Second best toll and capacity optimisation in network: solution algorithm and policy implications

This paper looks at the first and second-best jointly optimal toll and road capacity investment problems from both policy and technical oriented perspectives. On the technical side, the paper investigates the applicability of the constraint cutting algorithm for solving the second-best problem under elastic demand which is formulated as a bilevel programming problem. The approach is shown to perform well despite several problems encountered by our previous work in Shepherd and Sumalee (2004). The paper then applies the algorithm to a small sized network to investigate the policy implications of the first and second-best cases. This policy analysis demonstrates that the joint first best structure is to invest in the most direct routes while reducing capacities elsewhere. Whilst unrealistic this acts as a useful benchmark. The results also show that certain second best policies can achieve a high proportion of the first best benefits while in general generating a revenue surplus. We also show that unless costs of capacity are known to be low then second best tolls will be affected and so should be analysed in conjunction with investments in the network

Foraging distribution of breeding northern fulmars is predicted by commercial fisheries

Funding: J.H.D. was funded by the Irish Research Council Enterprise Partnership Scheme, supported by the Petroleum Infrastructure Program. Field work on Little Saltee in 2018 and 2019 and S.d.G. were funded by the BlueFish project, funded by the European Regional Development fund through the Ireland Wales Cooperation Programme 2014−2020. Fieldwork on Eynhallow and St. Kilda was supported by Orkney Islands Council, the University of Aberdeen, the National Trust for Scotland and Talisman Energy (UK) Ltd. E.W.J.E. was funded by a Marine Alliance for Science and Technology for Scotland and University of Aberdeen studentship. Fieldwork elsewhere was funded by the EU Atlantic area INTERREG program via the Future of the Atlantic Marine Environment (FAME) project and by the RSPB, JNCC, Fair Isle Bird Observatory Trust and Marine Scotland, through the Seabird Tracking And Research (STAR) project. G.E.A. was funded by the MarPAMM project supported by the EU INTERREG VA Programme, managed by the Special EU Programmes Body (SEUPB).Habitat-use and distribution models are essential tools of conservation biology. For wide-ranging species, such models may be challenged by the expanse, remoteness and variability of their habitat, these challenges often being compounded by the species' mobility. In marine environments, direct observations and sampling are usually impractical over broad regions, and instead remotely sensed proxies of prey availability are often used to link species abundance or foraging behaviour to areas that are expected to provide food consistently. One source of food consumed by many marine top predators is fisheries waste, but habitat-use models rarely account for this interaction. We assessed the utility of commercial fishing effort as a covariate in foraging habitat models for northern fulmars Fulmarus glacialis, a species known to exploit fisheries waste, during their summer breeding season. First, we investigated the prevalence of fulmar-vessel interactions using concurrently tracked fulmars and fishing vessels. We infer that over half of our study individuals associate with fishing vessels while foraging, mostly with trawl-type vessels. We then used hidden Markov models to explain the spatio-temporal distribution of putative foraging behaviour as a function of a range of covariates. Persistent commercial fishing effort was a significant predictor of foraging behaviour, and was more important than commonly used environmental covariates retained in the model. This study demonstrates the effect of commercial fisheries on the foraging distribution and behaviour of a marine top predator, and supports the idea that, in some systems, incorporating human activities into distribution studies can improve model fit substantially.Publisher PDFPeer reviewe

Contralateral hip fractures and other osteoporosis-related fractures in hip fracture patients: Incidence and risk factors. An observational cohort study of 1,229 patients

Purpose: To report risk factors, 1-year and overall risk for a contralateral hip and other osteoporosis-related fractures in a hip fracture population. Methods: An observational study on 1,229 consecutive patients of 50 years and older, who sustained a hip fracture between January 2005 and June 2009. Fractures were scored retrospectively for 2005-2008 and prospectively for 2008-2009. Rates of a contralateral hip and other osteoporosis- related fractures were compared between patients with and without a history of a fracture. Previous fractures, gender, age and ASA classification were analysed as possible risk factors. Results: The absolute risk for a contralateral hip fracture was 13.8 %, for one or more osteoporosis-related fracture( s) 28.6 %. First-, second- and third-year risk for a second hip fracture was 2, 1 and 0 %. Median (IQR) interval between both hip fractures was 18.5 (26.6) months. One-year incidence of other fractures was 6 %. Only age was a risk factor for a contralateral hip fracture, hazard ratio (HR) 1.02 (1.006-1.042, p = 0.008). Patients with a history of a fracture (33.1 %) did not have a higher incidence of fractures during follow-up (16.7 %) than patients without fractures in their history (14 %). HR for a contralateral hip fracture for the fracture versus the non-fracture group was 1.29 (0.75-2.23, p = 0.360). Conclusion: The absolute risk of a contralateral hip fracture after a hip fracture is 13.8 %, the 1-year risk was 2 %, with a short interval between the 2 hip fractures. Age was a risk factor for sustaining a contralateral hip fracture; a fracture in history was not

Delirium risk screening and haloperidol prophylaxis program in hip fracture patients is a helpful tool in identifying high-risk patients, but does not reduce the incidence of delirium

Background: Delirium in patients with hip fractures lead to higher morbidity and mortality. Prevention in high-risk patients by prescribing low dose haloperidol is currently under investigation. Methods. This prospective cohort surveillance assessed hip fracture patients for risk of developing a delirium with the Risk Model for Delirium (RD) score. High-risk patients (score ≥5 points) were treated with a prophylactic low-dose of haloperidol according to hospital protocol. Primary outcome was delirium incidence. Secondary outcomes were differences between high- and low-risk patients in delirium, length of stay (LOS), return to pre-fracture living situation and mortality. Logistic regression analysis was performed with age, ASA-classification, known dementia, having a partner, type of fracture, institutional residence and psychotropic drug use as possible confounders. Results: 445 hip fracture patients aged 65 years and older were admitted from January 2008 to December 2009. The RD-score was completed in 378 patients, 173 (45.8%) high-risk patients were treated with prophylactic medication. Sensitivity was 71.6%, specificity 63.8% and the negative predictive value (NPV) of a score < 5 was 85.9%. Delirium incidence (27.0%) was not significantly different compared to 2007 (27.8%) 2006 (23.9%) and 2005 (29.0%) prior to implementation of the RD- protocol. Logistic regression analysis showed that high-risk patients did have a significant higher delirium incidence (42.2% vs. 14.1%, OR 4.1, CI 2.43-7.02). They were more likely to be residing at an alternative living situation after 3 months (62.3% vs. 17.0%, OR 6.57, CI 3.23-13.37) and less likely to be discharged from hospital before 10 days (34.9% vs. 55.9%, OR 1.63, CI 1.03-2.59). Significant independent risk factors for a delirium were a RD-score 5 (OR 4.13, CI 2.43-7.02), male gender (OR 1.93, CI 0.99-1.07) and age (OR 1.03, CI 0.99-1.07). Conclusions: Introducing the delirium prevention protocol did not reduce delirium incidence. The RD-score did identify patients with a high risk to develop a delirium. This high-risk group had a longer LOS and returned to pre-fracture living situation less often. The NPV of a score < 5 was high, as it should be for a screening instrument. Concluding, the RD-score is a useful tool to identify patients with poorer outcome

Home on the Range: Factors Explaining Partial Migration of African Buffalo in a Tropical Environment

Partial migration (when only some individuals in a population undertake seasonal migrations) is common in many species and geographical contexts. Despite the development of modern statistical methods for analyzing partial migration, there have been no studies on what influences partial migration in tropical environments. We present research on factors affecting partial migration in African buffalo (Syncerus caffer) in northeastern Namibia. Our dataset is derived from 32 satellite tracking collars, spans 4 years and contains over 35,000 locations. We used remotely sensed data to quantify various factors that buffalo experience in the dry season when making decisions on whether and how far to migrate, including potential man-made and natural barriers, as well as spatial and temporal heterogeneity in environmental conditions. Using an information-theoretic, non-linear regression approach, our analyses showed that buffalo in this area can be divided into 4 migratory classes: migrants, non-migrants, dispersers, and a new class that we call “expanders”. Multimodel inference from least-squares regressions of wet season movements showed that environmental conditions (rainfall, fires, woodland cover, vegetation biomass), distance to the nearest barrier (river, fence, cultivated area) and social factors (age, size of herd at capture) were all important in explaining variation in migratory behaviour. The relative contributions of these variables to partial migration have not previously been assessed for ungulates in the tropics. Understanding the factors driving migratory decisions of wildlife will lead to better-informed conservation and land-use decisions in this area

Double vs single internal thoracic artery harvesting in diabetic patients: role in perioperative infection rate

Background: The aim of this prospective study is to evaluate the role in the onset of surgical site infections of bilateral internal thoracic arteries harvesting in patients with decompensated preoperative glycemia. Methods: 81 consecutive patients with uncontrolled diabetes mellitus underwent elective CABG harvesting single or double internal thoracic arteries. Single left ITA was harvested in 41 patients (Group 1, 50.6%), BITAs were harvested in 40 (Group 2, 49.4%). The major clinical end points analyzed in this study were infection rate, type of infection, duration of infection, infection relapse rate and total hospital length of stay. Results: Five patients developed sternal SSI in the perioperative period, 2 in group 1 and 3 in group 2 without significant difference. All sternal SSIs were superficial with no sternal dehiscence. The development of infection from the time of surgery took 18.5 ± 2.1 and 7.3 ± 3.0 days for Groups 1 and 2 respectively. The infections were treated with wound irrigation and debridement, and with VAC therapy as well as with antibiotics. The VAC system was removed after a mean of 12.8 ± 5.1 days, when sterilization was achieved. The overall survival estimate at 1 year was 98.7%. Only BMI was a significant predictor of SSI using multivariate stepwise logistic regression analysis (Odds Ratio: 1.34; 95%Conficdence Interval: 1.02–1.83; p value: 0.04). In the model, the use of BITA was not an independent predictor of SSI. Conclusion: CABG with bilateral pedicled ITAs grafting could be performed safely even in diabetics with poor preoperative glycaemic control

Genome-Scale Metabolic Modeling Elucidates the Role of Proliferative Adaptation in Causing the Warburg Effect

The Warburg effect - a classical hallmark of cancer metabolism - is a counter-intuitive phenomenon in which rapidly proliferating cancer cells resort to inefficient ATP production via glycolysis leading to lactate secretion, instead of relying primarily on more efficient energy production through mitochondrial oxidative phosphorylation, as most normal cells do. The causes for the Warburg effect have remained a subject of considerable controversy since its discovery over 80 years ago, with several competing hypotheses. Here, utilizing a genome-scale human metabolic network model accounting for stoichiometric and enzyme solvent capacity considerations, we show that the Warburg effect is a direct consequence of the metabolic adaptation of cancer cells to increase biomass production rate. The analysis is shown to accurately capture a three phase metabolic behavior that is observed experimentally during oncogenic progression, as well as a prominent characteristic of cancer cells involving their preference for glutamine uptake over other amino acids