Feasibility of using red cell distribution width for prediction of postoperative mortality in severe burn patients: an association with acute kidney injury after surgery

Background Severe burns cause pathophysiological processes that result in mortality. A laboratory biomarker, red cell distribution width (RDW), is known as a predictor of mortality in critically-ill patients. We examined the association between RDW and postoperative mortality in severe burn patients. Methods We retrospectively analyzed medical data of 731 severely burned patients who underwent surgery under general anesthesia. We evaluated whether preoperative RDW value can predict 3-month mortality after burn surgery using receiver operating characteristic (ROC) curve analysis, logistic regression, and Cox proportional-hazards regression analysis. Mortality was also analyzed according to preoperative RDW values and incidence of postoperative acute kidney injury (AKI). Results The 3-month mortality rate after burn surgery was 27.1% (198/731). The area under the ROC curve of preoperative RDW to predict mortality after burn surgery was 0.701 (95% confidence interval [CI], 0.667–0.734; P 12.9 was 1.238 (95% CI, 1.138–1.347; P 12.9. Preoperative RDW was considered an independent risk factor for mortality (odds ratio, 1.679; 95% CI, 1.378–2.046; P 12.9 and postoperative AKI may further increase mortality after burn surgery

Teatro e ensino da matemática: atividade desenvolvida num curso de formação docente

Anais do II Seminário Seminário Estadual PIBID do Paraná: tecendo saberes / organizado por Dulcyene Maria Ribeiro e Catarina Costa Fernandes — Foz do Iguaçu: Unioeste; Unila, 2014Este trabalho relata uma aula desenvolvida pelas alunas do Curso de Formação de Docentes do Instituto Estadual de Educação de Londrina com a colaboração dos Bolsistas do Programa Institucional de Bolsas de Iniciação à Docência – PIBID – Subprojeto de Matemática, para alunos de primeiro ano do Ensino Fundamental utilizando o teatro como forma de apresentar conteúdos matemáticos como números, sequência de números, operações básicas como adição, subtração e conteúdos de língua portuguesa como leitura e escrita de número

Cost- And Dataset-free Stuck-at Fault Mitigation for ReRAM-based Deep Learning Accelerators

Resistive RAMs can implement extremely efficient matrix vector multiplication, drawing much attention for deep learning accelerator research. However, high fault rate is one of the fundamental challenges of ReRAM crossbar array-based deep learning accelerators. In this paper we propose a dataset-free, cost-free method to mitigate the impact of stuck-at faults in ReRAM crossbar arrays for deep learning applications. Our technique exploits the statistical properties of deep learning applications, hence complementary to previous hardware or algorithmic methods. Our experimental results using MNIST and CIFAR-10 datasets in binary networks demonstrate that our technique is very effective, both alone and together with previous methods, up to 20 % fault rate, which is higher than the previous remapping methods. We also evaluate our method in the presence of other non-idealities such as variability and IR drop

Learning to Predict IR Drop with Effective Training for ReRAM-based Neural Network Hardware

Due to the inevitability of the IR drop problem in passive ReRAM crossbar arrays, finding a software solution that can predict the effect of IR drop without the need of expensive SPICE simulations, is very desirable. In this paper, two simple neural networks are proposed as software solution to predict the effect of IR drop. These networks can be easily integrated in any deep neural network framework to incorporate the IR drop problem during training. As an example, the proposed solution is integrated in BinaryNet framework and the test validation results, done through SPICE simulations, show very high improvement in performance close to the baseline performance, which demonstrates the efficacy of the proposed method. In addition, the proposed solution outperforms the prior work on challenging datasets such as CIFAR10 and SVHN

Fast Prototyping of an IS-95 CDMA Cellular Phone: a Case Study

This paper presents a case study on fast prototyping of a wireless CDMA cellular phone system. We set up a fast prototyping flow which aims at reducing the coverification time. We captured executable specifications of the system with Ptolemy and Polis tools. We developed a prototyping board, a board debugger which provides in-circuit emulation functions, and a debug agent which works as a debugger and cosimulation interface to the prototyping board. Using the debug agent and cosimulation interface stars in Ptolemy, we performed coverification of the system by executing the SW part on a real processor and the HW part on an FPGA while simulating the remaining part on Ptolemy

I2CRF: Incremental interconnect customization for embedded reconfigurable fabrics

Integrating coarse-grained reconfigurable architectures (CGRAs) into a System-on-a-Chip (SoC) presents many benefits as well as important challenges. One of the challenges is how to customize the architecture for the target applications efficiently and effectively without explicit design space exploration. In this paper we present a novel methodology for incremental interconnect customization of CGRAs that can suggest a new interconnection architecture that can maximize the performance for a given set of application kernels while minimizing the hardware cost. Applying the inexact graph matching analogy, we translate our problem into graph matching taking into account the cost of various graph edit operations, which we solve using the A* search algorithm with a heuristic tailored to our problem. Our experimental results demonstrate that our customization method can quickly find application-optimized interconnections that exhibit 70% higher performance on average compared to the base architecture, with relatively little hardware increase in interconnections and muxes

Training-Free Stuck-at Fault Mitigation for ReRAM-based Deep Learning Accelerators

Although Resistive RAMs can support highly efficient matrix-vector multiplication, which is very useful for machine learning and other applications, the non-ideal behavior of hardware such as stuck-at fault and IR drop is an important concern in making ReRAM crossbar array-based deep learning accelerators. Previous work has addressed the nonideality problem through either redundancy in hardware, which requires a permanent increase of hardware cost, or software retraining, which may be even more costly or unacceptable due to its need for a training dataset as well as high computation overhead. In this paper we propose a very light-weight method that can be applied on top of existing hardware or software solutions. Our method, called FPT (Forward-Parameter Tuning), takes advantage of a certain statistical property existing in the activation data of neural network layers, and can mitigate the impact of mild nonidealities in ReRAM crossbar arrays for deep learning applications without using any hardware, a dataset, or gradientbased training. Our experimental results using MNIST, CIFAR-10, CIFAR-100, and ImageNet datasets in binary and multibit networks demonstrate that our technique is very effective, both alone and together with previous methods, up to 20rate, which is higher than even some of the previous remapping methods. We also evaluate our method in the presence of other nonidealities such as variability and IR drop. Further, we provide an analysis based on the concept of effective fault rate, which not only demonstrates that effective fault rate can be a useful tool to predict the accuracy of faulty RCA-based neural networks, but also explains why mitigating the SAF problem is more difficult with multi-bit neural networks

Development of a certified reference material for the analysis of vitamins in multivitamin tablets

Abstract Multivitamin tablet certified reference material (CRM, 108-10-019) was developed for the analysis of seven water-soluble vitamins, including thiamine, riboflavin, nicotinamide, pantothenic acid, pyridoxine, biotin, and folic acid. The CRM was prepared in powder form by grinding multivitamin tablets and then mixing, sieving, and bottling the powder. For the certification of each water-soluble vitamin, the isotope dilution mass spectrometry based on the liquid chromatography was applied. The methods for each analyte were validated by confirming the repeatability and reproducibility and by comparing with other CRMs. The property values and uncertainties for the vitamins were determined with 10 units from sample stored at − 20 °C. The homogeneity of each certified component was also examined in the range of 0.48–2.2%. All certified values for the seven water-soluble vitamins were stable for 3 or 6 years after the initial certification under storage conditions at − 20 °C. For fat-soluble vitamins, including retinol, α-tocopherol, cholecalciferol, and phylloquinone, two expert laboratories participated in analyses based on official methods, and the mean values of the reported results were assigned as reference values. The multivitamin tablet CRM (108-10-019) will be useful for validating analytical methods and for ensuring the quality of results for vitamin analysis in multivitamin tablets or similar products

Immunohistochemical (IHC) staining of dental follicle and permanent PDL tissues.

<p>(<b>A</b>, <b>F</b>) IHC staining for AMTN in the dental follicle and (<b>K</b>, <b>P</b>) permanent PDL tissues. (<b>B</b>, <b>G</b>) IHC staining for CXCL13 in the dental follicle and (<b>L</b>, <b>Q</b>) permanent PDL tissue. (<b>C</b>, <b>H</b>) IHC staining for DMP1 in the dental follicle and (<b>M</b>, <b>R</b>) permanent PDL tissue. (<b>D</b>, <b>I</b>) IHC staining for WIF1 in the dental follicle and (<b>N</b>, <b>S</b>) permanent PDL tissue. (<b>E</b>, <b>J</b>) IHC staining for MMP9 in the dental follicle and (<b>O</b>, <b>T</b>) permanent PDL tissue. (Scale bars: 20 μm in <b>A–E</b> and <b>P–T</b>; 100 μm in F–O.)</p