The vascular targeting agent combretastatin-A4 and a novel cis-Restricted {beta}-Lactam Analogue, CA-432, induce apoptosis in human chronic myeloid leukemia cells and ex vivo patient samples including those displaying multidrug resistance

Combretastatin-A4 (CA-4) is a natural derivative of the African willow tree Combretum caffrum. CA-4 is one of the most potent antimitotic components of natural origin, but it is, however, intrinsically unstable. A novel series of CA-4 analogs incorporating a 3,4-diaryl-2-azetidinone (β-lactam) ring were designed and synthesized with the objective to prevent cis -trans isomerization and improve the intrinsic stability without altering the biological activity of CA-4. Evaluation of selected β-lactam CA-4 analogs demonstrated potent antitubulin, antiproliferative, and antimitotic effects in human leukemia cells. A lead β-lactam analog, CA-432, displayed comparable antiproliferative activities with CA-4. CA-432 induced rapid apoptosis in HL-60 acute myeloid leukemia cells, which was accompanied by depolymerization of the microtubular network, poly(ADP-ribose) polymerase cleavage, caspase-3 activation, and Bcl-2 cleavage. A prolonged G(2)M cell cycle arrest accompanied by a sustained phosphorylation of mitotic spindle checkpoint protein, BubR1, and the antiapoptotic proteins Bcl-2 and Bcl-x(L) preceded apoptotic events in K562 chronic myeloid leukemia (CML) cells. Molecular docking studies in conjunction with comprehensive cell line data rule out CA-4 and β-lactam derivatives as P-glycoprotein substrates. Furthermore, both CA-4 and CA-432 induced significantly more apoptosis compared with imatinib mesylate in ex vivo samples from patients with CML, including those positive for the T315I mutation displaying resistance to imatinib mesylate and dasatinib. In summary, synthetic intrinsically stable analogs of CA-4 that display significant clinical potential as antileukemic agents have been designed and synthesized.

c-Kit+ progenitors generate vascular cells for tissue-engineered grafts through modulation of the Wnt/Klf4 pathway

The development of decellularised scaffolds for small diameter vascular grafts is hampered by their limited patency, due to the lack of luminal cell coverage by endothelial cells (EC) and to the low tone of the vessel due to absence of a contractile smooth muscle cells (SMC). In this study, we identify a population of vascular progenitor c-Kit+/Sca-1- cells available in large numbers and derived from immuno-privileged embryonic stem cells (ESCs). We also define an efficient and controlled differentiation protocol yielding fully to differentiated ECs and SMCs in sufficient numbers to allow the repopulation of a tissue engineered vascular graft. When seeded ex vivo on a decellularised vessel, c-Kit+/Sca-1-derived cells recapitulated the native vessel structure and upon in vivo implantation in the mouse, markedly reduced neointima formation and mortality, restoring functional vascularisation. We showed that Krüppel-like transcription factor 4 (Klf4) regulates the choice of differentiation pathway of these cells through β-catenin activation and was itself regulated by the canonical Wnt pathway activator lithium chloride. Our data show that ESC-derived c-Kit+/Sca-1-cells can be differentiated through a Klf4/β-catenin dependent pathway and are a suitable source of vascular progenitors for the creation of superior tissue-engineered vessels from decellularised scaffolds.

Two-part Segmentation of Text Documents

We consider the problem of segmenting text documents that have a
two-part structure such as a problem part and a solution part. Documents
of this genre include incident reports that typically involve
description of events relating to a problem followed by those pertaining
to the solution that was tried. Segmenting such documents
into the component two parts would render them usable in knowledge
reuse frameworks such as Case-Based Reasoning. This segmentation
problem presents a hard case for traditional text segmentation
due to the lexical inter-relatedness of the segments. We develop
a two-part segmentation technique that can harness a corpus
of similar documents to model the behavior of the two segments
and their inter-relatedness using language models and translation
models respectively. In particular, we use separate language models
for the problem and solution segment types, whereas the interrelatedness
between segment types is modeled using an IBM Model
1 translation model. We model documents as being generated starting
from the problem part that comprises of words sampled from
the problem language model, followed by the solution part whose
words are sampled either from the solution language model or from
a translation model conditioned on the words already chosen in the
problem part. We show, through an extensive set of experiments on
real-world data, that our approach outperforms the state-of-the-art
text segmentation algorithms in the accuracy of segmentation, and
that such improved accuracy translates well to improved usability
in Case-based Reasoning systems. We also analyze the robustness
of our technique to varying amounts and types of noise and empirically
illustrate that our technique is quite noise tolerant, and
degrades gracefully with increasing amounts of noise

Automated retinal image quality assessment on the UK Biobank dataset for epidemiological studies

Morphological changes in the retinal vascular network are associated with future risk of many systemic and vascular diseases. However, uncertainty over the presence and nature of some of these associations exists. Analysis of data from large population based studies will help to resolve these uncertainties. The QUARTZ (QUantitative Analysis of Retinal vessel Topology and siZe) retinal image analysis system allows automated processing of large numbers of retinal images. However, an image quality assessment module is needed to achieve full automation. In this paper, we propose such an algorithm, which uses the segmented vessel map to determine the suitability of retinal images for use in the creation of vessel morphometric data suitable for epidemiological studies. This includes an effective 3-dimensional feature set and support vector machine classification. A random subset of 800 retinal images from UK Biobank (a large prospective study of 500,000 middle aged adults; where 68,151 underwent retinal imaging) was used to examine the performance of the image quality algorithm. The algorithm achieved a sensitivity of 95.33% and a specificity of 91.13% for the detection of inadequate images. The strong performance of this image quality algorithm will make rapid automated analysis of vascular morphometry feasible on the entire UK Biobank dataset (and other large retinal datasets), with minimal operator involvement, and at low cost.

Fast segmentation of 3D data using an octree

The algorithm developed uses an octree pyramid in which noise is reduced at the expense of the spatial resolution. At a certain level an unsupervised clustering without spatial connectivity constraints is applied. After the classification, isolated voxels and insignificant regions are removed by assigning them to their neighbours. The spatial resolution is then increased by the downprojection of the regions, level by level. At each level the uncertainty of the boundary voxels is minimised by a dynamic selection and classification of these, using an adaptive 3D filtering. The algorithm is tested using different data sets, including NMR data.

Analysis of underwater acoustic data via 3-D segmentation

The analysis of seabed structure is important in a wide variety of scientific and industrial applications. In this paper, underwater acoustic data produced by bottom-penetrating sonar (Topas) are analyzed using unsupervised volumetric segmentation, based on a three dimensional Gibbs-Markov model. The result is a concise and accurate description of the seabed, in which key structures are emphasized. This description is also very well suited to further operations, such as the enhancement and automatic recognition of important structures. Experimental results demonstrating the effectiveness of this approach are shown, using Topas data gathered in the North Sea off Horten, Norway.

Comparing segmentation strategies for efficient video passage retrieval

We compare the effect of different text segmentation strategies on speech based passage retrieval of video. Passage retrieval has mainly been studied to improve document retrieval and to enable question answering. In these domains best results were obtained using passages defined by the paragraph structure of the source documents or by using arbitrary overlapping passages. For the retrieval of relevant passages in a video, using speech transcripts, no author defined segmentation is available. We compare retrieval results from 4 different types of segments based on the speech channel of the video: fixed length segments, a sliding window, semantically coherent segments and prosodic segments. We evaluated the methods on the corpus of the MediaEval 2011 Rich Speech Retrieval task. Our main conclusion is that the retrieval results highly depend on the right choice for the segment length. However, results using the segmentation into semantically coherent parts depend much less on the segment length. Especially, the quality of fixed length and sliding window segmentation drops fast when the segment length increases, while quality of the semantically coherent segments is much more stable. Thus, if coherent segments are defined, longer segments can be used and consequently less segments have to be considered at retrieval time.