40 research outputs found
Student experiences of undergraduate interprofessional education in Scotland:Emerging views of teamwork and professional identity
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Using an e-learning tool to support qualitative e-research in occupational therapy
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Children who are born preterm: Are their handwriting difficulties different to those of their full-term peers
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Emerging tools in qualitative research methods : asynchronous online discussion and the use of WebCT
The use of the Internet as a medium for conducting research
is not a new concept and certainly one that is constantly
evolving. The online environment represents numerous
opportunities for methodological innovations. Online
discussions are a permutation- of the traditional focus
groups, which have been closely associated to qualitative
research and the production of rich, textual data relating to the participants' lives and experiences.sch_occunpub133unpu
Occupational therapists' perceptions on the academic difficulties preterm children present with in their first years of mainstream schooling
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Qualitative Research via Internet: Asynchronous Online Discussions and the Use of WebCT
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The language profile of formal thought disorder
Formal thought disorder (FTD) is clinically manifested as disorganized speech, but there have been only few investigations of its linguistic properties. We examined how disturbance of thought may relate to the referential function of language as expressed in the use of noun phrases (NPs) and the complexity of sentence structures. We used a comic strip description task to elicit language samples from 30 participants with schizophrenia (SZ), 15 with moderate or severe FTD (SZ + FTD), and 15 minimal or no FTD (SZ−FTD), as well as 15 first-degree relatives of people with SZ (FDRs) and 15 neurotypical controls (NC). We predicted that anomalies in the normal referential use of NPs, sub-divided into definite and indefinite NPs, would identify FTD; and also that FTD would also be linked to reduced linguistic complexity as specifically measured by the number of embedded clauses and of grammatical dependents. Participants with SZ + FTD produced more referential anomalies than NC and produced the fewest definite NPs, while FDRs produced the most and thus also differed from NC. When referential anomalies were classed according to the NP type in which they occurred, the SZ + FTD group produced more anomalies in definite NPs than NC. Syntactic errors did not distinguish groups, but the SZ + FTD group exhibited significantly less syntactic complexity than non-SZ groups. Exploratory regression analyses suggested that production of definite NPs distinguished the two SZ groups. These results demonstrate that FTD can be identified in specific grammatical patterns which provide new targets for detection, intervention, and neurobiological studies
Deep learning models for predicting RNA degradation via dual crowdsourcing
Medicines based on messenger RNA (mRNA) hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition (‘Stanford OpenVaccine’) on Kaggle, involving single-nucleotide resolution measurements on 6,043 diverse 102–130-nucleotide RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504–1,588 nucleotides) with improved accuracy compared with previously published models. These results indicate that such models can represent in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for dataset creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales
Deep learning models for predicting RNA degradation via dual crowdsourcing
Messenger RNA-based medicines hold immense potential, as evidenced by their
rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA
molecules has been limited by their thermostability, which is fundamentally
limited by the intrinsic instability of RNA molecules to a chemical degradation
reaction called in-line hydrolysis. Predicting the degradation of an RNA
molecule is a key task in designing more stable RNA-based therapeutics. Here,
we describe a crowdsourced machine learning competition ("Stanford
OpenVaccine") on Kaggle, involving single-nucleotide resolution measurements on
6043 102-130-nucleotide diverse RNA constructs that were themselves solicited
through crowdsourcing on the RNA design platform Eterna. The entire experiment
was completed in less than 6 months, and 41% of nucleotide-level predictions
from the winning model were within experimental error of the ground truth
measurement. Furthermore, these models generalized to blindly predicting
orthogonal degradation data on much longer mRNA molecules (504-1588
nucleotides) with improved accuracy compared to previously published models.
Top teams integrated natural language processing architectures and data
augmentation techniques with predictions from previous dynamic programming
models for RNA secondary structure. These results indicate that such models are
capable of representing in-line hydrolysis with excellent accuracy, supporting
their use for designing stabilized messenger RNAs. The integration of two
crowdsourcing platforms, one for data set creation and another for machine
learning, may be fruitful for other urgent problems that demand scientific
discovery on rapid timescales