High‐Speed Large‐Field Multifocal Illumination Fluorescence Microscopy

Scanning optical microscopy techniques are commonly restricted to a sub‐millimeter field‐of‐view (FOV) or otherwise employ slow mechanical translation, limiting their applicability for imaging fast biological dynamics occurring over large areas. A rapid scanning large‐field multifocal illumination (LMI) fluorescence microscopy technique is devised based on a beam‐splitting grating and an acousto‐optic deflector synchronized with a high‐speed camera to attain real‐time fluorescence microscopy over a centimeter‐scale FOV. Owing to its large depth of focus, the approach allows noninvasive visualization of perfusion across the entire mouse cerebral cortex, not achievable with conventional wide‐field fluorescence microscopy methods. The new concept can readily be incorporated into conventional wide‐field microscopes to mitigate image blur due to tissue scattering and attain optimal trade‐off between spatial resolution and FOV. It further establishes a bridge between conventional wide‐field macroscopy and laser scanning confocal microscopy, thus it is anticipated to find broad applicability in functional neuroimaging, in vivo cell tracking, and other applications looking at large‐scale fluorescent‐based biodynamics

Deep learning facilitates fully automated brain image registration of optoacoustic tomography and magnetic resonance imaging

Multispectral optoacoustic tomography (MSOT) is an emerging optical imaging method providing multiplex molecular and functional information from the rodent brain. It can be greatly augmented by magnetic resonance imaging (MRI) which offers excellent soft-tissue contrast and high-resolution brain anatomy. Nevertheless, registration of MSOT-MRI images remains challenging, chiefly due to the entirely different image contrast rendered by these two modalities. Previously reported registration algorithms mostly relied on manual user-dependent brain segmentation, which compromised data interpretation and quantification. Here we propose a fully automated registration method for MSOT-MRI multimodal imaging empowered by deep learning. The automated workflow includes neural network-based image segmentation to generate suitable masks, which are subsequently registered using an additional neural network. The performance of the algorithm is showcased with datasets acquired by cross-sectional MSOT and high-field MRI preclinical scanners. The automated registration method is further validated with manual and half-automated registration, demonstrating its robustness and accuracy

Visualizing alpha-synuclein and iron deposition in M83 mouse model of Parkinson's disease in vivo

BACKGROUND Abnormal alpha-synuclein and iron accumulation in the brain play an important role in Parkinson's disease (PD). Herein, we aim at visualizing alpha-synuclein inclusions and iron deposition in the brains of M83 (A53T) mouse models of PD in vivo . METHODS Fluorescently labelled pyrimidoindole-derivative THK-565 was characterized by using recombinant fibrils and brains from 10-11 months old M83 mice, which subsequently underwent in vivo concurrent wide-field fluorescence and volumetric multispectral optoacoustic tomography (vMSOT) imaging. The in vivo results were verified against structural and susceptibility weighted imaging (SWI) magnetic resonance imaging (MRI) at 9.4 Tesla and scanning transmission X-ray microscopy (STXM) of perfused brains. Brain slice immunofluorescence and Prussian blue staining were further performed to validate the detection of alpha-synuclein inclusions and iron deposition in the brain, respectively. RESULTS THK-565 showed increased fluorescence upon binding to recombinant alpha-synuclein fibrils and alpha-synuclein inclusions in post-mortem brain slices from patients with Parkinson's disease and M83 mice. i.v. administration of THK-565 in M83 mice showed higher cerebral retention at 20 and 40 minutes post-injection by wide-field fluorescence compared to non-transgenic littermate mice, in congruence with the vMSOT findings. SWI/phase images and Prussian blue indicated the accumulation of iron deposits in the brains of M83 mice, presumably in the Fe $^{3+}$ form, as evinced by the STXM results. CONCLUSION We demonstrated in vivo mapping of alpha-synuclein by means of non-invasive epifluorescence and vMSOT imaging assisted with a targeted THK-565 label and SWI/STXM identification of iron deposits in M83 mouse brains ex vivo

Study of the B−→Λc+Λˉc−K−B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-} decay

The decay $B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-}$ is studied in proton-proton collisions at a center-of-mass energy of $\sqrt{s}=13$ TeV using data corresponding to an integrated luminosity of 5 $\mathrm{fb}^{-1}$ collected by the LHCb experiment. In the $\Lambda_{c}^+ K^{-}$ system, the $\Xi_{c}(2930)^{0}$ state observed at the BaBar and Belle experiments is resolved into two narrower states, $\Xi_{c}(2923)^{0}$ and $\Xi_{c}(2939)^{0}$, whose masses and widths are measured to be $$m(\Xi_{c}(2923)^{0}) = 2924.5 \pm 0.4 \pm 1.1 \,\mathrm{MeV}, \\ m(\Xi_{c}(2939)^{0}) = 2938.5 \pm 0.9 \pm 2.3 \,\mathrm{MeV}, \\ \Gamma(\Xi_{c}(2923)^{0}) = \phantom{000}4.8 \pm 0.9 \pm 1.5 \,\mathrm{MeV},\\ \Gamma(\Xi_{c}(2939)^{0}) = \phantom{00}11.0 \pm 1.9 \pm 7.5 \,\mathrm{MeV},$$ where the first uncertainties are statistical and the second systematic. The results are consistent with a previous LHCb measurement using a prompt $\Lambda_{c}^{+} K^{-}$ sample. Evidence of a new $\Xi_{c}(2880)^{0}$ state is found with a local significance of $3.8\,\sigma$, whose mass and width are measured to be $2881.8 \pm 3.1 \pm 8.5\,\mathrm{MeV}$ and $12.4 \pm 5.3 \pm 5.8 \,\mathrm{MeV}$, respectively. In addition, evidence of a new decay mode $\Xi_{c}(2790)^{0} \to \Lambda_{c}^{+} K^{-}$ is found with a significance of $3.7\,\sigma$. The relative branching fraction of $B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-}$ with respect to the $B^{-} \to D^{+} D^{-} K^{-}$ decay is measured to be $2.36 \pm 0.11 \pm 0.22 \pm 0.25$, where the first uncertainty is statistical, the second systematic and the third originates from the branching fractions of charm hadron decays.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-028.html (LHCb public pages

Measurement of the ratios of branching fractions R(D∗)\mathcal{R}(D^{*}) and R(D0)\mathcal{R}(D^{0})

The ratios of branching fractions $\mathcal{R}(D^{*})\equiv\mathcal{B}(\bar{B}\to D^{*}\tau^{-}\bar{\nu}_{\tau})/\mathcal{B}(\bar{B}\to D^{*}\mu^{-}\bar{\nu}_{\mu})$ and $\mathcal{R}(D^{0})\equiv\mathcal{B}(B^{-}\to D^{0}\tau^{-}\bar{\nu}_{\tau})/\mathcal{B}(B^{-}\to D^{0}\mu^{-}\bar{\nu}_{\mu})$ are measured, assuming isospin symmetry, using a sample of proton-proton collision data corresponding to 3.0 fb${ }^{-1}$ of integrated luminosity recorded by the LHCb experiment during 2011 and 2012. The tau lepton is identified in the decay mode $\tau^{-}\to\mu^{-}\nu_{\tau}\bar{\nu}_{\mu}$. The measured values are $\mathcal{R}(D^{*})=0.281\pm0.018\pm0.024$ and $\mathcal{R}(D^{0})=0.441\pm0.060\pm0.066$, where the first uncertainty is statistical and the second is systematic. The correlation between these measurements is $\rho=-0.43$. Results are consistent with the current average of these quantities and are at a combined 1.9 standard deviations from the predictions based on lepton flavor universality in the Standard Model.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-039.html (LHCb public pages

Editorial: Advances in image formation methods for optoacoustic and ultrasound imaging

ISSN:2296-424