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We present the results of the re-discovery of the decay $B^0 \to \pi^- \ell^+ \nu_\ell$ in 34.6 fb$^{-1}$ of Belle II data using hadronic $B$-tagging via the Full Event Interpretation algorithm. We observe 21 signal events on a background of 155 in a fit to the distribution of the square of the missing mass, $M_{\mathrm{miss}}^2$, with a significance of 5.69$\sigma$, and determine a total branching fraction of (1.58 $\pm$ 0.43$_{\mathrm{stat}}$ $\pm$ 0.07$_{\mathrm{sys}}$) $\times 10^{-4}$.

Speech perception is known to rely on both auditory and visual information. However, sound specific somatosensory input has been shown also to influence speech perceptual processing (Ito et al., 2009). In the present study we addressed further the relationship between somatosensory information and speech perceptual processing by addressing the hypothesis that the temporal relationship between orofacial movement and sound processing contributes to somatosensory-auditory interaction in speech perception. We examined the changes in event-related potentials in response to multisensory synchronous (simultaneous) and asynchronous (90 ms lag and lead) somatosensory and auditory stimulation compared to individual unisensory auditory and somatosensory stimulation alone. We used a robotic device to apply facial skin somatosensory deformations that were similar in timing and duration to those experienced in speech production. Following synchronous multisensory stimulation the amplitude of the event-related potential was reliably different from the two unisensory potentials. More importantly, the magnitude of the event-related potential difference varied as a function of the relative timing of the somatosensory-auditory stimulation. Event-related activity change due to stimulus timing was seen between 160-220 ms following somatosensory onset, mostly around the parietal area. The results demonstrate a dynamic modulation of somatosensory-auditory convergence and suggest the contribution of somatosensory information for speech processing process is dependent on the specific temporal order of sensory inputs in speech production. International audience

We report measurements of the $\bar{B}^0 \to D^{*+} \ell^{-} \bar��_l$ and $B^- \to D^{0} \ell^{-} \bar��_l$ processes using 34.6 fb$^{-1}$ of collision events recorded by the Belle II experiment at the SuperKEKB asymmetric-energy $e^+ e^-$ collider. For the $B^-\to D^{0}\ell^-\bar��_\ell$ channel, we present first studies that isolate this decay from other semileptonic processes and backgrounds. We report a measurement of the $\bar{B}^0 \to D^{*+} \ell^{-} \bar��_l$ branching fraction and obtain ${\cal B}(\bar{B}^0 \to D^{*+} \ell^{-} \bar��_l) = \left(4.60 \pm 0.05_{\mathrm{stat}}\pm0.17_{\mathrm{syst}} \pm 0.45_{��_s}\right) \%$, in agreement with the world average. Here, the uncertainties are statistical, systematic, and related to slow pion reconstruction, respectively. The systematic uncertainties are limited by the statistics of auxiliary measurements and will improve in the future. We also report differential branching fractions in five bins of the hadronic recoil parameter $w$ for $\bar{B}^0 \to D^{*+} \ell^{-} \bar��_l$, unfolded to account for resolution and efficiency effects. 19 pages, 6 figures

We introduce Sleep, a new Python open-source graphical user interface (GUI) dedicated to visualization, scoring and analyses of sleep data. Among its most prominent features are: (1) Dynamic display of polysomnographic data, spectrogram, hypnogram and topographic maps with several customizable parameters, (2) Implementation of several automatic detection of sleep features such as spindles, K-complexes, slow waves, and rapid eye movements (REM), (3) Implementation of practical signal processing tools such as re-referencing or filtering, and (4) Display of main descriptive statistics including publication-ready tables and figures. The software package supports loading and reading raw EEG data from standard file formats such as European Data Format, in addition to a range of commercial data formats. Most importantly, Sleep is built on top of the VisPy library, which provides GPU-based fast and high-level visualization. As a result, it is capable of efficiently handling and displaying large sleep datasets. Sleep is freely available (http://visbrain.org/sleep) and comes with sample datasets and an extensive documentation. Novel functionalities will continue to be added and open-science community efforts are expected to enhance the capacities of this module.

Super-resolution and single-molecule microscopy are increasingly being applied to complex biological systems. While this can lead greater insight, it also means data analysis becomes more complex: fluorescent puncta must reliably and efficiently be detected in the low signal, high noise, heterogeneous background environments of cells and tissue. We present a bioimaging-segmentation method that accomplishes this. It detects features over a broad range of spatial scales: from single proteins to complex cell phenotypes. Our method outperforms the state-of-the-art in both precision and speed, using image gradients to separate Gaussian-shaped objects from background features. We demonstrate that this approach both works robustly on simple test systems, and that it enables us to extract previously unseen correlations between Parkinsonís Disease-relevant $\alpha$-synuclein oligomers and multiple different cells types in the human brain. This sensitive, computationally efficient approach will enable fluorescent puncta and cellular features to be distinguished in cellular and tissue environments with a sensitivity down to the level of the single protein. Experiment Description We took 5400 field of views from three Parkinson's disease (PD) cases in the anterior cingulate gyrus for microglia and neuron markers respectively, covering dimensions of 4mm x 4mm x 13m per patient. Cells were stained in 488nm channel while alpha-synuclein protein was labelled in 561nm channel. The colocalization study was performed based on the locations of detected cells and alpha-synucleins.

Tag-side reconstruction is an important method for reconstructing $B$ meson decays with missing energy. The Belle II tag-side reconstruction algorithm, Full Event Interpretation, relies on a hierarchical reconstruction of $B$ meson decays with multivariate classification employed at each stage of reconstruction. Given the large numbers of classifiers employed and decay chains reconstructed, the performance of the algorithm on data and simulation differs significantly. Here, calibration factors are derived for hadronic tag-side $B$ decays by measuring a signal side decay, $B \rightarrow X\ell \nu$, in $34.6$ fb$^{-1}$ of Belle II data. For a very loose selection on the tag-side $B$ multivariate classifier, the calibration factors are $0.65 \pm 0.02$ and $0.83 \pm 0.03$ for tag-side $B^{+}$ and $B^{0}$ mesons, respectively.

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