We examined temporal variations in overall Emergency Medical Services (EMS) demand, as well as medical and trauma cases separately. We analyzed cases according to time of day and day of week to determine whether population level demand demonstrates temporal patterns that will increase baseline knowledge for EMS planning. We conducted a secondary analysis of data from the Ambulance Victoria data warehouse covering the period 2008-2011. We included all cases of EMS attendance which resulted in 1,203,803 cases for review. Data elements comprised age, gender, date and time of call to the EMS emergency number along with the clinical condition of the patient. We employed Poisson regression to analyze case numbers and trigonometric regression to quantify distribution patterns. EMS demand exhibited a bimodal distribution with the highest peak at 10:00 and a second smaller peak at 19:00. The highest number of cases occurred on Fridays, and the lowest on Tuesdays and Wednesdays. However, the distribution of cases throughout the day differed by day of week. Distribution patterns on Fridays, Saturdays and Sundays differed significantly from the rest of the week (p < 0.001). When categorized into medical or trauma cases, medical cases were more frequent during working hours and involved patients of higher mean age (57 years vs. 49 years for trauma, p < 0.001). Trauma cases peaked on Friday and Saturday nights around midnight. Day of week EMS demand distribution patterns reveal differences that can be masked in aggregate data. Day of week EMS demand distribution patterns showed not only which days have differences in demand but the times of day at which the demand changes. Patterns differed by case type as well. These differences in distribution are important for EMS demand planning. Increased understanding of EMS demand patterns is imperative in a climate of ever-increasing demand and fiscal constraints. Further research is needed into the effect of age and case type on EMS
In cryo-electron microscopy (cryo-EM), a microscope generates a top view of a sample of randomly oriented copies of a molecule. The problem of single particle reconstruction (SPR) from cryo-EM is to use the resulting set of noisy two-dimensional projection images taken at unknown directions to reconstruct the three-dimensional (3D) structure of the molecule. In some situations, the molecule under examination exhibits structural variability, which poses a fundamental challenge in SPR. The heterogeneity problem is the task of mapping the space of conformational states of a molecule. It has been previously suggested that the leading eigenvectors of the covariance matrix of the 3D molecules can be used to solve the heterogeneity problem. Estimating the covariance matrix is challenging, since only projections of the molecules are observed, but not the molecules themselves. In this paper, we formulate a general problem of covariance estimation from noisy projections of samples. This problem has intimate connections with matrix completion problems and high-dimensional principal component analysis. We propose an estimator and prove its consistency. When there are finitely many heterogeneity classes, the spectrum of the estimated covariance matrix reveals the number of classes. The estimator can be found as the solution to a certain linear system. In the cryo-EM case, the linear operator to be inverted, which we term the projection covariance transform, is an important object in covariance estimation for tomographic problems involving structural variation. Inverting it involves applying a filter akin to the ramp filter in tomography. We design a basis in which this linear operator is sparse and thus can be tractably inverted despite its large size. We demonstrate via numerical experiments on synthetic datasets the robustness of our algorithm to high levels of noise. PMID:25699132
Investigating organelles such as the Golgi complex depends increasingly on high-throughput quantitative morphological analyses from multiple experimental or genetic conditions. Light microscopy (LM) has been an effective tool for screening but fails to reveal fine details of Golgi structures such as vesicles, tubules and cisternae. Electron microscopy (EM) has sufficient resolution but traditional transmission EM (TEM) methods are slow and inefficient. Newer volume scanning EM (volume-SEM) methods now have the potential to speed up 3D analysis by automated sectioning and imaging. However, they produce large arrays of sections and/or images, which require labour-intensive 3D reconstruction for quantitation on limited cell numbers. Here, we show that the information storage, digital waste and workload involved in using volume-SEM can be reduced substantially using sampling-based stereology. Using the Golgi as an example, we describe how Golgi populations can be sensed quantitatively using single random slices and how accurate quantitative structural data on Golgi organelles of individual cells can be obtained using only 5-10 sections/images taken from a volume-SEM series (thereby sensing population parameters and cell-cell variability). The approach will be useful in techniques such as correlative LM and EM (CLEM) where small samples of cells are treated and where there may be variable responses. For Golgi study, we outline a series of stereological estimators that are suited to these analyses and suggest workflows, which have the potential to enhance the speed and relevance of data acquisition in volume-SEM.
Ice-repellent coatings can have significant impact on global energy savings and improving safety in many infrastructures, transportation, and cooling systems. Recent efforts for developing ice-phobic surfaces have been mostly devoted to utilizing lotus-leaf-inspired superhydrophobic surfaces, yet these surfaces fail in high-humidity conditions due to water condensation and frost formation and even lead to increased ice adhesion due to a large surface area. We report a radically different type of ice-repellent material based on slippery, liquid-infused porous surfaces (SLIPS), where a stable, ultrasmooth, low-hysteresis lubricant overlayer is maintained by infusing a water-immiscible liquid into a nanostructured surface chemically functionalized to have a high affinity to the infiltrated liquid and lock it in place. We develop a direct fabrication method of SLIPS on industrially relevant metals, particularly aluminum, one of the most widely used lightweight structural materials. We demonstrate that SLIPS-coated Al surfaces not only suppress ice/frost accretion by effectively removing condensed moisture but also exhibit at least an order of magnitude lower ice adhesion than state-of-the-art materials. On the basis of a theoretical analysis followed by extensive icing/deicing experiments, we discuss special advantages of SLIPS as ice-repellent surfaces: highly reduced sliding droplet sizes resulting from the extremely low contact angle hysteresis. We show that our surfaces remain essentially frost-free in which any conventional materials accumulate ice. These results indicate that SLIPS is a promising candidate for developing robust anti-icing materials for broad applications, such as refrigeration, aviation, roofs, wires, outdoor signs, railings, and wind turbines. 2b1af7f3a8