The incidence of venous thromboembolism and pharmacologic thromboprophylaxis following major urologic surgery: a population-based analysis

Thesis (M.A.)--Boston UniversityINTRODUCTION: The incidence of symptomatic venous thromboembolism (VTE), which comprises deep venous thrombosis (DVT) and pulmonary embolism (PE) at a population-based level remains unknown in patients undergoing major urologic surgery. Our aim was to determine the incidence of VTE in major urologic surgery, identify patients who are at high risk for developing these events, and to examine whether the use of pharmacologic thromboprophylaxis is associated with a reduction in the incidence of VTE in major urologic surgery. METHODS: We captured all adult patients who underwent major urologic surgery between January 2005 and December 2010 based on 1CD-9-CM codes from the Perspective Database (Premier, Inc, Charlotte, NC), a nationally representative dataset capturing 25% of US hospital discharges. Major urologic surgery was defined as a radical prostatectomy, radical cystectomy, radical nephrectomy or partial nephrectomy. We used ICD-9-CM codes to identify VTE and major bleeding after major urologic surgery within 90 days after the procedure and hospital billing descriptions to identify if patients had received pharmacologic thromboprophylaxis beginning the day of surgery. Univariate and multivariate analyses were performed using STATA 12 (StataCorp LP, CollegeStation, Texas) after adjusting for sample weights. [TRUNCATED

3DGRAPE/AL User's Manual

This document is a users' manual for a new three-dimensional structured multiple-block volume g generator called 3DGRAPE/AL. It is a significantly improved version of the previously-released a widely-distributed programs 3DGRAPE and 3DMAGGS. It generates volume grids by iteratively solving the Poisson Equations in three-dimensions. The right-hand-side terms are designed so that user-specific; grid cell heights and user-specified grid cell skewness near boundary surfaces result automatically, with little user intervention. The code is written in Fortran-77, and can be installed with or without a simple graphical user interface which allows the user to watch as the grid is generated. An introduction describing the improvements over the antecedent 3DGRAPE code is presented first. Then follows a chapter on the basic grid generator program itself, and comments on installing it. The input is then described in detail. After that is a description of the Graphical User Interface. Five example cases are shown next, with plots of the results. Following that is a chapter on two input filters which allow use of input data generated elsewhere. Last is a treatment of the theory embodied in the code

A 3DGRAPE/AL: The Ames/Langley technology upgrade

This paper describes a new three-dimensional structured multiple-block volume grid generator called 3DGRAPE/AL. It is a significantly improved version of the previously-released and widely-distributed program 3DGRAPE, with many of the improvements taken from the grid-generator program 3DMAGGS. It generates volume grids by iteratively solving the Poisson Equations in three-dimensions. The right-hand-side terms are designed so that user-specified grid cell heights and user-specified grid cell skewness near boundary surfaces result automatically, with little user intervention. Versatility was a high priority in this code's development, and as a result it can generate grids in almost any three-dimensional physical domain. Improvements include added kinds of forcing functions, improved control of cell skewness, improved initial conditions, convergence acceleration, the ability to take as input the output from GRIDGEN, and a simple but powerful graphical user interface (GUI)

From Random to Regular: Variation in the Patterning of Retinal Mosaics

The various types of retinal neurons are each positioned at their respective depths within the retina where they are believed to be assembled as orderly mosaics, in which like-type neurons minimize proximity to one another. Two common statistical analyses for assessing the spatial properties of retinal mosaics include the nearest neighbor analysis, from which an index of their "regularity" is commonly calculated, and the density recovery profile derived from auto-correlation analysis, revealing the presence of an exclusion zone indicative of anti-clustering. While each of the spatial statistics derived from these analyses, the regularity index and the effective radius, can be useful in characterizing such properties of orderly retinal mosaics, they are rarely sufficient for conveying the natural variation in the self-spacing behavior of different types of retinal neurons and the extent to which that behavior generates uniform intercellular spacing across the mosaic. We consider the strengths and limitations of different spatial statistical analyses for assessing the patterning in retinal mosaics, highlighting a number of misconceptions and their frequent misuse. Rather than being diagnostic criteria for determining simply whether a population is "regular", they should be treated as descriptive statistics that convey variation in the factors that influence neuronal positioning. We subsequently apply multiple spatial statistics to the analysis of eight different mosaics in the mouse retina, demonstrating conspicuous variability in the degree of patterning present, from essentially random to notably regular. This variability in patterning has both a developmental as well as a functional significance, reflecting the rules governing the positioning of different types of neurons as the architecture of the retina is assembled (abstract truncated).Comment: 11 Figure

FALCON: Framework for Anomaly Detection in Industrial Control Systems

Industrial Control Systems (ICS) are used to control physical processes in critical infrastructure. These systems are used in a wide variety of operations such as water treatment, power generation and distribution, and manufacturing. While the safety and security of these systems are of serious concern, recent reports have shown an increase in targeted attacks aimed at manipulating physical processes to cause catastrophic consequences. This trend emphasizes the need for algorithms and tools that provide resilient and smart attack detection mechanisms to protect ICS. In this paper, we propose an anomaly detection framework for ICS based on a deep neural network. The proposed methodology uses dilated convolution and long short-term memory (LSTM) layers to learn temporal as well as long term dependencies within sensor and actuator data in an ICS. The sensor/actuator data are passed through a unique feature engineering pipeline where wavelet transformation is applied to the sensor signals to extract features that are fed into the model. Additionally, this paper explores four variations of supervised deep learning models, as well as an unsupervised support vector machine (SVM) model for this problem. The proposed framework is validated on Secure Water Treatment testbed results. This framework detects more attacks in a shorter period of time than previously published methods

Characterization of high temperature mechanical properties using laser ultrasound

Mechanical properties are controlled to a large degree by defect structures such as dislocations and grain boundaries. These microstructural features involve a perturbation of the perfect crystal lattice (i.e. strain fields). Viewed in this context, high frequency strain waves (i.e. ultrasound) provide a natural choice to study microstructure mediated mechanical properties. In this presentation we use laser ultrasound to probe mechanical properties of materials. This approach utilizes lasers to excite and detect ultrasonic waves, and as a consequence has unique advantages over other methods—it is noncontacting, requires no couplant or invasive sample preparation (other than that used in metallurgical analysis), and has the demonstrated capability to probe microstructure on a micron scale. Laser techniques are highly reproducible enabling sophisticated, microstructurally informed data analysis. Since light is being used for generation and detection of the ultrasonic wave, the specimen being examined is not mechanically coupled to the transducer. As a result, laser ultrasound can be carried out remotely, an especially attractive characteristic for in situ measurements in severe environments. Several examples involving laser ultrasound to measure mechanical properties in high temperature environments will be presented. Emphasis will be place on understanding the role of grain microstructure

ICA model order selection of task co-activation networks

Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders

Persistent accumulation of calcium/calmodulin-dependent protein kinase II in dendritic spines after induction of NMDA receptor-dependent chemical long-term potentiation

Author Posting. © Society for Neuroscience, 2004. This article is posted here by permission of Society for Neuroscience for personal use, not for redistribution. The definitive version was published in Journal of Neuroscience 24 (2004): 9324-9331, doi:10.1523/JNEUROSCI.2350-04.2004.Calcium/calmodulin-dependent protein kinase II (CaMKII) is a leading candidate for a synaptic memory molecule because it is persistently activated after long-term potentiation (LTP) induction and because mutations that block this persistent activity prevent LTP and learning. Previous work showed that synaptic stimulation causes a rapidly reversible translocation of CaMKII to the synaptic region. We have now measured green fluorescent protein (GFP)-CaMKIIα translocation into synaptic spines during NMDA receptor-dependent chemical LTP (cLTP) and find that under these conditions, translocation is persistent. Using red fluorescent protein as a cell morphology marker, we found that there are two components of the persistent accumulation. cLTP produces a persistent increase in spine volume, and some of the increase in GFP-CaMKIIα is secondary to this volume change. In addition, cLTP results in a dramatic increase in the bound fraction of GFP-CaMKIIα in spines. To further study the bound pool, immunogold electron microscopy was used to measure CaMKIIα in the postsynaptic density (PSD), an important regulator of synaptic function. cLTP produced a persistent increase in the PSD-associated pool of CaMKIIα. These results are consistent with the hypothesis that CaMKIIα accumulation at synapses is a memory trace of past synaptic activity.This work was supported by Grant R01 NS-27337 from the National Institutes of Health/National Institute of Neurological Disorders and Stroke

CARMA Measurements of the Sunyaev-Zel'dovich Effect in RXJ1347.5-1145

We demonstrate the Sunyaev-Zel'dovich (SZ) effect imaging capabilities of the Combined Array for Research in Millimeter-wave Astronomy (CARMA) by presenting an SZ map of the galaxy cluster RXJ1347.5-1145. By combining data from multiple CARMA bands and configurations, we are able to capture the structure of this cluster over a wide range of angular scales, from its bulk properties to its core morphology. We find that roughly 9% of this cluster's thermal energy is associated with sub-arcminute-scale structure imparted by a merger, illustrating the value of high-resolution SZ measurements for pursuing cluster astrophysics and for understanding the scatter in SZ scaling relations. We also find that the cluster's SZ signal is lower in amplitude than suggested by a spherically-symmetric model derived from X-ray data, consistent with compression along the line of sight relative to the plane of the sky. Finally, we discuss the impact of upgrades currently in progress that will further enhance CARMA's power as an SZ imaging instrument.Comment: 8 pages, 6 figure