40,761 research outputs found
Developments in the tools and methodologies of synthetic biology.
Synthetic biology is principally concerned with the rational design and engineering of biologically based parts, devices, or systems. However, biological systems are generally complex and unpredictable, and are therefore, intrinsically difficult to engineer. In order to address these fundamental challenges, synthetic biology is aiming to unify a body of knowledge from several foundational scientific fields, within the context of a set of engineering principles. This shift in perspective is enabling synthetic biologists to address complexity, such that robust biological systems can be designed, assembled, and tested as part of a biological design cycle. The design cycle takes a forward-design approach in which a biological system is specified, modeled, analyzed, assembled, and its functionality tested. At each stage of the design cycle, an expanding repertoire of tools is being developed. In this review, we highlight several of these tools in terms of their applications and benefits to the synthetic biology community
Synthesizing Training Data for Object Detection in Indoor Scenes
Detection of objects in cluttered indoor environments is one of the key
enabling functionalities for service robots. The best performing object
detection approaches in computer vision exploit deep Convolutional Neural
Networks (CNN) to simultaneously detect and categorize the objects of interest
in cluttered scenes. Training of such models typically requires large amounts
of annotated training data which is time consuming and costly to obtain. In
this work we explore the ability of using synthetically generated composite
images for training state-of-the-art object detectors, especially for object
instance detection. We superimpose 2D images of textured object models into
images of real environments at variety of locations and scales. Our experiments
evaluate different superimposition strategies ranging from purely image-based
blending all the way to depth and semantics informed positioning of the object
models into real scenes. We demonstrate the effectiveness of these object
detector training strategies on two publicly available datasets, the
GMU-Kitchens and the Washington RGB-D Scenes v2. As one observation, augmenting
some hand-labeled training data with synthetic examples carefully composed onto
scenes yields object detectors with comparable performance to using much more
hand-labeled data. Broadly, this work charts new opportunities for training
detectors for new objects by exploiting existing object model repositories in
either a purely automatic fashion or with only a very small number of
human-annotated examples.Comment: Added more experiments and link to project webpag
Genome engineering of isogenic human ES cells to model autism disorders.
Isogenic pluripotent stem cells are critical tools for studying human neurological diseases by allowing one to study the effects of a mutation in a fixed genetic background. Of particular interest are the spectrum of autism disorders, some of which are monogenic such as Timothy syndrome (TS); others are multigenic such as the microdeletion and microduplication syndromes of the 16p11.2 chromosomal locus. Here, we report engineered human embryonic stem cell (hESC) lines for modeling these two disorders using locus-specific endonucleases to increase the efficiency of homology-directed repair (HDR). We developed a system to: (1) computationally identify unique transcription activator-like effector nuclease (TALEN) binding sites in the genome using a new software program, TALENSeek, (2) assemble the TALEN genes by combining golden gate cloning with modified constructs from the FLASH protocol, and (3) test the TALEN pairs in an amplification-based HDR assay that is more sensitive than the typical non-homologous end joining assay. We applied these methods to identify, construct, and test TALENs that were used with HDR donors in hESCs to generate an isogenic TS cell line in a scarless manner and to model the 16p11.2 copy number disorder without modifying genomic loci with high sequence similarity
Chip and Skim: cloning EMV cards with the pre-play attack
EMV, also known as "Chip and PIN", is the leading system for card payments
worldwide. It is used throughout Europe and much of Asia, and is starting to be
introduced in North America too. Payment cards contain a chip so they can
execute an authentication protocol. This protocol requires point-of-sale (POS)
terminals or ATMs to generate a nonce, called the unpredictable number, for
each transaction to ensure it is fresh. We have discovered that some EMV
implementers have merely used counters, timestamps or home-grown algorithms to
supply this number. This exposes them to a "pre-play" attack which is
indistinguishable from card cloning from the standpoint of the logs available
to the card-issuing bank, and can be carried out even if it is impossible to
clone a card physically (in the sense of extracting the key material and
loading it into another card). Card cloning is the very type of fraud that EMV
was supposed to prevent. We describe how we detected the vulnerability, a
survey methodology we developed to chart the scope of the weakness, evidence
from ATM and terminal experiments in the field, and our implementation of
proof-of-concept attacks. We found flaws in widely-used ATMs from the largest
manufacturers. We can now explain at least some of the increasing number of
frauds in which victims are refused refunds by banks which claim that EMV cards
cannot be cloned and that a customer involved in a dispute must therefore be
mistaken or complicit. Pre-play attacks may also be carried out by malware in
an ATM or POS terminal, or by a man-in-the-middle between the terminal and the
acquirer. We explore the design and implementation mistakes that enabled the
flaw to evade detection until now: shortcomings of the EMV specification, of
the EMV kernel certification process, of implementation testing, formal
analysis, or monitoring customer complaints. Finally we discuss
countermeasures
- …