bíogo/hts: high throughput sequence handling for the Go language

biogo/hts provides a Go native implementation of the SAM specification (Group 2016) for SAM and BAM alignment formats (H. et al. 2012) commonly used for representation of high throughput genomic data, the BAI, CSI and tabix indexing formats, and the BGZF blocked compression format. The biogo/hts packages perform parallelized read and write operations and are able to cache recent reads according to user-specified caching methods. The parallelisation approach used by the biogo/hts package is influenced by the approach of the D implementation, sambamba by Tarazov et al. (T. A. et al. 2015). The biogo/hts APIs have been constructed to provide a consistent interface to sequence alignment data and the underlying compression system in order to aid ease of use and tool development.R. Daniel Kortschak, Brent S. Pedersen, and David L. Adelso

Playing with Puffball: Simple Scale-Invariant Inflation for Use in Vision and Graphics

We describe how inflation, the act of mapping a 2D silhouette to a 3D region, can be applied in two disparate problems to offer insight and improvement: silhouette part segmentation and image-based material transfer. To demonstrate this, we introduce Puffball, a novel inflation technique, which achieves similar results to existing inflation approaches -- including smoothness, robustness, and scale and shift-invariance -- through an exceedingly simple and accessible formulation. The part segmentation algorithm avoids many of the pitfalls of previous approaches by finding part boundaries on a canonical 3-D shape rather than in the contour of the 2-D shape; the algorithm gives reliable and intuitive boundaries, even in cases where traditional approaches based on the 2D Minima Rule are misled. To demonstrate its effectiveness, we present data in which subjects prefer Puffball's segmentations to more traditional Minima Rule-based segmentations across several categories of silhouettes. The texture transfer algorithm utilizes Puffball's estimated shape information to produce visually pleasing and realistically synthesized surface textures with no explicit knowledge of either underlying shape.National Eye Institute (Special Training Grant

Millimeter and Submillimeter Survey of the R Corona Australis Region

Using a combination of data from the Antarctic Submillimeter Telescope and Remote Observatory (AST/RO), the Arizona Radio Observatory Kitt Peak 12m telescope and the Arizona Radio Observatory 10m Heinrich Hertz Telescope, we have studied the most active part of the R CrA molecular cloud in multiple transitions of Carbon Monoxide, HCO$^+$ and 870\micron continuum emission. Since R CrA is nearby (130 pc), we are able to obtain physical spatial resolution as high as 0.01pc over an area of 0.16 pc$^2$, with velocity resolution finer than 1 km/s. Mass estimates of the protostar driving the mm-wave emission derived from HCO$^+$, dust continuum emission and kinematic techniques point to a young, deeply embedded protostar of $\sim$0.5-0.75 M$_\odot$, with a gaseous envelope of similar mass. A molecular outflow is driven by this source that also contains at least 0.8 M$_\odot$ of molecular gas with $\sim$0.5 L$_\odot$ of mechanical luminosity. HCO$^+$ lines show the kinematic signature of infall motions as well as bulk rotation. The source is most likely a Class 0 protostellar object not yet visible at near-IR wavelengths. With the combination of spatial and spectral resolution in our data set, we are able to disentangle the effects of infall, rotation and outflow towards this young object.Comment: 29 pages, 9 figures. Accepted for publication in the Astrophysical Journa

Tracking objects with point clouds from vision and touch

We present an object-tracking framework that fuses point cloud information from an RGB-D camera with tactile information from a GelSight contact sensor. GelSight can be treated as a source of dense local geometric information, which we incorporate directly into a conventional point-cloud-based articulated object tracker based on signed-distance functions. Our implementation runs at 12 Hz using an online depth reconstruction algorithm for GelSight and a modified second-order update for the tracking algorithm. We present data from hardware experiments demonstrating that the addition of contact-based geometric information significantly improves the pose accuracy during contact, and provides robustness to occlusions of small objects by the robot's end effector

Molecular Line Profile Fitting with Analytic Radiative Transfer Models

We present a study of analytic models of starless cores whose line profiles have ``infall asymmetry,'' or blue-skewed shapes indicative of contracting motions. We compare the ability of two types of analytical radiative transfer models to reproduce the line profiles and infall speeds of centrally condensed starless cores whose infall speeds are spatially constant and range between 0 and 0.2 km s-1. The model line profiles of HCO+ (J=1-0) and HCO+ (J=3-2) are produced by a self-consistent Monte Carlo radiative transfer code. The analytic models assume that the excitation temperature in the front of the cloud is either constant (``two-layer'' model) or increases inward as a linear function of optical depth (``hill'' model). Each analytic model is matched to the line profile by rapid least-squares fitting. The blue-asymmetric line profiles with two peaks, or with a blue shifted peak and a red shifted shoulder, can be well fit by the ``HILL5'' model (a five parameter version of the hill model), with an RMS error of 0.02 km s-1. A peak signal to noise ratio of at least 30 in the molecular line observations is required for performing these analytic radiative transfer fits to the line profiles.Comment: 48 pages, 20 figures, accepted for publication in Ap

ViTac: Feature Sharing between Vision and Tactile Sensing for Cloth Texture Recognition

Vision and touch are two of the important sensing modalities for humans and they offer complementary information for sensing the environment. Robots could also benefit from such multi-modal sensing ability. In this paper, addressing for the first time (to the best of our knowledge) texture recognition from tactile images and vision, we propose a new fusion method named Deep Maximum Covariance Analysis (DMCA) to learn a joint latent space for sharing features through vision and tactile sensing. The features of camera images and tactile data acquired from a GelSight sensor are learned by deep neural networks. But the learned features are of a high dimensionality and are redundant due to the differences between the two sensing modalities, which deteriorates the perception performance. To address this, the learned features are paired using maximum covariance analysis. Results of the algorithm on a newly collected dataset of paired visual and tactile data relating to cloth textures show that a good recognition performance of greater than 90% can be achieved by using the proposed DMCA framework. In addition, we find that the perception performance of either vision or tactile sensing can be improved by employing the shared representation space, compared to learning from unimodal data

Dichotomy in the Dynamical Status of Massive Cores in Orion

To study the evolution of high mass cores, we have searched for evidence of collapse motions in a large sample of starless cores in the Orion molecular cloud. We used the Caltech Submillimeter Observatory telescope to obtain spectra of the optically thin (\H13CO+) and optically thick (\HCO+) high density tracer molecules in 27 cores with masses $>$ 1 \Ms. The red- and blue-asymmetries seen in the line profiles of the optically thick line with respect to the optically thin line indicate that 2/3 of these cores are not static. We detect evidence for infall (inward motions) in 9 cores and outward motions for 10 cores, suggesting a dichotomy in the kinematic state of the non-static cores in this sample. Our results provide an important observational constraint on the fraction of collapsing (inward motions) versus non-collapsing (re-expanding) cores for comparison with model simulations.Comment: 9 pages, 2 Figures. To appear in ApJ(Letters

Infall, Outflow, Rotation, and Turbulent Motions of Dense Gas within NGC 1333 IRAS 4

Millimeter wavelength observations are presented of NGC 1333 IRAS 4, a group of highly-embedded young stellar objects in Perseus, that reveal motions of infall, outflow, rotation, and turbulence in the dense gas around its two brightest continuum objects, 4A and 4B. These data have finest angular resolution of approximately 2" (0.0034 pc) and finest velocity resolution of 0.13 km/s. Infall motions are seen from inverse P-Cygni profiles observed in H2CO 3_12-2_11 toward both objects, but also in CS 3-2 and N2H+ 1-0 toward 4A, providing the least ambiguous evidence for such motions toward low-mass protostellar objects. Outflow motions are probed by bright line wings of H2CO 3_12-2_11 and CS 3-2 observed at positions offset from 4A and 4B, likely tracing dense cavity walls. Rotational motions of dense gas are traced by a systematic variation of the N2H+ line velocities, and such variations are found around 4A but not around 4B. Turbulent motions appear reduced with scale, given N2H+ line widths around both 4A and 4B that are narrower by factors of 2 or 3 than those seen from single-dish observations. Minimum observed line widths of approximately 0.2 km/s provide a new low, upper bound to the velocity dispersion of the parent core to IRAS 4, and demonstrate that turbulence within regions of clustered star formation can be reduced significantly. A third continuum object in the region, 4B', shows no detectable line emission in any of the observed molecular species.Comment: LateX, 51 pages, 9 figures, accepted by Ap

Deep Depth From Focus

Depth from focus (DFF) is one of the classical ill-posed inverse problems in computer vision. Most approaches recover the depth at each pixel based on the focal setting which exhibits maximal sharpness. Yet, it is not obvious how to reliably estimate the sharpness level, particularly in low-textured areas. In this paper, we propose `Deep Depth From Focus (DDFF)' as the first end-to-end learning approach to this problem. One of the main challenges we face is the hunger for data of deep neural networks. In order to obtain a significant amount of focal stacks with corresponding groundtruth depth, we propose to leverage a light-field camera with a co-calibrated RGB-D sensor. This allows us to digitally create focal stacks of varying sizes. Compared to existing benchmarks our dataset is 25 times larger, enabling the use of machine learning for this inverse problem. We compare our results with state-of-the-art DFF methods and we also analyze the effect of several key deep architectural components. These experiments show that our proposed method `DDFFNet' achieves state-of-the-art performance in all scenes, reducing depth error by more than 75% compared to the classical DFF methods.Comment: accepted to Asian Conference on Computer Vision (ACCV) 201

Identification of candidate anti-cancer molecular mechanisms of compound kushen injection using functional genomics

Compound Kushen Injection (CKI) has been clinically used in China for over 15 years to treat various types of solid tumours. However, because such Traditional Chinese Medicine (TCM) preparations are complex mixtures of plant secondary metabolites, it is essential to explore their underlying molecular mechanisms in a systematic fashion. We have used the MCF-7 human breast cancer cell line as an initial in vitro model to identify CKI induced changes in gene expression. Cells were treated with CKI for 24 and 48 hours at two concentrations (1 and 2 mg/mL total alkaloids), and the effect of CKI on cell proliferation and apoptosis were measured using XTT and Annexin V/Propidium Iodide staining assays respectively. Transcriptome data of cells treated with CKI or 5-Fluorouracil (5-FU) for 24 and 48 hours were subsequently acquired using high-throughput Illumina RNA-seq technology. In this report we show that CKI inhibited MCF-7 cell proliferation and induced apoptosis in a dose-dependent fashion. We integrated and applied a series of transcriptome analysis methods, including gene differential expression analysis, pathway over-representation analysis, de novo identification of long non-coding RNAs (lncRNA) as well as co-expression network reconstruction, to identify candidate anti-cancer molecular mechanisms of CKI. Multiple pathways were perturbed and the cell cycle was identified as the potential primary target pathway of CKI in MCF-7 cells. CKI may also induce apoptosis in MCF-7 cells via a p53 independent mechanism. In addition, we identified novel lncRNAs and showed that many of them might be expressed as a response to CKI treatment.Zhipeng Qu, Jian Cui, Yuka Harata-Lee, Thazin Nwe Aung, Qianjin Feng, Joy M. Raison, Robert Daniel Kortschak, David L. Adelso