Evidence map›Paper›PMID 29969104›Full record

ArticleActa crystallographica. Section F, Structural biology communications2018

Cinder: keeping crystallographers app-y.

Nicholas Rosa, Marko Ristic, Bevan Marshall, Janet Newman

Abstract read
In one paragraph

Article in Acta crystallographica. Section F, Structural biology communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Moving beyond MARCO.PloS one · 2023
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Nicholas RosaManufacturing (Biomedical), CSIRO, 343 Royal Parade, Parkville, VIC 3052, Australia.
Marko RisticManufacturing (Biomedical), CSIRO, 343 Royal Parade, Parkville, VIC 3052, Australia.ORCID 0000-0002-6027-3508
Bevan MarshallManufacturing (Biomedical), CSIRO, 343 Royal Parade, Parkville, VIC 3052, Australia.
Janet NewmanManufacturing (Biomedical), CSIRO, 343 Royal Parade, Parkville, VIC 3052, Australia.ORCID 0000-0003-2666-3219

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The process of producing suitable crystals for X-ray diffraction analysis most often involves the setting up of hundreds (or thousands) of individual crystallization trials, each of which must be repeatedly examined for crystals or hints of crystallinity. Currently, the only real way to address this bottleneck is to use an automated imager to capture images of the trials. However, the images still need to be assessed for crystals or other outcomes. Ideally, there would exist some rapid and reliable machine-analysis tool to translate the images into a quantitative result. However, as yet no such tool exists in wide usage, despite this being a well recognized problem. One of the issues in creating robust automatic image-analysis software is the lack of reliable data for training machine-learning algorithms. Here, a mobile application, Cinder, has been developed which allows crystallization images to be scored quickly on a smartphone or tablet. The Cinder scores are inserted into the appropriate table in a crystallization database and are immediately available to the user through a more sophisticated web interface, allowing more detailed analyses. A sharp increase in the number of scored images was observed after Cinder was released, which in turn provides more data for training machine-learning tools.

Indexed as

CrystallizationCrystallography, X-RayMobile ApplicationsCindercrystallizationimagesmachine learningmobile appsscoring

Identifiers

PMID29969104
PMCPMC6038447

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.