186,322 research outputs found
Orientation-disturbing magnetic treatment affects the pigeon opioid system
No full textKeeping homing pigeons in an oscillating magnetic field of low intensity is known to increase the scattering of initial bearings and/or their deflection towards a specific direction. To determine whether these effects on orientation are the outcome of direct interference with the birds' navigational mechanism or are the side-effect of problems in another biological system, experiments were performed to test whether the same effects could be induced by non-magnetic treatments. The initial orientation of pigeons treated with the prototypic opiate antagonist naloxone (1 mg kg-1) displayed similar disturbances to those observed in magnetically treated birds. In both cases, the orientation was significantly different from that of control birds.
The concentration and affinity of the brain's mu-opiate receptors were then assessed in magnetically treated birds by using [H-3]dihydromorphine as a ligand. The concentration of u-opiate receptors fell significantly in these birds, whereas the affinity of the receptors was unaffected.
We conclude that it appears improbable that the navigational mechanism of pigeons is directly influenced by magnetic treatments. What these do seem to produce is a lack of compensation for the stress experienced by pigeons subjected to a test release
alessioluschi/HITBert: v1.1
No full text<p>A fine-tuned BERT-based NLP model for classyfing HIT-related adverse events reports.</p><p>When using this model, the provided Python code, or the dataset for any other projects, please cite the original work:</p><p>Luschi, A., Nesi, P., Iadanza, E. "Evidence-based Clinical Engineering: Health Information Technology Adverse Events Identification and Classification with Natural Language Processing", Heliyon, Vol. 9(11), 2023 [DOI: 10.1016/j.heliyon.2023.e21723]</p>
Evidence-based clinical engineering: Health information technology adverse events identification and classification with natural language processing
No full textThe primary goal of this project is to create a framework to extract Real-World Evidence to support Health Technology Assessment, Health Technology Management, Evidence-Based Maintenance, and Post Market Surveillance (as outlined in the EU Medical Device Regulation 2017/745) of medical devices using Natural Language Processing (NLP) and Artificial Intelligence. An initial literature review on Spontaneous Reporting System databases, Health Information Technologies (HIT) fault classification, and Natural Language Processing has been conducted, from which it clearly emerges that adverse events related to HIT are increasing over time. The proposed framework uses NLP techniques and Explainable Artificial Intelligence models to automatically identify HIT-related adverse event reports. The designed model employs a pre-trained version of ClinicalBERT that has been fine-tuned and tested on 3,075 adverse event reports extracted from the FDA MAUDE database and manually labelled by experts
Health Information Technology Adverse Events Identification and Classification with Natural Language Processing and Deep Learning
No full textThe main topic of this work is to develop a framework to extract Real-World Evidence through Natural Language Processing (NLP) and Neural Networks. An initial literature analysis has been performed, from which it clearly emerges that adverse events concerning Health Information Technology (HIT) are gradually growing over time. The goal of the proposed framework is to automatically identify adverse event reports related to HIT, aiming to support Health Technology Assessment and Post Market Surveillance as outlined in European Regulation 2017/745 on Medical Devices. The designed model uses a pre-trained version of ClinicalBERT, additionally fine-tuned on 3, 705 adverse events reports extracted from the FDA MAUDE database, which had been previously manually labelled by experts. Results show better metrics than other existing HIT adverse events reports text classifiers based on non-BERT models, performing with an accuracy of 0.9906, precision of 0.9840, recall of 0.9973, and F1 score of 0.9906
Detecting female precise natal philopatry in green turtles using assignment methods
No full textIt is well established that sea turtles return to natal rookeries to mate and lay their eggs, and that individual females are faithful to particular nesting sites within the rookery. Less certain is whether females are precisely returning to their natal beach. Attempts to demonstrate such precise natal philopatry with genetic data have had mixed success. Here we focused on the green turtles of three nesting sites in the Ascension Island rookery, separated by 5–5 km. Our approach differed from previous work in two key areas. First, we used male microsatellite data (five loci) reconstructed from samples collected from their offspring (N = 17) in addition to data for samples taken directly from females ( N = 139). Second, we employed assignment methods in addition to the more traditional F-statistics. No significant genetic structure could be demonstrated with F ST . However, when average assignment probabilities of females were examined, those for nesting populations in which they were sampled were indeed significantly higher than their probabilities for other populations (Mann–Whitney U -test: P < 0.001). Further evidence was provided by a significant result for the mAI C test ( P < 0.001), supporting greater natal philopatry for females compared with males. The results suggest that female natal site fidelity was not sufficient for significant genetic differentiation among the nesting populations within the rookery, but detectable with assignment tests
Movements of three loggerhead sea turtles in Tuscany waters
No full textThe coastal waters along Tuscany (Central Italy)
are thought to represent a good foraging ground for loggerhead
turtles (Caretta caretta), especially, but not only, during the
juvenile phase. We describe the movements of three juvenile
loggerheads released along the Tuscany coast after having been
accidentally caught by fishermen and rehabilitated by recovery
centres of the region. The turtles were tracked by satellite for
20-125 days, displaying two main movement patterns. Two
turtles remained in neritic coastal waters for the whole duration
of tracking, while the third one moved north soon after
release, reaching the northern Ligurian Sea with an open-sea
route. These findings show that loggerheads can use Tuscany
waters both as a profitable long-term foraging site and as a
transit area during their movements towards other destinations
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