KudoZ home » Spanish to English » Medical (general)

codificaciones altas y bajas

English translation: high and low codifications

Advertisement

Login or register (free and only takes a few minutes) to participate in this question.

You will also have access to many other tools and opportunities designed for those who have language-related jobs
(or are passionate about them). Participation is free and the site has a strict confidentiality policy.
GLOSSARY ENTRY (DERIVED FROM QUESTION BELOW)
Spanish term or phrase:codificaciones altas y bajas
English translation:high and low codifications
Entered by: liz askew
Options:
- Contribute to this entry
- Include in personal glossary

09:36 Feb 22, 2008
Spanish to English translations [PRO]
Medical - Medical (general)
Spanish term or phrase: codificaciones altas y bajas
TB bacilloscopy and culture laboratory techniques...

El laboratorio supervisor evaluará el desempeño del personal del laboratorio supervisado a través del análisis de un panel de láminas (PL).
Estos paneles estarán constituidos por 10 láminas que incluirán láminas negativas y positivas con **codificaciones altas y bajas**.
Jason Willis-Lee
Local time: 06:00
high or low codifications
Explanation:
http://www.google.co.uk/search?hl=en&as_qdr=all&q=codificati...

--------------------------------------------------
Note added at 8 mins (2008-02-22 09:44:53 GMT)
--------------------------------------------------

sorry

high AND low

--------------------------------------------------
Note added at 4 hrs (2008-02-22 13:52:18 GMT)
--------------------------------------------------

Hi

Here is a better reference:

http://216.239.59.104/search?q=cache:NMQNoO8m0yoJ:scielo.sld...


Las láminas de esputo, fueron recoloreadas antes de efectuar la relectura. El procedimiento de recoloración se hizo con fucsina básica 1 %, dejándola actuar 10 min luego de aplicar calor hasta la emisión de vapores,2 modalidad que hasta el momento no se había realizado en Cuba; luego de enjuagar con suficiente agua, se aplicó una solución de alcohol clorhídrico a 0,3 % por 2 min, se enjuagó con suficiente agua de nuevo y posteriormente se aplicó azul de metileno 45 s.

Para la interpretación de los resultados del rechequeo, se calcularon las tasas de falsos positivos (FP), falsos negativos (FN) y errores de codificación (EC). Se tomó 5 % como tolerancia para los FP y 1 % para los FN. Los errores de codificación no debieron superar el 5 %.2,5-7


--------------------------------------------------
Note added at 4 hrs (2008-02-22 13:52:58 GMT)
--------------------------------------------------

So maybe it is "Tasas altas y bajas...

--------------------------------------------------
Note added at 4 hrs (2008-02-22 13:53:37 GMT)
--------------------------------------------------

high and low codification rates

You'll need to look into this yourself now.

--------------------------------------------------
Note added at 4 hrs (2008-02-22 13:55:47 GMT)
--------------------------------------------------

Material and Methods

We carried out this work through successive steps, in order to construct a neural network capable of learning the RBS concept, quite independently from the enormous data quantity generated by genome sequencing. For each step, a different model was designed for both architecture and data codification.

Three models of MLNN were built and, for each one, tests were performed with several different parameters, such as training strategy, learning step, and maximum error rate. For each of the models the best results are shown, and one network from each model is indicated as having the best performance. This performance is calculated using these values:

• True Positive (TP): percentage of instances that the network correctly classified a RBS sequence;

• True Negative (TN): percentage of instances that the network correctly classified as not being a RBS sequence;

• False Positive (FP): percentage of instances that are not RBS sequences, but the network classified as a RBS sequence;

• False Negative (FN): percentage of instances that are RBS sequences, but the network classified as not being a RBS sequence.

First model

The training of the first model was done using examples of sequences, which were taken from a well-known software for RBS identification: the RBSFinder (Suzek, 2001), whose default output is five bases length sequences. Therefore, this will be the size of the input of the networks of this model.

The codification used in this work is a normalized Binary Four Digit One (known as BIN4) (see Table 1), which in Wu (2000) is the most recommended for Bioinformatics applications.

Selected response from:

liz askew
United Kingdom
Local time: 05:00
Grading comment
Ta Liz...
4 KudoZ points were awarded for this answer

Advertisement


Summary of answers provided
3high or low codificationsliz askew
3with high and low degrees of positivityYasser El Helw


  

Answers


49 mins   confidence: Answerer confidence 3/5Answerer confidence 3/5
with high and low degrees of positivity


Explanation:
Suerte

Yasser El Helw
Local time: 06:00
Specializes in field
Native speaker of: Native in ArabicArabic, Native in EnglishEnglish
PRO pts in category: 473
Login to enter a peer comment (or grade)

8 mins   confidence: Answerer confidence 3/5Answerer confidence 3/5
high or low codifications


Explanation:
http://www.google.co.uk/search?hl=en&as_qdr=all&q=codificati...

--------------------------------------------------
Note added at 8 mins (2008-02-22 09:44:53 GMT)
--------------------------------------------------

sorry

high AND low

--------------------------------------------------
Note added at 4 hrs (2008-02-22 13:52:18 GMT)
--------------------------------------------------

Hi

Here is a better reference:

http://216.239.59.104/search?q=cache:NMQNoO8m0yoJ:scielo.sld...


Las láminas de esputo, fueron recoloreadas antes de efectuar la relectura. El procedimiento de recoloración se hizo con fucsina básica 1 %, dejándola actuar 10 min luego de aplicar calor hasta la emisión de vapores,2 modalidad que hasta el momento no se había realizado en Cuba; luego de enjuagar con suficiente agua, se aplicó una solución de alcohol clorhídrico a 0,3 % por 2 min, se enjuagó con suficiente agua de nuevo y posteriormente se aplicó azul de metileno 45 s.

Para la interpretación de los resultados del rechequeo, se calcularon las tasas de falsos positivos (FP), falsos negativos (FN) y errores de codificación (EC). Se tomó 5 % como tolerancia para los FP y 1 % para los FN. Los errores de codificación no debieron superar el 5 %.2,5-7


--------------------------------------------------
Note added at 4 hrs (2008-02-22 13:52:58 GMT)
--------------------------------------------------

So maybe it is "Tasas altas y bajas...

--------------------------------------------------
Note added at 4 hrs (2008-02-22 13:53:37 GMT)
--------------------------------------------------

high and low codification rates

You'll need to look into this yourself now.

--------------------------------------------------
Note added at 4 hrs (2008-02-22 13:55:47 GMT)
--------------------------------------------------

Material and Methods

We carried out this work through successive steps, in order to construct a neural network capable of learning the RBS concept, quite independently from the enormous data quantity generated by genome sequencing. For each step, a different model was designed for both architecture and data codification.

Three models of MLNN were built and, for each one, tests were performed with several different parameters, such as training strategy, learning step, and maximum error rate. For each of the models the best results are shown, and one network from each model is indicated as having the best performance. This performance is calculated using these values:

• True Positive (TP): percentage of instances that the network correctly classified a RBS sequence;

• True Negative (TN): percentage of instances that the network correctly classified as not being a RBS sequence;

• False Positive (FP): percentage of instances that are not RBS sequences, but the network classified as a RBS sequence;

• False Negative (FN): percentage of instances that are RBS sequences, but the network classified as not being a RBS sequence.

First model

The training of the first model was done using examples of sequences, which were taken from a well-known software for RBS identification: the RBSFinder (Suzek, 2001), whose default output is five bases length sequences. Therefore, this will be the size of the input of the networks of this model.

The codification used in this work is a normalized Binary Four Digit One (known as BIN4) (see Table 1), which in Wu (2000) is the most recommended for Bioinformatics applications.



liz askew
United Kingdom
Local time: 05:00
Specializes in field
Native speaker of: Native in EnglishEnglish
PRO pts in category: 3841
Grading comment
Ta Liz...
Login to enter a peer comment (or grade)




Return to KudoZ list


Changes made by editors
Feb 27, 2008 - Changes made by liz askew:
Edited KOG entry<a href="/profile/46918">Jason Willis-Lee's</a> old entry - "codificaciones altas y bajas" » "high and low codifications"


KudoZ™ translation help
The KudoZ network provides a framework for translators and others to assist each other with translations or explanations of terms and short phrases.



See also:



Term search
  • All of ProZ.com
  • Term search
  • Jobs
  • Forums
  • Multiple search