Re-negotiating Ideologies of Bilingualism on the Margins of Education

This article reports on an ethnographic study carried out in three interrelated sites: two contrasting secondary schools and a Youth-Club (the principal focus of this article), in an area of southwest Wales. This article highlights the incongruence between the language at home and the language of the school and posits that the relationship between language use at school and in the wider community needs to be problematised and questioned far more than has been done thus far. This study questions whether school-based ideologies and school-based practices are re-negotiated or contested on the margins of education and whether this re-negotiation and contestation plays an important role in whether a young person chooses to use Welsh or English outside of school. It will be argued that recreational spaces, even though loosely connected to schools as institutions, function as more open spaces where institutional ideologies are actively reworked and renegotiated, either through choosing to use English or by mixing and blending different aspects of linguistic resources, or by re-negotiating and questioning which version of Welshness is more valuable, ‘the removed and authentic’ (as seen at the Welsh school) or the ‘new and hybrid’ as seen at the Youth-Club

Information Hiding Using Convolutional Encoding

We consider two functions f1(r) and f2(r), for r 2 Rn and the problem of ‘Diffusing’ these functions together, followed by the application of an encryption process we call ‘Stochastic Diffusion’ and then hiding the output of this process in to one or other of the same functions. The coupling of these two processes (i.e., data diffusion and stochastic diffusion) is considered using a form of conditioning that generates a wellposed and data consistent inverse solution for the purpose of decrypting the output. After presenting the basic encryption method and (encrypted) information hiding model, coupled with a mathematical analysis (within the context of ‘convolutional encoding’), we provide a case study which is concerned with the implementation of the approach for full-colour 24-bit digital images. The ideas considered yields the foundations for a number of wide-ranging applications that include covert signal and image information interchange, data authentication, copyright protection and digital rights management, for example

Phase-only Digital Encryption using a Three-pass Protocol

Abstract—This paper considers an application of phase-only digital encryption to the three-pass protocol leading to a new ‘nokey- exchange algorithm’. After providing a study on the theoretical background to the method, an algorithm is presented on a step-by-step basis together with three examples of cryptanalysis. A prototype MATLAB function is provided for validation of the approach and for further development by interested readers

Bioprospecting Finds the Toughest Biological Material: Extraordinary Silk from a Giant Riverine Orb Spider

Background Combining high strength and elasticity, spider silks are exceptionally tough, i.e., able to absorb massive kinetic energy before breaking. Spider silk is therefore a model polymer for development of high performance biomimetic fibers. There are over 41.000 described species of spiders, most spinning multiple types of silk. Thus we have available some 200.000+ unique silks that may cover an amazing breadth of material properties. To date, however, silks from only a few tens of species have been characterized, most chosen haphazardly as model organisms (Nephila) or simply from researchers' backyards. Are we limited to ‘blindly fishing’ in efforts to discover extraordinary silks? Or, could scientists use ecology to predict which species are likely to spin silks exhibiting exceptional performance properties? Methodology We examined the biomechanical properties of silk produced by the remarkable Malagasy ‘Darwin's bark spider’ (Caerostris darwini), which we predicted would produce exceptional silk based upon its amazing web. The spider constructs its giant orb web (up to 2.8 m2) suspended above streams, rivers, and lakes. It attaches the web to substrates on each riverbank by anchor threads as long as 25 meters. Dragline silk from both Caerostris webs and forcibly pulled silk, exhibits an extraordinary combination of high tensile strength and elasticity previously unknown for spider silk. The toughness of forcibly silked fibers averages 350 MJ/m3, with some samples reaching 520 MJ/m3. Thus, C. darwini silk is more than twice tougher than any previously described silk, and over 10 times better than Kevlar®. Caerostris capture spiral silk is similarly exceptionally tough. Conclusions Caerostris darwini produces the toughest known biomaterial. We hypothesize that this extraordinary toughness coevolved with the unusual ecology and web architecture of these spiders, decreasing the likelihood of bridgelines breaking and collapsing the web into the river. This hypothesis predicts that rapid change in material properties of silk co-occurred with ecological shifts within the genus, and can thus be tested by combining material science, behavioral observations, and phylogenetics. Our findings highlight the potential benefits of natural history–informed bioprospecting to discover silks, as well as other materials, with novel and exceptional properties to serve as models in biomimicry.Primary funding for this work came from the Slovenian Research Agency (grant Z1-9799-0618-07 to I. Agnarsson), the National Geographic Society (grant 8655-09 to the authors), and the National Science Foundation (grants DBI-0521261, DEB-0516038 and IOS-0745379 to T. Blackledge). Additional funding came from the European Community 6th Framework Programme (a Marie Curie International Reintegration Grant MIRG-CT-2005 036536 to M. Kuntner). The 2001 field work was supported by the Sallee Charitable Trust grant to I. Agnarsson and M. Kuntner and by a United States National Science Foundation grant (DEB-9712353) to G. Hormiga and J. A. Coddington. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewe

Deep-learned estimation of uncertainty in measurements of apparent diffusion coefficient from whole-body diffusion-weighted MRI.

PURPOSE: To use deep learning to calculate the uncertainty in apparent diffusion coefficient (σADC) voxel-wise measurements to clinically impact the monitoring of treatment response and improve the quality of ADC maps. MATERIALS AND METHODS: We use a uniquely designed diffusion-weighted imaging (DWI) acquisition protocol that provides gold-standard measurements of σADC to train a deep learning model on two separate cohorts: 16 patients with prostate cancer and 28 patients with mesothelioma. Our network was trained with a novel cost function, which incorporates a perception metric and a b-value regularisation term, on ADC maps calculated by combinations of 2 or 3 b-values (e.g. 50/600/900, 50/900, 50/600, 600/900 s/mm2). We compare the accuracy of the deep-learning based approach for estimation of σADC with gold-standard measurements. RESULTS: The model accurately predicted the σADC for every b-value combination in both cohorts. Mean values of σADC within areas of active disease deviated from those measured by the gold-standard by 4.3% (range, 2.87-6.13%) for the prostate and 3.7% (range, 3.06-4.54%) for the mesothelioma cohort. We also showed that the model can easily be adapted for a different DWI protocol and field-of-view with only a few images (as little as a single patient) using transfer learning. CONCLUSION: Deep learning produces maps of σADC from standard clinical diffusion-weighted images (DWI) when 2 or more b-values are available

Virtual Biopsy in Soft Tissue Sarcoma. How Close Are We?

A shift in radiology to a data-driven specialty has been unlocked by synergistic developments in imaging biomarkers (IB) and computational science. This is advancing the capability to deliver "virtual biopsies" within oncology. The ability to non-invasively probe tumour biology both spatially and temporally would fulfil the potential of imaging to inform management of complex tumours; improving diagnostic accuracy, providing new insights into inter- and intra-tumoral heterogeneity and individualised treatment planning and monitoring. Soft tissue sarcomas (STS) are rare tumours of mesenchymal origin with over 150 histological subtypes and notorious heterogeneity. The combination of inter- and intra-tumoural heterogeneity and the rarity of the disease remain major barriers to effective treatments. We provide an overview of the process of successful IB development, the key imaging and computational advancements in STS including quantitative magnetic resonance imaging, radiomics and artificial intelligence, and the studies to date that have explored the potential biological surrogates to imaging metrics. We discuss the promising future directions of IBs in STS and illustrate how the routine clinical implementation of a virtual biopsy has the potential to revolutionise the management of this group of complex cancers and improve clinical outcomes

Repeatability of quantitative individual lesion and total disease multiparametric whole-body MRI measurements in prostate cancer bone metastases.

OBJECTIVES: To assess the repeatability of quantitative multiparametric whole-body MRI (mpWB-MRI) parameters in advanced prostate cancer (APC) bone metastases. METHODS: 1.5T MRI was performed twice on the same day in 10 APC patients. MpWB-MRI-included diffusion weighted imaging (DWI) and T1-weighted gradient-echo 2-point Dixon sequences. ADC and relative fat-fraction percentage (rFF%) maps were calculated, respectively. A radiologist delineated up to 10 target bone metastases per study. Means of ADC, b900 signal intensity(SI), normalised b900 SI, rFF% and maximum diameter (MD) for each target lesion and overall parameter averages across all targets per patient were recorded. The total disease volume (tDV in ml) was manually delineated on b900 images and mean global (g)ADC was derived. Bland-Altman analyses were performed with calculation of 95% repeatability coefficients (RC). RESULTS: Seventy-three individual targets (median MD 26 mm) were included. Lesion mean ADC RC was 12.5%, mean b900 SI RC 137%, normalised mean b900 SI RC 110%, rFF% RC 3.2 and target MD RC 5.5 mm (16.3%). Patient target lesion average mean ADC RC was 6.4%, b900 SI RC 104% and normalised mean b900 SI RC 39.6%. Target average rFF% RC was 1.8, average MD RC 1.3 mm (4.8%). tDV segmentation RC was 6.4% and mean gADC RC 5.3%. CONCLUSIONS: APC bone metastases' ADC, rFF% and maximum diameter, tDV and gADC show good repeatability. ADVANCES IN KNOWLEDGE: APC bone metastases' mean ADC and rFF% measurements of single lesions and global disease volumes are repeatable, supporting their potential role as quantitative biomarkers in metastatic bone disease

Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imaging.

T2-weighted magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) are essential components of cervical cancer diagnosis. However, combining these channels for the training of deep learning models is challenging due to image misalignment. Here, we propose a novel multi-head framework that uses dilated convolutions and shared residual connections for the separate encoding of multiparametric MRI images. We employ a residual U-Net model as a baseline, and perform a series of architectural experiments to evaluate the tumor segmentation performance based on multiparametric input channels and different feature encoding configurations. All experiments were performed on a cohort of 207 patients with locally advanced cervical cancer. Our proposed multi-head model using separate dilated encoding for T2W MRI and combined b1000 DWI and apparent diffusion coefficient (ADC) maps achieved the best median Dice similarity coefficient (DSC) score, 0.823 (confidence interval (CI), 0.595-0.797), outperforming the conventional multi-channel model, DSC 0.788 (95% CI, 0.568-0.776), although the difference was not statistically significant (p > 0.05). We investigated channel sensitivity using 3D GRAD-CAM and channel dropout, and highlighted the critical importance of T2W and ADC channels for accurate tumor segmentation. However, our results showed that b1000 DWI had a minor impact on the overall segmentation performance. We demonstrated that the use of separate dilated feature extractors and independent contextual learning improved the model's ability to reduce the boundary effects and distortion of DWI, leading to improved segmentation performance. Our findings could have significant implications for the development of robust and generalizable models that can extend to other multi-modal segmentation applications