UK permanent residence: where can EU students get information?

The issue of Comprehensive Sickness Insurance (CSI) and residency for EU students has become a focal point following the outcome of the Brexit referendum. Bethan Ovens has been advising on the requirements for CSI in relation to dual-EU/Non-EU nationals accessing their right of free movement for five years at the LSE. She writes that dual-EU/Non-EU students often do not have the right to a European Health Insurance Card (EHIC) and the requirement for CSI is often not found out until their dependant is refused a residency permit

She’s Such a Tease: The Feminine as Burlesque Performance in Margaret Atwood’s _The Edible Woman_

This paper proposes that the dominant imageof Margaret Atwood's The Edible Woman is that of the female as prey or commodity, and that this image can also be understood as the female as burlesque performer. As her marriage looms, Marian negotiates a complex set of social instructions on how to present herself as the ideal woman. This desired image involves strategic teasing and revealing for a male spectator: Marian’s appearance, as well as her behaviour, should be calculated to satisfy, but not sate, her male audience’s appetites. Lucy, Marian’s colleague, and Ainsley, Marian’s roommate, perform their own versions of femininity within what Judith Butler has since identified as “the obligatory frame of reproductive heterosexuality.” The cake that Marian bakes in her own image, and then consumes, demonstrates her rejection of this compulsory, commodified bawdy role. However, she searches in vain for an alternative definition of femininity—one that did not yet exist when the novel was written in the early 1960s

Utilizing gene co-expression networks for comparative transcriptomic analyses

The development of high-throughput technologies such as microarray and next-generation RNA sequencing (RNA-seq) has generated numerous transcriptomic data that can be used for comparative transcriptomics studies. Transcriptomes obtained from different species can reveal differentially expressed genes that underlie species-specific traits. It also has the potential to identify genes that have conserved gene expression patterns. However, differential expression alone does not provide information about how the genes relate to each other in terms of gene expression or if groups of genes are correlated in similar ways across species, tissues, etc. This makes gene expression networks, such as co-expression networks, valuable in terms of finding similarities or differences between genes based on their relationships with other genes. The desired outcome of this research was to develop methods for comparative transcriptomics, specifically for comparing gene co-expression networks (GCNs), either within or between any set of organisms. These networks represent genes as nodes in the network, and pairs of genes may be connected by an edge representing the strength of the relationship between the pairs. We begin with a review of currently utilized techniques available that can be used or adapted to compare gene co-expression networks. We also work to systematically determine the appropriate number of samples needed to construct reproducible gene co-expression networks for comparison purposes. In order to systematically compare these replicate networks, software to visualize the relationship between replicate networks was created to determine when the consistency of the networks begins to plateau and if this is affected by factors such as tissue type and sample size. Finally, we developed a tool called Juxtapose that utilizes gene embedding to functionally interpret the commonalities and differences between a given set of co-expression networks constructed using transcriptome datasets from various organisms. A set of transcriptome datasets were utilized from publicly available sources as well as from collaborators. GTEx and Gene Expression Omnibus (GEO) RNA-seq datasets were used for the evaluation of the techniques proposed in this research. Skeletal cell datasets of closely related species and more evolutionarily distant organisms were also analyzed to investigate the evolutionary relationships of several skeletal cell types. We found evidence that data characteristics such as tissue origin, as well as the method used to construct gene co-expression networks, can substantially impact the number of samples required to generate reproducible networks. In particular, if a threshold is used to construct a gene co-expression network for downstream analyses, the number of samples used to construct the networks is an important consideration as many samples may be required to generate networks that have a reproducible edge order when sorted by edge weight. We also demonstrated the capabilities of our proposed method for comparing GCNs, Juxtapose, showing that it is capable of consistently matching up genes in identical networks, and it also reflects the similarity between different networks using cosine distance as a measure of gene similarity. Finally, we applied our proposed method to skeletal cell networks and find evidence of conserved gene relationships within skeletal GCNs from the same species and identify modules of genes with similar embeddings across species that are enriched for biological processes involved in cartilage and osteoblast development. Furthermore, smaller sub-networks of genes reflect the phylogenetic relationships of the species analyzed using our gene embedding strategy to compare the GCNs. This research has produced methodologies and tools that can be used for evolutionary studies and generalizable to scenarios other than cross-species comparisons, including co-expression network comparisons across tissues or conditions within the same species

The Space Complexity of Scannable Objects with Bounded Components

A fundamental task in the asynchronous shared memory model is obtaining a consistent view of a collection of shared objects while they are being modified concurrently by other processes. A scannable object addresses this problem. A scannable object is a sequence of readable objects called components, each of which can be accessed independently. It also supports the Scan operation, which simultaneously reads all of the components of the object. In this paper, we consider the space complexity of an n-process, k-component scannable object implementation from objects with bounded domain sizes. If the value of each component can change only a finite number of times, then there is a simple lock-free implementation from k objects. However, more objects are needed if each component is fully reusable, i.e. for every pair of values v, v\u27, there is a sequence of operations that changes the value of the component from v to v\u27. We considered the special case of scannable binary objects, where each component has domain {0, 1}, in PODC 2021. Here, we present upper and lower bounds on the space complexity of any n-process implementation of a scannable object O with k fully reusable components from an arbitrary set of objects with bounded domain sizes. We construct a lock-free implementation from k objects of the same types as the components of O along with ?n/b? objects with domain size 2^b. By weakening the progress condition to obstruction-freedom, we construct an implementation from k objects of the same types as the components of O along with ?n/(b-1)? objects with domain size b. When the domain size of each component and each object used to implement O is equal to b and n ? b^k - bk + k, we prove that 1/2? (k + (n-1)/b - log_b n) objects are required. This asymptotically matches our obstruction-free upper bound. When n > b^k - bk + k, we prove that 1/2? (b^{k-1} - {(b-1)k + 1}/b) objects are required. We also present a lower bound on the number of objects needed when the domain sizes of the components and the objects used by the implementation are arbitrary and finite

Integrating biclustering techniques with de novo gene regulatory network discovery using RNA-seq from skeletal tissues

In order to improve upon stem cell therapy for osteoarthritis, it is necessary to understand the molecular and cellular processes behind bone development and the differences from cartilage formation. To further elucidate these processes would provide a means to analyze the relatedness of bone and cartilage tissue by determining genes that are expressed and regulated for stem cells to differentiate into skeletal tissues. It would also contribute to the classification of differences in normal skeletogenesis and degenerative conditions involving these tissues. The three predominant skeletal tissues of interest are bone, immature cartilage and mature cartilage. Analysis of the transcriptome of these skeletal tissues using RNA-seq technology was performed using differential expression, clustering and biclustering algorithms, to detect similarly expressed genes, which provides evidence for genes potentially interacting together to produce a particular phenotype. Identifying key regulators in the gene regulatory networks (GRNs) driving cartilage and bone development and the differences in the GRNs they drive will facilitate a means to make comparisons between the tissues at the transcriptomic level. Due to a small number of available samples for gene expression data in bone, immature and mature cartilage, it is necessary to determine how the number of samples influences the ability to make accurate GRN predictions. Machine learning techniques for GRN prediction that can incorporate multiple data types have not been well evaluated for complex organisms, nor has RNA-seq data been used often for evaluating these methods. Therefore, techniques identified to work well with microarray data were applied to RNA-seq data from mouse embryonic stem cells, where more samples are available for evaluation compared to the skeletal tissue RNA-seq samples. The RNA-seq data was combined with ChIP-seq data to determine if the machine learning methods outperform simple, correlation-based methods that have been evaluated using RNA-seq data alone. Two of the best performing GRN prediction algorithms from previous large-scale evaluations, which are incapable of incorporating data beyond expression data, were used as a baseline to determine if the addition of multiple data types could help reduce the number of gene expression samples. It was also necessary to identify a biclustering algorithm that could identify potentially biologically relevant modules. Publicly available ChIP-seq and RNA-seq samples from embryonic stem cells were used to measure the performance and consistency of each method, as there was a well-established network in mouse embryonic stem cells to compare results. The methods were then compared to cMonkey2, a biclustering method used in conjunction with ChIP-seq for two important transcription factors in the embryonic stem cell network. This was done to determine if any of these GRN prediction methods could potentially use the small number of skeletal tissue samples available to determine transcription factors orchestrating the expression of other genes driving cartilage and bone formation. Using the embryonic stem cell RNA-seq samples, it was found that sample size, if above 10, does not have a significant impact on the number of true positives in the top predicted interactions. Random forest methods outperform correlation-based methods when using RNA-seq, with area under ROC (AUROC) for evaluation, but the number of true positive interactions predicted when compared to a literature network were similar when using a strict cut-off. Using a limited set of ChIP-seq data was found to not improve the confidence in the transcription factor interactions and had no obvious affect on biclustering results. Correlation-based methods are likely the safest option when based on consistency of the results over multiple runs, but there is still the challenge of determining an appropriate cut-off to the predictions. To predict the skeletal tissue GRNs, cMonkey was used as an initial feature selection method to identify important genes in skeletal tissues and compared with other biclustering methods that do not use ChIP-seq. The predicted skeletal tissue GRNs will be utilized in future analyses of skeletal tissues, focussing on the evolutionary relationship between the GRNs driving skeletal tissue development