Spin controlled atom-ion inelastic collisions

The control of the ultracold collisions between neutral atoms is an extensive and successful field of study. The tools developed allow for ultracold chemical reactions to be managed using magnetic fields, light fields and spin-state manipulation of the colliding particles among other methods. The control of chemical reactions in ultracold atom-ion collisions is a young and growing field of research. Recently, the collision energy and the ion electronic state were used to control atom-ion interactions. Here, we demonstrate spin-controlled atom-ion inelastic processes. In our experiment, both spin-exchange and charge-exchange reactions are controlled in an ultracold Rb-Sr$^+$ mixture by the atomic spin state. We prepare a cloud of atoms in a single hyperfine spin-state. Spin-exchange collisions between atoms and ion subsequently polarize the ion spin. Electron transfer is only allowed for (RbSr)$^+$ colliding in the singlet manifold. Initializing the atoms in various spin states affects the overlap of the collision wavefunction with the singlet molecular manifold and therefore also the reaction rate. We experimentally show that by preparing the atoms in different spin states one can vary the charge-exchange rate in agreement with theoretical predictions

Biobeam—Multiplexed wave-optical simulations of light-sheet microscopy

<div><p>Sample-induced image-degradation remains an intricate wave-optical problem in light-sheet microscopy. Here we present <i>biobeam</i>, an open-source software package that enables simulation of operational light-sheet microscopes by combining data from 10⁵–10⁶ multiplexed and GPU-accelerated point-spread-function calculations. The wave-optical nature of these simulations leads to the faithful reproduction of spatially varying aberrations, diffraction artifacts, geometric image distortions, adaptive optics, and emergent wave-optical phenomena, and renders image-formation in light-sheet microscopy computationally tractable.</p></div

Rigorous wave-optical simulation of image formation process in light-sheet microscopy.

<p>(a) Synthetic tissue phantom of a multicellular organism (100 × 200 × 100<i>μm</i>) comprising a complex refractive index distribution (left, <i>n</i> = 1.33–1.38) and a fluorophore distribution of interest (right). (b) Wave optical simulation of the illuminating light sheet and resulting excitation distribution within the sample at a given z position. (c) Partially coherent simulation of the detection path by multiplexed calculation of all independent point spread functions (left) and the resulting simulated camera image combining illumination and fluorescence path of light through the scattering sample. (d) Alternative light-sheet modalities (see also <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006079#pcbi.1006079.s002" target="_blank">S1</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006079#pcbi.1006079.s006" target="_blank">S5</a> Videos and main text for details).</p

Optical capabilities of the biobeam image-formation pipeline.

<p>(a) A test chart at the mid-section of an optically heterogeneous embryo-model (<i>n</i> = 1.35–1.39 diameter 140<i>μm</i>) is illuminated by cylindrical light sheet (<i>NA</i> = 0.15), and imaged from an orthogonal position (<i>NA</i> = 0.6). (b) Details of these wave-optically calculated images reveal <i>i)</i> spatially varying image blur, contrast loss and absorption induced by the heterogeneity of the sample, <i>ii)</i> diffraction artifacts from the light-sheet-typical coherent illumination, and <i>iii)</i> geometric image distortions such as lensing, split-screen type image distortions, and object displacements lensing. (c) <i>Biobeam</i> is further capable of adaptive optics simulations by which reversal of guide star emitted light fields yields perfect foci in scattering tissues. (d) Adaptive optics simulations faithfully reproduces the shift-shift memory effect, an emergent wave-optical phenomenon, here at 4 mean-free-paths deep inside the tissue.</p

Tracking whiskers from high-speed (500 Hz) videos during an object detection task.

<p>(A) A typical field of view. (B) Typical imaging configuration. (C–G) Automated results of tracing and linking. (C) Facial hairs and whiskers are traced in each video frame and then identified by a separate tracking step. (D) A whisker (blue) touches the pole. (E) Two whiskers (blue & green) are bent by the pole. The most posterior whisker is strongly retracted so that only a small segment is visible. (F) Tracking measures whisker orientation, such as the angle at base. (G) Tracking measures whisker shape, such as mean curvature, which can be observed over time. Changes in curvature allow the calculation of forces acting on the whisker follicle <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002591#pcbi.1002591-Birdwell1" target="_blank">[16]</a>.</p

Analysis of curvature change on contact.

<p>(A–E) The sequence of steps used to extract detailed curvature measurements. (A) Whiskers in raw video frames are automatically traced and linked, yielding (B) an identified set of curves for each frame. (C) The raw curve is fit with a 5^th-degree parametric polynomial. (D) A mask is specified to determine where the curve intersects the face. Within a small interval (1–2.5 mm path length) about an interest point chosen for high signal to noise, the raw curve is re-fit to ensure measurements are not biased by whisker shape outside the interval. This new fit is to a 2^nd-degree polynomial. The curvature at the interest point is then measured as the curvature of this 2^nd fitted curve. (E) Follicle position is estimated by extrapolating a fixed distance into the face from the mask. Similarly, curves are extrapolated, when necessary, to contact points on the pole. Trajectories for curvature (F) and the angle of the whisker at its base (G) are shown for the first contacting whisker in 10 trials grouped by whether the first contact was during a retraction (top 5) or protraction (bottom 5). Trajectories are aligned to first contact. The intervals when the whisker is in whisker-pole contact are highlighted in red. (H) Histograms of peak contact curvature change (from resting) for the first whisker-pole contact in each trial (green) and all whisker-pole contacts prior to an answer-lick (red).</p

Tracing illustrated for cases with whisker crossing and pole contact.

<p>(A,B) As a curve is extended from an initiation point, a local test region within the image is queried at each step to detect cases where the line detector may be unreliable. This happens near (C) crossing whiskers and (D) whisker-pole contacts. (E,F) When such a cases are encountered, the curve is linearly extended from the last trusted point, up to a threshold distance. (G,H) If all tests are satisfied at one of the points, a line segment is used to jump the gap and normal tracing is resumed. Otherwise, the trace is terminated at the last trusted point.</p

Changes in whisker behavior as mice successfully learn the object detection task.

<p>(A–B) Normalized histograms of whisker angle (1° resolution, whisker C1, log scale, 0° lies on the lateral axis) were computed over 100 ms time bins during the first 6 training sessions over correct rejection trials. Each histogram shows data from 21–150 trials. (A) Some mice, such as JF25607, increase the frequency of large deflections during stimulus presentation (trial counts: 99, 150, 135, 109, 102, and 90 respectively). (B) Others, such as JF27332, do not (trial counts: 21, 96, 90, 109, 107 and 86 respectively). (C) During stimulus presentation, an increase in mean deflection angle was correlated with a decrease in the false alarm (FA) rate, a measure of behavioral performance, for two mice (JF25609, JF25607). Two mice did not exhibit this correlation (R²: 0.14, 0.61, 0.97, 0.84 for JF27332, JF26706, JF25609, and JF25607 respectively).</p

When linking <i>N</i> whiskers, each curve in a frame is assigned a label <i>W₁</i> to <i>W_N</i>, or <i>F₀</i> to <i>F_N</i>.

<p>Rules constrain the labeling to enforce consistent anterior-posterior ordering of whiskers. The most proximal point of curves labeled W_i or F_i must be posterior to that of curves labeled W_j or F_j when <i>i, and at most one curve may be labeled <i>W_i</i> for a given <i>i</i>. (A) A correct labeling is schematically illustrated. (B) These rules are encoded as transitions in a hidden Markov model. (C) Normalized feature histograms are used to compute the likelihood a curve is, or is not, a whisker.</i></p

Boothe_et_al_eLife_2017_Figure_4b_Source_Data

Boothe_et_al_eLife_2017_Figure_4b_Source_Dat