838 research outputs found
Object recognition using multi-view imaging
Single view imaging data has been used in most previous research in computer vision and
image understanding and lots of techniques have been developed. Recently with the fast
development and dropping cost of multiple cameras, it has become possible to have many
more views to achieve image processing tasks. This thesis will consider how to use the
obtained multiple images in the application of target object recognition.
In this context, we present two algorithms for object recognition based on scale-
invariant feature points. The first is single view object recognition method (SOR), which
operates on single images and uses a chirality constraint to reduce the recognition errors
that arise when only a small number of feature points are matched. The procedure is
extended in the second multi-view object recognition algorithm (MOR) which operates on
a multi-view image sequence and, by tracking feature points using a dynamic programming
method in the plenoptic domain subject to the epipolar constraint, is able to fuse feature
point matches from all the available images, resulting in more robust recognition.
We evaluated these algorithms using a number of data sets of real images capturing
both indoor and outdoor scenes. We demonstrate that MOR is better than SOR particularly for noisy and low resolution images, and it is also able to recognize objects that are
partially occluded by combining it with some segmentation techniques
Systems analysis of guard cell membrane transport for enhanced stomatal dynamics and water use efficiency
Stomatal transpiration is at the centre of a crisis in water availability and crop production that is expected to unfold over the next 20-30 years. Global water usage has increased 6-fold in the past 100 years, twice as fast as the human population, and is expected to double again before 2030, driven mainly by irrigation and agriculture. Guard cell membrane transport is integral to controlling stomatal aperture and offers important targets for genetic manipulation to improve crop performance. However, its complexity presents a formidable barrier to exploring such possibilities. With few exceptions, mutations that increase water use efficiency commonly have been found to do so with substantial costs to the rate of carbon assimilation, reflecting the trade-off in CO2 availability with suppressed stomatal transpiration. One approach yet to be explored in any detail relies on quantitative systems analysis of the guard cell. Our deep knowledge of transport and homeostasis in these cells gives real substance to the prospect for ‘reverse engineering’ of stomatal responses, using in silico design in directing genetic manipulation for improved water use and crop yields. Here we address this problem with a focus on stomatal kinetics, taking advantage of the OnGuard software and models of the stomatal guard cell (www.psrg.org.uk) recently developed for exploring stomatal physiology. Our analysis suggests that manipulations of single transporter populations are likely to have unforeseen consequences. Channel gating, especially of the dominant K+ channels, appears the most favorable target for experimental manipulation
Harvesting Discriminative Meta Objects with Deep CNN Features for Scene Classification
Recent work on scene classification still makes use of generic CNN features
in a rudimentary manner. In this ICCV 2015 paper, we present a novel pipeline
built upon deep CNN features to harvest discriminative visual objects and parts
for scene classification. We first use a region proposal technique to generate
a set of high-quality patches potentially containing objects, and apply a
pre-trained CNN to extract generic deep features from these patches. Then we
perform both unsupervised and weakly supervised learning to screen these
patches and discover discriminative ones representing category-specific objects
and parts. We further apply discriminative clustering enhanced with local CNN
fine-tuning to aggregate similar objects and parts into groups, called meta
objects. A scene image representation is constructed by pooling the feature
response maps of all the learned meta objects at multiple spatial scales. We
have confirmed that the scene image representation obtained using this new
pipeline is capable of delivering state-of-the-art performance on two popular
scene benchmark datasets, MIT Indoor 67~\cite{MITIndoor67} and
Sun397~\cite{Sun397}Comment: To Appear in ICCV 201
Probing the ligand receptor interface of TNF ligand family members RANKL and TRAIL
During the last two decades, research has shown that the tumor necrosis factor (TNF) superfamily is of importance in numerous biological activities, such as mediating cellular apoptosis, survival, differentiation or proliferation. The binding between the TNF superfamily ligands and receptors regulates normal physiological processes, while the deregulation may cause harmful effects. Therefore, targeting TNF superfamily ligands or receptors with either agonistic or antagonistic molecules may provide novel approaches for therapy. The work described in this thesis is focused on the ligand-receptor interface of TNF super family members Ligand of Receptor Activator of Nuclear Factor κB (RANKL) and TNF-Related Apoptosis Inducing Ligand (TRAIL), to design and characterization novel recombinant RANKL and TRAIL variants for their use as potential therapeutics
Engineering receptor-based decoys for the treatment of cancer using a yeast surface display platform
Decoy receptors form a distinct class of targeted therapeutics that offer unique advantages compared to traditional antibody therapy. Since they are derived from preexisting signaling processes, they possess inherent ligand specificity and immune tolerance, which can significantly reduce off-target effects and enhance clinical efficacy. The use of receptor-based biologics to target aberrant signalling pathways that promote cancer cell proliferation and metastasis has emerged as a new frontier in cancer therapy. The major drawback of using “wild-type” receptor-based biologics is their limited competitive advantage over cellular receptors. Therefore, in this thesis, I employed directed evolution approaches to engineer high affinity TACI and RAGE receptor-based therapeutics that target tumour-promoting components in diffused large B cell lymphoma (DLBCL) and triple-negative breast cancer (TNBC), respectively.
DLBCL is characterised by the expansion of malignant B cells in the lymphocytes and the overexpression of autoantibodies. APRIL and BAFF are essential ligands for B cell development, which have been shown to promote DLBCL growth. In this study, I used yeast surface display-based affinity maturation processes to develop TACI variants with enhanced APRIL and BAFF binding affinity. Structural and biochemical analyses were carried out to investigate the basis of the enhanced ligand binding affinity. The TACI variants were reformatted into Fc fusion proteins to examine their in vitro and in vivo antitumour activity. The affinity-enhanced TACI variant, namely BS7-10, was shown to have an 8-fold increase in binding affinity to both APRIL and BAFF compared to atacicept, a commercially available WT-TACI decoy receptor. Also, BS7-10-Fc showed higher efficacy in B cell proliferation assay but demonstrated modest anti-tumour activity in a DLBCL mouse model. Collectively, our findings indicated that BS7-10 is an affinity-enhanced decoy receptor that binds APRIL and BAFF with higher affinity that should perform better than atacicept in clinical indications for both malignant diseases as well as not malignant indications such as lupus erythematosus.
TNBC is an aggressive type of breast cancer that fails to express three hormonal regulatory receptors at targetable levels: estrogen, progesterone and HER2. Due to the lack of these three receptors, there is limited approaches for the treatment of TNBC. Receptor for advanced glycation end productions (RAGE) is a multi-ligand receptor that has been associated with TNBC metastasis. RAGE ligands pleiotropically interact with a range of receptors that can trigger TNBC progression and inflammation. In this study, I used yeast surface display and directed evolution approaches to engineer a high affinity RAGE V domain, the ligand binding domain of the RAGE receptor, that has an enhanced binding capacity to multiple RAGE ligands. Structural analysis was performed to investigate the basis of the improved binding ability. IgG1 Fc fusion proteins of RAGE V decoy receptors were produced to evaluate their anti-tumour activity in both in vitro and in vivo studies. IP-MS and RNA sequencing were used to investigate the underlying tumour-suppressive mechanism of the RAGE V variants. Overall, RAGE V13 was the most robust variant which had elevated binding capacity to multiple RAGE ligands compared to WT-RAGE V and demonstrated superior anti-proliferative and antimigratory activity in both in vitro and in vivo studies. I found through IP-MS analysis that GRP78 was a novel interactor with both WT-RAGE V-Fc and RAGE V13-Fc. In addition, RNA-seq data revealed that RAGV13-Fc treatment resulted in CXCL3 downregulation in MDA-MB-231. I hypothesize that RAGE V13-Fc suppresses TLR4 signalling by sequestering GRP78, which in turn downregulates CXCL3 expression by reducing phosphor-IRF3 level. Given the complexity of RAGE-ligands interactions, further studies are needed to fully understand the underlying anti-tumour mechanism of RAGE V13-Fc
Collaborative Deep Reinforcement Learning for Joint Object Search
We examine the problem of joint top-down active search of multiple objects
under interaction, e.g., person riding a bicycle, cups held by the table, etc..
Such objects under interaction often can provide contextual cues to each other
to facilitate more efficient search. By treating each detector as an agent, we
present the first collaborative multi-agent deep reinforcement learning
algorithm to learn the optimal policy for joint active object localization,
which effectively exploits such beneficial contextual information. We learn
inter-agent communication through cross connections with gates between the
Q-networks, which is facilitated by a novel multi-agent deep Q-learning
algorithm with joint exploitation sampling. We verify our proposed method on
multiple object detection benchmarks. Not only does our model help to improve
the performance of state-of-the-art active localization models, it also reveals
interesting co-detection patterns that are intuitively interpretable
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