| 1. |
- Bachimanchi, Harshith, et al.
(author)
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Deep-learning-powered data analysis in plankton ecology
- 2024
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In: Limnology And Oceanography Letters. - 2378-2242.
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Research review (peer-reviewed)abstract
- The implementation of deep learning algorithms has brought new perspectives to plankton ecology. Emerging as an alternative approach to established methods, deep learning offers objective schemes to investigate plankton organisms in diverse environments. We provide an overview of deep-learning-based methods including detection and classification of phytoplankton and zooplankton images, foraging and swimming behavior analysis, and finally ecological modeling. Deep learning has the potential to speed up the analysis and reduce the human experimental bias, thus enabling data acquisition at relevant temporal and spatial scales with improved reproducibility. We also discuss shortcomings and show how deep learning architectures have evolved to mitigate imprecise readouts. Finally, we suggest opportunities where deep learning is particularly likely to catalyze plankton research. The examples are accompanied by detailed tutorials and code samples that allow readers to apply the methods described in this review to their own data.
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| 2. |
- Canal-Garcia, Anna, et al.
(author)
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Dynamic multilayer functional connectivity detects preclinical and clinical Alzheimer's disease
- 2024
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In: CEREBRAL CORTEX. - 1047-3211 .- 1460-2199.
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Journal article (peer-reviewed)abstract
- Increasing evidence suggests that patients with Alzheimer's disease present alterations in functional connectivity but previous results have not always been consistent. One of the reasons that may account for this inconsistency is the lack of consideration of temporal dynamics. To address this limitation, here we studied the dynamic modular organization on resting-state functional magnetic resonance imaging across different stages of Alzheimer's disease using a novel multilayer brain network approach. Participants from preclinical and clinical Alzheimer's disease stages were included. Temporal multilayer networks were used to assess time-varying modular organization. Logistic regression models were employed for disease stage discrimination, and partial least squares analyses examined associations between dynamic measures with cognition and pathology. Temporal multilayer functional measures distinguished all groups, particularly preclinical stages, overcoming the discriminatory power of risk factors such as age, sex, and APOE epsilon 4 carriership. Dynamic multilayer functional measures exhibited strong associations with cognition as well as amyloid and tau pathology. Dynamic multilayer functional connectivity shows promise as a functional imaging biomarker for both early- and late-stage Alzheimer's disease diagnosis.
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| 3. |
- Olsén, Erik, 1994, et al.
(author)
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Dual-Angle Interferometric Scattering Microscopy for Optical Multiparametric Particle Characterization
- 2024
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In: NANO LETTERS. - 1530-6984 .- 1530-6992. ; 24:6, s. 1874-1881
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Journal article (peer-reviewed)abstract
- Traditional single-nanoparticle sizing using optical microscopy techniques assesses size via the diffusion constant, which requires suspended particles to be in a medium of known viscosity. However, these assumptions are typically not fulfilled in complex natural sample environments. Here, we introduce dual-angle interferometric scattering microscopy (DAISY), enabling optical quantification of both size and polarizability of individual nanoparticles (radius <170 nm) without requiring a priori information regarding the surrounding media or super-resolution imaging. DAISY achieves this by combining the information contained in concurrently measured forward and backward scattering images through twilight off-axis holography and interferometric scattering (iSCAT). Going beyond particle size and polarizability, single-particle morphology can be deduced from the fact that the hydrodynamic radius relates to the outer particle radius, while the scattering-based size estimate depends on the internal mass distribution of the particles. We demonstrate this by differentiating biomolecular fractal aggregates from spherical particles in fetal bovine serum at the single-particle level.
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| 4. |
- Srikrishna, Meera, et al.
(author)
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CT-based volumetric measures obtained through deep learning: Association with biomarkers of neurodegeneration
- 2024
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In: Alzheimers & Dementia. - 1552-5260. ; 20:1, s. 629-640
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Journal article (peer-reviewed)abstract
- INTRODUCTIONCranial computed tomography (CT) is an affordable and widely available imaging modality that is used to assess structural abnormalities, but not to quantify neurodegeneration. Previously we developed a deep-learning-based model that produced accurate and robust cranial CT tissue classification.MATERIALS AND METHODSWe analyzed 917 CT and 744 magnetic resonance (MR) scans from the Gothenburg H70 Birth Cohort, and 204 CT and 241 MR scans from participants of the Memory Clinic Cohort, Singapore. We tested associations between six CT-based volumetric measures (CTVMs) and existing clinical diagnoses, fluid and imaging biomarkers, and measures of cognition.RESULTSCTVMs differentiated cognitively healthy individuals from dementia and prodromal dementia patients with high accuracy levels comparable to MR-based measures. CTVMs were significantly associated with measures of cognition and biochemical markers of neurodegeneration.DISCUSSIONThese findings suggest the potential future use of CT-based volumetric measures as an informative first-line examination tool for neurodegenerative disease diagnostics after further validation.HIGHLIGHTSComputed tomography (CT)-based volumetric measures can distinguish between patients with neurodegenerative disease and healthy controls, as well as between patients with prodromal dementia and controls.CT-based volumetric measures associate well with relevant cognitive, biochemical, and neuroimaging markers of neurodegenerative diseases.Model performance, in terms of brain tissue classification, was consistent across two cohorts of diverse nature.Intermodality agreement between our automated CT-based and established magnetic resonance (MR)-based image segmentations was stronger than the agreement between visual CT and MR imaging assessment.
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| 5. |
- Tomecek, David, 1991, et al.
(author)
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Neural network enabled nanoplasmonic hydrogen sensors with 100 ppm limit of detection in humid air
- 2024
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In: Nature Communications. - 2041-1723 .- 2041-1723. ; 15:1
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Journal article (peer-reviewed)abstract
- Environmental humidity variations are ubiquitous and high humidity characterizes fuel cell and electrolyzer operation conditions. Since hydrogen-air mixtures are highly flammable, humidity tolerant H2 sensors are important from safety and process monitoring perspectives. Here, we report an optical nanoplasmonic hydrogen sensor operated at elevated temperature that combined with Deep Dense Neural Network or Transformer data treatment involving the entire spectral response of the sensor enables a 100 ppm H2 limit of detection in synthetic air at 80% relative humidity. This significantly exceeds the <1000 ppm US Department of Energy performance target. Furthermore, the sensors pass the ISO 26142:2010 stability requirement in 80% relative humidity in air down to 0.06% H2 and show no signs of performance loss after 140 h continuous operation. Our results thus demonstrate the potential of plasmonic hydrogen sensors for use in high humidity and how neural-network-based data treatment can significantly boost their performance.
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| 6. |
- Viot, Pascal, et al.
(author)
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Destructive effect of fluctuations on the performance of a Brownian gyrator
- 2024
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In: SOFT MATTER. - 1744-683X .- 1744-6848. ; 20:4, s. 3154-3160
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Journal article (peer-reviewed)abstract
- The Brownian gyrator (BG) is often called a minimal model of a nano-engine performing a rotational motion, judging solely upon the fact that in non-equilibrium conditions its torque, specific angular momentum L and specific angular velocity W have non-zero mean values. For a time-discretised (with time-step δt) model we calculate here the previously unknown probability density functions (PDFs) of L and W. We show that for finite δt, the PDF of L has exponential tails and all moments are therefore well-defined. At the same time, this PDF appears to be effectively broad – the noise-to-signal ratio is generically bigger than unity meaning that L is strongly not self-averaging. Concurrently, the PDF of W exhibits heavy power-law tails and its mean Image ID:d3sm01606d-t1.gif is the only existing moment. The BG is therefore not an engine in the common sense: it does not exhibit regular rotations on each run and its fluctuations are not only a minor nuisance – on contrary, their effect is completely destructive for the performance. Our theoretical predictions are confirmed by numerical simulations and experimental data. We discuss some plausible improvements of the model which may result in a more systematic rotational motion.
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