Discrete Conditional Phase-type model (DC_Ph) for patient waiting time with a logistic regression component to predict patient admission to hospital

Discrete Conditional Phase-type (DC-Ph) models are a family of models which represent skewed survival data conditioned on specific inter-related discrete variables. The survival data is modeled using a Coxian phase-type distribution which is associated with the inter-related variables using a range of possible data mining approaches such as Bayesian networks (BNs), the Naïve Bayes Classification method and classification regression trees. This paper utilizes the Discrete Conditional Phase-type model (DC-Ph) to explore the modeling of patient waiting times in an Accident and Emergency Department of a UK hospital. The resulting DC-Ph model takes on the form of the Coxian phase-type distribution conditioned on the outcome of a logistic regression model.

Hardware elliptic curve cryptographic processor over GF(p)

A novel hardware architecture for elliptic curve cryptography (ECC) over GF(p) is introduced. This can perform the main prime field arithmetic functions needed in these cryptosystems including modular inversion and multiplication. This is based on a new unified modular inversion algorithm that offers considerable improvement over previous ECC techniques that use Fermat's Little Theorem for this operation. The processor described uses a full-word multiplier which requires much fewer clock cycles than previous methods, while still maintaining a competitive critical path delay. The benefits of the approach have been demonstrated by utilizing these techniques to create a field-programmable gate array (FPGA) design. This can perform a 256-bit prime field scalar point multiplication in 3.86 ms, the fastest FPGA time reported to date. The ECC architecture described can also perform four different types of modular inversion, making it suitable for use in many different ECC applications. © 2006 IEEE.

First-Order Reversal Curve Probing of Spatially Resolved Polarization Switching Dynamics in Ferroelectric Nanocapacitors

Spatially resolved polarization switching In ferroelectric nanocapacitors was studied on the sub-25 nm scale using the first-order reversal curve (FORC) method. The chosen capacitor geometry allows both high-veracity observation of the domain structure and mapping of polarization switching in a uniform field, synergistically combining microstructural observations and probing of uniform-field polarization responses as relevant to device operation. A classical Kolmogorov-Avrami-Ishibashi model has been adapted to the voltage domain, and the individual switching dynamics of the FORC response curves are well approximated by the adapted model. The comparison with microstructures suggests a strong spatial variability of the switching dynamics inside the nanocapacitors.

The role of angiogenic and antiangiogenic factors in the second trimester in the prediction of preeclampsia in pregnant women with type 1 diabetes

OBJECTIVE To assess the association between circulating angiogenic and antiangiogenic factors in the second trimester and risk of preeclampsia in women with type 1 diabetes.

RESEARCH DESIGN AND METHODS Maternal plasma concentrations of placental growth factor (PlGF), soluble fms-like tyrosine kinase 1 (sFlt-1), and soluble endoglin (sEng) were available at 26 weeks of gestation in 540 women with type 1 diabetes enrolled in the Diabetes and Preeclampsia Intervention Trial.

RESULTS Preeclampsia developed in 17% of pregnancies (n = 94). At 26 weeks of gestation, women in whom preeclampsia developed later had significantly lower PlGF (median [interquartile range]: 231 pg/mL [120–423] vs. 365 pg/mL [237–582]; P < 0.001), higher sFlt-1 (1,522 pg/mL [1,108–3,393] vs. 1,193 pg/mL [844–1,630] P < 0.001), and higher sEng (6.2 ng/mL [4.9–7.9] vs. 5.1 ng/mL[(4.3–6.2]; P < 0.001) compared with women who did not have preeclampsia. In addition, the ratio of PlGF to sEng was significantly lower (40 [17–71] vs. 71 [44–114]; P < 0.001) and the ratio of sFlt-1 to PlGF was significantly higher (6.3 [3.4–15.7] vs. 3.1 [1.8–5.8]; P < 0.001) in women who later developed preeclampsia. The addition of the ratio of PlGF to sEng or the ratio of sFlt-1 to PlGF to a logistic model containing established risk factors (area under the curve [AUC], 0.813) significantly improved the predictive value (AUC, 0.850 and 0.846, respectively; P < 0.01) and significantly improved reclassification according to the integrated discrimination improvement index (IDI) (IDI scores 0.086 and 0.065, respectively; P < 0.001).

CONCLUSIONS These data suggest that angiogenic and antiangiogenic factors measured during the second trimester are predictive of preeclampsia in women with type 1 diabetes. The addition of the ratio of PlGF to sEng or the ratio of sFlt-1 to PlGF to established clinical risk factors significantly improves the prediction of preeclampsia in women with type 1 diabetes.

Preeclampsia is characterized by the development of hypertension and new-onset proteinuria during the second half of pregnancy (1,2), leading to increased maternal morbidity and mortality (3). Women with type 1 diabetes are at increased risk for development of preeclampsia during pregnancy, with rates being two-times to four-times higher than that of the background maternity population (4,5). Small advances have come from preventive measures, such as low-dose aspirin in women at high risk (6); however, delivery remains the only effective intervention, and preeclampsia is responsible for up to 15% of preterm births and a consequent increase in infant mortality and morbidity (7).

Although the etiology of preeclampsia remains unclear, abnormal placental vascular remodeling and placental ischemia, together with maternal endothelial dysfunction, hemodynamic changes, and renal pathology, contribute to its pathogenesis (8). In addition, over the past decade accumulating evidence has suggested that an imbalance between angiogenic factors, such as placental growth factor (PlGF), and antiangiogenic factors, such as soluble fms-like tyrosine kinase 1 (sFlt-1) and soluble endoglin (sEng), plays a key role in the pathogenesis of preeclampsia (8,9). In women at low risk (10–13) and women at high risk (14,15), concentrations of angiogenic and antiangiogenic factors are significantly different between women who later develop preeclampsia (lower PlGF, higher sFlt-1, and higher sEng levels) compared with women who do not.

Few studies have specifically focused on circulating angiogenic factors and risk of preeclampsia in women with diabetes, and the results have been conflicting. In a small study, higher sFlt-1 and lower PlGF were reported at the time of delivery in women with diabetes who developed preeclampsia (16). In a longitudinal prospective cohort of pregnant women with diabetes, Yu et al. (17) reported increased sFlt-1 and reduced PlGF in the early third trimester as potential predictors of preeclampsia in women with type 1 diabetes, but they did not show any difference in sEng levels in women with preeclampsia compared with women without preeclampsia. By contrast, Powers et al. (18) reported only increased sEng in the second trimester in women with pregestational diabetes who developed preeclampsia.

The aim of this study, which was significantly larger than the previous studies highlighted, was to assess the association between circulating angiogenic (PlGF) and antiangiogenic (sFlt-1 and sEng) factors and the risk of preeclampsia in women with type 1 diabetes. A further aim was to evaluate the added predictive ability and clinical usefulness of angiogenic factors and established risk factors for preeclampsia risk prediction in women with type 1 diabetes.

Capability Curve Based Enhanced Reactive Power Control Strategy for Stability Enhancement and Network Voltage Management

Reactive power has become a vital resource in modern electricity networks due to increased penetration of distributed generation. This paper examines the extended reactive power capability of DFIGs to improve network stability and capability to manage network voltage profile during transient faults and dynamic operating conditions. A coordinated reactive power controller is designed by considering the reactive power capabilities of the rotor-side converter (RSC) and the grid-side converter (GSC) of the DFIG in order to maximise the reactive power support from DFIGs. The study has illustrated that, a significant reactive power contribution can be obtained from partially loaded DFIG wind farms for stability enhancement by using the proposed capability curve based reactive power controller; hence DFIG wind farms can function as vital dynamic reactive power resources for power utilities without commissioning additional dynamic reactive power devices. Several network adaptive droop control schemes are also proposed for network voltage management and their performance has been investigated during variable wind conditions. Furthermore, the influence of reactive power capability on network adaptive droop control strategies has been investigated and it has also been shown that enhanced reactive power capability of DFIGs can substantially improve the voltage control performance.

A New Principal Curve Algorithm for Nonlinear Principal Component Analysis

This paper emerged from work supported by EPSRC grant GR/S84354/01 and proposes a method of determining principal curves, using spline functions, in principal component analysis (PCA) for the representation of non-linear behaviour in process monitoring. Although principal curves are well established, they are difficult to implement in practice if a large number of variables are analysed. The significant contribution of this paper is that the proposed method has minimal complexity, assuming simple spline geometry, thus enabling efficient computation. The paper provides a foundation for further work where multiple curves may be required to represent underlying non-linear information in complex data.

Asynchrony between the Greenland ice core chronology and the radiocarbon calibration curve? Evidence from Irish bogs

A significant cold event, deduced from the Greenland ice cores, took place between 8200 and 8000 cal. BP. Modeling of the event suggests that higher northern latitudes would have also experienced considerable decreases in precipitation and that Ireland would have witnessed one of the greatest depressions. However, no well-dated proxy record exists from the British Isles to test the model results. Here we present independent evidence for a phase of major pine recruitment on Irish bogs at around 8150 cal. BP. Dendrochronological dating of subfossil trees from three sites reveal synchronicity in germination across the region, indicative of a regional forcing, and allows for high-precision radiocarbon based dating. The inner-rings of 40% of all samples from the north of Ireland dating to the period 8500-7500 cal. BP fall within a 25-yr window. The concurrent colonization of pine on peatland is interpreted as drier conditions in the region and provides the first substantive proxy data in support of a significant hydrological change in the north of Ireland accompanying the 8.2 ka event. The dating uncertainties associated with the Irish pine record and the Greenland Ice Core Chronology 2005 (GICC05) do not allow for any overlap between the two. Our results indicate that the discrepancy could be an artifact of dating inaccuracy, and support a similar claim by Lohne et al. (2013) for the Younger Dryas boundaries. If real, this asynchrony will most likely have affected interpretations of previous proxy alignments.

Multiple Biomarkers for the prediction of ischemic stroke: the PRIME study

Objective: To simultaneously evaluate 14 biomarkers from distinct biological pathways for risk prediction of ischemic stroke, including biomarkers of hemostasis, inflammation, and endothelial activation as well as chemokines and adipocytokines.
Methods and Results: The Prospective Epidemiological Study on Myocardial Infarction (PRIME) is a cohort of 9771 healthy men 50 to 59 years of age who were followed up over 10 years. In a nested case–control study, 95 ischemic stroke cases were matched with 190 controls. After multivariable adjustment for traditional risk factors, fibrinogen (odds ratio [OR], 1.53; 95% confidence interval [CI], 1.03–2.28), E-selectin (OR, 1.76; 95% CI, 1.06–2.93), interferon-γ-inducible-protein-10 (OR, 1.72; 95% CI, 1.06–2.78), resistin (OR, 2.86; 95% CI, 1.30–6.27), and total adiponectin (OR, 1.82; 95% CI, 1.04–3.19) were significantly associated with ischemic stroke. Adding E-selectin and resistin to a traditional risk factor model significantly increased the area under the receiver-operating characteristic curve from 0.679 (95% CI, 0.612–0.745) to 0.785 and 0.788, respectively, and yielded a categorical net reclassification improvement of 29.9% (P=0.001) and 28.4% (P=0.002), respectively. Their simultaneous inclusion in the traditional risk factor model increased the area under the receiver-operating characteristic curve to 0.824 (95% CI, 0.770–0.877) and resulted in an net reclassification improvement of 41.4% (P<0.001). Results were confirmed when using continuous net reclassification improvement.
Conclusion: Among multiple biomarkers from distinct biological pathways, E-selectin and resistin provided incremental and additive value to traditional risk factors in predicting ischemic stroke.

Risk minimization in logistic processes with oil products

13.Vidovic M., Miljus M., Vlajic J., (2002), "Risk minimization in logistic processes with oil products", Proceedings of the 6th International Conference on Traffic Science, ICTS 2002, Portorož, Slovenia, pp. 568-577;

Antimicrobial peptides and proinflammatory cytokines in periprosthetic joint infection

Background: Differentiation between septic and aseptic loosening of joint replacements is essential for successful revision surgery, but reliable markers for the diagnosis of low-grade infection are lacking. The present study was performed to assess intra-articular and systemic levels of antimicrobial peptides and proinflammatory cytokines as diagnostic markers for periprosthetic joint infection. Methods: Fifteen consecutive patients with staphylococcal periprosthetic joint infections and twenty control patients with aseptic loosening of total hip and knee replacements were included in this prospective, single-center, controlled clinical trial. Expression of the antimicrobial peptides human β-defensin-2 (HBD-2), human β-defensin-3 (HBD-3), and cathelicidin LL-37 (LL-37) was determined by ELISA (enzyme-linked immunosorbent assay) in serum and joint aspirates. Proinflammatory cytokines were assessed in serum and joint aspirates with use of cytometric bead arrays. C-reactive protein in serum, microbiology, and histopathology of periprosthetic tissue served as the “gold standard” for the diagnosis of infection. Results: The antimicrobial peptides HBD-3 and LL-37 were significantly elevated in joint aspirates from patients with periprosthetic joint infection compared with patients with aseptic loosening, and the area under the curve (AUC) in a receiver operating characteristic curve analysis was equal to 0.745 and 0.875, respectively. Additionally, significant local increases in the proinflammatory cytokines interleukin (IL)-1β, IL-4, IL-6, IL-17A, interferon (IFN)-γ, and tumor necrosis factor (TNF)-α were observed to be associated with infection. Logistic regression analysis indicated that the combination of an antimicrobial peptide with another synovial fluid biomarker improved diagnostic accuracy; the AUC value was 0.916 for LL-37 and IL-4, 0.895 for LL-37 and IL-6, 0.972 for HBD-3 and IL-4, and 0.849 for HBD-3 and IL-6. In contrast, the only antimicrobial peptides and cytokines in serum that showed a significant systemic increase in association with infection were HBD-2, IL-4, and IL-6 (all of which had an AUC value of <0.75). Conclusions: The present study showed promising results for the use of antimicrobial peptides and other biomarkers in synovial fluid for the diagnosis of periprosthetic joint infection, and analysis of the levels in synovial fluid was more accurate than analysis of serum.

Rapid single-nucleotide polymorphism-based identification of clonal Pseudomonas aeruginosa isolates from patients with cystic fibrosis by the use of real-time PCR and high-resolution melting curve analysis

Pseudomonas aeruginosa genotyping relies mainly upon DNA fingerprinting methods, which can be subjective, expensive and time-consuming. The detection of at least three different clonal P. aeruginosa strains in patients attending two cystic fibrosis (CF) centres in a single Australian city prompted the design of a non-gel-based PCR method to enable clinical microbiology laboratories to readily identify these clonal strains. We designed a detection method utilizing heat-denatured P. aeruginosa isolates and a ten-single-nucleotide polymorphism (SNP) profile. Strain differences were detected by SYBR Green-based real-time PCR and high-resolution melting curve analysis (HRM10SNP assay). Overall, 106 P. aeruginosa sputum isolates collected from 74 patients with CF, as well as five reference strains, were analysed with the HRM10SNP assay, and the results were compared with those obtained by pulsed-field gel electrophoresis (PFGE). The HRM10SNP assay accurately identified all 45 isolates as members of one of the three major clonal strains characterized by PFGE in two Brisbane CF centres (Australian epidemic strain-1, Australian epidemic strain-2 and P42) from 61 other P. aeruginosa strains from Australian CF patients and two representative overseas epidemic strain isolates. The HRM10SNP method is simple, is relatively inexpensive and can be completed in <3 h. In our setting, it could be made easily available for clinical microbiology laboratories to screen for local P. aeruginosa strains and to guide infection control policies. Further studies are needed to determine whether the HRM10SNP assay can also be modified to detect additional clonal strains that are prevalent in other CF centres.

Nanoscale reinforced orthopaedic bone cement with graphene oxide - a possible solution to aseptic loosening of cemented hip implants? Preliminary results of an ongoing study

Hip replacement surgery is amongst the most common orthopaedic operations performed in the UK. Aseptic loosening is responsible for 40% of hip revision procedures. Aseptic loosening is a result of cement mantle fatigue. The aim of the current study is to analyse the effect of nanoscale Graphene Oxide (GO) on the mechanical properties of orthopaedic bone cement. Study Design A experimental thermal and mechanical analysis was conducted in a laboratory set up conforming to international standards for bone cement testing according to ISO 5583. Testing was performed on control cement samples of Colacryl bone cement, and additional samples reinforced with variable wt% of Graphene Oxide containing composites – 0.1%, 0.25%, 0.5% and 1.0% GO loading. Pilot Data Porosity demonstrated a linear relationship with increasing wt% loading compared to control (p<0.001). Thermal characterisation demonstrated maximal temperature during polymerization, and generated exotherm were inversely proportional to w%t loading (p<0.05) Fatigue strength performed on the control and 0.1 and 0.25%wt loadings of GO demonstrate increased average cycles to failure compared to control specimens. A right shift of the Weibull curve was demonstrated for both wt% available currently. Logistic regression analysis for failure demonstrated significant increases in number of cycles to failure for both specimens compared to a control (p<0.001). Forward Plan Early results convey positive benefits at low wt% loadings of GO containing bone cement. Study completion and further analysis is required in order to elude to the optimum w%t of GO which conveys the greatest mechanical advantage.

Sector analysis provides additional spatial information on the permanent prostate brachytherapy learning curve

PURPOSE: To evaluate the permanent prostate brachytherapy (PPB) learning curve using postimplant multisector dosimetric analysis and to assess the correlation between sector -specific dosimetry and patient-reported outcome measures (PROMs).

METHODS AND METHODS: First 200 patients treated with (125)I PPB monotherapy (145 Gy) at a single institution were assessed. Postimplant dosimetry (PID) using CT was evaluated for whole prostate (global) and 12 sectors, assessing minimum dose to 90% of prostate (D90) and dose to 0.1 cm(3) of rectum (D0.1cc). Global and sector PID results were evaluated to investigate changes in D90 with case number. Urinary and bowel PROMs were assessed using the International Prostate Symptom Score and the Expanded Prostate Cancer Index Composite questionnaire. The correlation between global and individual sector PID and urinary/bowel PROMs was also evaluated.

RESULTS: Linear regression confirmed a significant improvement in global D90 with case number (r(2) = 0.20; p = 0.001) at a rate of 0.11 Gy/case. Postimplant D90 of base sectors increased at a rate of 0.11-0.15 Gy/case (p = 0.0001) and matched global improvement. The regression lines of midgland and apex sectors were significantly different from global D90 (p = 0.01). Posterior midgland sectors showed a significant reduction in D90 with case number at a rate of 0.13-0.19 Gy/case (p = 0.01). Dose to posterior midgland sectors correlated with rectal D0.1cc dose but not bowel PROMs. Dose to posterior midgland sectors correlated with urinary International Prostate Symptom Score change, which was not apparent when global D90 alone was considered.

CONCLUSIONS: Sector analysis provided increased spatial information regarding the PPB learning curve. Furthermore, sector analysis correlated with urinary PROMs and rectal dose.