Metodologia modificada para determinação de óxido de titânio como marcador em fezes de bovinos.

JIPE 2013

Análise e gerenciamento de resíduos do pH-SMP.

JIPE 2012

Integrated optical fiber force myography sensor as pervasive predictor of hand postures

Force myography (FMG) is an appealing alternative to traditional electromyography in biomedical applications, mainly due to its simpler signal pattern and immunity to electrical interference. Most FMG sensors, however, send data to a computer for further processing, which reduces the user mobility and, thus, the chances for practical application. In this sense, this work proposes to remodel a typical optical fiber FMG sensor with smaller portable components. Moreover, all data acquisition and processing routines were migrated to a Raspberry Pi 3 Model B microprocessor, ensuring the comfort of use and portability. The sensor was successfully demonstrated for 2 input channels and 9 postures classification with an average precision and accuracy of ~99.5% and ~99.8%, respectively, using a feedforward artificial neural network of 2 hidden layers and a competitive output layer11CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPNão tem0012017/25666-

Reassessing associations between white matter and behaviour with multimodal microstructural imaging

Several studies have established specific relationships between White Matter (WM) and behaviour. However, these studies have typically focussed on fractional anisotropy (FA), a neuroimaging metric that is sensitive to multiple tissue properties, making it difficult to identify what biological aspects of WM may drive such relationships. Here, we carry out a pre-registered assessment of WM-behaviour relationships in 50 healthy individuals across multiple behavioural and anatomical domains, and complementing FA with myelin-sensitive quantitative MR modalities (MT, R1, R2∗). Surprisingly, we only find support for predicted relationships between FA and behaviour in one of three pre-registered tests. For one behavioural domain, where we failed to detect an FA-behaviour correlation, we instead find evidence for a correlation between behaviour and R1. This hints that multimodal approaches are able to identify a wider range of WM-behaviour relationships than focusing on FA alone. To test whether a common biological substrate such as myelin underlies WM-behaviour relationships, we then ran joint multimodal analyses, combining across all MRI parameters considered. No significant multimodal signatures were found and power analyses suggested that sample sizes of 40–200 may be required to detect such joint multimodal effects, depending on the task being considered. These results demonstrate that FA-behaviour relationships from the literature can be replicated, but may not be easily generalisable across domains. Instead, multimodal microstructural imaging may be best placed to detect a wider range of WM-behaviour relationships, as different MRI modalities provide distinct biological sensitivities. Our findings highlight a broad heterogeneity in WM\u27s relationship with behaviour, suggesting that variable biological effects may be shaping their interaction

Microscopy-BIDS: An extension to the brain imaging data structure for microscopy data

The Brain Imaging Data Structure (BIDS) is a specification for organizing, sharing, and archiving neuroimaging data and metadata in a reusable way. First developed for magnetic resonance imaging (MRI) datasets, the community-led specification evolved rapidly to include other modalities such as magnetoencephalography, positron emission tomography, and quantitative MRI (qMRI). In this work, we present an extension to BIDS for microscopy imaging data, along with example datasets. Microscopy-BIDS supports common imaging methods, including 2D/3D, ex/in vivo, micro-CT, and optical and electron microscopy. Microscopy-BIDS also includes comprehensible metadata definitions for hardware, image acquisition, and sample properties. This extension will facilitate future harmonization efforts in the context of multi-modal, multi-scale imaging such as the characterization of tissue microstructure with qMRI

Assessment of Multivessel Coronary Artery Disease Using Cardiovascular Magnetic Resonance Pixelwise Quantitative Perfusion Mapping

OBJECTIVES: The authors sought to compare the diagnostic accuracy of quantitative perfusion maps to visual assessment (VA) of first-pass perfusion images for the detection of multivessel coronary artery disease (MVCAD). BACKGROUND: VA of first-pass stress perfusion cardiac magnetic resonance (CMR) may underestimate ischemia in MVCAD. Pixelwise perfusion mapping allows quantitative measurement of regional myocardial blood flow, which may improve ischemia detection in MVCAD. METHODS: One hundred fifty-one subjects recruited at 2 centers underwent stress perfusion CMR with myocardial perfusion mapping, and invasive coronary angiography with coronary physiology assessment. Ischemic burden was assessed by VA of first-pass images and by quantitative measurement of stress myocardial blood flow using perfusion maps. RESULTS: In patients with MVCAD (2-vessel [2VD] or 3-vessel disease [3VD]; n = 95), perfusion mapping identified significantly more segments with perfusion defects (median segments per patient 12 [interquartile range (IQR): 9 to 16] by mapping vs. 8 [IQR: 5 to 9.5] by VA; p < 0.001). Ischemic burden (IB) measured using mapping was higher in MVCAD compared with IB measured using VA (3VD mapping 100 % (75% to 100%) vs. first-pass 56% (38% to 81%) ; 2VD mapping 63% (50% to 75%) vs. first-pass 41% (31% to 50%); both p < 0.001), but there was no difference in single-vessel disease (mapping 25% (13% to 44%) vs. 25% (13% to 31%). Perfusion mapping was superior to VA for the correct identification of extent of coronary disease (78% vs. 58%; p < 0.001) due to better identification of 3VD (87% vs. 40%) and 2VD (71% vs. 48%). CONCLUSIONS: VA of first-pass stress perfusion underestimates ischemic burden in MVCAD. Pixelwise quantitative perfusion mapping increases the accuracy of CMR in correctly identifying extent of coronary disease. This has important implications for assessment of ischemia and therapeutic decision-making

Remote practicals in the time of coronavirus, a multidisciplinary approach

Due to the COVID-19 pandemic, universities across the world have curtailed face to face teaching. Associated with this is the halt to the delivery of the practical experience required of engineering students. The Multidisciplinary Engineering Education (MEE) team at The University of Sheffield have responded to this problem in an efficient and effective way by recording laboratory experiences and putting videos, quizzes and data online for students to engage with. The focus of this work was on ensuring all Learning Outcomes (LOs) for modules and courses were preserved. Naturally, practical skills cannot be easily provided using this approach, but it is an effective way of getting students to interact with real data, uncertainty and equipment which they cannot access directly. A number of short case studies from across the range of engineering disciplines are provided to inspire and guide other educators in how they can move experiments on line in an efficient and effective manner. No student feedback is available at the time of writing, but anecdotal evidence is that this approach is at least acceptable for students and a way of collecting future feedback is suggested. The effort expended on this approach and the artefacts produced will support student learning after the initial disruption of the lockdown has passed