Pre-selection Method Research Articles

Betting on the fastest horse: Using computer simulation to design a combination HIV intervention for future projects in Maharashtra, India.

ObjectiveTo inform the design of a combination intervention strategy targeting HIV-infected unhealthy alcohol users in Maharashtra, India, that could be tested in future randomized control trials.MethodsUsing probabilistic compartmental simulation modeling we compared intervention strategies targeting HIV-infected unhealthy alcohol users on antiretroviral therapy (ART) in Maharashtra, India. We tested interventions targeting four behaviors (unhealthy alcohol consumption, risky sexual behavior, depression and antiretroviral adherence), in three formats (individual, group based, community) and two durations (shorter versus longer). A total of 5,386 possible intervention combinations were tested across the population for a 20-year time horizon and intervention bundles were narrowed down based on incremental cost-effectiveness analysis using a two-step probabilistic uncertainty analysis approach.ResultsTaking into account uncertainty in transmission variables and intervention cost and effectiveness values, we were able to reduce the number of possible intervention combinations to be used in a randomized control trial from over 5,000 to less than 5. The most robust intervention bundle identified was a combination of three interventions: long individual alcohol counseling; weekly Short Message Service (SMS) adherence counseling; and brief sex risk group counseling.ConclusionsIn addition to guiding policy design, simulation modeling of HIV transmission can be used as a preparatory step to trial design, offering a method for intervention pre-selection at a reduced cost.

Using Worker Self-Assessments for Competence-Based Pre-Selection in Crowdsourcing Microtasks

Paid crowdsourcing platforms have evolved into remarkable marketplaces where requesters can tap into human intelligence to serve a multitude of purposes, and the workforce can benefit through monetary returns for investing their efforts. In this work, we focus on individual crowd worker competencies. By drawing from self-assessment theories in psychology, we show that crowd workers often lack awareness about their true level of competence. Due to this, although workers intend to maintain a high reputation, they tend to participate in tasks that are beyond their competence. We reveal the diversity of individual worker competencies, and make a case for competence-based pre-selection in crowdsourcing marketplaces. We show the implications of flawed self-assessments on real-world microtasks, and propose a novel worker pre-selection method that considers accuracy of worker self-assessments. We evaluated our method in a sentiment analysis task and observed an improvement in the accuracy by over 15%, when compared to traditional performance-based worker pre-selection. Similarly, our proposed method resulted in an improvement in accuracy of nearly 6% in an image validation task. Our results show that requesters in crowdsourcing platforms can benefit by considering worker self-assessments in addition to their performance for pre-selection.

Hydrogeochemical characterization and Natural Background Levels in urbanized areas: Milan Metropolitan area (Northern Italy)

Although aquifers in densely populated and industrialized areas are extremely valuable and sensitive to contamination, an estimate of the groundwater quality status relative to baseline conditions is lacking for many of them. This paper provides a hydrogeochemical characterization of the groundwater in the Milan metropolitan area, one of the most densely populated areas in Europe. First, a conceptual model of the study area based on the analysis of the spatial distribution of natural chemical species and indicator contaminants is presented. The hydrochemical facies of the study area depend on the lithology of catchments drained by the main contributing rivers and on the aquifer settings. The anthropogenic influence on the groundwater quality of superficial aquifers is studied by means of probability plots, concentration versus depth plots and spatial–temporal plots for nitrate, sulfate and chloride. These allow differentiation of contaminated superficial aquifers from deep confined aquifers with baseline water quality. Natural Background Levels (NBL) of selected species (Cl, Na, NH4, SO4, NO3, As, Fe, Mn and Zn) are estimated by means of the pre-selection (PS) and the component separation (CS) statistical approaches. The NBLs depend on hydrogeological settings of the study area; sodium, chloride, sulfate and zinc NBL values never exceed the environmental water quality standards. NBL values of ammonium, iron, arsenic and manganese exceed the environmental water quality standards in the anaerobic portion of the aquifers. On the basis of observations, a set of criteria and precautions are suggested for adoption with both PS and CS methods in the aquifer characterization of highly urbanized areas.

Brilliant cresyl blue staining does not present cytotoxic effects on human luteinized follicular cells, according to gene/protein expression, as well as to cytotoxicity tests

In vitro oocyte maturation presents many advantages and its success is related to the selection of fully grown oocytes. In animal models, staining of cumulus-oocyte complexes (COCs) with Brilliant Cresyl Blue (BCB) is widely used for this selection. However, a lack of information about the safety of BCB makes its applicability in humans questionable. Because granulosa and cumulus cells have a close relationship with the oocyte and taking into account that follicular cells are also exposed to BCB when this pre-selection method is applied, we aimed to evaluate the effects of BCB on human follicular cells exposed to BCB. Cytotoxicity tests (Sulforhodamine B and Neutral Red Uptake) and gene and protein expression of elements related to the cell cycle (BAX, BCL2, TP53 and CDKN1A), as well as to cell death and metabolism (GAPDH), glucose consumption, and estradiol and progesterone secretion, were examined in granulosa and cumulus cells with and without exposure to BCB. Regardless estradiol secretion and glucose consumption, all other evaluations presented similar results between control and treated (BCB) groups, independently of cell type. Therefore, our results demonstrate convincingly that BCB seems to be safe for use in humans and it should encourage future studies to evaluate the development of embryos derived from human oocytes selected by BCB staining.

Large‐Scale Portfolio Optimization Using Multiobjective Evolutionary Algorithms and Preselection Methods

Portfolio optimization problems involve selection of different assets to invest in order to maximize the overall return and minimize the overall risk simultaneously. The complexity of the optimal asset allocation problem increases with an increase in the number of assets available to select from for investing. The optimization problem becomes computationally challenging when there are more than a few hundreds of assets to select from. To reduce the complexity of large‐scale portfolio optimization, two asset preselection procedures that consider return and risk of individual asset and pairwise correlation to remove assets that may not potentially be selected into any portfolio are proposed in this paper. With these asset preselection methods, the number of assets considered to be included in a portfolio can be increased to thousands. To test the effectiveness of the proposed methods, a Normalized Multiobjective Evolutionary Algorithm based on Decomposition (NMOEA/D) algorithm and several other commonly used multiobjective evolutionary algorithms are applied and compared. Six experiments with different settings are carried out. The experimental results show that with the proposed methods the simulation time is reduced while return‐risk trade‐off performances are significantly improved. Meanwhile, the NMOEA/D is able to outperform other compared algorithms on all experiments according to the comparative analysis.

Early germinal vesicle breakdown is a predictor of high preimplantation developmental competent oocytes in mice.

The preselection of highly developmentally competent oocytes for in vitro maturation (IVM) is crucial for improving assisted reproductive technology. Although several intrinsic markers of oocyte quality are known to be closely related to the onset of nuclear maturation (germinal vesicle break down, GVBD), a direct comparison between GVBD timing and oocyte quality has never been reported. In this study, we established a non-invasive oocyte evaluation method based on GVBD timing for preselecting more developmental competent oocytes in mice. Because the O2 concentration during IVM may affect the nuclear kinetics, all experiments were performed under two distinct O2 concentrations: 20% and 5% O2. First, we determined the time course of changes in nuclear maturation and preimplantation developmental competence of in vitro-matured oocytes to estimate GVBD timing in high developmental competent oocytes. Two-thirds of oocytes that underwent GVBD in early IVM seemed to mainly contribute to the blastocyst yield. To confirm this result, we compared the preimplantation developmental competence of the early and late GVBD oocytes. Cleavage and blastocyst formation rates of early GVBD oocytes (80.2% and 52.7% under 20% O2, respectively, and 67.6% and 47.3% under 5% O2, respectively) were almost double those of late GVBD oocytes (44.8% and 26.0% under 20% O2, respectively, and 40.4% and 17.9% under 5% O2, respectively). With no observable alterations by checking the timing of GVBD in preimplantation developmental competence, oocyte evaluation based on GVBD timing can be used as an efficient and non-invasive preselection method for high developmental competent oocytes.

Genomic Selection Using Extreme Phenotypes and Pre-Selection of SNPs in Large Yellow Croaker (Larimichthys crocea).

Genomic selection (GS) is an effective method to improve predictive accuracies of genetic values. However, high cost in genotyping will limit the application of this technology in some species. Therefore, it is necessary to find some methods to reduce the genotyping costs in genomic selection. Large yellow croaker is one of the most commercially important marine fish species in southeast China and Eastern Asia. In this study, genotyping-by-sequencing was used to construct the libraries for the NGS sequencing and find 29,748 SNPs in the genome. Two traits, eviscerated weight (EW) and the ratio between eviscerated weight and whole body weight (REW), were chosen to study. Two strategies to reduce the costs were proposed as follows: selecting extreme phenotypes (EP) for genotyping in reference population or pre-selecting SNPs to construct low-density marker panels in candidates. Three methods of pre-selection of SNPs, i.e., pre-selecting SNPs by absolute effects (SE), by single marker analysis (SMA), and by fixed intervals of sequence number (EL), were studied. The results showed that using EP was a feasible method to save the genotyping costs in reference population. Heritability did not seem to have obvious influences on the predictive abilities estimated by EP. Using SMA was the most feasible method to save the genotyping costs in candidates. In addition, the combination of EP and SMA in genomic selection also showed good results, especially for trait of REW. We also described how to apply the new methods in genomic selection and compared the genotyping costs before and after using the new methods. Our study may not only offer a reference for aquatic genomic breeding but also offer a reference for genomic prediction in other species including livestock and plants, etc.

Combining natural background levels (NBLs) assessment with indicator kriging analysis to improve groundwater quality data interpretation and management

The natural background level (NBL) concept is revisited and combined with indicator kriging method to analyze the spatial distribution of groundwater quality within a groundwater body (GWB). The aim is to provide a methodology to easily identify areas with the same probability of exceeding a given threshold (which may be a groundwater quality criteria, standards, or recommended limits for selected properties and constituents). Three case studies with different hydrogeological settings and located in two countries (Portugal and Italy) are used to derive NBL using the preselection method and validate the proposed methodology illustrating its main advantages over conventional statistical water quality analysis. Indicator kriging analysis was used to create probability maps of the three potential groundwater contaminants. The results clearly indicate the areas within a groundwater body that are potentially contaminated because the concentrations exceed the drinking water standards or even the local NBL, and cannot be justified by geogenic origin. The combined methodology developed facilitates the management of groundwater quality because it allows for the spatial interpretation of NBL values.

Geological Distribution and Background Level of Copper and Zinc in Non-drinking Groundwater, South Korea

To add new groundwater standard, 7 candidate materials (copper, zinc, selenium, manganese, iron, chromium, aluminum) were calculated by CROWN (Chemical Ranking Of groundwater pollutaNts). Copper and zinc were selected as groundwater candidates through the process and monitored total 430 samples for 2 years with 113 groundwater sampling sites. In this study, geological distribution characteristics (igneous rock, metamorphic rock, sedimentary rock) of copper and zinc were evaluated and the geological background levels obtained by a cumulative probability distribution and pre-selection methods were compared. In the results, the highest average concentrations of the copper and zinc were observed both in the igneous rock. The detection concentration ranges of copper and zinc in 430 groundwater samples were 0.002∼0.931 ㎎ L -1 , and 0.002∼32.293 ㎎ L -1 , respectively. In addition, detection concentration ranges of copper and zinc were 0.002∼0.931 ㎎ L -1 , 0.002∼32.293 ㎎ L -1 in the igneous rock, 0.002∼0.134 ㎎ L -1 , 0.004∼7.038 ㎎ L -1 in the metamorphic rock and 0.002∼0.008 ㎎ L -1 , 0.003∼3.948 ㎎ L -1 in the sedimentary rock, respectively. As a result of the background concentrations with two methods, zinc concentrations with the pre-selected method are comparatively higher than that of the others with the cumulative distribution.

Prognostic and prescriptive predictors of improvement in a naturalistic study on inpatient and day hospital treatment of depression

BackgroundThe study aimed to identify prognostic (associated with general outcome) and prescriptive (associated with differential outcome in two different settings) predictors of improvement in a naturalistic multi-center study on inpatient and day hospital treatment in major depressive disorder (MDD). Methods250 inpatients and 250day hospital patients of eight psychosomatic hospitals were assessed at admission, discharge and a 3-months follow-up. Primary outcome was defined as a reduction of depressive symptomatology from admission to discharge and from discharge to follow-up (QIDS-C, total score). Percent improvement scores at discharge and at follow-up were entered as dependent variables into two General Linear Models with a set of predictor variables and the respective interaction terms with treatment setting. The selection of predictor sets was guided by statistical methods of variable preselection (LASSO). ResultsThree variables were associated with less improvement from admission to discharge: the number of additional axis-I diagnoses, axis-II co-morbidity (SCID) and lower motivation (expert assessment). Social support (F-SozU) predicted symptom course between discharge and 3-month follow-up. Patients with no absent / sick days prior to admission showed a less favorable symptom course after discharge when treated as inpatients. ConclusionsPatients with co-morbidity show less improvement during the active treatment phase. Motivation can be considered a prerequisite for symptom reduction, whereas social support seems to be an important factor for the maintenance of treatment gains. The lack in prescriptive predictors found may point to the fact that inpatient and day hospital treatment have comparable effects for most subgroups of patients with MDD.

A pharmacogenetic pilot study reveals MTHFR, DRD3, and MDR1 polymorphisms as biomarker candidates for slow atorvastatin metabolizers.

BackgroundThe genetic variation underlying atorvastatin (ATV) pharmacokinetics was evaluated in a Mexican population. Aims of this study were: 1) to reveal the frequency of 87 polymorphisms in 36 genes related to drug metabolism in healthy Mexican volunteers, 2) to evaluate the impact of these polymorphisms on ATV pharmacokinetics, 3) to classify the ATV metabolic phenotypes of healthy volunteers, and 4) to investigate a possible association between genotypes and metabolizer phenotypes.MethodsA pharmacokinetic study of ATV (single 80-mg dose) was conducted in 60 healthy male volunteers. ATV plasma concentrations were measured by high-performance liquid chromatography mass spectrometry. Pharmacokinetic parameters were calculated by the non-compartmental method. The polymorphisms were determined with the PHARMAchip® microarray and the TaqMan® probes genotyping assay.ResultsThree metabolic phenotypes were found in our population: slow, normal, and rapid. Six gene polymorphisms were found to have a significant effect on ATV pharmacokinetics: MTHFR (rs1801133), DRD3 (rs6280), GSTM3 (rs1799735), TNFα (rs1800629), MDR1 (rs1045642), and SLCO1B1 (rs4149056). The combination of MTHFR, DRD3 and MDR1 polymorphisms associated with a slow ATV metabolizer phenotype.ConclusionFurther studies using a genetic preselection method and a larger population are needed to confirm these polymorphisms as predictive biomarkers for ATV slow metabolizers.Trial registrationAustralian New Zealand Clinical Trials Registry: ACTRN12614000851662, date registered: August 8, 2014.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-016-2062-2) contains supplementary material, which is available to authorized users.

Type (I, II) errors variable selection in quantitative structure activity relationships

Selection of valid and informative descriptors is one of the most crucial steps in the QSAR studies. In this contribution, type (I, II) errors variable selection is proposed as a statistically based variable selection method for selecting valid descriptors. Developed selection strategy can be considered as a sophisticated combination of jackknifing and y-randomization. Type (I) error is a measure of descriptors' importance, and it is provided using jackknifing for each descriptor. Type (II) error is a measure of chance correlation and it is calculated using y-randomization. Successive projection algorithm and Gram–Schmidt orthogonalization were utilized as pre-selection methods for the initial reduction of collinear and uninformative variables. Selwood data including 31 molecules and 53 descriptors, anti-HIV data including 107 molecules and 160, and Flour data including 116 molecules and 1268 descriptors were utilized in this research. Model parameters for each data set before and after variable selection confirm the adequacy of the proposed novel variable selection.

Macroeconomic Now- and Forecasting Based on the Factor Error Correction Model Using Targeted Mixed Frequency Indicators

Since the influential paper of Stock and Watson (2002), the dynamic factor model (DFM) has been widely used for forecasting macroeconomic key variables such as GDP. However, the DFM has some weaknesses. For nowcasting, the dynamic factor model is modified by using the mixed data sampling technique. Other improvements are also studied mostly in two directions: a pre-selection is used to optimally choose a small number of indicators from a large number of indicators. The error correction mechanism takes into account the co-integrating relationship between the key variables and factors and, hence, captures the long-run dynamics of the non-stationary macroeconomic variables. This papers proposes the factor error correction model using targeted mixedfrequency indicators, which combines the three refinements for the dynamic factor model, namely the mixed data sampling technique, pre-selection methods, and the error correction mechanism. The empirical results based on euro-area data show that the now- and forecasting performance of our new model is superior to that of the subset models.

Optimal Signal Selection for Power System Ambient Mode Estimation Using a Prediction Error Criterion

This paper formulates an optimality criterion for the selection of synchrophasor signals to be used in ambient mode estimators. This criterion, which is associated with each measured signal and each dominant mode, is defined as the asymptotic variance of the corresponding estimated mode damping ratio. The value of the criterion is computed directly from an estimated autoregressive moving average (ARMA) model. Because the online computation of the defined criterion (for each measured signal in the system) may be computationally expensive, a fast pre-selection method for initial signal ranking is formulated. The pre-selection method is used to effectively determine a set of the candidate signals for which the formal criterion is evaluated in a second stage. The methodology is illustrated using synthetic measurements from the KTH Nordic 32 and the IEEE 39-bus test systems.

NMFP: a non-negative matrix factorization based preselection method to increase accuracy of identifying mRNA isoforms from RNA-seq data.

BackgroundThe advent of next-generation RNA sequencing (RNA-seq) has greatly advanced transcriptomic studies, including system-wide identification and quantification of mRNA isoforms under various biological conditions. A number of computational methods have been developed to systematically identify mRNA isoforms in a high-throughput manner from RNA-seq data. However, a common drawback of these methods is that their identified mRNA isoforms contain a high percentage of false positives, especially for genes with complex splicing structures, e.g., many exons and exon junctions.ResultsWe have developed a preselection method called “Non-negative Matrix Factorization Preselection” (NMFP) which is designed to improve the accuracy of computational methods in identifying mRNA isoforms from RNA-seq data. We demonstrated through simulation and real data studies that NMFP can effectively shrink the search space of isoform candidates and increase the accuracy of two mainstream computational methods, Cufflinks and SLIDE, in their identification of mRNA isoforms.ConclusionNMFP is a useful tool to preselect mRNA isoform candidates for downstream isoform discovery methods. It can greatly reduce the number of isoform candidates while maintaining a good coverage of unknown true isoforms. Adding NMFP as an upstream step, computational methods are expected to achieve better accuracy in identifying mRNA isoforms from RNA-seq data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2304-8) contains supplementary material, which is available to authorized users.

Stitching of hyper-spectral UAV images based on feature bands selection

Abstract: This study employed the feature band pre-selection and composite process to overcome the mosaic difficulties of large number of bands and huge data volume of the hyperspectral UAV images before the mosaic process being done. The application and comparison were done by using the UHD 185 hyperspectral camera, and the result showed that the band pre-selection and composite method used in this study can go up as high as being 390 percent faster than the single band mosaic (for the Normalized Differential Vegetation Index application), and about 46 times faster than full band mosaic. Besides, compared to the full band mosaic method, the method used in the study can reduce the maximum memory usage amount and final mosaic file size significantly.

Distributed Active Learning

Active learning aims at obtaining high-accuracy models with as a few labeled data as possible, by iteratively and elaborately selecting most valuable data to query labels during the learning process, thereby the cost of labeling data can be reduced. Most previous active learning approaches consider the situation of centralized processing, where all the unlabeled data are supposed to be gathered together in one place. Due to the development of distributed applications, distributed processing has attracted a lot of interests given the situation that data are distributed at different nodes over network. In this paper, we focus on the issue of distributed active learning (DAL) for the classification problem. We propose a fully decentralized active learning approach, which consists of two parts, namely, a distributed sample selection strategy and a distributed classification algorithm. The former helps nodes to cooperatively select data based on uncertainty, diversity, and representativeness of data. Due to the introducing of a randomized preselection method in the strategy, we can achieve diversity of the selected data without any information exchange among nodes. The latter helps each node to train its local multi-class classification model in a global sense without transmitting original data among nodes. We demonstrate the effectiveness of the proposed DAL approach on several real data sets. Simulation results show that the proposed approach can significantly reduce the number of labeled data needed for obtaining a high-accuracy classifier in distributed case.

Groundwater chemical status assessment considering geochemical background: an example from Northern Latium (Central Italy)

The evaluation of the chemical status of groundwater bodies, pursuant to the European Groundwater Directive 2006/118/EC, requires the assessment of the natural background levels when concentration exceeds the national standards. In large part of Italy, including the case-study region Latium, the presence of substances of natural origin has long been recognized. Nonetheless, a systematic assessment of background levels is still missing. Guidelines have been published both at the European and Italian level, but there are still unanswered questions, including the appropriate geographical scale, the time and spatial variation. In this paper, the evaluation of the groundwater chemical status at the groundwater body scale for the Latium region for the above-mentioned elements is presented. We used the preselection method, choosing the 95th percentile of the preselected datasets for various groundwater bodies. Results show that the natural background levels differ at the groundwater body scale reflecting the complexity of the geological asset. At a smaller scale, the variation of the background even at short distances may be dramatic, and this should be considered especially when dealing with site scale assessment. Conversely, the time variation of the considered parameters seems to be modest. In the case-study region, the chemical status assessment considering the background levels would result “good” as for the exceedances of arsenic and fluoride, solving the problem of erroneous classification of water bodies characterized by contamination of natural origin.

Finite Control Set Model Predictive Control of AC/DC Matrix Converter for Grid-Connected Battery Energy Storage Application

This paper presents a finite control set model predictive control (FCS-MPC) strategy for the AC/DC matrix converter used in grid-connected battery energy storage system (BESS). First, to control the grid current properly, the DC current is also included in the cost function because of input and output direct coupling. The DC current reference is generated based on the dynamic relationship of the two currents, so the grid current gains improved transient state performance. Furthermore, the steady state error is reduced by adding a closed-loop. Second, a Luenberger observer is adopted to detect the AC input voltage instead of sensors, so the cost is reduced and the reliability can be enhanced. Third, a switching state pre-selection method that only needs to evaluate half of the active switching states is presented, with the advantages of shorter calculation time, no high dv/dt at the DC terminal, and less switching loss. The robustness under grid voltage distortion and parameter sensibility are discussed as well. Simulation and experimental results confirm the good performance of the proposed scheme for battery charging and discharging control.

COMPSEC, a new tool to derive natural background levels by the component separation approach: application in two different hydrogeological contexts in northern Italy

The assessment of natural background levels (NBLs) is crucial in the management of groundwater resources and the definition of the status of groundwater bodies according to the Water Framework Directive 2000/60/EC. This work presents a new MATLAB code to assess NBLs by the component separation method, which concerns the identification and separation of a natural and an anthropogenic component within geochemical data. To increase the validity of the estimated NBL, the code calculates a set of possible solutions that should then be evaluated by the user. The following evaluation criteria are proposed: (i) stability of solution, (ii) hydrogeochemical reliability, (iii) agreement with the conceptual model and (iv) goodness of fit.The code is tested on groundwater Mn, Fe, As, Ni, Cr(VI) and NH4 concentrations observed in two study areas with different hydrogeological and geochemical features in northern Italy: the multi-layer aquifer of Cremona (lower Po Plain) and the alluvial aquifer of the Aosta Plain (western Alpine sector). The pre-selection method is also applied to estimate NBLs and threshold values are assessed.The NBLs calculated through the component separation method (4292, 27 and 1220μg/L for Fe, As and NH4 in Cremona and 56, 261, 9 and 2.3μg/L for Mn, Fe, Ni and Cr(VI) in Aosta, respectively) approach those estimated by the pre-selection (2798, 25 and 1820μg/L for Fe, As and NH4 in Cremona and 52, 264, 10 and 1.8μg/L for Mn, Fe, Ni and Cr(VI) in Aosta, respectively). This outcome increases the validity of the estimated NBLs. The code showed versatile applicability in different contexts and conditions (e.g., different numbers of samples and monitoring points, different sampling networks). This work highlights the strength of the component separation method although some criticisms are pointed out, mainly related to the superimposition of a log-normal function for modeling the natural component.