Generic Dataset Research Articles

A nursing minimum dataset for documenting nutritional care for adults in primary healthcare: a scoping review protocol

A nursing minimum dataset for documenting nutritional care for adults in primary healthcare: a scoping review protocol

Association Rule Mining Based on Bat Algorithm

In this paper, we propose a bat-based algorithm (BA) for association rule mining (ARM Bat). Our algorithm aims to maximize the fitness function to generate the best rules in the defined dataset starting from a specific minimum support and minimum confidence. The efficiency of our proposed algorithm is tested on several generic datasets with different number of transactions and items. The results are compared to FPgrowth algorithm results on the same datasets. ARM bat algorithm perform better than the FPgrowth algorithm in term of computation speed and memory usage,

Evolutionary plant radiations: where, when, why and how?

Evolutionary plant radiations: where, when, why and how?

NativeLCA - a systematic approach for the selection of environmental datasets as generic data: application to construction products in a national context

Purpose The aim of this paper is to propose guidelines for the selection ofanaccurate lifecycle assessment(LCA) dataset of building products to be used as generic data for a national context. Methods The guidelines are structured within a methodology, called NativeLCA. First, a review of available datasets for construction products is presented such as generic LCA and Environmental Product Declaration (EPD) databases for both national (e.g. France, Germany, Spain etc.) and Europeancontext. Secondly, a method is proposed to choose appropriate generic datasets by means of a hybrid methodology. A metaanalysis is conducted in the first step on the sample of collected datasets from the literature. When relevant, productspecific data (EPD of the different producers) are averaged to represent an average data or existing generic data are adapted to be more suitable for the context. Then, a data quality assessment enables to rank the different datasets according to the goal and scope of the study. Results and discussion This study provides consistent guidelines that can be used by building LCA practitioners to select relevant datasets depending on their goal and scope. A full case study for stone wool boards illustrates and demonstrates the applicability and usefulness of the proposed methodology, namely in the selection of a coherent dataset as generic data for a national context. This work highlights the issues in terms of choice and adaptation of existing data for a national context. Industry data cannot be adapted due to confidentiality issues unlike unit process generic data. The use of data quality indicators then helps to select the relevant generic data for each context according to user needs. Conclusions While further efforts are needed to develop regional and sector-specific LCA databases adapted for each national context, the proposed guidelines showed that the current use or adaptation of existing data, if consistently done, can lead practitioners to increase the reliability of building LCA studies according to their goal and scope definition.

A Human Facial Expression Recognition Model Based on Eigen Face Approach

Emotion recognition through facial expression detection is one of the important fields of study for human-computer interaction. To detect a facial Expression one system need to come across various variability of human faces such as colour, posture, expression, orientation, etc. To detect the expression of a human face first it is required to detect the different facial features such as the movements of eye, nose, lips, etc. and then classify them comparing with trained data using a suitable classifier for expression recognition. In this research, a human facial expression recognition system is modelled using eigenface approach. The proposed method uses the HSV (Hue-Saturation-Value) colour model to detect the face in an image. PCA has been used for reducing the high dimensionality of the eigenspace and then by projecting the test image upon the eigenspace and calculating the Euclidean distance between the test image and mean of the eigenfaces of the training dataset the expressions are classified. A generic dataset is used for training purpose. The gray scale images of the face is used by the system to classify five basic emotions such as surprise, sorrow, fear, anger and happiness.

Evaluation of Inhaled Versus Deposited Dose Using the Exponential Dose-Response Model for Inhalational Anthrax in Nonhuman Primate, Rabbit, and Guinea Pig.

The application of the exponential model is extended by the inclusion of new nonhuman primate (NHP), rabbit, and guinea pig dose-lethality data for inhalation anthrax. Because deposition is a critical step in the initiation of inhalation anthrax, inhaled doses may not provide the most accurate cross-species comparison. For this reason, species-specific deposition factors were derived to translate inhaled dose to deposited dose. Four NHP, three rabbit, and two guinea pig data sets were utilized. Results from species-specific pooling analysis suggested all four NHP data sets could be pooled into a single NHP data set, which was also true for the rabbit and guinea pig data sets. The three species-specific pooled data sets could not be combined into a single generic mammalian data set. For inhaled dose, NHPs were the most sensitive (relative lowest LD50) species and rabbits the least. Improved inhaled LD50 s proposed for use in risk assessment are 50,600, 102,600, and 70,800 inhaled spores for NHP, rabbit, and guinea pig, respectively. Lung deposition factors were estimated for each species using published deposition data from Bacillus spore exposures, particle deposition studies, and computer modeling. Deposition was estimated at 22%, 9%, and 30% of the inhaled dose for NHP, rabbit, and guinea pig, respectively. When the inhaled dose was adjusted to reflect deposited dose, the rabbit animal model appears the most sensitive with the guinea pig the least sensitive species.

A non-parametric Bayesian model for joint cell clustering and cluster matching: identification of anomalous sample phenotypes with random effects.

BackgroundFlow cytometry (FC)-based computer-aided diagnostics is an emerging technique utilizing modern multiparametric cytometry systems.The major difficulty in using machine-learning approaches for classification of FC data arises from limited access to a wide variety of anomalous samples for training. In consequence, any learning with an abundance of normal cases and a limited set of specific anomalous cases is biased towards the types of anomalies represented in the training set. Such models do not accurately identify anomalies, whether previously known or unknown, that may exist in future samples tested. Although one-class classifiers trained using only normal cases would avoid such a bias, robust sample characterization is critical for a generalizable model. Owing to sample heterogeneity and instrumental variability, arbitrary characterization of samples usually introduces feature noise that may lead to poor predictive performance. Herein, we present a non-parametric Bayesian algorithm called ASPIRE (anomalous sample phenotype identification with random effects) that identifies phenotypic differences across a batch of samples in the presence of random effects. Our approach involves simultaneous clustering of cellular measurements in individual samples and matching of discovered clusters across all samples in order to recover global clusters using probabilistic sampling techniques in a systematic way.ResultsWe demonstrate the performance of the proposed method in identifying anomalous samples in two different FC data sets, one of which represents a set of samples including acute myeloid leukemia (AML) cases, and the other a generic 5-parameter peripheral-blood immunophenotyping. Results are evaluated in terms of the area under the receiver operating characteristics curve (AUC). ASPIRE achieved AUCs of 0.99 and 1.0 on the AML and generic blood immunophenotyping data sets, respectively.ConclusionsThese results demonstrate that anomalous samples can be identified by ASPIRE with almost perfect accuracy without a priori access to samples of anomalous subtypes in the training set. The ASPIRE approach is unique in its ability to form generalizations regarding normal and anomalous states given only very weak assumptions regarding sample characteristics and origin. Thus, ASPIRE could become highly instrumental in providing unique insights about observed biological phenomena in the absence of full information about the investigated samples.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-314) contains supplementary material, which is available to authorized users.

A tree-based statistical classification algorithm (CHAID) for identifying variables responsible for the occurrence of faecal indicator bacteria during waterworks operations

Microbial contamination of groundwater used for drinking water can affect public health and is of major concern to local water authorities and water suppliers. Potential hazards need to be identified in order to protect raw water resources. We propose a non-parametric data mining technique for exploring the presence of total coliforms (TC) in a groundwater abstraction well and its relationship to readily available, continuous time series of hydrometric monitoring parameters (seven year records of precipitation, river water levels, and groundwater heads). The original monitoring parameters were used to create an extensive generic dataset of explanatory variables by considering different accumulation or averaging periods, as well as temporal offsets of the explanatory variables. A classification tree based on the Chi-Squared Automatic Interaction Detection (CHAID) recursive partitioning algorithm revealed statistically significant relationships between precipitation and the presence of TC in both a production well and a nearby monitoring well. Different secondary explanatory variables were identified for the two wells. Elevated water levels and short-term water table fluctuations in the nearby river were found to be associated with TC in the observation well. The presence of TC in the production well was found to relate to elevated groundwater heads and fluctuations in groundwater levels. The generic variables created proved useful for increasing significance levels. The tree-based model was used to predict the occurrence of TC on the basis of hydrometric variables.

Local Metric Learning for Exemplar-Based Object Detection

Object detection has been widely studied in the com- puter vision community and it has many real applications, despite its variations, such as scale, pose, lighting, and background. Most classical object detection methods heavily rely on category- based training to handle intra-class variations. In contrast to classical methods that use a rigid category-based representation, exemplar-based methods try to model variations among positives by learning from specific positive samples. However, current existing exemplar-based methods either fail to use any training information or suffer from a significant performance drop when few exemplars are available. In this paper, we design a novel local metric learning approach to well handle exemplar- based object detection task. The main works are two-fold: 1) a novel local metric learning algorithm called exemplar metric learning (EML) is designed and 2) an exemplar-based object detection algorithm based on EML is implemented. We evaluate our method on two generic object detection data sets: UIUC-Car and UMass FDDB. Experiments show that compared with other exemplar-based methods, our approach can effectively enhance object detection performance when few exemplars are available.

Dated phylogenies of the sister genera Macaranga and Mallotus (Euphorbiaceae): congruence in historical biogeographic patterns?

Molecular phylogenies and estimates of divergence times within the sister genera Macaranga and Mallotus were estimated using Bayesian relaxed clock analyses of two generic data sets, one per genus. Both data sets were based on different molecular markers and largely different samples. Per genus three calibration points were utilised. The basal calibration point (crown node of all taxa used) was taken from literature and used for both taxa. The other three calibrations were based on fossils of which two were used per genus. We compared patterns of dispersal and diversification in Macaranga and Mallotus using ancestral area reconstruction in RASP (S-DIVA option) and contrasted our results with biogeographical and geological records to assess accuracy of inferred age estimates. A check of the fossil calibration point showed that the Japanese fossil, used for dating the divergence of Mallotus, probably had to be attached to a lower node, the stem node of all pioneer species, but even then the divergence time was still younger than the estimated age of the fossil. The African (only used in the Macaranga data set) and New Zealand fossils (used for both genera) seemed reliably placed. Our results are in line with existing geological data and the presence of stepping stones that provided dispersal pathways from Borneo to New Guinea-Australia, from Borneo to mainland Asia and additionally at least once to Africa and Madagascar via land and back to India via Indian Ocean island chains. The two genera show congruence in dispersal patterns, which corroborate divergence time estimates, although the overall mode and tempo of dispersal and diversification differ significantly as shown by distribution patterns of extant species.

Learning Prototype Hyperplanes for Face Verification in the Wild

In this paper, we propose a new scheme called Prototype Hyperplane Learning (PHL) for face verification in the wild using only weakly labeled training samples (i.e., we only know whether each pair of samples are from the same class or different classes without knowing the class label of each sample) by leveraging a large number of unlabeled samples in a generic data set. Our scheme represents each sample in the weakly labeled data set as a mid-level feature with each entry as the corresponding decision value from the classification hyperplane (referred to as the prototype hyperplane) of one Support Vector Machine (SVM) model, in which a sparse set of support vectors is selected from the unlabeled generic data set based on the learnt combination coefficients. To learn the optimal prototype hyperplanes for the extraction of mid-level features, we propose a Fisher’s Linear Discriminant-like (FLD-like) objective function by maximizing the discriminability on the weakly labeled data set with a constraint enforcing sparsity on the combination coefficients of each SVM model, which is solved by using an alternating optimization method. Then, we use the recent work called Side-Information based Linear Discriminant (SILD) analysis for dimensionality reduction and a cosine similarity measure for final face verification. Comprehensive experiments on two data sets, Labeled Faces in the Wild (LFW) and YouTube Faces, demonstrate the effectiveness of our scheme.

Head direction estimation from low resolution images with scene adaptation

This paper presents an appearance-based method for estimating head direction that automatically adapts to individual scenes. Appearance-based estimation methods usually require a ground-truth dataset taken from a scene that is similar to test video sequences. However, it is almost impossible to acquire many manually labeled head images for each scene. We introduce an approach that automatically aggregates labeled head images by inferring head direction labels from walking direction. Furthermore, in order to deal with large variations that occur in head appearance even within the same scene, we introduce an approach that segments a scene into multiple regions according to the similarity of head appearances. Experimental results demonstrate that our proposed method achieved higher accuracy in head direction estimation than conventional approaches that use a scene-independent generic dataset.

MycPermCheck: the Mycobacterium tuberculosispermeability prediction tool for small molecules

With over 8 million new cases in 2010, particularly documented in developing countries, tuberculosis (TB) is still a highly present pandemic and often terminal [1]. This is also due to the emergence of antibiotic-resistant strains (MDR-TB and XDR-TB) of the primary causative agent Mycobacterium tuberculosis (MTB). Efforts to develop new effective drugs against MTB are restrained by the unique and largely impermeable mycobacterial cell wall [2]. Accordingly, tools for estimating the ability of chemical compounds to pass this barrier would significantly support TB drug discovery. Using the CDD TB database of antimycobacterial substances [3], 3,815 active and, thus, permeable compounds could be retrieved. A data mining approach was conducted to gather the similarities of these substances in terms of physico-chemical descriptors and to delimit them from a generic dataset of drug-like molecules. Principal component analyses were performed and a logistic regression model was generated on the basis of the differences of these datasets in the first principal component. This regression model was thoroughly evaluated using highly active MTB inhibitors absent in the training dataset, as well as inhibitors of the validated MTB drug target InhA. Based on these validation results, its performance is expected to be of high practical value for virtual screening purposes. The regression model was implemented into the online tool MycPermCheck to predict the permeability probability of small organic compounds against the MTB cell wall. Given the current lack of precise molecular criteria determining mycobacterial permeability, MycPermCheck represents an unprecedented knowledge-driven prediction tool intended to support antimycobacterial drug discovery. It can be used intuitively as an additional selection criterion for potential new inhibitors against MTB. The MycPermCheck online tool is freely accessible under the URL: http://www.mycpermcheck.aksotriffer.pharmazie.uni-wuerzburg.de.

MycPermCheck: the Mycobacterium tuberculosis permeability prediction tool for small molecules

With >8 million new cases in 2010, particularly documented in developing countries, tuberculosis (TB) is still a highly present pandemic and often terminal. This is also due to the emergence of antibiotic-resistant strains (MDR-TB and XDR-TB) of the primary causative TB agent Mycobacterium tuberculosis (MTB). Efforts to develop new effective drugs against MTB are restrained by the unique and largely impermeable composition of the mycobacterial cell wall. Based on a database of antimycobacterial substances (CDD TB), 3815 compounds were classified as active and thus permeable. A data mining approach was conducted to gather the physico-chemical similarities of these substances and delimit them from a generic dataset of drug-like molecules. On the basis of the differences in these datasets, a regression model was generated and implemented into the online tool MycPermCheck to predict the permeability probability of small organic compounds. Given the current lack of precise molecular criteria determining mycobacterial permeability, MycPermCheck represents an unprecedented prediction tool intended to support antimycobacterial drug discovery. It follows a novel knowledge-driven approach to estimate the permeability probability of small organic compounds. As such, MycPermCheck can be used intuitively as an additional selection criterion for potential new inhibitors against MTB. Based on the validation results, its performance is expected to be of high practical value for virtual screening purposes. The online tool is freely accessible under the URL http://www.mycpermcheck.aksotriffer.pharmazie.uni-wuerzburg.de

A tiered approach to estimate inventory data and impacts of chemical products and mixtures

Mixtures of organic chemicals are a part of virtually all life cycles, but LCI data exist for only relatively few chemicals. Thus, estimation methods are required. However, these are often either very time-consuming or deliver results of low quality. This article compares existing and new methods in two scenarios and recommends a tiered approach of different methods for an efficient estimation of the production impacts of chemical mixtures. Four approaches to estimate impacts of a large number of chemicals are compared in this article: extrapolation from existing data, substitution with generic datasets on chemicals, molecular structure-based models (MSMs, in this case the Finechem tool), and using process-based estimation methods. Two scenarios were analyzed as case studies: soft PVC plastic and a tobacco flavor, a mixture of 20 chemicals. Process models have the potential to deliver the best estimations, as existing information on production processes can be integrated. However, their estimation quality suffers when such data are not available and they are time-consuming to apply, which is problematic when estimating large numbers of chemicals. Extrapolation from known to unknown components and use of generic datasets are generally not recommended. In both case studies, these two approaches significantly underestimated the impacts of the chemicals compared to the process models. MSMs were generally able to estimate impacts on the same level as the more complex process models. A tiered approach using MSMs to determine the relevance of individual components in mixtures and applying process models to the most relevant components offered a simpler and faster estimation process while delivering results on the level of most process models. The application of the tiered combination of MSMs and process models allows LCA practitioners a relatively fast and simple estimation of the LCIA results of chemicals, even for mixtures with a large number of components. Such mixtures previously presented a problem, as the application of process models for all components was very time-consuming, while the existing, simple approaches were shown to be inadequate in this study. We recommend the tiered approach as a significant improvement over previous approaches for estimating LCA results of chemical mixtures.

Automatic annotation of image databases based on implicit crowdsourcing, visual concept modeling and evolution

In this paper a novel approach for automatically annotating image databases is proposed. Despite most current schemes that are just based on spatial content analysis, the proposed method properly combines several innovative modules for semantically annotating images. In particular it includes: (a) a GWAP-oriented interface for optimized collection of implicit crowdsourcing data, (b) a new unsupervised visual concept modeling algorithm for content description and (c) a hierarchical visual content display method for easy data navigation, based on graph partitioning. The proposed scheme can be easily adopted by any multimedia search engine, providing an intelligent way to even annotate completely non-annotated content or correct wrongly annotated images. The proposed approach currently provides very interesting results in limited-size both standard and generic datasets and it is expected to add significant value especially to billions of non-annotated images existing in the Web. Furthermore expert annotators can gain important knowledge relevant to user new trends, language idioms and styles of searching.

Advanced Methods and Tools for Reconstruction and Analysis of Grain Boundaries from 3D-EBSD Data Sets

We report the recent development of a 3D orientation data post-processing software, which we refer to as QUBE. Amongst other functionalities, it offers the possibility to specify the spatial and orientational distribution of boundary normals. We describe a method to reconstruct a voxel-accurate and smooth 3D boundary triangle mesh by algorithmic means. A proof of concept is given by a benchmark on a generic dataset and we demonstrate a first result with the description of selected grain boundaries in an Fe-28%Ni sample.

Using advanced surface complexation models for modelling soil chemistry under forests: Solling forest, Germany

Various dynamic soil chemistry models have been developed to gain insight into impacts of atmospheric deposition of sulphur, nitrogen and other elements on soil and soil solution chemistry. Sorption parameters for anions and cations are generally calibrated for each site, which hampers extrapolation in space and time. On the other hand, recently developed surface complexation models (SCMs) have been successful in predicting ion sorption for static systems using generic parameter sets. This study reports the inclusion of an assemblage of these SCMs in the dynamic soil chemistry model SMARTml and applies this model to a spruce forest site in Solling Germany. Parameters for SCMs were taken from generic datasets and not calibrated. Nevertheless, modelling results for major elements matched observations well. Further, trace metals were included in the model, also using the existing framework of SCMs. The model predicted sorption for most trace elements well.

Late-time Kerr tails: generic and non-generic initial data sets, ‘up’ modes, and superposition

Three interrelated questions concerning Kerr spacetime late-time scalar-field tails are considered numerically, specifically the evolutions of generic and non-generic initial data sets, the excitation of ‘up’ modes, and the resolution of an apparent paradox related to the superposition principle. We propose to generalize the Barack–Ori formula for the decay rate of any tail multipole given a generic initial data set, to the contribution of any initial multipole mode. Our proposal leads to a much simpler expression for the late-time power-law index. Specifically, we propose that the late-time decay rate of a kinematically allowed Yℓm spherical harmonic multipole moment because of an initial multipole is independent of the azimuthal number m and is given by t−n, where n = ℓ′ + ℓ + 1 for ℓ < ℓ′ and n = ℓ′ + ℓ + 3 for ℓ ⩾ ℓ′. We also show explicitly that the angular symmetry group of a multipole does not determine its late-time decay rate.

Using a GIS filtering approach to replicate patterns of glacial erosion

AbstractIn order to extend our knowledge of glacial relief production in mountainous areas new methods are required for landscape reconstructions on a temporal resolution of a glacial cycle and a spatial resolution that includes the most important terrain components. A generic data set and a 50 m resolution digital elevation model over a study area in northern Sweden and Norway (the present day landscape data set) were employed to portray spatial patterns of erosion by reconstructing the landscape over successive cycles of glacial erosion. A maximum‐value geographic information system (GIS) filtering technique using variable neighbourhoods was applied such that existing highpoints in the landscape were used as erosional anchor points for the reconstruction of past landscape topography. An inherent assumption, therefore, is that the highest surfaces have experienced insignificant down‐wearing over the Quaternary. Over multiple reconstruction cycles, proceeding backwards in time, the highest summits increase in area, valleys become shallower, and the valley pattern becomes increasingly simplified as large valleys become in‐filled from the sides. The sum of these changes reduces relief. The pattern of glacial erosion, which is to 60% correlated to slope angle and to 70% correlated to relative relief, is characterized by (i) an abrupt erosional boundary below preserved summit areas, (ii) enhanced erosion in narrow valleys, (iii) restricted erosion of smooth areas, independently of elevation, (iv) eradication of small‐scale irregularities, (v) restricted erosion on isolated hills in low‐relief terrain, and (vi) a valley widening independent of valley directions. The method outlined in this paper shows how basic GIS filtering techniques can mimic some of the observed patterns of glacial erosion and thereby help deduce the key controls on the processes that govern large‐scale landscape evolution beneath ice sheets. Copyright © 2010 John Wiley &amp; Sons, Ltd.