Flexible dynamics of floating wind turbines

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012."February 2012." Cataloged from PDF version of thesis.Includes bibliographical references (p. 84-86).This work presents Tower Flex, a structural dynamics model for a coupled analysis of offshore floating wind turbines consisting of a tower, a floating platform and a mooring system. In this multi-body, linear frequency-domain model, the tower is represented as a series of uniform Timoshenko beams connected to each other. The deflections of the tower are solved analytically in each beam while the mass, damping and stiffness coming from the rotor, the floating platform and the mooring lines are taken into account via generalized boundary conditions. Tower Flex is used for the evaluation of a 3MW offshore floating wind turbine mounted on a Tension Leg Platform (TLP). Natural frequencies, motion responses and fatigue damage are analyzed to illustrate the features of Tower Flex and assess the performance of the proposed design.by Thomas Luypaert.S.M

Ensembl Genomes 2022: an expanding genome resource for non-vertebrates

Ensembl Genomes (https://www.ensemblgenomes.org) provides access to non-vertebrate genomes and analysis complementing vertebrate resources developed by the Ensembl project (https://www.ensembl.org). The two resources collectively present genome annotation through a consistent set of interfaces spanning the tree of life presenting genome sequence, annotation, variation, transcriptomic data and comparative analysis. Here we present our largest increase in plant, metazoan and fungal genomes since the project’s inception creating one of the world’s most comprehensive genomic resources and describe our efforts to reduce genome redundancy in our Bacteria portal. We also detail our new efforts in gene annotation, our emerging support for pangenome analysis and efforts to accelerate data dissemination through the Ensembl Rapid Release resource. We also present our new AlphaFold visualisation. Finally, we present details of our future plans including updates on our integration with Ensembl, and how we plan to improve our support for the microbial research community. Software and data are made available without restriction via our website, online tools platform and programmatic interfaces (available under an Apache 2.0 license). Data updates are synchronised with Ensembl’s release cycle

Neoadjuvant chemotherapy in breast cancer-response evaluation and prediction of response to treatment using dynamic contrast-enhanced and diffusion-weighted MR imaging

Objective To explore the predictive value of MRI parameters and tumour characteristics before neoadjuvant chemotherapy (NAC) and to compare changes in tumour size and tumour apparent diffusion coefficient (ADC) during treatment, between patients who achieved pathological complete response (pCR) and those who did not. Methods Approval by the Regional Ethics Committee and written informed consent were obtained. Thirty-one patients with invasive breast carcinoma scheduled for NAC were enrolled (mean age, 50.7; range, 37–72). Study design included MRI before treatment (Tp0), after four cycles of NAC (Tp1) and before surgery (Tp2). Data in pCR versus non-pCR groups were compared and cut-off values for pCR prediction were evaluated. Results Before NAC, HER2 overexpression was the single significant predictor of pCR (p=0.006). At Tp1 ADC, tumour size and changes in tumour size were all significantly different in the pCR and non-pCR groups. Using 1.42×10−3 mm2/s as the cut-off value for ADC, pCR was predicted with sensitivity and specificity of 88% and 80%, respectively. Using a cut-off value of 83% for tumour volume reduction, sensitivity and specificity for pCR were 91% and 80%. Conclusion ADC, tumour size and tumour size reduction at Tp1 were strong independent predictors of pCR

The Acoustic Index User's Guide: a practical manual for defining, generating and understanding current and future acoustic indices

1. Ecoacoustics, the study of environmental sound, is a rapidly growing discipline offering ecological insights at scales ranging from individual organisms to whole ecosystems. Substantial methodological developments over the last 15 years have streamlined extraction of ecological information from audio recordings. One widely used set of methods are acoustic indices, which offer numerical summaries of the spectral, temporal and amplitude patterns in audio recordings. 2. Currently, the specifics of each index's background, methodology and the soundscape patterns they are designed to summarise, are spread across multiple sources. Critically, details of index calculation are sometimes scarce, making it challenging for users to understand how index values are generated. Discrepancies in understanding can lead to misuse of acoustic indices or reporting of spurious results. This hinders ecological inference, replicability and discourages adoption of these tools for conservation and ecosystem monitoring, where they might otherwise provide useful insight. 3. Here we present the Acoustic Index User's Guide—an interactive RShiny web app that defines and deconstructs eight of the most commonly used acoustic indices to facilitate consistent application across the discipline. We break the acoustic indices calculations down into easy-to-follow steps to better enable practical application and critical interpretation of acoustic indices. We demonstrate typical soundscape patterns using a suite of 91 example audio recordings: 66 real-world soundscapes from terrestrial, aquatic and subterranean systems around the world, and 25 synthetic files demonstrating archetypal soundscape patterns. Our interpretation figures signpost specific soundscape patterns likely to be reflected in acoustic indices' values. 4. This RShiny app is a living resource; additional acoustic indices will be added in the future through collaboration with authors of pre-existing and new indices. The app also serves as a best-practice template for the information required when publishing new acoustic indices, so that authors can facilitate the widest possible understanding and uptake of their indices. In turn, improved understanding of acoustic indices will aid effective hypothesis generation, application and interpretation in ecological research, ecosystem monitoring and conservation management