A weibull approach for improving climate model projections of tropical cyclone wind-speed distributions

This is the final version of the article. Available from the publisher via the DOI in this record.Open Access ArticleReliable estimates of future changes in extreme weather phenomena, such as tropical cyclone maximum wind speeds, are critical for climate change impact assessments and the development of appropriate adaptation strategies. However, global and regional climate model outputs are often too coarse for direct use in these applications, with variables such as wind speed having truncated probability distributions compared to those of observations. This poses two problems: How canmodel-simulated variables best be adjusted to make themmore realistic? And how can such adjustments be used to make more reliable predictions of future changes in their distribution? This study investigates North Atlantic tropical cyclone maximum wind speeds from observations (1950- 2010) and regional climate model simulations (1995-2005 and 2045-55 at 12- and 36-km spatial resolutions). The wind speed distributions in these datasets are well represented by the Weibull distribution, albeit with different scale and shape parameters. A power-law transfer function is used to recalibrate the Weibull variables and obtain future projections of wind speeds. Two different strategies, bias correction and change factor, are tested by using 36-km model data to predict future 12-km model data (pseudo-observations). The strategies are also applied to the observations to obtain likely predictions of the future distributions of wind speeds. The strategies yield similar predictions of likely changes in the fraction of events within Saffir-Simpson categories-for example, an increase from 21% (1995-2005) to 27%-37% (2045-55) for category 3 or above events and an increase from 1.6% (1995- 2005) to 2.8%-9.8% (2045-55) for category 5 events. © 2014 American Meteorological Society.Acknowledgments. Support for this work was provided by theWillis Research Network, the Research Program to Secure Energy for America, NSF EASM Grant S1048841, and the NCARWeather and Climate Assessment Science Program. We thank Sherrie Fredrick for extracting data, and Cindy Bruyère, James Done, and Ben Youngman for productive discussions that enhanced this research. We also thank Dr. Adam Monahan and one anonymous reviewer for their insightful comments and suggestions

Optimal schedule of home care visits for a health care center

The provision of home health care services is becoming an important research area, mainly because in Portugal the population is ageing. Home care visits are organized taking into account the medical treatments and general support that elder/sick people need at home. This health service can be provided by nurse teams from Health Care Centers. Usually, the visits are manually planned and without computer support. The main goal of this work is to carry out the automatic schedule of home care visits, of one Portuguese Health Care Center, in order to minimize the time spent in all home care visits and, consequently, reduce the costs involved. The developed algorithms were coded in MatLab Software and the problem was efficiently solved, obtaining several schedule solutions of home care visits for the presented data. Solutions found by genetic and particle swarm algorithms lead to significant time reductions for both nurse teams and patients.This work has been supported by COMPETE: POCI-01-0145- FEDER-007043 and FCT - Fundru;ao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Super-resolving phase measurements with a multi-photon entangled state

Using a linear optical elements and post-selection, we construct an entangled polarization state of three photons in the same spatial mode. This state is analogous to a ``photon-number path entangled state'' and can be used for super-resolving interferometry. Measuring a birefringent phase shift, we demonstrate two- and three-fold improvements in phase resolution.Comment: 4 pages, 3 figure

A randomized control trial of intensive aphasia therapy after acute stroke: The Very Early Rehabilitation for SpEech (VERSE) study.

BACKGROUND:Effectiveness of early intensive aphasia rehabilitation after stroke is unknown. The Very Early Rehabilitation for SpEech trial (VERSE) aimed to determine whether intensive aphasia therapy, beginning within 14 days after stroke, improved communication recovery compared to usual care. METHODS:Prospective, randomized, single-blinded trial conducted at 17 acute-care hospitals across Australia/New Zealand from 2014 to 2018. Participants with aphasia following acute stroke were randomized to receive usual care (direct usual care aphasia therapy), or one of two higher intensity regimens (20 sessions of either non-prescribed (usual care-plus or prescribed (VERSE) direct aphasia therapy). The primary outcome was improvement of communication on the Western Aphasia Battery-Revised Aphasia Quotient (AQ) at 12 weeks after stroke. Our pre-planned intention to treat analysis combined high intensity groups for the primary outcome. FINDINGS:Among 13,654 acute stroke patients screened, 25% (3477) had aphasia, of whom 25% (866) were eligible and 246 randomized to usual care (n = 81; 33%), usual care-plus (n = 82; 33%) or VERSE (n = 83; 34%). At 12 weeks after stroke, the primary outcome was assessed in 217 participants (88%); 14 had died, 9 had withdrawn, and 6 were too unwell for assessment. Communication recovery was 50.3% (95% CI 45.7-54.8) in the high intensity group (n = 147) and 52.1% (95% CI 46.1-58.1) in the usual care group (n = 70; difference -1.8, 95% CI -8.7-5.0). There was no difference between groups in non-fatal or fatal adverse events (p = 0.72). INTERPRETATION:Early, intensive aphasia therapy did not improve communication recovery within 12 weeks post stroke compared to usual care

The spatial ecology of phytoplankton blooms in UK canals

Environmental change is expected to increase the frequency and severity of problems caused by harmful algal blooms. We investigated the ecology of phytoplankton blooms in UK canals to determine the environmental predictors and spatial structure of bloom communities. The results revealed a significant increase in bloom presence with increasing elevation. As predicted, higher temperatures were associated with a greater probability of blooms, but the relationship between temperature and bloom occurrence changed across landscapes. At the minimum level of agricultural land, the probability of bloom presence increased with increasing temperature. Conversely, at the maximum level, the probability decreased with increasing temperature. This pattern could be due to higher temperatures increasing phytoplankton growth rates despite lower nutrient concentrations at low levels of agricultural land, and nutrient depletion by rapidly growing blooms at high levels of agricultural land and temperatures. Community composition exhibited spatial autocorrelation; nearby blooms were more similar than distant blooms. Hydrological distances through the canal network showed a stronger association with community dissimilarity than Euclidean distances, suggesting a role for hydrological connectivity in driving bloom formation and composition. This new knowledge regarding canal phytoplankton bloom origin and ecology could help inform measures to inhibit bloom formation

Novel, synergistic antifungal combinations that target translation fidelity

There is an unmet need for new antifungal or fungicide treatments, as resistance to existing treatments grows. Combination treatments help to combat resistance. Here we develop a novel, effective target for combination antifungal therapy. Different aminoglycoside antibiotics combined with different sulphate-transport inhibitors produced strong, synergistic growth-inhibition of several fungi. Combinations decreased the respective MICs by ≥8 fold. Synergy was suppressed in yeast mutants resistant to effects of sulphate-mimetics (like chromate or molybdate) on sulphate transport. By different mechanisms, aminoglycosides and inhibition of sulphate transport cause errors in mRNA translation. The mistranslation rate was stimulated up to 10-fold when the agents were used in combination, consistent with this being the mode of synergistic action. A range of undesirable fungi were susceptible to synergistic inhibition by the combinations, including the human pathogens Candida albicans, C. glabrata and Cryptococcus neoformans, the food spoilage organism Zygosaccharomyces bailii and the phytopathogens Rhizoctonia solani and Zymoseptoria tritici. There was some specificity as certain fungi were unaffected. There was no synergy against bacterial or mammalian cells. The results indicate that translation fidelity is a promising new target for combinatorial treatment of undesirable fungi, the combinations requiring substantially decreased doses of active components compared to each agent alone

Correlation between 5-fluorouracil metabolism and treatment response in two variants of C26 murine colon carcinoma

Following an i.p. dose of 150 mg x kg(-1) 5-fluorouracil (5-FU), drug uptake and metabolism over a 2-h period were studied by in vivo (19)F magnetic resonance spectroscopy (MRS) for the murine colon carcinoma lines C26-B (5-FU-insensitive; n=11) and C26-10 (5-FU-sensitive; n=15) implanted s.c. in Balb/C mice. Time courses for tumour growth, intracellular levels of FdUMP, thymidylate synthase (TS) activity, and 5-FU in RNA were also determined, and the effects of a 9.5-min period of carbogen breathing, starting 1 min before drug administration, on MRS-detected 5-FU metabolism and tumour growth curves were examined. Both tumour variants generated MRS-detectable 5-FU nucleotides and showed similar initial growth inhibition after treatment. However, the growth rate of C26-B tumours returned to normal, while the sensitive C26-10 tumours, which produced larger fluoronucleotide pools, still showed moderate growth inhibition. Carbogen breathing did not significantly influence 5-FU uptake or fluoronucleotide production but did significantly enhance growth inhibition in C26-10 tumours. While both tumour variants exhibited incorporation of 5-FU into RNA and inhibition of TS via FdUMP, clearance of 5-FU from RNA and recovery of TS activity were greater for the insensitive C26-B line, indicating that these processes, in addition to 5-FU uptake and metabolism, may be important determinants of drug sensitivity and treatment respons

Using ordinal logistic regression to evaluate the performance of laser-Doppler predictions of burn-healing time

Background Laser-Doppler imaging (LDI) of cutaneous blood flow is beginning to be used by burn surgeons to predict the healing time of burn wounds; predicted healing time is used to determine wound treatment as either dressings or surgery. In this paper, we do a statistical analysis of the performance of the technique. Methods We used data from a study carried out by five burn centers: LDI was done once between days 2 to 5 post burn, and healing was assessed at both 14 days and 21 days post burn. Random-effects ordinal logistic regression and other models such as the continuation ratio model were used to model healing-time as a function of the LDI data, and of demographic and wound history variables. Statistical methods were also used to study the false-color palette, which enables the laser-Doppler imager to be used by clinicians as a decision-support tool. Results Overall performance is that diagnoses are over 90% correct. Related questions addressed were what was the best blood flow summary statistic and whether, given the blood flow measurements, demographic and observational variables had any additional predictive power (age, sex, race, % total body surface area burned (%TBSA), site and cause of burn, day of LDI scan, burn center). It was found that mean laser-Doppler flux over a wound area was the best statistic, and that, given the same mean flux, women recover slightly more slowly than men. Further, the likely degradation in predictive performance on moving to a patient group with larger %TBSA than those in the data sample was studied, and shown to be small. Conclusion Modeling healing time is a complex statistical problem, with random effects due to multiple burn areas per individual, and censoring caused by patients missing hospital visits and undergoing surgery. This analysis applies state-of-the art statistical methods such as the bootstrap and permutation tests to a medical problem of topical interest. New medical findings are that age and %TBSA are not important predictors of healing time when the LDI results are known, whereas gender does influence recovery time, even when blood flow is controlled for. The conclusion regarding the palette is that an optimum three-color palette can be chosen 'automatically', but the optimum choice of a 5-color palette cannot be made solely by optimizing the percentage of correct diagnoses