Virus Propagation in Multiple Profile Networks

Suppose we have a virus or one competing idea/product that propagates over a multiple profile (e.g., social) network. Can we predict what proportion of the network will actually get "infected" (e.g., spread the idea or buy the competing product), when the nodes of the network appear to have different sensitivity based on their profile? For example, if there are two profiles $\mathcal{A}$ and $\mathcal{B}$ in a network and the nodes of profile $\mathcal{A}$ and profile $\mathcal{B}$ are susceptible to a highly spreading virus with probabilities $\beta_{\mathcal{A}}$ and $\beta_{\mathcal{B}}$ respectively, what percentage of both profiles will actually get infected from the virus at the end? To reverse the question, what are the necessary conditions so that a predefined percentage of the network is infected? We assume that nodes of different profiles can infect one another and we prove that under realistic conditions, apart from the weak profile (great sensitivity), the stronger profile (low sensitivity) will get infected as well. First, we focus on cliques with the goal to provide exact theoretical results as well as to get some intuition as to how a virus affects such a multiple profile network. Then, we move to the theoretical analysis of arbitrary networks. We provide bounds on certain properties of the network based on the probabilities of infection of each node in it when it reaches the steady state. Finally, we provide extensive experimental results that verify our theoretical results and at the same time provide more insight on the problem

Phase-contrast imaging using radiation sources based on laser-plasma wakefield accelerators : state of the art and future development

Both the laser-plasma wakefield accelerator (LWFA) and X-ray phase-contrast imaging (XPCi) are promising technologies that are attracting the attention of the scientific community. Conventional X-ray absorption imaging cannot be used as a means of imaging biological material because of low contrast. XPCi overcomes this limitation by exploiting the variation of the refraction index of materials. The contrast obtained is higher than for conventional absorption imaging and requires a lower dose. The LWFA is a new concept of acceleration where electrons are accelerated to very high energy (~150 MeV) in very short distances (mm scale) by surfing plasma waves excited by the passage of an ultra-intense laser pulse (~1018 Wcm-2) through plasma. Electrons in the LWFA can undergo transverse oscillation and emit synchrotron-like (betatron) radiation in a narrow cone around the propagation axis. The properties of the betatron radiation produced by LWFA, such as source size and spectrum, make it an excellent candidate for XPCi. In this work we present the characterization of betatron radiation produced by the LWFA in the ALPHA-X laboratory (University of Strathclyde). We show how phase contrast images can be obtained using the betatron radiation in a free-space propagation configuration and we discuss the potential and limitation of the LWFA driven XPCi

Financial diversification before modern portfolio theory: UK financial advice documents in the late nineteenth and the beginning of the twentieth century

The paper offers textual evidence from a series of financial advice documents in the late nineteenth century and the early twentieth century of how UK investors perceived of and managed risk. In the world’s largest financial centre of the time, UK investors were familiar with the concept of correlation and financial advisers’ suggestions were consistent with the recommendations of modern portfolio theory in relation to portfolio selection strategies. From the 1870s, there was an increased awareness of the benefits of financial diversification - primarily putting equal amounts into a number of different securities - with much of the emphasis being on geographical rather than sectoral diversification and some discussion of avoiding highly correlated investments. Investors in the past were not so naïve as mainstream financial discussions suggest today

Lack of genetic structure and evidence for long-distance dispersal in ash (Fraxinus excelsior) populations under threat from an emergent fungal pathogen:Implications for restorative planting

Genetic analysis on populations of European ash (Fraxinus excelsior) throughout Ireland was carried out to determine the levels and patterns of genetic diversity in naturally seeded trees in ash woodlands and hedgerows, with the aim of informing conservation and replanting strategies in the face of potential loss of trees as a result of ash dieback. Samples from 33 sites across Northern Ireland and three sites in the Republic of Ireland were genotyped for eight nuclear and ten chloroplast microsatellites. Levels of diversity were (h)igh (mean A(R)=10.53; mean H-O=0.709; mean H-E=0.765) and were similar to those in Great Britain and continental Europe, whilst levels of population genetic differentiation based on nuclear microsatellites were extremely low ((Phi) over bar (ST)=0.0131). Levels of inbreeding (mean F-IS=0.067) were significantly lower than those reported for populations from Great Britain. Fine-scale analysis of seed dispersal indicated potential for dispersal over hundreds of metres. Our results suggest that ash woodlands across Ireland could be treated as a single management unit, and thus native material from anywhere in Ireland could be used as a source for replanting. In addition, high potential for dispersal has implications for recolonization processes post-ash dieback (Chalara fraxinea) infection, and could aid in our assessment of the capacity of ash to shift its range in response to global climate change.authorsversionPeer reviewe

A Comparison between the Compass Fundus Perimeter and the Humphrey Field Analyzer

Purpose: To evaluate relative diagnostic precision and test–retest variability of 2 devices, the Compass (CMP, CenterVue, Padova, Italy) fundus perimeter and the Humphrey Field Analyzer (HFA, Zeiss, Dublin, CA), in detecting glaucomatous optic neuropathy (GON). Design: Multicenter, cross-sectional, case-control study. Participants: We sequentially enrolled 499 patients with glaucoma and 444 normal subjects to analyze relative precision. A separate group of 44 patients with glaucoma and 54 normal subjects was analyzed to assess test–retest variability. Methods: One eye of recruited subjects was tested with the index tests: HFA (Swedish interactive thresholding algorithm [SITA] standard strategy) and CMP (Zippy Estimation by Sequential Testing [ZEST] strategy), 24-2 grid. The reference test for GON was specialist evaluation of fundus photographs or OCT, independent of the visual field (VF). For both devices, linear regression was used to calculate the sensitivity decrease with age in the normal group to compute pointwise total deviation (TD) values and mean deviation (MD). We derived 5% and 1% pointwise normative limits. The MD and the total number of TD values below 5% (TD 5%) or 1% (TD 1%) limits per field were used as classifiers. Main Outcome Measures: We used partial receiver operating characteristic (pROC) curves and partial area under the curve (pAUC) to compare the diagnostic precision of the devices. Pointwise mean absolute deviation and Bland–Altman plots for the mean sensitivity (MS) were computed to assess test–retest variability. Results: Retinal sensitivity was generally lower with CMP, with an average mean difference of 1.85±0.06 decibels (dB) (mean ± standard error, P < 0.001) in healthy subjects and 1.46±0.05 dB (mean ± standard error, P < 0.001) in patients with glaucoma. Both devices showed similar discriminative power. The MD metric had marginally better discrimination with CMP (pAUC difference ± standard error, 0.019±0.009, P = 0.035). The 95% limits of agreement for the MS were reduced by 13% in CMP compared with HFA in participants with glaucoma and by 49% in normal participants. Mean absolute deviation was similar, with no significant differences. Conclusions: Relative diagnostic precision of the 2 devices is equivalent. Test–retest variability of MS for CMP was better than for HFA

Detecting glaucoma from multi-modal data using probabilistic deep learning

Objective: To assess the accuracy of probabilistic deep learning models to discriminate normal eyes and eyes with glaucoma from fundus photographs and visual fields. Design: Algorithm development for discriminating normal and glaucoma eyes using data from multicenter, cross-sectional, case-control study. Subjects and participants: Fundus photograph and visual field data from 1,655 eyes of 929 normal and glaucoma subjects to develop and test deep learning models and an independent group of 196 eyes of 98 normal and glaucoma patients to validate deep learning models. Main outcome measures: Accuracy and area under the receiver-operating characteristic curve (AUC). Methods: Fundus photographs and OCT images were carefully examined by clinicians to identify glaucomatous optic neuropathy (GON). When GON was detected by the reader, the finding was further evaluated by another clinician. Three probabilistic deep convolutional neural network (CNN) models were developed using 1,655 fundus photographs, 1,655 visual fields, and 1,655 pairs of fundus photographs and visual fields collected from Compass instruments. Deep learning models were trained and tested using 80% of fundus photographs and visual fields for training set and 20% of the data for testing set. Models were further validated using an independent validation dataset. The performance of the probabilistic deep learning model was compared with that of the corresponding deterministic CNN model. Results: The AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and combined modalities using development dataset were 0.90 (95% confidence interval: 0.89–0.92), 0.89 (0.88–0.91), and 0.94 (0.92–0.96), respectively. The AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and both modalities using the independent validation dataset were 0.94 (0.92–0.95), 0.98 (0.98–0.99), and 0.98 (0.98–0.99), respectively. The AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and both modalities using an early glaucoma subset were 0.90 (0.88,0.91), 0.74 (0.73,0.75), 0.91 (0.89,0.93), respectively. Eyes that were misclassified had significantly higher uncertainty in likelihood of diagnosis compared to eyes that were classified correctly. The uncertainty level of the correctly classified eyes is much lower in the combined model compared to the model based on visual fields only. The AUCs of the deterministic CNN model using fundus images, visual field, and combined modalities based on the development dataset were 0.87 (0.85,0.90), 0.88 (0.84,0.91), and 0.91 (0.89,0.94), and the AUCs based on the independent validation dataset were 0.91 (0.89,0.93), 0.97 (0.95,0.99), and 0.97 (0.96,0.99), respectively, while the AUCs based on an early glaucoma subset were 0.88 (0.86,0.91), 0.75 (0.73,0.77), and 0.92 (0.89,0.95), respectively. Conclusion and relevance: Probabilistic deep learning models can detect glaucoma from multi-modal data with high accuracy. Our findings suggest that models based on combined visual field and fundus photograph modalities detects glaucoma with higher accuracy. While probabilistic and deterministic CNN models provided similar performance, probabilistic models generate certainty level of the outcome thus providing another level of confidence in decision making

Pharmacoeconomic analysis of adjuvant oral capecitabine vs intravenous 5-FU/LV in Dukes' C colon cancer: the X-ACT trial

Oral capecitabine (Xeloda&lt;sup&gt;&#174;&lt;/sup&gt;) is an effective drug with favourable safety in adjuvant and metastatic colorectal cancer. Oxaliplatin-based therapy is becoming standard for Dukes' C colon cancer in patients suitable for combination therapy, but is not yet approved by the UK National Institute for Health and Clinical Excellence (NICE) in the adjuvant setting. Adjuvant capecitabine is at least as effective as 5-fluorouracil/leucovorin (5-FU/LV), with significant superiority in relapse-free survival and a trend towards improved disease-free and overall survival. We assessed the cost-effectiveness of adjuvant capecitabine from payer (UK National Health Service (NHS)) and societal perspectives. We used clinical trial data and published sources to estimate incremental direct and societal costs and gains in quality-adjusted life months (QALMs). Acquisition costs were higher for capecitabine than 5-FU/LV, but higher 5-FU/LV administration costs resulted in 57% lower chemotherapy costs for capecitabine. Capecitabine vs 5-FU/LV-associated adverse events required fewer medications and hospitalisations (cost savings £3653). Societal costs, including patient travel/time costs, were reduced by &gt;75% with capecitabine vs 5-FU/LV (cost savings £1318), with lifetime gain in QALMs of 9 months. Medical resource utilisation is significantly decreased with capecitabine vs 5-FU/LV, with cost savings to the NHS and society. Capecitabine is also projected to increase life expectancy vs 5-FU/LV. Cost savings and better outcomes make capecitabine a preferred adjuvant therapy for Dukes' C colon cancer. This pharmacoeconomic analysis strongly supports replacing 5-FU/LV with capecitabine in the adjuvant treatment of colon cancer in the UK