Mastermind Acts Downstream of Notch to Specify Neuronal Cell Fates in theDrosophilaCentral Nervous System

AbstractIn theDrosophilacentral nervous system, cellular diversity is generated through the asymmetric partitioning of cell fate determinants at cell division. Neural precursors (or neuroblasts) divide in a stem cell lineage to generate a series of ganglion mother cells, each of which divides once to produce a pair of postmitotic neurons or glial cells. An exception to this rule is the MP2 neuroblast, which divides only once to generate two neurons. We screened for genes expressed in the MP2 neuroblast and its progeny as a means of identifying the factors that specify cell fate in the MP2 lineage. We identified a P-element insertion line that expresses the reporter gene, tau-β-galactosidase, in the MP2 precursor and its progeny, the vMP2 and dMP2 neurons. The transposon disrupts the neurogenic gene,mastermind,but does not lead to neural hyperplasia. However, the vMP2 neuron is transformed into its sibling cell, dMP2. By contrast, expression of a dominant activated form of the Notch receptor in the MP2 lineage transforms dMP2 to vMP2. Notch signalling requires Mastermind, suggesting that Mastermind acts downstream of Notch to determine the vMP2 cell fate. We show that Mastermind plays a similar role in the neurons derived from ganglion mother cells 1-1a and 4-2a, where it specifies the pCC and RP2sib fates, respectively. This suggests that Notch signalling through Mastermind plays a wider role in specifying neuronal identity in theDrosophilacentral nervous system

The inner dark matter distribution of the Cosmic Horseshoe (J1148+1930) with gravitational lensing and dynamics

We present a detailed analysis of the inner mass structure of the Cosmic Horseshoe (J1148+1930) strong gravitational lens system observed with the Hubble Space Telescope (HST) Wide Field Camera 3 (WFC3). In addition to the spectacular Einstein ring, this systems shows a radial arc. We obtained the redshift of the radial arc counter image $z_\text{s,r} = 1.961 \pm 0.001$ from Gemini observations. To disentangle the dark and luminous matter, we consider three different profiles for the dark matter distribution: a power-law profile, the NFW, and a generalized version of the NFW profile. For the luminous matter distribution, we base it on the observed light distribution that is fitted with three components: a point mass for the central light component resembling an active galactic nucleus, and the remaining two extended light components scaled by a constant M/L. To constrain the model further, we include published velocity dispersion measurements of the lens galaxy and perform a self-consistent lensing and axisymmetric Jeans dynamical modeling. Our model fits well to the observations including the radial arc, independent of the dark matter profile. Depending on the dark matter profile, we get a dark matter fraction between 60 % and 70 %. With our composite mass model we find that the radial arc helps to constrain the inner dark matter distribution of the Cosmic Hoseshoe independently of the dark matter profile.Comment: 19 pages, 14 figures, 8 tables, submitted to A&

The Right Angle: Visual Portrayal of Products Affects Observers’ Impressions of Owners

Consumer products have long been known to influence observers’ impressions of product owners. The angle at which products are visually portrayed in advertisements, however, may be an overlooked factor in these effects. We hypothesize and find that portrayals of the same product from different viewpoints can prime different associations that color impressions of product and owner in parallel ways. In Study 1, automobiles were rated higher on status‐ and power‐related traits (e.g., dominant , powerful ) when portrayed head‐on versus in side profile, an effect found for sport utility vehicles (SUVs)—a category with a reputation for dominance—but not sedans. In Study 2, these portrayal‐based associations influenced the impressions formed about the product's owner: a target person was rated higher on status‐ and power‐related traits when his SUV was portrayed head‐on versus in side profile. These results suggest that the influence of visual portrayal extends beyond general evaluations of products to affect more specific impressions of products and owners alike, and highlight that primed traits are likely to influence impressions when compatible with other knowledge about the target.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93734/1/mar20557.pd

Sprays for Control of Sycamore Anthracnose

Sycamore anthracnose, Gnomonia veneta, (Sacc. & Speg.) Klebahn (4), prevalent in Iowa (1), belongs to a group of plant diseases with life cycle patterns which suggest the use of fungicidal sprays for their control. In early spring this disease appears as a blight on the young leaves soon after they emerge from the bud (Fig. 1). The blighted leaves become brown to black and frequently appear as if they had been injured by frost. Mature leaf infection characteristically produces elongate brown streaks along the midveins and laterals (Fig. 2), and many such infected leaves fall within two or three weeks. In addition to defoliation, sycamores undergo extensive twig blight. The death of terminal buds stimulates the development of numerous lateral branches which later may also be destroyed. This results in dead clusters of branches, witches\u27 broom , around a swollen terminal area (Fig. 3)

Time-to-explosion thermal initiation test for explosives

A controlled series of experiments have been performed in an attempt to evaluate the Henkin time-to-explosion test as a reactivity test and a quality assurance tool. This was done by comparing the Henkin results with those obtained with the presently used gas chromatograph chemical reactivity test, Two explosives, PBX 9404 and LX-04-1, were tested with four ``inerts``: sodium chloride, lead powder, Silastic Q-300030 and epoxy 633111. The experimental data from the two tests are given; a method for mathematically reducing the Henkin data is considered. The data from the two tests are not in complete agreement. Both predict that the epoxy is reactive with and that NaCl is unreactive with both explosives. However, the Henkin data indicated reactivity of lead powder and Silastic with LX-04-1; the gas chromatograph did not. For PBX 9404, the Henkin test indicated that the lead was borderline reactive and the Silastic unreactive, while the gas chromatograph predicted the opposite. DTA thermograms are included to help interpret the comparison test results. A section in the appendix is devoted to the experimental problems encountered in getting the Henkin test operational. All raw data generated during that time are included in the appendix. In addition, data obtained subsequently with a fairly wide variety of explosives (and propellants) are given; these indicate the present quality of data

HOLISMOKES -- X. Comparison between neural network and semi-automated traditional modeling of strong lenses

Modeling of strongly gravitationally lensed galaxies is often required in order to use them as astrophysical or cosmological probes. With current and upcoming wide-field imaging surveys, the number of detected lenses is increasing significantly such that automated and fast modeling procedures for ground-based data are urgently needed. This is especially pertinent to short-lived lensed transients in order to plan follow-up observations. Therefore, we present in a companion paper (submitted) a neural network predicting the parameter values with corresponding uncertainties of a Singular Isothermal Ellipsoid (SIE) mass profile with external shear. In this work, we present a newly-developed pipeline glee_auto.py to model consistently any galaxy-scale lensing system. In contrast to previous automated modeling pipelines that require high-resolution images, glee_auto.py is optimized for ground-based images such as those from the Hyper-Suprime-Cam (HSC) or the upcoming Rubin Observatory Legacy Survey of Space and Time. We further present glee_tools.py, a flexible automation code for individual modeling that has no direct decisions and assumptions implemented. Both pipelines, in addition to our modeling network, minimize the user input time drastically and thus are important for future modeling efforts. We apply the network to 31 real galaxy-scale lenses of HSC and compare the results to the traditional models. In the direct comparison, we find a very good match for the Einstein radius especially for systems with $\theta_E \gtrsim 2$". The lens mass center and ellipticity show reasonable agreement. The main discrepancies are on the external shear as expected from our tests on mock systems. In general, our study demonstrates that neural networks are a viable and ultra fast approach for measuring the lens-galaxy masses from ground-based data in the upcoming era with $\sim10^5$ lenses expected.Comment: 17+28 pages, 7+31 figures, 2+5 tables, submitted to A&

HOLISMOKES -- IV. Efficient mass modeling of strong lenses through deep learning

Modelling the mass distributions of strong gravitational lenses is often necessary to use them as astrophysical and cosmological probes. With the high number of lens systems ($>10^5$) expected from upcoming surveys, it is timely to explore efficient modeling approaches beyond traditional MCMC techniques that are time consuming. We train a CNN on images of galaxy-scale lenses to predict the parameters of the SIE mass model ($x,y,e_x,e_y$, and $\theta_E$). To train the network, we simulate images based on real observations from the HSC Survey for the lens galaxies and from the HUDF as lensed galaxies. We tested different network architectures, the effect of different data sets, and using different input distributions of $\theta_E$. We find that the CNN performs well and obtain with the network trained with a uniform distribution of $\theta_E$ $>0.5"$ the following median values with $1\sigma$ scatter: $\Delta x=(0.00^{+0.30}_{-0.30})"$, $\Delta y=(0.00^{+0.30}_{-0.29})"$, $\Delta \theta_E=(0.07^{+0.29}_{-0.12})"$, $\Delta e_x = -0.01^{+0.08}_{-0.09}$ and $\Delta e_y = 0.00^{+0.08}_{-0.09}$. The bias in $\theta_E$ is driven by systems with small $\theta_E$. Therefore, when we further predict the multiple lensed image positions and time delays based on the network output, we apply the network to the sample limited to $\theta_E>0.8"$. In this case, the offset between the predicted and input lensed image positions is $(0.00_{-0.29}^{+0.29})"$ and $(0.00_{-0.31}^{+0.32})"$ for $x$ and $y$, respectively. For the fractional difference between the predicted and true time delay, we obtain $0.04_{-0.05}^{+0.27}$. Our CNN is able to predict the SIE parameters in fractions of a second on a single CPU and with the output we can predict the image positions and time delays in an automated way, such that we are able to process efficiently the huge amount of expected lens detections in the near future.Comment: 17 pages, 14 Figure

HOLISMOKES -- IX. Neural network inference of strong-lens parameters and uncertainties from ground-based images

Modeling of strong gravitational lenses is a necessity for further applications in astrophysics and cosmology. Especially with the large number of detections in current and upcoming surveys such as the Rubin Legacy Survey of Space and Time (LSST), it is timely to investigate in automated and fast analysis techniques beyond the traditional and time consuming Markov chain Monte Carlo sampling methods. Building upon our convolutional neural network (CNN) presented in Schuldt et al. (2021b), we present here another CNN, specifically a residual neural network (ResNet), that predicts the five mass parameters of a Singular Isothermal Ellipsoid (SIE) profile (lens center $x$ and $y$, ellipticity $e_x$ and $e_y$, Einstein radius $\theta_E$) and the external shear ($\gamma_{ext,1}$, $\gamma_{ext,2}$) from ground-based imaging data. In contrast to our CNN, this ResNet further predicts a 1$\sigma$ uncertainty for each parameter. To train our network, we use our improved pipeline from Schuldt et al. (2021b) to simulate lens images using real images of galaxies from the Hyper Suprime-Cam Survey (HSC) and from the Hubble Ultra Deep Field as lens galaxies and background sources, respectively. We find overall very good recoveries for the SIE parameters, while differences remain in predicting the external shear. From our tests, most likely the low image resolution is the limiting factor for predicting the external shear. Given the run time of milli-seconds per system, our network is perfectly suited to predict the next appearing image and time delays of lensed transients in time. Therefore, we also present the performance of the network on these quantities in comparison to our simulations. Our ResNet is able to predict the SIE and shear parameter values in fractions of a second on a single CPU such that we are able to process efficiently the huge amount of expected galaxy-scale lenses in the near future.Comment: 16 pages, including 11 figures, accepted for publication by A&

Constraining the multi-scale dark-matter distribution in CASSOWARY 31 with strong gravitational lensing and stellar dynamics

We study the inner structure of the group-scale lens CASSOWARY 31 (CSWA 31) by adopting both strong lensing and dynamical modeling. CSWA 31 is a peculiar lens system. The brightest group galaxy (BGG) is an ultra-massive elliptical galaxy at z = 0.683 with a weighted mean velocity dispersion of $\sigma = 432 \pm 31$ km s$^{-1}$. It is surrounded by group members and several lensed arcs probing up to ~150 kpc in projection. Our results significantly improve previous analyses of CSWA 31 thanks to the new HST imaging and MUSE integral-field spectroscopy. From the secure identification of five sets of multiple images and measurements of the spatially-resolved stellar kinematics of the BGG, we conduct a detailed analysis of the multi-scale mass distribution using various modeling approaches, both in the single and multiple lens-plane scenarios. Our best-fit mass models reproduce the positions of multiple images and provide robust reconstructions for two background galaxies at z = 1.4869 and z = 2.763. The relative contributions from the BGG and group-scale halo are remarkably consistent in our three reference models, demonstrating the self-consistency between strong lensing analyses based on image position and extended image modeling. We find that the ultra-massive BGG dominates the projected total mass profiles within 20 kpc, while the group-scale halo dominates at larger radii. The total projected mass enclosed within $R_{eff}$ = 27.2 kpc is $1.10_{-0.04}^{+0.02} \times 10^{13}$ M$_\odot$. We find that CSWA 31 is a peculiar fossil group, strongly dark-matter dominated towards the central region, and with a projected total mass profile similar to higher-mass cluster-scale halos. The total mass-density slope within the effective radius is shallower than isothermal, consistent with previous analyses of early-type galaxies in overdense environments.Comment: 22 pages, 12 figures, 5 tables, submitted to Astronomy & Astrophysics. We welcome the comments from reader