Reducing the size and number of linear programs in a dynamic Gr\"obner basis algorithm

The dynamic algorithm to compute a Gr\"obner basis is nearly twenty years old, yet it seems to have arrived stillborn; aside from two initial publications, there have been no published followups. One reason for this may be that, at first glance, the added overhead seems to outweigh the benefit; the algorithm must solve many linear programs with many linear constraints. This paper describes two methods of reducing the cost substantially, answering the problem effectively.Comment: 11 figures, of which half are algorithms; submitted to journal for refereeing, December 201

The Importance of Perceived Relevance: A Qualitative Evaluation of Patient’s Perceptions of Value and Impact Following a Low-Intensity Group-Based Pain Management Program

Background and objectives: Limited evidence exists exploring perceptions of which aspects of a pain management program are perceived as valuable and impactful. The aim of this study was to explore patient beliefs about which aspects of a pain management program were valued and/or had perceived impact. Materials and Methods: One-on-one structured interviews were conducted with 11 adults three months after their completion of the Spark Pain Program at Westmead Hospital, Sydney, Australia. Concepts in the transcripts were inductively identified and explored, utilizing thematic analysis to better understand their relevance to the study aim. Results: Four themes emerged: (1) “The program overall was positive, but…”; (2) “I valued my improved knowledge and understanding of pain, but…”; (3) “I valued the stretching/relaxation/pacing/activity monitoring”; and (4) “I valued being part of a supportive and understanding group”. Participants reported that they liked being treated as an individual within the group. A lack of perceived personal relevance of key messages was identified in some participants; it appears that patients in pain programs must determine that changes in knowledge, beliefs, and attitudes are personally relevant in order for the changes to have a significant impact on them. Conclusions: This study provides new insights into aspects of a pain management program that were perceived as valuable and impactful, areas that “missed the mark”, and hypotheses to guide the implementation of service delivery and program redesig

Quality of life and visual function in Nigeria: findings from the National Survey of Blindness and Visual Impairment

Aims To assess associations of visual function (VF) and quality of life (QOL) by visual acuity (VA), causes of blindness and types of cataract procedures in Nigeria. Methods Multi-stage stratified cluster random sampling was used to identify a nationally representative sample of persons aged >= 40 years. VF/QOL questionnaires were administered to participants with VA = 6/12. Results VF/QOL questionnaires were administered to 2076 participants. Spearman's rank correlation showed a strong correlation between decreasing VA and VF/QOL scores (p< 0.0001) with greatest impact on social (p< 0.0001) and mobility-related activities (p< 0.0001). People who were blind due to glaucoma had lower VF and QOL scores than those who were blind due to cataract. Mean VF and QOL scores were lower after couching compared with conventional cataract surgery (mean VF score=51.0 vs 63.0 and mean QOL score=71.3 vs 79.3). Finally, VF and QOL scores were lower among populations with specific characteristics. Conclusions Populations with the following characteristics should be targeted to improve VF and QOL: people who are blind, older people, women, manual labourers, people living in rural areas, those living in the northern geopolitical zones, those practising Islamic and Traditionalism faith, those not currently married and those who have undergone couching

Versatile method for template-free synthesis of single crystalline metal and metal alloy nanowires

© 2016 The Royal Society of Chemistry. Metal and metal alloy nanowires have applications ranging from spintronics to drug delivery, but high quality, high density single crystalline materials have been surprisingly difficult to fabricate. Here we report a versatile, template-free, self-assembly method for fabrication of single crystalline metal and metal alloy nanowires (Co, Ni, NiCo, CoFe, and NiFe) by reduction of metal nitride precursors formed in situ by reaction of metal salts with a nitrogen source. Thiol reduction of the metal nitrides to the metallic phase at 550-600 °C results in nanowire growth. In this process, sulfur acts as a uniaxial structure-directing agent, passivating the surface of the growing nanowires and preventing radial growth. The versatility of the method is demonstrated by achieving nanowire growth from gas-phase, solution-phase or a combination of gas- and solution-phase precursors. The fabrication method is suited to large-area CVD on a wide range of solid substrates

Development of a life expectancy table for individuals with type 1 diabetes

Aims/hypothesis:Tables reporting life expectancies by common risk factors are available for individuals with type 2 diabetes; however, there is currently no published equivalent for individuals with type 1 diabetes. We aimed to develop a life expectancy table using a recently published simulation model for individuals with type 1 diabetes.Methods: The simulation model was developed using data from a real-world population of patients with type 1 diabetes selected from the Swedish National Diabetes Register. The following six important risk factors were included in the life table: sex; age; current smoking status; BMI; eGFR; and HbA1c. For each of 1024 cells in the life expectancy table, a synthetic cohort containing 1000 individuals was created, with other risk factors assigned values representative of the real-world population. The simulations were executed for all synthetic cohorts and life expectancy for each cell was calculated as mean survival time of the individuals in the respective cohort.Results: There was a substantial variation in life expectancy across patients with different risk factor levels. Life expectancy of 20-year-old men varied from 29.3 years to 50.6 years, constituting a gap of 21.3 years between those with worst and best risk factor levels. In 20-year-old women, this gap was 18.9 years (life expectancy range 35.0-53.9 years). The variation in life expectancy was a function of the combination of risk factor values, with HbA1c and eGFR consistently showing a negative and positive correlation, respectively, with life expectancy at any level combination of other risk factors. Individuals with the lowest level (20 kg/m2) and highest level of BMI (35 kg/m2) had a lower life expectancy compared with those with a BMI of 25 kg/m2. Non-smokers and women had a higher life expectancy than smokers and men, respectively, with the difference in life expectancy ranging from 0.4 years to 2.7 years between non-smokers and smokers, and from 1.9 years to 5.9 years between women and men, depending on levels of other risk factors.Conclusions/interpretation: The life expectancy table generated in this study shows a substantial variation in life expectancy across individuals with different modifiable risk factors. The table allows for rapid communications of risk in an easily understood format between healthcare professionals, health economists, researchers, policy makers and patients. Particularly, it supports clinicians in their discussion with patients about the benefits of improving risk factors

Annelid functional genomics reveal the origins of bilaterian life cycles.

Indirect development with an intermediate larva exists in all major animal lineages1, which makes larvae central to most scenarios of animal evolution2-11. Yet how larvae evolved remains disputed. Here we show that temporal shifts (that is, heterochronies) in trunk formation underpin the diversification of larvae and bilaterian life cycles. We performed chromosome-scale genome sequencing in the annelid Owenia fusiformis with transcriptomic and epigenomic profiling during the life cycles of this and two other annelids. We found that trunk development is deferred to pre-metamorphic stages in the feeding larva of O. fusiformis but starts after gastrulation in the non-feeding larva with gradual metamorphosis of Capitella teleta and the direct developing embryo of Dimorphilus gyrociliatus. Accordingly, the embryos of O. fusiformis develop first into an enlarged anterior domain that forms larval tissues and the adult head12. Notably, this also occurs in the so-called 'head larvae' of other bilaterians13-17, with which the O. fusiformis larva shows extensive transcriptomic similarities. Together, our findings suggest that the temporal decoupling of head and trunk formation, as maximally observed in head larvae, facilitated larval evolution in Bilateria. This diverges from prevailing scenarios that propose either co-option9,10 or innovation11 of gene regulatory programmes to explain larva and adult origins

Primary cilia elongation in response to interleukin-1 mediates the inflammatory response

Primary cilia are singular, cytoskeletal organelles present in the majority of mammalian cell types where they function as coordinating centres for mechanotransduction, Wnt and hedgehog signalling. The length of the primary cilium is proposed to modulate cilia function, governed in part by the activity of intraflagellar transport (IFT). In articular cartilage, primary cilia length is increased and hedgehog signaling activated in osteoarthritis (OA). Here, we examine primary cilia length with exposure to the quintessential inflammatory cytokine interleukin-1 (IL-1), which is up-regulated in OA. We then test the hypothesis that the cilium is involved in mediating the downstream inflammatory response. Primary chondrocytes treated with IL-1 exhibited a 50 % increase in cilia length after 3 h exposure. IL-1-induced cilia elongation was also observed in human fibroblasts. In chondrocytes, this elongation occurred via a protein kinase A (PKA)-dependent mechanism. G-protein coupled adenylate cyclase also regulated the length of chondrocyte primary cilia but not downstream of IL-1. Chondrocytes treated with IL-1 exhibit a characteristic increase in the release of the inflammatory chemokines, nitric oxide and prostaglandin E2. However, in cells with a mutation in IFT88 whereby the cilia structure is lost, this response to IL-1 was significantly attenuated and, in the case of nitric oxide, completely abolished. Inhibition of IL-1-induced cilia elongation by PKA inhibition also attenuated the chemokine response. These results suggest that cilia assembly regulates the response to inflammatory cytokines. Therefore, the cilia proteome may provide a novel therapeutic target for the treatment of inflammatory pathologies, including OA

Multiple Imputation Ensembles (MIE) for dealing with missing data

Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation Ensembles (MIE). Our method integrates two approaches: multiple imputation and ensemble methods and compares two types of ensembles: bagging and stacking. We also propose a robust experimental set-up using 20 benchmark datasets from the UCI machine learning repository. For each dataset, we introduce increasing amounts of data Missing Completely at Random. Firstly, we use a number of single/multiple imputation methods to recover the missing values and then ensemble a number of different classifiers built on the imputed data. We assess the quality of the imputation by using dissimilarity measures. We also evaluate the MIE performance by comparing classification accuracy on the complete and imputed data. Furthermore, we use the accuracy of simple imputation as a benchmark for comparison. We find that our proposed approach combining multiple imputation with ensemble techniques outperform others, particularly as missing data increases

Deep Learning Combined with De - noising Data for Network Intrusion Detection

Anomaly-based Network Intrusion Detection Systems (NIDSs) are a common security defense for modern networks. The success of their operation depends upon vast quantities of training data. However, one major limitation is the inability of NIDS to be reliably trained using imbalanced datasets. Network observations are naturally imbalanced, yet without substantial data pre-processing, NIDS accuracy can be significantly reduced. With the diversity and dynamicity of modern network traffic, there are concerns that the current reliance upon un-natural balanced datasets cannot remain feasible in modern networks. This paper details our de-noising method, which when combined with deep learning techniques can address these concerns and offer accuracy improvements of between 1.5% and 4.5%. Promising results have been obtained from our model thus far, demonstrating improvements over existing approaches and the strong potential for use in modern NIDSs