oxidation of hydrocarbons. Environmental Science & Technology 1998; 32(16): 2357-2370. |
R824970 (Final) |
not available |
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Yu JZ, Cocker DR, Griffin RJ, Flagan RC, Seinfeld JH. Gas-phase ozone oxidation of monoterpenes: Gaseous and particulate products. Journal of Atmospheric Chemistry 1999;34(2):207-258 |
R824970 (Final) |
not available |
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Yu JZ, Griffin RJ, Cocker DR, Flagan RC, Seinfeld JH, Blanchard P. Observation of gaseous and particulate products of monoterpene oxidation in forest atmospheres. Geophysical Research Letters 1999;26(8):1145-1148 |
R824970 (Final) |
not available |
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Zhong X, Bozzelli JW. Thermochemical and kinetic analysis of the H, OH, HO2, O and O2 association reactions with cyclopentadienyl radical. Journal of Physical Chemistry A 1998; 102(20): 3537-3555. |
R824970 (Final) |
not available |
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Zhong X, Bozzelli JW. Thermochemical and kinetic analysis on the addition reactions of H, O, OH, and HO2 with 1,3 cyclopentadiene. International Journal of Chemical Kinetics 1997; 29(12): 893-913. |
R824970 (Final) |
not available |
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Zhu L, Bozzelli JW, Ho WP. Reaction of OH radical with C2H3Cl: Rate constant and reaction pathway analysis. Journal of Physical Chemistry A 1999; 103(39): 7800-7810. |
R824970 (Final) |
not available |
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Zhu L, Chen C-J, Bozzelli JW. Structures, rotational barriers, and thermodynamic properties of C2 vinyl and chlorovinyl alcohols and additivity groups. Journal of Physical Chemistry A 2000;104(40):9197-9206. |
R824970 (Final) R826371C009 (Final) |
Exit |
Supplemental Keywords:
RFA, Scientific Discipline, Air, Waste, particulate matter, Environmental Chemistry, Analytical Chemistry, Environmental Monitoring, Atmospheric Sciences, Ecology and Ecosystems, Incineration/Combustion, emission control strategies, atmospheric particulate matter, atmospheric particles, air quality models, combustion emissions, air pollution control, airborne organics, airborne particulate matter, air pollution, atmospheric transport, combustion, aerosol dynamics, combustion kineticsProgress and Final Reports:
Final Report Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
R824970C001 Chemical Kinetic Modeling of Formation of Products of Incomplete Combustion
from Spark-ignition Engines
R824970C002 Combustion Chamber Deposit Effects on Engine Hydrocarbon Emissions
R824970C003 Atmospheric Transformation of Volatile Organic Compounds: Gas-Phase
Photooxidation and Gas-to-Particle Conversion
R824970C004 Mathematical Models of the Transport and Fate of Airborne Organics
R824970C005 Elementary Reaction Mechanism and Pathways for Atmospheric Reactions
of Aromatics - Benzene and Toluene
R824970C006 Simultaneous Removal of Soot and NOx from the Exhaust of Diesel Powered
Vehicles
R824970C007 Modeling Gas-Phase Chemistry and Heterogeneous Reaction of Polycyclic
Aromatic Compounds
R824970C008 Fundamental Study on High Temperature Chemistry of Oxygenated Hydrocarbons
as Alternate Motor Fuels and Additives
R824970C009 Markers for Emissions from Combustion Sources
R824970C010 Experimental Investigation of the Evolution of the Size and Composition Distribution of Atmospheric Organic Aerosols
R824970C011 Microengineered Mass Spectrometer for in-situ Measurement of Airborne
Contaminants