scholarly journals CROSS-CONTAMINATION OF FITTING PARAMETERS IN MULTIDIMENSIONAL TUNNELING TREATMENTS

2017 ◽  
Author(s):  
Jon Hougen ◽  
Nobukimi Ohashi
Keyword(s):  
Cross Contamination ◽  
Multidimensional Tunneling ◽  
Fitting Parameters

Preliminary Evaluation of a Weibull Function for Fitting Slow-Component Eye Velocity over the Time Course of Caloric-Induced Nystagmus

1989 ◽  
Vol 32 (3) ◽  
pp. 681-687 ◽  
Author(s):  
C. Formby ◽  
B. Albritton ◽  
I. M. Rivera
Keyword(s):  
Time Course ◽  
Slow Component ◽  
Weibull Function ◽  
Preliminary Evaluation ◽  
Mathematical Function ◽  
Before And After ◽  
Best Fitting ◽  
Eye Velocity ◽  
Fitting Parameters ◽  
Weibull Equation

We describe preliminary attempts to fit a mathematical function to the slow-component eye velocity (SCV) over the time course of caloric-induced nystagmus. Initially, we consider a Weibull equation with three parameters. These parameters are estimated by a least-squares procedure to fit digitized SCV data. We present examples of SCV data and fitted curves to show how adjustments in the parameters of the model affect the fitted curve. The best fitting parameters are presented for curves fit to 120 warm caloric responses. The fitting parameters and the efficacy of the fitted curves are compared before and after the SCV data were smoothed to reduce response variability. We also consider a more flexible four-parameter Weibull equation that, for 98% of the smoothed caloric responses, yields fits that describe the data more precisely than a line through the mean. Finally, we consider advantages and problems in fitting the Weibull function to caloric data.

Restorative dental treatment in times of COVID-19

2020 ◽  
Vol 68 ◽  
Author(s):  
Robson de Lima GOMES ◽  
Marlus da Silva PEDROSA ◽  
Claudio Heliomar Vicente da SILVA
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Dental Treatment ◽  
Close Contact ◽  
Cross Contamination ◽  
Clinical Protocols ◽  
Know How ◽  
Dental Procedures ◽  
Dental Staff ◽  
Dental Professional ◽  
Restrictive Measures

ABSTRACT Since the outbreak of the Coronavirus Disease 2019 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), numerous restrictive measures have been adopted by governments of different countries. The return to elective dental care in Brazil is a reality even during the COVID-19 pandemic. During restorative dental procedures, the dental professional requires close contact with the patient, being exposed to contaminated saliva and fluids. In addition, transmission of COVID-19 by the generation of aerosol produced by dental handipieces may be possible. Thus, the dental staff must know how to act during restorative dental procedures, putting into practice the correct clinical protocols to avoid cross-contamination and COVID-19 spread. The purpose of this article is to review the literature on the biosafety practices especially in the context of restorative dental procedures in times of COVID-19.

An Effective SIMS Methodology for GOI Contamination Analysis

2013 ◽  
Author(s):  
Yanhua Huang ◽  
Lei Zhu ◽  
Kenny Ong ◽  
Hanwei Teo ◽  
Younan Hua
Keyword(s):  
Oxide Layer ◽  
Secondary Ion Mass Spectrometry ◽  
Gate Oxide ◽  
Sample Surface ◽  
Low Energy ◽  
Cross Contamination ◽  
Common Effect ◽  
Sims Analysis ◽  
Secondary Ion ◽  
Lower Background

Abstract Contamination in the gate oxide layer is the most common effect which cause the gate oxide integrate (GOI) issue. Dynamic Secondary Ion Mass Spectrometry (SIMS) is a mature tool for GOI contamination analysis. During the sample preparation, all metal and IDL layers above poly should be removed because the presence of these layers added complexity for the subsequent SIMS analysis. The normal delayering process is simply carried out by soaking the sample in the HF solution. However, the poly surface is inevitably contaminated by surroundings even though it is already a practice to clean with DI rinse and tape. In this article, TOFSIMS with low energy sputter gun is used to clean the sample surface after the normal delayering process. The residue signals also can be monitored by TOF SIMS during sputtering to confirm the cross contamination is cleared. After that, a much lower background desirable by dynamic SIMS. Thus an accurate depth profile in gate oxide layer can be achieved without the interference from surface.

An artificial intelligent platform for live cell identification and the detection of cross-contamination (Preprint)

2019 ◽  
Author(s):  
Ruixin Wang ◽  
Dongni Wang ◽  
Dekai Kang ◽  
Xusen Guo ◽  
Chong Guo ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Line ◽  
Cell Lines ◽  
Cell Morphology ◽  
Single Cell Level ◽  
Cross Contamination ◽  
Cell Level ◽  
Cell Identification ◽  
Pure Cell ◽  
Microscopy Images

BACKGROUND In vitro human cell line models have been widely used for biomedical research to predict clinical response, identify novel mechanisms and drug response. However, one-fifth to one-third of cell lines have been cross-contaminated, which can seriously result in invalidated experimental results, unusable therapeutic products and waste of research funding. Cell line misidentification and cross-contamination may occur at any time, but authenticating cell lines is infrequent performed because the recommended genetic approaches are usually require extensive expertise and may take a few days. Conversely, the observation of live-cell morphology is a direct and real-time technique. OBJECTIVE The purpose of this study was to construct a novel computer vision technology based on deep convolutional neural networks (CNN) for “cell face” recognition. This was aimed to improve cell identification efficiency and reduce the occurrence of cell-line cross contamination. METHODS Unstained optical microscopy images of cell lines were obtained for model training (about 334 thousand patch images), and testing (about 153 thousand patch images). The AI system first trained to recognize the pure cell morphology. In order to find the most appropriate CNN model，we explored the key image features in cell morphology classification tasks using the classical CNN model-Alexnet. After that, a preferred fine-grained recognition model BCNN was used for the cell type identification (seven classifications). Next, we simulated the situation of cell cross-contamination and mixed the cells in pairs at different ratios. The detection of the cross-contamination was divided into two levels, whether the cells are mixed and what the contaminating cell is. The specificity, sensitivity, and accuracy of the model were tested separately by external validation. Finally, the segmentation model DialedNet was used to present the classification results at the single cell level. RESULTS The cell texture and density were the influencing factors that can be better recognized by the bilinear convolutional neural network (BCNN) comparing to AlexNet. The BCNN achieved 99.5% accuracy in identifying seven pure cell lines and 86.3% accuracy for detecting cross-contamination (mixing two of the seven cell lines). DilatedNet was applied to the semantic segment for analyzing in single-cell level and achieved an accuracy of 98.2%. CONCLUSIONS This study successfully demonstrated that cell lines can be morphologically identified using deep learning models. Only light-microscopy images and no reagents are required, enabling most labs to routinely perform cell identification tests.

Determination of Dimetridazole and Ipronidazole in Feeds at Cross-Contamination Levels

1988 ◽  
Vol 71 (3) ◽  
pp. 474-477 ◽  
Author(s):  
Duane D Hughes
Keyword(s):  
Thin Layer ◽  
Rapid Method ◽  
Aqueous Phase ◽  
Borate Buffer ◽  
Cross Contamination ◽  
Chromatographic System ◽  
Arsanilic Acid ◽  
Contamination Levels ◽  
Benzene Extract

Abstract A rapid method for the determination of dimetridazole and ipronidazole in feeds is described. The compounds are extracted from a borate buffer (pH 8.65) with benzene, partitioned into IN HC1, and then partitioned back into benzene from a basic aqueous phase. The benzene extract is concentrated and injected onto a nonpolar (Apiezon L) gas chromatographic column for determination by 63Ni electroncapture detection. Recoveries from feeds of various composition, spiked at 0.2 ppm with both dimetridazole and ipronidazole, ranged from 70 to 115%; for the same feeds spiked at 1 ppm or more, the recoveries were greater than 80%. Carbadox, furazolidone, levamisole, oxytetracycline, chlortetracycline, sulfamethazine, sulfaquinoxaline, arsanilic acid, piperazine, penicillin, and commonly added vitamins and minerals do not interfere. A 2-dimensional thin layer chromatographic system is presented as a means of additional identification.

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