Gy 1736

We are using prodigy to ner. I read ner.

Nineteen hens 13 dead, 6 culled had intussusceptions of the proventriculus into the ventriculus. Mean age of affected hens was wk range wk. None of the hens in the study had an intestinal intussusception, and none of the hens euthanized at the end of the study had a proventricular intussusception. Hens with proventricular intussusceptions were severely emaciated; mean body weights were and g for affected and cohort hens, respectively. Necropsy findings included prominent keel, marked muscle atrophy, generalized serous atrophy of fat, no visible proventriculus, esophagus directly entering the ventriculus, and an enlarged, spherical, firm ventriculus, which contained an invaginated, swollen, diffusely ulcerated proventriculus. Eighteen affected hens were anovulatory Severe, diffuse necrosis and ulceration of the proventricular mucosa was confirmed microscopically, but no etiologic agent was identified.

Gy 1736

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Hi Ines, Thanks for the quick response, gy 1736. Keywords: avian; birds; chicken; digestive system; intussusception; laying hen; noninfectious disease; poultry; proventriculus; stomach; ventriculus. Is that a best practice for the future, to split the docs to smaller chunks when annotations start to pile up?

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The digital motion processor can be used to process complex algorithms directly on the board. Usually, the DMP processes algorithms that turn the raw values from the sensors into stable position data. In particular, it is shown how to retrieve the raw sensor values. If you plan to use the full range of features or require reliable and stable position data, then I recommend to have also a look at ready-to-use libraries. Next, we have to set up the I2C connection between the module and the Arduino. Unfortunately, you cannot use just any pin.

Gy 1736

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But I think the absolute fastest solution would be the pre-processing approach I described above. Are we doing anything wrong? Las gallinas con intususcepciones proventriculares estaban severamente emaciadas; los pesos corporales medios fueron g y g para las gallinas afectadas y para las gallinas cohorte, respectivamente. Also in case it makes any difference we are running prodigy and the database inside a docker container. Keywords: avian; birds; chicken; digestive system; intussusception; laying hen; noninfectious disease; poultry; proventriculus; stomach; ventriculus. We have annotated segments from 12 docs which does not explain the wait as it takes only 12 seconds for a naive script that passes the doc from nlp and checks if the pdf is annotated to find 10 documents that are not annotated. Interface is slow when running textcat recipe usage , textcat , front-end. Running nlp text to each document is slow as well but not that slow, it takes approx 2s per document We assume prodigy does not use pipe. Nineteen hens 13 dead, 6 culled had intussusceptions of the proventriculus into the ventriculus. Managing long annotation sessions usage , streams. From looking at the logs, it seems like Prodigy spends the 40 minutes going through your source data trying to find the next batch to send out. Let me know if you need more information on our environment, data or anything. La edad media de las gallinas afectadas fue de semanas con un rango de a semanas. Hi Ines, Thanks for the quick response.

Applications: Molecular works of ethanol precipitation, phenol extraction, NA preparation, cell collection, spin-down of temperature-sensitive reaction mixtures, etc.

Dieciocho gallinas afectadas fueron anovulatorias In conclusion, proventricular intussusception of undetermined etiology was identified as a cause of sporadic emaciation, culling, and mortality in older laying hens. Publication types Case Reports. Are we doing anything wrong? Also, if you want to really optimize for processing performance, another option could be to do the pre-processing separately, e. Hi Ines, Thanks for the quick response. Loading message prodigy UI usage , solved. Eighteen affected hens were anovulatory Is that a best practice for the future, to split the docs to smaller chunks when annotations start to pile up? The hackier version of this would be to just remove all lines from the top of your JSONL that you know are already annotated in the data.