Semantic Pose using Deep Networks Trained on Synthetic RGB-D

In this work we address the problem of indoor scene understanding from RGB-D images. Specifically, we propose to find instances of common furniture classes, their spatial extent, and their pose with respect to generalized class models. To accomplish this, we use a deep, wide, multi-output convolutional neural network (CNN) that predicts class, pose, and location of possible objects simultaneously. To overcome the lack of large annotated RGB-D training sets (especially those with pose), we use an on-the-fly rendering pipeline that generates realistic cluttered room scenes in parallel to training. We then perform transfer learning on the relatively small amount of publicly available annotated RGB-D data, and find that our model is able to successfully annotate even highly challenging real scenes. Importantly, our trained network is able to understand noisy and sparse observations of highly cluttered scenes with a remarkable degree of accuracy, inferring class and pose from a very limited set of cues. Additionally, our neural network is only moderately deep and computes class, pose and position in tandem, so the overall run-time is significantly faster than existing methods, estimating all output parameters simultaneously in parallel on a GPU in seconds.Comment: ICCV 2015 Submissio

Ultrastable cellulosome-adhesion complex tightens under load

Challenging environments have guided nature in the development of ultrastable protein complexes. Specialized bacteria produce discrete multi-component protein networks called cellulosomes to effectively digest lignocellulosic biomass. While network assembly is enabled by protein interactions with commonplace affinities, we show that certain cellulosomal ligand-receptor interactions exhibit extreme resistance to applied force. Here, we characterize the ligand-receptor complex responsible for substrate anchoring in the Ruminococcus flavefaciens cellulosome using single-molecule force spectroscopy and steered molecular dynamics simulations. The complex withstands forces of 600-750 pN, making it one of the strongest bimolecular interactions reported, equivalent to half the mechanical strength of a covalent bond. Our findings demonstrate force activation and inter-domain stabilization of the complex, and suggest that certain network components serve as mechanical effectors for maintaining network integrity. This detailed understanding of cellulosomal network components may help in the development of biocatalysts for production of fuels and chemicals from renewable plant-derived biomass

A model-based approach to finding substitute tools in 3D vision data

A robot can feasibly be given knowledge of a set of tools for manipulation activities (e.g. hammer, knife, spatula). If the robot then operates outside a closed environment it is likely to face situations where the tool it knows is not available, but alternative unknown tools are present. We tackle the problem of finding the best substitute tool based solely on 3D vision data. Our approach has simple hand-coded models of known tools in terms of superquadrics and relationships among them. Our system attempts to fit these models to point clouds of unknown tools, producing a numeric value for how good a fit is. This value can be used to rate candidate substitutes. We explicitly control how closely each part of a tool must match our model, under direction from parameters of a target task. We allow bottom-up information from segmentation to dictate the sizes that should be considered for various parts of the tool. These ideas allow for a flexible matching so that tools may be superficially quite different, but similar in the way that matters. We evaluate our system's ratings relative to other approaches and relative to human performance in the same task. This is an approach to knowledge transfer, via a suitable representation and reasoning engine, and we discuss how this could be extended to transfer in planning

Single-molecule force spectroscopy on polyproteins and receptor–ligand complexes: The current toolbox

Single-molecule force spectroscopy sheds light onto the free energy landscapes governing protein folding and molecular recognition. Since only a single molecule or single molecular complex is probed at any given point in time, the technique is capable of identifying low-probability conformations within a large ensemble of possibilities. It furthermore allows choosing certain unbinding pathways through careful selection of the points at which the force acts on the protein or molecular complex. This review focuses on recent innovations in construct design, site-specific bioconjugation, measurement techniques, instrumental advances, and data analysis methods for improving workflow, throughput, and data yield of AFM-based single-molecule force spectroscopy experiments. Current trends that we highlight include customized fingerprint domains, peptide tags for site-specific covalent surface attachment, and polyproteins that are formed through mechanostable receptor-ligand interactions. Recent methods to improve measurement stability, signal-to-noise ratio, and force precision are presented, and theoretical considerations, analysis methods, and algorithms for analyzing large numbers of force-extension curves are further discussed. The various innovations identified here will serve as a starting point to researchers in the field looking for opportunities to push the limits of the technique further

Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC) and proteome quantitation of mouse embryonic stem cells to a depth of 5,111 proteins

Embryonic stem [ES) cells are pluripotent cells isolated from mammalian preimplantation embryos. They are capable of differentiating into all cell types and therefore hold great promise in regenerative medicine. Here we show that murine ES cells can be fully SILAC (stable isotope labeling by amino acids in cell culture)-labeled when grown feeder-free during the last phase of cell culture. We fractionated the SILAC-labeled ES cell proteome by one-dimensional gel electrophoresis and by isoelectric focusing of peptides. High resolution analysis on a linear ion trap-orbitrap instrument (LTO-Orbitrap) at sub-ppm mass accuracy resulted in confident identification and quantitation of more than 5,000 distinct proteins. This is the largest quantified proteome reported to date and contains prominent stem cell markers such as OCT4, NANOG, SOX2, and UTF1 along with the embryonic form of RAS (ERAS). We also quantified the proportion of the ES cell proteome present in cytosolic, nucleoplasmic, and membrane/chromatin fractions. We compared two different preparation approaches, cell fractionation followed by one-dimensional gel separation and in-solution digestion of total cell lysate combined with isoelectric focusing, and found comparable proteome coverage with no apparent bias for any functional protein classes for either approach. Bioinformatics analysis of the ES cell proteome revealed a broad distribution of cellular functions with overrepresentation of proteins involved in proliferation. We compared the proteome with a recently published map of chromatin states of promoters in ES cells and found excellent correlation between protein expression and the presence of active and repressive chromatin marks.close17617

Induction of pluripotency in human cord blood unrestricted somatic stem cells

Objective: Generation of induced pluripotent stem (iPS) cells from human cord blood (CB)-derived unrestricted somatic stem cells and evaluation of their molecular signature and differentiation potential in comparison to human embryonic stem cells. Materials and Methods: Unrestricted somatic stem cells isolated from human CB were reprogrammed to iPS cells using retroviral expression of the transcription factors OCT4, SOX2, KLF4, and C-MYC. The reprogrammed cells were analyzed morphologically, by quantitative reverse transcription polymerase chain reaction, genome-wide microRNA and methylation profiling, and gene expression microarrays, as well as in their pluripotency potential by in vivo teratoma formation in severe combined immunodeficient mice and in vitro differentiation. Results: CB iPS cells are very similar to human embryonic stem cells morphologically, at their molecular signature, and in their differentiation potential. Conclusions: Human CB-derived unrestricted somatic stem cells offer an attractive source of cells for generation of iPS cells. Our findings open novel perspectives to generate human leukocyte antigen-matched pluripotent stem cell banks based on existing CB banks. Besides the obvious relevance of a second-generation CB iPS cell bank for pharmacological and toxicological testing, its application for autologous or allogenic regenerative cell transplantation appears feasible.close201

Being extraordinary: How CEOS' uncommon names explain strategic distinctiveness

Research summary: We build upon recent theories and studies about relational self to explain how a CEO's uncommon name may be related to a firm's strategic distinctiveness. Our theory explains why CEOs with uncommon names tend to develop a conception of being different from peers and accordingly pursue strategies that deviate from industry norms. We further suggest that the positive relationship between CEO name uncommonness and strategic distinctiveness is strengthened by the CEO's confidence, power, and environmental munificence. Using name commonness data from the U.S. Social Security Administration and financial data of 1,172 public firms over a 19-year period, we find support for our theoretical predictions. Managerial summary: Using 19 years of data on 1,172 public firms, we show that firms' distinctive strategies are systematically linked to their CEOs' uncommon names. Psychological studies suggest that individuals with uncommon names tend to have a self-conception of being different from their peers. Although many people may not have the confidence to exhibit how unique they believe themselves to be, CEOs do???they are generally confident individuals. It is thus predicted that CEOs with more uncommon names tend to pursue strategies that deviate more from their peer firms'. This pattern is even clearer when CEOs have a higher level of confidence, possess greater power, and operate businesses in an environment with more growth opportunities. Looking for unconventional leaders? You can often tell by their names. ?? 2020 Strategic Management Societ