Macrophages are one of the most abundant immune cells in the tumour microenvironment of solid tumours and their presence correlates with reduced survival in most cancers. Macrophages are present at ...all stages of tumour progression and stimulate angiogenesis, tumour cell invasion, and intravasation at the primary site. At the metastatic site, macrophages and monocytes prepare for the arrival of disseminated tumour cells and promote their extravasation and survival by inhibiting immune-mediated clearance or by directly engaging with tumour cells to activate prosurvival signalling pathways. In addition, macrophages promote the growth of disseminated tumour cells at the metastatic site by organising the formation of a supportive metastatic niche. The development of agents inhibiting the recruitment or the protumorigenic effector functions of macrophages in both the primary tumour and at the metastatic site is a promising strategy to improve cancer survival in the future.
Product Quantization for Nearest Neighbor Search Jégou, H; Douze, M; Schmid, C
IEEE transactions on pattern analysis and machine intelligence,
2011-Jan., 2011, 2011-Jan, 2011-01-00, 20110101, 2011-01, Letnik:
33, Številka:
1
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This paper introduces a product quantization-based approach for approximate nearest neighbor search. The idea is to decompose the space into a Cartesian product of low-dimensional subspaces and to ...quantize each subspace separately. A vector is represented by a short code composed of its subspace quantization indices. The euclidean distance between two vectors can be efficiently estimated from their codes. An asymmetric version increases precision, as it computes the approximate distance between a vector and a code. Experimental results show that our approach searches for nearest neighbors efficiently, in particular in combination with an inverted file system. Results for SIFT and GIST image descriptors show excellent search accuracy, outperforming three state-of-the-art approaches. The scalability of our approach is validated on a data set of two billion vectors.
Color names are required in real-world applications such as image retrieval and image annotation. Traditionally, they are learned from a collection of labeled color chips. These color chips are ...labeled with color names within a well-defined experimental setup by human test subjects. However, naming colors in real-world images differs significantly from this experimental setting. In this paper, we investigate how color names learned from color chips compare to color names learned from real-world images. To avoid hand labeling real-world images with color names, we use Google image to collect a data set. Due to the limitations of Google image, this data set contains a substantial quantity of wrongly labeled data. We propose several variants of the PLSA model to learn color names from this noisy data. Experimental results show that color names learned from real-world images significantly outperform color names learned from labeled color chips for both image retrieval and image annotation.
Electrical microstimulation and more recently optogenetics are widely used to map large-scale brain circuits. However, the neuronal specificity achieved with both methods is not well understood. Here ...we compare cell-targeted optogenetics and electrical microstimulation in the macaque monkey brain to functionally map the koniocellular lateral geniculate nucleus (LGN) projection to primary visual cortex (V1). Selective activation of the LGN konio neurons with CamK-specific optogenetics caused selective electrical current inflow in the supra-granular layers of V1. Electrical microstimulation targeted at LGN konio layers revealed the same supra-granular V1 activation pattern as the one elicited by optogenetics. Taken together, these findings establish a selective koniocellular LGN influence on V1 supra-granular layers, and they indicate comparable capacities of both stimulation methods to isolate thalamo-cortical circuits in the primate brain.
•Konio cells of the macaque LGN directly influence the supra-granular layers of V1•CamKII-selective, AAV-mediated optogenetics can be used to activate the konio system•Electrical stimulation of konio cells affirms selective V1 supra-granular activation•Both methods prove highly comparable in eliciting selective feedforward activation
Klein et al. show a selective influence of the lateral geniculate nucleus koniocellular projections on the supra-granular layers of visual cortex in the macaque, using cell-specific optogenetics and electrical microstimulation. Both stimulation methods result in similar feedforward activation selectivity.
A performance evaluation of local descriptors Mikolajczyk, K.; Schmid, C.
IEEE transactions on pattern analysis and machine intelligence,
10/2005, Letnik:
27, Številka:
10
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In this paper, we compare the performance of descriptors computed for local interest regions, as, for example, extracted by the Harris-Affine detector Mikolajczyk, K and Schmid, C, 2004. Many ...different descriptors have been proposed in the literature. It is unclear which descriptors are more appropriate and how their performance depends on the interest region detector. The descriptors should be distinctive and at the same time robust to changes in viewing conditions as well as to errors of the detector. Our evaluation uses as criterion recall with respect to precision and is carried out for different image transformations. We compare shape context Belongie, S, et al., April 2002, steerable filters Freeman, W and Adelson, E, Setp. 1991, PCA-SIFT Ke, Y and Sukthankar, R, 2004, differential invariants Koenderink, J and van Doorn, A, 1987, spin images Lazebnik, S, et al., 2003, SIFT Lowe, D. G., 1999, complex filters Schaffalitzky, F and Zisserman, A, 2002, moment invariants Van Gool, L, et al., 1996, and cross-correlation for different types of interest regions. We also propose an extension of the SIFT descriptor and show that it outperforms the original method. Furthermore, we observe that the ranking of the descriptors is mostly independent of the interest region detector and that the SIFT-based descriptors perform best. Moments and steerable filters show the best performance among the low dimensional descriptors.
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