Incremental dimension reduction of tensors with random index

We present an incremental, scalable and efficient dimension reduction technique for tensors that is based on sparse random linear coding. Data is stored in a compactified representation with fixed size, which makes memory requirements low and predictable. Component encoding and decoding are performed on-line without computationally expensive re-analysis of the data set. The range of tensor indices can be extended dynamically without modifying the component representation. This idea originates from a mathematical model of semantic memory and a method known as random indexing in natural language processing. We generalize the random-indexing algorithm to tensors and present signal-to-noise-ratio simulations for representations of vectors and matrices. We present also a mathematical analysis of the approximate orthogonality of high-dimensional ternary vectors, which is a property that underpins this and other similar random-coding approaches to dimension reduction. To further demonstrate the properties of random indexing we present results of a synonym identification task. The method presented here has some similarities with random projection and Tucker decomposition, but it performs well at high dimensionality only (n>10^3). Random indexing is useful for a range of complex practical problems, e.g., in natural language processing, data mining, pattern recognition, event detection, graph searching and search engines. Prototype software is provided. It supports encoding and decoding of tensors of order >= 1 in a unified framework, i.e., vectors, matrices and higher order tensors.Comment: 36 pages, 9 figure

Quranic Topic Modelling Using Paragraph Vectors

The Quran is known for its linguistic and spiritual value. It comprises knowledge and topics that govern different aspects of people’s life. Acquiring and encoding this knowledge is not a trivial task due to the overlapping of meanings over its documents and passages. Analysing a text like the Quran requires learning approaches that go beyond word level to achieve sentence level representation. Thus, in this work, we follow a deep learning approach: paragraph vector to learn an informative representation of Quranic Verses. We use a recent breakthrough in embeddings that maps the passages of the Quran to vector representation that preserves more semantic and syntactic information. These vectors can be used as inputs for machine learning models, and leveraged for the topic analysis. Moreover, we evaluated the derived clusters of related verses against a tagged corpus, to add more significance to our conclusions. Using the paragraph vectors model, we managed to generate a document embedding space that model and explain word distribution in the Holy Quran. The dimensions in the space represent the semantic structure in the data and ultimately help to identify main topics and concepts in the text

Gamma secretase inhibition promotes hypoxic necrosis in mouse pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDA) is a highly lethal disease that is refractory to medical intervention. Notch pathway antagonism has been shown to prevent pancreatic preneoplasia progression in mouse models, but potential benefits in the setting of an established PDA tumor have not been established. We demonstrate that the gamma secretase inhibitor MRK003 effectively inhibits intratumoral Notch signaling in the KPC mouse model of advanced PDA. Although MRK003 monotherapy fails to extend the lifespan of KPC mice, the combination of MRK003 with the chemotherapeutic gemcitabine prolongs survival. Combination treatment kills tumor endothelial cells and synergistically promotes widespread hypoxic necrosis. These results indicate that the paucivascular nature of PDA can be exploited as a therapeutic vulnerability, and the dual targeting of the tumor endothelium and neoplastic cells by gamma secretase inhibition constitutes a rationale for clinical translation

Modelling semantic transparency

We present models of semantic transparency in which the perceived trans- parency of English noun–noun compounds, and of their constituent words, is pre- dicted on the basis of the expectedness of their semantic structure. We show that such compounds are perceived as more transparent when the first noun is more frequent, hence more expected, in the language generally; when the compound semantic rela- tion is more frequent, hence more expected, in association with the first noun; and when the second noun is more productive, hence more expected, as the second ele- ment of a noun–noun compound. Taken together, our models of compound and con- stituent transparency lead us to two conclusions. Firstly, although compound trans- parency is a function of the transparencies of the constituents, the two constituents differ in the nature of their contribution. Secondly, since all the significant predictors in our models of compound transparency are also known predictors of processing speed, perceived transparency may itself be a reflex of ease of processing

Semantically linking molecular entities in literature through entity relationships

Background Text mining tools have gained popularity to process the vast amount of available research articles in the biomedical literature. It is crucial that such tools extract information with a sufficient level of detail to be applicable in real life scenarios. Studies of mining non-causal molecular relations attribute to this goal by formally identifying the relations between genes, promoters, complexes and various other molecular entities found in text. More importantly, these studies help to enhance integration of text mining results with database facts. Results We describe, compare and evaluate two frameworks developed for the prediction of non-causal or 'entity' relations (REL) between gene symbols and domain terms. For the corresponding REL challenge of the BioNLP Shared Task of 2011, these systems ranked first (57.7% F-score) and second (41.6% F-score). In this paper, we investigate the performance discrepancy of 16 percentage points by benchmarking on a related and more extensive dataset, analysing the contribution of both the term detection and relation extraction modules. We further construct a hybrid system combining the two frameworks and experiment with intersection and union combinations, achieving respectively high-precision and high-recall results. Finally, we highlight extremely high-performance results (F-score > 90%) obtained for the specific subclass of embedded entity relations that are essential for integrating text mining predictions with database facts. Conclusions The results from this study will enable us in the near future to annotate semantic relations between molecular entities in the entire scientific literature available through PubMed. The recent release of the EVEX dataset, containing biomolecular event predictions for millions of PubMed articles, is an interesting and exciting opportunity to overlay these entity relations with event predictions on a literature-wide scale

Expression of Nestin by Neural Cells in the Adult Rat and Human Brain

Neurons and glial cells in the developing brain arise from neural progenitor cells (NPCs). Nestin, an intermediate filament protein, is thought to be expressed exclusively by NPCs in the normal brain, and is replaced by the expression of proteins specific for neurons or glia in differentiated cells. Nestin expressing NPCs are found in the adult brain in the subventricular zone (SVZ) of the lateral ventricle and the subgranular zone (SGZ) of the dentate gyrus. While significant attention has been paid to studying NPCs in the SVZ and SGZ in the adult brain, relatively little attention has been paid to determining whether nestin-expressing neural cells (NECs) exist outside of the SVZ and SGZ. We therefore stained sections immunocytochemically from the adult rat and human brain for NECs, observed four distinct classes of these cells, and present here the first comprehensive report on these cells. Class I cells are among the smallest neural cells in the brain and are widely distributed. Class II cells are located in the walls of the aqueduct and third ventricle. Class IV cells are found throughout the forebrain and typically reside immediately adjacent to a neuron. Class III cells are observed only in the basal forebrain and closely related areas such as the hippocampus and corpus striatum. Class III cells resemble neurons structurally and co-express markers associated exclusively with neurons. Cell proliferation experiments demonstrate that Class III cells are not recently born. Instead, these cells appear to be mature neurons in the adult brain that express nestin. Neurons that express nestin are not supposed to exist in the brain at any stage of development. That these unique neurons are found only in brain regions involved in higher order cognitive function suggests that they may be remodeling their cytoskeleton in supporting the neural plasticity required for these functions

Cross talk between hedgehog and epithelial–mesenchymal transition pathways in gastric pit cells and in diffuse-type gastric cancers

We previously reported hedgehog (Hh) signal activation in the mucus-secreting pit cell of the stomach and in diffuse-type gastric cancer (GC). Epithelial–mesenchymal transition (EMT) is known to be involved in tumour malignancy. However, little is known about whether and how both signallings cooperatively act in diffuse-type GC. By microarray and reverse transcription–PCR, we investigated the expression of those Hh and EMT signalling molecules in pit cells and in diffuse-type GCs. How both signallings act cooperatively in those cells was also investigated by the treatment of an Hh-signal inhibitor and siRNAs of Hh and EMT transcriptional key regulator genes on a mouse primary culture and on human GC cell lines. Pit cells and diffuse-type GCs co-expressed many Hh and EMT signalling genes. Mesenchymal-related genes (WNT5A, CDH2, PDGFRB, EDNRA, ROBO1, ROR2, and MEF2C) were found to be activated by an EMT regulator, SIP1/ZFHX1B/ZEB2, which was a target of a primary transcriptional regulator GLI1 in Hh signal. Furthermore, we identified two cancer-specific Hh targets, ELK1 and MSX2, which have an essential role in GC cell growth. These findings suggest that the gastric pit cell exhibits mesenchymal-like gene expression, and that diffuse-type GC maintains expression through the Hh–EMT pathway. Our proposed extensive Hh–EMT signal pathway has the potential to an understanding of diffuse-type GC and to the development of new drugs

Notch signaling in glioblastoma: a developmental drug target?

Malignant gliomas are among the most devastating tumors for which conventional therapies have not significantly improved patient outcome. Despite advances in imaging, surgery, chemotherapy and radiotherapy, survival is still less than 2 years from diagnosis and more targeted therapies are urgently needed. Notch signaling is central to the normal and neoplastic development of the central nervous system, playing important roles in proliferation, differentiation, apoptosis and cancer stem cell regulation. Notch is also involved in the regulation response to hypoxia and angiogenesis, which are typical tumor and more specifically glioblastoma multiforme (GBM) features. Targeting Notch signaling is therefore a promising strategy for developing future therapies for the treatment of GBM. In this review we give an overview of the mechanisms of Notch signaling, its networking pathways in gliomas, and discuss its potential for designing novel therapeutic approaches

In Situ Dividing and Phagocytosing Retinal Microglia Express Nestin, Vimentin, and NG2 In Vivo

BACKGROUND: Following injury, microglia become activated with subsets expressing nestin as well as other neural markers. Moreover, cerebral microglia can give rise to neurons in vitro. In a previous study, we analysed the proliferation potential and nestin re-expression of retinal macroglial cells such as astrocytes and Müller cells after optic nerve (ON) lesion. However, we were unable to identify the majority of proliferative nestin(+) cells. Thus, the present study evaluates expression of nestin and other neural markers in quiescent and proliferating microglia in naïve retina and following ON transection in adult rats in vivo. METHODOLOGY/PRINCIPAL FINDINGS: For analysis of cell proliferation and cells fates, rats received BrdU injections. Microglia in retinal sections or isolated cells were characterized using immunofluorescence labeling with markers for microglia (e.g., Iba1, CD11b), cell proliferation, and neural cells (e.g., nestin, vimentin, NG2, GFAP, Doublecortin etc.). Cellular analyses were performed using confocal laser scanning microscopy. In the naïve adult rat retina, about 60% of resting ramified microglia expressed nestin. After ON transection, numbers of nestin(+) microglia peaked to a maximum at 7 days, primarily due to in situ cell proliferation of exclusively nestin(+) microglia. After 8 weeks, microglia numbers re-attained control levels, but 20% were still BrdU(+) and nestin(+), although no further local cell proliferation occurred. In addition, nestin(+) microglia co-expressed vimentin and NG2, but not GFAP or neuronal markers. Fourteen days after injury and following retrograde labeling of retinal ganglion cells (RGCs) with Fluorogold (FG), nestin(+)NG2(+) microglia were positive for the dye indicating an active involvement of a proliferating cell population in phagocytosing apoptotic retinal neurons. CONCLUSIONS/SIGNIFICANCE: The current study provides evidence that in adult rat retina, a specific resident population of microglia expresses proteins of immature neural cells that are involved in injury-induced cell proliferation and phagocytosis while transdifferentiation was not observed