Risk‐Treatment Paradox in the Selection of Transradial Access for Percutaneous Coronary Intervention

Background: Access site complications contribute to morbidity and mortality during percutaneous coronary intervention (PCI). Transradial arterial access significantly lowers the risk of access site complications compared to transfemoral arteriotomy. We sought to develop a prediction model for access site complications in patients undergoing PCI with femoral arteriotomy, and assess whether transradial access was selectively used in patients at high risk for complications. Methods and Results: We analyzed 17 509 patients who underwent PCI without circulatory support from 2008 to 2011 at 5 institutions. Transradial arterial access was used in 17.8% of patients. In those who underwent transfemoral access, 177 (1.2%) patients had access site complications. Using preprocedural clinical and demographic data, a prediction model for femoral arteriotomy complications was generated. The variables retained in the model included: elevated age (P<0.001), female gender (P<0.001), elevated troponin (P<0.001), decreased renal function or dialysis (P=0.002), emergent PCI (P=0.01), prior PCI (P=0.005), diabetes (P=0.008), and peripheral artery disease (P=0.003). The model showed moderate discrimination (optimism‐adjusted c‐statistic=0.72) and was internally validated via bootstrap resampling. Patients with higher predicted risk of complications via transfemoral access were less likely to receive transradial access (P<0.001). Similar results were seen in patients presenting with and without ST‐segment myocardial infarction and when adjusting for individual physician operator. Conclusions: We generated and validated a model for transfemoral access site complications during PCI. Paradoxically, patients most likely to develop access site complications from transfemoral access, and therefore benefit from transradial access, were the least likely to receive transradial access

The type IC SN 1990B in NGC 4568

We present a study of the Type Ic supernova (SN) 1990B that includes most of the observations obtained from around the world. The combined data set comprises 84 BV(RI)c photometric points spanning approximately 360 days after maximum light and 14 spectra from 5 up to ~150 days after maximum light. In contrast to other Type Ic SNe, SN 1990B did not display a weak but distinct He I λ5876 line indicating that its He content was smaller or that the He layers were rather effectively shielded from the radioactive matter in the ejecta. The behavior of the Na I D line, however, suggests that He I λ5876 was blended with it. SN 1990B appeared on a sharply varying background that complicates the usual techniques of digital photometry. In order to do unbiased photometry, we modeled and subtracted the background of each image with the SN using images of NGC 4568 taken ~2500 days after the explosion, when SN 1990B had faded beyond detection. We compare the performance of standard point-spread function fitting photometry of the SN in the images with and without the background of the parent galaxy and find the results to differ systematically at late times. The photometry done on the images with the background light of NGC 4568 subtracted shows the light curves of SN 1990B to be of the slow Type Ic variety, with a slope steeper than that of the Type Ib SN 1983N or the Type II transition (Type IIb) SN 1993J but slower than that of the Type Ic SN 1994I. We estimate the reddening by foreground matter in the Galaxy and NGC 4568 and compute BV(RI)c light curves spanning ~110 days after maximum light

The ALICE experiment at the CERN LHC

ALICE (A Large Ion Collider Experiment) is a general-purpose, heavy-ion detector at the CERN LHC which focuses on QCD, the strong-interaction sector of the Standard Model. It is designed to address the physics of strongly interacting matter and the quark-gluon plasma at extreme values of energy density and temperature in nucleus-nucleus collisions. Besides running with Pb ions, the physics programme includes collisions with lighter ions, lower energy running and dedicated proton-nucleus runs. ALICE will also take data with proton beams at the top LHC energy to collect reference data for the heavy-ion programme and to address several QCD topics for which ALICE is complementary to the other LHC detectors. The ALICE detector has been built by a collaboration including currently over 1000 physicists and engineers from 105 Institutes in 30 countries. Its overall dimensions are 161626 m3 with a total weight of approximately 10 000 t. The experiment consists of 18 different detector systems each with its own specific technology choice and design constraints, driven both by the physics requirements and the experimental conditions expected at LHC. The most stringent design constraint is to cope with the extreme particle multiplicity anticipated in central Pb-Pb collisions. The different subsystems were optimized to provide high-momentum resolution as well as excellent Particle Identification (PID) over a broad range in momentum, up to the highest multiplicities predicted for LHC. This will allow for comprehensive studies of hadrons, electrons, muons, and photons produced in the collision of heavy nuclei. Most detector systems are scheduled to be installed and ready for data taking by mid-2008 when the LHC is scheduled to start operation, with the exception of parts of the Photon Spectrometer (PHOS), Transition Radiation Detector (TRD) and Electro Magnetic Calorimeter (EMCal). These detectors will be completed for the high-luminosity ion run expected in 2010. This paper describes in detail the detector components as installed for the first data taking in the summer of 2008

Modelling habitat requirements of bullhead (Cottus gobio) in Alpine streams

In the context of water resources planning and management, the prediction of fish distribution related to habitat characteristics is fundamental for the definition of environmental flows and habitat restoration measures. In particular, threatened and endemic fish species should be the targets of biodiversity safeguard and wildlife conservation actions. The recently developed meso-scale habitat model (MesoHABSIM) can provide solutions in this sense by using multivariate statistical techniques to predict fish species distribution and to define habitat suitability criteria. In this research, Random Forests (RF) and Logistic Regressions (LR) models were used to predict the distribution of bullhead (Cottus gobio) as a function of habitat conditions. In ten reference streams of the Alps (NW Italy), 95 mesohabitats were sampled for hydro-morphologic and biological parameters, and RF and LR were used to distinguish between absence/presence and presence/abundance of fish. The obtained models were compared on the basis of their performances (model accuracy, sensitivity, specificity, Cohen's kappa and area under ROC curve). Results indicate that RF outperformed LR, for both absence/presence (RF: 84 % accuracy, k = 0.58 and AUC = 0.88; LR: 78 % accuracy, k = 0.54 and AUC = 0.85) and presence/abundance models (RF: 79 % accuracy, k = 0.57 and AUC = 0.87; LR: 69 % accuracy, k = 0.43 and AUC = 0.81). The most important variables, selected in each model, are discussed and compared to the available literature. Lastly, results from models' application in regulated sites are presented to show the possible use of RF in predicting habitat availability for fish in Alpine stream