Decision tree methods: applications for classification and prediction.

PubMed

Song, Yan-Yan; Lu, Ying

2015-04-25

Decision tree methodology is a commonly used data mining method for establishing classification systems based on multiple covariates or for developing prediction algorithms for a target variable. This method classifies a population into branch-like segments that construct an inverted tree with a root node, internal nodes, and leaf nodes. The algorithm is non-parametric and can efficiently deal with large, complicated datasets without imposing a complicated parametric structure. When the sample size is large enough, study data can be divided into training and validation datasets. Using the training dataset to build a decision tree model and a validation dataset to decide on the appropriate tree size needed to achieve the optimal final model. This paper introduces frequently used algorithms used to develop decision trees (including CART, C4.5, CHAID, and QUEST) and describes the SPSS and SAS programs that can be used to visualize tree structure.

Automated Decision Tree Classification of Corneal Shape

PubMed Central

Twa, Michael D.; Parthasarathy, Srinivasan; Roberts, Cynthia; Mahmoud, Ashraf M.; Raasch, Thomas W.; Bullimore, Mark A.

2011-01-01

Purpose The volume and complexity of data produced during videokeratography examinations present a challenge of interpretation. As a consequence, results are often analyzed qualitatively by subjective pattern recognition or reduced to comparisons of summary indices. We describe the application of decision tree induction, an automated machine learning classification method, to discriminate between normal and keratoconic corneal shapes in an objective and quantitative way. We then compared this method with other known classification methods. Methods The corneal surface was modeled with a seventh-order Zernike polynomial for 132 normal eyes of 92 subjects and 112 eyes of 71 subjects diagnosed with keratoconus. A decision tree classifier was induced using the C4.5 algorithm, and its classification performance was compared with the modified Rabinowitz–McDonnell index, Schwiegerling’s Z3 index (Z3), Keratoconus Prediction Index (KPI), KISA%, and Cone Location and Magnitude Index using recommended classification thresholds for each method. We also evaluated the area under the receiver operator characteristic (ROC) curve for each classification method. Results Our decision tree classifier performed equal to or better than the other classifiers tested: accuracy was 92% and the area under the ROC curve was 0.97. Our decision tree classifier reduced the information needed to distinguish between normal and keratoconus eyes using four of 36 Zernike polynomial coefficients. The four surface features selected as classification attributes by the decision tree method were inferior elevation, greater sagittal depth, oblique toricity, and trefoil. Conclusions Automated decision tree classification of corneal shape through Zernike polynomials is an accurate quantitative method of classification that is interpretable and can be generated from any instrument platform capable of raw elevation data output. This method of pattern classification is extendable to other classification

Parallel object-oriented decision tree system

DOEpatents

Kamath,; Chandrika, Cantu-Paz [Dublin, CA; Erick, [Oakland, CA

2006-02-28

A data mining decision tree system that uncovers patterns, associations, anomalies, and other statistically significant structures in data by reading and displaying data files, extracting relevant features for each of the objects, and using a method of recognizing patterns among the objects based upon object features through a decision tree that reads the data, sorts the data if necessary, determines the best manner to split the data into subsets according to some criterion, and splits the data.

Fast Image Texture Classification Using Decision Trees

NASA Technical Reports Server (NTRS)

Thompson, David R.

2011-01-01

Texture analysis would permit improved autonomous, onboard science data interpretation for adaptive navigation, sampling, and downlink decisions. These analyses would assist with terrain analysis and instrument placement in both macroscopic and microscopic image data products. Unfortunately, most state-of-the-art texture analysis demands computationally expensive convolutions of filters involving many floating-point operations. This makes them infeasible for radiation- hardened computers and spaceflight hardware. A new method approximates traditional texture classification of each image pixel with a fast decision-tree classifier. The classifier uses image features derived from simple filtering operations involving integer arithmetic. The texture analysis method is therefore amenable to implementation on FPGA (field-programmable gate array) hardware. Image features based on the "integral image" transform produce descriptive and efficient texture descriptors. Training the decision tree on a set of training data yields a classification scheme that produces reasonable approximations of optimal "texton" analysis at a fraction of the computational cost. A decision-tree learning algorithm employing the traditional k-means criterion of inter-cluster variance is used to learn tree structure from training data. The result is an efficient and accurate summary of surface morphology in images. This work is an evolutionary advance that unites several previous algorithms (k-means clustering, integral images, decision trees) and applies them to a new problem domain (morphology analysis for autonomous science during remote exploration). Advantages include order-of-magnitude improvements in runtime, feasibility for FPGA hardware, and significant improvements in texture classification accuracy.

The Decision Tree: A Tool for Achieving Behavioral Change.

ERIC Educational Resources Information Center

Saren, Dru

1999-01-01

Presents a "Decision Tree" process for structuring team decision making and problem solving about specific student behavioral goals. The Decision Tree involves a sequence of questions/decisions that can be answered in "yes/no" terms. Questions address reasonableness of the goal, time factors, importance of the goal, responsibilities, safety,…

Flow visualization around an apple with and without bagging

NASA Astrophysics Data System (ADS)

Matsumoto, H.; Kubota, Y.; Ohishi, M.; Mochizuki, O.

2017-04-01

The typhoon often causes the vast damage to drop the apple before harvest. Many apples fall from trees by the strong wind. These apples are usually bagged to protect them from insects and control sun light for the apples colouring while they are ripening on the tree. We directly measured the drag force acting on an apple with and without bagging experimentally to bare the influence of the bagging on the dropping mechanism. There are two interesting results through the experiment: the drag coefficient of a naked apple is smaller than a sphere, and the bagging is a cause of increasing drag coefficient. To know the reason of these results, we visualized flow around the apple with and without bagging by using the hydrogen bubbles method in an open water channel in this study. We found two facts as follows: the hollow on the top of an apple plays reduction of width of the wake of an apple and reason of increasing the wake width is the flow separation from peripheral edge of the bagging.

An automated approach to the design of decision tree classifiers

NASA Technical Reports Server (NTRS)

Argentiero, P.; Chin, R.; Beaudet, P.

1982-01-01

An automated technique is presented for designing effective decision tree classifiers predicated only on a priori class statistics. The procedure relies on linear feature extractions and Bayes table look-up decision rules. Associated error matrices are computed and utilized to provide an optimal design of the decision tree at each so-called 'node'. A by-product of this procedure is a simple algorithm for computing the global probability of correct classification assuming the statistical independence of the decision rules. Attention is given to a more precise definition of decision tree classification, the mathematical details on the technique for automated decision tree design, and an example of a simple application of the procedure using class statistics acquired from an actual Landsat scene.

A survey of decision tree classifier methodology

NASA Technical Reports Server (NTRS)

Safavian, S. R.; Landgrebe, David

1991-01-01

Decision tree classifiers (DTCs) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. Perhaps the most important feature of DTCs is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. A survey of current methods is presented for DTC designs and the various existing issues. After considering potential advantages of DTCs over single-state classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed.

A survey of decision tree classifier methodology

NASA Technical Reports Server (NTRS)

Safavian, S. Rasoul; Landgrebe, David

1990-01-01

Decision Tree Classifiers (DTC's) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition. Perhaps, the most important feature of DTC's is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. A survey of current methods is presented for DTC designs and the various existing issue. After considering potential advantages of DTC's over single stage classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed.

Building of fuzzy decision trees using ID3 algorithm

NASA Astrophysics Data System (ADS)

Begenova, S. B.; Avdeenko, T. V.

2018-05-01

Decision trees are widely used in the field of machine learning and artificial intelligence. Such popularity is due to the fact that with the help of decision trees graphic models, text rules can be built and they are easily understood by the final user. Because of the inaccuracy of observations, uncertainties, the data, collected in the environment, often take an unclear form. Therefore, fuzzy decision trees becoming popular in the field of machine learning. This article presents a method that includes the features of the two above-mentioned approaches: a graphical representation of the rules system in the form of a tree and a fuzzy representation of the data. The approach uses such advantages as high comprehensibility of decision trees and the ability to cope with inaccurate and uncertain information in fuzzy representation. The received learning method is suitable for classifying problems with both numerical and symbolic features. In the article, solution illustrations and numerical results are given.

Multivariate analysis of flow cytometric data using decision trees.

PubMed

Simon, Svenja; Guthke, Reinhard; Kamradt, Thomas; Frey, Oliver

2012-01-01

Characterization of the response of the host immune system is important in understanding the bidirectional interactions between the host and microbial pathogens. For research on the host site, flow cytometry has become one of the major tools in immunology. Advances in technology and reagents allow now the simultaneous assessment of multiple markers on a single cell level generating multidimensional data sets that require multivariate statistical analysis. We explored the explanatory power of the supervised machine learning method called "induction of decision trees" in flow cytometric data. In order to examine whether the production of a certain cytokine is depended on other cytokines, datasets from intracellular staining for six cytokines with complex patterns of co-expression were analyzed by induction of decision trees. After weighting the data according to their class probabilities, we created a total of 13,392 different decision trees for each given cytokine with different parameter settings. For a more realistic estimation of the decision trees' quality, we used stratified fivefold cross validation and chose the "best" tree according to a combination of different quality criteria. While some of the decision trees reflected previously known co-expression patterns, we found that the expression of some cytokines was not only dependent on the co-expression of others per se, but was also dependent on the intensity of expression. Thus, for the first time we successfully used induction of decision trees for the analysis of high dimensional flow cytometric data and demonstrated the feasibility of this method to reveal structural patterns in such data sets.

Decision tree modeling using R.

PubMed

Zhang, Zhongheng

2016-08-01

In machine learning field, decision tree learner is powerful and easy to interpret. It employs recursive binary partitioning algorithm that splits the sample in partitioning variable with the strongest association with the response variable. The process continues until some stopping criteria are met. In the example I focus on conditional inference tree, which incorporates tree-structured regression models into conditional inference procedures. While growing a single tree is subject to small changes in the training data, random forests procedure is introduced to address this problem. The sources of diversity for random forests come from the random sampling and restricted set of input variables to be selected. Finally, I introduce R functions to perform model based recursive partitioning. This method incorporates recursive partitioning into conventional parametric model building.

A framework for sensitivity analysis of decision trees.

PubMed

Kamiński, Bogumił; Jakubczyk, Michał; Szufel, Przemysław

2018-01-01

In the paper, we consider sequential decision problems with uncertainty, represented as decision trees. Sensitivity analysis is always a crucial element of decision making and in decision trees it often focuses on probabilities. In the stochastic model considered, the user often has only limited information about the true values of probabilities. We develop a framework for performing sensitivity analysis of optimal strategies accounting for this distributional uncertainty. We design this robust optimization approach in an intuitive and not overly technical way, to make it simple to apply in daily managerial practice. The proposed framework allows for (1) analysis of the stability of the expected-value-maximizing strategy and (2) identification of strategies which are robust with respect to pessimistic/optimistic/mode-favoring perturbations of probabilities. We verify the properties of our approach in two cases: (a) probabilities in a tree are the primitives of the model and can be modified independently; (b) probabilities in a tree reflect some underlying, structural probabilities, and are interrelated. We provide a free software tool implementing the methods described.

Decision Tree Approach for Soil Liquefaction Assessment

PubMed Central

Gandomi, Amir H.; Fridline, Mark M.; Roke, David A.

2013-01-01

In the current study, the performances of some decision tree (DT) techniques are evaluated for postearthquake soil liquefaction assessment. A database containing 620 records of seismic parameters and soil properties is used in this study. Three decision tree techniques are used here in two different ways, considering statistical and engineering points of view, to develop decision rules. The DT results are compared to the logistic regression (LR) model. The results of this study indicate that the DTs not only successfully predict liquefaction but they can also outperform the LR model. The best DT models are interpreted and evaluated based on an engineering point of view. PMID:24489498

Decision tree approach for soil liquefaction assessment.

PubMed

Gandomi, Amir H; Fridline, Mark M; Roke, David A

2013-01-01

In the current study, the performances of some decision tree (DT) techniques are evaluated for postearthquake soil liquefaction assessment. A database containing 620 records of seismic parameters and soil properties is used in this study. Three decision tree techniques are used here in two different ways, considering statistical and engineering points of view, to develop decision rules. The DT results are compared to the logistic regression (LR) model. The results of this study indicate that the DTs not only successfully predict liquefaction but they can also outperform the LR model. The best DT models are interpreted and evaluated based on an engineering point of view.

An Isometric Mapping Based Co-Location Decision Tree Algorithm

NASA Astrophysics Data System (ADS)

Zhou, G.; Wei, J.; Zhou, X.; Zhang, R.; Huang, W.; Sha, H.; Chen, J.

2018-05-01

Decision tree (DT) induction has been widely used in different pattern classification. However, most traditional DTs have the disadvantage that they consider only non-spatial attributes (ie, spectral information) as a result of classifying pixels, which can result in objects being misclassified. Therefore, some researchers have proposed a co-location decision tree (Cl-DT) method, which combines co-location and decision tree to solve the above the above-mentioned traditional decision tree problems. Cl-DT overcomes the shortcomings of the existing DT algorithms, which create a node for each value of a given attribute, which has a higher accuracy than the existing decision tree approach. However, for non-linearly distributed data instances, the euclidean distance between instances does not reflect the true positional relationship between them. In order to overcome these shortcomings, this paper proposes an isometric mapping method based on Cl-DT (called, (Isomap-based Cl-DT), which is a method that combines heterogeneous and Cl-DT together. Because isometric mapping methods use geodetic distances instead of Euclidean distances between non-linearly distributed instances, the true distance between instances can be reflected. The experimental results and several comparative analyzes show that: (1) The extraction method of exposed carbonate rocks is of high accuracy. (2) The proposed method has many advantages, because the total number of nodes, the number of leaf nodes and the number of nodes are greatly reduced compared to Cl-DT. Therefore, the Isomap -based Cl-DT algorithm can construct a more accurate and faster decision tree.

EEG feature selection method based on decision tree.

PubMed

Duan, Lijuan; Ge, Hui; Ma, Wei; Miao, Jun

2015-01-01

This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.

Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets

PubMed Central

Doubravsky, Karel; Dohnal, Mirko

2015-01-01

Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details. PMID:26158662

Reconciliation of Decision-Making Heuristics Based on Decision Trees Topologies and Incomplete Fuzzy Probabilities Sets.

PubMed

Doubravsky, Karel; Dohnal, Mirko

2015-01-01

Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details.

Multi-test decision tree and its application to microarray data classification.

PubMed

Czajkowski, Marcin; Grześ, Marek; Kretowski, Marek

2014-05-01

The desirable property of tools used to investigate biological data is easy to understand models and predictive decisions. Decision trees are particularly promising in this regard due to their comprehensible nature that resembles the hierarchical process of human decision making. However, existing algorithms for learning decision trees have tendency to underfit gene expression data. The main aim of this work is to improve the performance and stability of decision trees with only a small increase in their complexity. We propose a multi-test decision tree (MTDT); our main contribution is the application of several univariate tests in each non-terminal node of the decision tree. We also search for alternative, lower-ranked features in order to obtain more stable and reliable predictions. Experimental validation was performed on several real-life gene expression datasets. Comparison results with eight classifiers show that MTDT has a statistically significantly higher accuracy than popular decision tree classifiers, and it was highly competitive with ensemble learning algorithms. The proposed solution managed to outperform its baseline algorithm on 14 datasets by an average 6%. A study performed on one of the datasets showed that the discovered genes used in the MTDT classification model are supported by biological evidence in the literature. This paper introduces a new type of decision tree which is more suitable for solving biological problems. MTDTs are relatively easy to analyze and much more powerful in modeling high dimensional microarray data than their popular counterparts. Copyright © 2014 Elsevier B.V. All rights reserved.

15 CFR Supplement 1 to Part 732 - Decision Tree

Code of Federal Regulations, 2010 CFR

2010-01-01

... 15 Commerce and Foreign Trade 2 2010-01-01 2010-01-01 false Decision Tree 1 Supplement 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) BUREAU... THE EAR Pt. 732, Supp. 1 Supplement 1 to Part 732—Decision Tree ER06FE04.000 [69 FR 5687, Feb. 6, 2004] ...

15 CFR Supplement 1 to Part 732 - Decision Tree

Code of Federal Regulations, 2012 CFR

2012-01-01

... 15 Commerce and Foreign Trade 2 2012-01-01 2012-01-01 false Decision Tree 1 Supplement 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) BUREAU... THE EAR Pt. 732, Supp. 1 Supplement 1 to Part 732—Decision Tree ER06FE04.000 [69 FR 5687, Feb. 6, 2004] ...

15 CFR Supplement 1 to Part 732 - Decision Tree

Code of Federal Regulations, 2011 CFR

2011-01-01

... 15 Commerce and Foreign Trade 2 2011-01-01 2011-01-01 false Decision Tree 1 Supplement 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) BUREAU... THE EAR Pt. 732, Supp. 1 Supplement 1 to Part 732—Decision Tree ER06FE04.000 [69 FR 5687, Feb. 6, 2004] ...

Computerized Adaptive Test vs. decision trees: Development of a support decision system to identify suicidal behavior.

PubMed

Delgado-Gomez, D; Baca-Garcia, E; Aguado, D; Courtet, P; Lopez-Castroman, J

2016-12-01

Several Computerized Adaptive Tests (CATs) have been proposed to facilitate assessments in mental health. These tests are built in a standard way, disregarding useful and usually available information not included in the assessment scales that could increase the precision and utility of CATs, such as the history of suicide attempts. Using the items of a previously developed scale for suicidal risk, we compared the performance of a standard CAT and a decision tree in a support decision system to identify suicidal behavior. We included the history of past suicide attempts as a class for the separation of patients in the decision tree. The decision tree needed an average of four items to achieve a similar accuracy than a standard CAT with nine items. The accuracy of the decision tree, obtained after 25 cross-validations, was 81.4%. A shortened test adapted for the separation of suicidal and non-suicidal patients was developed. CATs can be very useful tools for the assessment of suicidal risk. However, standard CATs do not use all the information that is available. A decision tree can improve the precision of the assessment since they are constructed using a priori information. Copyright © 2016 Elsevier B.V. All rights reserved.

Sequential decision tree using the analytic hierarchy process for decision support in rectal cancer.

PubMed

Suner, Aslı; Çelikoğlu, Can Cengiz; Dicle, Oğuz; Sökmen, Selman

2012-09-01

The aim of the study is to determine the most appropriate method for construction of a sequential decision tree in the management of rectal cancer, using various patient-specific criteria and treatments such as surgery, chemotherapy, and radiotherapy. An analytic hierarchy process (AHP) was used to determine the priorities of variables. Relevant criteria used in two decision steps and their relative priorities were established by a panel of five general surgeons. Data were collected via a web-based application and analyzed using the "Expert Choice" software specifically developed for the AHP. Consistency ratios in the AHP method were calculated for each set of judgments, and the priorities of sub-criteria were determined. A sequential decision tree was constructed for the best treatment decision process, using priorities determined by the AHP method. Consistency ratios in the AHP method were calculated for each decision step, and the judgments were considered consistent. The tumor-related criterion "presence of perforation" (0.331) and the patient-surgeon-related criterion "surgeon's experience" (0.630) had the highest priority in the first decision step. In the second decision step, the tumor-related criterion "the stage of the disease" (0.230) and the patient-surgeon-related criterion "surgeon's experience" (0.281) were the paramount criteria. The results showed some variation in the ranking of criteria between the decision steps. In the second decision step, for instance, the tumor-related criterion "presence of perforation" was just the fifth. The consistency of decision support systems largely depends on the quality of the underlying decision tree. When several choices and variables have to be considered in a decision, it is very important to determine priorities. The AHP method seems to be effective for this purpose. The decision algorithm developed by this method is more realistic and will improve the quality of the decision tree. Copyright © 2012 Elsevier

Decision tree and ensemble learning algorithms with their applications in bioinformatics.

PubMed

Che, Dongsheng; Liu, Qi; Rasheed, Khaled; Tao, Xiuping

2011-01-01

Machine learning approaches have wide applications in bioinformatics, and decision tree is one of the successful approaches applied in this field. In this chapter, we briefly review decision tree and related ensemble algorithms and show the successful applications of such approaches on solving biological problems. We hope that by learning the algorithms of decision trees and ensemble classifiers, biologists can get the basic ideas of how machine learning algorithms work. On the other hand, by being exposed to the applications of decision trees and ensemble algorithms in bioinformatics, computer scientists can get better ideas of which bioinformatics topics they may work on in their future research directions. We aim to provide a platform to bridge the gap between biologists and computer scientists.

MRI-based decision tree model for diagnosis of biliary atresia.

PubMed

Kim, Yong Hee; Kim, Myung-Joon; Shin, Hyun Joo; Yoon, Haesung; Han, Seok Joo; Koh, Hong; Roh, Yun Ho; Lee, Mi-Jung

2018-02-23

To evaluate MRI findings and to generate a decision tree model for diagnosis of biliary atresia (BA) in infants with jaundice. We retrospectively reviewed features of MRI and ultrasonography (US) performed in infants with jaundice between January 2009 and June 2016 under approval of the institutional review board, including the maximum diameter of periportal signal change on MRI (MR triangular cord thickness, MR-TCT) or US (US-TCT), visibility of common bile duct (CBD) and abnormality of gallbladder (GB). Hepatic subcapsular flow was reviewed on Doppler US. We performed conditional inference tree analysis using MRI findings to generate a decision tree model. A total of 208 infants were included, 112 in the BA group and 96 in the non-BA group. Mean age at the time of MRI was 58.7 ± 36.6 days. Visibility of CBD, abnormality of GB and MR-TCT were good discriminators for the diagnosis of BA and the MRI-based decision tree using these findings with MR-TCT cut-off 5.1 mm showed 97.3 % sensitivity, 94.8 % specificity and 96.2 % accuracy. MRI-based decision tree model reliably differentiates BA in infants with jaundice. MRI can be an objective imaging modality for the diagnosis of BA. • MRI-based decision tree model reliably differentiates biliary atresia in neonatal cholestasis. • Common bile duct, gallbladder and periportal signal changes are the discriminators. • MRI has comparable performance to ultrasonography for diagnosis of biliary atresia.

RE-Powering’s Electronic Decision Tree

EPA Pesticide Factsheets

Developed by US EPA's RE-Powering America's Land Initiative, the RE-Powering Decision Trees tool guides interested parties through a process to screen sites for their suitability for solar photovoltaics or wind installations

A Decision Tree for Nonmetric Sex Assessment from the Skull.

PubMed

Langley, Natalie R; Dudzik, Beatrix; Cloutier, Alesia

2018-01-01

This study uses five well-documented cranial nonmetric traits (glabella, mastoid process, mental eminence, supraorbital margin, and nuchal crest) and one additional trait (zygomatic extension) to develop a validated decision tree for sex assessment. The decision tree was built and cross-validated on a sample of 293 U.S. White individuals from the William M. Bass Donated Skeletal Collection. Ordinal scores from the six traits were analyzed using the partition modeling option in JMP Pro 12. A holdout sample of 50 skulls was used to test the model. The most accurate decision tree includes three variables: glabella, zygomatic extension, and mastoid process. This decision tree yielded 93.5% accuracy on the training sample, 94% on the cross-validated sample, and 96% on a holdout validation sample. Linear weighted kappa statistics indicate acceptable agreement among observers for these variables. Mental eminence should be avoided, and definitions and figures should be referenced carefully to score nonmetric traits. © 2017 American Academy of Forensic Sciences.

Learning accurate very fast decision trees from uncertain data streams

NASA Astrophysics Data System (ADS)

Liang, Chunquan; Zhang, Yang; Shi, Peng; Hu, Zhengguo

2015-12-01

Most existing works on data stream classification assume the streaming data is precise and definite. Such assumption, however, does not always hold in practice, since data uncertainty is ubiquitous in data stream applications due to imprecise measurement, missing values, privacy protection, etc. The goal of this paper is to learn accurate decision tree models from uncertain data streams for classification analysis. On the basis of very fast decision tree (VFDT) algorithms, we proposed an algorithm for constructing an uncertain VFDT tree with classifiers at tree leaves (uVFDTc). The uVFDTc algorithm can exploit uncertain information effectively and efficiently in both the learning and the classification phases. In the learning phase, it uses Hoeffding bound theory to learn from uncertain data streams and yield fast and reasonable decision trees. In the classification phase, at tree leaves it uses uncertain naive Bayes (UNB) classifiers to improve the classification performance. Experimental results on both synthetic and real-life datasets demonstrate the strong ability of uVFDTc to classify uncertain data streams. The use of UNB at tree leaves has improved the performance of uVFDTc, especially the any-time property, the benefit of exploiting uncertain information, and the robustness against uncertainty.

PRIA 3 Fee Determination Decision Tree

EPA Pesticide Factsheets

The PRIA 3 decision tree will help applicants requesting a pesticide registration or certain tolerance action to accurately identify the category of their application and the amount of the required fee before they submit the application.

The decision tree classifier - Design and potential. [for Landsat-1 data

NASA Technical Reports Server (NTRS)

Hauska, H.; Swain, P. H.

1975-01-01

A new classifier has been developed for the computerized analysis of remote sensor data. The decision tree classifier is essentially a maximum likelihood classifier using multistage decision logic. It is characterized by the fact that an unknown sample can be classified into a class using one or several decision functions in a successive manner. The classifier is applied to the analysis of data sensed by Landsat-1 over Kenosha Pass, Colorado. The classifier is illustrated by a tree diagram which for processing purposes is encoded as a string of symbols such that there is a unique one-to-one relationship between string and decision tree.

78 FR 69817 - Polyethylene Retail Carrier Bags From Thailand: Final Court Decision and Amended Final Results of...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-21

... Bags From Thailand: Final Court Decision and Amended Final Results of Administrative Review of the... Thailand produced or exported by King Pac Industrial Co., Ltd. (King Pac) and Master Packaging Co., Ltd... administrative review of the antidumping duty order on PRCBs from Thailand covering the POR, in accordance with...

A new approach to enhance the performance of decision tree for classifying gene expression data.

PubMed

Hassan, Md; Kotagiri, Ramamohanarao

2013-12-20

Gene expression data classification is a challenging task due to the large dimensionality and very small number of samples. Decision tree is one of the popular machine learning approaches to address such classification problems. However, the existing decision tree algorithms use a single gene feature at each node to split the data into its child nodes and hence might suffer from poor performance specially when classifying gene expression dataset. By using a new decision tree algorithm where, each node of the tree consists of more than one gene, we enhance the classification performance of traditional decision tree classifiers. Our method selects suitable genes that are combined using a linear function to form a derived composite feature. To determine the structure of the tree we use the area under the Receiver Operating Characteristics curve (AUC). Experimental analysis demonstrates higher classification accuracy using the new decision tree compared to the other existing decision trees in literature. We experimentally compare the effect of our scheme against other well known decision tree techniques. Experiments show that our algorithm can substantially boost the classification performance of the decision tree.

Creating ensembles of oblique decision trees with evolutionary algorithms and sampling

DOEpatents

Cantu-Paz, Erick [Oakland, CA; Kamath, Chandrika [Tracy, CA

2006-06-13

A decision tree system that is part of a parallel object-oriented pattern recognition system, which in turn is part of an object oriented data mining system. A decision tree process includes the step of reading the data. If necessary, the data is sorted. A potential split of the data is evaluated according to some criterion. An initial split of the data is determined. The final split of the data is determined using evolutionary algorithms and statistical sampling techniques. The data is split. Multiple decision trees are combined in ensembles.

Learning in data-limited multimodal scenarios: Scandent decision forests and tree-based features.

PubMed

Hor, Soheil; Moradi, Mehdi

2016-12-01

Incomplete and inconsistent datasets often pose difficulties in multimodal studies. We introduce the concept of scandent decision trees to tackle these difficulties. Scandent trees are decision trees that optimally mimic the partitioning of the data determined by another decision tree, and crucially, use only a subset of the feature set. We show how scandent trees can be used to enhance the performance of decision forests trained on a small number of multimodal samples when we have access to larger datasets with vastly incomplete feature sets. Additionally, we introduce the concept of tree-based feature transforms in the decision forest paradigm. When combined with scandent trees, the tree-based feature transforms enable us to train a classifier on a rich multimodal dataset, and use it to classify samples with only a subset of features of the training data. Using this methodology, we build a model trained on MRI and PET images of the ADNI dataset, and then test it on cases with only MRI data. We show that this is significantly more effective in staging of cognitive impairments compared to a similar decision forest model trained and tested on MRI only, or one that uses other kinds of feature transform applied to the MRI data. Copyright © 2016. Published by Elsevier B.V.

IND - THE IND DECISION TREE PACKAGE

NASA Technical Reports Server (NTRS)

Buntine, W.

1994-01-01

A common approach to supervised classification and prediction in artificial intelligence and statistical pattern recognition is the use of decision trees. A tree is "grown" from data using a recursive partitioning algorithm to create a tree which has good prediction of classes on new data. Standard algorithms are CART (by Breiman Friedman, Olshen and Stone) and ID3 and its successor C4 (by Quinlan). As well as reimplementing parts of these algorithms and offering experimental control suites, IND also introduces Bayesian and MML methods and more sophisticated search in growing trees. These produce more accurate class probability estimates that are important in applications like diagnosis. IND is applicable to most data sets consisting of independent instances, each described by a fixed length vector of attribute values. An attribute value may be a number, one of a set of attribute specific symbols, or it may be omitted. One of the attributes is delegated the "target" and IND grows trees to predict the target. Prediction can then be done on new data or the decision tree printed out for inspection. IND provides a range of features and styles with convenience for the casual user as well as fine-tuning for the advanced user or those interested in research. IND can be operated in a CART-like mode (but without regression trees, surrogate splits or multivariate splits), and in a mode like the early version of C4. Advanced features allow more extensive search, interactive control and display of tree growing, and Bayesian and MML algorithms for tree pruning and smoothing. These often produce more accurate class probability estimates at the leaves. IND also comes with a comprehensive experimental control suite. IND consists of four basic kinds of routines: data manipulation routines, tree generation routines, tree testing routines, and tree display routines. The data manipulation routines are used to partition a single large data set into smaller training and test sets. The

Evaluation of Decision Trees for Cloud Detection from AVHRR Data

NASA Technical Reports Server (NTRS)

Shiffman, Smadar; Nemani, Ramakrishna

2005-01-01

Automated cloud detection and tracking is an important step in assessing changes in radiation budgets associated with global climate change via remote sensing. Data products based on satellite imagery are available to the scientific community for studying trends in the Earth's atmosphere. The data products include pixel-based cloud masks that assign cloud-cover classifications to pixels. Many cloud-mask algorithms have the form of decision trees. The decision trees employ sequential tests that scientists designed based on empirical astrophysics studies and simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In a previous study we compared automatically learned decision trees to cloud masks included in Advanced Very High Resolution Radiometer (AVHRR) data products from the year 2000. In this paper we report the replication of the study for five-year data, and for a gold standard based on surface observations performed by scientists at weather stations in the British Islands. For our sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks p < 0.001.

15 CFR Supplement No 1 to Part 732 - Decision Tree

Code of Federal Regulations, 2013 CFR

2013-01-01

... 15 Commerce and Foreign Trade 2 2013-01-01 2013-01-01 false Decision Tree No Supplement No 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued... THE EAR Pt. 732, Supp. 1 Supplement No 1 to Part 732—Decision Tree ER06FE04.000 [69 FR 5687, Feb. 6...

15 CFR Supplement No 1 to Part 732 - Decision Tree

Code of Federal Regulations, 2014 CFR

2014-01-01

... 15 Commerce and Foreign Trade 2 2014-01-01 2014-01-01 false Decision Tree No Supplement No 1 to Part 732 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued... THE EAR Pt. 732, Supp. 1 Supplement No 1 to Part 732—Decision Tree ER06FE04.000 [69 FR 5687, Feb. 6...

An automated approach to the design of decision tree classifiers

NASA Technical Reports Server (NTRS)

Argentiero, P.; Chin, P.; Beaudet, P.

1980-01-01

The classification of large dimensional data sets arising from the merging of remote sensing data with more traditional forms of ancillary data is considered. Decision tree classification, a popular approach to the problem, is characterized by the property that samples are subjected to a sequence of decision rules before they are assigned to a unique class. An automated technique for effective decision tree design which relies only on apriori statistics is presented. This procedure utilizes a set of two dimensional canonical transforms and Bayes table look-up decision rules. An optimal design at each node is derived based on the associated decision table. A procedure for computing the global probability of correct classfication is also provided. An example is given in which class statistics obtained from an actual LANDSAT scene are used as input to the program. The resulting decision tree design has an associated probability of correct classification of .76 compared to the theoretically optimum .79 probability of correct classification associated with a full dimensional Bayes classifier. Recommendations for future research are included.

The value of decision tree analysis in planning anaesthetic care in obstetrics.

PubMed

Bamber, J H; Evans, S A

2016-08-01

The use of decision tree analysis is discussed in the context of the anaesthetic and obstetric management of a young pregnant woman with joint hypermobility syndrome with a history of insensitivity to local anaesthesia and a previous difficult intubation due to a tongue tumour. The multidisciplinary clinical decision process resulted in the woman being delivered without complication by elective caesarean section under general anaesthesia after an awake fibreoptic intubation. The decision process used is reviewed and compared retrospectively to a decision tree analytical approach. The benefits and limitations of using decision tree analysis are reviewed and its application in obstetric anaesthesia is discussed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Decision tree analysis in subarachnoid hemorrhage: prediction of outcome parameters during the course of aneurysmal subarachnoid hemorrhage using decision tree analysis.

PubMed

Hostettler, Isabel Charlotte; Muroi, Carl; Richter, Johannes Konstantin; Schmid, Josef; Neidert, Marian Christoph; Seule, Martin; Boss, Oliver; Pangalu, Athina; Germans, Menno Robbert; Keller, Emanuela

2018-01-19

OBJECTIVE The aim of this study was to create prediction models for outcome parameters by decision tree analysis based on clinical and laboratory data in patients with aneurysmal subarachnoid hemorrhage (aSAH). METHODS The database consisted of clinical and laboratory parameters of 548 patients with aSAH who were admitted to the Neurocritical Care Unit, University Hospital Zurich. To examine the model performance, the cohort was randomly divided into a derivation cohort (60% [n = 329]; training data set) and a validation cohort (40% [n = 219]; test data set). The classification and regression tree prediction algorithm was applied to predict death, functional outcome, and ventriculoperitoneal (VP) shunt dependency. Chi-square automatic interaction detection was applied to predict delayed cerebral infarction on days 1, 3, and 7. RESULTS The overall mortality was 18.4%. The accuracy of the decision tree models was good for survival on day 1 and favorable functional outcome at all time points, with a difference between the training and test data sets of < 5%. Prediction accuracy for survival on day 1 was 75.2%. The most important differentiating factor was the interleukin-6 (IL-6) level on day 1. Favorable functional outcome, defined as Glasgow Outcome Scale scores of 4 and 5, was observed in 68.6% of patients. Favorable functional outcome at all time points had a prediction accuracy of 71.1% in the training data set, with procalcitonin on day 1 being the most important differentiating factor at all time points. A total of 148 patients (27%) developed VP shunt dependency. The most important differentiating factor was hyperglycemia on admission. CONCLUSIONS The multiple variable analysis capability of decision trees enables exploration of dependent variables in the context of multiple changing influences over the course of an illness. The decision tree currently generated increases awareness of the early systemic stress response, which is seemingly pertinent for

The Decision Tree for Teaching Management of Uncertainty

ERIC Educational Resources Information Center

Knaggs, Sara J.; And Others

1974-01-01

A 'decision tree' consists of an outline of the patient's symptoms and a logic for decision and action. It is felt that this approach to the decisionmaking process better facilitates each learner's application of his own level of knowledge and skills. (Author)

A universal hybrid decision tree classifier design for human activity classification.

PubMed

Chien, Chieh; Pottie, Gregory J

2012-01-01

A system that reliably classifies daily life activities can contribute to more effective and economical treatments for patients with chronic conditions or undergoing rehabilitative therapy. We propose a universal hybrid decision tree classifier for this purpose. The tree classifier can flexibly implement different decision rules at its internal nodes, and can be adapted from a population-based model when supplemented by training data for individuals. The system was tested using seven subjects each monitored by 14 triaxial accelerometers. Each subject performed fourteen different activities typical of daily life. Using leave-one-out cross validation, our decision tree produced average classification accuracies of 89.9%. In contrast, the MATLAB personalized tree classifiers using Gini's diversity index as the split criterion followed by optimally tuning the thresholds for each subject yielded 69.2%.

Improved Frame Mode Selection for AMR-WB+ Based on Decision Tree

NASA Astrophysics Data System (ADS)

Kim, Jong Kyu; Kim, Nam Soo

In this letter, we propose a coding mode selection method for the AMR-WB+ audio coder based on a decision tree. In order to reduce computation while maintaining good performance, decision tree classifier is adopted with the closed loop mode selection results as the target classification labels. The size of the decision tree is controlled by pruning, so the proposed method does not increase the memory requirement significantly. Through an evaluation test on a database covering both speech and music materials, the proposed method is found to achieve a much better mode selection accuracy compared with the open loop mode selection module in the AMR-WB+.

Predicting metabolic syndrome using decision tree and support vector machine methods.

PubMed

Karimi-Alavijeh, Farzaneh; Jalili, Saeed; Sadeghi, Masoumeh

2016-05-01

Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome. This study aims to employ decision tree and support vector machine (SVM) to predict the 7-year incidence of metabolic syndrome. This research is a practical one in which data from 2107 participants of Isfahan Cohort Study has been utilized. The subjects without metabolic syndrome according to the ATPIII criteria were selected. The features that have been used in this data set include: gender, age, weight, body mass index, waist circumference, waist-to-hip ratio, hip circumference, physical activity, smoking, hypertension, antihypertensive medication use, systolic blood pressure (BP), diastolic BP, fasting blood sugar, 2-hour blood glucose, triglycerides (TGs), total cholesterol, low-density lipoprotein, high density lipoprotein-cholesterol, mean corpuscular volume, and mean corpuscular hemoglobin. Metabolic syndrome was diagnosed based on ATPIII criteria and two methods of decision tree and SVM were selected to predict the metabolic syndrome. The criteria of sensitivity, specificity and accuracy were used for validation. SVM and decision tree methods were examined according to the criteria of sensitivity, specificity and accuracy. Sensitivity, specificity and accuracy were 0.774 (0.758), 0.74 (0.72) and 0.757 (0.739) in SVM (decision tree) method. The results show that SVM method sensitivity, specificity and accuracy is more efficient than decision tree. The results of decision tree method show that the TG is the most important feature in predicting metabolic syndrome. According

Using histograms to introduce randomization in the generation of ensembles of decision trees

DOEpatents

Kamath, Chandrika; Cantu-Paz, Erick; Littau, David

2005-02-22

A system for decision tree ensembles that includes a module to read the data, a module to create a histogram, a module to evaluate a potential split according to some criterion using the histogram, a module to select a split point randomly in an interval around the best split, a module to split the data, and a module to combine multiple decision trees in ensembles. The decision tree method includes the steps of reading the data; creating a histogram; evaluating a potential split according to some criterion using the histogram, selecting a split point randomly in an interval around the best split, splitting the data, and combining multiple decision trees in ensembles.

A Decision Tree for Psychology Majors: Supplying Questions as Well as Answers.

ERIC Educational Resources Information Center

Poe, Retta E.

1988-01-01

Outlines the development of a psychology careers decision tree to help faculty advise students plan their program. States that students using the decision tree may benefit by learning more about their career options and by acquiring better question-asking skills. (GEA)

Solar and Wind Site Screening Decision Trees

EPA Pesticide Factsheets

EPA and NREL created a decision tree to guide state and local governments and other stakeholders through a process for screening sites for their suitability for future redevelopment with solar photovoltaic (PV) energy and wind energy.

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data.

PubMed

Barros, Rodrigo C; Winck, Ana T; Machado, Karina S; Basgalupp, Márcio P; de Carvalho, André C P L F; Ruiz, Duncan D; de Souza, Osmar Norberto

2012-11-21

This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.

The Utility of Decision Trees in Oncofertility Care in Japan.

PubMed

Ito, Yuki; Shiraishi, Eriko; Kato, Atsuko; Haino, Takayuki; Sugimoto, Kouhei; Okamoto, Aikou; Suzuki, Nao

2017-03-01

To identify the utility and issues associated with the use of decision trees in oncofertility patient care in Japan. A total of 35 women who had been diagnosed with cancer, but had not begun anticancer treatment, were enrolled. We applied the oncofertility decision tree for women published by Gardino et al. to counsel a consecutive series of women on fertility preservation (FP) options following cancer diagnosis. Percentage of women who decided to undergo oocyte retrieval for embryo cryopreservation and the expected live-birth rate for these patients were calculated using the following equation: expected live-birth rate = pregnancy rate at each age per embryo transfer × (1 - miscarriage rate) × No. of cryopreserved embryos. Oocyte retrieval was performed for 17 patients (48.6%; mean ± standard deviation [SD] age, 36.35 ± 3.82 years). The mean ± SD number of cryopreserved embryos was 5.29 ± 4.63. The expected live-birth rate was 0.66. The expected live-birth rate with FP indicated that one in three oncofertility patients would not expect to have a live birth following oocyte retrieval and embryo cryopreservation. While the decision trees were useful as decision-making tools for women contemplating FP, in the context of the current restrictions on oocyte donation and the extremely small number of adoptions in Japan, the remaining options for fertility after cancer are limited. In order for cancer survivors to feel secure in their decisions, the decision tree may need to be adapted simultaneously with improvements to the social environment, such as greater support for adoption.

Learning from examples - Generation and evaluation of decision trees for software resource analysis

NASA Technical Reports Server (NTRS)

Selby, Richard W.; Porter, Adam A.

1988-01-01

A general solution method for the automatic generation of decision (or classification) trees is investigated. The approach is to provide insights through in-depth empirical characterization and evaluation of decision trees for software resource data analysis. The trees identify classes of objects (software modules) that had high development effort. Sixteen software systems ranging from 3,000 to 112,000 source lines were selected for analysis from a NASA production environment. The collection and analysis of 74 attributes (or metrics), for over 4,700 objects, captured information about the development effort, faults, changes, design style, and implementation style. A total of 9,600 decision trees were automatically generated and evaluated. The trees correctly identified 79.3 percent of the software modules that had high development effort or faults, and the trees generated from the best parameter combinations correctly identified 88.4 percent of the modules on the average.

New Splitting Criteria for Decision Trees in Stationary Data Streams.

PubMed

Jaworski, Maciej; Duda, Piotr; Rutkowski, Leszek; Jaworski, Maciej; Duda, Piotr; Rutkowski, Leszek; Rutkowski, Leszek; Duda, Piotr; Jaworski, Maciej

2018-06-01

The most popular tools for stream data mining are based on decision trees. In previous 15 years, all designed methods, headed by the very fast decision tree algorithm, relayed on Hoeffding's inequality and hundreds of researchers followed this scheme. Recently, we have demonstrated that although the Hoeffding decision trees are an effective tool for dealing with stream data, they are a purely heuristic procedure; for example, classical decision trees such as ID3 or CART cannot be adopted to data stream mining using Hoeffding's inequality. Therefore, there is an urgent need to develop new algorithms, which are both mathematically justified and characterized by good performance. In this paper, we address this problem by developing a family of new splitting criteria for classification in stationary data streams and investigating their probabilistic properties. The new criteria, derived using appropriate statistical tools, are based on the misclassification error and the Gini index impurity measures. The general division of splitting criteria into two types is proposed. Attributes chosen based on type- splitting criteria guarantee, with high probability, the highest expected value of split measure. Type- criteria ensure that the chosen attribute is the same, with high probability, as it would be chosen based on the whole infinite data stream. Moreover, in this paper, two hybrid splitting criteria are proposed, which are the combinations of single criteria based on the misclassification error and Gini index.

Real-Time Speech/Music Classification With a Hierarchical Oblique Decision Tree

DTIC Science & Technology

2008-04-01

REAL-TIME SPEECH/ MUSIC CLASSIFICATION WITH A HIERARCHICAL OBLIQUE DECISION TREE Jun Wang, Qiong Wu, Haojiang Deng, Qin Yan Institute of Acoustics...time speech/ music classification with a hierarchical oblique decision tree. A set of discrimination features in frequency domain are selected...handle signals without discrimination and can not work properly in the existence of multimedia signals. This paper proposes a real-time speech/ music

Decision-Tree Models of Categorization Response Times, Choice Proportions, and Typicality Judgments

ERIC Educational Resources Information Center

Lafond, Daniel; Lacouture, Yves; Cohen, Andrew L.

2009-01-01

The authors present 3 decision-tree models of categorization adapted from T. Trabasso, H. Rollins, and E. Shaughnessy (1971) and use them to provide a quantitative account of categorization response times, choice proportions, and typicality judgments at the individual-participant level. In Experiment 1, the decision-tree models were fit to…

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

PubMed Central

2012-01-01

Background This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. Results The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. Conclusions We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor. PMID:23171000

Metric Sex Determination of the Human Coxal Bone on a Virtual Sample using Decision Trees.

PubMed

Savall, Frédéric; Faruch-Bilfeld, Marie; Dedouit, Fabrice; Sans, Nicolas; Rousseau, Hervé; Rougé, Daniel; Telmon, Norbert

2015-11-01

Decision trees provide an alternative to multivariate discriminant analysis, which is still the most commonly used in anthropometric studies. Our study analyzed the metric characterization of a recent virtual sample of 113 coxal bones using decision trees for sex determination. From 17 osteometric type I landmarks, a dataset was built with five classic distances traditionally reported in the literature and six new distances selected using the two-step ratio method. A ten-fold cross-validation was performed, and a decision tree was established on two subsamples (training and test sets). The decision tree established on the training set included three nodes and its application to the test set correctly classified 92% of individuals. This percentage was similar to the data of the literature. The usefulness of decision trees has been demonstrated in numerous fields. They have been already used in sex determination, body mass prediction, and ancestry estimation. This study shows another use of decision trees enabling simple and accurate sex determination. © 2015 American Academy of Forensic Sciences.

Generation and Termination of Binary Decision Trees for Nonparametric Multiclass Classification.

DTIC Science & Technology

1984-10-01

O M coF=F;; UMBER2. GOVT ACCE5SION NO.1 3 . REC,PINS :A7AL:,G NUMBER ( ’eneration and Terminat_,on :)f Binary D-ecision jC j ik; Trees for Nonnararetrc...1-I . v)IAMO 0~I4 EDvt" O F I 00 . 3 15I OR%.OL.ETL - S-S OCTOBER 1984 LIDS-P-1411 GENERATION AND TERMINATION OF BINARY DECISION TREES FOR...minimizes the Bayes risk. Tree generation and termination are based on the training and test samples, respectively. 0 0 0/ 6 0¢ A 3 I. Introduction We state

Applied Swarm-based medicine: collecting decision trees for patterns of algorithms analysis.

PubMed

Panje, Cédric M; Glatzer, Markus; von Rappard, Joscha; Rothermundt, Christian; Hundsberger, Thomas; Zumstein, Valentin; Plasswilm, Ludwig; Putora, Paul Martin

2017-08-16

The objective consensus methodology has recently been applied in consensus finding in several studies on medical decision-making among clinical experts or guidelines. The main advantages of this method are an automated analysis and comparison of treatment algorithms of the participating centers which can be performed anonymously. Based on the experience from completed consensus analyses, the main steps for the successful implementation of the objective consensus methodology were identified and discussed among the main investigators. The following steps for the successful collection and conversion of decision trees were identified and defined in detail: problem definition, population selection, draft input collection, tree conversion, criteria adaptation, problem re-evaluation, results distribution and refinement, tree finalisation, and analysis. This manuscript provides information on the main steps for successful collection of decision trees and summarizes important aspects at each point of the analysis.

Aneurysmal subarachnoid hemorrhage prognostic decision-making algorithm using classification and regression tree analysis.

PubMed

Lo, Benjamin W Y; Fukuda, Hitoshi; Angle, Mark; Teitelbaum, Jeanne; Macdonald, R Loch; Farrokhyar, Forough; Thabane, Lehana; Levine, Mitchell A H

2016-01-01

Classification and regression tree analysis involves the creation of a decision tree by recursive partitioning of a dataset into more homogeneous subgroups. Thus far, there is scarce literature on using this technique to create clinical prediction tools for aneurysmal subarachnoid hemorrhage (SAH). The classification and regression tree analysis technique was applied to the multicenter Tirilazad database (3551 patients) in order to create the decision-making algorithm. In order to elucidate prognostic subgroups in aneurysmal SAH, neurologic, systemic, and demographic factors were taken into account. The dependent variable used for analysis was the dichotomized Glasgow Outcome Score at 3 months. Classification and regression tree analysis revealed seven prognostic subgroups. Neurological grade, occurrence of post-admission stroke, occurrence of post-admission fever, and age represented the explanatory nodes of this decision tree. Split sample validation revealed classification accuracy of 79% for the training dataset and 77% for the testing dataset. In addition, the occurrence of fever at 1-week post-aneurysmal SAH is associated with increased odds of post-admission stroke (odds ratio: 1.83, 95% confidence interval: 1.56-2.45, P < 0.01). A clinically useful classification tree was generated, which serves as a prediction tool to guide bedside prognostication and clinical treatment decision making. This prognostic decision-making algorithm also shed light on the complex interactions between a number of risk factors in determining outcome after aneurysmal SAH.

An Improved Decision Tree for Predicting a Major Product in Competing Reactions

ERIC Educational Resources Information Center

Graham, Kate J.

2014-01-01

When organic chemistry students encounter competing reactions, they are often overwhelmed by the task of evaluating multiple factors that affect the outcome of a reaction. The use of a decision tree is a useful tool to teach students to evaluate a complex situation and propose a likely outcome. Specifically, a decision tree can help students…

Implementation of Data Mining to Analyze Drug Cases Using C4.5 Decision Tree

NASA Astrophysics Data System (ADS)

Wahyuni, Sri

2018-03-01

Data mining was the process of finding useful information from a large set of databases. One of the existing techniques in data mining was classification. The method used was decision tree method and algorithm used was C4.5 algorithm. The decision tree method was a method that transformed a very large fact into a decision tree which was presenting the rules. Decision tree method was useful for exploring data, as well as finding a hidden relationship between a number of potential input variables with a target variable. The decision tree of the C4.5 algorithm was constructed with several stages including the selection of attributes as roots, created a branch for each value and divided the case into the branch. These stages would be repeated for each branch until all the cases on the branch had the same class. From the solution of the decision tree there would be some rules of a case. In this case the researcher classified the data of prisoners at Labuhan Deli prison to know the factors of detainees committing criminal acts of drugs. By applying this C4.5 algorithm, then the knowledge was obtained as information to minimize the criminal acts of drugs. From the findings of the research, it was found that the most influential factor of the detainee committed the criminal act of drugs was from the address variable.

Using decision tree models to depict primary care physicians CRC screening decision heuristics.

PubMed

Wackerbarth, Sarah B; Tarasenko, Yelena N; Curtis, Laurel A; Joyce, Jennifer M; Haist, Steven A

2007-10-01

The purpose of this study was to identify decision heuristics utilized by primary care physicians in formulating colorectal cancer screening recommendations. Qualitative research using in-depth semi-structured interviews. We interviewed 66 primary care internists and family physicians evenly drawn from academic and community practices. A majority of physicians were male, and almost all were white, non-Hispanic. Three researchers independently reviewed each transcript to determine the physician's decision criteria and developed decision trees. Final trees were developed by consensus. The constant comparative methodology was used to define the categories. Physicians were found to use 1 of 4 heuristics ("age 50," "age 50, if family history, then earlier," "age 50, if family history, then screen at age 40," or "age 50, if family history, then adjust relative to reference case") for the timing recommendation and 5 heuristics ["fecal occult blood test" (FOBT), "colonoscopy," "if not colonoscopy, then...," "FOBT and another test," and "a choice between options"] for the type decision. No connection was found between timing and screening type heuristics. We found evidence of heuristic use. Further research is needed to determine the potential impact on quality of care.

[Comparison of Discriminant Analysis and Decision Trees for the Detection of Subclinical Keratoconus].

PubMed

Kleinhans, Sonja; Herrmann, Eva; Kohnen, Thomas; Bühren, Jens

2017-08-15

Background Iatrogenic keratectasia is one of the most dreaded complications of refractive surgery. In most cases, keratectasia develops after refractive surgery of eyes suffering from subclinical stages of keratoconus with few or no signs. Unfortunately, there has been no reliable procedure for the early detection of keratoconus. In this study, we used binary decision trees (recursive partitioning) to assess their suitability for discrimination between normal eyes and eyes with subclinical keratoconus. Patients and Methods The method of decision tree analysis was compared with discriminant analysis which has shown good results in previous studies. Input data were 32 eyes of 32 patients with newly diagnosed keratoconus in the contralateral eye and preoperative data of 10 eyes of 5 patients with keratectasia after laser in-situ keratomileusis (LASIK). The control group was made up of 245 normal eyes after LASIK and 12-month follow-up without any signs of iatrogenic keratectasia. Results Decision trees gave better accuracy and specificity than did discriminant analysis. The sensitivity of decision trees was lower than the sensitivity of discriminant analysis. Conclusion On the basis of the patient population of this study, decision trees did not prove to be superior to linear discriminant analysis for the detection of subclinical keratoconus. Georg Thieme Verlag KG Stuttgart · New York.

[Prediction of regional soil quality based on mutual information theory integrated with decision tree algorithm].

PubMed

Lin, Fen-Fang; Wang, Ke; Yang, Ning; Yan, Shi-Guang; Zheng, Xin-Yu

2012-02-01

In this paper, some main factors such as soil type, land use pattern, lithology type, topography, road, and industry type that affect soil quality were used to precisely obtain the spatial distribution characteristics of regional soil quality, mutual information theory was adopted to select the main environmental factors, and decision tree algorithm See 5.0 was applied to predict the grade of regional soil quality. The main factors affecting regional soil quality were soil type, land use, lithology type, distance to town, distance to water area, altitude, distance to road, and distance to industrial land. The prediction accuracy of the decision tree model with the variables selected by mutual information was obviously higher than that of the model with all variables, and, for the former model, whether of decision tree or of decision rule, its prediction accuracy was all higher than 80%. Based on the continuous and categorical data, the method of mutual information theory integrated with decision tree could not only reduce the number of input parameters for decision tree algorithm, but also predict and assess regional soil quality effectively.

Using Decision Trees to Detect and Isolate Simulated Leaks in the J-2X Rocket Engine

NASA Technical Reports Server (NTRS)

Schwabacher, Mark A.; Aguilar, Robert; Figueroa, Fernando F.

2009-01-01

The goal of this work was to use data-driven methods to automatically detect and isolate faults in the J-2X rocket engine. It was decided to use decision trees, since they tend to be easier to interpret than other data-driven methods. The decision tree algorithm automatically "learns" a decision tree by performing a search through the space of possible decision trees to find one that fits the training data. The particular decision tree algorithm used is known as C4.5. Simulated J-2X data from a high-fidelity simulator developed at Pratt & Whitney Rocketdyne and known as the Detailed Real-Time Model (DRTM) was used to "train" and test the decision tree. Fifty-six DRTM simulations were performed for this purpose, with different leak sizes, different leak locations, and different times of leak onset. To make the simulations as realistic as possible, they included simulated sensor noise, and included a gradual degradation in both fuel and oxidizer turbine efficiency. A decision tree was trained using 11 of these simulations, and tested using the remaining 45 simulations. In the training phase, the C4.5 algorithm was provided with labeled examples of data from nominal operation and data including leaks in each leak location. From the data, it "learned" a decision tree that can classify unseen data as having no leak or having a leak in one of the five leak locations. In the test phase, the decision tree produced very low false alarm rates and low missed detection rates on the unseen data. It had very good fault isolation rates for three of the five simulated leak locations, but it tended to confuse the remaining two locations, perhaps because a large leak at one of these two locations can look very similar to a small leak at the other location.

Application of preprocessing filtering on Decision Tree C4.5 and rough set theory

NASA Astrophysics Data System (ADS)

Chan, Joseph C. C.; Lin, Tsau Y.

2001-03-01

This paper compares two artificial intelligence methods: the Decision Tree C4.5 and Rough Set Theory on the stock market data. The Decision Tree C4.5 is reviewed with the Rough Set Theory. An enhanced window application is developed to facilitate the pre-processing filtering by introducing the feature (attribute) transformations, which allows users to input formulas and create new attributes. Also, the application produces three varieties of data set with delaying, averaging, and summation. The results prove the improvement of pre-processing by applying feature (attribute) transformations on Decision Tree C4.5. Moreover, the comparison between Decision Tree C4.5 and Rough Set Theory is based on the clarity, automation, accuracy, dimensionality, raw data, and speed, which is supported by the rules sets generated by both algorithms on three different sets of data.

Ultrasonographic Diagnosis of Biliary Atresia Based on a Decision-Making Tree Model.

PubMed

Lee, So Mi; Cheon, Jung-Eun; Choi, Young Hun; Kim, Woo Sun; Cho, Hyun-Hae; Cho, Hyun-Hye; Kim, In-One; You, Sun Kyoung

2015-01-01

To assess the diagnostic value of various ultrasound (US) findings and to make a decision-tree model for US diagnosis of biliary atresia (BA). From March 2008 to January 2014, the following US findings were retrospectively evaluated in 100 infants with cholestatic jaundice (BA, n = 46; non-BA, n = 54): length and morphology of the gallbladder, triangular cord thickness, hepatic artery and portal vein diameters, and visualization of the common bile duct. Logistic regression analyses were performed to determine the features that would be useful in predicting BA. Conditional inference tree analysis was used to generate a decision-making tree for classifying patients into the BA or non-BA groups. Multivariate logistic regression analysis showed that abnormal gallbladder morphology and greater triangular cord thickness were significant predictors of BA (p = 0.003 and 0.001; adjusted odds ratio: 345.6 and 65.6, respectively). In the decision-making tree using conditional inference tree analysis, gallbladder morphology and triangular cord thickness (optimal cutoff value of triangular cord thickness, 3.4 mm) were also selected as significant discriminators for differential diagnosis of BA, and gallbladder morphology was the first discriminator. The diagnostic performance of the decision-making tree was excellent, with sensitivity of 100% (46/46), specificity of 94.4% (51/54), and overall accuracy of 97% (97/100). Abnormal gallbladder morphology and greater triangular cord thickness (> 3.4 mm) were the most useful predictors of BA on US. We suggest that the gallbladder morphology should be evaluated first and that triangular cord thickness should be evaluated subsequently in cases with normal gallbladder morphology.

Comparison of Taxi Time Prediction Performance Using Different Taxi Speed Decision Trees

NASA Technical Reports Server (NTRS)

Lee, Hanbong

2017-01-01

In the STBO modeler and tactical surface scheduler for ATD-2 project, taxi speed decision trees are used to calculate the unimpeded taxi times of flights taxiing on the airport surface. The initial taxi speed values in these decision trees did not show good prediction accuracy of taxi times. Using the more recent, reliable surveillance data, new taxi speed values in ramp area and movement area were computed. Before integrating these values into the STBO system, we performed test runs using live data from Charlotte airport, with different taxi speed settings: 1) initial taxi speed values and 2) new ones. Taxi time prediction performance was evaluated by comparing various metrics. The results show that the new taxi speed decision trees can calculate the unimpeded taxi-out times more accurately.

Diagnostic classification scheme in Iranian breast cancer patients using a decision tree.

PubMed

Malehi, Amal Saki

2014-01-01

The objective of this study was to determine a diagnostic classification scheme using a decision tree based model. The study was conducted as a retrospective case-control study in Imam Khomeini hospital in Tehran during 2001 to 2009. Data, including demographic and clinical-pathological characteristics, were uniformly collected from 624 females, 312 of them were referred with positive diagnosis of breast cancer (cases) and 312 healthy women (controls). The decision tree was implemented to develop a diagnostic classification scheme using CART 6.0 Software. The AUC (area under curve), was measured as the overall performance of diagnostic classification of the decision tree. Five variables as main risk factors of breast cancer and six subgroups as high risk were identified. The results indicated that increasing age, low age at menarche, single and divorced statues, irregular menarche pattern and family history of breast cancer are the important diagnostic factors in Iranian breast cancer patients. The sensitivity and specificity of the analysis were 66% and 86.9% respectively. The high AUC (0.82) also showed an excellent classification and diagnostic performance of the model. Decision tree based model appears to be suitable for identifying risk factors and high or low risk subgroups. It can also assists clinicians in making a decision, since it can identify underlying prognostic relationships and understanding the model is very explicit.

Cloud Detection from Satellite Imagery: A Comparison of Expert-Generated and Automatically-Generated Decision Trees

NASA Technical Reports Server (NTRS)

Shiffman, Smadar

2004-01-01

Automated cloud detection and tracking is an important step in assessing global climate change via remote sensing. Cloud masks, which indicate whether individual pixels depict clouds, are included in many of the data products that are based on data acquired on- board earth satellites. Many cloud-mask algorithms have the form of decision trees, which employ sequential tests that scientists designed based on empirical astrophysics studies and astrophysics simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In this study we explored the potential benefits of automatically-learned decision trees for detecting clouds from images acquired using the Advanced Very High Resolution Radiometer (AVHRR) instrument on board the NOAA-14 weather satellite of the National Oceanic and Atmospheric Administration. We constructed three decision trees for a sample of 8km-daily AVHRR data from 2000 using a decision-tree learning procedure provided within MATLAB(R), and compared the accuracy of the decision trees to the accuracy of the cloud mask. We used ground observations collected by the National Aeronautics and Space Administration Clouds and the Earth s Radiant Energy Systems S COOL project as the gold standard. For the sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks included in the AVHRR data product.

Improving medical diagnosis reliability using Boosted C5.0 decision tree empowered by Particle Swarm Optimization.

PubMed

Pashaei, Elnaz; Ozen, Mustafa; Aydin, Nizamettin

2015-08-01

Improving accuracy of supervised classification algorithms in biomedical applications is one of active area of research. In this study, we improve the performance of Particle Swarm Optimization (PSO) combined with C4.5 decision tree (PSO+C4.5) classifier by applying Boosted C5.0 decision tree as the fitness function. To evaluate the effectiveness of our proposed method, it is implemented on 1 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. Moreover, the results of PSO + Boosted C5.0 implementation are compared to eight well-known benchmark classification methods (PSO+C4.5, support vector machine under the kernel of Radial Basis Function, Classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Boosted C5.0 decision tree, Naive Bayes and Weighted K-Nearest neighbor). Repeated five-fold cross-validation method was used to justify the performance of classifiers. Experimental results show that our proposed method not only improve the performance of PSO+C4.5 but also obtains higher classification accuracy compared to the other classification methods.

Predicting the probability of mortality of gastric cancer patients using decision tree.

PubMed

Mohammadzadeh, F; Noorkojuri, H; Pourhoseingholi, M A; Saadat, S; Baghestani, A R

2015-06-01

Gastric cancer is the fourth most common cancer worldwide. This reason motivated us to investigate and introduce gastric cancer risk factors utilizing statistical methods. The aim of this study was to identify the most important factors influencing the mortality of patients who suffer from gastric cancer disease and to introduce a classification approach according to decision tree model for predicting the probability of mortality from this disease. Data on 216 patients with gastric cancer, who were registered in Taleghani hospital in Tehran,Iran, were analyzed. At first, patients were divided into two groups: the dead and alive. Then, to fit decision tree model to our data, we randomly selected 20% of dataset to the test sample and remaining dataset considered as the training sample. Finally, the validity of the model examined with sensitivity, specificity, diagnosis accuracy and the area under the receiver operating characteristic curve. The CART version 6.0 and SPSS version 19.0 softwares were used for the analysis of the data. Diabetes, ethnicity, tobacco, tumor size, surgery, pathologic stage, age at diagnosis, exposure to chemical weapons and alcohol consumption were determined as effective factors on mortality of gastric cancer. The sensitivity, specificity and accuracy of decision tree were 0.72, 0.75 and 0.74 respectively. The indices of sensitivity, specificity and accuracy represented that the decision tree model has acceptable accuracy to prediction the probability of mortality in gastric cancer patients. So a simple decision tree consisted of factors affecting on mortality of gastric cancer may help clinicians as a reliable and practical tool to predict the probability of mortality in these patients.

Pruning a decision tree for selecting computer-related assistive devices for people with disabilities.

PubMed

Chi, Chia-Fen; Tseng, Li-Kai; Jang, Yuh

2012-07-01

Many disabled individuals lack extensive knowledge about assistive technology, which could help them use computers. In 1997, Denis Anson developed a decision tree of 49 evaluative questions designed to evaluate the functional capabilities of the disabled user and choose an appropriate combination of assistive devices, from a selection of 26, that enable the individual to use a computer. In general, occupational therapists guide the disabled users through this process. They often have to go over repetitive questions in order to find an appropriate device. A disabled user may require an alphanumeric entry device, a pointing device, an output device, a performance enhancement device, or some combination of these. Therefore, the current research eliminates redundant questions and divides Anson's decision tree into multiple independent subtrees to meet the actual demand of computer users with disabilities. The modified decision tree was tested by six disabled users to prove it can determine a complete set of assistive devices with a smaller number of evaluative questions. The means to insert new categories of computer-related assistive devices was included to ensure the decision tree can be expanded and updated. The current decision tree can help the disabled users and assistive technology practitioners to find appropriate computer-related assistive devices that meet with clients' individual needs in an efficient manner.

Wrist postures and forces in tree planters during three tree unloading conditions.

PubMed

Denbeigh, Kathleen; Slot, Tegan R; Dumas, Geneviève A

2013-01-01

The aims of this study were to investigate wrist postures and forces while operating the shovel during tree planting and to determine if different tree unloading techniques result in variations in wrist postures and forces. Experienced tree planters performed the planting task in a laboratory environment for three conditions: (1) symmetric tree unloading, and asymmetric unloading resulting in (2) right-loaded planting bags and (3) left-loaded planting bags. An optoelectric system and a shovel instrumented with strain gauges captured wrist posture and forces at the wrist, respectively. Wrist extension of up to 45° was observed, and this posture, in combination with varying degrees of wrist deviation, may be a primary risk factor for musculoskeletal pain. Average resultant forces at the wrist were moderately high (>30 N) for each unloading condition, indicating increased risk for the development of repetitive strain injuries such as carpal tunnel syndrome. No significant differences in wrist posture or forces existed between unloading conditions. Wrist pain is a major musculoskeletal complaint among tree planters. This study measured wrist postures and forces at the wrist while operating the shovel during tree planting. The wrist extension observed, in combination with deviation, may be a key risk factor for musculoskeletal pain. Forces at the wrist indicate increased risk for repetitive strain injuries.

Minimizing the cost of translocation failure with decision-tree models that predict species' behavioral response in translocation sites.

PubMed

Ebrahimi, Mehregan; Ebrahimie, Esmaeil; Bull, C Michael

2015-08-01

The high number of failures is one reason why translocation is often not recommended. Considering how behavior changes during translocations may improve translocation success. To derive decision-tree models for species' translocation, we used data on the short-term responses of an endangered Australian skink in 5 simulated translocations with different release conditions. We used 4 different decision-tree algorithms (decision tree, decision-tree parallel, decision stump, and random forest) with 4 different criteria (gain ratio, information gain, gini index, and accuracy) to investigate how environmental and behavioral parameters may affect the success of a translocation. We assumed behavioral changes that increased dispersal away from a release site would reduce translocation success. The trees became more complex when we included all behavioral parameters as attributes, but these trees yielded more detailed information about why and how dispersal occurred. According to these complex trees, there were positive associations between some behavioral parameters, such as fight and dispersal, that showed there was a higher chance, for example, of dispersal among lizards that fought than among those that did not fight. Decision trees based on parameters related to release conditions were easier to understand and could be used by managers to make translocation decisions under different circumstances. © 2015 Society for Conservation Biology.

Decision Trees Predicting Tumor Shrinkage for Head and Neck Cancer: Implications for Adaptive Radiotherapy.

PubMed

Surucu, Murat; Shah, Karan K; Mescioglu, Ibrahim; Roeske, John C; Small, William; Choi, Mehee; Emami, Bahman

2016-02-01

To develop decision trees predicting for tumor volume reduction in patients with head and neck (H&N) cancer using pretreatment clinical and pathological parameters. Forty-eight patients treated with definitive concurrent chemoradiotherapy for squamous cell carcinoma of the nasopharynx, oropharynx, oral cavity, or hypopharynx were retrospectively analyzed. These patients were rescanned at a median dose of 37.8 Gy and replanned to account for anatomical changes. The percentages of gross tumor volume (GTV) change from initial to rescan computed tomography (CT; %GTVΔ) were calculated. Two decision trees were generated to correlate %GTVΔ in primary and nodal volumes with 14 characteristics including age, gender, Karnofsky performance status (KPS), site, human papilloma virus (HPV) status, tumor grade, primary tumor growth pattern (endophytic/exophytic), tumor/nodal/group stages, chemotherapy regimen, and primary, nodal, and total GTV volumes in the initial CT scan. The C4.5 Decision Tree induction algorithm was implemented. The median %GTVΔ for primary, nodal, and total GTVs was 26.8%, 43.0%, and 31.2%, respectively. Type of chemotherapy, age, primary tumor growth pattern, site, KPS, and HPV status were the most predictive parameters for primary %GTVΔ decision tree, whereas for nodal %GTVΔ, KPS, site, age, primary tumor growth pattern, initial primary GTV, and total GTV volumes were predictive. Both decision trees had an accuracy of 88%. There can be significant changes in primary and nodal tumor volumes during the course of H&N chemoradiotherapy. Considering the proposed decision trees, radiation oncologists can select patients predicted to have high %GTVΔ, who would theoretically gain the most benefit from adaptive radiotherapy, in order to better use limited clinical resources. © The Author(s) 2015.

Development of a New Decision Tree to Rapidly Screen Chemical Estrogenic Activities of Xenopus laevis.

PubMed

Wang, Ting; Li, Weiying; Zheng, Xiaofeng; Lin, Zhifen; Kong, Deyang

2014-02-01

During the last past decades, there is an increasing number of studies about estrogenic activities of the environmental pollutants on amphibians and many determination methods have been proposed. However, these determination methods are time-consuming and expensive, and a rapid and simple method to screen and test the chemicals for estrogenic activities to amphibians is therefore imperative. Herein is proposed a new decision tree formulated not only with physicochemical parameters but also a biological parameter that was successfully used to screen estrogenic activities of the chemicals on amphibians. The biological parameter, CDOCKER interaction energy (Ebinding ) between chemicals and the target proteins was calculated based on the method of molecular docking, and it was used to revise the decision tree formulated by Hong only with physicochemical parameters for screening estrogenic activity of chemicals in rat. According to the correlation between Ebinding of rat and Xenopus laevis, a new decision tree for estrogenic activities in Xenopus laevis is finally proposed. Then it was validated by using the randomly 8 chemicals which can be frequently exposed to Xenopus laevis, and the agreement between the results from the new decision tree and the ones from experiments is generally satisfactory. Consequently, the new decision tree can be used to screen the estrogenic activities of the chemicals, and combinational use of the Ebinding and classical physicochemical parameters can greatly improves Hong's decision tree. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Assessing School Readiness for a Practice Arrangement Using Decision Tree Methodology.

ERIC Educational Resources Information Center

Barger, Sara E.

1998-01-01

Questions in a decision-tree address mission, faculty interest, administrative support, and practice plan as a way of assessing arrangements for nursing faculty's clinical practice. Decisions should be based on congruence between the human resource allocation and the reward systems. (SK)

Ethnographic Decision Tree Modeling: A Research Method for Counseling Psychology.

ERIC Educational Resources Information Center

Beck, Kirk A.

2005-01-01

This article describes ethnographic decision tree modeling (EDTM; C. H. Gladwin, 1989) as a mixed method design appropriate for counseling psychology research. EDTM is introduced and located within a postpositivist research paradigm. Decision theory that informs EDTM is reviewed, and the 2 phases of EDTM are highlighted. The 1st phase, model…

Comparative Issues and Methods in Organizational Diagnosis. Report II. The Decision Tree Approach.

DTIC Science & Technology

organizational diagnosis . The advantages and disadvantages of the decision-tree approach generally, and in this study specifically, are examined. A pre-test, using a civilian sample of 174 work groups with Survey of Organizations data, was conducted to assess various decision-tree classification criteria, in terms of their similarity to the distance function used by Bowers and Hausser (1977). The results suggested the use of a large developmental sample, which should result in more distinctly defined boundary lines between classification profiles. Also, the decision matrix

Decision tree and PCA-based fault diagnosis of rotating machinery

NASA Astrophysics Data System (ADS)

Sun, Weixiang; Chen, Jin; Li, Jiaqing

2007-04-01

After analysing the flaws of conventional fault diagnosis methods, data mining technology is introduced to fault diagnosis field, and a new method based on C4.5 decision tree and principal component analysis (PCA) is proposed. In this method, PCA is used to reduce features after data collection, preprocessing and feature extraction. Then, C4.5 is trained by using the samples to generate a decision tree model with diagnosis knowledge. At last the tree model is used to make diagnosis analysis. To validate the method proposed, six kinds of running states (normal or without any defect, unbalance, rotor radial rub, oil whirl, shaft crack and a simultaneous state of unbalance and radial rub), are simulated on Bently Rotor Kit RK4 to test C4.5 and PCA-based method and back-propagation neural network (BPNN). The result shows that C4.5 and PCA-based diagnosis method has higher accuracy and needs less training time than BPNN.

Using decision trees to characterize verbal communication during change and stuck episodes in the therapeutic process

PubMed Central

Masías, Víctor H.; Krause, Mariane; Valdés, Nelson; Pérez, J. C.; Laengle, Sigifredo

2015-01-01

Methods are needed for creating models to characterize verbal communication between therapists and their patients that are suitable for teaching purposes without losing analytical potential. A technique meeting these twin requirements is proposed that uses decision trees to identify both change and stuck episodes in therapist-patient communication. Three decision tree algorithms (C4.5, NBTree, and REPTree) are applied to the problem of characterizing verbal responses into change and stuck episodes in the therapeutic process. The data for the problem is derived from a corpus of 8 successful individual therapy sessions with 1760 speaking turns in a psychodynamic context. The decision tree model that performed best was generated by the C4.5 algorithm. It delivered 15 rules characterizing the verbal communication in the two types of episodes. Decision trees are a promising technique for analyzing verbal communication during significant therapy events and have much potential for use in teaching practice on changes in therapeutic communication. The development of pedagogical methods using decision trees can support the transmission of academic knowledge to therapeutic practice. PMID:25914657

Using decision trees to characterize verbal communication during change and stuck episodes in the therapeutic process.

PubMed

Masías, Víctor H; Krause, Mariane; Valdés, Nelson; Pérez, J C; Laengle, Sigifredo

2015-01-01

Methods are needed for creating models to characterize verbal communication between therapists and their patients that are suitable for teaching purposes without losing analytical potential. A technique meeting these twin requirements is proposed that uses decision trees to identify both change and stuck episodes in therapist-patient communication. Three decision tree algorithms (C4.5, NBTree, and REPTree) are applied to the problem of characterizing verbal responses into change and stuck episodes in the therapeutic process. The data for the problem is derived from a corpus of 8 successful individual therapy sessions with 1760 speaking turns in a psychodynamic context. The decision tree model that performed best was generated by the C4.5 algorithm. It delivered 15 rules characterizing the verbal communication in the two types of episodes. Decision trees are a promising technique for analyzing verbal communication during significant therapy events and have much potential for use in teaching practice on changes in therapeutic communication. The development of pedagogical methods using decision trees can support the transmission of academic knowledge to therapeutic practice.

Modifiable risk factors predicting major depressive disorder at four year follow-up: a decision tree approach.

PubMed

Batterham, Philip J; Christensen, Helen; Mackinnon, Andrew J

2009-11-22

Relative to physical health conditions such as cardiovascular disease, little is known about risk factors that predict the prevalence of depression. The present study investigates the expected effects of a reduction of these risks over time, using the decision tree method favoured in assessing cardiovascular disease risk. The PATH through Life cohort was used for the study, comprising 2,105 20-24 year olds, 2,323 40-44 year olds and 2,177 60-64 year olds sampled from the community in the Canberra region, Australia. A decision tree methodology was used to predict the presence of major depressive disorder after four years of follow-up. The decision tree was compared with a logistic regression analysis using ROC curves. The decision tree was found to distinguish and delineate a wide range of risk profiles. Previous depressive symptoms were most highly predictive of depression after four years, however, modifiable risk factors such as substance use and employment status played significant roles in assessing the risk of depression. The decision tree was found to have better sensitivity and specificity than a logistic regression using identical predictors. The decision tree method was useful in assessing the risk of major depressive disorder over four years. Application of the model to the development of a predictive tool for tailored interventions is discussed.

A decision tree for differentiating multiple system atrophy from Parkinson's disease using 3-T MR imaging.

PubMed

Nair, Shalini Rajandran; Tan, Li Kuo; Mohd Ramli, Norlisah; Lim, Shen Yang; Rahmat, Kartini; Mohd Nor, Hazman

2013-06-01

To develop a decision tree based on standard magnetic resonance imaging (MRI) and diffusion tensor imaging to differentiate multiple system atrophy (MSA) from Parkinson's disease (PD). 3-T brain MRI and DTI (diffusion tensor imaging) were performed on 26 PD and 13 MSA patients. Regions of interest (ROIs) were the putamen, substantia nigra, pons, middle cerebellar peduncles (MCP) and cerebellum. Linear, volumetry and DTI (fractional anisotropy and mean diffusivity) were measured. A three-node decision tree was formulated, with design goals being 100 % specificity at node 1, 100 % sensitivity at node 2 and highest combined sensitivity and specificity at node 3. Nine parameters (mean width, fractional anisotropy (FA) and mean diffusivity (MD) of MCP; anteroposterior diameter of pons; cerebellar FA and volume; pons and mean putamen volume; mean FA substantia nigra compacta-rostral) showed statistically significant (P < 0.05) differences between MSA and PD with mean MCP width, anteroposterior diameter of pons and mean FA MCP chosen for the decision tree. Threshold values were 14.6 mm, 21.8 mm and 0.55, respectively. Overall performance of the decision tree was 92 % sensitivity, 96 % specificity, 92 % PPV and 96 % NPV. Twelve out of 13 MSA patients were accurately classified. Formation of the decision tree using these parameters was both descriptive and predictive in differentiating between MSA and PD. • Parkinson's disease and multiple system atrophy can be distinguished on MR imaging. • Combined conventional MRI and diffusion tensor imaging improves the accuracy of diagnosis. • A decision tree is descriptive and predictive in differentiating between clinical entities. • A decision tree can reliably differentiate Parkinson's disease from multiple system atrophy.

Development of a diagnostic decision tree for obstructive pulmonary diseases based on real-life data

PubMed Central

in ’t Veen, Johannes C.C.M.; Dekhuijzen, P.N. Richard; van Heijst, Ellen; Kocks, Janwillem W.H.; Muilwijk-Kroes, Jacqueline B.; Chavannes, Niels H.; van der Molen, Thys

2016-01-01

The aim of this study was to develop and explore the diagnostic accuracy of a decision tree derived from a large real-life primary care population. Data from 9297 primary care patients (45% male, mean age 53±17 years) with suspicion of an obstructive pulmonary disease was derived from an asthma/chronic obstructive pulmonary disease (COPD) service where patients were assessed using spirometry, the Asthma Control Questionnaire, the Clinical COPD Questionnaire, history data and medication use. All patients were diagnosed through the Internet by a pulmonologist. The Chi-squared Automatic Interaction Detection method was used to build the decision tree. The tree was externally validated in another real-life primary care population (n=3215). Our tree correctly diagnosed 79% of the asthma patients, 85% of the COPD patients and 32% of the asthma–COPD overlap syndrome (ACOS) patients. External validation showed a comparable pattern (correct: asthma 78%, COPD 83%, ACOS 24%). Our decision tree is considered to be promising because it was based on real-life primary care patients with a specialist's diagnosis. In most patients the diagnosis could be correctly predicted. Predicting ACOS, however, remained a challenge. The total decision tree can be implemented in computer-assisted diagnostic systems for individual patients. A simplified version of this tree can be used in daily clinical practice as a desk tool. PMID:27730177

Decision-Tree Program

NASA Technical Reports Server (NTRS)

Buntine, Wray

1994-01-01

IND computer program introduces Bayesian and Markov/maximum-likelihood (MML) methods and more-sophisticated methods of searching in growing trees. Produces more-accurate class-probability estimates important in applications like diagnosis. Provides range of features and styles with convenience for casual user, fine-tuning for advanced user or for those interested in research. Consists of four basic kinds of routines: data-manipulation, tree-generation, tree-testing, and tree-display. Written in C language.

PCA based feature reduction to improve the accuracy of decision tree c4.5 classification

NASA Astrophysics Data System (ADS)

Nasution, M. Z. F.; Sitompul, O. S.; Ramli, M.

2018-03-01

Splitting attribute is a major process in Decision Tree C4.5 classification. However, this process does not give a significant impact on the establishment of the decision tree in terms of removing irrelevant features. It is a major problem in decision tree classification process called over-fitting resulting from noisy data and irrelevant features. In turns, over-fitting creates misclassification and data imbalance. Many algorithms have been proposed to overcome misclassification and overfitting on classifications Decision Tree C4.5. Feature reduction is one of important issues in classification model which is intended to remove irrelevant data in order to improve accuracy. The feature reduction framework is used to simplify high dimensional data to low dimensional data with non-correlated attributes. In this research, we proposed a framework for selecting relevant and non-correlated feature subsets. We consider principal component analysis (PCA) for feature reduction to perform non-correlated feature selection and Decision Tree C4.5 algorithm for the classification. From the experiments conducted using available data sets from UCI Cervical cancer data set repository with 858 instances and 36 attributes, we evaluated the performance of our framework based on accuracy, specificity and precision. Experimental results show that our proposed framework is robust to enhance classification accuracy with 90.70% accuracy rates.

Tree Planting - Forests Made by Hand

Treesearch

Jim Barnett

1999-01-01

The typical tree-planting scene hasn't changed much in 50 years. Workers armed with tree bags on their backs and hoedads or dibbles will line up on a frosy morning to begin the process of putting seedlings in the ground one by one.

The application of a decision tree to establish the parameters associated with hypertension.

PubMed

Tayefi, Maryam; Esmaeili, Habibollah; Saberi Karimian, Maryam; Amirabadi Zadeh, Alireza; Ebrahimi, Mahmoud; Safarian, Mohammad; Nematy, Mohsen; Parizadeh, Seyed Mohammad Reza; Ferns, Gordon A; Ghayour-Mobarhan, Majid

2017-02-01

Hypertension is an important risk factor for cardiovascular disease (CVD). The goal of this study was to establish the factors associated with hypertension by using a decision-tree algorithm as a supervised classification method of data mining. Data from a cross-sectional study were used in this study. A total of 9078 subjects who met the inclusion criteria were recruited. 70% of these subjects (6358 cases) were randomly allocated to the training dataset for the constructing of the decision-tree. The remaining 30% (2720 cases) were used as the testing dataset to evaluate the performance of decision-tree. Two models were evaluated in this study. In model I, age, gender, body mass index, marital status, level of education, occupation status, depression and anxiety status, physical activity level, smoking status, LDL, TG, TC, FBG, uric acid and hs-CRP were considered as input variables and in model II, age, gender, WBC, RBC, HGB, HCT MCV, MCH, PLT, RDW and PDW were considered as input variables. The validation of the model was assessed by constructing a receiver operating characteristic (ROC) curve. The prevalence rates of hypertension were 32% in our population. For the decision-tree model I, the accuracy, sensitivity, specificity and area under the ROC curve (AUC) value for identifying the related risk factors of hypertension were 73%, 63%, 77% and 0.72, respectively. The corresponding values for model II were 70%, 61%, 74% and 0.68, respectively. We have developed a decision tree model to identify the risk factors associated with hypertension that maybe used to develop programs for hypertension management. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Vlsi implementation of flexible architecture for decision tree classification in data mining

NASA Astrophysics Data System (ADS)

Sharma, K. Venkatesh; Shewandagn, Behailu; Bhukya, Shankar Nayak

2017-07-01

The Data mining algorithms have become vital to researchers in science, engineering, medicine, business, search and security domains. In recent years, there has been a terrific raise in the size of the data being collected and analyzed. Classification is the main difficulty faced in data mining. In a number of the solutions developed for this problem, most accepted one is Decision Tree Classification (DTC) that gives high precision while handling very large amount of data. This paper presents VLSI implementation of flexible architecture for Decision Tree classification in data mining using c4.5 algorithm.

FDT 2.0: Improving scalability of the fuzzy decision tree induction tool - integrating database storage.

PubMed

Durham, Erin-Elizabeth A; Yu, Xiaxia; Harrison, Robert W

2014-12-01

Effective machine-learning handles large datasets efficiently. One key feature of handling large data is the use of databases such as MySQL. The freeware fuzzy decision tree induction tool, FDT, is a scalable supervised-classification software tool implementing fuzzy decision trees. It is based on an optimized fuzzy ID3 (FID3) algorithm. FDT 2.0 improves upon FDT 1.0 by bridging the gap between data science and data engineering: it combines a robust decisioning tool with data retention for future decisions, so that the tool does not need to be recalibrated from scratch every time a new decision is required. In this paper we briefly review the analytical capabilities of the freeware FDT tool and its major features and functionalities; examples of large biological datasets from HIV, microRNAs and sRNAs are included. This work shows how to integrate fuzzy decision algorithms with modern database technology. In addition, we show that integrating the fuzzy decision tree induction tool with database storage allows for optimal user satisfaction in today's Data Analytics world.

A fuzzy decision tree for fault classification.

PubMed

Zio, Enrico; Baraldi, Piero; Popescu, Irina C

2008-02-01

In plant accident management, the control room operators are required to identify the causes of the accident, based on the different patterns of evolution of the monitored process variables thereby developing. This task is often quite challenging, given the large number of process parameters monitored and the intense emotional states under which it is performed. To aid the operators, various techniques of fault classification have been engineered. An important requirement for their practical application is the physical interpretability of the relationships among the process variables underpinning the fault classification. In this view, the present work propounds a fuzzy approach to fault classification, which relies on fuzzy if-then rules inferred from the clustering of available preclassified signal data, which are then organized in a logical and transparent decision tree structure. The advantages offered by the proposed approach are precisely that a transparent fault classification model is mined out of the signal data and that the underlying physical relationships among the process variables are easily interpretable as linguistic if-then rules that can be explicitly visualized in the decision tree structure. The approach is applied to a case study regarding the classification of simulated faults in the feedwater system of a boiling water reactor.

Ontology based decision system for breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Trabelsi Ben Ameur, Soumaya; Cloppet, Florence; Wendling, Laurent; Sellami, Dorra

2018-04-01

In this paper, we focus on analysis and diagnosis of breast masses inspired by expert concepts and rules. Accordingly, a Bag of Words is built based on the ontology of breast cancer diagnosis, accurately described in the Breast Imaging Reporting and Data System. To fill the gap between low level knowledge and expert concepts, a semantic annotation is developed using a machine learning tool. Then, breast masses are classified into benign or malignant according to expert rules implicitly modeled with a set of classifiers (KNN, ANN, SVM and Decision Tree). This semantic context of analysis offers a frame where we can include external factors and other meta-knowledge such as patient risk factors as well as exploiting more than one modality. Based on MRI and DECEDM modalities, our developed system leads a recognition rate of 99.7% with Decision Tree where an improvement of 24.7 % is obtained owing to semantic analysis.

Development and acceptability testing of decision trees for self-management of prosthetic socket fit in adults with lower limb amputation.

PubMed

Lee, Daniel Joseph; Veneri, Diana A

2018-05-01

The most common complaint lower limb prosthesis users report is inadequacy of a proper socket fit. Adjustments to the residual limb-socket interface can be made by the prosthesis user without consultation of a clinician in many scenarios through skilled self-management. Decision trees guide prosthesis wearers through the self-management process, empowering them to rectify fit issues, or referring them to a clinician when necessary. This study examines the development and acceptability testing of patient-centered decision trees for lower limb prosthesis users. Decision trees underwent a four-stage process: literature review and expert consultation, designing, two-rounds of expert panel review and revisions, and target audience testing. Fifteen lower limb prosthesis users (average age 61 years) reviewed the decision trees and completed an acceptability questionnaire. Participants reported agreement of 80% or above in five of the eight questions related to acceptability of the decision trees. Disagreement was related to the level of experience of the respondent. Decision trees were found to be easy to use, illustrate correct solutions to common issues, and have terminology consistent with that of a new prosthesis user. Some users with greater than 1.5 years of experience would not use the decision trees based on their own self-management skills. Implications for Rehabilitation Discomfort of the residual limb-prosthetic socket interface is the most common reason for clinician visits. Prosthesis users can use decision trees to guide them through the process of obtaining a proper socket fit independently. Newer users may benefit from using the decision trees more than experienced users.

It's in the Bag!

ERIC Educational Resources Information Center

Renfro, Nancy

1983-01-01

Thirteen ideas for using paper bags for class art activities are given. Directions for making bag barracudas, bionic bags, bigfoot bags, bag sculptures, bag beads, and body bag superstars are included. (PP)

Online adaptive decision trees: pattern classification and function approximation.

PubMed

Basak, Jayanta

2006-09-01

Recently we have shown that decision trees can be trained in the online adaptive (OADT) mode (Basak, 2004), leading to better generalization score. OADTs were bottlenecked by the fact that they are able to handle only two-class classification tasks with a given structure. In this article, we provide an architecture based on OADT, ExOADT, which can handle multiclass classification tasks and is able to perform function approximation. ExOADT is structurally similar to OADT extended with a regression layer. We also show that ExOADT is capable not only of adapting the local decision hyperplanes in the nonterminal nodes but also has the potential of smoothly changing the structure of the tree depending on the data samples. We provide the learning rules based on steepest gradient descent for the new model ExOADT. Experimentally we demonstrate the effectiveness of ExOADT in the pattern classification and function approximation tasks. Finally, we briefly discuss the relationship of ExOADT with other classification models.

Circum-Arctic petroleum systems identified using decision-tree chemometrics

USGS Publications Warehouse

Peters, K.E.; Ramos, L.S.; Zumberge, J.E.; Valin, Z.C.; Scotese, C.R.; Gautier, D.L.

2007-01-01

Source- and age-related biomarker and isotopic data were measured for more than 1000 crude oil samples from wells and seeps collected above approximately 55??N latitude. A unique, multitiered chemometric (multivariate statistical) decision tree was created that allowed automated classification of 31 genetically distinct circumArctic oil families based on a training set of 622 oil samples. The method, which we call decision-tree chemometrics, uses principal components analysis and multiple tiers of K-nearest neighbor and SIMCA (soft independent modeling of class analogy) models to classify and assign confidence limits for newly acquired oil samples and source rock extracts. Geochemical data for each oil sample were also used to infer the age, lithology, organic matter input, depositional environment, and identity of its source rock. These results demonstrate the value of large petroleum databases where all samples were analyzed using the same procedures and instrumentation. Copyright ?? 2007. The American Association of Petroleum Geologists. All rights reserved.

C-fuzzy variable-branch decision tree with storage and classification error rate constraints

NASA Astrophysics Data System (ADS)

Yang, Shiueng-Bien

2009-10-01

The C-fuzzy decision tree (CFDT), which is based on the fuzzy C-means algorithm, has recently been proposed. The CFDT is grown by selecting the nodes to be split according to its classification error rate. However, the CFDT design does not consider the classification time taken to classify the input vector. Thus, the CFDT can be improved. We propose a new C-fuzzy variable-branch decision tree (CFVBDT) with storage and classification error rate constraints. The design of the CFVBDT consists of two phases-growing and pruning. The CFVBDT is grown by selecting the nodes to be split according to the classification error rate and the classification time in the decision tree. Additionally, the pruning method selects the nodes to prune based on the storage requirement and the classification time of the CFVBDT. Furthermore, the number of branches of each internal node is variable in the CFVBDT. Experimental results indicate that the proposed CFVBDT outperforms the CFDT and other methods.

Data mining for multiagent rules, strategies, and fuzzy decision tree structure

NASA Astrophysics Data System (ADS)

Smith, James F., III; Rhyne, Robert D., II; Fisher, Kristin

2002-03-01

A fuzzy logic based resource manager (RM) has been developed that automatically allocates electronic attack resources in real-time over many dissimilar platforms. Two different data mining algorithms have been developed to determine rules, strategies, and fuzzy decision tree structure. The first data mining algorithm uses a genetic algorithm as a data mining function and is called from an electronic game. The game allows a human expert to play against the resource manager in a simulated battlespace with each of the defending platforms being exclusively directed by the fuzzy resource manager and the attacking platforms being controlled by the human expert or operating autonomously under their own logic. This approach automates the data mining problem. The game automatically creates a database reflecting the domain expert's knowledge. It calls a data mining function, a genetic algorithm, for data mining of the database as required and allows easy evaluation of the information mined in the second step. The criterion for re- optimization is discussed as well as experimental results. Then a second data mining algorithm that uses a genetic program as a data mining function is introduced to automatically discover fuzzy decision tree structures. Finally, a fuzzy decision tree generated through this process is discussed.

Application of decision tree model for the ground subsidence hazard mapping near abandoned underground coal mines.

PubMed

Lee, Saro; Park, Inhye

2013-09-30

Subsidence of ground caused by underground mines poses hazards to human life and property. This study analyzed the hazard to ground subsidence using factors that can affect ground subsidence and a decision tree approach in a geographic information system (GIS). The study area was Taebaek, Gangwon-do, Korea, where many abandoned underground coal mines exist. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping ground-subsidence hazard (GSH). The subsidence area was randomly split 50/50 for training and validation of the models. A data-mining classification technique was applied to the GSH mapping, and decision trees were constructed using the chi-squared automatic interaction detector (CHAID) and the quick, unbiased, and efficient statistical tree (QUEST) algorithms. The frequency ratio model was also applied to the GSH mapping for comparing with probabilistic model. The resulting GSH maps were validated using area-under-the-curve (AUC) analysis with the subsidence area data that had not been used for training the model. The highest accuracy was achieved by the decision tree model using CHAID algorithm (94.01%) comparing with QUEST algorithms (90.37%) and frequency ratio model (86.70%). These accuracies are higher than previously reported results for decision tree. Decision tree methods can therefore be used efficiently for GSH analysis and might be widely used for prediction of various spatial events. Copyright © 2013. Published by Elsevier Ltd.

Classification and Progression Based on CFS-GA and C5.0 Boost Decision Tree of TCM Zheng in Chronic Hepatitis B.

PubMed

Chen, Xiao Yu; Ma, Li Zhuang; Chu, Na; Zhou, Min; Hu, Yiyang

2013-01-01

Chronic hepatitis B (CHB) is a serious public health problem, and Traditional Chinese Medicine (TCM) plays an important role in the control and treatment for CHB. In the treatment of TCM, zheng discrimination is the most important step. In this paper, an approach based on CFS-GA (Correlation based Feature Selection and Genetic Algorithm) and C5.0 boost decision tree is used for zheng classification and progression in the TCM treatment of CHB. The CFS-GA performs better than the typical method of CFS. By CFS-GA, the acquired attribute subset is classified by C5.0 boost decision tree for TCM zheng classification of CHB, and C5.0 decision tree outperforms two typical decision trees of NBTree and REPTree on CFS-GA, CFS, and nonselection in comparison. Based on the critical indicators from C5.0 decision tree, important lab indicators in zheng progression are obtained by the method of stepwise discriminant analysis for expressing TCM zhengs in CHB, and alterations of the important indicators are also analyzed in zheng progression. In conclusion, all the three decision trees perform better on CFS-GA than on CFS and nonselection, and C5.0 decision tree outperforms the two typical decision trees both on attribute selection and nonselection.

Ventriculogram segmentation using boosted decision trees

NASA Astrophysics Data System (ADS)

McDonald, John A.; Sheehan, Florence H.

2004-05-01

Left ventricular status, reflected in ejection fraction or end systolic volume, is a powerful prognostic indicator in heart disease. Quantitative analysis of these and other parameters from ventriculograms (cine xrays of the left ventricle) is infrequently performed due to the labor required for manual segmentation. None of the many methods developed for automated segmentation has achieved clinical acceptance. We present a method for semi-automatic segmentation of ventriculograms based on a very accurate two-stage boosted decision-tree pixel classifier. The classifier determines which pixels are inside the ventricle at key ED (end-diastole) and ES (end-systole) frames. The test misclassification rate is about 1%. The classifier is semi-automatic, requiring a user to select 3 points in each frame: the endpoints of the aortic valve and the apex. The first classifier stage is 2 boosted decision-trees, trained using features such as gray-level statistics (e.g. median brightness) and image geometry (e.g. coordinates relative to user supplied 3 points). Second stage classifiers are trained using the same features as the first, plus the output of the first stage. Border pixels are determined from the segmented images using dilation and erosion. A curve is then fit to the border pixels, minimizing a penalty function that trades off fidelity to the border pixels with smoothness. ED and ES volumes, and ejection fraction are estimated from border curves using standard area-length formulas. On independent test data, the differences between automatic and manual volumes (and ejection fractions) are similar in size to the differences between two human observers.

[Analysis of the characteristics of the older adults with depression using data mining decision tree analysis].

PubMed

Park, Myonghwa; Choi, Sora; Shin, A Mi; Koo, Chul Hoi

2013-02-01

The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method. A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs. The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease. The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

Data Clustering and Evolving Fuzzy Decision Tree for Data Base Classification Problems

NASA Astrophysics Data System (ADS)

Chang, Pei-Chann; Fan, Chin-Yuan; Wang, Yen-Wen

Data base classification suffers from two well known difficulties, i.e., the high dimensionality and non-stationary variations within the large historic data. This paper presents a hybrid classification model by integrating a case based reasoning technique, a Fuzzy Decision Tree (FDT), and Genetic Algorithms (GA) to construct a decision-making system for data classification in various data base applications. The model is major based on the idea that the historic data base can be transformed into a smaller case-base together with a group of fuzzy decision rules. As a result, the model can be more accurately respond to the current data under classifying from the inductions by these smaller cases based fuzzy decision trees. Hit rate is applied as a performance measure and the effectiveness of our proposed model is demonstrated by experimentally compared with other approaches on different data base classification applications. The average hit rate of our proposed model is the highest among others.

Breaking BAG: The Co-Chaperone BAG3 in Health and Disease.

PubMed

Behl, Christian

2016-08-01

Human BAG (Bcl-2-associated athanogene) proteins form a family of antiapoptotic proteins that currently consists of six members (BAG1-6) all sharing the BAG protein domain from which the name arises. Via this domain, BAG proteins bind to the heat shock protein 70 (Hsp70), thereby acting as a co-chaperone regulating the activity of Hsp70. In addition to their antiapoptotic activity, all human BAG proteins have distinct functions in health and disease, and BAG3 in particular is the focus of many investigations. BAG3 has a modular protein domain composition offering the possibility for manifold interactions with other proteins. Various BAG3 functions are implicated in disorders including cancer, myopathies, and neurodegeneration. The discovery of its role in selective autophagy and the description of BAG3-mediated selective macroautophagy as an adaptive mechanism to maintain cellular homeostasis, under stress as well as during aging, make BAG3 a highly interesting target for future pharmacological interventions. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predicting membrane protein types using various decision tree classifiers based on various modes of general PseAAC for imbalanced datasets.

PubMed

Sankari, E Siva; Manimegalai, D

2017-12-21

Predicting membrane protein types is an important and challenging research area in bioinformatics and proteomics. Traditional biophysical methods are used to classify membrane protein types. Due to large exploration of uncharacterized protein sequences in databases, traditional methods are very time consuming, expensive and susceptible to errors. Hence, it is highly desirable to develop a robust, reliable, and efficient method to predict membrane protein types. Imbalanced datasets and large datasets are often handled well by decision tree classifiers. Since imbalanced datasets are taken, the performance of various decision tree classifiers such as Decision Tree (DT), Classification And Regression Tree (CART), C4.5, Random tree, REP (Reduced Error Pruning) tree, ensemble methods such as Adaboost, RUS (Random Under Sampling) boost, Rotation forest and Random forest are analysed. Among the various decision tree classifiers Random forest performs well in less time with good accuracy of 96.35%. Another inference is RUS boost decision tree classifier is able to classify one or two samples in the class with very less samples while the other classifiers such as DT, Adaboost, Rotation forest and Random forest are not sensitive for the classes with fewer samples. Also the performance of decision tree classifiers is compared with SVM (Support Vector Machine) and Naive Bayes classifier. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prediction of the compression ratio for municipal solid waste using decision tree.

PubMed

Heshmati R, Ali Akbar; Mokhtari, Maryam; Shakiba Rad, Saeed

2014-01-01

The compression ratio of municipal solid waste (MSW) is an essential parameter for evaluation of waste settlement and landfill design. However, no appropriate model has been proposed to estimate the waste compression ratio so far. In this study, a decision tree method was utilized to predict the waste compression ratio (C'c). The tree was constructed using Quinlan's M5 algorithm. A reliable database retrieved from the literature was used to develop a practical model that relates C'c to waste composition and properties, including dry density, dry weight water content, and percentage of biodegradable organic waste using the decision tree method. The performance of the developed model was examined in terms of different statistical criteria, including correlation coefficient, root mean squared error, mean absolute error and mean bias error, recommended by researchers. The obtained results demonstrate that the suggested model is able to evaluate the compression ratio of MSW effectively.

Tools of the Future: How Decision Tree Analysis Will Impact Mission Planning

NASA Technical Reports Server (NTRS)

Otterstatter, Matthew R.

2005-01-01

The universe is infinitely complex; however, the human mind has a finite capacity. The multitude of possible variables, metrics, and procedures in mission planning are far too many to address exhaustively. This is unfortunate because, in general, considering more possibilities leads to more accurate and more powerful results. To compensate, we can get more insightful results by employing our greatest tool, the computer. The power of the computer will be utilized through a technology that considers every possibility, decision tree analysis. Although decision trees have been used in many other fields, this is innovative for space mission planning. Because this is a new strategy, no existing software is able to completely accommodate all of the requirements. This was determined through extensive research and testing of current technologies. It was necessary to create original software, for which a short-term model was finished this summer. The model was built into Microsoft Excel to take advantage of the familiar graphical interface for user input, computation, and viewing output. Macros were written to automate the process of tree construction, optimization, and presentation. The results are useful and promising. If this tool is successfully implemented in mission planning, our reliance on old-fashioned heuristics, an error-prone shortcut for handling complexity, will be reduced. The computer algorithms involved in decision trees will revolutionize mission planning. The planning will be faster and smarter, leading to optimized missions with the potential for more valuable data.

Three-dimensional object recognition using similar triangles and decision trees

NASA Technical Reports Server (NTRS)

Spirkovska, Lilly

1993-01-01

A system, TRIDEC, that is capable of distinguishing between a set of objects despite changes in the objects' positions in the input field, their size, or their rotational orientation in 3D space is described. TRIDEC combines very simple yet effective features with the classification capabilities of inductive decision tree methods. The feature vector is a list of all similar triangles defined by connecting all combinations of three pixels in a coarse coded 127 x 127 pixel input field. The classification is accomplished by building a decision tree using the information provided from a limited number of translated, scaled, and rotated samples. Simulation results are presented which show that TRIDEC achieves 94 percent recognition accuracy in the 2D invariant object recognition domain and 98 percent recognition accuracy in the 3D invariant object recognition domain after training on only a small sample of transformed views of the objects.

MODIS Snow Cover Mapping Decision Tree Technique: Snow and Cloud Discrimination

NASA Technical Reports Server (NTRS)

Riggs, George A.; Hall, Dorothy K.

2010-01-01

Accurate mapping of snow cover continues to challenge cryospheric scientists and modelers. The Moderate-Resolution Imaging Spectroradiometer (MODIS) snow data products have been used since 2000 by many investigators to map and monitor snow cover extent for various applications. Users have reported on the utility of the products and also on problems encountered. Three problems or hindrances in the use of the MODIS snow data products that have been reported in the literature are: cloud obscuration, snow/cloud confusion, and snow omission errors in thin or sparse snow cover conditions. Implementation of the MODIS snow algorithm in a decision tree technique using surface reflectance input to mitigate those problems is being investigated. The objective of this work is to use a decision tree structure for the snow algorithm. This should alleviate snow/cloud confusion and omission errors and provide a snow map with classes that convey information on how snow was detected, e.g. snow under clear sky, snow tinder cloud, to enable users' flexibility in interpreting and deriving a snow map. Results of a snow cover decision tree algorithm are compared to the standard MODIS snow map and found to exhibit improved ability to alleviate snow/cloud confusion in some situations allowing up to about 5% increase in mapped snow cover extent, thus accuracy, in some scenes.

Office of Legacy Management Decision Tree for Solar Photovoltaic Projects - 13317

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elmer, John; Butherus, Michael; Barr, Deborah L.

2013-07-01

To support consideration of renewable energy power development as a land reuse option, the DOE Office of Legacy Management (LM) and the National Renewable Energy Laboratory (NREL) established a partnership to conduct an assessment of wind and solar renewable energy resources on LM lands. From a solar capacity perspective, the larger sites in the western United States present opportunities for constructing solar photovoltaic (PV) projects. A detailed analysis and preliminary plan was developed for three large sites in New Mexico, assessing the costs, the conceptual layout of a PV system, and the electric utility interconnection process. As a result ofmore » the study, a 1,214-hectare (3,000-acre) site near Grants, New Mexico, was chosen for further study. The state incentives, utility connection process, and transmission line capacity were key factors in assessing the feasibility of the project. LM's Durango, Colorado, Disposal Site was also chosen for consideration because the uranium mill tailings disposal cell is on a hillside facing south, transmission lines cross the property, and the community was very supportive of the project. LM worked with the regulators to demonstrate that the disposal cell's long-term performance would not be impacted by the installation of a PV solar system. A number of LM-unique issues were resolved in making the site available for a private party to lease a portion of the site for a solar PV project. A lease was awarded in September 2012. Using a solar decision tree that was developed and launched by the EPA and NREL, LM has modified and expanded the decision tree structure to address the unique aspects and challenges faced by LM on its multiple sites. The LM solar decision tree covers factors such as land ownership, usable acreage, financial viability of the project, stakeholder involvement, and transmission line capacity. As additional sites are transferred to LM in the future, the decision tree will assist in determining whether

Discovering Decision Knowledge from Web Log Portfolio for Managing Classroom Processes by Applying Decision Tree and Data Cube Technology.

ERIC Educational Resources Information Center

Chen, Gwo-Dong; Liu, Chen-Chung; Ou, Kuo-Liang; Liu, Baw-Jhiune

2000-01-01

Discusses the use of Web logs to record student behavior that can assist teachers in assessing performance and making curriculum decisions for distance learning students who are using Web-based learning systems. Adopts decision tree and data cube information processing methodologies for developing more effective pedagogical strategies. (LRW)

Comparing wavefront-optimized, wavefront-guided and topography-guided laser vision correction: clinical outcomes using an objective decision tree.

PubMed

Stonecipher, Karl; Parrish, Joseph; Stonecipher, Megan

2018-05-18

This review is intended to update and educate the reader on the currently available options for laser vision correction, more specifically, laser-assisted in-situ keratomileusis (LASIK). In addition, some related clinical outcomes data from over 1000 cases performed over a 1-year are presented to highlight some differences between the various treatment profiles currently available including the rapidity of visual recovery. The cases in question were performed on the basis of a decision tree to segregate patients on the basis of anatomical, topographic and aberrometry findings; the decision tree was formulated based on the data available in some of the reviewed articles. Numerous recent studies reported in the literature provide data related to the risks and benefits of LASIK; alternatives to a laser refractive procedure are also discussed. The results from these studies have been used to prepare a decision tree to assist the surgeon in choosing the best option for the patient based on the data from several standard preoperative diagnostic tests. The data presented here should aid surgeons in understanding the effects of currently available LASIK treatment profiles. Surgeons should also be able to appreciate how the findings were used to create a decision tree to help choose the most appropriate treatment profile for patients. Finally, the retrospective evaluation of clinical outcomes based on the decision tree should provide surgeons with a realistic expectation for their own outcomes should they adopt such a decision tree in their own practice.

A Modified Decision Tree Algorithm Based on Genetic Algorithm for Mobile User Classification Problem

PubMed Central

Liu, Dong-sheng; Fan, Shu-jiang

2014-01-01

In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity. PMID:24688389

Peripheral Exophytic Oral Lesions: A Clinical Decision Tree

PubMed Central

Safi, Yaser; Jafari, Soudeh

2017-01-01

Diagnosis of peripheral oral exophytic lesions might be quite challenging. This review article aimed to introduce a decision tree for oral exophytic lesions according to their clinical features. General search engines and specialized databases including PubMed, PubMed Central, Medline Plus, EBSCO, Science Direct, Scopus, Embase, and authenticated textbooks were used to find relevant topics by means of keywords such as “oral soft tissue lesion,” “oral tumor like lesion,” “oral mucosal enlargement,” and “oral exophytic lesion.” Related English-language articles published since 1988 to 2016 in both medical and dental journals were appraised. Upon compilation of data, peripheral oral exophytic lesions were categorized into two major groups according to their surface texture: smooth (mesenchymal or nonsquamous epithelium-originated) and rough (squamous epithelium-originated). Lesions with smooth surface were also categorized into three subgroups according to their general frequency: reactive hyperplastic lesions/inflammatory hyperplasia, salivary gland lesions (nonneoplastic and neoplastic), and mesenchymal lesions (benign and malignant neoplasms). In addition, lesions with rough surface were summarized in six more common lesions. In total, 29 entities were organized in the form of a decision tree in order to help clinicians establish a logical diagnosis by a stepwise progression method. PMID:28757870

Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree.

PubMed

Özdemir, Merve Erkınay; Telatar, Ziya; Eroğul, Osman; Tunca, Yusuf

2018-05-01

Dysmorphic syndromes have different facial malformations. These malformations are significant to an early diagnosis of dysmorphic syndromes and contain distinctive information for face recognition. In this study we define the certain features of each syndrome by considering facial malformations and classify Fragile X, Hurler, Prader Willi, Down, Wolf Hirschhorn syndromes and healthy groups automatically. The reference points are marked on the face images and ratios between the points' distances are taken into consideration as features. We suggest a neural network based hierarchical decision tree structure in order to classify the syndrome types. We also implement k-nearest neighbor (k-NN) and artificial neural network (ANN) classifiers to compare classification accuracy with our hierarchical decision tree. The classification accuracy is 50, 73 and 86.7% with k-NN, ANN and hierarchical decision tree methods, respectively. Then, the same images are shown to a clinical expert who achieve a recognition rate of 46.7%. We develop an efficient system to recognize different syndrome types automatically in a simple, non-invasive imaging data, which is independent from the patient's age, sex and race at high accuracy. The promising results indicate that our method can be used for pre-diagnosis of the dysmorphic syndromes by clinical experts.

Prognostic Factors and Decision Tree for Long-term Survival in Metastatic Uveal Melanoma.

PubMed

Lorenzo, Daniel; Ochoa, María; Piulats, Josep Maria; Gutiérrez, Cristina; Arias, Luis; Català, Jaum; Grau, María; Peñafiel, Judith; Cobos, Estefanía; Garcia-Bru, Pere; Rubio, Marcos Javier; Padrón-Pérez, Noel; Dias, Bruno; Pera, Joan; Caminal, Josep Maria

2017-12-04

The purpose of this study was to demonstrate the existence of a bimodal survival pattern in metastatic uveal melanoma. Secondary aims were to identify the characteristics and prognostic factors associated with long-term survival and to develop a clinical decision tree. The medical records of 99 metastatic uveal melanoma patients were retrospectively reviewed. Patients were classified as either short (≤ 12 months) or long-term survivors (> 12 months) based on a graphical interpretation of the survival curve after diagnosis of the first metastatic lesion. Ophthalmic and oncological characteristics were assessed in both groups. Of the 99 patients, 62 (62.6%) were classified as short-term survivors, and 37 (37.4%) as long-term survivors. The multivariate analysis identified the following predictors of long-term survival: age ≤ 65 years (p=0.012) and unaltered serum lactate dehydrogenase levels (p=0.018); additionally, the size (smaller vs. larger) of the largest liver metastasis showed a trend towards significance (p=0.063). Based on the variables significantly associated with long-term survival, we developed a decision tree to facilitate clinical decision-making. The findings of this study demonstrate the existence of a bimodal survival pattern in patients with metastatic uveal melanoma. The presence of certain clinical characteristics at diagnosis of distant disease is associated with long-term survival. A decision tree was developed to facilitate clinical decision-making and to counsel patients about the expected course of disease.

Decision tree analysis of factors influencing rainfall-related building damage

NASA Astrophysics Data System (ADS)

Spekkers, M. H.; Kok, M.; Clemens, F. H. L. R.; ten Veldhuis, J. A. E.

2014-04-01

Flood damage prediction models are essential building blocks in flood risk assessments. Little research has been dedicated so far to damage of small-scale urban floods caused by heavy rainfall, while there is a need for reliable damage models for this flood type among insurers and water authorities. The aim of this paper is to investigate a wide range of damage-influencing factors and their relationships with rainfall-related damage, using decision tree analysis. For this, district-aggregated claim data from private property insurance companies in the Netherlands were analysed, for the period of 1998-2011. The databases include claims of water-related damage, for example, damages related to rainwater intrusion through roofs and pluvial flood water entering buildings at ground floor. Response variables being modelled are average claim size and claim frequency, per district per day. The set of predictors include rainfall-related variables derived from weather radar images, topographic variables from a digital terrain model, building-related variables and socioeconomic indicators of households. Analyses were made separately for property and content damage claim data. Results of decision tree analysis show that claim frequency is most strongly associated with maximum hourly rainfall intensity, followed by real estate value, ground floor area, household income, season (property data only), buildings age (property data only), ownership structure (content data only) and fraction of low-rise buildings (content data only). It was not possible to develop statistically acceptable trees for average claim size, which suggest that variability in average claim size is related to explanatory variables that cannot be defined at the district scale. Cross-validation results show that decision trees were able to predict 22-26% of variance in claim frequency, which is considerably better compared to results from global multiple regression models (11-18% of variance explained). Still, a

Urine drainage bags

MedlinePlus

... this page: //medlineplus.gov/ency/patientinstructions/000142.htm Urine drainage bags To use the sharing features on this page, please enable JavaScript. Urine drainage bags collect urine. Your bag will attach ...

Bag molding processes

NASA Astrophysics Data System (ADS)

Slobodzinsky, A.

Features, materials, and techniques of vacuum, pressure, and autoclave FRP bag molding processes are described. The bags are used in sealed environments, inflated to flexibly force a curing FRP laminate to conform to a stiff mold form which defines the shape of the finished product. Densification is achieved as the bag presses out the voids and excess resin from the laminate, and consolidation occurs as the plies and adherends are bonded by the bag pressure. Curing techniques nominally involved room temperature or high temperature, and investigations of alternative techniques, such as induction, dielectric, microwave, xenon flash, UV, electron beam, and gamma radiation heating are proceeding. Polysulfone is the most common thermoplastic. Details are given of mold preparations, peel plies or release films and fabrics, bagging techniques, and reusable venting blankets and silicone rubber bags.

Bayesian additive decision trees of biomarker by treatment interactions for predictive biomarker detection and subgroup identification.

PubMed

Zhao, Yang; Zheng, Wei; Zhuo, Daisy Y; Lu, Yuefeng; Ma, Xiwen; Liu, Hengchang; Zeng, Zhen; Laird, Glen

2017-10-11

Personalized medicine, or tailored therapy, has been an active and important topic in recent medical research. Many methods have been proposed in the literature for predictive biomarker detection and subgroup identification. In this article, we propose a novel decision tree-based approach applicable in randomized clinical trials. We model the prognostic effects of the biomarkers using additive regression trees and the biomarker-by-treatment effect using a single regression tree. Bayesian approach is utilized to periodically revise the split variables and the split rules of the decision trees, which provides a better overall fitting. Gibbs sampler is implemented in the MCMC procedure, which updates the prognostic trees and the interaction tree separately. We use the posterior distribution of the interaction tree to construct the predictive scores of the biomarkers and to identify the subgroup where the treatment is superior to the control. Numerical simulations show that our proposed method performs well under various settings comparing to existing methods. We also demonstrate an application of our method in a real clinical trial.

Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed

USGS Publications Warehouse

Balk, Benjamin; Elder, Kelly

2000-01-01

We model the spatial distribution of snow across a mountain basin using an approach that combines binary decision tree and geostatistical techniques. In April 1997 and 1998, intensive snow surveys were conducted in the 6.9‐km2 Loch Vale watershed (LVWS), Rocky Mountain National Park, Colorado. Binary decision trees were used to model the large‐scale variations in snow depth, while the small‐scale variations were modeled through kriging interpolation methods. Binary decision trees related depth to the physically based independent variables of net solar radiation, elevation, slope, and vegetation cover type. These decision tree models explained 54–65% of the observed variance in the depth measurements. The tree‐based modeled depths were then subtracted from the measured depths, and the resulting residuals were spatially distributed across LVWS through kriging techniques. The kriged estimates of the residuals were added to the tree‐based modeled depths to produce a combined depth model. The combined depth estimates explained 60–85% of the variance in the measured depths. Snow densities were mapped across LVWS using regression analysis. Snow‐covered area was determined from high‐resolution aerial photographs. Combining the modeled depths and densities with a snow cover map produced estimates of the spatial distribution of snow water equivalence (SWE). This modeling approach offers improvement over previous methods of estimating SWE distribution in mountain basins.

Extracting decision rules from police accident reports through decision trees.

PubMed

de Oña, Juan; López, Griselda; Abellán, Joaquín

2013-01-01

Given the current number of road accidents, the aim of many road safety analysts is to identify the main factors that contribute to crash severity. To pinpoint those factors, this paper shows an application that applies some of the methods most commonly used to build decision trees (DTs), which have not been applied to the road safety field before. An analysis of accidents on rural highways in the province of Granada (Spain) between 2003 and 2009 (both inclusive) showed that the methods used to build DTs serve our purpose and may even be complementary. Applying these methods has enabled potentially useful decision rules to be extracted that could be used by road safety analysts. For instance, some of the rules may indicate that women, contrary to men, increase their risk of severity under bad lighting conditions. The rules could be used in road safety campaigns to mitigate specific problems. This would enable managers to implement priority actions based on a classification of accidents by types (depending on their severity). However, the primary importance of this proposal is that other databases not used here (i.e. other infrastructure, roads and countries) could be used to identify unconventional problems in a manner easy for road safety managers to understand, as decision rules. Copyright © 2012 Elsevier Ltd. All rights reserved.

Using decision trees to understand structure in missing data

PubMed Central

Tierney, Nicholas J; Harden, Fiona A; Harden, Maurice J; Mengersen, Kerrie L

2015-01-01

Objectives Demonstrate the application of decision trees—classification and regression trees (CARTs), and their cousins, boosted regression trees (BRTs)—to understand structure in missing data. Setting Data taken from employees at 3 different industrial sites in Australia. Participants 7915 observations were included. Materials and methods The approach was evaluated using an occupational health data set comprising results of questionnaires, medical tests and environmental monitoring. Statistical methods included standard statistical tests and the ‘rpart’ and ‘gbm’ packages for CART and BRT analyses, respectively, from the statistical software ‘R’. A simulation study was conducted to explore the capability of decision tree models in describing data with missingness artificially introduced. Results CART and BRT models were effective in highlighting a missingness structure in the data, related to the type of data (medical or environmental), the site in which it was collected, the number of visits, and the presence of extreme values. The simulation study revealed that CART models were able to identify variables and values responsible for inducing missingness. There was greater variation in variable importance for unstructured as compared to structured missingness. Discussion Both CART and BRT models were effective in describing structural missingness in data. CART models may be preferred over BRT models for exploratory analysis of missing data, and selecting variables important for predicting missingness. BRT models can show how values of other variables influence missingness, which may prove useful for researchers. Conclusions Researchers are encouraged to use CART and BRT models to explore and understand missing data. PMID:26124509

Fault trees for decision making in systems analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lambert, Howard E.

1975-10-09

The application of fault tree analysis (FTA) to system safety and reliability is presented within the framework of system safety analysis. The concepts and techniques involved in manual and automated fault tree construction are described and their differences noted. The theory of mathematical reliability pertinent to FTA is presented with emphasis on engineering applications. An outline of the quantitative reliability techniques of the Reactor Safety Study is given. Concepts of probabilistic importance are presented within the fault tree framework and applied to the areas of system design, diagnosis and simulation. The computer code IMPORTANCE ranks basic events and cut setsmore » according to a sensitivity analysis. A useful feature of the IMPORTANCE code is that it can accept relative failure data as input. The output of the IMPORTANCE code can assist an analyst in finding weaknesses in system design and operation, suggest the most optimal course of system upgrade, and determine the optimal location of sensors within a system. A general simulation model of system failure in terms of fault tree logic is described. The model is intended for efficient diagnosis of the causes of system failure in the event of a system breakdown. It can also be used to assist an operator in making decisions under a time constraint regarding the future course of operations. The model is well suited for computer implementation. New results incorporated in the simulation model include an algorithm to generate repair checklists on the basis of fault tree logic and a one-step-ahead optimization procedure that minimizes the expected time to diagnose system failure.« less

Predictability of the future development of aggressive behavior of cranial dural arteriovenous fistulas based on decision tree analysis.

PubMed

Satomi, Junichiro; Ghaibeh, A Ammar; Moriguchi, Hiroki; Nagahiro, Shinji

2015-07-01

The severity of clinical signs and symptoms of cranial dural arteriovenous fistulas (DAVFs) are well correlated with their pattern of venous drainage. Although the presence of cortical venous drainage can be considered a potential predictor of aggressive DAVF behaviors, such as intracranial hemorrhage or progressive neurological deficits due to venous congestion, accurate statistical analyses are currently not available. Using a decision tree data mining method, the authors aimed at clarifying the predictability of the future development of aggressive behaviors of DAVF and at identifying the main causative factors. Of 266 DAVF patients, 89 were eligible for analysis. Under observational management, 51 patients presented with intracranial hemorrhage/infarction during the follow-up period. The authors created a decision tree able to assess the risk for the development of aggressive DAVF behavior. Evaluated by 10-fold cross-validation, the decision tree's accuracy, sensitivity, and specificity were 85.28%, 88.33%, and 80.83%, respectively. The tree shows that the main factor in symptomatic patients was the presence of cortical venous drainage. In its absence, the lesion location determined the risk of a DAVF developing aggressive behavior. Decision tree analysis accurately predicts the future development of aggressive DAVF behavior.

Comparative study of biodegradability prediction of chemicals using decision trees, functional trees, and logistic regression.

PubMed

Chen, Guangchao; Li, Xuehua; Chen, Jingwen; Zhang, Ya-Nan; Peijnenburg, Willie J G M

2014-12-01

Biodegradation is the principal environmental dissipation process of chemicals. As such, it is a dominant factor determining the persistence and fate of organic chemicals in the environment, and is therefore of critical importance to chemical management and regulation. In the present study, the authors developed in silico methods assessing biodegradability based on a large heterogeneous set of 825 organic compounds, using the techniques of the C4.5 decision tree, the functional inner regression tree, and logistic regression. External validation was subsequently carried out by 2 independent test sets of 777 and 27 chemicals. As a result, the functional inner regression tree exhibited the best predictability with predictive accuracies of 81.5% and 81.0%, respectively, on the training set (825 chemicals) and test set I (777 chemicals). Performance of the developed models on the 2 test sets was subsequently compared with that of the Estimation Program Interface (EPI) Suite Biowin 5 and Biowin 6 models, which also showed a better predictability of the functional inner regression tree model. The model built in the present study exhibits a reasonable predictability compared with existing models while possessing a transparent algorithm. Interpretation of the mechanisms of biodegradation was also carried out based on the models developed. © 2014 SETAC.

Decision Tree based Prediction and Rule Induction for Groundwater Trichloroethene (TCE) Pollution Vulnerability

NASA Astrophysics Data System (ADS)

Park, J.; Yoo, K.

2013-12-01

For groundwater resource conservation, it is important to accurately assess groundwater pollution sensitivity or vulnerability. In this work, we attempted to use data mining approach to assess groundwater pollution vulnerability in a TCE (trichloroethylene) contaminated Korean industrial site. The conventional DRASTIC method failed to describe TCE sensitivity data with a poor correlation with hydrogeological properties. Among the different data mining methods such as Artificial Neural Network (ANN), Multiple Logistic Regression (MLR), Case Base Reasoning (CBR), and Decision Tree (DT), the accuracy and consistency of Decision Tree (DT) was the best. According to the following tree analyses with the optimal DT model, the failure of the conventional DRASTIC method in fitting with TCE sensitivity data may be due to the use of inaccurate weight values of hydrogeological parameters for the study site. These findings provide a proof of concept that DT based data mining approach can be used in predicting and rule induction of groundwater TCE sensitivity without pre-existing information on weights of hydrogeological properties.

Block-Based Connected-Component Labeling Algorithm Using Binary Decision Trees

PubMed Central

Chang, Wan-Yu; Chiu, Chung-Cheng; Yang, Jia-Horng

2015-01-01

In this paper, we propose a fast labeling algorithm based on block-based concepts. Because the number of memory access points directly affects the time consumption of the labeling algorithms, the aim of the proposed algorithm is to minimize neighborhood operations. Our algorithm utilizes a block-based view and correlates a raster scan to select the necessary pixels generated by a block-based scan mask. We analyze the advantages of a sequential raster scan for the block-based scan mask, and integrate the block-connected relationships using two different procedures with binary decision trees to reduce unnecessary memory access. This greatly simplifies the pixel locations of the block-based scan mask. Furthermore, our algorithm significantly reduces the number of leaf nodes and depth levels required in the binary decision tree. We analyze the labeling performance of the proposed algorithm alongside that of other labeling algorithms using high-resolution images and foreground images. The experimental results from synthetic and real image datasets demonstrate that the proposed algorithm is faster than other methods. PMID:26393597

Using decision-tree classifier systems to extract knowledge from databases

NASA Technical Reports Server (NTRS)

St.clair, D. C.; Sabharwal, C. L.; Hacke, Keith; Bond, W. E.

1990-01-01

One difficulty in applying artificial intelligence techniques to the solution of real world problems is that the development and maintenance of many AI systems, such as those used in diagnostics, require large amounts of human resources. At the same time, databases frequently exist which contain information about the process(es) of interest. Recently, efforts to reduce development and maintenance costs of AI systems have focused on using machine learning techniques to extract knowledge from existing databases. Research is described in the area of knowledge extraction using a class of machine learning techniques called decision-tree classifier systems. Results of this research suggest ways of performing knowledge extraction which may be applied in numerous situations. In addition, a measurement called the concept strength metric (CSM) is described which can be used to determine how well the resulting decision tree can differentiate between the concepts it has learned. The CSM can be used to determine whether or not additional knowledge needs to be extracted from the database. An experiment involving real world data is presented to illustrate the concepts described.

Exploratory Use of Decision Tree Analysis in Classification of Outcome in Hypoxic-Ischemic Brain Injury.

PubMed

Phan, Thanh G; Chen, Jian; Singhal, Shaloo; Ma, Henry; Clissold, Benjamin B; Ly, John; Beare, Richard

2018-01-01

Prognostication following hypoxic ischemic encephalopathy (brain injury) is important for clinical management. The aim of this exploratory study is to use a decision tree model to find clinical and MRI associates of severe disability and death in this condition. We evaluate clinical model and then the added value of MRI data. The inclusion criteria were as follows: age ≥17 years, cardio-respiratory arrest, and coma on admission (2003-2011). Decision tree analysis was used to find clinical [Glasgow Coma Score (GCS), features about cardiac arrest, therapeutic hypothermia, age, and sex] and MRI (infarct volume) associates of severe disability and death. We used the area under the ROC (auROC) to determine accuracy of model. There were 41 (63.7% males) patients having MRI imaging with the average age 51.5 ± 18.9 years old. The decision trees showed that infarct volume and age were important factors for discrimination between mild to moderate disability and severe disability and death at day 0 and day 2. The auROC for this model was 0.94 (95% CI 0.82-1.00). At day 7, GCS value was the only predictor; the auROC was 0.96 (95% CI 0.86-1.00). Our findings provide proof of concept for further exploration of the role of MR imaging and decision tree analysis in the early prognostication of hypoxic ischemic brain injury.

Applying of Decision Tree Analysis to Risk Factors Associated with Pressure Ulcers in Long-Term Care Facilities.

PubMed

Moon, Mikyung; Lee, Soo-Kyoung

2017-01-01

The purpose of this study was to use decision tree analysis to explore the factors associated with pressure ulcers (PUs) among elderly people admitted to Korean long-term care facilities. The data were extracted from the 2014 National Inpatient Sample (NIS)-data of Health Insurance Review and Assessment Service (HIRA). A MapReduce-based program was implemented to join and filter 5 tables of the NIS. The outcome predicted by the decision tree model was the prevalence of PUs as defined by the Korean Standard Classification of Disease-7 (KCD-7; code L89 * ). Using R 3.3.1, a decision tree was generated with the finalized 15,856 cases and 830 variables. The decision tree displayed 15 subgroups with 8 variables showing 0.804 accuracy, 0.820 sensitivity, and 0.787 specificity. The most significant primary predictor of PUs was length of stay less than 0.5 day. Other predictors were the presence of an infectious wound dressing, followed by having diagnoses numbering less than 3.5 and the presence of a simple dressing. Among diagnoses, "injuries to the hip and thigh" was the top predictor ranking 5th overall. Total hospital cost exceeding 2,200,000 Korean won (US $2,000) rounded out the top 7. These results support previous studies that showed length of stay, comorbidity, and total hospital cost were associated with PUs. Moreover, wound dressings were commonly used to treat PUs. They also show that machine learning, such as a decision tree, could effectively predict PUs using big data.

Effects of multilayered bags vs ethylvinyl-acetate bags on oxidation of parenteral nutrition.

PubMed

Balet, Antònia; Cardona, Daniel; Jané, Salvador; Molins-Pujol, Antoni M; Sánchez Quesada, José Luís; Gich, Ignasi; Mangues, Ma Antònia

2004-01-01

We evaluate the effects of multilayered bags vs ethylvinyl-acetate bags on peroxidate formation of various emulsions for all-in-one total parenteral nutrition solutions (TPN) during storage. Twenty-four parenteral nutritions were prepared with 4 commercial i.v. lipid emulsions (Soyacal 20%, Grifols; Intralipid 20%, Fresenius-Kabi; Lipofundina 20%, Braun; and Clinoleic 20%, Clintex) and 2 different bags (multilayered [ML] bag, Miramed; and 1 ethylvinyl-acetate [EVA] bag, Miramed). Each kind of TPN was prepared in triplicate. Samples were taken at 3 different times: immediately after preparation (time 0), after 6 days at 4 degrees C and 48 hours at 37 degrees C (time 1), and finally after a total of 14 days at 37 degrees C (time 2). Oxidation of TPN was evaluated by analysis of hydroperoxides by ferrous oxidation-xylenol orange (FOX) reactive, lipoperoxides by thiobarbituric acid reactive species (TBARS), alpha-tocopherol by high-performance liquid chromatography (HPLC), and ascorbic acid and dehydroascorbic acid by HPLC. TPN admixtures in ML bag showed less oxidation evaluated by peroxide determination using FOX than EVA bag. Lipoperoxides by TBARS did not show significant differences between 2 bags. Ascorbic acid and dehydroascorbic acid disappeared in EVA bags at time 1. No important differences were found in alpha-tocopherol content. Multilayered bags minimize oxidation.

Decision tree analysis to stratify risk of de novo non-melanoma skin cancer following liver transplantation.

PubMed

Tanaka, Tomohiro; Voigt, Michael D

2018-03-01

Non-melanoma skin cancer (NMSC) is the most common de novo malignancy in liver transplant (LT) recipients; it behaves more aggressively and it increases mortality. We used decision tree analysis to develop a tool to stratify and quantify risk of NMSC in LT recipients. We performed Cox regression analysis to identify which predictive variables to enter into the decision tree analysis. Data were from the Organ Procurement Transplant Network (OPTN) STAR files of September 2016 (n = 102984). NMSC developed in 4556 of the 105984 recipients, a mean of 5.6 years after transplant. The 5/10/20-year rates of NMSC were 2.9/6.3/13.5%, respectively. Cox regression identified male gender, Caucasian race, age, body mass index (BMI) at LT, and sirolimus use as key predictive or protective factors for NMSC. These factors were entered into a decision tree analysis. The final tree stratified non-Caucasians as low risk (0.8%), and Caucasian males > 47 years, BMI < 40 who did not receive sirolimus, as high risk (7.3% cumulative incidence of NMSC). The predictions in the derivation set were almost identical to those in the validation set (r 2  = 0.971, p < 0.0001). Cumulative incidence of NMSC in low, moderate and high risk groups at 5/10/20 year was 0.5/1.2/3.3, 2.1/4.8/11.7 and 5.6/11.6/23.1% (p < 0.0001). The decision tree model accurately stratifies the risk of developing NMSC in the long-term after LT.

Air bags: an update.

PubMed

Mikhail, J N; Huelke, D F

1997-10-01

Overwhelming evidence shows that air bags save lives and reduce morbidity associated with MVCs. The resulting benefits far outweigh the risks of air bag injury or death. Emergency nurses play a pivotal role in educating the public about active seat belt use in conjunction with passive restraint systems such as air bags. Air bags cannot be viewed as a single solution or panacea to occupant protection. Air bags are designed as supplemental devices to be used with seat belts and require the active participation of the user for maximum benefit and safety.

Binary Decision Trees for Preoperative Periapical Cyst Screening Using Cone-beam Computed Tomography.

PubMed

Pitcher, Brandon; Alaqla, Ali; Noujeim, Marcel; Wealleans, James A; Kotsakis, Georgios; Chrepa, Vanessa

2017-03-01

Cone-beam computed tomographic (CBCT) analysis allows for 3-dimensional assessment of periradicular lesions and may facilitate preoperative periapical cyst screening. The purpose of this study was to develop and assess the predictive validity of a cyst screening method based on CBCT volumetric analysis alone or combined with designated radiologic criteria. Three independent examiners evaluated 118 presurgical CBCT scans from cases that underwent apicoectomies and had an accompanying gold standard histopathological diagnosis of either a cyst or granuloma. Lesion volume, density, and specific radiologic characteristics were assessed using specialized software. Logistic regression models with histopathological diagnosis as the dependent variable were constructed for cyst prediction, and receiver operating characteristic curves were used to assess the predictive validity of the models. A conditional inference binary decision tree based on a recursive partitioning algorithm was constructed to facilitate preoperative screening. Interobserver agreement was excellent for volume and density, but it varied from poor to good for the radiologic criteria. Volume and root displacement were strong predictors for cyst screening in all analyses. The binary decision tree classifier determined that if the volume of the lesion was >247 mm 3 , there was 80% probability of a cyst. If volume was <247 mm 3 and root displacement was present, cyst probability was 60% (78% accuracy). The good accuracy and high specificity of the decision tree classifier renders it a useful preoperative cyst screening tool that can aid in clinical decision making but not a substitute for definitive histopathological diagnosis after biopsy. Confirmatory studies are required to validate the present findings. Published by Elsevier Inc.

A simple plug-in bagging ensemble based on threshold-moving for classifying binary and multiclass imbalanced data.

PubMed

Collell, Guillem; Prelec, Drazen; Patil, Kaustubh R

2018-01-31

Class imbalance presents a major hurdle in the application of classification methods. A commonly taken approach is to learn ensembles of classifiers using rebalanced data. Examples include bootstrap averaging (bagging) combined with either undersampling or oversampling of the minority class examples. However, rebalancing methods entail asymmetric changes to the examples of different classes, which in turn can introduce their own biases. Furthermore, these methods often require specifying the performance measure of interest a priori, i.e., before learning. An alternative is to employ the threshold moving technique, which applies a threshold to the continuous output of a model, offering the possibility to adapt to a performance measure a posteriori , i.e., a plug-in method. Surprisingly, little attention has been paid to this combination of a bagging ensemble and threshold-moving. In this paper, we study this combination and demonstrate its competitiveness. Contrary to the other resampling methods, we preserve the natural class distribution of the data resulting in well-calibrated posterior probabilities. Additionally, we extend the proposed method to handle multiclass data. We validated our method on binary and multiclass benchmark data sets by using both, decision trees and neural networks as base classifiers. We perform analyses that provide insights into the proposed method.

Lunar regolith bagging system

NASA Technical Reports Server (NTRS)

Cannon, Reuben; Henninger, Scott; Levandoski, Mark; Perkins, Jim; Pitchon, Jack; Swats, Robin; Wessels, Roger

1990-01-01

A design of a lunar regolith bag and bagging system is described. The bags of regolith are to be used for construction applications on the lunar surface. The machine is designed to be used in conjunction with the lunar SKITTER currently under development. The bags for this system are 1 cu ft volume and are made from a fiberglass composite weave. The machinery is constructed mostly from a boron/aluminum composite. The machine can fill 120 bags per hour and work for 8 hours a day. The man hours to machine hours ratio to operate the machine is .5/8.

Toward the Decision Tree for Inferring Requirements Maturation Types

NASA Astrophysics Data System (ADS)

Nakatani, Takako; Kondo, Narihito; Shirogane, Junko; Kaiya, Haruhiko; Hori, Shozo; Katamine, Keiichi

Requirements are elicited step by step during the requirements engineering (RE) process. However, some types of requirements are elicited completely after the scheduled requirements elicitation process is finished. Such a situation is regarded as problematic situation. In our study, the difficulties of eliciting various kinds of requirements is observed by components. We refer to the components as observation targets (OTs) and introduce the word “Requirements maturation.” It means when and how requirements are elicited completely in the project. The requirements maturation is discussed on physical and logical OTs. OTs Viewed from a logical viewpoint are called logical OTs, e.g. quality requirements. The requirements of physical OTs, e.g., modules, components, subsystems, etc., includes functional and non-functional requirements. They are influenced by their requesters' environmental changes, as well as developers' technical changes. In order to infer the requirements maturation period of each OT, we need to know how much these factors influence the OTs' requirements maturation. According to the observation of actual past projects, we defined the PRINCE (Pre Requirements Intelligence Net Consideration and Evaluation) model. It aims to guide developers in their observation of the requirements maturation of OTs. We quantitatively analyzed the actual cases with their requirements elicitation process and extracted essential factors that influence the requirements maturation. The results of interviews of project managers are analyzed by WEKA, a data mining system, from which the decision tree was derived. This paper introduces the PRINCE model and the category of logical OTs to be observed. The decision tree that helps developers infer the maturation type of an OT is also described. We evaluate the tree through real projects and discuss its ability to infer the requirements maturation types.

Comparison of Naive Bayes and Decision Tree on Feature Selection Using Genetic Algorithm for Classification Problem

NASA Astrophysics Data System (ADS)

Rahmadani, S.; Dongoran, A.; Zarlis, M.; Zakarias

2018-03-01

This paper discusses the problem of feature selection using genetic algorithms on a dataset for classification problems. The classification model used is the decicion tree (DT), and Naive Bayes. In this paper we will discuss how the Naive Bayes and Decision Tree models to overcome the classification problem in the dataset, where the dataset feature is selectively selected using GA. Then both models compared their performance, whether there is an increase in accuracy or not. From the results obtained shows an increase in accuracy if the feature selection using GA. The proposed model is referred to as GADT (GA-Decision Tree) and GANB (GA-Naive Bayes). The data sets tested in this paper are taken from the UCI Machine Learning repository.

Decision-Tree Analysis for Predicting First-Time Pass/Fail Rates for the NCLEX-RN® in Associate Degree Nursing Students.

PubMed

Chen, Hsiu-Chin; Bennett, Sean

2016-08-01

Little evidence shows the use of decision-tree algorithms in identifying predictors and analyzing their associations with pass rates for the NCLEX-RN(®) in associate degree nursing students. This longitudinal and retrospective cohort study investigated whether a decision-tree algorithm could be used to develop an accurate prediction model for the students' passing or failing the NCLEX-RN. This study used archived data from 453 associate degree nursing students in a selected program. The chi-squared automatic interaction detection analysis of the decision trees module was used to examine the effect of the collected predictors on passing/failing the NCLEX-RN. The actual percentage scores of Assessment Technologies Institute®'s RN Comprehensive Predictor(®) accurately identified students at risk of failing. The classification model correctly classified 92.7% of the students for passing. This study applied the decision-tree model to analyze a sequence database for developing a prediction model for early remediation in preparation for the NCLEXRN. [J Nurs Educ. 2016;55(8):454-457.]. Copyright 2016, SLACK Incorporated.

Diagnostic Features of Common Oral Ulcerative Lesions: An Updated Decision Tree

PubMed Central

Safi, Yaser

2016-01-01

Diagnosis of oral ulcerative lesions might be quite challenging. This narrative review article aims to introduce an updated decision tree for diagnosing oral ulcerative lesions on the basis of their diagnostic features. Various general search engines and specialized databases including PubMed, PubMed Central, Medline Plus, EBSCO, Science Direct, Scopus, Embase, and authenticated textbooks were used to find relevant topics by means of MeSH keywords such as “oral ulcer,” “stomatitis,” and “mouth diseases.” Thereafter, English-language articles published since 1983 to 2015 in both medical and dental journals including reviews, meta-analyses, original papers, and case reports were appraised. Upon compilation of the relevant data, oral ulcerative lesions were categorized into three major groups: acute, chronic, and recurrent ulcers and into five subgroups: solitary acute, multiple acute, solitary chronic, multiple chronic, and solitary/multiple recurrent, based on the number and duration of lesions. In total, 29 entities were organized in the form of a decision tree in order to help clinicians establish a logical diagnosis by stepwise progression. PMID:27781066

View of special 'gift bag' of exchange items to be carried during ASTP

NASA Technical Reports Server (NTRS)

1975-01-01

A close-up view of the special 'gift bag' in which several exchange/gift items will be carried aboard the Apollo Command Module during the joint U.S.-U.S.S.R. Apollo Soyuz Test Project (ASTP) mission. Some of these items are seen here alongside the bag. They are the sectionized ASTP Commemorative Plaque, ten 8X12 inch American flags, 8.07X16.14 inch (205mm x 410mm) Soviet Union flag, a special box of white spruce tree seeds, and the ASTP Certification to authorize the ASTP docking. The gifts will be presented and the exchange made while the Apollo and Soyuz spacecraft are docked in Earth orbit.

Evaluation with Decision Trees of Efficacy and Safety of Semirigid Ureteroscopy in the Treatment of Proximal Ureteral Calculi.

PubMed

Sancak, Eyup Burak; Kılınç, Muhammet Fatih; Yücebaş, Sait Can

2017-01-01

The decision on the choice of proximal ureteral stone therapy depends on many factors, and sometimes urologists have difficulty in choosing the treatment option. This study is aimed at evaluating the factors affecting the success of semirigid ureterorenoscopy (URS) using the "decision tree" method. From January 2005 to November 2015, the data of consecutive patients treated for proximal ureteral stone were retrospectively analyzed. A total of 920 patients with proximal ureteral stone treated with semirigid URS were included in the study. All statistically significant attributes were tested using the decision tree method. The model created using decision tree had a sensitivity of 0.993 and an accuracy of 0.857. While URS treatment was successful in 752 patients (81.7%), it was unsuccessful in 168 patients (18.3%). According to the decision tree method, the most important factor affecting the success of URS is whether the stone is impacted to the ureteral wall. The second most important factor affecting treatment was intramural stricture requiring dilatation if the stone is impacted, and the size of the stone if not impacted. Our study suggests that the impacted stone, intramural stricture requiring dilatation and stone size may have a significant effect on the success rate of semirigid URS for proximal ureteral stone. Further studies with population-based and longitudinal design should be conducted to confirm this finding. © 2017 S. Karger AG, Basel.

Comparison of neurofuzzy logic and decision trees in discovering knowledge from experimental data of an immediate release tablet formulation.

PubMed

Shao, Q; Rowe, R C; York, P

2007-06-01

Understanding of the cause-effect relationships between formulation ingredients, process conditions and product properties is essential for developing a quality product. However, the formulation knowledge is often hidden in experimental data and not easily interpretable. This study compares neurofuzzy logic and decision tree approaches in discovering hidden knowledge from an immediate release tablet formulation database relating formulation ingredients (silica aerogel, magnesium stearate, microcrystalline cellulose and sodium carboxymethylcellulose) and process variables (dwell time and compression force) to tablet properties (tensile strength, disintegration time, friability, capping and drug dissolution at various time intervals). Both approaches successfully generated useful knowledge in the form of either "if then" rules or decision trees. Although different strategies are employed by the two approaches in generating rules/trees, similar knowledge was discovered in most cases. However, as decision trees are not able to deal with continuous dependent variables, data discretisation procedures are generally required.

Determinants of farmers' tree-planting investment decisions as a degraded landscape management strategy in the central highlands of Ethiopia

NASA Astrophysics Data System (ADS)

Gessesse, Berhan; Bewket, Woldeamlak; Bräuning, Achim

2016-04-01

Land degradation due to lack of sustainable land management practices is one of the critical challenges in many developing countries including Ethiopia. This study explored the major determinants of farm-level tree-planting decisions as a land management strategy in a typical farming and degraded landscape of the Modjo watershed, Ethiopia. The main data were generated from household surveys and analysed using descriptive statistics and a binary logistic regression model. The model significantly predicted farmers' tree-planting decisions (χ2 = 37.29, df = 15, P < 0.001). Besides, the computed significant value of the model revealed that all the considered predictor variables jointly influenced the farmers' decisions to plant trees as a land management strategy. The findings of the study demonstrated that the adoption of tree-growing decisions by local land users was a function of a wide range of biophysical, institutional, socioeconomic and household-level factors. In this regard, the likelihood of household size, productive labour force availability, the disparity of schooling age, level of perception of the process of deforestation and the current land tenure system had a critical influence on tree-growing investment decisions in the study watershed. Eventually, the processes of land-use conversion and land degradation were serious, which in turn have had adverse effects on agricultural productivity, local food security and poverty trap nexus. Hence, the study recommended that devising and implementing sustainable land management policy options would enhance ecological restoration and livelihood sustainability in the study watershed.

Determinants of farmers' tree planting investment decision as a degraded landscape management strategy in the central highlands of Ethiopia

NASA Astrophysics Data System (ADS)

Gessesse, B.; Bewket, W.; Bräuning, A.

2015-11-01

Land degradation due to lack of sustainable land management practices are one of the critical challenges in many developing countries including Ethiopia. This study explores the major determinants of farm level tree planting decision as a land management strategy in a typical framing and degraded landscape of the Modjo watershed, Ethiopia. The main data were generated from household surveys and analysed using descriptive statistics and binary logistic regression model. The model significantly predicted farmers' tree planting decision (Chi-square = 37.29, df = 15, P<0.001). Besides, the computed significant value of the model suggests that all the considered predictor variables jointly influenced the farmers' decision to plant trees as a land management strategy. In this regard, the finding of the study show that local land-users' willingness to adopt tree growing decision is a function of a wide range of biophysical, institutional, socioeconomic and household level factors, however, the likelihood of household size, productive labour force availability, the disparity of schooling age, level of perception of the process of deforestation and the current land tenure system have positively and significantly influence on tree growing investment decisions in the study watershed. Eventually, the processes of land use conversion and land degradation are serious which in turn have had adverse effects on agricultural productivity, local food security and poverty trap nexus. Hence, devising sustainable and integrated land management policy options and implementing them would enhance ecological restoration and livelihood sustainability in the study watershed.

Coronal Mass Ejection Data Clustering and Visualization of Decision Trees

NASA Astrophysics Data System (ADS)

Ma, Ruizhe; Angryk, Rafal A.; Riley, Pete; Filali Boubrahimi, Soukaina

2018-05-01

Coronal mass ejections (CMEs) can be categorized as either “magnetic clouds” (MCs) or non-MCs. Features such as a large magnetic field, low plasma-beta, and low proton temperature suggest that a CME event is also an MC event; however, so far there is neither a definitive method nor an automatic process to distinguish the two. Human labeling is time-consuming, and results can fluctuate owing to the imprecise definition of such events. In this study, we approach the problem of MC and non-MC distinction from a time series data analysis perspective and show how clustering can shed some light on this problem. Although many algorithms exist for traditional data clustering in the Euclidean space, they are not well suited for time series data. Problems such as inadequate distance measure, inaccurate cluster center description, and lack of intuitive cluster representations need to be addressed for effective time series clustering. Our data analysis in this work is twofold: clustering and visualization. For clustering we compared the results from the popular hierarchical agglomerative clustering technique to a distance density clustering heuristic we developed previously for time series data clustering. In both cases, dynamic time warping will be used for similarity measure. For classification as well as visualization, we use decision trees to aggregate single-dimensional clustering results to form a multidimensional time series decision tree, with averaged time series to present each decision. In this study, we achieved modest accuracy and, more importantly, an intuitive interpretation of how different parameters contribute to an MC event.

Validating a decision tree for serious infection: diagnostic accuracy in acutely ill children in ambulatory care.

PubMed

Verbakel, Jan Y; Lemiengre, Marieke B; De Burghgraeve, Tine; De Sutter, An; Aertgeerts, Bert; Bullens, Dominique M A; Shinkins, Bethany; Van den Bruel, Ann; Buntinx, Frank

2015-08-07

Acute infection is the most common presentation of children in primary care with only few having a serious infection (eg, sepsis, meningitis, pneumonia). To avoid complications or death, early recognition and adequate referral are essential. Clinical prediction rules have the potential to improve diagnostic decision-making for rare but serious conditions. In this study, we aimed to validate a recently developed decision tree in a new but similar population. Diagnostic accuracy study validating a clinical prediction rule. Acutely ill children presenting to ambulatory care in Flanders, Belgium, consisting of general practice and paediatric assessment in outpatient clinics or the emergency department. Physicians were asked to score the decision tree in every child. The outcome of interest was hospital admission for at least 24 h with a serious infection within 5 days after initial presentation. We report the diagnostic accuracy of the decision tree in sensitivity, specificity, likelihood ratios and predictive values. In total, 8962 acute illness episodes were included, of which 283 lead to admission to hospital with a serious infection. Sensitivity of the decision tree was 100% (95% CI 71.5% to 100%) at a specificity of 83.6% (95% CI 82.3% to 84.9%) in the general practitioner setting with 17% of children testing positive. In the paediatric outpatient and emergency department setting, sensitivities were below 92%, with specificities below 44.8%. In an independent validation cohort, this clinical prediction rule has shown to be extremely sensitive to identify children at risk of hospital admission for a serious infection in general practice, making it suitable for ruling out. NCT02024282. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks.

PubMed

Alauthaman, Mohammad; Aslam, Nauman; Zhang, Li; Alasem, Rafe; Hossain, M A

2018-01-01

In recent years, Botnets have been adopted as a popular method to carry and spread many malicious codes on the Internet. These malicious codes pave the way to execute many fraudulent activities including spam mail, distributed denial-of-service attacks and click fraud. While many Botnets are set up using centralized communication architecture, the peer-to-peer (P2P) Botnets can adopt a decentralized architecture using an overlay network for exchanging command and control data making their detection even more difficult. This work presents a method of P2P Bot detection based on an adaptive multilayer feed-forward neural network in cooperation with decision trees. A classification and regression tree is applied as a feature selection technique to select relevant features. With these features, a multilayer feed-forward neural network training model is created using a resilient back-propagation learning algorithm. A comparison of feature set selection based on the decision tree, principal component analysis and the ReliefF algorithm indicated that the neural network model with features selection based on decision tree has a better identification accuracy along with lower rates of false positives. The usefulness of the proposed approach is demonstrated by conducting experiments on real network traffic datasets. In these experiments, an average detection rate of 99.08 % with false positive rate of 0.75 % was observed.

Identifying Risk Factors for Drug Use in an Iranian Treatment Sample: A Prediction Approach Using Decision Trees.

PubMed

Amirabadizadeh, Alireza; Nezami, Hossein; Vaughn, Michael G; Nakhaee, Samaneh; Mehrpour, Omid

2018-05-12

Substance abuse exacts considerable social and health care burdens throughout the world. The aim of this study was to create a prediction model to better identify risk factors for drug use. A prospective cross-sectional study was conducted in South Khorasan Province, Iran. Of the total of 678 eligible subjects, 70% (n: 474) were randomly selected to provide a training set for constructing decision tree and multiple logistic regression (MLR) models. The remaining 30% (n: 204) were employed in a holdout sample to test the performance of the decision tree and MLR models. Predictive performance of different models was analyzed by the receiver operating characteristic (ROC) curve using the testing set. Independent variables were selected from demographic characteristics and history of drug use. For the decision tree model, the sensitivity and specificity for identifying people at risk for drug abuse were 66% and 75%, respectively, while the MLR model was somewhat less effective at 60% and 73%. Key independent variables in the analyses included first substance experience, age at first drug use, age, place of residence, history of cigarette use, and occupational and marital status. While study findings are exploratory and lack generalizability they do suggest that the decision tree model holds promise as an effective classification approach for identifying risk factors for drug use. Convergent with prior research in Western contexts is that age of drug use initiation was a critical factor predicting a substance use disorder.

Identifying Risk and Protective Factors in Recidivist Juvenile Offenders: A Decision Tree Approach

PubMed Central

Ortega-Campos, Elena; García-García, Juan; Gil-Fenoy, Maria José; Zaldívar-Basurto, Flor

2016-01-01

Research on juvenile justice aims to identify profiles of risk and protective factors in juvenile offenders. This paper presents a study of profiles of risk factors that influence young offenders toward committing sanctionable antisocial behavior (S-ASB). Decision tree analysis is used as a multivariate approach to the phenomenon of repeated sanctionable antisocial behavior in juvenile offenders in Spain. The study sample was made up of the set of juveniles who were charged in a court case in the Juvenile Court of Almeria (Spain). The period of study of recidivism was two years from the baseline. The object of study is presented, through the implementation of a decision tree. Two profiles of risk and protective factors are found. Risk factors associated with higher rates of recidivism are antisocial peers, age at baseline S-ASB, problems in school and criminality in family members. PMID:27611313

Which Types of Leadership Styles Do Followers Prefer? A Decision Tree Approach

ERIC Educational Resources Information Center

Salehzadeh, Reza

2017-01-01

Purpose: The purpose of this paper is to propose a new method to find the appropriate leadership styles based on the followers' preferences using the decision tree technique. Design/methodology/approach: Statistical population includes the students of the University of Isfahan. In total, 750 questionnaires were distributed; out of which, 680…

The Americans with Disabilities Act: A Decision Tree for Social Services Administrators

ERIC Educational Resources Information Center

O'Brien, Gerald V.; Ellegood, Christina

2005-01-01

The 1990 Americans with Disabilities Act has had a profound influence on social workers and social services administrators in virtually all work settings. Because of the multiple elements of the act, however, assessing the validity of claims can be a somewhat arduous and complicated task. This article provides a "decision tree" for…

A decision tree approach using silvics to guide planning for forest restoration

Treesearch

Sharon M. Hermann; John S. Kush; John C. Gilbert

2013-01-01

We created a decision tree based on silvics of longleaf pine (Pinus palustris) and historical descriptions to develop approaches for restoration management at Horseshoe Bend National Military Park located in central Alabama. A National Park Service goal is to promote structure and composition of a forest that likely surrounded the 1814 battlefield....

Advanced Air Bag Technology Assessment

NASA Technical Reports Server (NTRS)

Phen, R. L.; Dowdy, M. W.; Ebbeler, D. H.; Kim. E.-H.; Moore, N. R.; VanZandt, T. R.

1998-01-01

As a result of the concern for the growing number of air-bag-induced injuries and fatalities, the administrators of the National Highway Traffic Safety Administration (NHTSA) and the National Aeronautics and Space Administration (NASA) agreed to a cooperative effort that "leverages NHTSA's expertise in motor vehicle safety restraint systems and biomechanics with NASAs position as one of the leaders in advanced technology development... to enable the state of air bag safety technology to advance at a faster pace..." They signed a NASA/NHTSA memorandum of understanding for NASA to "evaluate air bag to assess advanced air bag performance, establish the technological potential for improved technology (smart) air bag systems, and identify key expertise and technology within the agency (i.e., NASA) that can potentially contribute significantly to the improved effectiveness of air bags." NASA is committed to contributing to NHTSAs effort to: (1) understand and define critical parameters affecting air bag performance; (2) systematically assess air bag technology state of the art and its future potential; and (3) identify new concepts for air bag systems. The Jet Propulsion Laboratory (JPL) was selected by NASA to respond to the memorandum of understanding by conducting an advanced air bag technology assessment. JPL analyzed the nature of the need for occupant restraint, how air bags operate alone and with safety belts to provide restraint, and the potential hazards introduced by the technology. This analysis yielded a set of critical parameters for restraint systems. The researchers examined data on the performance of current air bag technology, and searched for and assessed how new technologies could reduce the hazards introduced by air bags while providing the restraint protection that is their primary purpose. The critical parameters which were derived are: (1) the crash severity; (2) the use of seat belts; (3) the physical characteristics of the occupants; (4) the

Does the 'old bag' make a good 'wind bag'?: Comparison of four fabrics commonly used as exclusion bags in studies of pollination and reproductive biology.

PubMed

Neal, Paul R; Anderson, Gregory J

2004-05-01

Fabrics used in pollination bags may exclude pollen carried by biotic vectors, but have varying degrees of permeability to wind-borne pollen. The permeability of bags to wind-borne pollen may have important consequences in studies of pollination and reproductive biology. The permeability of four fabrics commonly used in the construction of pollination bags was examined. Deposition of wind-borne pollen on horizontally and vertically oriented microscope slides was assessed on slides enclosed in pollination bags, as well as on control slides. It was found that the permeability of fabrics to wind-borne pollen, as measured by deposition on both horizontally and vertically oriented slides, decreased with pore size. However, deposition on horizontal slides was always greater than on vertical slides for a given fabric; this could manifest itself as differential success of pollination of flowers in bags-dependent on flower orientation. Obviously, bags with mesh size smaller than most pollen grains are impermeable to pollen. However, material for such bags is very expensive. In addition, it was also observed that bags with even moderately small pore size, such as pores (approx. 200 microm) in twisted fibre cotton muslin, offered highly significant barriers to passage of wind-borne pollen. Such bags are sufficiently effective in most large-sample-size reproductive biology studies.

What Satisfies Students?: Mining Student-Opinion Data with Regression and Decision Tree Analysis

ERIC Educational Resources Information Center

Thomas, Emily H.; Galambos, Nora

2004-01-01

To investigate how students' characteristics and experiences affect satisfaction, this study uses regression and decision tree analysis with the CHAID algorithm to analyze student-opinion data. A data mining approach identifies the specific aspects of students' university experience that most influence three measures of general satisfaction. The…

49 CFR 173.166 - Air bag inflators, air bag modules and seat-belt pretensioners.

Code of Federal Regulations, 2011 CFR

2011-10-01

... an inflatable bag assembly. A seat-belt pre-tensioner contains similar hazardous materials and is... manufacturer has submitted each design type air bag inflator, air bag module, or seat-belt pretensioner to a... or pretensioner design type for which approval is sought and details on the complete package. The...

49 CFR 173.166 - Air bag inflators, air bag modules and seat-belt pretensioners.

Code of Federal Regulations, 2010 CFR

2010-10-01

... an inflatable bag assembly. A seat-belt pre-tensioner contains similar hazardous materials and is... manufacturer has submitted each design type air bag inflator, air bag module, or seat-belt pretensioner to a... or pretensioner design type for which approval is sought and details on the complete package. The...

49 CFR 173.166 - Air bag inflators, air bag modules and seat-belt pretensioners.

Code of Federal Regulations, 2012 CFR

2012-10-01

... an inflatable bag assembly. A seat-belt pre-tensioner contains similar hazardous materials and is... manufacturer has submitted each design type air bag inflator, air bag module, or seat-belt pretensioner to a... or pretensioner design type for which approval is sought and details on the complete package. The...

Classification of Liss IV Imagery Using Decision Tree Methods

NASA Astrophysics Data System (ADS)

Verma, Amit Kumar; Garg, P. K.; Prasad, K. S. Hari; Dadhwal, V. K.

2016-06-01

Image classification is a compulsory step in any remote sensing research. Classification uses the spectral information represented by the digital numbers in one or more spectral bands and attempts to classify each individual pixel based on this spectral information. Crop classification is the main concern of remote sensing applications for developing sustainable agriculture system. Vegetation indices computed from satellite images gives a good indication of the presence of vegetation. It is an indicator that describes the greenness, density and health of vegetation. Texture is also an important characteristics which is used to identifying objects or region of interest is an image. This paper illustrate the use of decision tree method to classify the land in to crop land and non-crop land and to classify different crops. In this paper we evaluate the possibility of crop classification using an integrated approach methods based on texture property with different vegetation indices for single date LISS IV sensor 5.8 meter high spatial resolution data. Eleven vegetation indices (NDVI, DVI, GEMI, GNDVI, MSAVI2, NDWI, NG, NR, NNIR, OSAVI and VI green) has been generated using green, red and NIR band and then image is classified using decision tree method. The other approach is used integration of texture feature (mean, variance, kurtosis and skewness) with these vegetation indices. A comparison has been done between these two methods. The results indicate that inclusion of textural feature with vegetation indices can be effectively implemented to produce classifiedmaps with 8.33% higher accuracy for Indian satellite IRS-P6, LISS IV sensor images.

Re-Construction of Reference Population and Generating Weights by Decision Tree

DTIC Science & Technology

2017-07-21

2017 Claflin University Orangeburg, SC 29115 DEFENSE EQUAL OPPORTUNITY MANAGEMENT INSTITUTE RESEARCH, DEVELOPMENT, AND STRATEGIC...Original Dataset 32 List of Figures in Appendix B Figure 1: Flow and Components of Project 20 Figure 2: Decision Tree 31 Figure 3: Effects of Weight...can compare the sample data. The dataset of this project has the reference population on unit level for group and gender, which is in red-dotted box

Air bag restraint device

DOEpatents

Marts, D.J.; Richardson, J.G.

1995-10-17

A rear-seat air bag restraint device is disclosed that prevents an individual, or individuals, from continuing violent actions while being transported in a patrol vehicle`s rear seat without requiring immediate physical contact by the law enforcement officer. The air bag is activated by a control switch in the front seat and inflates to independently restrict the amount of physical activity occurring in the rear seat of the vehicle while allowing the officer to safely stop the vehicle. The air bag can also provide the officer additional time to get backup personnel to aid him if the situation warrants it. The bag is inflated and maintains a constant pressure by an air pump. 8 figs.

Air bag restraint device

DOEpatents

Marts, Donna J.; Richardson, John G.

1995-01-01

A rear-seat air bag restraint device is disclosed that prevents an individual, or individuals, from continuing violent actions while being transported in a patrol vehicle's rear seat without requiring immediate physical contact by the law enforcement officer. The air bag is activated by a control switch in the front seat and inflates to independently restrict the amount of physical activity occurring in the rear seat of the vehicle while allowing the officer to safely stop the vehicle. The air bag can also provide the officer additional time to get backup personnel to aid him if the situation warrants it. The bag is inflated and maintains a constant pressure by an air pump.

Unit-Dose Bags For Formulating Intravenous Solutions

NASA Technical Reports Server (NTRS)

Finley, Mike; Kipp, Jim; Scharf, Mike; Packard, Jeff; Owens, Jim

1993-01-01

Smaller unit-dose flowthrough bags devised for use with large-volume parenteral (LVP) bags in preparing sterile intravenous solutions. Premeasured amount of solute stored in such unit-dose bag flushed by predetermined amount of water into LVP bag. Relatively small number of LVP bags used in conjunction with smaller unit-dose bags to formulate large number of LVP intravenous solutions in nonsterile environment.

ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography.

PubMed

Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano

2016-07-07

Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

ATLAAS: an automatic decision tree-based learning algorithm for advanced image segmentation in positron emission tomography

NASA Astrophysics Data System (ADS)

Berthon, Beatrice; Marshall, Christopher; Evans, Mererid; Spezi, Emiliano

2016-07-01

Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.

Blast-Absorbing Bag

NASA Technical Reports Server (NTRS)

Kahn, Jon B.

1992-01-01

Proposed expandable bag contains debris from explosion. Permanently surrounds vessel or devices prone to explosive disintegration or slipped around small bomb. Finned cells shaped like outward-opening cups. Cells built up from overlapped sheets of fabric and stitched together to form expandable polyhedral bag. Cells pentagonal, triangular or square.

Gravitating lepton bag model

NASA Astrophysics Data System (ADS)

Burinskii, A.

2015-08-01

The Kerr-Newman (KN) black hole (BH) solution exhibits the external gravitational and electromagnetic field corresponding to that of the Dirac electron. For the large spin/mass ratio, a ≫ m, the BH loses horizons and acquires a naked singular ring creating two-sheeted topology. This space is regularized by the Higgs mechanism of symmetry breaking, leading to an extended particle that has a regular spinning core compatible with the external KN solution. We show that this core has much in common with the known MIT and SLAC bag models, but has the important advantage of being in accordance with the external gravitational and electromagnetic fields of the KN solution. A peculiar two-sheeted structure of Kerr's gravity provides a framework for the implementation of the Higgs mechanism of symmetry breaking in configuration space in accordance with the concept of the electroweak sector of the Standard Model. Similar to other bag models, the KN bag is flexible and pliant to deformations. For parameters of a spinning electron, the bag takes the shape of a thin rotating disk of the Compton radius, with a ring-string structure and a quark-like singular pole formed at the sharp edge of this disk, indicating that the considered lepton bag forms a single bag-string-quark system.

Computerization of guidelines: a knowledge specification method to convert text to detailed decision tree for electronic implementation.

PubMed

Aguirre-Junco, Angel-Ricardo; Colombet, Isabelle; Zunino, Sylvain; Jaulent, Marie-Christine; Leneveut, Laurence; Chatellier, Gilles

2004-01-01

The initial step for the computerization of guidelines is the knowledge specification from the prose text of guidelines. We describe a method of knowledge specification based on a structured and systematic analysis of text allowing detailed specification of a decision tree. We use decision tables to validate the decision algorithm and decision trees to specify and represent this algorithm, along with elementary messages of recommendation. Edition tools are also necessary to facilitate the process of validation and workflow between expert physicians who will validate the specified knowledge and computer scientist who will encode the specified knowledge in a guide-line model. Applied to eleven different guidelines issued by an official agency, the method allows a quick and valid computerization and integration in a larger decision support system called EsPeR (Personalized Estimate of Risks). The quality of the text guidelines is however still to be developed further. The method used for computerization could help to define a framework usable at the initial step of guideline development in order to produce guidelines ready for electronic implementation.

Bayesian averaging over Decision Tree models for trauma severity scoring.

PubMed

Schetinin, V; Jakaite, L; Krzanowski, W

2018-01-01

Health care practitioners analyse possible risks of misleading decisions and need to estimate and quantify uncertainty in predictions. We have examined the "gold" standard of screening a patient's conditions for predicting survival probability, based on logistic regression modelling, which is used in trauma care for clinical purposes and quality audit. This methodology is based on theoretical assumptions about data and uncertainties. Models induced within such an approach have exposed a number of problems, providing unexplained fluctuation of predicted survival and low accuracy of estimating uncertainty intervals within which predictions are made. Bayesian method, which in theory is capable of providing accurate predictions and uncertainty estimates, has been adopted in our study using Decision Tree models. Our approach has been tested on a large set of patients registered in the US National Trauma Data Bank and has outperformed the standard method in terms of prediction accuracy, thereby providing practitioners with accurate estimates of the predictive posterior densities of interest that are required for making risk-aware decisions. Copyright © 2017 Elsevier B.V. All rights reserved.

a Rough Set Decision Tree Based Mlp-Cnn for Very High Resolution Remotely Sensed Image Classification

NASA Astrophysics Data System (ADS)

Zhang, C.; Pan, X.; Zhang, S. Q.; Li, H. P.; Atkinson, P. M.

2017-09-01

Recent advances in remote sensing have witnessed a great amount of very high resolution (VHR) images acquired at sub-metre spatial resolution. These VHR remotely sensed data has post enormous challenges in processing, analysing and classifying them effectively due to the high spatial complexity and heterogeneity. Although many computer-aid classification methods that based on machine learning approaches have been developed over the past decades, most of them are developed toward pixel level spectral differentiation, e.g. Multi-Layer Perceptron (MLP), which are unable to exploit abundant spatial details within VHR images. This paper introduced a rough set model as a general framework to objectively characterize the uncertainty in CNN classification results, and further partition them into correctness and incorrectness on the map. The correct classification regions of CNN were trusted and maintained, whereas the misclassification areas were reclassified using a decision tree with both CNN and MLP. The effectiveness of the proposed rough set decision tree based MLP-CNN was tested using an urban area at Bournemouth, United Kingdom. The MLP-CNN, well capturing the complementarity between CNN and MLP through the rough set based decision tree, achieved the best classification performance both visually and numerically. Therefore, this research paves the way to achieve fully automatic and effective VHR image classification.

Modeling flash floods in ungauged mountain catchments of China: A decision tree learning approach for parameter regionalization

NASA Astrophysics Data System (ADS)

Ragettli, S.; Zhou, J.; Wang, H.; Liu, C.

2017-12-01

Flash floods in small mountain catchments are one of the most frequent causes of loss of life and property from natural hazards in China. Hydrological models can be a useful tool for the anticipation of these events and the issuing of timely warnings. Since sub-daily streamflow information is unavailable for most small basins in China, one of the main challenges is finding appropriate parameter values for simulating flash floods in ungauged catchments. In this study, we use decision tree learning to explore parameter set transferability between different catchments. For this purpose, the physically-based, semi-distributed rainfall-runoff model PRMS-OMS is set up for 35 catchments in ten Chinese provinces. Hourly data from more than 800 storm runoff events are used to calibrate the model and evaluate the performance of parameter set transfers between catchments. For each catchment, 58 catchment attributes are extracted from several data sets available for whole China. We then use a data mining technique (decision tree learning) to identify catchment similarities that can be related to good transfer performance. Finally, we use the splitting rules of decision trees for finding suitable donor catchments for ungauged target catchments. We show that decision tree learning allows to optimally utilize the information content of available catchment descriptors and outperforms regionalization based on a conventional measure of physiographic-climatic similarity by 15%-20%. Similar performance can be achieved with a regionalization method based on spatial proximity, but decision trees offer flexible rules for selecting suitable donor catchments, not relying on the vicinity of gauged catchments. This flexibility makes the method particularly suitable for implementation in sparsely gauged environments. We evaluate the probability to detect flood events exceeding a given return period, considering measured discharge and PRMS-OMS simulated flows with regionalized parameters

The risk of disabling, surgery and reoperation in Crohn's disease - A decision tree-based approach to prognosis.

PubMed

Dias, Cláudia Camila; Pereira Rodrigues, Pedro; Fernandes, Samuel; Portela, Francisco; Ministro, Paula; Martins, Diana; Sousa, Paula; Lago, Paula; Rosa, Isadora; Correia, Luis; Moura Santos, Paula; Magro, Fernando

2017-01-01

Crohn's disease (CD) is a chronic inflammatory bowel disease known to carry a high risk of disabling and many times requiring surgical interventions. This article describes a decision-tree based approach that defines the CD patients' risk or undergoing disabling events, surgical interventions and reoperations, based on clinical and demographic variables. This multicentric study involved 1547 CD patients retrospectively enrolled and divided into two cohorts: a derivation one (80%) and a validation one (20%). Decision trees were built upon applying the CHAIRT algorithm for the selection of variables. Three-level decision trees were built for the risk of disabling and reoperation, whereas the risk of surgery was described in a two-level one. A receiver operating characteristic (ROC) analysis was performed, and the area under the curves (AUC) Was higher than 70% for all outcomes. The defined risk cut-off values show usefulness for the assessed outcomes: risk levels above 75% for disabling had an odds test positivity of 4.06 [3.50-4.71], whereas risk levels below 34% and 19% excluded surgery and reoperation with an odds test negativity of 0.15 [0.09-0.25] and 0.50 [0.24-1.01], respectively. Overall, patients with B2 or B3 phenotype had a higher proportion of disabling disease and surgery, while patients with later introduction of pharmacological therapeutic (1 months after initial surgery) had a higher proportion of reoperation. The decision-tree based approach used in this study, with demographic and clinical variables, has shown to be a valid and useful approach to depict such risks of disabling, surgery and reoperation.

A multivariate decision tree analysis of biophysical factors in tropical forest fire occurrence

Treesearch

Rey S. Ofren; Edward Harvey

2000-01-01

A multivariate decision tree model was used to quantify the relative importance of complex hierarchical relationships between biophysical variables and the occurrence of tropical forest fires. The study site is the Huai Kha Kbaeng wildlife sanctuary, a World Heritage Site in northwestern Thailand where annual fires are common and particularly destructive. Thematic...

The risk factors of laryngeal pathology in Korean adults using a decision tree model.

PubMed

Byeon, Haewon

2015-01-01

The purpose of this study was to identify risk factors affecting laryngeal pathology in the Korean population and to evaluate the derived prediction model. Cross-sectional study. Data were drawn from the 2008 Korea National Health and Nutritional Examination Survey. The subjects were 3135 persons (1508 male and 2114 female) aged 19 years and older living in the community. The independent variables were age, sex, occupation, smoking, alcohol drinking, and self-reported voice problems. A decision tree analysis was done to identify risk factors for predicting a model of laryngeal pathology. The significant risk factors of laryngeal pathology were age, gender, occupation, smoking, and self-reported voice problem in decision tree model. Four significant paths were identified in the decision tree model for the prediction of laryngeal pathology. Those identified as high risk groups for laryngeal pathology included those who self-reported a voice problem, those who were males in their 50s who did not recognize a voice problem, those who were not economically active males in their 40s, and male workers aged 19 and over and under 50 or 60 and over who currently smoked. The results of this study suggest that individual risk factors, such as age, sex, occupation, health behavior, and self-reported voice problem, affect the onset of laryngeal pathology in a complex manner. Based on the results of this study, early management of the high-risk groups is needed for the prevention of laryngeal pathology. Copyright © 2015 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Identification of pests and diseases of Dalbergia hainanensis based on EVI time series and classification of decision tree

NASA Astrophysics Data System (ADS)

Luo, Qiu; Xin, Wu; Qiming, Xiong

2017-06-01

In the process of vegetation remote sensing information extraction, the problem of phenological features and low performance of remote sensing analysis algorithm is not considered. To solve this problem, the method of remote sensing vegetation information based on EVI time-series and the classification of decision-tree of multi-source branch similarity is promoted. Firstly, to improve the time-series stability of recognition accuracy, the seasonal feature of vegetation is extracted based on the fitting span range of time-series. Secondly, the decision-tree similarity is distinguished by adaptive selection path or probability parameter of component prediction. As an index, it is to evaluate the degree of task association, decide whether to perform migration of multi-source decision tree, and ensure the speed of migration. Finally, the accuracy of classification and recognition of pests and diseases can reach 87%--98% of commercial forest in Dalbergia hainanensis, which is significantly better than that of MODIS coverage accuracy of 80%--96% in this area. Therefore, the validity of the proposed method can be verified.

Using high-resolution topography and hyperspectral data to classify tree species at the San Joaquin Experimental Range

NASA Astrophysics Data System (ADS)

Dibb, S. D.; Ustin, S.; Grigsby, S.

2015-12-01

Air- and space-borne remote sensing instruments allow for rapid and precise study of the diversity of the Earth's ecosystems. After atmospheric correction and ground validation are performed, the gathered hyperspectral and topographic data can be assembled into a stack of layers for land cover classification. Data for this project were collected in multiple field campaigns, including the 2013 NSF NEON California campaign and 2015 NASA SARP campaign. Using hyperspectral and high resolution topography data, 25 discriminatory attributes were processed in Exelis' ENVI software and collected for use in a decision forest to classify the four major tree species (Blue Oak, Live Oak, California Buckeye, and Foothill Pine) at the San Joaquin Experimental Range near Fresno, CA. These attributes include 21 classic vegetation indices and a number of other spectral characteristics, such as color and albedo, and four topographic layers, including slope, aspect, elevation, and tree height. Additionally, a number of nearby terrain classes, including bare earth, asphalt, water, rock, shadow, structures, and grass were created. Fifty training pixels were used for each class. The training pixels for each tree species came from collected GPS points in the field. Ensemble bootstrap aggregation of decision trees was performed in MATLAB, and an arbitrary number of 500 trees were selected to be grown. The tree that produced the minimum out-of-bag classification error (4.65%) was selected to classify the entire scene. Classification results accurately distinguished between oak species, but was suboptimal in dense areas. The entire San Joaquin Experimental Range was mapped with an overall accuracy of 94.7% and a Kappa coefficient 0.94. Finally, the Commission and Omission percentage averages were 5.3% each. A highly accurate map of tree species at this scale supports studies on drought effects, disease, and species-specific growth traits.

Zebrafish models of BAG3 myofibrillar myopathy suggest a toxic gain of function leading to BAG3 insufficiency.

PubMed

Ruparelia, Avnika A; Oorschot, Viola; Vaz, Raquel; Ramm, Georg; Bryson-Richardson, Robert J

2014-12-01

Mutations in the co-chaperone Bcl2-associated athanogene 3 (BAG3) can cause myofibrillar myopathy (MFM), a childhood-onset progressive muscle disease, characterized by the formation of protein aggregates and myofibrillar disintegration. In contrast to other MFM-causing proteins, BAG3 has no direct structural role, but regulates autophagy and the degradation of misfolded proteins. To investigate the mechanism of disease in BAG3-related MFM, we expressed wild-type BAG3 or the dominant MFM-causing BAG3 (BAG3(P209L)) in zebrafish. Expression of the mutant protein results in the formation of aggregates that contain wild-type BAG3. Through the stimulation and inhibition of autophagy, we tested the prevailing hypothesis that impaired autophagic function is responsible for the formation of protein aggregates. Contrary to the existing theory, our studies reveal that inhibition of autophagy is not sufficient to induce protein aggregation. Expression of the mutant protein, however, did not induce myofibrillar disintegration and we therefore examined the effect of knocking down Bag3 function. Loss of Bag3 resulted in myofibrillar disintegration, but not in the formation of protein aggregates. Remarkably, BAG3(P209L) is able to rescue the myofibrillar disintegration phenotype, further demonstrating that its function is not impaired. Together, our knockdown and overexpression experiments identify a mechanism whereby BAG3(P209L) aggregates form, gradually reducing the pool of available BAG3, which eventually results in BAG3 insufficiency and myofibrillar disintegration. This mechanism is consistent with the childhood onset and progressive nature of MFM and suggests that reducing aggregation through enhanced degradation or inhibition of nucleation would be an effective therapy for this disease.

Using Decision Trees for Estimating Mode Choice of Trips in Buca-Izmir

NASA Astrophysics Data System (ADS)

Oral, L. O.; Tecim, V.

2013-05-01

Decision makers develop transportation plans and models for providing sustainable transport systems in urban areas. Mode Choice is one of the stages in transportation modelling. Data mining techniques can discover factors affecting the mode choice. These techniques can be applied with knowledge process approach. In this study a data mining process model is applied to determine the factors affecting the mode choice with decision trees techniques by considering individual trip behaviours from household survey data collected within Izmir Transportation Master Plan. From this perspective transport mode choice problem is solved on a case in district of Buca-Izmir, Turkey with CRISP-DM knowledge process model.

42 CFR 84.88 - Breathing bag test.

Code of Federal Regulations, 2014 CFR

2014-10-01

... Apparatus § 84.88 Breathing bag test. (a) Breathing bags will be tested in an air atmosphere saturated with... Institute upon request. (d) The air within the bag(s) shall not contain more than 100 parts per million of... 42 Public Health 1 2014-10-01 2014-10-01 false Breathing bag test. 84.88 Section 84.88 Public...

Defender-Attacker Decision Tree Analysis to Combat Terrorism.

PubMed

Garcia, Ryan J B; von Winterfeldt, Detlof

2016-12-01

We propose a methodology, called defender-attacker decision tree analysis, to evaluate defensive actions against terrorist attacks in a dynamic and hostile environment. Like most game-theoretic formulations of this problem, we assume that the defenders act rationally by maximizing their expected utility or minimizing their expected costs. However, we do not assume that attackers maximize their expected utilities. Instead, we encode the defender's limited knowledge about the attacker's motivations and capabilities as a conditional probability distribution over the attacker's decisions. We apply this methodology to the problem of defending against possible terrorist attacks on commercial airplanes, using one of three weapons: infrared-guided MANPADS (man-portable air defense systems), laser-guided MANPADS, or visually targeted RPGs (rocket propelled grenades). We also evaluate three countermeasures against these weapons: DIRCMs (directional infrared countermeasures), perimeter control around the airport, and hardening airplanes. The model includes deterrence effects, the effectiveness of the countermeasures, and the substitution of weapons and targets once a specific countermeasure is selected. It also includes a second stage of defensive decisions after an attack occurs. Key findings are: (1) due to the high cost of the countermeasures, not implementing countermeasures is the preferred defensive alternative for a large range of parameters; (2) if the probability of an attack and the associated consequences are large, a combination of DIRCMs and ground perimeter control are preferred over any single countermeasure. © 2016 Society for Risk Analysis.

Using Boosting Decision Trees in Gravitational Wave Searches triggered by Gamma-ray Bursts

NASA Astrophysics Data System (ADS)

Zuraw, Sarah; LIGO Collaboration

2015-04-01

The search for gravitational wave bursts requires the ability to distinguish weak signals from background detector noise. Gravitational wave bursts are characterized by their transient nature, making them particularly difficult to detect as they are similar to non-Gaussian noise fluctuations in the detector. The Boosted Decision Tree method is a powerful machine learning algorithm which uses Multivariate Analysis techniques to explore high-dimensional data sets in order to distinguish between gravitational wave signal and background detector noise. It does so by training with known noise events and simulated gravitational wave events. The method is tested using waveform models and compared with the performance of the standard gravitational wave burst search pipeline for Gamma-ray Bursts. It is shown that the method is able to effectively distinguish between signal and background events under a variety of conditions and over multiple Gamma-ray Burst events. This example demonstrates the usefulness and robustness of the Boosted Decision Tree and Multivariate Analysis techniques as a detection method for gravitational wave bursts. LIGO, UMass, PREP, NEGAP.

Type 2 Diabetes Mellitus Screening and Risk Factors Using Decision Tree: Results of Data Mining.

PubMed

Habibi, Shafi; Ahmadi, Maryam; Alizadeh, Somayeh

2015-03-18

The aim of this study was to examine a predictive model using features related to the diabetes type 2 risk factors. The data were obtained from a database in a diabetes control system in Tabriz, Iran. The data included all people referred for diabetes screening between 2009 and 2011. The features considered as "Inputs" were: age, sex, systolic and diastolic blood pressure, family history of diabetes, and body mass index (BMI). Moreover, we used diagnosis as "Class". We applied the "Decision Tree" technique and "J48" algorithm in the WEKA (3.6.10 version) software to develop the model. After data preprocessing and preparation, we used 22,398 records for data mining. The model precision to identify patients was 0.717. The age factor was placed in the root node of the tree as a result of higher information gain. The ROC curve indicates the model function in identification of patients and those individuals who are healthy. The curve indicates high capability of the model, especially in identification of the healthy persons. We developed a model using the decision tree for screening T2DM which did not require laboratory tests for T2DM diagnosis.

Predicting Potential Changes in Suitable Habitat and Distribution by 2100 for Tree Species of the Eastern United States

Treesearch

Louis R Iverson; Anantha M. Prasad; Mark W. Schwartz; Mark W. Schwartz

2005-01-01

We predict current distribution and abundance for tree species present in eastern North America, and subsequently estimate potential suitable habitat for those species under a changed climate with 2 x CO2. We used a series of statistical models (i.e., Regression Tree Analysis (RTA), Multivariate Adaptive Regression Splines (MARS), Bagging Trees (...

Decision support for mitigating the risk of tree induced transmission line failure in utility rights-of-way.

PubMed

Poulos, H M; Camp, A E

2010-02-01

Vegetation management is a critical component of rights-of-way (ROW) maintenance for preventing electrical outages and safety hazards resulting from tree contact with conductors during storms. Northeast Utility's (NU) transmission lines are a critical element of the nation's power grid; NU is therefore under scrutiny from federal agencies charged with protecting the electrical transmission infrastructure of the United States. We developed a decision support system to focus right-of-way maintenance and minimize the potential for a tree fall episode that disables transmission capacity across the state of Connecticut. We used field data on tree characteristics to develop a system for identifying hazard trees (HTs) in the field using limited equipment to manage Connecticut power line ROW. Results from this study indicated that the tree height-to-diameter ratio, total tree height, and live crown ratio were the key characteristics that differentiated potential risk trees (danger trees) from trees with a high probability of tree fall (HTs). Products from this research can be transferred to adaptive right-of-way management, and the methods we used have great potential for future application to other regions of the United States and elsewhere where tree failure can disrupt electrical power.

A Decision Tree to Identify Children Affected by Prenatal Alcohol Exposure

PubMed Central

Goh, Patrick K.; Doyle, Lauren R.; Glass, Leila; Jones, Kenneth L.; Riley, Edward P.; Coles, Claire D.; Hoyme, H. Eugene; Kable, Julie A.; May, Philip A.; Kalberg, Wendy O.; Elizabeth, R. Sowell; Wozniak, Jeffrey R.; Mattson, Sarah N.

2017-01-01

Objective To develop and validate a hierarchical decision tree model, combining neurobehavioral and physical measures, for identification of children affected by prenatal alcohol exposure even when facial dysmorphology is not present. Study design Data were collected as part of a multisite study across the United States. The model was developed after evaluating over 1000 neurobehavioral and dysmorphology variables collected from 434 children (8–16y) with prenatal alcohol exposure, with and without fetal alcohol syndrome (FAS), and non-exposed controls, with and without other clinically-relevant behavioral or cognitive concerns. The model was subsequently validated in an independent sample of 454 children in two age ranges (5–7y or 10–16y). In all analyses, the discriminatory ability of each model step was tested with logistic regression. Classification accuracies and positive and negative predictive values were calculated. Results The model consisted of variables from 4 measures (2 parent questionnaires, an IQ score, and a physical examination). Overall accuracy rates for both the development and validation samples met or exceeded our goal of 80% overall accuracy. Conclusions The decision tree model distinguished children affected by prenatal alcohol exposure from non-exposed controls, including those with other behavioral concerns or conditions. Improving identification of this population will streamline access to clinical services, including multidisciplinary evaluation and treatment. PMID:27476634

Chi-squared Automatic Interaction Detection Decision Tree Analysis of Risk Factors for Infant Anemia in Beijing, China

PubMed Central

Ye, Fang; Chen, Zhi-Hua; Chen, Jie; Liu, Fang; Zhang, Yong; Fan, Qin-Ying; Wang, Lin

2016-01-01

Background: In the past decades, studies on infant anemia have mainly focused on rural areas of China. With the increasing heterogeneity of population in recent years, available information on infant anemia is inconclusive in large cities of China, especially with comparison between native residents and floating population. This population-based cross-sectional study was implemented to determine the anemic status of infants as well as the risk factors in a representative downtown area of Beijing. Methods: As useful methods to build a predictive model, Chi-squared automatic interaction detection (CHAID) decision tree analysis and logistic regression analysis were introduced to explore risk factors of infant anemia. A total of 1091 infants aged 6–12 months together with their parents/caregivers living at Heping Avenue Subdistrict of Beijing were surveyed from January 1, 2013 to December 31, 2014. Results: The prevalence of anemia was 12.60% with a range of 3.47%–40.00% in different subgroup characteristics. The CHAID decision tree model has demonstrated multilevel interaction among risk factors through stepwise pathways to detect anemia. Besides the three predictors identified by logistic regression model including maternal anemia during pregnancy, exclusive breastfeeding in the first 6 months, and floating population, CHAID decision tree analysis also identified the fourth risk factor, the maternal educational level, with higher overall classification accuracy and larger area below the receiver operating characteristic curve. Conclusions: The infant anemic status in metropolis is complex and should be carefully considered by the basic health care practitioners. CHAID decision tree analysis has demonstrated a better performance in hierarchical analysis of population with great heterogeneity. Risk factors identified by this study might be meaningful in the early detection and prompt treatment of infant anemia in large cities. PMID:27174328

Chi-squared Automatic Interaction Detection Decision Tree Analysis of Risk Factors for Infant Anemia in Beijing, China.

PubMed

Ye, Fang; Chen, Zhi-Hua; Chen, Jie; Liu, Fang; Zhang, Yong; Fan, Qin-Ying; Wang, Lin

2016-05-20

In the past decades, studies on infant anemia have mainly focused on rural areas of China. With the increasing heterogeneity of population in recent years, available information on infant anemia is inconclusive in large cities of China, especially with comparison between native residents and floating population. This population-based cross-sectional study was implemented to determine the anemic status of infants as well as the risk factors in a representative downtown area of Beijing. As useful methods to build a predictive model, Chi-squared automatic interaction detection (CHAID) decision tree analysis and logistic regression analysis were introduced to explore risk factors of infant anemia. A total of 1091 infants aged 6-12 months together with their parents/caregivers living at Heping Avenue Subdistrict of Beijing were surveyed from January 1, 2013 to December 31, 2014. The prevalence of anemia was 12.60% with a range of 3.47%-40.00% in different subgroup characteristics. The CHAID decision tree model has demonstrated multilevel interaction among risk factors through stepwise pathways to detect anemia. Besides the three predictors identified by logistic regression model including maternal anemia during pregnancy, exclusive breastfeeding in the first 6 months, and floating population, CHAID decision tree analysis also identified the fourth risk factor, the maternal educational level, with higher overall classification accuracy and larger area below the receiver operating characteristic curve. The infant anemic status in metropolis is complex and should be carefully considered by the basic health care practitioners. CHAID decision tree analysis has demonstrated a better performance in hierarchical analysis of population with great heterogeneity. Risk factors identified by this study might be meaningful in the early detection and prompt treatment of infant anemia in large cities.

Spatial distribution of block falls using volumetric GIS-decision-tree models

NASA Astrophysics Data System (ADS)

Abdallah, C.

2010-10-01

Block falls are considered a significant aspect of surficial instability contributing to losses in land and socio-economic aspects through their damaging effects to natural and human environments. This paper predicts and maps the geographic distribution and volumes of block falls in central Lebanon using remote sensing, geographic information systems (GIS) and decision-tree modeling (un-pruned and pruned trees). Eleven terrain parameters (lithology, proximity to fault line, karst type, soil type, distance to drainage line, elevation, slope gradient, slope aspect, slope curvature, land cover/use, and proximity to roads) were generated to statistically explain the occurrence of block falls. The latter were discriminated using SPOT4 satellite imageries, and their dimensions were determined during field surveys. The un-pruned tree model based on all considered parameters explained 86% of the variability in field block fall measurements. Once pruned, it classifies 50% in block falls' volumes by selecting just four parameters (lithology, slope gradient, soil type, and land cover/use). Both tree models (un-pruned and pruned) were converted to quantitative 1:50,000 block falls' maps with different classes; starting from Nil (no block falls) to more than 4000 m 3. These maps are fairly matching with coincidence value equal to 45%; however, both can be used to prioritize the choice of specific zones for further measurement and modeling, as well as for land-use management. The proposed tree models are relatively simple, and may also be applied to other areas (i.e. the choice of un-pruned or pruned model is related to the availability of terrain parameters in a given area).

A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran.

PubMed

Khosravi, Khabat; Pham, Binh Thai; Chapi, Kamran; Shirzadi, Ataollah; Shahabi, Himan; Revhaug, Inge; Prakash, Indra; Tien Bui, Dieu

2018-06-15

Floods are one of the most damaging natural hazards causing huge loss of property, infrastructure and lives. Prediction of occurrence of flash flood locations is very difficult due to sudden change in climatic condition and manmade factors. However, prior identification of flood susceptible areas can be done with the help of machine learning techniques for proper timely management of flood hazards. In this study, we tested four decision trees based machine learning models namely Logistic Model Trees (LMT), Reduced Error Pruning Trees (REPT), Naïve Bayes Trees (NBT), and Alternating Decision Trees (ADT) for flash flood susceptibility mapping at the Haraz Watershed in the northern part of Iran. For this, a spatial database was constructed with 201 present and past flood locations and eleven flood-influencing factors namely ground slope, altitude, curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), land use, rainfall, river density, distance from river, lithology, and Normalized Difference Vegetation Index (NDVI). Statistical evaluation measures, the Receiver Operating Characteristic (ROC) curve, and Freidman and Wilcoxon signed-rank tests were used to validate and compare the prediction capability of the models. Results show that the ADT model has the highest prediction capability for flash flood susceptibility assessment, followed by the NBT, the LMT, and the REPT, respectively. These techniques have proven successful in quickly determining flood susceptible areas. Copyright © 2018 Elsevier B.V. All rights reserved.

Decision Tree Algorithm-Generated Single-Nucleotide Polymorphism Barcodes of rbcL Genes for 38 Brassicaceae Species Tagging.

PubMed

Yang, Cheng-Hong; Wu, Kuo-Chuan; Chuang, Li-Yeh; Chang, Hsueh-Wei

2018-01-01

DNA barcode sequences are accumulating in large data sets. A barcode is generally a sequence larger than 1000 base pairs and generates a computational burden. Although the DNA barcode was originally envisioned as straightforward species tags, the identification usage of barcode sequences is rarely emphasized currently. Single-nucleotide polymorphism (SNP) association studies provide us an idea that the SNPs may be the ideal target of feature selection to discriminate between different species. We hypothesize that SNP-based barcodes may be more effective than the full length of DNA barcode sequences for species discrimination. To address this issue, we tested a r ibulose diphosphate carboxylase ( rbcL ) S NP b arcoding (RSB) strategy using a decision tree algorithm. After alignment and trimming, 31 SNPs were discovered in the rbcL sequences from 38 Brassicaceae plant species. In the decision tree construction, these SNPs were computed to set up the decision rule to assign the sequences into 2 groups level by level. After algorithm processing, 37 nodes and 31 loci were required for discriminating 38 species. Finally, the sequence tags consisting of 31 rbcL SNP barcodes were identified for discriminating 38 Brassicaceae species based on the decision tree-selected SNP pattern using RSB method. Taken together, this study provides the rational that the SNP aspect of DNA barcode for rbcL gene is a useful and effective sequence for tagging 38 Brassicaceae species.

Air bags and the skin.

PubMed

Corazza, Monica; Trincone, Silvana; Zampino, Maria Rosaria; Virgili, Annarosa

2004-01-01

Air bags, fitted in the majority of new automobiles, are safety devices activated when a sudden deceleration causes the ignition of a propellant cartridge containing sodium azide. The bag is inflated by nitrogen liberated during the combustion. Deployment releases various high-temperature gases, including nitrogen and carbon dioxide, and produces sodium hydroxide, a highly irritant alkaline substance. In about 7%-8% of cases, air bags cause dermatologic injuries such as traumatic lesions, irritant dermatitis, and chemical and thermal burns. Nondermatologic lesions, such as ocular damage (alkali keratitis, corneal abrasions), ear lesions, bone fractures, and contusive damage can also be caused by air bag deployment.

49 CFR 178.520 - Standards for textile bags.

Code of Federal Regulations, 2011 CFR

2011-10-01

... for a sift-proof textile bag; and (3) 5L3 for a water-resistant textile bag. (b) Construction... bag. (2) Bags, sift-proof, 5L2: The bag must be made sift-proof, by appropriate means, such as by the...

50 CFR 622.456 - Bag limits.

Code of Federal Regulations, 2013 CFR

2013-10-01

... of Puerto Rico and the U.S. Virgin Islands § 622.456 Bag limits. (a) Applicability. Section 622.11(a... fishing license issued by Puerto Rico or the U.S. Virgin Islands. (b) Bag limit. The bag limit for spiny...

Prevalence and Determinants of Preterm Birth in Tehran, Iran: A Comparison between Logistic Regression and Decision Tree Methods.

PubMed

Amini, Payam; Maroufizadeh, Saman; Samani, Reza Omani; Hamidi, Omid; Sepidarkish, Mahdi

2017-06-01

Preterm birth (PTB) is a leading cause of neonatal death and the second biggest cause of death in children under five years of age. The objective of this study was to determine the prevalence of PTB and its associated factors using logistic regression and decision tree classification methods. This cross-sectional study was conducted on 4,415 pregnant women in Tehran, Iran, from July 6-21, 2015. Data were collected by a researcher-developed questionnaire through interviews with mothers and review of their medical records. To evaluate the accuracy of the logistic regression and decision tree methods, several indices such as sensitivity, specificity, and the area under the curve were used. The PTB rate was 5.5% in this study. The logistic regression outperformed the decision tree for the classification of PTB based on risk factors. Logistic regression showed that multiple pregnancies, mothers with preeclampsia, and those who conceived with assisted reproductive technology had an increased risk for PTB ( p < 0.05). Identifying and training mothers at risk as well as improving prenatal care may reduce the PTB rate. We also recommend that statisticians utilize the logistic regression model for the classification of risk groups for PTB.

78 FR 28192 - Polyethylene Retail Carrier Bags From Thailand: Preliminary Results of Antidumping Duty...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-14

... Bags From Thailand: Preliminary Results of Antidumping Duty Administrative Review; 2011-2012 AGENCY... Import Administration, ``Decision Memorandum for Preliminary Results of the 2011/12 Antidumping Duty... (August 12, 2010) (Section 129 Determination). Preliminary Results of Review As a result of our review, we...

49 CFR 178.520 - Standards for textile bags.

Code of Federal Regulations, 2010 CFR

2010-10-01

...) 5L2 for a sift-proof textile bag; and (3) 5L3 for a water-resistant textile bag. (b) Construction... bag. (2) Bags, sift-proof, 5L2: The bag must be made sift-proof, by appropriate means, such as by the...

Multipurpose Cargo Transfer Bag

NASA Technical Reports Server (NTRS)

Broyan, James; Baccus, Shelley

2014-01-01

The Logistics Reduction (LR) project within the Advanced Exploration Systems (AES) program is tasked with reducing logistical mass and repurposing logistical items. Multipurpose Cargo Transfer Bags (MCTB) have been designed such that they can serve the same purpose as a Cargo Transfer Bag, the suitcase-shaped common logistics carrying bag for Shuttle and the International Space Station. After use as a cargo carrier, a regular CTB becomes trash, whereas the MCTB can be unzipped, unsnapped, and unfolded to be reused. Reuse ideas that have been investigated include partitions, crew quarters, solar radiation storm shelters, acoustic blankets, and forward osmosis water processing.

Bag free in the Bay State.

PubMed

Hurwitz, Anita Gupta; Farrow, Phillip R; Preer, Genevieve; Philipp, Barbara L

2014-06-01

The deleterious effect of formula company-sponsored discharge bags on breastfeeding is well established. As of July 2012, all 49 maternity facilities in Massachusetts had banned these bags, making it the second "bag-free" state in the United States. Obstacles to changing this long-standing practice were numerous, including concerns regarding the cost of a substitute gift. This study was designed to describe what practices maternity facilities in Massachusetts have adopted in place of giving out a formula company-sponsored discharge bag. Maternity facilities in Massachusetts were surveyed regarding discharge gift practices. Information was collected regarding gifts given and cost. The response rate was 100%. Fifty-nine percent of the facilities replaced the formula company-sponsored bag with their own gift bag carrying the hospital's logo. Bags were either given empty or contained educational materials and/or a gift such as a T-shirt, hat, or baby book. Fourteen percent of the facilities gave a gift that did not include a bag. Twenty-seven percent of facilities gave no gift. Cost of the gifts ranged from $1 to $35, with a mean cost of $10.67. The hospital budget was used to partially or fully fund 58% of gifts; 22% were covered in part by donations. Although most maternity facilities surveyed replaced the formula company-sponsored discharge bag with a different gift, one-quarter gave no replacement. These data indicate that discontinuing discharge gifts can be a readily accepted, cost-neutral step toward evidence-based breastfeeding best practice.

Optimization of matrix tablets controlled drug release using Elman dynamic neural networks and decision trees.

PubMed

Petrović, Jelena; Ibrić, Svetlana; Betz, Gabriele; Đurić, Zorica

2012-05-30

The main objective of the study was to develop artificial intelligence methods for optimization of drug release from matrix tablets regardless of the matrix type. Static and dynamic artificial neural networks of the same topology were developed to model dissolution profiles of different matrix tablets types (hydrophilic/lipid) using formulation composition, compression force used for tableting and tablets porosity and tensile strength as input data. Potential application of decision trees in discovering knowledge from experimental data was also investigated. Polyethylene oxide polymer and glyceryl palmitostearate were used as matrix forming materials for hydrophilic and lipid matrix tablets, respectively whereas selected model drugs were diclofenac sodium and caffeine. Matrix tablets were prepared by direct compression method and tested for in vitro dissolution profiles. Optimization of static and dynamic neural networks used for modeling of drug release was performed using Monte Carlo simulations or genetic algorithms optimizer. Decision trees were constructed following discretization of data. Calculated difference (f(1)) and similarity (f(2)) factors for predicted and experimentally obtained dissolution profiles of test matrix tablets formulations indicate that Elman dynamic neural networks as well as decision trees are capable of accurate predictions of both hydrophilic and lipid matrix tablets dissolution profiles. Elman neural networks were compared to most frequently used static network, Multi-layered perceptron, and superiority of Elman networks have been demonstrated. Developed methods allow simple, yet very precise way of drug release predictions for both hydrophilic and lipid matrix tablets having controlled drug release. Copyright © 2012 Elsevier B.V. All rights reserved.

Detection of clinical mastitis with sensor data from automatic milking systems is improved by using decision-tree induction.

PubMed

Kamphuis, C; Mollenhorst, H; Heesterbeek, J A P; Hogeveen, H

2010-08-01

The objective was to develop and validate a clinical mastitis (CM) detection model by means of decision-tree induction. For farmers milking with an automatic milking system (AMS), it is desirable that the detection model has a high level of sensitivity (Se), especially for more severe cases of CM, at a very high specificity (Sp). In addition, an alert for CM should be generated preferably at the quarter milking (QM) at which the CM infection is visible for the first time. Data were collected from 9 Dutch dairy herds milking automatically during a 2.5-yr period. Data included sensor data (electrical conductivity, color, and yield) at the QM level and visual observations of quarters with CM recorded by the farmers. Visual observations of quarters with CM were combined with sensor data of the most recent automatic milking recorded for that same quarter, within a 24-h time window before the visual assessment time. Sensor data of 3.5 million QM were collected, of which 348 QM were combined with a CM observation. Data were divided into a training set, including two-thirds of all data, and a test set. Cows in the training set were not included in the test set and vice versa. A decision-tree model was trained using only clear examples of healthy (n=24,717) or diseased (n=243) QM. The model was tested on 105 QM with CM and a random sample of 50,000 QM without CM. While keeping the Se at a level comparable to that of models currently used by AMS, the decision-tree model was able to decrease the number of false-positive alerts by more than 50%. At an Sp of 99%, 40% of the CM cases were detected. Sixty-four percent of the severe CM cases were detected and only 12.5% of the CM that were scored as watery milk. The Se increased considerably from 40% to 66.7% when the time window increased from less than 24h before the CM observation, to a time window from 24h before to 24h after the CM observation. Even at very wide time windows, however, it was impossible to reach an Se of 100

21 CFR 868.5320 - Reservoir bag.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Reservoir bag. 868.5320 Section 868.5320 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES ANESTHESIOLOGY DEVICES Therapeutic Devices § 868.5320 Reservoir bag. (a) Identification. A reservoir bag is a...

21 CFR 868.5320 - Reservoir bag.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Reservoir bag. 868.5320 Section 868.5320 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES ANESTHESIOLOGY DEVICES Therapeutic Devices § 868.5320 Reservoir bag. (a) Identification. A reservoir bag is a...

Comparison of glass vessels and plastic bags for enclosing living plant parts for headspace analysis.

PubMed

Stewart-Jones, Alex; Poppy, Guy M

2006-04-01

Plants release volatile chemicals into their surrounding air space that can affect the physiology of neighboring plants and influence the behavior of insects. In studying these interactions, it is desirable to collect volatiles from plants that have not been excised and are growing under as natural conditions as possible. We compared a vessel of borosilicate glass and Nylon-6 or polyester [poly(ethyleneterephthalate) or PET] cooking bags for enclosing plants during collection of volatiles. A push-pull airflow system was used, and volatiles were trapped on Tenax TA and analyzed by gas chromatography after thermal desorption. Low levels of impurities were found for the glass vessel and polyester bags. Nylon bags contained higher levels and more impurities. Recoveries of standards of 10 plant volatiles were measured in static and dynamic systems. In a static air system, there was good recovery only from the glass vessel. In a dynamic system, there was generally good recovery from both the glass vessel and polyester bags. Recoveries of alpha-pinene and (Z)-jasmone were poor throughout. The former was shown to have a very low breakthrough volume on the Tenax TA adsorbent, and the latter may be strongly adsorbed on glass. All three materials were essentially transparent in the IR and visible (photosynthetic) range but with significantly different absorptions in the UV range. In a simulated dynamic entrainment in full sunlight, internal vessel temperatures were higher than ambient by up to 9.5 degrees C in the glass vessel and 7.5 degrees C in the polyester bag. Lower increases in temperature relative to ambient (<1 degrees C) were recorded when entrainments were conducted in the shade. In a field trial, the profiles of volatiles collected from an apple tree infested with rosy apple aphid using a glass vessel and a polyester bag were similar. Polyester bags are recommended as more convenient than glass vessels for the enclosure of plants during the collection of volatiles.

Gravitating lepton bag model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burinskii, A., E-mail: burinskii@mail.ru

The Kerr–Newman (KN) black hole (BH) solution exhibits the external gravitational and electromagnetic field corresponding to that of the Dirac electron. For the large spin/mass ratio, a ≫ m, the BH loses horizons and acquires a naked singular ring creating two-sheeted topology. This space is regularized by the Higgs mechanism of symmetry breaking, leading to an extended particle that has a regular spinning core compatible with the external KN solution. We show that this core has much in common with the known MIT and SLAC bag models, but has the important advantage of being in accordance with the external gravitationalmore » and electromagnetic fields of the KN solution. A peculiar two-sheeted structure of Kerr’s gravity provides a framework for the implementation of the Higgs mechanism of symmetry breaking in configuration space in accordance with the concept of the electroweak sector of the Standard Model. Similar to other bag models, the KN bag is flexible and pliant to deformations. For parameters of a spinning electron, the bag takes the shape of a thin rotating disk of the Compton radius, with a ring–string structure and a quark-like singular pole formed at the sharp edge of this disk, indicating that the considered lepton bag forms a single bag–string–quark system.« less

Decision Tree Repository and Rule Set Based Mingjiang River Estuarine Wetlands Classifaction

NASA Astrophysics Data System (ADS)

Zhang, W.; Li, X.; Xiao, W.

2018-05-01

The increasing urbanization and industrialization have led to wetland losses in estuarine area of Mingjiang River over past three decades. There has been increasing attention given to produce wetland inventories using remote sensing and GIS technology. Due to inconsistency training site and training sample, traditionally pixel-based image classification methods can't achieve a comparable result within different organizations. Meanwhile, object-oriented image classification technique shows grate potential to solve this problem and Landsat moderate resolution remote sensing images are widely used to fulfill this requirement. Firstly, the standardized atmospheric correct, spectrally high fidelity texture feature enhancement was conducted before implementing the object-oriented wetland classification method in eCognition. Secondly, we performed the multi-scale segmentation procedure, taking the scale, hue, shape, compactness and smoothness of the image into account to get the appropriate parameters, using the top and down region merge algorithm from single pixel level, the optimal texture segmentation scale for different types of features is confirmed. Then, the segmented object is used as the classification unit to calculate the spectral information such as Mean value, Maximum value, Minimum value, Brightness value and the Normalized value. The Area, length, Tightness and the Shape rule of the image object Spatial features and texture features such as Mean, Variance and Entropy of image objects are used as classification features of training samples. Based on the reference images and the sampling points of on-the-spot investigation, typical training samples are selected uniformly and randomly for each type of ground objects. The spectral, texture and spatial characteristics of each type of feature in each feature layer corresponding to the range of values are used to create the decision tree repository. Finally, with the help of high resolution reference images, the

Bag-Tanks for Your Classroom.

ERIC Educational Resources Information Center

Wulfson, Stephen E.

1981-01-01

Suggests using plastic bags as aquaria and terraria. Describes techniques for converting plastic sheets into aquaria, how to set them up for classroom use, and other uses for plastic bag aquaria. (DS)

Development and Validation of a Primary Care-Based Family Health History and Decision Support Program (MeTree)

PubMed Central

Orlando, Lori A.; Buchanan, Adam H.; Hahn, Susan E.; Christianson, Carol A.; Powell, Karen P.; Skinner, Celette Sugg; Chesnut, Blair; Blach, Colette; Due, Barbara; Ginsburg, Geoffrey S.; Henrich, Vincent C.

2016-01-01

INTRODUCTION Family health history is a strong predictor of disease risk. To reduce the morbidity and mortality of many chronic diseases, risk-stratified evidence-based guidelines strongly encourage the collection and synthesis of family health history to guide selection of primary prevention strategies. However, the collection and synthesis of such information is not well integrated into clinical practice. To address barriers to collection and use of family health histories, the Genomedical Connection developed and validated MeTree, a Web-based, patient-facing family health history collection and clinical decision support tool. MeTree is designed for integration into primary care practices as part of the genomic medicine model for primary care. METHODS We describe the guiding principles, operational characteristics, algorithm development, and coding used to develop MeTree. Validation was performed through stakeholder cognitive interviewing, a genetic counseling pilot program, and clinical practice pilot programs in 2 community-based primary care clinics. RESULTS Stakeholder feedback resulted in changes to MeTree’s interface and changes to the phrasing of clinical decision support documents. The pilot studies resulted in the identification and correction of coding errors and the reformatting of clinical decision support documents. MeTree’s strengths in comparison with other tools are its seamless integration into clinical practice and its provision of action-oriented recommendations guided by providers’ needs. LIMITATIONS The tool was validated in a small cohort. CONCLUSION MeTree can be integrated into primary care practices to help providers collect and synthesize family health history information from patients with the goal of improving adherence to risk-stratified evidence-based guidelines. PMID:24044145

A Clinical Decision Tree to Predict Whether a Bacteremic Patient Is Infected With an Extended-Spectrum β-Lactamase-Producing Organism.

PubMed

Goodman, Katherine E; Lessler, Justin; Cosgrove, Sara E; Harris, Anthony D; Lautenbach, Ebbing; Han, Jennifer H; Milstone, Aaron M; Massey, Colin J; Tamma, Pranita D

2016-10-01

Timely identification of extended-spectrum β-lactamase (ESBL) bacteremia can improve clinical outcomes while minimizing unnecessary use of broad-spectrum antibiotics, including carbapenems. However, most clinical microbiology laboratories currently require at least 24 additional hours from the time of microbial genus and species identification to confirm ESBL production. Our objective was to develop a user-friendly decision tree to predict which organisms are ESBL producing, to guide appropriate antibiotic therapy. We included patients ≥18 years of age with bacteremia due to Escherichia coli or Klebsiella species from October 2008 to March 2015 at Johns Hopkins Hospital. Isolates with ceftriaxone minimum inhibitory concentrations ≥2 µg/mL underwent ESBL confirmatory testing. Recursive partitioning was used to generate a decision tree to determine the likelihood that a bacteremic patient was infected with an ESBL producer. Discrimination of the original and cross-validated models was evaluated using receiver operating characteristic curves and by calculation of C-statistics. A total of 1288 patients with bacteremia met eligibility criteria. For 194 patients (15%), bacteremia was due to a confirmed ESBL producer. The final classification tree for predicting ESBL-positive bacteremia included 5 predictors: history of ESBL colonization/infection, chronic indwelling vascular hardware, age ≥43 years, recent hospitalization in an ESBL high-burden region, and ≥6 days of antibiotic exposure in the prior 6 months. The decision tree's positive and negative predictive values were 90.8% and 91.9%, respectively. Our findings suggest that a clinical decision tree can be used to estimate a bacteremic patient's likelihood of infection with ESBL-producing bacteria. Recursive partitioning offers a practical, user-friendly approach for addressing important diagnostic questions. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of

Acoustic Multipurpose Cargo Transfer Bag

NASA Technical Reports Server (NTRS)

Baccus, Shelley

2015-01-01

The Logistics Reduction (LR) project within the Advanced Exploration Systems (AES) program is tasked with reducing logistical mass and repurposing logistical items. Multipurpose Cargo Transfer Bags (MCTB) are designed to be the same external volume as a regular cargo transfer bag, the common logistics carrier for the International Space Station. After use as a cargo bag, the MCTB can be unzipped and unfolded to be reused. This Acoustic MCTBs transform into acoustic blankets after the initial logistics carrying objective is complete.

Bagworm bags as portable armour against invertebrate predators

PubMed Central

2016-01-01

Some animals have evolved the use of environmental materials as “portable armour” against natural enemies. Portable bags that bagworm larvae (Lepidoptera: Psychidae) construct using their own silk and plant parts are generally believed to play an important role as a physical barrier against natural enemies. However, no experimental studies have tested the importance of bags as portable armour against predators. To clarify the defensive function, I studied the bagworm Eumeta minuscula and a potential predator Calosoma maximoviczi (Coleoptera: Carabidae). Under laboratory conditions, all bagworm larvae were attacked by carabid adults, but successfully defended themselves against the predators’ mandibles using their own bags. The portable bags, which are composed mainly of host plant twigs, may function as a physical barrier against predator mandibles. To test this hypothesis, I removed the twig bags and replaced some with herb leaf bags; all bag-removed larvae were easily caught and predated by carabids, while all bag-replaced larvae could successfully defend themselves against carabid attacks. Therefore, various types of portable bags can protect bagworm larvae from carabid attacks. This is the first study to test the defensive function of bagworm portable bags against invertebrate predators. PMID:26893969

Bagworm bags as portable armour against invertebrate predators.

PubMed

Sugiura, Shinji

2016-01-01

Some animals have evolved the use of environmental materials as "portable armour" against natural enemies. Portable bags that bagworm larvae (Lepidoptera: Psychidae) construct using their own silk and plant parts are generally believed to play an important role as a physical barrier against natural enemies. However, no experimental studies have tested the importance of bags as portable armour against predators. To clarify the defensive function, I studied the bagworm Eumeta minuscula and a potential predator Calosoma maximoviczi (Coleoptera: Carabidae). Under laboratory conditions, all bagworm larvae were attacked by carabid adults, but successfully defended themselves against the predators' mandibles using their own bags. The portable bags, which are composed mainly of host plant twigs, may function as a physical barrier against predator mandibles. To test this hypothesis, I removed the twig bags and replaced some with herb leaf bags; all bag-removed larvae were easily caught and predated by carabids, while all bag-replaced larvae could successfully defend themselves against carabid attacks. Therefore, various types of portable bags can protect bagworm larvae from carabid attacks. This is the first study to test the defensive function of bagworm portable bags against invertebrate predators.

hs-CRP is strongly associated with coronary heart disease (CHD): A data mining approach using decision tree algorithm.

PubMed

Tayefi, Maryam; Tajfard, Mohammad; Saffar, Sara; Hanachi, Parichehr; Amirabadizadeh, Ali Reza; Esmaeily, Habibollah; Taghipour, Ali; Ferns, Gordon A; Moohebati, Mohsen; Ghayour-Mobarhan, Majid

2017-04-01

Coronary heart disease (CHD) is an important public health problem globally. Algorithms incorporating the assessment of clinical biomarkers together with several established traditional risk factors can help clinicians to predict CHD and support clinical decision making with respect to interventions. Decision tree (DT) is a data mining model for extracting hidden knowledge from large databases. We aimed to establish a predictive model for coronary heart disease using a decision tree algorithm. Here we used a dataset of 2346 individuals including 1159 healthy participants and 1187 participant who had undergone coronary angiography (405 participants with negative angiography and 782 participants with positive angiography). We entered 10 variables of a total 12 variables into the DT algorithm (including age, sex, FBG, TG, hs-CRP, TC, HDL, LDL, SBP and DBP). Our model could identify the associated risk factors of CHD with sensitivity, specificity, accuracy of 96%, 87%, 94% and respectively. Serum hs-CRP levels was at top of the tree in our model, following by FBG, gender and age. Our model appears to be an accurate, specific and sensitive model for identifying the presence of CHD, but will require validation in prospective studies. Copyright © 2017 Elsevier B.V. All rights reserved.

A Comparison between Decision Tree and Random Forest in Determining the Risk Factors Associated with Type 2 Diabetes.

PubMed

Esmaily, Habibollah; Tayefi, Maryam; Doosti, Hassan; Ghayour-Mobarhan, Majid; Nezami, Hossein; Amirabadizadeh, Alireza

2018-04-24

We aimed to identify the associated risk factors of type 2 diabetes mellitus (T2DM) using data mining approach, decision tree and random forest techniques using the Mashhad Stroke and Heart Atherosclerotic Disorders (MASHAD) Study program. A cross-sectional study. The MASHAD study started in 2010 and will continue until 2020. Two data mining tools, namely decision trees, and random forests, are used for predicting T2DM when some other characteristics are observed on 9528 subjects recruited from MASHAD database. This paper makes a comparison between these two models in terms of accuracy, sensitivity, specificity and the area under ROC curve. The prevalence rate of T2DM was 14% among these subjects. The decision tree model has 64.9% accuracy, 64.5% sensitivity, 66.8% specificity, and area under the ROC curve measuring 68.6%, while the random forest model has 71.1% accuracy, 71.3% sensitivity, 69.9% specificity, and area under the ROC curve measuring 77.3% respectively. The random forest model, when used with demographic, clinical, and anthropometric and biochemical measurements, can provide a simple tool to identify associated risk factors for type 2 diabetes. Such identification can substantially use for managing the health policy to reduce the number of subjects with T2DM .

50 CFR 622.456 - Bag limits.

Code of Federal Regulations, 2014 CFR

2014-10-01

..., DEPARTMENT OF COMMERCE FISHERIES OF THE CARIBBEAN, GULF OF MEXICO, AND SOUTH ATLANTIC Spiny Lobster Fishery of Puerto Rico and the U.S. Virgin Islands § 622.456 Bag limits. (a) Applicability. Section 622.11(a... fishing license issued by Puerto Rico or the U.S. Virgin Islands. (b) Bag limit. The bag limit for spiny...

50 CFR 622.494 - Bag limit.

Code of Federal Regulations, 2014 CFR

2014-10-01

..., DEPARTMENT OF COMMERCE FISHERIES OF THE CARIBBEAN, GULF OF MEXICO, AND SOUTH ATLANTIC Queen Conch Resources of Puerto Rico and the U.S. Virgin Islands § 622.494 Bag limit. (a) Applicability. Section 622.11(a... fishing license issued by Puerto Rico or the U.S. Virgin Islands. (b) Bag limit. The bag limit for queen...

50 CFR 622.494 - Bag limit.

Code of Federal Regulations, 2013 CFR

2013-10-01

..., DEPARTMENT OF COMMERCE FISHERIES OF THE CARIBBEAN, GULF OF MEXICO, AND SOUTH ATLANTIC Queen Conch Resources of Puerto Rico and the U.S. Virgin Islands § 622.494 Bag limit. (a) Applicability. Section 622.11(a... fishing license issued by Puerto Rico or the U.S. Virgin Islands. (b) Bag limit. The bag limit for queen...

Lunar Regolith Bagging System

NASA Technical Reports Server (NTRS)

Brown, Sebrina; Lundberg, Kimberly; Mcgarity, Ginger; Silverman, Philip

1990-01-01

A regolith container to be used as a fundamental building block in radiation protection of a habitable lunar base was designed. Parameters for the container are its: size, shape, material, and structural design. Also, a machine was designed to fill the regolith container which is capable of grasping and opening an empty container, filling it, closing it when full, and depositing it on the surface of the Moon. The simple design will bag lunar soil in a relatively short amount of time, with a low equipment weight, and with moving parts distanced from the dirt. The bags are made out of Kevlar 149 with a fabric weight of 6 oz. per square yard. All machine parts are composed of aluminum 6061-T6. Assuming that the vehicle runs at 7 km/hr for 8 hours a day, the machine will bag the necessary 450 cu m of soil in about 12 days. The total mass of the bags and the machine to be shipped to the Moon will be 687 kg. The cost of shipping this weight will be $6.23 million.

Applying decision tree for identification of a low risk population for type 2 diabetes. Tehran Lipid and Glucose Study.

PubMed

Ramezankhani, Azra; Pournik, Omid; Shahrabi, Jamal; Khalili, Davood; Azizi, Fereidoun; Hadaegh, Farzad

2014-09-01

The aim of this study was to create a prediction model using data mining approach to identify low risk individuals for incidence of type 2 diabetes, using the Tehran Lipid and Glucose Study (TLGS) database. For a 6647 population without diabetes, aged ≥20 years, followed for 12 years, a prediction model was developed using classification by the decision tree technique. Seven hundred and twenty-nine (11%) diabetes cases occurred during the follow-up. Predictor variables were selected from demographic characteristics, smoking status, medical and drug history and laboratory measures. We developed the predictive models by decision tree using 60 input variables and one output variable. The overall classification accuracy was 90.5%, with 31.1% sensitivity, 97.9% specificity; and for the subjects without diabetes, precision and f-measure were 92% and 0.95, respectively. The identified variables included fasting plasma glucose, body mass index, triglycerides, mean arterial blood pressure, family history of diabetes, educational level and job status. In conclusion, decision tree analysis, using routine demographic, clinical, anthropometric and laboratory measurements, created a simple tool to predict individuals at low risk for type 2 diabetes. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Applications of urban tree canopy assessment and prioritization tools: supporting collaborative decision making to achieve urban sustainability goals

Treesearch

Dexter H. Locke; J. Morgan Grove; Michael Galvin; Jarlath P.M. ONeil-Dunne; Charles Murphy

2013-01-01

Urban Tree Canopy (UTC) Prioritizations can be both a set of geographic analysis tools and a planning process for collaborative decision-making. In this paper, we describe how UTC Prioritizations can be used as a planning process to provide decision support to multiple government agencies, civic groups and private businesses to aid in reaching a canopy target. Linkages...

Establishing Decision Trees for Predicting Successful Postpyloric Nasoenteric Tube Placement in Critically Ill Patients.

PubMed

Chen, Weisheng; Sun, Cheng; Wei, Ru; Zhang, Yanlin; Ye, Heng; Chi, Ruibin; Zhang, Yichen; Hu, Bei; Lv, Bo; Chen, Lifang; Zhang, Xiunong; Lan, Huilan; Chen, Chunbo

2016-08-31

Despite the use of prokinetic agents, the overall success rate for postpyloric placement via a self-propelled spiral nasoenteric tube is quite low. This retrospective study was conducted in the intensive care units of 11 university hospitals from 2006 to 2016 among adult patients who underwent self-propelled spiral nasoenteric tube insertion. Success was defined as postpyloric nasoenteric tube placement confirmed by abdominal x-ray scan 24 hours after tube insertion. Chi-square automatic interaction detection (CHAID), simple classification and regression trees (SimpleCart), and J48 methodologies were used to develop decision tree models, and multiple logistic regression (LR) methodology was used to develop an LR model for predicting successful postpyloric nasoenteric tube placement. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of these models. Successful postpyloric nasoenteric tube placement was confirmed in 427 of 939 patients enrolled. For predicting successful postpyloric nasoenteric tube placement, the performance of the 3 decision trees was similar in terms of the AUCs: 0.715 for the CHAID model, 0.682 for the SimpleCart model, and 0.671 for the J48 model. The AUC of the LR model was 0.729, which outperformed the J48 model. Both the CHAID and LR models achieved an acceptable discrimination for predicting successful postpyloric nasoenteric tube placement and were useful for intensivists in the setting of self-propelled spiral nasoenteric tube insertion. © 2016 American Society for Parenteral and Enteral Nutrition.

Establishing Decision Trees for Predicting Successful Postpyloric Nasoenteric Tube Placement in Critically Ill Patients.

PubMed

Chen, Weisheng; Sun, Cheng; Wei, Ru; Zhang, Yanlin; Ye, Heng; Chi, Ruibin; Zhang, Yichen; Hu, Bei; Lv, Bo; Chen, Lifang; Zhang, Xiunong; Lan, Huilan; Chen, Chunbo

2018-01-01

Despite the use of prokinetic agents, the overall success rate for postpyloric placement via a self-propelled spiral nasoenteric tube is quite low. This retrospective study was conducted in the intensive care units of 11 university hospitals from 2006 to 2016 among adult patients who underwent self-propelled spiral nasoenteric tube insertion. Success was defined as postpyloric nasoenteric tube placement confirmed by abdominal x-ray scan 24 hours after tube insertion. Chi-square automatic interaction detection (CHAID), simple classification and regression trees (SimpleCart), and J48 methodologies were used to develop decision tree models, and multiple logistic regression (LR) methodology was used to develop an LR model for predicting successful postpyloric nasoenteric tube placement. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of these models. Successful postpyloric nasoenteric tube placement was confirmed in 427 of 939 patients enrolled. For predicting successful postpyloric nasoenteric tube placement, the performance of the 3 decision trees was similar in terms of the AUCs: 0.715 for the CHAID model, 0.682 for the SimpleCart model, and 0.671 for the J48 model. The AUC of the LR model was 0.729, which outperformed the J48 model. Both the CHAID and LR models achieved an acceptable discrimination for predicting successful postpyloric nasoenteric tube placement and were useful for intensivists in the setting of self-propelled spiral nasoenteric tube insertion. © 2016 American Society for Parenteral and Enteral Nutrition.

Vapor pressure measured with inflatable plastic bag

NASA Technical Reports Server (NTRS)

1965-01-01

Deflated plastic bag in a vacuum chamber measures initial low vapor pressures of materials. The bag captures the test sample vapors and visual observation of the vapor-inflated bag under increasing external pressures yields pertinent data.

Plastic bag clip discovered in partial colectomy accompanying proposal for phylogenic plastic bag clip classification

PubMed Central

Lehmer, Larisa M; Ragsdale, Bruce D; Daniel, John; Hayashi, Edwin; Kvalstad, Robert

2011-01-01

A plastic bag clip was incidentally found anchored in the mucosa of a partial colectomy specimen 2.6 cm proximal to a ruptured diverticulum for which the patient, a mentally retarded, diabetic, 58-year-old man, underwent surgery. Over 20 cases of accidental ingestion of plastic bag clips have been published. Known complications include small bowel perforation, obstruction, dysphagia, gastrointestinal bleeding and colonic impaction. Preoperative diagnosis of plastic clips lodged in the gastrointestinal tract is frustrated due to radiographic translucency. This occult threat could likely be prevented by the design of gastrointestinally safe, plastic-bag-sealing devices. Presented here is a morphologically based classification of bag clips as a possible guide for determining the most hazardous varieties and to aid further discussions of their impact on health. PMID:22679182

21 CFR 880.6050 - Ice bag.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Ice bag. 880.6050 Section 880.6050 Food and Drugs....6050 Ice bag. (a) Identification. An ice bag is a device intended for medical purposes that is in the form of a container intended to be filled with ice that is used to apply dry cold therapy to an area of...

21 CFR 880.6050 - Ice bag.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Ice bag. 880.6050 Section 880.6050 Food and Drugs....6050 Ice bag. (a) Identification. An ice bag is a device intended for medical purposes that is in the form of a container intended to be filled with ice that is used to apply dry cold therapy to an area of...

21 CFR 880.6050 - Ice bag.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Ice bag. 880.6050 Section 880.6050 Food and Drugs....6050 Ice bag. (a) Identification. An ice bag is a device intended for medical purposes that is in the form of a container intended to be filled with ice that is used to apply dry cold therapy to an area of...

21 CFR 880.6050 - Ice bag.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Ice bag. 880.6050 Section 880.6050 Food and Drugs....6050 Ice bag. (a) Identification. An ice bag is a device intended for medical purposes that is in the form of a container intended to be filled with ice that is used to apply dry cold therapy to an area of...

21 CFR 880.6050 - Ice bag.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Ice bag. 880.6050 Section 880.6050 Food and Drugs....6050 Ice bag. (a) Identification. An ice bag is a device intended for medical purposes that is in the form of a container intended to be filled with ice that is used to apply dry cold therapy to an area of...

W-Z-top-quark bags

DOE Office of Scientific and Technical Information (OSTI.GOV)

Crichigno, Marcos P.; Shuryak, Edward; Flambaum, Victor V.

2010-10-01

We discuss a new family of multiquanta-bound states in the standard model which exist due to the mutual Higgs-based attraction of the heaviest members of the standard model, namely, gauge quanta W, Z, and (anti)top quarks, t, t. We use a self-consistent mean-field approximation, up to a rather large particle number N. In this paper we do not focus on weakly bound, nonrelativistic bound states, but rather on 'bags' in which the Higgs vacuum expectation value is significantly modified or depleted. The minimal number N above which such states appear strongly depends on the ratio of the Higgs mass tomore » the masses of W, Z, t, t: For a light Higgs mass, m{sub H{approx}}50 GeV, bound states start from N{approx}O(10), but for a ''realistic'' Higgs mass, m{sub H{approx}}100 GeV, one finds metastable/bound W, Z bags only for N{approx}O(1000). We also found that in the latter case pure top bags disappear for all N, although top quarks can still be well bound to the W bags. Anticipating the cosmological applications (discussed in the following Article [Phys. Rev. D 82, 073019]) of these bags as 'doorway states' for baryosynthesis, we also consider here the existence of such metastable bags at finite temperatures, when standard-model parameters such as Higgs, gauge, and top masses are significantly modified.« less

Automobile air bags: friend or foe? A case of air bag-associated ocular trauma and a related literature review.

PubMed

Kenney, Kristin S; Fanciullo, Lisa M

2005-07-01

Although air bags are placed in automobiles to act as safety devices, they have been shown to carry a risk of injury themselves. Ocular injury, in particular, can often be a direct consequence of air bag deployment. A case of ocular air bag injury is presented. A discussion and review of the current literature on this issue follows. A 63-year-old man was transferred to our clinic after sustaining injuries related to a motor vehicle accident, during which the automobile's air bag was deployed. Initial examination revealed many signs of blunt ocular trauma of the O.D., including iridodialysis, dislocated lens with traumatic cataract, and traumatic/inflammatory glaucoma. Initial B-scan showed an attached retina O.D. One month later, the patient underwent an attempted pars plana vitrectomy with lensectomy, iris repair, and insertion of an anterior chamber intraocular lens. Complications arose during the procedure, and a total retinal detachment developed. Resultant acuity is no light perception O.D. Although ocular morbidity can be a direct consequence of air bag deployment, most eye injuries are minimal, and seem to be outweighed by the benefits of air bags. Drivers, as well as passengers, can minimize associated injuries by adhering to specific safety guidelines. This, as well as continual modification and improvement in air bag design, will maximize the safety of air bags and decrease the incidence of vision-threatening ocular injury caused by air bag deployment.

A decision tree model for predicting mediastinal lymph node metastasis in non-small cell lung cancer with F-18 FDG PET/CT.

PubMed

Pak, Kyoungjune; Kim, Keunyoung; Kim, Mi-Hyun; Eom, Jung Seop; Lee, Min Ki; Cho, Jeong Su; Kim, Yun Seong; Kim, Bum Soo; Kim, Seong Jang; Kim, In Joo

2018-01-01

We aimed to develop a decision tree model to improve diagnostic performance of positron emission tomography/computed tomography (PET/CT) to detect metastatic lymph nodes (LN) in non-small cell lung cancer (NSCLC). 115 patients with NSCLC were included in this study. The training dataset included 66 patients. A decision tree model was developed with 9 variables, and validated with 49 patients: short and long diameters of LNs, ratio of short and long diameters, maximum standardized uptake value (SUVmax) of LN, mean hounsfield unit, ratio of LN SUVmax and ascending aorta SUVmax (LN/AA), and ratio of LN SUVmax and superior vena cava SUVmax. A total of 301 LNs of 115 patients were evaluated in this study. Nodular calcification was applied as the initial imaging parameter, and LN SUVmax (≥3.95) was assessed as the second. LN/AA (≥2.92) was required to high LN SUVmax. Sensitivity was 50% for training dataset, and 40% for validation dataset. However, specificity was 99.28% for training dataset, and 96.23% for validation dataset. In conclusion, we have developed a new decision tree model for interpreting mediastinal LNs. All LNs with nodular calcification were benign, and LNs with high LN SUVmax and high LN/AA were metastatic Further studies are needed to incorporate subjective parameters and pathologic evaluations into a decision tree model to improve the test performance of PET/CT.

Application of Decision Tree on Collision Avoidance System Design and Verification for Quadcopter

NASA Astrophysics Data System (ADS)

Chen, C.-W.; Hsieh, P.-H.; Lai, W.-H.

2017-08-01

The purpose of the research is to build a collision avoidance system with decision tree algorithm used for quadcopters. While the ultrasonic range finder judges the distance is in collision avoidance interval, the access will be replaced from operator to the system to control the altitude of the UAV. According to the former experiences on operating quadcopters, we can obtain the appropriate pitch angle. The UAS implement the following three motions to avoid collisions. Case1: initial slow avoidance stage, Case2: slow avoidance stage and Case3: Rapid avoidance stage. Then the training data of collision avoidance test will be transmitted to the ground station via wireless transmission module to further analysis. The entire decision tree algorithm of collision avoidance system, transmission data, and ground station have been verified in some flight tests. In the flight test, the quadcopter can implement avoidance motion in real-time and move away from obstacles steadily. In the avoidance area, the authority of the collision avoidance system is higher than the operator and implements the avoidance process. The quadcopter can successfully fly away from the obstacles in 1.92 meter per second and the minimum distance between the quadcopter and the obstacle is 1.05 meters.

Decision tree analysis of treatment strategies for mild and moderate cases of clinical mastitis occurring in early lactation.

PubMed

Pinzón-Sánchez, C; Cabrera, V E; Ruegg, P L

2011-04-01

The objective of this study was to develop a decision tree to evaluate the economic impact of different durations of intramammary treatment for the first case of mild or moderate clinical mastitis (CM) occurring in early lactation with various scenarios of pathogen distributions and use of on-farm culture. The tree included 2 decision and 3 probability events. The first decision evaluated use of on-farm culture (OFC; 2 programs using OFC and 1 not using OFC) and the second decision evaluated treatment strategies (no intramammary antimicrobials or antimicrobials administered for 2, 5, or 8 d). The tree included probabilities for the distribution of etiologies (gram-positive, gram-negative, or no growth), bacteriological cure, and recurrence. The economic consequences of mastitis included costs of diagnosis and initial treatment, additional treatments, labor, discarded milk, milk production losses due to clinical and subclinical mastitis, culling, and transmission of infection to other cows (only for CM caused by Staphylococcus aureus). Pathogen-specific estimates for bacteriological cure and milk losses were used. The economically optimal path for several scenarios was determined by comparison of expected monetary values. For most scenarios, the optimal economic strategy was to treat CM caused by gram-positive pathogens for 2 d and to avoid antimicrobials for CM cases caused by gram-negative pathogens or when no pathogen was recovered. Use of extended intramammary antimicrobial therapy (5 or 8 d) resulted in the least expected monetary values. Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Career Path Suggestion using String Matching and Decision Trees

NASA Astrophysics Data System (ADS)

Nagpal, Akshay; P. Panda, Supriya

2015-05-01

High school and college graduates seemingly are often battling for the courses they should major in order to achieve their target career. In this paper, we worked on suggesting a career path to a graduate to reach his/her dream career given the current educational status. Firstly, we collected the career data of professionals and academicians from various career fields and compiled the data set by using the necessary information from the data. Further, this was used as the basis to suggest the most appropriate career path for the person given his/her current educational status. Decision trees and string matching algorithms were employed to suggest the appropriate career path for a person. Finally, an analysis of the result has been done directing to further improvements in the model.

Baryon bags in strong coupling QCD

NASA Astrophysics Data System (ADS)

Gattringer, Christof

2018-04-01

We discuss lattice QCD with one flavor of staggered fermions and show that in the path integral the baryon contributions can be fully separated from quark and diquark contributions. The baryonic degrees of freedom (d.o.f.) are independent of the gauge field, and the corresponding free fermion action describes the baryons through the joint propagation of three quarks. The nonbaryonic dynamics is described by quark and diquark terms that couple to the gauge field. When evaluating the quark and diquark contributions in the strong coupling limit, the partition function completely factorizes into baryon bags and a complementary domain. Baryon bags are regions in space-time where the dynamics is described by a single free fermion made out of three quarks propagating coherently as a baryon. Outside the baryon bags, the relevant d.o.f. are monomers and dimers for quarks and diquarks. The partition sum is a sum over all baryon bag configurations, and for each bag, a free fermion determinant appears as a weight factor.

Holoentropy enabled-decision tree for automatic classification of diabetic retinopathy using retinal fundus images.

PubMed

Mane, Vijay Mahadeo; Jadhav, D V

2017-05-24

Diabetic retinopathy (DR) is the most common diabetic eye disease. Doctors are using various test methods to detect DR. But, the availability of test methods and requirements of domain experts pose a new challenge in the automatic detection of DR. In order to fulfill this objective, a variety of algorithms has been developed in the literature. In this paper, we propose a system consisting of a novel sparking process and a holoentropy-based decision tree for automatic classification of DR images to further improve the effectiveness. The sparking process algorithm is developed for automatic segmentation of blood vessels through the estimation of optimal threshold. The holoentropy enabled decision tree is newly developed for automatic classification of retinal images into normal or abnormal using hybrid features which preserve the disease-level patterns even more than the signal level of the feature. The effectiveness of the proposed system is analyzed using standard fundus image databases DIARETDB0 and DIARETDB1 for sensitivity, specificity and accuracy. The proposed system yields sensitivity, specificity and accuracy values of 96.72%, 97.01% and 96.45%, respectively. The experimental result reveals that the proposed technique outperforms the existing algorithms.

Prediction of microRNA target genes using an efficient genetic algorithm-based decision tree.

PubMed

Rabiee-Ghahfarrokhi, Behzad; Rafiei, Fariba; Niknafs, Ali Akbar; Zamani, Behzad

2015-01-01

MicroRNAs (miRNAs) are small, non-coding RNA molecules that regulate gene expression in almost all plants and animals. They play an important role in key processes, such as proliferation, apoptosis, and pathogen-host interactions. Nevertheless, the mechanisms by which miRNAs act are not fully understood. The first step toward unraveling the function of a particular miRNA is the identification of its direct targets. This step has shown to be quite challenging in animals primarily because of incomplete complementarities between miRNA and target mRNAs. In recent years, the use of machine-learning techniques has greatly increased the prediction of miRNA targets, avoiding the need for costly and time-consuming experiments to achieve miRNA targets experimentally. Among the most important machine-learning algorithms are decision trees, which classify data based on extracted rules. In the present work, we used a genetic algorithm in combination with C4.5 decision tree for prediction of miRNA targets. We applied our proposed method to a validated human datasets. We nearly achieved 93.9% accuracy of classification, which could be related to the selection of best rules.

Prediction of microRNA target genes using an efficient genetic algorithm-based decision tree

PubMed Central

Rabiee-Ghahfarrokhi, Behzad; Rafiei, Fariba; Niknafs, Ali Akbar; Zamani, Behzad

2015-01-01

MicroRNAs (miRNAs) are small, non-coding RNA molecules that regulate gene expression in almost all plants and animals. They play an important role in key processes, such as proliferation, apoptosis, and pathogen–host interactions. Nevertheless, the mechanisms by which miRNAs act are not fully understood. The first step toward unraveling the function of a particular miRNA is the identification of its direct targets. This step has shown to be quite challenging in animals primarily because of incomplete complementarities between miRNA and target mRNAs. In recent years, the use of machine-learning techniques has greatly increased the prediction of miRNA targets, avoiding the need for costly and time-consuming experiments to achieve miRNA targets experimentally. Among the most important machine-learning algorithms are decision trees, which classify data based on extracted rules. In the present work, we used a genetic algorithm in combination with C4.5 decision tree for prediction of miRNA targets. We applied our proposed method to a validated human datasets. We nearly achieved 93.9% accuracy of classification, which could be related to the selection of best rules. PMID:26649272

Identification of Potential Sources of Mercury (Hg) in Farmland Soil Using a Decision Tree Method in China.

PubMed

Zhong, Taiyang; Chen, Dongmei; Zhang, Xiuying

2016-11-09

Identification of the sources of soil mercury (Hg) on the provincial scale is helpful for enacting effective policies to prevent further contamination and take reclamation measurements. The natural and anthropogenic sources and their contributions of Hg in Chinese farmland soil were identified based on a decision tree method. The results showed that the concentrations of Hg in parent materials were most strongly associated with the general spatial distribution pattern of Hg concentration on a provincial scale. The decision tree analysis gained an 89.70% total accuracy in simulating the influence of human activities on the additions of Hg in farmland soil. Human activities-for example, the production of coke, application of fertilizers, discharge of wastewater, discharge of solid waste, and the production of non-ferrous metals-were the main external sources of a large amount of Hg in the farmland soil.

Identification of Potential Sources of Mercury (Hg) in Farmland Soil Using a Decision Tree Method in China

PubMed Central

Zhong, Taiyang; Chen, Dongmei; Zhang, Xiuying

2016-01-01

Identification of the sources of soil mercury (Hg) on the provincial scale is helpful for enacting effective policies to prevent further contamination and take reclamation measurements. The natural and anthropogenic sources and their contributions of Hg in Chinese farmland soil were identified based on a decision tree method. The results showed that the concentrations of Hg in parent materials were most strongly associated with the general spatial distribution pattern of Hg concentration on a provincial scale. The decision tree analysis gained an 89.70% total accuracy in simulating the influence of human activities on the additions of Hg in farmland soil. Human activities—for example, the production of coke, application of fertilizers, discharge of wastewater, discharge of solid waste, and the production of non-ferrous metals—were the main external sources of a large amount of Hg in the farmland soil. PMID:27834884

Air Bag Applies Uniform Bonding Pressure

NASA Technical Reports Server (NTRS)

Gillespie, C. A.

1982-01-01

Air-bag box applies constant uniform pressure to tiles and other objects undergoing adhesive bonding. Box is basically a compliant clamp with adjustable force and position. Can be used on irregular surfaces as well as on flat ones. Pressurized air is fed to bag through a tube so that it expands, filling the box and pressing against work. Bag adopts a contour that accommodates surface under open side of box.

The Role of the Multifunctional BAG3 Protein in Cellular Protein Quality Control and in Disease.

PubMed

Stürner, Elisabeth; Behl, Christian

2017-01-01

In neurons, but also in all other cells the complex proteostasis network is monitored and tightly regulated by the cellular protein quality control (PQC) system. Beyond folding of newly synthesized polypeptides and their refolding upon misfolding the PQC also manages the disposal of aberrant proteins either by the ubiquitin-proteasome machinery or by the autophagic-lysosomal system. Aggregated proteins are primarily degraded by a process termed selective macroautophagy (or aggrephagy). One such recently discovered selective macroautophagy pathway is mediated by the multifunctional HSP70 co-chaperone BAG3 ( BCL-2-associated athanogene 3 ). Under acute stress and during cellular aging, BAG3 in concert with the molecular chaperones HSP70 and HSPB8 as well as the ubiquitin receptor p62/SQSTM1 specifically targets aggregation-prone proteins to autophagic degradation. Thereby, BAG3-mediated selective macroautophagy represents a pivotal adaptive safeguarding and emergency system of the PQC which is activated under pathophysiological conditions to ensure cellular proteostasis. Interestingly, BAG3-mediated selective macroautophagy is also involved in the clearance of aggregated proteins associated with age-related neurodegenerative disorders, like Alzheimer's disease (tau-protein), Huntington's disease (mutated huntingtin/polyQ proteins), and amyotrophic lateral sclerosis (mutated SOD1). In addition, based on its initial description BAG3 is an anti-apoptotic protein that plays a decisive role in other widespread diseases, including cancer and myopathies. Therefore, in the search for novel therapeutic intervention avenues in neurodegeneration, myopathies and cancer BAG3 is a promising candidate.

Test Reviews: Euler, B. L. (2007). "Emotional Disturbance Decision Tree". Lutz, FL: Psychological Assessment Resources

ERIC Educational Resources Information Center

Tansy, Michael

2009-01-01

The Emotional Disturbance Decision Tree (EDDT) is a teacher-completed norm-referenced rating scale published by Psychological Assessment Resources, Inc., in Lutz, Florida. The 156-item EDDT was developed for use as part of a broader assessment process to screen and assist in the identification of 5- to 18-year-old children for the special…

Decision tree analysis to evaluate dry cow strategies under UK conditions.

PubMed

Berry, Elizabeth A; Hogeveen, Henk; Hillerton, J Eric

2004-11-01

Economic decisions on animal health strategies address the cost-benefit aspect along with animal welfare and public health concerns. Decision tree analysis at an individual cow level highlighted that there is little economic difference between the use of either dry cow antibiotic or an internal teat sealant in preventing a new intramammary infection in a cow free of infection in all quarters of the mammary gland at drying off. However, a potential net loss of over ł20 per cow might occur if the uninfected cow was left untreated. The only economically viable option, for a cow with one or more quarters infected at drying off, is antibiotic treatment, although a loss might still be incurred depending on the pathogen concerned and the cure rates achievable. There was a net loss for cows with quarters infected with Corynebacterium spp. at drying off, for both the teat sealant and untreated groups (ł22 and ł48, respectively) with only antibiotic-treated cows showing a gain.

The effectiveness of air bags.

PubMed

Barry, S; Ginpil, S; O'Neill, T J

1999-11-01

Previous research has shown that the installation of air bags in vehicles significantly reduces crash related deaths, but these analyses have used statistical techniques which have not been capable of controlling for other major determinants of crash survival. This study analysed data from the US FARS database of fatal crashes using conditional logistic regression which is simultaneously able to estimate occupant protection effects for a range of variables. Results of the analysis provided a comparative quantification of both the effect of the air bag as well as other well known determinants of occupant crash survival (age, seat belt use, and gender). When potentially confounding variables were controlled, both the driver and passenger side air bag devices were shown to significantly reduce the probability of death in direct frontal collisions, but the effect size calculated was small compared to the effect of the seat belt. The effect size may also be very small in absolute terms depending on the severity of the crash involved. Given the limited benefit of the air bag, efforts to promote air bags seem particularly difficult to justify in countries such as the United States where the vastly superior occupant protection of the seat belt is under-utilised.

49 CFR 178.520 - Standards for textile bags.

Code of Federal Regulations, 2012 CFR

2012-10-01

... for a sift-proof textile bag; and (3) 5L3 for a water-resistant textile bag. (b) Construction requirements for textile bags are as follows: (1) The textiles used must be of good quality. The strength of... use of paper bonded to the inner surface of the bag by a water-resistant adhesive such as bitumen...

49 CFR 178.520 - Standards for textile bags.

Code of Federal Regulations, 2014 CFR

2014-10-01

... for a sift-proof textile bag; and (3) 5L3 for a water-resistant textile bag. (b) Construction requirements for textile bags are as follows: (1) The textiles used must be of good quality. The strength of... use of paper bonded to the inner surface of the bag by a water-resistant adhesive such as bitumen...

49 CFR 178.520 - Standards for textile bags.

Code of Federal Regulations, 2013 CFR

2013-10-01

... for a sift-proof textile bag; and (3) 5L3 for a water-resistant textile bag. (b) Construction requirements for textile bags are as follows: (1) The textiles used must be of good quality. The strength of... use of paper bonded to the inner surface of the bag by a water-resistant adhesive such as bitumen...

Untethered Crewlock Bag Drifts Away from ISS

NASA Image and Video Library

2008-11-18

S126-E-008155 (18 Nov. 2008) --- An extravehicular activity (EVA) tool bag drifts away from the International Space Station during the mission's first scheduled spacewalk for STS-126. About halfway into the spacewalk, one of the grease guns that astronaut Heidemarie Stefanyshyn-Piper (out of frame), mission specialist, was preparing to use on the Solar Alpha Rotary Joint released some Braycote grease into her crew lock bag, which is the tool bag the spacewalkers use during their activities. As she was cleaning the inside of the bag, it drifted away from her and toward the aft and starboard portion of the International Space Station. Inside the bag were two grease guns, a scraper, a scraper debris container, several wipes in a caddy and tethers.

Untethered Crewlock Bag Drifts Away from ISS

NASA Image and Video Library

2008-11-18

S126-E-008143 (18 Nov. 2008) --- An extravehicular activity (EVA) tool bag drifts away from the International Space Station during the mission's first scheduled spacewalk for STS-126. About halfway into the spacewalk, one of the grease guns that astronaut Heidemarie Stefanyshyn-Piper (out of frame), mission specialist, was preparing to use on the Solar Alpha Rotary Joint released some Braycote grease into her crew lock bag, which is the tool bag the spacewalkers use during their activities. As she was cleaning the inside of the bag, it drifted away from her and toward the aft and starboard portion of the International Space Station. Inside the bag were two grease guns, a scraper, a scraper debris container, several wipes in a caddy and tethers.

Untethered Crewlock Bag Drifts Away from ISS

NASA Image and Video Library

2008-11-18

S126-E-008146 (18 Nov. 2008) --- An extravehicular activity (EVA) tool bag drifts away from the International Space Station during the mission's first scheduled spacewalk for STS-126. About halfway into the spacewalk, one of the grease guns that astronaut Heidemarie Stefanyshyn-Piper (out of frame), mission specialist, was preparing to use on the Solar Alpha Rotary Joint released some Braycote grease into her crew lock bag, which is the tool bag the spacewalkers use during their activities. As she was cleaning the inside of the bag, it drifted away from her and toward the aft and starboard portion of the International Space Station. Inside the bag were two grease guns, a scraper, a scraper debris container, several wipes in a caddy and tethers.

A Novel Treatment Decision Tree and Literature Review of Retrograde Peri-Implantitis.

PubMed

Sarmast, Nima D; Wang, Howard H; Soldatos, Nikolaos K; Angelov, Nikola; Dorn, Samuel; Yukna, Raymond; Iacono, Vincent J

2016-12-01

Although retrograde peri-implantitis (RPI) is not a common sequela of dental implant surgery, its prevalence has been reported in the literature to be 0.26%. Incidence of RPI is reported to increase to 7.8% when teeth adjacent to the implant site have a previous history of root canal therapy, and it is correlated with distance between implant and adjacent tooth and/or with time from endodontic treatment of adjacent tooth to implant placement. Minimum 2 mm space between implant and adjacent tooth is needed to decrease incidence of apical RPI, with minimum 4 weeks between completion of endodontic treatment and actual implant placement. The purpose of this study is to compile all available treatment modalities and to provide a decision tree as a general guide for clinicians to aid in diagnosis and treatment of RPI. Literature search was performed for articles published in English on the topic of RPI. Articles selected were case reports with study populations ranging from 1 to 32 patients. Any case report or clinical trial that attempted to treat or rescue an implant diagnosed with RPI was included. Predominant diagnostic presentation of a lesion was presence of sinus tract at buccal or facial abscess of apical portion of implant, and subsequent periapical radiographs taken demonstrated a radiolucent lesion. On the basis of case reports analyzed, RPI was diagnosed between 1 week and 4 years after implant placement. Twelve of 20 studies reported that RPI lesions were diagnosed within 6 months after implant placement. A step-by-step decision tree is provided to allow clinicians to triage and properly manage cases of RPI on the basis of recommendations and successful treatments provided in analyzed case reports. It is divided between symptomatic and asymptomatic implants and adjacent teeth with vital and necrotic pulps. Most common etiology of apical RPI is endodontic infection from neighboring teeth, which was diagnosed within 6 months after implant placement. Most

Storage of Maize in Purdue Improved Crop Storage (PICS) Bags

PubMed Central

2017-01-01

Interest in using hermetic technologies as a pest management solution for stored grain has risen in recent years. One hermetic approach, Purdue Improved Crop Storage (PICS) bags, has proven successful in controlling the postharvest pests of cowpea. This success encouraged farmers to use of PICS bags for storing other crops including maize. To assess whether maize can be safely stored in PICS bags without loss of quality, we carried out laboratory studies of maize grain infested with Sitophilus zeamais (Motshulsky) and stored in PICS triple bags or in woven polypropylene bags. Over an eight month observation period, temperatures in the bags correlated with ambient temperature for all treatments. Relative humidity inside PICS bags remained constant over this period despite the large changes that occurred in the surrounding environment. Relative humidity in the woven bags followed ambient humidity closely. PICS bags containing S. zeamais-infested grain saw a significant decline in oxygen compared to the other treatments. Grain moisture content declined in woven bags, but remained high in PICS bags. Seed germination was not significantly affected over the first six months in all treatments, but declined after eight months of storage when infested grain was held in woven bags. Relative damage was low across treatments and not significantly different between treatments. Overall, maize showed no signs of deterioration in PICS bags versus the woven bags and PICS bags were superior to woven bags in terms of specific metrics of grain quality. PMID:28072835

Application of a hybrid association rules/decision tree model for drought monitoring

NASA Astrophysics Data System (ADS)

Nourani, Vahid; Molajou, Amir

2017-12-01

The previous researches have shown that the incorporation of the oceanic-atmospheric climate phenomena such as Sea Surface Temperature (SST) into hydro-climatic models could provide important predictive information about hydro-climatic variability. In this paper, the hybrid application of two data mining techniques (decision tree and association rules) was offered to discover affiliation between drought of Tabriz and Kermanshah synoptic stations (located in Iran) and de-trend SSTs of the Black, Mediterranean and Red Seas. Two major steps of the proposed model were the classification of de-trend SST data and selecting the most effective groups and extracting hidden information involved in the data. The techniques of decision tree which can identify the good traits from a data set for the classification purpose were used for classification and selecting the most effective groups and association rules were employed to extract the hidden predictive information from the large observed data. To examine the accuracy of the rules, confidence and Heidke Skill Score (HSS) measures were calculated and compared for different considering lag times. The computed measures confirm reliable performance of the proposed hybrid data mining method to forecast drought and the results show a relative correlation between the Mediterranean, Black and Red Sea de-trend SSTs and drought of Tabriz and Kermanshah synoptic stations so that the confidence between the monthly Standardized Precipitation Index (SPI) values and the de-trend SST of seas is higher than 70 and 80% respectively for Tabriz and Kermanshah synoptic stations.

49 CFR 178.521 - Standards for paper bags.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 3 2011-10-01 2011-10-01 false Standards for paper bags. 178.521 Section 178.521...-Oriented Packaging Standards § 178.521 Standards for paper bags. (a) The following are identification codes for paper bags: (1) 5M1 for a multi-wall paper bag; and (2) 5M2 for a multi-wall water-resistant paper...

49 CFR 178.521 - Standards for paper bags.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 2 2010-10-01 2010-10-01 false Standards for paper bags. 178.521 Section 178.521...-bulk Performance-Oriented Packaging Standards § 178.521 Standards for paper bags. (a) The following are identification codes for paper bags: (1) 5M1 for a multi-wall paper bag; and (2) 5M2 for a multi-wall water...

Decision tree-based method for integrating gene expression, demographic, and clinical data to determine disease endotypes

PubMed Central

2013-01-01

Background Complex diseases are often difficult to diagnose, treat and study due to the multi-factorial nature of the underlying etiology. Large data sets are now widely available that can be used to define novel, mechanistically distinct disease subtypes (endotypes) in a completely data-driven manner. However, significant challenges exist with regard to how to segregate individuals into suitable subtypes of the disease and understand the distinct biological mechanisms of each when the goal is to maximize the discovery potential of these data sets. Results A multi-step decision tree-based method is described for defining endotypes based on gene expression, clinical covariates, and disease indicators using childhood asthma as a case study. We attempted to use alternative approaches such as the Student’s t-test, single data domain clustering and the Modk-prototypes algorithm, which incorporates multiple data domains into a single analysis and none performed as well as the novel multi-step decision tree method. This new method gave the best segregation of asthmatics and non-asthmatics, and it provides easy access to all genes and clinical covariates that distinguish the groups. Conclusions The multi-step decision tree method described here will lead to better understanding of complex disease in general by allowing purely data-driven disease endotypes to facilitate the discovery of new mechanisms underlying these diseases. This application should be considered a complement to ongoing efforts to better define and diagnose known endotypes. When coupled with existing methods developed to determine the genetics of gene expression, these methods provide a mechanism for linking genetics and exposomics data and thereby accounting for both major determinants of disease. PMID:24188919

Air bags and ocular injuries.

PubMed

Stein, J D; Jaeger, E A; Jeffers, J B

1999-01-01

This investigation retrospectively examined ocular injuries associated with air bag deployment to gain a better appreciation of potential risk factors in motor vehicle accidents. National statistics regarding the efficacy of air bags were reviewed. Review of the literature from 1991 to 1998 identified 44 articles describing 97 patients with air-bag-induced ocular injuries. Variables extracted from each case were age, sex, height, position in the car, eye wear, vehicle impact speed, visual acuity, and specific ocular injuries. Corneal abrasions occurred in 49% of occupants, hyphemas in 43%, vitreous or retinal hemorrhages in 25%, and retinal tears or detachments in 15%. The globe was ruptured in 10 patients. Patients involved in higher-speed accidents (over 30 mph) sustained a greater percentage of vitreous or retinal hemorrhages and traumatic cataracts, while those at slower speeds were more prone to retinal tears or detachments. In a subset of 14 patients with serious ocular injuries, the impact speed of 11 patients was recorded at 30 mph or less. Slower speed may be a risk factor for some ocular injuries. Occupant height was not a significant factor. National statistics confirm that air bags reduce fatalities in motor vehicle accidents. However, children sitting in the front seat without a seat belt and infants in passenger-side rear-facing car seats are at risk for fatal injury. Air bags combined with seat belts are an effective means of reducing injury and death in adults during motor vehicle accidents. However, this study has documented a wide variety of ocular injuries associated with air bag deployment. It is hoped that researchers can develop modifications that continue to save lives while minimizing additional harm.

CorRECTreatment: A Web-based Decision Support Tool for Rectal Cancer Treatment that Uses the Analytic Hierarchy Process and Decision Tree

PubMed Central

Karakülah, G.; Dicle, O.; Sökmen, S.; Çelikoğlu, C.C.

2015-01-01

Summary Background The selection of appropriate rectal cancer treatment is a complex multi-criteria decision making process, in which clinical decision support systems might be used to assist and enrich physicians’ decision making. Objective The objective of the study was to develop a web-based clinical decision support tool for physicians in the selection of potentially beneficial treatment options for patients with rectal cancer. Methods The updated decision model contained 8 and 10 criteria in the first and second steps respectively. The decision support model, developed in our previous study by combining the Analytic Hierarchy Process (AHP) method which determines the priority of criteria and decision tree that formed using these priorities, was updated and applied to 388 patients data collected retrospectively. Later, a web-based decision support tool named corRECTreatment was developed. The compatibility of the treatment recommendations by the expert opinion and the decision support tool was examined for its consistency. Two surgeons were requested to recommend a treatment and an overall survival value for the treatment among 20 different cases that we selected and turned into a scenario among the most common and rare treatment options in the patient data set. Results In the AHP analyses of the criteria, it was found that the matrices, generated for both decision steps, were consistent (consistency ratio<0.1). Depending on the decisions of experts, the consistency value for the most frequent cases was found to be 80% for the first decision step and 100% for the second decision step. Similarly, for rare cases consistency was 50% for the first decision step and 80% for the second decision step. Conclusions The decision model and corRECTreatment, developed by applying these on real patient data, are expected to provide potential users with decision support in rectal cancer treatment processes and facilitate them in making projections about treatment options

CorRECTreatment: a web-based decision support tool for rectal cancer treatment that uses the analytic hierarchy process and decision tree.

PubMed

Suner, A; Karakülah, G; Dicle, O; Sökmen, S; Çelikoğlu, C C

2015-01-01

The selection of appropriate rectal cancer treatment is a complex multi-criteria decision making process, in which clinical decision support systems might be used to assist and enrich physicians' decision making. The objective of the study was to develop a web-based clinical decision support tool for physicians in the selection of potentially beneficial treatment options for patients with rectal cancer. The updated decision model contained 8 and 10 criteria in the first and second steps respectively. The decision support model, developed in our previous study by combining the Analytic Hierarchy Process (AHP) method which determines the priority of criteria and decision tree that formed using these priorities, was updated and applied to 388 patients data collected retrospectively. Later, a web-based decision support tool named corRECTreatment was developed. The compatibility of the treatment recommendations by the expert opinion and the decision support tool was examined for its consistency. Two surgeons were requested to recommend a treatment and an overall survival value for the treatment among 20 different cases that we selected and turned into a scenario among the most common and rare treatment options in the patient data set. In the AHP analyses of the criteria, it was found that the matrices, generated for both decision steps, were consistent (consistency ratio<0.1). Depending on the decisions of experts, the consistency value for the most frequent cases was found to be 80% for the first decision step and 100% for the second decision step. Similarly, for rare cases consistency was 50% for the first decision step and 80% for the second decision step. The decision model and corRECTreatment, developed by applying these on real patient data, are expected to provide potential users with decision support in rectal cancer treatment processes and facilitate them in making projections about treatment options.

Analytical and CASE study on Limited Search, ID3, CHAID, C4.5, Improved C4.5 and OVA Decision Tree Algorithms to design Decision Support System

NASA Astrophysics Data System (ADS)

Kaur, Parneet; Singh, Sukhwinder; Garg, Sushil; Harmanpreet

2010-11-01

In this paper we study about classification algorithms for farm DSS. By applying classification algorithms i.e. Limited search, ID3, CHAID, C4.5, Improved C4.5 and One VS all Decision Tree on common data set of crop with specified class, results are obtained. The tool used to derive results is SPINA. The graphical results obtained from tool are compared to suggest best technique to develop farm Decision Support System. This analysis would help to researchers to design effective and fast DSS for farmer to take decision for enhancing their yield.

The Role of the Multifunctional BAG3 Protein in Cellular Protein Quality Control and in Disease

PubMed Central

Stürner, Elisabeth; Behl, Christian

2017-01-01

In neurons, but also in all other cells the complex proteostasis network is monitored and tightly regulated by the cellular protein quality control (PQC) system. Beyond folding of newly synthesized polypeptides and their refolding upon misfolding the PQC also manages the disposal of aberrant proteins either by the ubiquitin-proteasome machinery or by the autophagic-lysosomal system. Aggregated proteins are primarily degraded by a process termed selective macroautophagy (or aggrephagy). One such recently discovered selective macroautophagy pathway is mediated by the multifunctional HSP70 co-chaperone BAG3 (BCL-2-associated athanogene 3). Under acute stress and during cellular aging, BAG3 in concert with the molecular chaperones HSP70 and HSPB8 as well as the ubiquitin receptor p62/SQSTM1 specifically targets aggregation-prone proteins to autophagic degradation. Thereby, BAG3-mediated selective macroautophagy represents a pivotal adaptive safeguarding and emergency system of the PQC which is activated under pathophysiological conditions to ensure cellular proteostasis. Interestingly, BAG3-mediated selective macroautophagy is also involved in the clearance of aggregated proteins associated with age-related neurodegenerative disorders, like Alzheimer’s disease (tau-protein), Huntington’s disease (mutated huntingtin/polyQ proteins), and amyotrophic lateral sclerosis (mutated SOD1). In addition, based on its initial description BAG3 is an anti-apoptotic protein that plays a decisive role in other widespread diseases, including cancer and myopathies. Therefore, in the search for novel therapeutic intervention avenues in neurodegeneration, myopathies and cancer BAG3 is a promising candidate. PMID:28680391

Quark gluon bags as reggeons

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bugaev, K. A.; Petrov, V. K.; Zinovjev, G. M.

2009-05-15

The influence of the medium-dependent finite width of quark gluon plasma (QGP) bags on their equation of state is analyzed within an exactly solvable model. It is argued that the large width of the QGP bags not only explains the observed deficit in the number of hadronic resonances but also clarifies the reason why the heavy QGP bags cannot be directly observed as metastable states in a hadronic phase. The model allows us to estimate the minimal value of the width of QGP bags being heavier than 2 GeV from a variety of the lattice QCD data and get thatmore » the minimal resonance width at zero temperature is about 600 MeV, whereas the minimal resonance width at the Hagedorn temperature is about 2000 MeV. As shown, these estimates are almost insensitive to the number of the elementary degrees of freedom. The recent lattice QCD data are analyzed and it is found that in addition to the {sigma}T{sup 4} term the lattice QCD pressure contains T-linear and T{sup 4}lnT terms in the range of temperatures between 240 and 420 MeV. The presence of the last term in the pressure bears almost no effect on the width estimates. Our analysis shows that at high temperatures the average mass and width of the QGP bags behave in accordance with the upper bound of the Regge trajectory asymptotics (the linear asymptotics), whereas at low temperatures they obey the lower bound of the Regge trajectory asymptotics (the square root one). Since the model explicitly contains the Hagedorn mass spectrum, it allows us to remove an existing contradiction between the finite number of hadronic Regge families and the Hagedorn idea of the exponentially growing mass spectrum of hadronic bags.« less

Air bags and ocular injuries.

PubMed Central

Stein, J D; Jaeger, E A; Jeffers, J B

1999-01-01

PURPOSE: This investigation retrospectively examined ocular injuries associated with air bag deployment to gain a better appreciation of potential risk factors in motor vehicle accidents. National statistics regarding the efficacy of air bags were reviewed. METHODS: Review of the literature from 1991 to 1998 identified 44 articles describing 97 patients with air-bag-induced ocular injuries. Variables extracted from each case were age, sex, height, position in the car, eye wear, vehicle impact speed, visual acuity, and specific ocular injuries. RESULTS: Corneal abrasions occurred in 49% of occupants, hyphemas in 43%, vitreous or retinal hemorrhages in 25%, and retinal tears or detachments in 15%. The globe was ruptured in 10 patients. Patients involved in higher-speed accidents (over 30 mph) sustained a greater percentage of vitreous or retinal hemorrhages and traumatic cataracts, while those at slower speeds were more prone to retinal tears or detachments. In a subset of 14 patients with serious ocular injuries, the impact speed of 11 patients was recorded at 30 mph or less. Slower speed may be a risk factor for some ocular injuries. Occupant height was not a significant factor. National statistics confirm that air bags reduce fatalities in motor vehicle accidents. However, children sitting in the front seat without a seat belt and infants in passenger-side rear-facing car seats are at risk for fatal injury. CONCLUSION: Air bags combined with seat belts are an effective means of reducing injury and death in adults during motor vehicle accidents. However, this study has documented a wide variety of ocular injuries associated with air bag deployment. It is hoped that researchers can develop modifications that continue to save lives while minimizing additional harm. Images FIGURE 1 FIGURE 2A FIGURE 2B FIGURE 2C FIGURE 2D FIGURE 3A FIGURE 3B FIGURE 4 FIGURE 5 FIGURE 7 FIGURE 8 PMID:10703118

MSEBAG: a dynamic classifier ensemble generation based on `minimum-sufficient ensemble' and bagging

NASA Astrophysics Data System (ADS)

Chen, Lei; Kamel, Mohamed S.

2016-01-01

In this paper, we propose a dynamic classifier system, MSEBAG, which is characterised by searching for the 'minimum-sufficient ensemble' and bagging at the ensemble level. It adopts an 'over-generation and selection' strategy and aims to achieve a good bias-variance trade-off. In the training phase, MSEBAG first searches for the 'minimum-sufficient ensemble', which maximises the in-sample fitness with the minimal number of base classifiers. Then, starting from the 'minimum-sufficient ensemble', a backward stepwise algorithm is employed to generate a collection of ensembles. The objective is to create a collection of ensembles with a descending fitness on the data, as well as a descending complexity in the structure. MSEBAG dynamically selects the ensembles from the collection for the decision aggregation. The extended adaptive aggregation (EAA) approach, a bagging-style algorithm performed at the ensemble level, is employed for this task. EAA searches for the competent ensembles using a score function, which takes into consideration both the in-sample fitness and the confidence of the statistical inference, and averages the decisions of the selected ensembles to label the test pattern. The experimental results show that the proposed MSEBAG outperforms the benchmarks on average.

Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression.

PubMed

Westreich, Daniel; Lessler, Justin; Funk, Michele Jonsson

2010-08-01

Propensity scores for the analysis of observational data are typically estimated using logistic regression. Our objective in this review was to assess machine learning alternatives to logistic regression, which may accomplish the same goals but with fewer assumptions or greater accuracy. We identified alternative methods for propensity score estimation and/or classification from the public health, biostatistics, discrete mathematics, and computer science literature, and evaluated these algorithms for applicability to the problem of propensity score estimation, potential advantages over logistic regression, and ease of use. We identified four techniques as alternatives to logistic regression: neural networks, support vector machines, decision trees (classification and regression trees [CART]), and meta-classifiers (in particular, boosting). Although the assumptions of logistic regression are well understood, those assumptions are frequently ignored. All four alternatives have advantages and disadvantages compared with logistic regression. Boosting (meta-classifiers) and, to a lesser extent, decision trees (particularly CART), appear to be most promising for use in the context of propensity score analysis, but extensive simulation studies are needed to establish their utility in practice. Copyright (c) 2010 Elsevier Inc. All rights reserved.

Deaths among drivers and right-front passengers in frontal collisions: redesigned air bags relative to first-generation air bags.

PubMed

Braver, Elisa R; Scerbo, Marge; Kufera, Joseph A; Alexander, Melvin T; Volpini, Karen; Lloyd, Joseph P

2008-03-01

After automakers were allowed the option of using sled tests for unbelted male dummies to certify the frontal crash performance of vehicles, most frontal air bags were depowered, starting in model year 1998, to reduce deaths and serious injuries arising from air bag deployments. Concern has been expressed that depowering air bags could compromise the protection of adult occupants. This study aimed to determine the effects of changes in air bag designs on risk of death among front-seat occupants. Deaths among drivers and right-front passengers per involvement in frontal police-reported crashes during calendar years 1998-2004 were compared among vehicles with sled-certified air bags (model years 1998-2004) and first-generation air bags (model years 1994-97). Frontal crash deaths were identified from the Fatality Analysis Reporting System. National estimates of police-reported crashes were derived from the National Automotive Sampling System/General Estimates System. Sled certification status for model years 1998-2004 was ascertained from published federal data and a survey of automobile manufacturers. Passenger cars, pickup trucks, sport utility vehicles, and minivans were studied. Stratified analyses were done to compute risk ratios (RR) and 95% confidence intervals (95% CI) for driver and right-front passenger deaths by air bag generation and crash, vehicle, and driver characteristics. In frontal crashes, overall RRs were 0.89 for driver deaths (95% CI = 0.74-1.08) and 0.89 for right-front passenger deaths (95% CI = 0.74-1.07) in sled-certified vehicles compared with first-generation air bag-equipped vehicles. Child right-front passengers (ages 0-4, 5-9) in vehicles with sled-certified air bags had statistically significant reductions in risk of dying in frontal collisions, including a 65% reduced risk among ages 0-4 (RR = 0.35; 95% CI = 0.21-0.60). No differences in effects of sled-certified air bags were observed between drivers ages 15-59 and 60-74 in sled

50 CFR 622.437 - Bag limits.

Code of Federal Regulations, 2013 CFR

2013-10-01

... Puerto Rico and the U.S. Virgin Islands § 622.437 Bag limits. (a) Applicability. Section 622.11(a... fishing license issued by Puerto Rico or the U.S. Virgin Islands. (b) Bag limits. (1) Groupers, snappers...

Material bagging device

DOEpatents

Wach, Charles G.; Nelson, Robert E.; Brak, Stephen B.

1984-01-01

A bagging device for transferring material from one chamber through an opening in a wall to a second chamber includes a cylindrical housing communicating with the opening and defining a passage between the chambers. A cylindrical cartridge is slidably received within the housing. The cartridge has a substantially rigid cylindrical sleeve to which is affixed a pliable tube. The pliable tube is positioned concentrically about the sleeve and has a pleated portion capable of unfolding from the sleeve and a closed end extending over a terminal end of the sleeve. Sealing means are interposed in sealed relationship between the cartridge and the housing. Material from one chamber is inserted into the cartridge secured in the housing and received in the closed end of the tube which unfolds into the other chamber enclosing the material therein. The tube may then be sealed behind the material and then severed to form a bag-like enclosure defined by the tube's closed terminal end and the new seal. The new seal then forms a terminal end for the unsevered portion of the pliable tube into which additional material may be placed and the bagging process repeated.

Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory

EPA Science Inventory

Efforts are increasingly being made to classify the world’s wetland resources, an important ecosystem and habitat that is diminishing in abundance. There are multiple remote sensing classification methods, including a suite of nonparametric classifiers such as decision-tree...

Knife-edge seal for vacuum bagging

NASA Technical Reports Server (NTRS)

Rauschl, J. A.

1980-01-01

Cam actuated clamps pinch bagging material between long knife edge (mounted to clamps) and high temperature rubber cushion bonded to baseplate. No adhesive, tape, or sealing groove is needed to seal edge of bagging sheet against base plate.

Field evaluation of the long-lasting treated storage bag, deltamethrin-incorporated (ZeroFly® Storage Bag) as a barrier to insect pest infestation

USDA-ARS?s Scientific Manuscript database

The deltamethrin-incorporated polypropylene (PP) bag, ZeroFly® Storage Bag, is a new technology to reduce postharvest losses caused by stored-product insect pests. ZeroFly bags filled with untreated maize were compared to PP bags filled with maize treated with Betallic Super (80 g pirimiphos-methyl ...

Decision trees for the analysis of genes involved in Alzheimer's disease pathology.

PubMed

Mestizo Gutiérrez, Sonia L; Herrera Rivero, Marisol; Cruz Ramírez, Nicandro; Hernández, Elena; Aranda-Abreu, Gonzalo E

2014-09-21

Alzheimer's disease (AD) is characterized by a gradual loss of memory, orientation, judgement and language. There is still no cure for this disorder. AD pathogenesis remains fairly unknown and its underlying molecular mechanisms are not yet fully understood. Several studies have shown that the abnormal accumulation of beta-amyloid and tau proteins occurs 10 to 20 years before the onset of symptoms of the disease, so it is extremely important to identify changes in the brain before the first symptoms. We used decision trees to classify 31 individuals (9 healthy controls and 22 AD patients in three different stages of disease) according to the expression of 69 genes previously reported in a meta-analysis, plus the expression levels of APP, APOE, BACE1, NCSTN, PSEN1, PSEN2 and MAPT. We also included in our analysis the MMSE (Mini-Mental State Examination) scores and number of NFT (neurofibrillary tangles). Results allowed us to generate a model of classification values for different AD stages of severity, according to MMSE scores, and achieve the identification of the expression level of protein tau that may possibly determine the onset (incipient stage) of AD. We used decision trees to model the different stages of AD (severe, moderate, incipient and control) based on the meta-analysis of gene expression levels plus MMSE and NFT scores. Both classifiers reported the variable MMSE as most informative, however it we were found that the protein tau also an important role in the onset of AD. Copyright © 2014 Elsevier Ltd. All rights reserved.

Identifying controlling factors of ground-level ozone levels over southwestern Taiwan using a decision tree

NASA Astrophysics Data System (ADS)

Chu, Hone-Jay; Lin, Chuan-Yao; Liau, Churn-Jung; Kuo, Yi-Ming

2012-12-01

Kaohsiung City and the suburban region of southwestern Taiwan have suffered from severe air pollution since becoming the largest center of heavy industry in Taiwan. The complex process of ozone (O3) formation and its precursor compounds (the volatile organic compounds (VOCs) and nitrogen oxide (NOx) emissions), accompanied by meteorological conditions, make controlling ozone difficult. Using a decision tree is especially appropriate for analyzing time series data that contain ozone levels and meteorological and explanatory variables for ozone formation. Results show that dominant variables such as temperature, wind speed, VOCs, and NOx can play vital roles in describing ozone variations among observations. That temperature and wind speed are highly correlated with ozone levels indicates that these meteorological conditions largely affect ozone variability. The results also demonstrate that spatial heterogeneity of ozone patterns are in coastal and inland areas caused by sea-land breeze and pollutant sources during high ozone episodes over southwestern Taiwan. This study used a decision tree to obtain quantitative insight into spatial distributions of precursor compound emissions and effects of meteorological conditions on ozone levels that are useful for refining monitoring plans and developing management strategies.

49 CFR 178.519 - Standards for plastic film bags.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 3 2011-10-01 2011-10-01 false Standards for plastic film bags. 178.519 Section...-bulk Performance-Oriented Packaging Standards § 178.519 Standards for plastic film bags. (a) The identification code for a plastic film bag is 5H4. (b) Construction requirements for plastic film bags are as...

49 CFR 178.519 - Standards for plastic film bags.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 3 2014-10-01 2014-10-01 false Standards for plastic film bags. 178.519 Section...-bulk Performance-Oriented Packaging Standards § 178.519 Standards for plastic film bags. (a) The identification code for a plastic film bag is 5H4. (b) Construction requirements for plastic film bags are as...

49 CFR 178.519 - Standards for plastic film bags.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 49 Transportation 3 2013-10-01 2013-10-01 false Standards for plastic film bags. 178.519 Section...-bulk Performance-Oriented Packaging Standards § 178.519 Standards for plastic film bags. (a) The identification code for a plastic film bag is 5H4. (b) Construction requirements for plastic film bags are as...

49 CFR 178.519 - Standards for plastic film bags.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 49 Transportation 3 2012-10-01 2012-10-01 false Standards for plastic film bags. 178.519 Section...-bulk Performance-Oriented Packaging Standards § 178.519 Standards for plastic film bags. (a) The identification code for a plastic film bag is 5H4. (b) Construction requirements for plastic film bags are as...

Bag For Formulating And Dispersing Intravenous Solution

NASA Technical Reports Server (NTRS)

Kipp, Jim; Owens, Jim; Scharf, Mike; Finley, Mike; Dudar, Tom; Veillon, Joe; Ogle, Jim

1993-01-01

Large-volume parenteral (LVP) bag in which predetermined amount of sterile solution formulated by combining premeasured, prepackaged amount of sterile solute with predetermined amount of water. Bag designed to hold predetermined amount, typically 1 L, of sterile solution. Sterility of solution maintained during mixing by passing water into bag through sterilizing filter. System used in field or hospitals not having proper sterile facilities, and in field research.

Sodium azide-associated laryngospasm after air bag deployment.

PubMed

Francis, David; Warren, Samuel A; Warner, Keir J; Harris, William; Copass, Michael K; Bulger, Eileen M

2010-09-01

The advent and incorporation of the air bag into motor vehicles has resulted in the mitigation of many head and truncal injuries in motor vehicle collisions. However, air bag deployment is not risk free. We present a case of sodium azide-induced laryngospasm after air bag deployment. An unrestrained male driver was in a moderate-speed motor vehicle collision with air bag deployment. Medics found him awake, gasping for air with stridorous respirations and guarding his neck. The patient had no external signs of trauma and was presumed to have tracheal injury. The patient was greeted by the Anesthesiology service, which intubated him using glidescope-assisted laryngoscopy. The patient was admitted for overnight observation and treatment of alkaline ocular injury and laryngospasm. Although air bags represent an important advance in automobile safety, their use is not without risk. Bruising and tracheal rupture secondary to air bag deployment have been reported in out-of-position occupants. Additionally, alkaline by-products from the combustion of sodium azide in air bags have been implicated in ocular injury and facial burns. Laryngospasm after sodium azide exposure presents another diagnostic challenge for providers. Therefore, it is incumbent to maintain vigilance in the physical examination and diagnosis of occult injuries after air bag deployment. Copyright © 2010 Elsevier Inc. All rights reserved.

Reducing single-use plastic shopping bags in the USA.

PubMed

Wagner, Travis P

2017-12-01

In the USA, local governments have the primary responsibility to manage MSW. However, local governments lack the authority to explicitly shift costs or responsibility back onto the producer for specific problem wastes. A particularly problematic waste for local governments is the single-use plastic bag. In 2014, in the USA, 103.465 billion single-use plastic shopping bags were consumed. Because of their extremely low recyclability rate, plastic bags remain a significant source of land-based litter and marine debris and impair stormwater management systems. They also reduce the effectiveness of automated recycling systems. In response, local governments increasingly have adopted a variety of measures specifically intended to reduce the store-level consumption of single-use shopping bags in 5 major categories: bans, imposition of fees and taxes, establishing minimum product design of bags, requiring consumer education, and mandating retailer take-back programs. As of September 2017, there were 271 local governments in the USA with plastic bag ordinances covering 9.7% of the nation's population. The majority (95%) of the ordinances is a ban on single-use plastic bags; 56.9% of these bans also include a mandatory fee on paper and/or reusable bags. For the fee-based ordinances, the mode is $0.10 per bag; every tax/fee ordinance allows retailers to retain some or all the collected fee. As local governments continue to increase their actions on plastic bags, 11 states have enacted laws to prohibit local governments from regulating single-use plastic bags. Because of the success with single-use bags, local governments are also enacting similar ordinances on single-use expanded polystyrene consumer products and other single-use plastic products. Copyright © 2017 Elsevier Ltd. All rights reserved.

Seat belt use in cars with air bags.

PubMed Central

Williams, A F; Wells, J K; Lund, A K

1990-01-01

Seat belt use was observed in 1,628 cars with air bags and manual belts and 34,223 cars with manual seat belts only. Sixty-six percent of drivers in cars with air bags wore seat belts compared to 63 percent of drivers in cars with manual belts only. The study found no evidence for the speculation that drivers with air bags will reduce their seat belt use because they believe an air bag alone provides sufficient protection. PMID:2240346

A hybrid approach of stepwise regression, logistic regression, support vector machine, and decision tree for forecasting fraudulent financial statements.

PubMed

Chen, Suduan; Goo, Yeong-Jia James; Shen, Zone-De

2014-01-01

As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.

BPS magnetic monopole bags

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Ki-Myeong; Weinberg, Erick J.; Physics Department, Columbia University, New York, New York 10027

2009-01-15

We explore the characteristics of spherical bags made of large numbers of BPS magnetic monopoles. There are two extreme limits. In the Abelian bag, N zeros of the Higgs field are arranged in a quasiregular lattice on a sphere of radius R{sub cr}{approx}N/v, where v is the Higgs vacuum expectation value. The massive gauge fields of the theory are largely confined to a thin shell at this radius that separates an interior with almost vanishing magnetic and Higgs fields from an exterior region with long-range Coulomb magnetic and Higgs fields. In the other limiting case, which we term a non-Abelianmore » bag, the N zeros of the Higgs field are all the origin, but there is again a thin shell of radius R{sub cr}. In this case the region enclosed by this shell can be viewed as a large monopole core, with small Higgs field but nontrivial massive and massless gauge fields.« less

Water bag modeling of a multispecies plasma

DOE Office of Scientific and Technical Information (OSTI.GOV)

Morel, P.; Gravier, E.; Besse, N.

2011-03-15

We report in the present paper a new modeling method to study multiple species dynamics in magnetized plasmas. Such a method is based on the gyrowater bag modeling, which consists in using a multistep-like distribution function along the velocity direction parallel to the magnetic field. The choice of a water bag representation allows an elegant link between kinetic and fluid descriptions of a plasma. The gyrowater bag model has been recently adapted to the context of strongly magnetized plasmas. We present its extension to the case of multi ion species magnetized plasmas: each ion species being modeled via a multiwatermore » bag distribution function. The water bag modelization will be discussed in details, under the simplification of a cylindrical geometry that is convenient for linear plasma devices. As an illustration, results obtained in the linear framework for ion temperature gradient instabilities are presented, that are shown to agree qualitatively with older works.« less

A novel decision tree approach based on transcranial Doppler sonography to screen for blunt cervical vascular injuries.

PubMed

Purvis, Dianna; Aldaghlas, Tayseer; Trickey, Amber W; Rizzo, Anne; Sikdar, Siddhartha

2013-06-01

Early detection and treatment of blunt cervical vascular injuries prevent adverse neurologic sequelae. Current screening criteria can miss up to 22% of these injuries. The study objective was to investigate bedside transcranial Doppler sonography for detecting blunt cervical vascular injuries in trauma patients using a novel decision tree approach. This prospective pilot study was conducted at a level I trauma center. Patients undergoing computed tomographic angiography for suspected blunt cervical vascular injuries were studied with transcranial Doppler sonography. Extracranial and intracranial vasculatures were examined with a portable power M-mode transcranial Doppler unit. The middle cerebral artery mean flow velocity, pulsatility index, and their asymmetries were used to quantify flow patterns and develop an injury decision tree screening protocol. Student t tests validated associations between injuries and transcranial Doppler predictive measures. We evaluated 27 trauma patients with 13 injuries. Single vertebral artery injuries were most common (38.5%), followed by single internal carotid artery injuries (30%). Compared to patients without injuries, mean flow velocity asymmetry was higher for single internal carotid artery (P = .003) and single vertebral artery (P = .004) injuries. Similarly, pulsatility index asymmetry was higher in single internal carotid artery (P = .015) and single vertebral artery (P = .042) injuries, whereas the lowest pulsatility index was elevated for bilateral vertebral artery injuries (P = .006). The decision tree yielded 92% specificity, 93% sensitivity, and 93% correct classifications. In this pilot feasibility study, transcranial Doppler measures were significantly associated with the blunt cervical vascular injury status, suggesting that transcranial Doppler sonography might be a viable bedside screening tool for trauma. Patient-specific hemodynamic information from transcranial Doppler assessment has the potential to alter

50 CFR 660.721 - Recreational fishing bag limits.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 50 Wildlife and Fisheries 13 2013-10-01 2013-10-01 false Recreational fishing bag limits. 660.721... Migratory Fisheries § 660.721 Recreational fishing bag limits. This section applies to recreational fishing... daily bag limit of fishing during one calendar day. Federal recreational HMS regulations are not...

50 CFR 660.721 - Recreational fishing bag limits.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 50 Wildlife and Fisheries 13 2014-10-01 2014-10-01 false Recreational fishing bag limits. 660.721... Migratory Fisheries § 660.721 Recreational fishing bag limits. This section applies to recreational fishing... daily bag limit of fishing during one calendar day. Federal recreational HMS regulations are not...

50 CFR 660.721 - Recreational fishing bag limits.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 50 Wildlife and Fisheries 9 2010-10-01 2010-10-01 false Recreational fishing bag limits. 660.721... Migratory Fisheries § 660.721 Recreational fishing bag limits. This section applies to recreational fishing... daily bag limit of fishing during one calendar day. Federal recreational HMS regulations are not...

50 CFR 660.721 - Recreational fishing bag limits.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 50 Wildlife and Fisheries 11 2011-10-01 2011-10-01 false Recreational fishing bag limits. 660.721... Migratory Fisheries § 660.721 Recreational fishing bag limits. This section applies to recreational fishing... daily bag limit of fishing during one calendar day. Federal recreational HMS regulations are not...

50 CFR 660.721 - Recreational fishing bag limits.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 50 Wildlife and Fisheries 13 2012-10-01 2012-10-01 false Recreational fishing bag limits. 660.721... Migratory Fisheries § 660.721 Recreational fishing bag limits. This section applies to recreational fishing... daily bag limit of fishing during one calendar day. Federal recreational HMS regulations are not...

21 CFR 870.3650 - Pacemaker polymeric mesh bag.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Pacemaker polymeric mesh bag. 870.3650 Section 870...) MEDICAL DEVICES CARDIOVASCULAR DEVICES Cardiovascular Prosthetic Devices § 870.3650 Pacemaker polymeric mesh bag. (a) Identification. A pacemaker polymeric mesh bag is an implanted device used to hold a...

21 CFR 870.3650 - Pacemaker polymeric mesh bag.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Pacemaker polymeric mesh bag. 870.3650 Section 870...) MEDICAL DEVICES CARDIOVASCULAR DEVICES Cardiovascular Prosthetic Devices § 870.3650 Pacemaker polymeric mesh bag. (a) Identification. A pacemaker polymeric mesh bag is an implanted device used to hold a...

21 CFR 870.3650 - Pacemaker polymeric mesh bag.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Pacemaker polymeric mesh bag. 870.3650 Section 870...) MEDICAL DEVICES CARDIOVASCULAR DEVICES Cardiovascular Prosthetic Devices § 870.3650 Pacemaker polymeric mesh bag. (a) Identification. A pacemaker polymeric mesh bag is an implanted device used to hold a...

21 CFR 870.3650 - Pacemaker polymeric mesh bag.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Pacemaker polymeric mesh bag. 870.3650 Section 870...) MEDICAL DEVICES CARDIOVASCULAR DEVICES Cardiovascular Prosthetic Devices § 870.3650 Pacemaker polymeric mesh bag. (a) Identification. A pacemaker polymeric mesh bag is an implanted device used to hold a...

Air bags and children: a potentially lethal combination.

PubMed

McCaffrey, M; German, A; Lalonde, F; Letts, M

1999-01-01

Air bags have been responsible for saving thousands of lives since their introduction in the early 1970s. Now, however, it has become apparent that under certain conditions, these restraint mechanisms designed to save lives have in fact been the cause of needless morbidity and mortality in children. We reviewed 13 children injured by air bags in Canada, where seat belt wear is mandatory for all occupants. Although 12 of the children sustained relatively minor air bag trauma, one child was killed by the air bag deployment, sustaining an occipital-C1 dislocation. The pediatric population is at particular risk as many parents continue to unwittingly place their children in the front seat and thus in jeopardy of sustaining air bag-induced injuries should a collision occur.

Decision tree for accurate infection timing in individuals newly diagnosed with HIV-1 infection.

PubMed

Verhofstede, Chris; Fransen, Katrien; Van Den Heuvel, Annelies; Van Laethem, Kristel; Ruelle, Jean; Vancutsem, Ellen; Stoffels, Karolien; Van den Wijngaert, Sigi; Delforge, Marie-Luce; Vaira, Dolores; Hebberecht, Laura; Schauvliege, Marlies; Mortier, Virginie; Dauwe, Kenny; Callens, Steven

2017-11-29

There is today no gold standard method to accurately define the time passed since infection at HIV diagnosis. Infection timing and incidence measurement is however essential to better monitor the dynamics of local epidemics and the effect of prevention initiatives. Three methods for infection timing were evaluated using 237 serial samples from documented seroconversions and 566 cross sectional samples from newly diagnosed patients: identification of antibodies against the HIV p31 protein in INNO-LIA, SediaTM BED CEIA and SediaTM LAg-Avidity EIA. A multi-assay decision tree for infection timing was developed. Clear differences in recency window between BED CEIA, LAg-Avidity EIA and p31 antibody presence were observed with a switch from recent to long term infection a median of 169.5, 108.0 and 64.5 days after collection of the pre-seroconversion sample respectively. BED showed high reliability for identification of long term infections while LAg-Avidity is highly accurate for identification of recent infections. Using BED as initial assay to identify the long term infections and LAg-Avidity as a confirmatory assay for those classified as recent infection by BED, explores the strengths of both while reduces the workload. The short recency window of p31 antibodies allows to discriminate very early from early infections based on this marker. BED recent infection results not confirmed by LAg-Avidity are considered to reflect a period more distant from the infection time. False recency predictions in this group can be minimized by elimination of patients with a CD4 count of less than 100 cells/mm3 or without no p31 antibodies. For 566 cross sectional sample the outcome of the decision tree confirmed the infection timing based on the results of all 3 markers but reduced the overall cost from 13.2 USD to 5.2 USD per sample. A step-wise multi assay decision tree allows accurate timing of the HIV infection at diagnosis at affordable effort and cost and can be an important

BAG3: a multifaceted protein that regulates major cell pathways

PubMed Central

Rosati, A; Graziano, V; De Laurenzi, V; Pascale, M; Turco, M C

2011-01-01

Bcl2-associated athanogene 3 (BAG3) protein is a member of BAG family of co-chaperones that interacts with the ATPase domain of the heat shock protein (Hsp) 70 through BAG domain (110–124 amino acids). BAG3 is the only member of the family to be induced by stressful stimuli, mainly through the activity of heat shock factor 1 on bag3 gene promoter. In addition to the BAG domain, BAG3 contains also a WW domain and a proline-rich (PXXP) repeat, that mediate binding to partners different from Hsp70. These multifaceted interactions underlie BAG3 ability to modulate major biological processes, that is, apoptosis, development, cytoskeleton organization and autophagy, thereby mediating cell adaptive responses to stressful stimuli. In normal cells, BAG3 is constitutively present in a very few cell types, including cardiomyocytes and skeletal muscle cells, in which the protein appears to contribute to cell resistance to mechanical stress. A growing body of evidence indicate that BAG3 is instead expressed in several tumor types. In different tumor contexts, BAG3 protein was reported to sustain cell survival, resistance to therapy, and/or motility and metastatization. In some tumor types, down-modulation of BAG3 levels was shown, as a proof-of-principle, to inhibit neoplastic cell growth in animal models. This review attempts to outline the emerging mechanisms that can underlie some of the biological activities of the protein, focusing on implications in tumor progression. PMID:21472004

Eyelid bags

PubMed Central

2010-01-01

Eyelid bags are considered a sign of ageing, but they often appear prematurely due to the variety of causes that favor them. This brief report describes the case of a patient who was referred to us for the correction of a second degree bilateral palpebral ptosis that the patient had suffered from for several years and that in recent months had worsened to the point of interfering with vision and who, aside from modest eyelid bags, presented a massive protrusion of "preocular" fatty tissue. Despite the indication of classic blepharoplasty through a lower lid incision and, therefore, the possibility of removing excess skin, the patient opted instead only for the removal of the bulging fat. The patient's postoperative results were normal and the patient was extremely satisfied with both the correction of the ptosis and the "rejuvenating" effect of removing the protruding orbital fat in the eyelid. PMID:20565888

What Satisfies Students? Mining Student-Opinion Data with Regression and Decision-Tree Analysis. AIR 2002 Forum Paper.

ERIC Educational Resources Information Center

Thomas, Emily H.; Galambos, Nora

To investigate how students' characteristics and experiences affect satisfaction, this study used regression and decision-tree analysis with the CHAID algorithm to analyze student opinion data from a sample of 1,783 college students. A data-mining approach identifies the specific aspects of students' university experience that most influence three…

Generation of 2D Land Cover Maps for Urban Areas Using Decision Tree Classification

NASA Astrophysics Data System (ADS)

Höhle, J.

2014-09-01

A 2D land cover map can automatically and efficiently be generated from high-resolution multispectral aerial images. First, a digital surface model is produced and each cell of the elevation model is then supplemented with attributes. A decision tree classification is applied to extract map objects like buildings, roads, grassland, trees, hedges, and walls from such an "intelligent" point cloud. The decision tree is derived from training areas which borders are digitized on top of a false-colour orthoimage. The produced 2D land cover map with six classes is then subsequently refined by using image analysis techniques. The proposed methodology is described step by step. The classification, assessment, and refinement is carried out by the open source software "R"; the generation of the dense and accurate digital surface model by the "Match-T DSM" program of the Trimble Company. A practical example of a 2D land cover map generation is carried out. Images of a multispectral medium-format aerial camera covering an urban area in Switzerland are used. The assessment of the produced land cover map is based on class-wise stratified sampling where reference values of samples are determined by means of stereo-observations of false-colour stereopairs. The stratified statistical assessment of the produced land cover map with six classes and based on 91 points per class reveals a high thematic accuracy for classes "building" (99 %, 95 % CI: 95 %-100 %) and "road and parking lot" (90 %, 95 % CI: 83 %-95 %). Some other accuracy measures (overall accuracy, kappa value) and their 95 % confidence intervals are derived as well. The proposed methodology has a high potential for automation and fast processing and may be applied to other scenes and sensors.

Two cases of death due to plastic bag suffocation.

PubMed

Nadesan, K; Beng, O B

2001-01-01

Deaths due to plastic bag suffocation or plastic bag asphyxia are not reported in Malaysia. In the West many suicides by plastic bag asphyxia, particularly in the elderly and those who are chronically and terminally ill, have been reported. Accidental deaths too are not uncommon in the West, both among small children who play with shopping bags and adolescents who are solvent abusers. Another well-known but not so common form of accidental death from plastic bag asphyxia is sexual asphyxia, which is mostly seen among adult males. Homicide by plastic bag asphyxia too is reported in the West and the victims are invariably infants or adults who are frail or terminally ill and who cannot struggle. Two deaths due to plastic bag asphyxia are presented. Both the autopsies were performed at the University Hospital Mortuary, Kuala Lumpur. Both victims were 50-year old married Chinese males. One death was diagnosed as suicide and the other as sexual asphyxia. Sexual asphyxia is generally believed to be a problem associated exclusively with the West. Specific autopsy findings are often absent in deaths due to plastic bag asphyxia and therefore such deaths could be missed when some interested parties have altered the scene and most importantly have removed the plastic bag. A visit to the scene of death is invariably useful.

Lessons learned from Applications of a Decision Tree for Confronting Climate Change Uncertainty - the Short Term and the Long Term

NASA Astrophysics Data System (ADS)

Ray, P. A.; Wi, S.; Bonzanigo, L.; Taner, M. U.; Rodriguez, D.; Garcia, L.; Brown, C.

2016-12-01

The Decision Tree for Confronting Climate Change Uncertainty is a hierarchical, staged framework for accomplishing climate change risk management in water resources system investments. Since its development for the World Bank Water Group two years ago, the framework has been applied to pilot demonstration projects in Nepal (hydropower generation), Mexico (water supply), Kenya (multipurpose reservoir operation), and Indonesia (flood risks to dam infrastructure). An important finding of the Decision Tree demonstration projects has been the need to present the risks/opportunities of climate change to stakeholders and investors in proportion to risks/opportunities and hazards of other kinds. This presentation will provide an overview of tools and techniques used to quantify risks/opportunities to each of the project types listed above, with special attention to those found most useful for exploration of the risk space. Careful exploration of the risk/opportunity space shows that some interventions would be better taken now, whereas risks/opportunities of other types would be better instituted incrementally in order to maintain reversibility and flexibility. A number of factors contribute to the robustness/flexibility tradeoff: available capital, magnitude and imminence of potential risk/opportunity, modular (or not) character of investment, and risk aversion of the decision maker, among others. Finally, in each case, nuance was required in the translation of Decision Tree findings into actionable policy recommendations. Though the narrative of stakeholder solicitation, engagement, and ultimate partnership is unique to each case, summary lessons are available from the portfolio that can serve as a guideline to the community of climate change risk managers.

Better Physician's 'Black Bags'

NASA Technical Reports Server (NTRS)

1976-01-01

The "black bag" is outgrowth of astronaut monitoring technology from NASA's Johnson Space Center. Technically known as the portable medical status system, a highly advanced physician's "black bag" weighs less than 30 pounds, yet contains equipment for monitoring and recording vital signs, electrocardiograms, and electroencephalograms. Liquid crystal displays are used to present 15 digits of data simultaneously for long periods of time without excessive use of battery power. Single printed circuit card contains all circuitry required to measure and display vital signs such as heart and respiration rate, temperature, and blood pressure.

Non-stationary Bias Correction of Monthly CMIP5 Temperature Projections over China using a Residual-based Bagging Tree Model

NASA Astrophysics Data System (ADS)

Yang, T.; Lee, C.

2017-12-01

The biases in the Global Circulation Models (GCMs) are crucial for understanding future climate changes. Currently, most bias correction methodologies suffer from the assumption that model bias is stationary. This paper provides a non-stationary bias correction model, termed Residual-based Bagging Tree (RBT) model, to reduce simulation biases and to quantify the contributions of single models. Specifically, the proposed model estimates the residuals between individual models and observations, and takes the differences between observations and the ensemble mean into consideration during the model training process. A case study is conducted for 10 major river basins in Mainland China during different seasons. Results show that the proposed model is capable of providing accurate and stable predictions while including the non-stationarities into the modeling framework. Significant reductions in both bias and root mean squared error are achieved with the proposed RBT model, especially for the central and western parts of China. The proposed RBT model has consistently better performance in reducing biases when compared to the raw ensemble mean, the ensemble mean with simple additive bias correction, and the single best model for different seasons. Furthermore, the contribution of each single GCM in reducing the overall bias is quantified. The single model importance varies between 3.1% and 7.2%. For different future scenarios (RCP 2.6, RCP 4.5, and RCP 8.5), the results from RBT model suggest temperature increases of 1.44 ºC, 2.59 ºC, and 4.71 ºC by the end of the century, respectively, when compared to the average temperature during 1970 - 1999.

BAG3 induces the sequestration of proteasomal clients into cytoplasmic puncta

PubMed Central

Minoia, Melania; Boncoraglio, Alessandra; Vinet, Jonathan; Morelli, Federica F; Brunsting, Jeanette F; Poletti, Angelo; Krom, Sabine; Reits, Eric; Kampinga, Harm H; Carra, Serena

2014-01-01

Eukaryotic cells use autophagy and the ubiquitin–proteasome system as their major protein degradation pathways. Upon proteasomal impairment, cells switch to autophagy to ensure proper clearance of clients (the proteasome-to-autophagy switch). The HSPA8 and HSPA1A cochaperone BAG3 has been suggested to be involved in this switch. However, at present it is still unknown whether and to what extent BAG3 can indeed reroute proteasomal clients to the autophagosomal pathway. Here, we show that BAG3 induces the sequestration of ubiquitinated clients into cytoplasmic puncta colabeled with canonical autophagy linkers and markers. Following proteasome inhibition, BAG3 upregulation significantly contributes to the compensatory activation of autophagy and to the degradation of the (poly)ubiquitinated proteins. BAG3 binding to the ubiquitinated clients occurs through the BAG domain, in competition with BAG1, another BAG family member, that normally directs ubiquitinated clients to the proteasome. Therefore, we propose that following proteasome impairment, increasing the BAG3/BAG1 ratio ensures the “BAG-instructed proteasomal to autophagosomal switch and sorting” (BIPASS). PMID:25046115

Air bags alone compared with the combination of mechanical restraints and air bags: implications for the emergency evaluation of crash victims.

PubMed

Sutyak, J P; Passi, V; Hammond, J S

1997-09-01

Air bags (ABs) may be perceived by the public and physicians as protection for thoracoabdominal injuries. This study compares injury patterns when air bags are used alone with injury patterns when air bags plus mechanical restraints (MRs) are used. Patients treated over a 4-year period with emergency medical services-documented AB deployment alone (n = 16) or AB plus MR (n = 22) were identified by trauma registry query. Medical records were reviewed and injuries recorded. Air bag-alone users had more severe overall (injury severity score > or = 15:9 vs 5), chest (abbreviated injury score [AIS] > or = 3:5 vs 1), and abdominal injuries (AIS > or = 3:6 vs 0). They required more tube thoracostomies (5 vs 0) and laparotomies (6 vs 0), longer hospitalizations (11.9 +/- 3.2 vs 5.3 +/- 1.4 days), and more intensive care unit admissions (8 vs 1). Craniofacial injuries (AIS > or = 3:6 vs 6) and fractures were similar. More victims using air bags alone required impatient rehabilitation and some patients died (6 vs 1). Crash victims using air bags alone (vs AB plus MR) had increased injury severity, hospitalizations, thoracoabdominal procedures, and rehabilitation. Physicians must be aware of the incomplete protection by air bags alone.

A Hybrid Approach of Stepwise Regression, Logistic Regression, Support Vector Machine, and Decision Tree for Forecasting Fraudulent Financial Statements

PubMed Central

Goo, Yeong-Jia James; Shen, Zone-De

2014-01-01

As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%. PMID:25302338

Stability of Fentanyl Citrate in Polyolefin Bags.

PubMed

Donnelly, Ronald F

2016-01-01

Fentanyl is used to manage pain because it is a potent lipophilic opiate agonist. The stability of fentanyl in polyolefin bags when diluted to either 10 µg/mL or 50 µg/mL with sodium chloride 0.9% has not been studied. The chemical stability of fentanyl 50 µg/mL packaged in polyvinyl chloride bags has been studied, however, the stability in polyolefin bags is lacking. Polyolefin bags were aseptically filled with either 10-µg/mL or 50-µg/mL fentanyl solution. Containers were then stored at either 5°C and protected from light or 22°C and exposed to light for 93 days. Fentanyl peaks were monitored using a stability-indicatin high-performance liquid chromatographic method. Changes to color, clarity, and pH were also monitored. There were no signs of chemical degradation of fentanyl packaged in polyolefin bags at either 5°C or 22°C after storage for 93 days. Over the course of the study, all solutions remained colorless and clear. The pH showed a slight decrease during the 93 days of storage. The stability of both undiluted (50-µg/mL) and diluted (10-µg/mL) fentanyl solutions when packaged in polyolefin bags was 93 days when stored at either 5°C or 22°C. Copyright© by International Journal of Pharmaceutical Compounding, Inc.

Solid and Liquid Waste Drying Bag

NASA Technical Reports Server (NTRS)

Litwiller, Eric (Inventor); Hogan, John A. (Inventor); Fisher, John W. (Inventor)

2009-01-01

Method and system for processing waste from human activities, including solids, liquids and vapors. A fluid-impermeable bag, lined with a liquid-impermeable but vapor-permeable membrane, defining an inner bag, is provided. A vacuum force is provided to extract vapors so that the waste is moved toward a selected region in the inner bag, extracted vapors, including the waste vapors and vaporized portions of the waste liquids are transported across the membrane, and most or all of the solids remain within the liner. Extracted vapors are filtered, and sanitized components thereof are isolated and optionally stored. The solids remaining within the liner are optionally dried and isolated for ultimate disposal.

Factors controlling bark decomposition and its role in wood decomposition in five tropical tree species

PubMed Central

Dossa, Gbadamassi G. O.; Paudel, Ekananda; Cao, Kunfang; Schaefer, Douglas; Harrison, Rhett D.

2016-01-01

Organic matter decomposition represents a vital ecosystem process by which nutrients are made available for plant uptake and is a major flux in the global carbon cycle. Previous studies have investigated decomposition of different plant parts, but few considered bark decomposition or its role in decomposition of wood. However, bark can comprise a large fraction of tree biomass. We used a common litter-bed approach to investigate factors affecting bark decomposition and its role in wood decomposition for five tree species in a secondary seasonal tropical rain forest in SW China. For bark, we implemented a litter bag experiment over 12 mo, using different mesh sizes to investigate effects of litter meso- and macro-fauna. For wood, we compared the decomposition of branches with and without bark over 24 mo. Bark in coarse mesh bags decomposed 1.11–1.76 times faster than bark in fine mesh bags. For wood decomposition, responses to bark removal were species dependent. Three species with slow wood decomposition rates showed significant negative effects of bark-removal, but there was no significant effect in the other two species. Future research should also separately examine bark and wood decomposition, and consider bark-removal experiments to better understand roles of bark in wood decomposition. PMID:27698461

Factors controlling bark decomposition and its role in wood decomposition in five tropical tree species.

PubMed

Dossa, Gbadamassi G O; Paudel, Ekananda; Cao, Kunfang; Schaefer, Douglas; Harrison, Rhett D

2016-10-04

Organic matter decomposition represents a vital ecosystem process by which nutrients are made available for plant uptake and is a major flux in the global carbon cycle. Previous studies have investigated decomposition of different plant parts, but few considered bark decomposition or its role in decomposition of wood. However, bark can comprise a large fraction of tree biomass. We used a common litter-bed approach to investigate factors affecting bark decomposition and its role in wood decomposition for five tree species in a secondary seasonal tropical rain forest in SW China. For bark, we implemented a litter bag experiment over 12 mo, using different mesh sizes to investigate effects of litter meso- and macro-fauna. For wood, we compared the decomposition of branches with and without bark over 24 mo. Bark in coarse mesh bags decomposed 1.11-1.76 times faster than bark in fine mesh bags. For wood decomposition, responses to bark removal were species dependent. Three species with slow wood decomposition rates showed significant negative effects of bark-removal, but there was no significant effect in the other two species. Future research should also separately examine bark and wood decomposition, and consider bark-removal experiments to better understand roles of bark in wood decomposition.

BAG3 is involved in neuronal differentiation and migration.

PubMed

Santoro, Antonietta; Nicolin, Vanessa; Florenzano, Fulvio; Rosati, Alessandra; Capunzo, Mario; Nori, Stefania L

2017-05-01

Bcl2-associated athanogene 3 (BAG3) protein belongs to the family of co-chaperones interacting with several heat shock proteins. It plays a key role in protein quality control and mediates the clearance of misfolded proteins. Little is known about the expression and cellular localization of BAG3 during nervous system development and differentiation. Therefore, we analyze the subcellular distribution and expression of BAG3 in nerve-growth-factor-induced neurite outgrowth in PC12 cells and in developing and adult cortex of mouse brain. In differentiated PC12 cells, BAG3 was localized mainly in the neuritic domain rather than the cell body, whereas in control cells, it appeared to be confined to the cytoplasm near the nuclear membrane. Interestingly, the change of BAG3 localization during neuronal differentiation was associated only with a slight increase in total BAG3 expression. These data were coroborated by transmission electron microscopy showing that BAG3 was confined mainly within large dense-core vesicles of the axon in differentiated PC12 cells. In mouse developing cortex, BAG3 appeared to be intensely expressed in cellular processes of migrating cells, whereas in adult brain, a diffuse expression of low to medium intensity was detected in neuronal cell bodies. These findings suggest that BAG3 expression is required for neuronal differentiation and migration and that its role is linked to a change in its distribution pattern rather than to an increase in its protein expression levels.

A Decision-Tree-Oriented Guidance Mechanism for Conducting Nature Science Observation Activities in a Context-Aware Ubiquitous Learning

ERIC Educational Resources Information Center

Hwang, Gwo-Jen; Chu, Hui-Chun; Shih, Ju-Ling; Huang, Shu-Hsien; Tsai, Chin-Chung

2010-01-01

A context-aware ubiquitous learning environment is an authentic learning environment with personalized digital supports. While showing the potential of applying such a learning environment, researchers have also indicated the challenges of providing adaptive and dynamic support to individual students. In this paper, a decision-tree-oriented…

21 CFR 878.4100 - Organ bag.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Organ bag. 878.4100 Section 878.4100 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES GENERAL AND PLASTIC SURGERY DEVICES Surgical Devices § 878.4100 Organ bag. (a) Identification. An organ...

21 CFR 878.4100 - Organ bag.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Organ bag. 878.4100 Section 878.4100 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES GENERAL AND PLASTIC SURGERY DEVICES Surgical Devices § 878.4100 Organ bag. (a) Identification. An organ...

21 CFR 878.4100 - Organ bag.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Organ bag. 878.4100 Section 878.4100 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES GENERAL AND PLASTIC SURGERY DEVICES Surgical Devices § 878.4100 Organ bag. (a) Identification. An organ...

21 CFR 878.4100 - Organ bag.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Organ bag. 878.4100 Section 878.4100 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES GENERAL AND PLASTIC SURGERY DEVICES Surgical Devices § 878.4100 Organ bag. (a) Identification. An organ...

21 CFR 878.4100 - Organ bag.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Organ bag. 878.4100 Section 878.4100 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES GENERAL AND PLASTIC SURGERY DEVICES Surgical Devices § 878.4100 Organ bag. (a) Identification. An organ...

Boeing electronic flight bag

NASA Astrophysics Data System (ADS)

Trujillo, Eddie J.; Ellersick, Steven D.

2006-05-01

The Boeing Electronic Flight Bag (EFB) is a key element in the evolutionary process of an "e-enabled" flight deck. The EFB is designed to improve the overall safety, efficiency, and operation of the flight deck and corresponding airline operations by providing the flight crew with better information and enhanced functionality in a user-friendly digital format. The EFB is intended to increase the pilots' situational awareness of the airplane and systems, as well as improve the efficiency of information management. The system will replace documents and forms that are currently stored or carried onto the flight deck and put them, in digital format, at the crew's fingertips. This paper describes what the Boeing EFB is and the significant human factors and interface design issues, trade-offs, and decisions made during development of the display system. In addition, EFB formats, graphics, input control methods, challenges using COTS (commercial-off-the-shelf)-leveraged glass and formatting technology are discussed. The optical design requirements, display technology utilized, brightness control system, reflection challenge, and the resulting optical performance are presented.

Online Bagging and Boosting

NASA Technical Reports Server (NTRS)

Oza, Nikunji C.

2005-01-01

Bagging and boosting are two of the most well-known ensemble learning methods due to their theoretical performance guarantees and strong experimental results. However, these algorithms have been used mainly in batch mode, i.e., they require the entire training set to be available at once and, in some cases, require random access to the data. In this paper, we present online versions of bagging and boosting that require only one pass through the training data. We build on previously presented work by presenting some theoretical results. We also compare the online and batch algorithms experimentally in terms of accuracy and running time.

The cost-effectiveness of air bags by seating position.

PubMed

Graham, J D; Thompson, K M; Goldie, S J; Segui-Gomez, M; Weinstein, M C

1997-11-05

Motor vehicle crashes continue to cause significant mortality and morbidity in the United States. Installation of air bags in new passenger vehicles is a major initiative in the field of injury prevention. To assess the net health consequences and cost-effectiveness of driver's side and front passenger air bags from a societal perspective, taking into account the increased risk to children who occupy the front passenger seat and the diminished effectiveness for older adults. A deterministic state transition model tracked a hypothetical cohort of new vehicles over a 20-year period for 3 strategies: (1) installation of safety belts, (2) installation of driver's side air bags in addition to safety belts, and (3) installation of front passenger air bags in addition to safety belts and driver's side air bags. Changes in health outcomes, valued in terms of quality-adjusted life-years (QALYs) and costs (in 1993 dollars), were projected following the recommendations of the Panel on Cost-effectiveness in Health and Medicine. US population-based and convenience sample data were used. Incremental cost-effectiveness ratios. Safety belts are cost saving, even at 50% use. The addition of driver's side air bags to safety belts results in net health benefits at an incremental cost of $24000 per QALY saved. The further addition of front passenger air bags results in an incremental net benefit at a higher incremental cost of $61000 per QALY saved. Results were sensitive to the unit cost of air bag systems, their effectiveness, baseline fatality rates, the ratio of injuries to fatalities, and the real discount rate. Both air bag systems save life-years at costs that are comparable to many medical and public health practices. Immediate steps can be taken to enhance the cost-effectiveness of front passenger air bags, such as moving children to the rear seat.

Trees Are Terrific!

ERIC Educational Resources Information Center

Braus, Judy, Ed.

1992-01-01

Ranger Rick's NatureScope is a creative education series dedicated to inspiring in children an understanding and appreciation of the natural world while developing the skills they will need to make responsible decisions about the environment. Contents are organized into the following sections: (1) "What Makes a Tree a Tree?," including…

Fragmentation dynamics in the droplet bag breakup regime

NASA Astrophysics Data System (ADS)

Kulkarni, Varun; Sojka, Paul

2014-11-01

The closing stages of a droplet bag breakup event is marked by the appearance of several topological changes in the drop shape, followed by its fragmentation owing to hydrodynamics instabilities. In the present work we examine this breakup event, which occurs when a drop enters a continuous jet air stream. The deformed drop before eventual fragmentation is comprised of two main features: a bag and a bounding rim. Our investigation discusses the mechanism of rim/ bag breakup and the ensuing drop size distribution. The role of two possible instabilities, Plateau -Rayleigh and Rayleigh -Taylor, in rim breakup is examined and the dominant role of the Plateau -Rayleigh instability is revealed. In contrast, the Rayleigh -Taylor instability is seen to explain the disintegration of the bag well. The effects of viscosity and air jet velocity are also investigated. The formation of secondary features, such as nodes on the rim and holes on the bag, are also discussed. To conclude, a simple scaling argument based on the characteristic time scales of these instabilities is presented to explain the commonly observed early bursting of the bag, vis-à-vis the rim.

Industrial filter bags cleaned by high-frequency vibration: A concept

NASA Technical Reports Server (NTRS)

Kooy, A. V.

1973-01-01

System holds filter bag around fine-mesh metal screen and vibrates screen at its resonant frequency. This removes deposited byproducts and protects bag fibers from damaging forces. Because filter bags represent 20 to 40% of any industrial filtering investment, this method of extending bag life should be of interest to those responsible for plant maintenance.

50 CFR 622.437 - Bag limits.

Code of Federal Regulations, 2014 CFR

2014-10-01

..., DEPARTMENT OF COMMERCE FISHERIES OF THE CARIBBEAN, GULF OF MEXICO, AND SOUTH ATLANTIC Reef Fish Fishery of Puerto Rico and the U.S. Virgin Islands § 622.437 Bag limits. (a) Applicability. Section 622.11(a... fishing license issued by Puerto Rico or the U.S. Virgin Islands. (b) Bag limits. (1) Groupers, snappers...

Air bag-mediated fatal craniocervical trauma: a case report.

PubMed

Büyük, Yalçin; Uzün, Ibrahim; Erkol, Zerrin; Ağritmiş, Hasan; Ustündağ, Kasim T

2010-07-01

This case report describes a four-year-old girl (102 cm, 17 kg) who sustained fatal craniocerebral injuries as a result of an inflating automobile air bag. The car struck the lid of the sewer system, which was 15 cm above the ground level, at a low speed, and both the driver and passenger air bags inflated. Despite the fact that air bag usage has lessened both the possibility and severity of occupant injuries in frontal collisions, case reports of serious injuries and even deaths especially in children due to air bag deployment, particularly during low speed impacts, highlight the need for changes in both system design and possibly the threshold speed of air bag deployment.

49 CFR 178.518 - Standards for woven plastic bags.

Code of Federal Regulations, 2011 CFR

2011-10-01

... plastic bag; (2) 5H2 for a sift-proof woven plastic bag; and (3) 5H3 for a water-resistant woven plastic... other equally strong method of closure. (3) Bags, sift-proof, 5H2 must be made sift-proof by appropriate...

Modeling individual tree survial

Treesearch

Quang V. Cao

2016-01-01

Information provided by growth and yield models is the basis for forest managers to make decisions on how to manage their forests. Among different types of growth models, whole-stand models offer predictions at stand level, whereas individual-tree models give detailed information at tree level. The well-known logistic regression is commonly used to predict tree...

Ammonia Diffusion through Nalophan Double Bags: Effect of Concentration Gradient Reduction

PubMed Central

Capelli, Laura; Boiardi, Emanuela; Del Rosso, Renato

2014-01-01

The ammonia loss through Nalophan bags has been studied. The losses observed for storage conditions and times as allowed by the reference standard for dynamic olfactometry (EN 13725:2003) indicate that odour concentration values due to the presence of small molecules may be significantly underestimated if samples are not analysed immediately after sampling. A diffusion model was used in order to study diffusion through the bag. The study discusses the effect of concentration gradient (ΔC) across the polymeric membrane of the analyte. The ΔC was controlled by using a setup bag called “double bags.” Experimental data show a reduction of ammonia percentage losses due to the effect of the external multibarrier. The expedient of the double bag loaded with the same gas mixture allows a reduced diffusion of ammonia into the inner bag. Comparing the inner bag losses with those of the single bag stored in the same conditions (T, P, u) and with equal geometrical characteristics (S/V, z), it was observed that the inner bag of the double bag displays a 16% loss while the single bag displays a 37% loss. Acting on the ΔC it is possible to achieve a gross reduction of 57% in the ammonia leakage due to diffusion. PMID:25506608

A New Decision Tree to Solve the Puzzle of Alzheimer's Disease Pathogenesis Through Standard Diagnosis Scoring System.

PubMed

Kumar, Ashwani; Singh, Tiratha Raj

2017-03-01

Alzheimer's disease (AD) is a progressive, incurable and terminal neurodegenerative disorder of the brain and is associated with mutations in amyloid precursor protein, presenilin 1, presenilin 2 or apolipoprotein E, but its underlying mechanisms are still not fully understood. Healthcare sector is generating a large amount of information corresponding to diagnosis, disease identification and treatment of an individual. Mining knowledge and providing scientific decision-making for the diagnosis and treatment of disease from the clinical dataset are therefore increasingly becoming necessary. The current study deals with the construction of classifiers that can be human readable as well as robust in performance for gene dataset of AD using a decision tree. Models of classification for different AD genes were generated according to Mini-Mental State Examination scores and all other vital parameters to achieve the identification of the expression level of different proteins of disorder that may possibly determine the involvement of genes in various AD pathogenesis pathways. The effectiveness of decision tree in AD diagnosis is determined by information gain with confidence value (0.96), specificity (92 %), sensitivity (98 %) and accuracy (77 %). Besides this functional gene classification using different parameters and enrichment analysis, our finding indicates that the measures of all the gene assess in single cohorts are sufficient to diagnose AD and will help in the prediction of important parameters for other relevant assessments.

BAG3 affects the nucleocytoplasmic shuttling of HSF1 upon heat stress

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Young-Hee; Ahn, Sang-Gun; Kim, Soo-A., E-mail: ksooa@dongguk.ac.kr

2015-08-21

Bcl2-associated athoanogene (BAG) 3 is a member of the co-chaperone BAG family. It is induced by stressful stimuli such as heat shock and heavy metals, and it regulates cellular adaptive responses against stressful conditions. In this study, we identified a novel role for BAG3 in regulating the nuclear shuttling of HSF1 during heat stress. The expression level of BAG3 was induced by heat stress in HeLa cells. Interestingly, BAG3 rapidly translocalized to the nucleus upon heat stress. Immunoprecipitation assay showed that BAG3 interacts with HSF1 under normal and stressed conditions and co-translocalizes to the nucleus upon heat stress. We alsomore » demonstrated that BAG3 interacts with HSF1 via its BAG domain. Over-expression of BAG3 down-regulates the level of nuclear HSF1 by exporting it to the cytoplasm during the recovery period. Depletion of BAG3 using siRNA results in reduced nuclear HSF1 and decreased Hsp70 promoter activity. BAG3 in MEF(hsf1{sup −/−}) cells actively translocalizes to the nucleus upon heat stress suggesting that BAG3 plays a key role in the processing of the nucleocytoplasmic shuttling of HSF1 upon heat stress. - Highlights: • The expression level of BAG3 is induced by heat stress. • BAG3 translocates to the nucleus upon heat stress. • BAG3 interacts with HSF1 and co-localizes to the nucleus. • BAG3 is a key regulator for HSF1 nuclear shuttling.« less

Blood oxygen level dependent magnetic resonance imaging for detecting pathological patterns in lupus nephritis patients: a preliminary study using a decision tree model.

PubMed

Shi, Huilan; Jia, Junya; Li, Dong; Wei, Li; Shang, Wenya; Zheng, Zhenfeng

2018-02-09

Precise renal histopathological diagnosis will guide therapy strategy in patients with lupus nephritis. Blood oxygen level dependent (BOLD) magnetic resonance imaging (MRI) has been applicable noninvasive technique in renal disease. This current study was performed to explore whether BOLD MRI could contribute to diagnose renal pathological pattern. Adult patients with lupus nephritis renal pathological diagnosis were recruited for this study. Renal biopsy tissues were assessed based on the lupus nephritis ISN/RPS 2003 classification. The Blood oxygen level dependent magnetic resonance imaging (BOLD-MRI) was used to obtain functional magnetic resonance parameter, R2* values. Several functions of R2* values were calculated and used to construct algorithmic models for renal pathological patterns. In addition, the algorithmic models were compared as to their diagnostic capability. Both Histopathology and BOLD MRI were used to examine a total of twelve patients. Renal pathological patterns included five classes III (including 3 as class III + V) and seven classes IV (including 4 as class IV + V). Three algorithmic models, including decision tree, line discriminant, and logistic regression, were constructed to distinguish the renal pathological pattern of class III and class IV. The sensitivity of the decision tree model was better than that of the line discriminant model (71.87% vs 59.48%, P < 0.001) and inferior to that of the Logistic regression model (71.87% vs 78.71%, P < 0.001). The specificity of decision tree model was equivalent to that of the line discriminant model (63.87% vs 63.73%, P = 0.939) and higher than that of the logistic regression model (63.87% vs 38.0%, P < 0.001). The Area under the ROC curve (AUROCC) of the decision tree model was greater than that of the line discriminant model (0.765 vs 0.629, P < 0.001) and logistic regression model (0.765 vs 0.662, P < 0.001). BOLD MRI is a useful non-invasive imaging technique

Interacting with mobile devices by fusion eye and hand gestures recognition systems based on decision tree approach

NASA Astrophysics Data System (ADS)

Elleuch, Hanene; Wali, Ali; Samet, Anis; Alimi, Adel M.

2017-03-01

Two systems of eyes and hand gestures recognition are used to control mobile devices. Based on a real-time video streaming captured from the device's camera, the first system recognizes the motion of user's eyes and the second one detects the static hand gestures. To avoid any confusion between natural and intentional movements we developed a system to fuse the decision coming from eyes and hands gesture recognition systems. The phase of fusion was based on decision tree approach. We conducted a study on 5 volunteers and the results that our system is robust and competitive.

BAG3 controls angiogenesis through regulation of ERK phosphorylation.

PubMed

Falco, A; Festa, M; Basile, A; Rosati, A; Pascale, M; Florenzano, F; Nori, S L; Nicolin, V; Di Benedetto, M; Vecchione, M L; Arra, C; Barbieri, A; De Laurenzi, V; Turco, M C

2012-12-13

BAG3 is a co-chaperone of the heat shock protein (Hsp) 70, is expressed in many cell types upon cell stress, however, its expression is constitutive in many tumours. We and others have previously shown that in neoplastic cells BAG3 exerts an anti-apoptotic function thus favoring tumour progression. As a consequence we have proposed BAG3 as a target of antineoplastic therapies. Here we identify a novel role for BAG3 in regulation of neo-angiogenesis and show that its downregulation results in reduced angiogenesis therefore expanding the role of BAG3 as a therapeutical target. In brief we show that BAG3 is expressed in endothelial cells and is essential for the interaction between ERK and its phosphatase DUSP6, as a consequence its removal results in reduced binding of DUSP6 to ERK and sustained ERK phosphorylation that in turn determines increased levels of p21 and p15 and cell-cycle arrest in the G1 phase.

Prediction of Weather Impacted Airport Capacity using Ensemble Learning

NASA Technical Reports Server (NTRS)

Wang, Yao Xun

2011-01-01

Ensemble learning with the Bagging Decision Tree (BDT) model was used to assess the impact of weather on airport capacities at selected high-demand airports in the United States. The ensemble bagging decision tree models were developed and validated using the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) data and weather forecast at these airports. The study examines the performance of BDT, along with traditional single Support Vector Machines (SVM), for airport runway configuration selection and airport arrival rates (AAR) prediction during weather impacts. Testing of these models was accomplished using observed weather, weather forecast, and airport operation information at the chosen airports. The experimental results show that ensemble methods are more accurate than a single SVM classifier. The airport capacity ensemble method presented here can be used as a decision support model that supports air traffic flow management to meet the weather impacted airport capacity in order to reduce costs and increase safety.

[Effects of bagging on the microenvironment, yield and quality of overwintering tomato].

PubMed

Wang, Lei; Xu, Kun; Bei, Feng; Gao, Fang-Sheng

2007-04-01

Taking overwintering tomato variety "L402" as test material, this paper studied the effects of bagging with different texture bags on its fruit enlarging and quality, and the microenvironment within the bags. The results showed that bagging could change the microenvironment of fruit development, promote fruit maturing in advance, and improve fruit mass and quality. The light intensity and humidity in parchment bag were at the intermediate of those in plastic and no-spinning cloth bags, and the temperature was the highest among all treatments, except that it was lower than that in plastic bag at forenoon. The fruit enlarging rate in parchment bag was also the highest, and the single fruit mass increased by 15.34%, compared with the control. However, the fruit soluble solid, soluble sugar, and vitamine C contents and the sugar/acid ratio in all bagging treatments were lower than those of the control, suggesting that bagging debased the nutritive quality and the tastiness of tomato fruit. Due to the fact that bagging prevented the direct contact of fruit with pesticides, the fruit chlorpyrifos and betacypermethrin contents in parchment and plastic bags were lower than the control, except that the chlorpyrifos content in no-spinning cloth bag was higher.

Impact of storage environment on the efficacy of hermetic storage bags.

PubMed

Lane, Brett; Woloshuk, Charles

2017-05-01

Small hermetic bags (50 and 100 kg capacities) used by smallholder farmers in several African countries have proven to be a low-cost solution for preventing storage losses due to insects. The complexity of postharvest practices and the need for ideal drying conditions, especially in the Sub-Sahara, has led to questions about the efficacy of the hermetic bags for controlling spoilage by fungi and the potential for mycotoxin accumulation. This study compared the effects of environmental temperature and relative humidity at two locations (Indiana and Arkansas) on dry maize (14% moisture content) in woven polypropylene bags and Purdue Improved Crop Storage (PICS) hermetic bags. Temperature and relative humidity data loggers placed in the middle of each bag provided profiles of environmental influences on stored grain at the two locations. The results indicated that the PICS bags prevented moisture penetration over the three-month storage period. In contrast, maize in the woven bags increased in moisture content. For both bag types, no evidence was obtained indicating the spread of Aspergillus flavus from colonized maize to adjacent non-colonized maize. However, other storage fungi did increase during storage. The number of infected kernels did not increase in the PICS bags, but the numbers in the woven bags increased significantly. The warmer environment in Arkansas resulted in significantly higher insect populations in the woven bags than in Indiana. Insects in the PICS bags remained low at both locations. This study demonstrates that the PICS hermetic bags are effective at blocking the effects of external humidity fluctuations as well as the spread of fungi to non-infected kernels.

Performance comparison of plastic shopping bags in modern and traditional retail

NASA Astrophysics Data System (ADS)

Radini, F. A.; Wulandari, R.; Nasiri, S. J. A.; Winarto, D. A.

2017-07-01

Followed by implementation of paid plastic bag policy in Indonesia’s modern and traditional retail, community question related to plastic shopping bag performance arise. But, there is limited information about it. Therefore, the assessment of the performance to compare between plastic shopping bags in modern retail and traditional retail should be interesting. The observation performance of plastic shopping bag were weight holding capacity, tear resistant and elongation. This performance were tested using Universal Testing Machine. Physical and physico-chemical properties also identified to determine factor affecting the performance of plastic shopping bag. The physical properties were analysed using visual and thickness gauge to see the colour and measure the thickness. The analysis of physico-chemical properties were carried out using DSC (Differential Scanning Calorimetry), TGA (Thermal Gravimetry Analysis), Furnace and FTIR (Fourier Transform Infra Red Spectroscopy) to identify the materials, also its melting and decomposition temperature. The result showed that the performance difference between modern retail plastic bag with traditional retail plastic bag appears only in the performance of elongation. The elongation of modern retail plastic bag is 121 - 413%, while traditional has 170 - 609%. According to physico-chemical test result, modern retail and traditional retail plastic bag contain polyethylene as main material and has melting temperature in the range of High Density Polyethylene (HDPE) melting temperature. However, modern retail plastic bag has 18.31 - 33.87% of inorganic filler percentage, whereas the traditional retail plastic bag has 0.35 - 9.85%. This inorganic filler percentage probably a contributing factor in the elongation performance difference between modern retail plastic bag with traditional retail plastic bag.

How have changes in air bag designs affected frontal crash mortality?

PubMed

Braver, Elisa R; Shardell, Michelle; Teoh, Eric R

2010-07-01

To determine whether front air bag changes have affected occupant protection, frontal crash mortality rates were compared among front outboard occupants in vehicles having certified-advanced air bags (latest generation of air bags) or sled-certified air bags with and without advanced features. Poisson marginal structural models were used to calculate standardized mortality rate ratios (MRRs) for front occupants per registered vehicle. Vehicle age-corrected mortality rates were lower for drivers of vehicles having sled-certified air bags with advanced features than for drivers having sled-certified air bags without advanced features (MRR = 0.88; 95% confidence interval [CI]: 0.81-0.95), including unbelted men and drivers younger than 60. The mortality rate was higher, though not statistically significant, for drivers having certified-advanced air bags compared with sled-certified air bags with advanced features (vehicle age-corrected MRR = 1.13; 95% CI: 0.97-1.32) and significantly higher for belted drivers (MRR = 1.21; 95% CI: 1.04-1.39). Advanced air bag features appeared protective for some occupants. However, increased mortality rates among belted drivers of vehicles having certified-advanced air bags relative to those having sled-certified air bags with advanced features suggest that further study is needed to identify any potential problems with requirements for certification. 2010 Elsevier Inc. All rights reserved.

Improved material-bagging device

DOEpatents

Wach, C.G.; Nelson, R.E.; Brak, S.B.

1982-01-19

A bagging device for transferring material; specifically contaminated material, from one chamber through an opening in a wall to a second chamber includes a cylindrical housing communicating with the opening and defining a passage between the chambers. A cylindrical cartridge is slidably received within the housing. The cartridge has a substantially rigid cylindrical sleeve to which is affixed a pliable tube. The pliable tube is positioned concentrically about the sleeve and has a pleated portion capable of unfolding from the sleeve and a closed end extending over a terminal end of the sleeve. Sealing means are interposed in sealed relationship between the cartridge and the housing. Material from one chamber is inserted into the cartridge secured in the housing and received in the closed end of the tube which unfolds into the other chamber enclosing the material therein. The tube may then be sealed behind the material and then severed to form a bag-like enclosure defined by the tube's closed terminal end and the new seal. The new seal then forms a terminal end for the unsevered portion of the pliable tube into which additional material may be placed and the bagging process repeated.

Rapid decision support tool based on novel ecosystem service variables for retrofitting of permeable pavement systems in the presence of trees.

PubMed

Scholz, Miklas; Uzomah, Vincent C

2013-08-01

The retrofitting of sustainable drainage systems (SuDS) such as permeable pavements is currently undertaken ad hoc using expert experience supported by minimal guidance based predominantly on hard engineering variables. There is a lack of practical decision support tools useful for a rapid assessment of the potential of ecosystem services when retrofitting permeable pavements in urban areas that either feature existing trees or should be planted with trees in the near future. Thus the aim of this paper is to develop an innovative rapid decision support tool based on novel ecosystem service variables for retrofitting of permeable pavement systems close to trees. This unique tool proposes the retrofitting of permeable pavements that obtained the highest ecosystem service score for a specific urban site enhanced by the presence of trees. This approach is based on a novel ecosystem service philosophy adapted to permeable pavements rather than on traditional engineering judgement associated with variables based on quick community and environment assessments. For an example case study area such as Greater Manchester, which was dominated by Sycamore and Common Lime, a comparison with the traditional approach of determining community and environment variables indicates that permeable pavements are generally a preferred SuDS option. Permeable pavements combined with urban trees received relatively high scores, because of their great potential impact in terms of water and air quality improvement, and flood control, respectively. The outcomes of this paper are likely to lead to more combined permeable pavement and tree systems in the urban landscape, which are beneficial for humans and the environment. Copyright © 2013 Elsevier B.V. All rights reserved.

Peepoo bag: self-sanitising single use biodegradable toilet.

PubMed

Vinnerås, Björn; Hedenkvist, Mikael; Nordin, Annika; Wilhelmson, Anders

2009-01-01

Unsafe water, sanitation and hygiene together with deficient nutritional status are major contributors to the global burden of disease. Safe collection, disposal and reuse of human excreta would enable the risk of transmission of diseases to be decreased and household food security to be increased in many regions. However, the majority of the 2.5 billion people lacking improved sanitation comprise poor people in societies with weak infrastructure. This study developed a low cost sanitation option requiring little investment and maintenance--a single use, self-sanitising, biodegradable toilet (Peepoo bag) and tested it for smell, degradability and hygiene aspects. It was found that no smell was detectable from a 25 microm thick bag filled with faeces during 24 h in a 10 m2 room at 30 degrees C. Bags that had been in contact with urea-treated faeces or urine for 2 months in air, compost or water at 24 or 37 degrees C showed little signs of degradation. Furthermore, pathogen inactivation modelling of the 4 g of urea present in the bag indicated that appropriate sanitation of faecal material collected is achieved in the bag within 2-4 weeks, after which the bag can be degraded and reused as fertiliser.

Cold temperature disinfestation of bagged flour

USDA-ARS?s Scientific Manuscript database

We conducted studies using a commercial freezer maintained at -17.8°C to determine the time needed to kill Tribolium castaneum eggs in a pallet of flour. Each bag weighed 22.7 kg, and there were 5 bags in each of 10 layers. The dimensions of the pallet were 109-cm wide by 132-cm long by 123-cm tall,...

49 CFR 178.519 - Standards for plastic film bags.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 2 2010-10-01 2010-10-01 false Standards for plastic film bags. 178.519 Section... PACKAGINGS Non-bulk Performance-Oriented Packaging Standards § 178.519 Standards for plastic film bags. (a) The identification code for a plastic film bag is 5H4. (b) Construction requirements for plastic film...

49 CFR 178.518 - Standards for woven plastic bags.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 2 2010-10-01 2010-10-01 false Standards for woven plastic bags. 178.518 Section... PACKAGINGS Non-bulk Performance-Oriented Packaging Standards § 178.518 Standards for woven plastic bags. (a) The following are identification codes for woven plastic bags: (1) 5H1 for an unlined or non-coated...

49 CFR 178.518 - Standards for woven plastic bags.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 49 Transportation 3 2013-10-01 2013-10-01 false Standards for woven plastic bags. 178.518 Section...-bulk Performance-Oriented Packaging Standards § 178.518 Standards for woven plastic bags. (a) The following are identification codes for woven plastic bags: (1) 5H1 for an unlined or non-coated woven...

49 CFR 178.518 - Standards for woven plastic bags.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 49 Transportation 3 2012-10-01 2012-10-01 false Standards for woven plastic bags. 178.518 Section...-bulk Performance-Oriented Packaging Standards § 178.518 Standards for woven plastic bags. (a) The following are identification codes for woven plastic bags: (1) 5H1 for an unlined or non-coated woven...

49 CFR 178.518 - Standards for woven plastic bags.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 3 2014-10-01 2014-10-01 false Standards for woven plastic bags. 178.518 Section...-bulk Performance-Oriented Packaging Standards § 178.518 Standards for woven plastic bags. (a) The following are identification codes for woven plastic bags: (1) 5H1 for an unlined or non-coated woven...

14 CFR 223.24 - Transportation of empty mail bags.

Code of Federal Regulations, 2014 CFR

2014-01-01

... Transportation of empty mail bags. Any carrier authorized to engage in foreign air transportation may transport in foreign air transportation empty air mail bags from any country to the country of origin of such... 14 Aeronautics and Space 4 2014-01-01 2014-01-01 false Transportation of empty mail bags. 223.24...

14 CFR 223.24 - Transportation of empty mail bags.

Code of Federal Regulations, 2013 CFR

2013-01-01

... Transportation of empty mail bags. Any carrier authorized to engage in foreign air transportation may transport in foreign air transportation empty air mail bags from any country to the country of origin of such... 14 Aeronautics and Space 4 2013-01-01 2013-01-01 false Transportation of empty mail bags. 223.24...

14 CFR 223.24 - Transportation of empty mail bags.

Code of Federal Regulations, 2012 CFR

2012-01-01

... Transportation of empty mail bags. Any carrier authorized to engage in foreign air transportation may transport in foreign air transportation empty air mail bags from any country to the country of origin of such... 14 Aeronautics and Space 4 2012-01-01 2012-01-01 false Transportation of empty mail bags. 223.24...

BAG3 affects the nucleocytoplasmic shuttling of HSF1 upon heat stress.

PubMed

Jin, Young-Hee; Ahn, Sang-Gun; Kim, Soo-A

2015-08-21

Bcl2-associated athoanogene (BAG) 3 is a member of the co-chaperone BAG family. It is induced by stressful stimuli such as heat shock and heavy metals, and it regulates cellular adaptive responses against stressful conditions. In this study, we identified a novel role for BAG3 in regulating the nuclear shuttling of HSF1 during heat stress. The expression level of BAG3 was induced by heat stress in HeLa cells. Interestingly, BAG3 rapidly translocalized to the nucleus upon heat stress. Immunoprecipitation assay showed that BAG3 interacts with HSF1 under normal and stressed conditions and co-translocalizes to the nucleus upon heat stress. We also demonstrated that BAG3 interacts with HSF1 via its BAG domain. Over-expression of BAG3 down-regulates the level of nuclear HSF1 by exporting it to the cytoplasm during the recovery period. Depletion of BAG3 using siRNA results in reduced nuclear HSF1 and decreased Hsp70 promoter activity. BAG3 in MEF(hsf1(-/-)) cells actively translocalizes to the nucleus upon heat stress suggesting that BAG3 plays a key role in the processing of the nucleocytoplasmic shuttling of HSF1 upon heat stress. Copyright © 2015 Elsevier Inc. All rights reserved.

Prediction of adverse drug reactions using decision tree modeling.

PubMed

Hammann, F; Gutmann, H; Vogt, N; Helma, C; Drewe, J

2010-07-01

Drug safety is of great importance to public health. The detrimental effects of drugs not only limit their application but also cause suffering in individual patients and evoke distrust of pharmacotherapy. For the purpose of identifying drugs that could be suspected of causing adverse reactions, we present a structure-activity relationship analysis of adverse drug reactions (ADRs) in the central nervous system (CNS), liver, and kidney, and also of allergic reactions, for a broad variety of drugs (n = 507) from the Swiss drug registry. Using decision tree induction, a machine learning method, we determined the chemical, physical, and structural properties of compounds that predispose them to causing ADRs. The models had high predictive accuracies (78.9-90.2%) for allergic, renal, CNS, and hepatic ADRs. We show the feasibility of predicting complex end-organ effects using simple models that involve no expensive computations and that can be used (i) in the selection of the compound during the drug discovery stage, (ii) to understand how drugs interact with the target organ systems, and (iii) for generating alerts in postmarketing drug surveillance and pharmacovigilance.

The Portuguese plastic carrier bag tax: The effects on consumers' behavior.

PubMed

Martinho, Graça; Balaia, Natacha; Pires, Ana

2017-03-01

Marine litter from lightweight plastic bags is a global problem that must be solved. A plastic bag tax was implemented in February 2015 to reduce the consumption of plastic grocery bags in Portugal and in turn reduce the potential contribution to marine litter. This study analyzes the effect of the plastic bag tax on consumer behavior to learn how it was received and determine the perceived effectiveness of the tax 4months after its implementation. In addition, the study assessed how proximity to coastal areas could influence behaviors and opinions. The results showed a 74% reduction of plastic bag consumption with a simultaneously 61% increase of reusable plastic bags after the tax was implemented. Because plastic bags were then reused for shopping instead of garbage bags, however, the consumption of garbage bags increased by 12%. Although reduction was achieved, the tax had no effect on the perception of marine litter or the impact of plastic bags on environment and health. The majority of respondents agree with the tax but view it as an extra revenue to the State. The distance to the coast had no meaningful influence on consumer behavior or on the perception of the tax. Although the tax was able to promote the reduction of plastics, the role of hypermarkets and supermarkets in providing alternatives through the distribution of reusable plastic bags was determinant to ensuring the reduction. Copyright © 2017 Elsevier Ltd. All rights reserved.

Effectiveness of the ZeroFly® storage bag fabric against stored-product insects

USDA-ARS?s Scientific Manuscript database

The ZeroFly® Storage Bag is a polypropylene bag (PP) which has deltamethrin incorporated in its fibers, and represents a novel approach to reducing stored-product insect pest-related postharvest losses. Fabric samples from ZeroFly bags, polypropylene (PP) bags, jute bags, malathion-treated PP bags, ...

Wave energy absorption by a floating air bag

NASA Astrophysics Data System (ADS)

Kurniawan, A.; Chaplin, J. R.; Greaves, D. M.; Hann, M.

2017-02-01

A floating air bag, ballasted in water, expands and contracts as it heaves under wave action. Connecting the bag to a secondary volume via a turbine transforms the bag into a device capable of generating useful energy from the waves. Small-scale measurements of the device reveal some interesting properties, which are successfully predicted numerically. Owing to its compressibility, the device can have a heave resonance period longer than that of a rigid device of the same shape and size, without any phase control. Furthermore, varying the amount of air in the bag is found to change its shape and hence its dynamic response, while varying the turbine damping or the air volume ratio changes the dynamic response without changing the shape.

Application of Decision Tree in the Prediction of Periventricular Leukomalacia (PVL) Occurrence in Neonates After Neonatal Heart Surgery

PubMed Central

Jalali, Ali; Licht, Daniel J.; Nataraj, C.

2013-01-01

This paper is concerned with the prediction of the occurrence of Periventricular Leukomalacia (PVL) that occurs in neonates after heart surgery. The data which is collected over a period of 12 hours after the cardiac surgery contains vital measurements as well as blood gas measurements with different resolutions. The decision tree classification technique has been selected as a tool for prediction of the PVL because of its capacity for discovering rules and novel associations in the data. Vital data measured using near-inferred spectroscopy (NIRS) at the sampling rate of 0.25 Hz and blood gas measurement up to 12 times with irregular time intervals for 35 patients collected from Children's Hospital of Philadelphia (CHOP) are used for this study. Vital data contain heart rate (HR), mean arterial pressure (MAP), right atrium pressure (RAP), blood hemoglobin (Hb), hemoglobin oxygen content (HbO2), oxygen saturation (SpO2) and relative cerebral blood flow (rCBF). Features derived from the data include statistical moments (mean, variance, skewness and kurtosis), trend and min and max of the vital data and rate of change, time weighted mean and a custom defined out of range index (ORI) for the blood gas data. A decision tree is developed for the vital data in order to identify the most important vital measurements. In addition, a decision tree is developed for blood gas data to find important factors for the prediction of PVL occurrence. Results show that in blood gas data, maximum rate of change in the concentration of bicarbonate ions in blood (HCO3) and minimum rate of change in the partial pressure of dissolved CO2 in the blood (PaCO2) are the most important factors for prediction of the PVL. Among vital features the kurtosis of HR and Hb are the most important parameters. PMID:23367279

[Ecological effects of bagging on actinidia fruits].

PubMed

Chen, Zhijie; Zhang, Shulian; Znang, Feng; Shi, Yongqiang

2003-11-01

Studies on the ecological effects of bagging on actinidia fruits showed that under different types of bagging, the ecological effects on actinidia fruits were different. Bagging with membrane changed the conditions of temperature and humidity. The inside temperature raised by 0.7-0.9 degree C, humidity increased by 10.8%-11.8%, single fruit weight increased by 25.7%-37.7%, commercial fruit rate raised by 20.4%-30.1%, percentage of diseases and pests decreased by 85.7%-90.2%, and storage property was good. Additionally, the application rate of chemical pesticide was reduce by 72.2%, and the concentration of remnant pesticide inside fruits was only 0.31% mg.kg-1, which was decreased by 90.0%. The chemical pesticide pollution in ecological circumstance and actinidia fruits both reduced. It would be a new way for the rgeen fruits production in the future. Bagging with paper has obvious negative effects.

BOREAS TE-4 Branch Bag Data From Boreal Tree Species

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Papagno, Andrea (Editor); Berry, Joseph A.; Fu, Wei; Fredeen, Art; Gamon, John

2000-01-01

The BOREAS TE-4 team collected continuous records of gas exchange under ambient conditions from intact boreal forest trees in the BOREAS NSA from 23-Jul-1996 until 14-Aug-1996. These measurements can be used to test models of photosynthesis, stomatal conductance, and leaf respiration, such as SiB2 (Sellers et al., 1996) or the leaf model (Collatz et al., 1991), and programs can be obtained from the investigators. The data are stored in tabular ASCII files. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Behaviour change in overweight and obese pregnancy: a decision tree to support the development of antenatal lifestyle interventions.

PubMed

Ainscough, Kate M; Lindsay, Karen L; O'Sullivan, Elizabeth J; Gibney, Eileen R; McAuliffe, Fionnuala M

2017-10-01

Antenatal healthy lifestyle interventions are frequently implemented in overweight and obese pregnancy, yet there is inconsistent reporting of the behaviour-change methods and behavioural outcomes. This limits our understanding of how and why such interventions were successful or not. The current paper discusses the application of behaviour-change theories and techniques within complex lifestyle interventions in overweight and obese pregnancy. The authors propose a decision tree to help guide researchers through intervention design, implementation and evaluation. The implications for adopting behaviour-change theories and techniques, and using appropriate guidance when constructing and evaluating interventions in research and clinical practice are also discussed. To enhance the evidence base for successful behaviour-change interventions during pregnancy, adoption of behaviour-change theories and techniques, and use of published guidelines when designing lifestyle interventions are necessary. The proposed decision tree may be a useful guide for researchers working to develop effective behaviour-change interventions in clinical settings. This guide directs researchers towards key literature sources that will be important in each stage of study development.

Decision trees to characterise the roles of permeability and solubility on the prediction of oral absorption.

PubMed

Newby, Danielle; Freitas, Alex A; Ghafourian, Taravat

2015-01-27

Oral absorption of compounds depends on many physiological, physiochemical and formulation factors. Two important properties that govern oral absorption are in vitro permeability and solubility, which are commonly used as indicators of human intestinal absorption. Despite this, the nature and exact characteristics of the relationship between these parameters are not well understood. In this study a large dataset of human intestinal absorption was collated along with in vitro permeability, aqueous solubility, melting point, and maximum dose for the same compounds. The dataset allowed a permeability threshold to be established objectively to predict high or low intestinal absorption. Using this permeability threshold, classification decision trees incorporating a solubility-related parameter such as experimental or predicted solubility, or the melting point based absorption potential (MPbAP), along with structural molecular descriptors were developed and validated to predict oral absorption class. The decision trees were able to determine the individual roles of permeability and solubility in oral absorption process. Poorly permeable compounds with high solubility show low intestinal absorption, whereas poorly water soluble compounds with high or low permeability may have high intestinal absorption provided that they have certain molecular characteristics such as a small polar surface or specific topology. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Inferences from growing trees backwards

Treesearch

David W. Green; Kent A. McDonald

1997-01-01

The objective of this paper is to illustrate how longitudinal stress wave techniques can be useful in tracking the future quality of a growing tree. Monitoring the quality of selected trees in a plantation forest could provide early input to decisions on the effectiveness of management practices, or future utilization options, for trees in a plantation. There will...

Predicting the disease of Alzheimer with SNP biomarkers and clinical data using data mining classification approach: decision tree.

PubMed

Erdoğan, Onur; Aydin Son, Yeşim

2014-01-01

Single Nucleotide Polymorphisms (SNPs) are the most common genomic variations where only a single nucleotide differs between individuals. Individual SNPs and SNP profiles associated with diseases can be utilized as biological markers. But there is a need to determine the SNP subsets and patients' clinical data which is informative for the diagnosis. Data mining approaches have the highest potential for extracting the knowledge from genomic datasets and selecting the representative SNPs as well as most effective and informative clinical features for the clinical diagnosis of the diseases. In this study, we have applied one of the widely used data mining classification methodology: "decision tree" for associating the SNP biomarkers and significant clinical data with the Alzheimer's disease (AD), which is the most common form of "dementia". Different tree construction parameters have been compared for the optimization, and the most accurate tree for predicting the AD is presented.

Mycorrhizal networks and distance from mature trees alter patterns of competition and facilitation in dry Douglas-fir forests.

PubMed

Teste, François P; Simard, Suzanne W

2008-11-01

The distribution of dry Douglas-fir forests in western North America is expected to shift northward with climate change and disappear from the grassland interface in the southern interior of British Columbia. This shift may be accentuated by clearcutting, a common harvesting practice that aims to reduce the competitive effects of residual mature trees on new regeneration, but in so doing, ignores their facilitative effects. In this study, we investigated the net effects of competition from and facilitation by mature trees retained on harvested sites on seedling establishment in the dry interface Douglas-fir forests. We demonstrate that access to a mycorrhizal network (MN) and proximity to trees have important influences on seedling performance. On six sites, we established trenched plots around 24 mature Pseudotsuga menziesii var. glauca (Douglas-fir) trees, then planted Douglas-fir seedlings into four mesh treatments that served to restrict MN access (i.e., planted into mesh bags with 0.5-, 35-, or 250-microm pores, or without mesh) or into impermeable bags (grown in isolation) at four distances (0.5, 1.0, 2.5, or 5.0 m). Seedling survival tended to be greater and water stress lower where seedlings had full access to the MN. Seedling height, shoot biomass, needle biomass, and nutrient uptake peaked at 2.5-5.0 m from mature trees. Seedlings 0.5 m from mature trees had lower CO2 assimilation rates and wood delta(13)C compared to seedlings 5.0 m away. Competition for soil resources was highest near mature trees but facilitation was relatively greater at further distances, resulting in a zone of net benefit for seedlings. These results show that intraspecific tree-seedling interactions are both competitive and facilitative in dry Douglas-fir forests, and that they are spatially dependent. After disturbance, maintaining residual mature trees may be important for their beneficial regeneration zones.

2'-Hydroxycinnamaldehyde induces apoptosis through HSF1-mediated BAG3 expression.

PubMed

Nguyen, Hai-Anh; Kim, Soo-A

2017-01-01

BAG3, a member of BAG co-chaperone family, is induced by stressful stimuli such as heat shock and heavy metals. Through interaction with various binding partners, BAG3 is thought to play a role in cellular adaptive responses against stressful conditions in normal and neoplastic cells. 2'-Hydroxycinnamaldehyde (HCA) is a natural derivative of cinnamaldehyde and has antitumor activity in various cancer cells. In the present study, for the first time, we identified that HCA induced BAG3 expression and BAG3-mediated apoptosis in cancer cells. The apoptotic cell death induced by HCA was demonstrated by caspase-7, -9 and PARP activation, and confirmed by Annexin V staining in both SW480 and SW620 colon cancer cells. Notably, both the mRNA and protein levels of BAG3 were largely induced by HCA in a dose- and time-dependent manner. By showing transcription factor HSF1 activation, we demonstrated that HCA induces the expression of BAG3 through HSF1 activation. More importantly, knockdown of BAG3 expression using siRNA largely inhibited HCA-induced apoptosis, suggesting that BAG3 is actively involved in HCA-induced cancer cell death. Considering the importance of the stress response mechanism in cancer progression, our results strongly suggest that BAG3 could be a potential target for anticancer therapy.

Multi-output decision trees for lesion segmentation in multiple sclerosis

NASA Astrophysics Data System (ADS)

Jog, Amod; Carass, Aaron; Pham, Dzung L.; Prince, Jerry L.

2015-03-01

Multiple Sclerosis (MS) is a disease of the central nervous system in which the protective myelin sheath of the neurons is damaged. MS leads to the formation of lesions, predominantly in the white matter of the brain and the spinal cord. The number and volume of lesions visible in magnetic resonance (MR) imaging (MRI) are important criteria for diagnosing and tracking the progression of MS. Locating and delineating lesions manually requires the tedious and expensive efforts of highly trained raters. In this paper, we propose an automated algorithm to segment lesions in MR images using multi-output decision trees. We evaluated our algorithm on the publicly available MICCAI 2008 MS Lesion Segmentation Challenge training dataset of 20 subjects, and showed improved results in comparison to state-of-the-art methods. We also evaluated our algorithm on an in-house dataset of 49 subjects with a true positive rate of 0.41 and a positive predictive value 0.36.

Performance tests of three types of air-sampling bags on organic solvent vapor retention.

PubMed

Fukui, Yoshinari; Kanemaru, Ai; Nagasawa, Yasuhiro; Kawakami, Takuya; Iwata, Toyoto; Murata, Katsuyuki; Ohashi, Fumiko; Ikeda, Masayuki

2013-01-01

Performance of two new air sampling bags [the transparent bag (TP bag) and the semi-transparent bag (ST bag)] was examined as possible surrogates for the traditional PVF bag (the Ref bag). Solvent vapor mixture of butyl acetate, chloroform, ethyl acetate, isopropyl alcohol and toluene at administrative control levels were introduced to each bag (n=5 for each of the three types), and the decay in the concentrations (by%) was followed by use of a gas auto-sampler - FID-GC system. A trend of time-dependent decay was noted for all types including the Ref bag. When the performance was compared, the TP bag was equal to or even better than the Ref bag. In contrast, the performance of the ST bag was comparable to that of the other two types of bags with regard to toluene and chloroform when the storage time was short, but poorer than others for the other three solvents throughout the test period. The TP bag may be a bag of choice when the storage time is extended (e.g., up to 48 h) although this bag is physically less robust and requires careful handling. The ST bag may be used when analysis will be completed within 24 h.

[Study on extraction method of Panax notoginseng plots in Wenshan of Yunnan province based on decision tree model].

PubMed

Shi, Ting-Ting; Zhang, Xiao-Bo; Guo, Lan-Ping; Huang, Lu-Qi

2017-11-01

The herbs used as the material for traditional Chinese medicine are always planted in the mountainous area where the natural environment is suitable. As the mountain terrain is complex and the distribution of planting plots is scattered, the traditional survey method is difficult to obtain accurate planting area. It is of great significance to provide decision support for the conservation and utilization of traditional Chinese medicine resources by studying the method of extraction of Chinese herbal medicine planting area based on remote sensing and realizing the dynamic monitoring and reserve estimation of Chinese herbal medicines. In this paper, taking the Panax notoginseng plots in Wenshan prefecture of Yunnan province as an example, the China-made GF-1multispectral remote sensing images with a 16 m×16 m resolution were obtained. Then, the time series that can reflect the difference of spectrum of P. notoginseng shed and the background objects were selected to the maximum extent, and the decision tree model of extraction the of P. notoginseng plots was constructed according to the spectral characteristics of the surface features. The results showed that the remote sensing classification method based on the decision tree model could extract P. notoginseng plots in the study area effectively. The method can provide technical support for extraction of P. notoginseng plots at county level. Copyright© by the Chinese Pharmaceutical Association.

Ash Tree Leaf Litter (Fraxinus excelsior L.) Breakdown in Two Different Biotopes and Streams

NASA Astrophysics Data System (ADS)

Fleituch, Tadeusz; Leichtfried, Maria

2004-11-01

Coarse (0.5 mm) and fine (0.1 mm) mesh size bag methodology was used for comparing the breakdown of ash tree leaves (Fraxinus excelsior L.) in two biotopes (dry - terrestrial and wet - overflown stream zones) in two low order streams (the Oberer Seebach (OSB), Lower Austria and the Brzezowka stream (BRZ), Beskidy Mountains, southern Poland). In total, 96 bags were exposed in autumn 2000. Ash-free dry mass (AFDM) ranged in dry zones of both streams from 94-62% (OSB) and 85-53% (BRZ) respectively. The decomposition process was faster in wet zones: 96-33% (OSB) and 56-11% (B) during the study period. Significant differences in ash breakdown and its chemical content between studied streams were found. Total organic carbon (TOC) and total nitrogen content (TN) of AFDM of litter showed increased differences with experiment duration between zones and between two bag types for both streams. The strongest increase of TOC and TN content (100% on average initial content) for bag types, zones, and streams was observed in the first two weeks of the experiment. These results confirm the importance of chemical compounds for microbiological processes (biofilms) in different ecosystem biotopes. (

Alternative hospital gift bags and breastfeeding exclusivity.

PubMed

Bai, Yeon; Wunderlich, Shahla M; Kashdan, Rickie

2013-01-01

The type of gift bags given to new mothers at the time of discharge from the hospital can influence their confidence in breastfeeding. Most hospitals in the US continue to distribute commercial gift bags containing formula samples despite the reported negative influence of commercial bags on the duration of breastfeeding. This study compared breastfeeding outcomes in women receiving three different kinds of gift bags at discharge. A prospective intervention study was conducted during 2009-2010 in New Jersey. Three breastfeeding cohorts were recruited and assigned to three groups: COMMERCIAL received discharge bags containing formula samples, BF-INFO received breastfeeding information and supplies, and PUMP received breastfeeding information/supplies plus a manual breast pump. Follow-up contacts were at 2, 4, and 12 postpartum weeks to determine breastfeeding outcome. The mean durations of exclusive (EBF) and partial breastfeeding were compared between groups using ANOVA. A total of 386 participants completed the study. The mean EBF duration (weeks) in the PUMP (n = 138, 8.28 ± 4.86) and BF-INFO (n = 121, 7.87 ± 4.63) were significantly longer (P < 0.01) than COMMERCIAL (n = 127, 6.12 ± 4.49). The rate of EBF through 12 weeks in PUMP was most consistent. The mean duration of partial breastfeeding showed similar results: significantly longer in PUMP and BF-INFO than COMMERCIAL (P < 0.01).

Comparison of two data mining techniques in labeling diagnosis to Iranian pharmacy claim dataset: artificial neural network (ANN) versus decision tree model.

PubMed

Rezaei-Darzi, Ehsan; Farzadfar, Farshad; Hashemi-Meshkini, Amir; Navidi, Iman; Mahmoudi, Mahmoud; Varmaghani, Mehdi; Mehdipour, Parinaz; Soudi Alamdari, Mahsa; Tayefi, Batool; Naderimagham, Shohreh; Soleymani, Fatemeh; Mesdaghinia, Alireza; Delavari, Alireza; Mohammad, Kazem

2014-12-01

This study aimed to evaluate and compare the prediction accuracy of two data mining techniques, including decision tree and neural network models in labeling diagnosis to gastrointestinal prescriptions in Iran. This study was conducted in three phases: data preparation, training phase, and testing phase. A sample from a database consisting of 23 million pharmacy insurance claim records, from 2004 to 2011 was used, in which a total of 330 prescriptions were assessed and used to train and test the models simultaneously. In the training phase, the selected prescriptions were assessed by both a physician and a pharmacist separately and assigned a diagnosis. To test the performance of each model, a k-fold stratified cross validation was conducted in addition to measuring their sensitivity and specificity. Generally, two methods had very similar accuracies. Considering the weighted average of true positive rate (sensitivity) and true negative rate (specificity), the decision tree had slightly higher accuracy in its ability for correct classification (83.3% and 96% versus 80.3% and 95.1%, respectively). However, when the weighted average of ROC area (AUC between each class and all other classes) was measured, the ANN displayed higher accuracies in predicting the diagnosis (93.8% compared with 90.6%). According to the result of this study, artificial neural network and decision tree model represent similar accuracy in labeling diagnosis to GI prescription.

An in vitro evaluation of the Anew Zephyr open-bag IOL in the prevention of posterior capsule opacification using a human capsular bag model.

PubMed

Eldred, Julie A; Spalton, David J; Wormstone, I Michael

2014-09-18

During cataract surgery an IOL is placed within the capsular bag. Clinical studies show that IOLs with a square edge profile and complete contact between the IOL and the anterior capsule (AC) are currently the best way to prevent posterior capsule opacification (PCO). This has been challenged by recent clinical and experimental observations, which suggest that if the capsular bag is kept open with separation of contact between the AC and posterior capsule (PC) by an "open-bag device" PCO is dramatically reduced. Therefore, the current study set out to evaluate the putative merits of an open-bag IOL (Anew Zephyr) in a human capsular bag model. An in vitro organ culture model using the bag-zonular-ciliary body complex isolated from fellow human donor eyes was prepared. A capsulorhexis and lens extraction were performed, and an Alcon Acrysof IOL or Anew Zephyr IOL implanted. Preparations were secured by pinning the ciliary body to a silicone ring and maintained in 6 mL Eagle's minimum essential medium (EMEM) or EMEM supplemented with 2% vol/vol human serum (HS) and 10 ng/mL TGF-β2 for 28 days. Cell growth and capsular modifications were monitored with phase-contrast and modified dark-field microscopy. In serum-free EMEM culture conditions, cells were observed growing onto the PC of preparations implanted with an Anew Zephyr IOL, but this was retarded relative to observations in match-paired capsular bags implanted with an Alcon Acrysof IOL. In the case of cultures maintained in 2% HS-EMEM plus TGF-β2, the movement on to the PC was again delayed with the presence of an Anew Zephyr IOL. Differences in the degree of growth on the PC and matrix modifications were apparent with the different donors, but in each case the match-paired Alcon Acrysof implanted bag exhibited significantly greater coverage and modification of the capsule. The Anew Zephyr open-bag IOL performs consistently better than the Alcon Acrysof IOL in the human capsular bag model. We propose that the

High expression of BAG3 predicts a poor prognosis in human medulloblastoma.

PubMed

Yang, Dong; Zhou, Ji; Wang, Hao; Wang, Yutao; Yang, Ge; Zhang, Yundong

2016-10-01

Bcl2-associated athanogene 3 (BAG3), a co-chaperone of the heat shock protein (Hsp) 70, regulates various physiological and pathological processes. However, its role in human medulloblastoma has not been clarified. First of all, the expression of BAG3 was examined in formalin-fixed, paraffin-embedded specimens by immunohistochemical staining. And then, the prognostic role of BAG3 was analyzed in 51 medulloblastoma samples. Finally, the roles of BAG3 in the proliferation, migration, and invasion of Daoy medulloblastoma cell were investigated using a specific short hairpin RNA (shRNA). The expression of BAG3 in medulloblastoma tissues was higher than nontumorous samples. Furthermore, BAG3 overexpression significantly correlated with poor prognosis of patients with medulloblastoma. The overall survival and tumor-free survival in patients with BAG3 low expression were higher than high expression. Univariate and multivariate analysis showed that BAG3 overexpression was an independent prognostic marker for medulloblastoma. After the BAG3 knockdown, the Daoy cells exhibited decreased the ability to proliferate and form neurosphere. The preliminary mechanism study showed that overexpression of BAG3 might facilitate the cell cycle transition from G1 to S phase by modulating the cyclin-dependent kinase 2 (CDK2) and cyclin E expression. Additionally, we found that BAG3 might enhance the medulloblastoma cell migratory and invasive ability. In summary, BAG3 overexpression may regulate the survival and invasive properties of medulloblastoma and may serve as a potential therapy target for medulloblastoma.

Diagnostic assessment of intraoperative cytology for papillary thyroid carcinoma: using a decision tree analysis.

PubMed

Pyo, J-S; Sohn, J H; Kang, G

2017-03-01

The aim of this study was to elucidate the cytological characteristics and the diagnostic usefulness of intraoperative cytology (IOC) for papillary thyroid carcinoma (PTC). In addition, using decision tree analysis, effective features for accurate cytological diagnosis were sought. We investigated cellularity, cytological features and diagnosis based on the Bethesda System for Reporting Thyroid Cytopathology in IOC of 240 conventional PTCs. The cytological features were evaluated in terms of nuclear score with nuclear features, and additional figures such as presence of swirling sheets, psammoma bodies, and multinucleated giant cells. The nuclear score (range 0-7) was made via seven nuclear features, including (1) enlarged, (2) oval or irregularly shaped nuclei, (3) longitudinal nuclear grooves, (4) intranuclear cytoplasmic pseudoinclusion, (5) pale nuclei with powdery chromatin, (6) nuclear membrane thickening, and (7) marginally placed micronucleoli. Nuclear scores in PTC, suspicious for malignancy, and atypia of undetermined significance cases were 6.18 ± 0.80, 4.48 ± 0.82, and 3.15 ± 0.67, respectively. Additional figures more frequent in PTC than in other diagnostic categories were identified. Cellularity of IOC significantly correlated with tumor size, nuclear score, and presence of additional figures. Also, IOCs with higher nuclear scores (4-7) significantly correlated with larger tumor size and presence of additional figures. In decision tree analysis, IOCs with nuclear score >5 and swirling sheets could be considered diagnostic for PTCs. Our study suggests that IOCs using nuclear features and additional figures could be useful with decreasing the likelihood of inconclusive results.

A Decision-Tree Approach to Cost Comparison of Newborn Screening Strategies for Cystic Fibrosis

PubMed Central

Wells, Janelle; Rosenberg, Marjorie; Hoffman, Gary; Anstead, Michael

2012-01-01

OBJECTIVE: Because cystic fibrosis can be difficult to diagnose and treat early, newborn screening programs have rapidly developed nationwide but methods vary widely. We therefore investigated the costs and consequences or specific outcomes of the 2 most commonly used methods. METHODS: With available data on screening and follow-up, we used a simulation approach with decision trees to compare immunoreactive trypsinogen (IRT) screening followed by a second IRT test against an IRT/DNA analysis. By using a Monte Carlo simulation program, variation in the model parameters for counts at various nodes of the decision trees, as well as for costs, are included and applied to fictional cohorts of 100 000 newborns. The outcome measures included the numbers of newborns given a diagnosis of cystic fibrosis and costs of screening strategy at each branch and cost per newborn. RESULTS: Simulations revealed a substantial number of potential missed diagnoses for the IRT/IRT system versus IRT/DNA. Although the IRT/IRT strategy with commonly used cutoff values offers an average overall cost savings of $2.30 per newborn, a breakdown of costs by societal segments demonstrated higher out-of-pocket costs for families. Two potential system failures causing delayed diagnoses were identified relating to the screening protocols and the follow-up system. CONCLUSIONS: The IRT/IRT screening algorithm reduces the costs to laboratories and insurance companies but has more system failures. IRT/DNA offers other advantages, including fewer delayed diagnoses and lower out-of-pocket costs to families. PMID:22291119

Application of Decision Tree to Obtain Optimal Operation Rules for Reservoir Flood Control Considering Sediment Desilting-Case Study of Tseng Wen Reservoir

NASA Astrophysics Data System (ADS)

ShiouWei, L.

2014-12-01

Reservoirs are the most important water resources facilities in Taiwan.However,due to the steep slope and fragile geological conditions in the mountain area,storm events usually cause serious debris flow and flood,and the flood then will flush large amount of sediment into reservoirs.The sedimentation caused by flood has great impact on the reservoirs life.Hence,how to operate a reservoir during flood events to increase the efficiency of sediment desilting without risk the reservoir safety and impact the water supply afterward is a crucial issue in Taiwan. 　Therefore,this study developed a novel optimization planning model for reservoir flood operation considering flood control and sediment desilting,and proposed easy to use operating rules represented by decision trees.The decision trees rules have considered flood mitigation,water supply and sediment desilting.The optimal planning model computes the optimal reservoir release for each flood event that minimum water supply impact and maximum sediment desilting without risk the reservoir safety.Beside the optimal flood operation planning model,this study also proposed decision tree based flood operating rules that were trained by the multiple optimal reservoir releases to synthesis flood scenarios.The synthesis flood scenarios consists of various synthesis storm events,reservoir's initial storage and target storages at the end of flood operating. 　Comparing the results operated by the decision tree operation rules(DTOR) with that by historical operation for Krosa Typhoon in 2007,the DTOR removed sediment 15.4% more than that of historical operation with reservoir storage only8.38×106m3 less than that of historical operation.For Jangmi Typhoon in 2008,the DTOR removed sediment 24.4% more than that of historical operation with reservoir storage only 7.58×106m3 less than that of historical operation.The results show that the proposed DTOR model can increase the sediment desilting efficiency and extend the

Role of BAG3 in cancer progression: A therapeutic opportunity.

PubMed

De Marco, Margot; Basile, Anna; Iorio, Vittoria; Festa, Michelina; Falco, Antonia; Ranieri, Bianca; Pascale, Maria; Sala, Gianluca; Remondelli, Paolo; Capunzo, Mario; Firpo, Matthew A; Pezzilli, Raffaele; Marzullo, Liberato; Cavallo, Pierpaolo; De Laurenzi, Vincenzo; Turco, Maria Caterina; Rosati, Alessandra

2018-06-01

BAG3 is a multifunctional protein that can bind to heat shock proteins (Hsp) 70 through its BAG domain and to other partners through its WW domain, proline-rich (PXXP) repeat and IPV (Ile-Pro-Val) motifs. Its intracellular expression can be induced by stressful stimuli, while is constitutive in skeletal muscle, cardiac myocytes and several tumour types. BAG3 can modulate the levels, localisation or activity of its partner proteins, thereby regulating major cell pathways and functions, including apoptosis, autophagy, mechanotransduction, cytoskeleton organisation, motility. A secreted form of BAG3 has been identified in studies on pancreatic ductal adenocarcinoma (PDAC). Secreted BAG3 can bind to a specific receptor, IFITM2, expressed on macrophages, and induce the release of factors that sustain tumour growth and the metastatic process. BAG3 neutralisation therefore appears to constitute a novel potential strategy in the therapy of PDAC and, possibly, other tumours. Copyright © 2017 Elsevier Ltd. All rights reserved.

Short peptides derived from the BAG-1 C-terminus inhibit the interaction between BAG-1 and HSC70 and decrease breast cancer cell growth.

PubMed

Sharp, Adam; Cutress, Ramsey I; Johnson, Peter W M; Packham, Graham; Townsend, Paul A

2009-11-03

BAG-1, a multifunctional protein, interacts with a plethora of cellular targets where the interaction with HSC70 and HSP70, is considered vital. Structural studies have demonstrated the C-terminal of BAG-1 forms a bundle of three alpha-helices of which helices 2 and 3 are directly involved in binding to the chaperones. Here we found peptides derived from helices 2 and 3 of BAG-1 interfered with BAG-1:HSC70 binding. We confirmed that a 12 amino-acid peptide from helix 2 directly interacted with HSC70 and when introduced into MCF-7 and ZR-75-1 cells, these peptides inhibited their growth. In conclusion, we have identified a small domain within BAG-1 which appears to play a critical role in the interaction with HSC70.

Overexpressed BAG3 is a potential therapeutic target in chronic lymphocytic leukemia.

PubMed

Zhu, Huayuan; Wu, Wei; Fu, Yuan; Shen, Wenyi; Miao, Kourong; Hong, Min; Xu, Wei; Young, Ken H; Liu, Peng; Li, Jianyong

2014-03-01

Bcl-2-associated athanogene 3 (BAG3), a member of BAG family, is shown to sustain cell survival and underlie resistance to chemotherapy in human neoplastic cells. We aimed to determine the exact role and underlying mechanisms of BAG3 in human chronic lymphocytic leukemia (CLL). One hundred human CLL samples and 20 normal B-cell samples from healthy controls were collected. We measured the BAG3 expression in these cells and explored its relationship with known prognostic factors for CLL. The roles of BAG3 in cell apoptosis and migration were evaluated by small interfering RNA-mediated knockdown of BAG3 in primary CLL cells. We showed that BAG3 expression level was increased in CLL cells compared with normal B cells. Moreover, BAG3 expression was particularly upregulated in CD38 positive, unmutated immunoglobulin heavy-chain patients and those with lymphadenopathy and/or splenomegaly. Importantly, patients with increased BAG3 expression level have poor overall survival in subgroups with positive ZAP-70 or those without any "p53 abnormality". In addition, knocking down of BAG3 expression resulted in increased apoptotic ratio and decreased migration in primary CLL cells. Our data indicate that BAG3 is a marker of poor prognostic in specific subgroups of CLL patients and may be a potential therapeutic target for this disease.

50 CFR 640.23 - Bag/possession limits.

Code of Federal Regulations, 2011 CFR

2011-10-01

... ADMINISTRATION, DEPARTMENT OF COMMERCE SPINY LOBSTER FISHERY OF THE GULF OF MEXICO AND SOUTH ATLANTIC Management... daily bag or possession limit for spiny lobster in or from the EEZ off the southern Atlantic states... fishing season specified in § 640.20(b)(1), the daily bag or possession limit of spiny lobster in or from...

50 CFR 640.23 - Bag/possession limits.

Code of Federal Regulations, 2010 CFR

2010-10-01

... ADMINISTRATION, DEPARTMENT OF COMMERCE SPINY LOBSTER FISHERY OF THE GULF OF MEXICO AND SOUTH ATLANTIC Management... daily bag or possession limit for spiny lobster in or from the EEZ off the southern Atlantic states... fishing season specified in § 640.20(b)(1), the daily bag or possession limit of spiny lobster in or from...

A method of building of decision trees based on data from wearable device during a rehabilitation of patients with tibia fractures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kupriyanov, M. S., E-mail: mikhail.kupriyanov@gmail.com; Shukeilo, E. Y., E-mail: eyshukeylo@gmail.com; Shichkina, J. A., E-mail: strange.y@mail.ru

2015-11-17

Nowadays technologies which are used in traumatology are a combination of mechanical, electronic, calculating and programming tools. Relevance of development of mobile applications for an expeditious data processing which are received from medical devices (in particular, wearable devices), and formulation of management decisions increases. Using of a mathematical method of building of decision trees for an assessment of a patient’s health condition using data from a wearable device considers in this article.

A method of building of decision trees based on data from wearable device during a rehabilitation of patients with tibia fractures

NASA Astrophysics Data System (ADS)

Kupriyanov, M. S.; Shukeilo, E. Y.; Shichkina, J. A.

2015-11-01

Nowadays technologies which are used in traumatology are a combination of mechanical, electronic, calculating and programming tools. Relevance of development of mobile applications for an expeditious data processing which are received from medical devices (in particular, wearable devices), and formulation of management decisions increases. Using of a mathematical method of building of decision trees for an assessment of a patient's health condition using data from a wearable device considers in this article.

Is the Capsular Bag Perimeter Round or Elliptical?

PubMed Central

Amigó, Alfredo; Bonaque-González, Sergio

2016-01-01

Purpose: To report findings that could suggest an elliptical shape of the capsular bag. Methods: Five eyes of three patients with axial length greater than 24 mm underwent phacoemulsification cataract surgery with plate-haptic multifocal toric intraocular lens (IOL) implantation oriented in the vertical meridian. Results: In all cases, correct orientation of the IOLs was verified 30 minutes after surgery. After 24 hours, all eyes demonstrated unwanted rotation of the IOLs ranging from 15 to 45 degrees. The IOLs remained stable in the new position in all cases until adhesion of the capsular bag took place. Conclusion: These observations could suggest that the perimeter of the capsular bag has an elliptical shape. Therefore, the IOL tends to become fixated in a meridian of the capsular bag that best fits the diagonal diameter of the IOL. PMID:27413495

Structural Equation Model Trees

ERIC Educational Resources Information Center

Brandmaier, Andreas M.; von Oertzen, Timo; McArdle, John J.; Lindenberger, Ulman

2013-01-01

In the behavioral and social sciences, structural equation models (SEMs) have become widely accepted as a modeling tool for the relation between latent and observed variables. SEMs can be seen as a unification of several multivariate analysis techniques. SEM Trees combine the strengths of SEMs and the decision tree paradigm by building tree…

Study of the suitability of DUO plastic bags for the storage of dynamites.

PubMed

Sáiz, Jorge; Ferrando, José-Luis; Atoche, Juan-Carlos; García-Ruiz, Carmen

2013-10-10

A comparative study on the retentiveness of two plastic bags (DUO and Royal Pack) has been carried out by gas chromatography with mass spectrometry detection. Two types of dynamites were packed in both plastic bags. The bags were placed into glass jars and headspace analyses were performed over 11 weeks to detect whether the volatile constituents of the dynamites were released from the bags. DUO plastic bags showed much better retentiveness than Royal Pack plastic bags. Ethylene glycol dinitrate (EGDN) was quickly detected in the headspace of the glass jars containing Royal Pack plastic bags after 1 week of storage. On the contrary, only a weak signal of EGDN, which was not detectable in the total ion chromatogram, was detected after 11 weeks of storage. Moreover, DUO plastic bags have shown less background signals than the Royal Pack bags, being the former bags much more suitable for the storage of dynamites. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Reduction of plastic carrier bag use: An analysis of alternatives in Israel.

PubMed

Ayalon, Ofira; Goldrath, Tal; Rosenthal, Gad; Grossman, Michal

2009-07-01

Plastic carrier bags have been drawing the attention of the public and politicians. Different policy measures to reduce the environmental burden of these bags have been implemented and more are planned. The research analyzed the actual environmental aspects of consumption and use of plastic carrier bags and assessed the effectiveness of the proposed regulation in Israel. Since plastic bags are provided free of charge, people have a tendency to use these bags excessively, therefore a rigorous educational program should address this trend. However, the environmental load imposed by the bags is more a politically correct issue than an actual environmental hazard, and therefore the means for reducing their use should not include a high levy or total elimination of these bags.

BAG3 promotes chondrosarcoma progression by upregulating the expression of β-catenin

PubMed Central

Shi, Huijuan; Chen, Wenfang; Dong, Yu; Lu, Xiaofang; Zhang, Wenhui; Wang, Liantang

2018-01-01

To investigate the roles of B-cell lymphoma-2 associated athanogene 3 (BAG3) in human chondrosarcoma and the potential mechanisms, the expression levels of BAG3 were detected in the present study, and the associations between BAG3 and clinical pathological parameters, clinical stage as well as the survival of patients were analyzed. The present study detected BAG3 mRNA and protein expression in the normal cartilage cell line HC-a and in SW1353 chondrosarcoma cells by reverse transcription-quantitative polymerase chain reaction and western blot analysis. The BAG3 protein expression in 59 cases of chondrosarcoma, 30 patients with endogenous chondroma and 8 cases of normal cartilage was semi-quantitatively analyzed using the immunohistochemical method. In addition, the BAG3 protein expression level, the clinical pathological parameters, clinical stage and the survival time of patients with chondrosarcoma were analyzed. The plasmid transfection method was employed to upregulate the expression BAG3 and small RNA interference to downregulate the expression of BAG3 in SW1353 cells. The expression levels of BAG3 protein and mRNA were significantly increased in the chondrosarcoma cell line when compared with the normal cartilage cell line. The immunohistochemistry results indicated that BAG3 protein was overexpressed in the tissue of human chondrosarcoma. Statistical analysis showed that the expression level of BAG3 was significantly increased in the different Enneking staging of patients with chondrosarcoma and Tumor staging, and there were no statistical differences in age, gender, histological classification and tumor size. In the in vitro experiments, the data revealed that BAG3 significantly promoted chondrosarcoma cell proliferation, colony-formation, migration and invasion; however, it inhibited chondrosarcoma cell apoptosis. It was observed that BAG3 upregulated β-catenin expression at the mRNA and protein levels. In addition, BAG3 induced the expression of runt

BAG3 promotes chondrosarcoma progression by upregulating the expression of β-catenin.

PubMed

Shi, Huijuan; Chen, Wenfang; Dong, Yu; Lu, Xiaofang; Zhang, Wenhui; Wang, Liantang

2018-04-01

To investigate the roles of B‑cell lymphoma‑2 associated athanogene 3 (BAG3) in human chondrosarcoma and the potential mechanisms, the expression levels of BAG3 were detected in the present study, and the associations between BAG3 and clinical pathological parameters, clinical stage as well as the survival of patients were analyzed. The present study detected BAG3 mRNA and protein expression in the normal cartilage cell line HC‑a and in SW1353 chondrosarcoma cells by reverse transcription‑quantitative polymerase chain reaction and western blot analysis. The BAG3 protein expression in 59 cases of chondrosarcoma, 30 patients with endogenous chondroma and 8 cases of normal cartilage was semi-quantitatively analyzed using the immunohistochemical method. In addition, the BAG3 protein expression level, the clinical pathological parameters, clinical stage and the survival time of patients with chondrosarcoma were analyzed. The plasmid transfection method was employed to upregulate the expression BAG3 and small RNA interference to downregulate the expression of BAG3 in SW1353 cells. The expression levels of BAG3 protein and mRNA were significantly increased in the chondrosarcoma cell line when compared with the normal cartilage cell line. The immunohistochemistry results indicated that BAG3 protein was overexpressed in the tissue of human chondrosarcoma. Statistical analysis showed that the expression level of BAG3 was significantly increased in the different Enneking staging of patients with chondrosarcoma and Tumor staging, and there were no statistical differences in age, gender, histological classification and tumor size. In the in vitro experiments, the data revealed that BAG3 significantly promoted chondrosarcoma cell proliferation, colony‑formation, migration and invasion; however, it inhibited chondrosarcoma cell apoptosis. It was observed that BAG3 upregulated β‑catenin expression at the mRNA and protein levels. In addition, BAG3 induced the

A BAG3 chaperone complex maintains cardiomyocyte function during proteotoxic stress

PubMed Central

Judge, Luke M.; Perez-Bermejo, Juan A.; Truong, Annie; Ribeiro, Alexandre J.S.; Yoo, Jennie C.; Jensen, Christina L.; Mandegar, Mohammad A.; Huebsch, Nathaniel; Kaake, Robyn M.; So, Po-Lin; Srivastava, Deepak; Krogan, Nevan J.

2017-01-01

Molecular chaperones regulate quality control in the human proteome, pathways that have been implicated in many diseases, including heart failure. Mutations in the BAG3 gene, which encodes a co-chaperone protein, have been associated with heart failure due to both inherited and sporadic dilated cardiomyopathy. Familial BAG3 mutations are autosomal dominant and frequently cause truncation of the coding sequence, suggesting a heterozygous loss-of-function mechanism. However, heterozygous knockout of the murine BAG3 gene did not cause a detectable phenotype. To model BAG3 cardiomyopathy in a human system, we generated an isogenic series of human induced pluripotent stem cells (iPSCs) with loss-of-function mutations in BAG3. Heterozygous BAG3 mutations reduced protein expression, disrupted myofibril structure, and compromised contractile function in iPSC-derived cardiomyocytes (iPS-CMs). BAG3-deficient iPS-CMs were particularly sensitive to further myofibril disruption and contractile dysfunction upon exposure to proteasome inhibitors known to cause cardiotoxicity. We performed affinity tagging of the endogenous BAG3 protein and mass spectrometry proteomics to further define the cardioprotective chaperone complex that BAG3 coordinates in the human heart. Our results establish a model for evaluating protein quality control pathways in human cardiomyocytes and their potential as therapeutic targets and susceptibility factors for cardiac drug toxicity. PMID:28724793

A BAG3 chaperone complex maintains cardiomyocyte function during proteotoxic stress.

PubMed

Judge, Luke M; Perez-Bermejo, Juan A; Truong, Annie; Ribeiro, Alexandre Js; Yoo, Jennie C; Jensen, Christina L; Mandegar, Mohammad A; Huebsch, Nathaniel; Kaake, Robyn M; So, Po-Lin; Srivastava, Deepak; Pruitt, Beth L; Krogan, Nevan J; Conklin, Bruce R

2017-07-20

Molecular chaperones regulate quality control in the human proteome, pathways that have been implicated in many diseases, including heart failure. Mutations in the BAG3 gene, which encodes a co-chaperone protein, have been associated with heart failure due to both inherited and sporadic dilated cardiomyopathy. Familial BAG3 mutations are autosomal dominant and frequently cause truncation of the coding sequence, suggesting a heterozygous loss-of-function mechanism. However, heterozygous knockout of the murine BAG3 gene did not cause a detectable phenotype. To model BAG3 cardiomyopathy in a human system, we generated an isogenic series of human induced pluripotent stem cells (iPSCs) with loss-of-function mutations in BAG3. Heterozygous BAG3 mutations reduced protein expression, disrupted myofibril structure, and compromised contractile function in iPSC-derived cardiomyocytes (iPS-CMs). BAG3-deficient iPS-CMs were particularly sensitive to further myofibril disruption and contractile dysfunction upon exposure to proteasome inhibitors known to cause cardiotoxicity. We performed affinity tagging of the endogenous BAG3 protein and mass spectrometry proteomics to further define the cardioprotective chaperone complex that BAG3 coordinates in the human heart. Our results establish a model for evaluating protein quality control pathways in human cardiomyocytes and their potential as therapeutic targets and susceptibility factors for cardiac drug toxicity.

The prosurvival protein BAG3: a new participant in vascular homeostasis

PubMed Central

Carrizzo, Albino; Damato, Antonio; Ambrosio, Mariateresa; Falco, Antonia; Rosati, Alessandra; Capunzo, Mario; Madonna, Michele; Turco, Maria C; Januzzi, James L; De Laurenzi, Vincenzo; Vecchione, Carmine

2016-01-01

Bcl2-associated athanogene 3 (BAG3), is constitutively expressed in a few normal cell types, including myocytes, peripheral nerves and in the brain, and is also expressed in certain tumors. To date, the main studies about the role of BAG3 are focused on its pro-survival effect in tumors through various mechanisms that vary according to cellular type. Recently, elevated concentrations of a soluble form of BAG3 were described in patients affected by advanced stage of heart failure (HF), identifying BAG3 as a potentially useful biomarker in monitoring HF progression. Despite the finding of high levels of BAG3 in the sera of HF patients, there are no data on its possible role on the modulation of vascular tone and blood pressure levels. The aim of this study was to investigate the possible hemodynamic effects of BAG3 performing both in vitro and in vivo experiments. Through vascular reactivity studies, we demonstrate that BAG3 is capable of evoking dose-dependent vasorelaxation. Of note, BAG3 exerts its vasorelaxant effect on resistance vessels, typically involved in the blood pressure regulation. Our data further show that the molecular mechanism through which BAG3 exerts this effect is the activation of the PI3K/Akt signalling pathway leading to nitric oxide release by endothelial cells. Finally, we show that in vivo BAG3 administration is capable of regulating blood pressure and that this is dependent on eNOS regulation since this ability is lost in eNOS KO animals. PMID:27763645

The prosurvival protein BAG3: a new participant in vascular homeostasis.

PubMed

Carrizzo, Albino; Damato, Antonio; Ambrosio, Mariateresa; Falco, Antonia; Rosati, Alessandra; Capunzo, Mario; Madonna, Michele; Turco, Maria C; Januzzi, James L; De Laurenzi, Vincenzo; Vecchione, Carmine

2016-10-20

Bcl2-associated athanogene 3 (BAG3), is constitutively expressed in a few normal cell types, including myocytes, peripheral nerves and in the brain, and is also expressed in certain tumors. To date, the main studies about the role of BAG3 are focused on its pro-survival effect in tumors through various mechanisms that vary according to cellular type. Recently, elevated concentrations of a soluble form of BAG3 were described in patients affected by advanced stage of heart failure (HF), identifying BAG3 as a potentially useful biomarker in monitoring HF progression. Despite the finding of high levels of BAG3 in the sera of HF patients, there are no data on its possible role on the modulation of vascular tone and blood pressure levels. The aim of this study was to investigate the possible hemodynamic effects of BAG3 performing both in vitro and in vivo experiments. Through vascular reactivity studies, we demonstrate that BAG3 is capable of evoking dose-dependent vasorelaxation. Of note, BAG3 exerts its vasorelaxant effect on resistance vessels, typically involved in the blood pressure regulation. Our data further show that the molecular mechanism through which BAG3 exerts this effect is the activation of the PI3K/Akt signalling pathway leading to nitric oxide release by endothelial cells. Finally, we show that in vivo BAG3 administration is capable of regulating blood pressure and that this is dependent on eNOS regulation since this ability is lost in eNOS KO animals.