17 research outputs found
Book Review: Ecolinguistics: Language, Ecology and the Stories We Live By
No full textBook Title: Ecolinguistics: Language, Ecology and the Stories We Live ByBook Author: Arran Stibbe2015, Routledge, London and New York. ISBN 978-0-415-83781-1. viii+210 pages. Price: £34.9
A reduced complexity-centralized power control scheme for DS/CDMA cellular communication
No full textTheoretical calculations of optical and EPR spectra and their pressure-induced shifts for ruby
No full textAdditional file 2: of Oct4 regulates DNA methyltransferase 1 transcription by direct binding of the regulatory element
No full textFigure S2. The results of the sensitive two-color EMSA assay showed direct binding of Oct4 to the mouse Dnmt1 promoter in vitro. A quantity of 20 ng of mouse Dnmt1 promoter (the region from − 554 to − 294 bp relative to TSS) was added to samples containing different amounts of Oct4 in 1 × binding buffer as described in the Methods section. The images were taken using an alpha gel imaging system. Lanes: (1) DL2000 markers; (2) 20 ng mouse Dnmt1 promoter; (3–9) 20 ng mouse Dnmt1 promoter interacting with increasing amounts (95, 190, 380, 570, 760, 950 and 1140 ng) of mouse Oct4 protein; (10, 11) 40 ng Oct4 protein without any mouse Dnmt1 promoter. A – Image of the EMSA gel stained with SYBR Green EMSA DNA stain to show DNA. B – The same gel stained with SYPRO Ruby EMSA protein stain to show the protein. (TIF 589 kb
Influence of Superparameterization and a Higher‐Order Turbulence Closure on Rainfall Bias Over Amazonia in Community Atmosphere Model Version 5
No full textHydroclimatic variability and predictability: a survey of recent research
No full textRecent research in large-scale hydroclimatic variability is
surveyed, focusing on five topics: (i) variability in general, (ii) droughts,
(iii) floods, (iv) land–atmosphere coupling, and (v) hydroclimatic
prediction. Each surveyed topic is supplemented by illustrative examples of
recent research, as presented at a 2016 symposium honoring the career of
Professor Eric Wood. Taken together, the recent literature and the
illustrative examples clearly show that current research into hydroclimatic
variability is strong, vibrant, and multifaceted
Scalable Text Mining with Sparse Generative Models
No full textThe information age has brought a deluge of data. Much of this is in text form, insurmountable in scope for humans and incomprehensible in structure for computers. Text mining is an expanding field of research that seeks to utilize the information contained in vast document collections. General data mining methods based on machine learning face challenges with the scale of text data, posing a need for scalable text mining methods.
This thesis proposes a solution to scalable text mining: generative models combined with sparse computation. A unifying formalization for generative text models is defined, bringing together research traditions that have used formally equivalent models, but ignored parallel developments. This framework allows the use of methods developed in different processing tasks such as retrieval and classification, yielding effective solutions across different text mining tasks. Sparse computation using inverted indices is proposed for inference on probabilistic models. This reduces the computational complexity of the common text mining operations according to sparsity, yielding probabilistic models with the scalability of modern search engines.
The proposed combination provides sparse generative models: a solution for text mining that is general, effective, and scalable. Extensive experimentation on text classification and ranked retrieval datasets are conducted, showing that the proposed solution matches or outperforms the leading task-specific methods in effectiveness, with a order of magnitude decrease in classification times for Wikipedia article categorization with a million classes. The developed methods were further applied in two 2014 Kaggle data mining prize competitions with over a hundred competing teams, earning first and second places
Semantic labelling of road scenes using supervised and unsupervised machine learning with lidar-stereo sensor fusion
No full textAt the highest level the aim of this thesis is to review and develop reliable and efficient
algorithms for classifying road scenery primarily using vision based technology mounted
on vehicles. The purpose of this technology is to enhance vehicle safety systems in order
to prevent accidents which cause injuries to drivers and pedestrians.
This thesis uses LIDAR–stereo sensor fusion to analyse the scene in the path of the vehicle
and apply semantic labels to the different content types within the images. It details every
step of the process from raw sensor data to automatically labelled images.
At each stage of the process currently used methods are investigated and evaluated. In
cases where existingmethods do not produce satisfactory results improvedmethods have
been suggested. In particular, this thesis presents a novel, automated,method for aligning
LIDAR data to the stereo camera frame without the need for specialised alignment grids.
For image segmentation a hybrid approach is presented, combining the strengths of both
edge detection and mean-shift segmentation. For texture analysis the presented method
uses GLCM metrics which allows texture information to be captured and summarised
using only four feature descriptors compared to the 100’s produced by SURF descriptors.
In addition to texture descriptors, the ìD information provided by the stereo system is
also exploited. The segmented point cloud is used to determine orientation and curvature
using polynomial surface fitting, a technique not yet applied to this application.
Regarding classification methods a comprehensive study was carried out comparing the
performance of the SVM and neural network algorithms for this particular application.
The outcome shows that for this particular set of learning features the SVM classifiers
offer slightly better performance in the context of image and depth based classification
which was not made clear in existing literature.
Finally a novel method of making unsupervised classifications is presented. Segments are
automatically grouped into sub-classes which can then be mapped to more expressive
super-classes as needed. Although the method in its current state does not yet match the
performance of supervised methods it does produce usable classification results without
the need for any training data. In addition, the method can be used to automatically
sub-class classes with significant inter-class variation into more specialised groups prior
to being used as training targets in a supervised method
The RNA Polymerase II-associated factor 1 complex represses small-RNA-mediated heterochromatin formation and gene silencing
No full textRNAi interference (RNAi) is a highly conserved regulatory mechanism employed by almost all Eukaryotes. With RNAi organisms can modulate the expression of endogenous genes and protect the integrity and identity of their genomes. All RNAi-based processes depend on a complex containing small non-coding RNAs (sRNA) associated with Argonaute proteins. In this sRNA-Argonaute complex, sRNA recognizes its sequence-specific target messenger RNA (mRNA) via a base-pairing interaction, and directs the Argonaute protein to it. Upon binding, the Argonaute protein can repress target gene expression at different stages.
In the case of the most studied class of sRNAs, the microRNAs, the repression of gene expression occurs at the post-transcriptional level. MicroRNAs inhibit the translation of target mRNAs and promote their degradation in the cytoplasm of a cell. In contrast, nuclear RNAi-based processes have been implicated in directing chromatin modifications and repressing gene activity at the transcriptional level. RNAi-mediated chromatin modifications have been linked to epigenetic gene silencing across kingdoms but the mechanistic details of the small RNA-dependent transgenerational silencing remain uncovered. One of the obstacles in the way to understanding these regulatory processes is the fact that attempts to stably silence genes by ectopic small RNA mediated, locus-independent heterochromatin formation, have proven to be inherently difficult.
By performing a mutagenesis screen we identified the highly conserved RNA Polymerase II-associated factor 1 (Paf1) complex as a repressor of sRNA-directed heterochromatin formation in the fission yeast Schizosaccharomyces pombe. We showed that small RNAs produced from a hairpin construct effectively silenced the expression of the target gene in trans, if the function of Paf1 complex was impaired. The induced repression was locus- and sequence-independent, and involved de novo formation of a functional heterochromatic domain. Importantly, we observed that the silent state could be transmitted through meiosis and was subsequently inherited through tens of generations, even in the absence of the primary siRNAs source. Thus, the Paf1 complex represses sRNA-induced heterochromatin formation in an epigenetic fashion. By performing a genetic analysis, we found that the Paf1 complex represses sRNA-mediated heterochromatin formation by contributing to efficient transcription termination and nascent transcript release. Thereby, we demonstrate that defective transcription termination exposes genes to sRNA-mediated repression.
The findings described in this dissertation are not only an advancement to the mechanistic research on sRNA-directed transgenerational gene silencing. The ability to stably repress gene activity without changing the underlying DNA sequence may also provide important technological implications, in particular in plant biotechnology
