robust method
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Statistical modeling and decision science
[1st ed.] xv, 296 s.
The aim of this study was to develop a simple extraction procedure and a multiresidual liquid chromatography-tandem mass spectrometry method for determination of a wide range of pharmaceuticals from various soil types. An extraction procedure for 91 pharmaceuticals from 13 soil types, followed by liquid chromatography-tandem mass spectrometry analysis, was optimized. The extraction efficiencies of three solvent mixtures for ultrasonic extraction were evaluated for 91 pharmaceuticals. The best results were obtained using acetonitrile/water (1/1 v/v with 0.1 % formic acid) followed by acetonitrile/2-propanol/water (3/3/4 v/v/v with 0.1 % formic acid) for extracting 63 pharmaceuticals. The method was validated at three fortification levels (10, 100, and 1000 ng/g) in all types of representative soils; recovery of 44 pharmaceuticals ranged between 55 and 135 % across all tested soils. The method was applied to analyze actual environmental samples of sediments, soils, and sludge, and 24 pharmaceuticals were found above limit of quantification with concentrations ranging between 0.83 ng/g (fexofenadine) and 223 ng/g (citalopram).
- MeSH
- 2-propanol MeSH
- acetonitrily MeSH
- chromatografie kapalinová metody MeSH
- látky znečišťující půdu analýza MeSH
- léčivé přípravky analýza MeSH
- monitorování životního prostředí metody MeSH
- odpadní vody chemie MeSH
- půda chemie MeSH
- tandemová hmotnostní spektrometrie metody MeSH
- Publikační typ
- časopisecké články MeSH
- validační studie MeSH
Image analysis methods commonly used in forensic anthropology do not have desirable robustness properties, which can be ensured by robust statistical methods. In this paper, the face localization in images is carried out by detecting symmetric areas in the images. Symmetry is measured between two neighboring rectangular areas in the images using a new robust correlation coefficient, which down-weights regions in the face violating the symmetry. Raw images of faces without usual preliminary transformations are considered. The robust correlation coefficient based on the least weighted squares regression yields very promising results also in the localization of such faces, which are not entirely symmetric. Standard methods of statistical machine learning are applied for comparison. The robust correlation analysis can be applicable to other problems of forensic anthropology.
- MeSH
- dospělí MeSH
- lidé MeSH
- lineární modely MeSH
- mladiství MeSH
- mladý dospělý MeSH
- obličej anatomie a histologie MeSH
- počítačové zpracování obrazu MeSH
- soudní antropologie MeSH
- statistické modely MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
A method is presented in which conventional speech algorithms are applied, with no modifications, to improve their performance in extremely noisy environments. It has been demonstrated that, for eigen-channel algorithms, pre-training multiple speaker identification (SID) models at a lattice of signal-to-noise-ratio (SNR) levels and then performing SID using the appropriate SNR dependent model was successful in mitigating noise at all SNR levels. In those tests, it was found that SID performance was optimized when the SNR of the testing and training data were close or identical. In this current effort multiple i-vector algorithms were used, greatly improving both processing throughput and equal error rate classification accuracy. Using identical approaches in the same noisy environment, performance of SID, language identification, gender identification, and diarization were significantly improved. A critical factor in this improvement is speech activity detection (SAD) that performs reliably in extremely noisy environments, where the speech itself is barely audible. To optimize SAD operation at all SNR levels, two algorithms were employed. The first maximized detection probability at low levels (-10 dB ≤ SNR < +10 dB) using just the voiced speech envelope, and the second exploited features extracted from the original speech to improve overall accuracy at higher quality levels (SNR ≥ +10 dB).
- MeSH
- algoritmy * MeSH
- hluk * MeSH
- lidé MeSH
- percepce řeči fyziologie MeSH
- počítačové zpracování signálu MeSH
- poměr signál - šum * MeSH
- řeč * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Resource specialization is a key concept in ecology, but it is unexpectedly difficult to parameterize. Differences in resource availability, sampling effort and abundances preclude comparisons of incompletely sampled biotic interaction webs. Here, we extend the distance-based specialization index (DSI) that measures trophic specialization by taking resource phylogenetic relatedness and availability into account into a rescaled version, DSI*. It is a versatile metric of specialization that expands considerably the scope and applicability, hence the usefulness, of DSI. The new metric also accounts for differences in abundance and sampling effort of consumers, which enables robust comparisons among distinct guilds of consumers. It also provides an abundance threshold for the reliability of the metric for rare species, a very desirable property given the difficulty of assessing any aspect of rare species accurately. We apply DSI* to an extensive dataset on interactions between insect herbivores from four folivorous guilds and their host plants in Papua New Guinean rainforests. We demonstrate that DSI*, contrary to the original DSI, is largely independent of sample size and weakly and non-linearly related with several host specificity measures that do not adjust for plant phylogeny. Thus, DSI* provides further insights into host specificity patterns; moreover, it is robust to the number and phylogenetic diversity of plant species selected to be sampled for herbivores. DSI* can be used for a broad range of comparisons of distinct feeding guilds, geographical locations and ecological conditions. This is a key advance in elucidating the interaction structure and evolution of highly diversified systems.
- MeSH
- býložravci * MeSH
- fylogeneze * MeSH
- hmyz klasifikace genetika MeSH
- nutriční stav MeSH
- potravní řetězec MeSH
- reprodukovatelnost výsledků MeSH
- rostliny klasifikace MeSH
- zvířata MeSH
- Check Tag
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
Intrinsically disordered proteins (IDPs) are subject to post-translational modifications. This allows the same polypeptide to be involved in different interaction networks with different consequences, ranging from regulatory signalling networks to the formation of membrane-less organelles. We report a robust method for co-expression of modification enzyme and SUMO-tagged IDPs with a subsequent purification procedure that allows for the production of modified IDP. The robustness of our protocol is demonstrated using a challenging system: RNA polymerase II C-terminal domain (CTD); that is, a low-complexity repetitive region with multiple phosphorylation sites. In vitro phosphorylation approaches fail to yield multiple-site phosphorylated CTD, whereas our in vivo protocol allows the rapid production of near homogeneous phosphorylated CTD at a low cost. These samples can be used in functional and structural studies.
- MeSH
- Escherichia coli genetika MeSH
- exprese genu MeSH
- fosforylace MeSH
- lidé MeSH
- nukleární magnetická rezonance biomolekulární MeSH
- posttranslační úpravy proteinů MeSH
- proteinové domény MeSH
- spektrometrie hmotnostní - ionizace laserem za účasti matrice MeSH
- transformace genetická MeSH
- tyrosin analýza genetika MeSH
- vnitřně neuspořádané proteiny chemie genetika MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Analysis of population genetic structure has become a standard approach in population genetics. In polyploid complexes, clustering analyses can elucidate the origin of polyploid populations and patterns of admixture between different cytotypes. However, combining diploid and polyploid data can theoretically lead to biased inference with (artefactual) clustering by ploidy. We used simulated mixed-ploidy (diploid-autotetraploid) data to systematically compare the performance of k-means clustering and the model-based clustering methods implemented in STRUCTURE, ADMIXTURE, FASTSTRUCTURE and INSTRUCT under different scenarios of differentiation and with different marker types. Under scenarios of strong population differentiation, the tested applications performed equally well. However, when population differentiation was weak, STRUCTURE was the only method that allowed unbiased inference with markers with limited genotypic information (co-dominant markers with unknown dosage or dominant markers). Still, since STRUCTURE was comparatively slow, the much faster but less powerful FASTSTRUCTURE provides a reasonable alternative for large datasets. Finally, although bias makes k-means clustering unsuitable for markers with incomplete genotype information, for large numbers of loci (>1000) with known dosage k-means clustering was superior to FASTSTRUCTURE in terms of power and speed. We conclude that STRUCTURE is the most robust method for the analysis of genetic structure in mixed-ploidy populations, although alternative methods should be considered under some specific conditions.
- MeSH
- diploidie MeSH
- genetická variace genetika MeSH
- genetické markery genetika MeSH
- genotyp MeSH
- jednonukleotidový polymorfismus genetika MeSH
- mikrosatelitní repetice genetika MeSH
- ploidie * MeSH
- populační genetika statistika a číselné údaje MeSH
- shluková analýza MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Markov Random Walks (MRW) has proven to be an effective way to understand spectral clustering and embedding. However, due to less global structural measure, conventional MRW (e.g., the Gaussian kernel MRW) cannot be applied to handle data points drawn from a mixture of subspaces. In this paper, we introduce a regularized MRW learning model, using a low-rank penalty to constrain the global subspace structure, for subspace clustering and estimation. In our framework, both the local pairwise similarity and the global subspace structure can be learnt from the transition probabilities of MRW. We prove that under some suitable conditions, our proposed local/global criteria can exactly capture the multiple subspace structure and learn a low-dimensional embedding for the data, in which giving the true segmentation of subspaces. To improve robustness in real situations, we also propose an extension of the MRW learning model based on integrating transition matrix learning and error correction in a unified framework. Experimental results on both synthetic data and real applications demonstrate that our proposed MRW learning model and its robust extension outperform the state-of-the-art subspace clustering methods.
- MeSH
- algoritmy MeSH
- emoce fyziologie MeSH
- lidé MeSH
- limbický systém fyziologie MeSH
- modely neurologické MeSH
- neuronové sítě * MeSH
- rozpoznávání automatizované metody MeSH
- shluková analýza MeSH
- teoretické modely MeSH
- učení MeSH
- umělá inteligence MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- přehledy MeSH
