遥感探测土地植被覆盖指数的准确度评估

GIS数据的准确度一直是GIS应用的考虑事项。用虚例和实例揭示了土地植被覆盖指数的准确度与图像分类的准确度之间的复杂关系,并进一步用数学方式进行了解释。土地植被覆盖指数的准确度取决于图像分类准确度,但与分类总准确度没有直接关系。用户和产家准确度比总准确度对土地植被覆盖指数的准确度具有更直接的控制,所以在图像分类报告中不应被忽略为了保证土地植被覆盖指数的准确度,某土地类型的用户和产家准确度应该尽可能一致,同时要使得这两个准确度数值越高越好。     

Measuring and comparing the accuracy of species distribution models with presence–absence data

Species distribution models have been widely used to predict species distributions for various purposes, including conservation planning, and climate change impact assessment. The success of these applications relies heavily on the accuracy of the models. Various measures have been proposed to assess the accuracy of the models. Rigorous statistical analysis should be incorporated in model accuracy assessment. However, since relevant information about the statistical properties of accuracy measures is scattered across various disciplines, ecologists find it difficult to select the most appropriate ones for their research. In this paper, we review accuracy measures that are currently used in species distribution modelling (SDM), and introduce additional metrics that have potential applications in SDM. For the commonly used measures (which are also intensively studied by statisticians), including overall accuracy, sensitivity, specificity, kappa, and area and partial area under the ROC curves, promising methods to construct confidence intervals and statistically compare the accuracy between two models are given. For other accuracy measures, methods to estimate standard errors are given, which can be used to construct approximate confidence intervals. We also suggest that as general tools, computer‐intensive methods, especially bootstrap and randomization methods can be used in constructing confidence intervals and statistical tests if suitable analytic methods cannot be found. Usually, these computer‐intensive methods provide robust results.     

Diffusion control in biochemical specificity

Biochemical specificity is critical in enzyme function, evolution, and engineering. Here we employ an established kinetic model to dissect the effects of reactant geometry and diffusion on product formation speed and accuracy in the presence of cognate (correct) and near-cognate (incorrect) substrates. Using this steady-state model for spherical geometries, we find that, for distinct kinetic regimes, the speed and accuracy of the reactions are optimized on different regions of the geometric landscape. From this model we deduce that accuracy can be strongly dependent on reactant geometric properties even for chemically limited reactions. Notably, substrates with a specific geometry and reactivity can be discriminated by the enzyme with higher efficacy than others through purely diffusive effects. For similar cognate and near-cognate substrate geometries (as is the case for polymerases or the ribosome), we observe that speed and accuracy are maximized in opposing regions of the geometric landscape. We also show that, in relevant environments, diffusive effects on accuracy can be substantial even far from extreme kinetic conditions. Finally, we find how reactant chemical discrimination and diffusion can be related to simultaneously optimize steady-state flux and accuracy. These results highlight how diffusion and geometry can be employed to enhance reaction speed and discrimination, and similarly how they impose fundamental restraints on these quantities.     

Assessing the dependence of sensitivity and specificity on prevalence in meta-analysis

We consider modeling the dependence of sensitivity and specificity on the disease prevalence in diagnostic accuracy studies. Many meta-analyses compare test accuracy across studies and fail to incorporate the possible connection between the accuracy measures and the prevalence. We propose a Pearson type correlation coefficient and an estimating equation-based regression framework to help understand such a practical dependence. The results we derive may then be used to better interpret the results from meta-analyses. In the biomedical examples analyzed in this paper, the diagnostic accuracy of biomarkers are shown to be associated with prevalence, providing insights into the utility of these biomarkers in low- and high-prevalence populations.     

Evaluating the Accuracy of Modern Glucose Meters

Background: Self-mmonitoring of blood glucose (SSMBG) is important for all patients with diabetes, as it provides valuable feedback on the effects of diet, exercise, and medications. To maximize the potential benefits of SMBG, clinicians must have confidence in the accuracy of their patients' glucose meters.Objective: The aim of this article is to review several issues related to glucose meter accuracy and ways that accuracy can be enhanced.Methods: A MeDLINE search of English-language articles using the terms SMBG, glucose meter,and accuracy as an initial screen was performed. After articles describing the use of outdated technologies or vague methodologies were excluded, appropriate articles that analyzed various aspects regarding meter accuracy were selected.Results: Glucose meter accuracy studies are complicated by issues related to the reference method, the sample being assayed, nd he ay n which accuracy s reported. error rid analysis ives linicians a means to valuate the clinical importance of meter error. Modern glucose meters have many technological improvements and enhanced clinical accuracy; however, the accuracy of readings depends not only on the instrument but also on patient technique and other aspects of the overall testing process.Conclusions: SMBG has proven to be a valuable tool for the management of diabetes whether it is used to guide insulin dosing, provide feedback on the effect of meals, or detect hypoglycemia. Accuracy of SMBG can be optimized by patient education and continued improvements in meter technology.     

Statistical measurements of accuracy in cervical cytology

Statistical measures that can be used to monitor the level of accuracy of cervical cytologic screening are examined: sensitivity, the false-negative rate, the interval cancer rate among women with negative reports and the predictive value of a positive test. Sensitivity and the false-negative rate are subject to biases and problems of determination that may make them less useful as measures of accuracy. The interval cancer rate and the positive predictive value may be better methods of assessing the frequency of serious abnormalities following negative cytologic reports and the accuracy of cytologic reports of serious abnormalities, respectively. It is important to recognize that no single measure of accuracy can adequately define a laboratory's performance. The use of statistical analyses can be invaluable in placing in context the criticisms of cervical cytology as an appropriate screening test for the prevention of cervical cancer; statistical analysis should be used as one component of quality control.     

Relative Accuracy Evaluation

The quality of data plays an important role in business analysis and decision making, and data accuracy is an important aspect in data quality. Thus one necessary task for data quality management is to evaluate the accuracy of the data. And in order to solve the problem that the accuracy of the whole data set is low while a useful part may be high, it is also necessary to evaluate the accuracy of the query results, called relative accuracy. However, as far as we know, neither measure nor effective methods for the accuracy evaluation methods are proposed. Motivated by this, for relative accuracy evaluation, we propose a systematic method. We design a relative accuracy evaluation framework for relational databases based on a new metric to measure the accuracy using statistics. We apply the methods to evaluate the precision and recall of basic queries, which show the result''s relative accuracy. We also propose the method to handle data update and to improve accuracy evaluation using functional dependencies. Extensive experimental results show the effectiveness and efficiency of our proposed framework and algorithms.     

Retrieval accuracy, statistical significance and compositional similarity in protein sequence database searches

Protein sequence database search programs may be evaluated both for their retrieval accuracy—the ability to separate meaningful from chance similarities—and for the accuracy of their statistical assessments of reported alignments. However, methods for improving statistical accuracy can degrade retrieval accuracy by discarding compositional evidence of sequence relatedness. This evidence may be preserved by combining essentially independent measures of alignment and compositional similarity into a unified measure of sequence similarity. A version of the BLAST protein database search program, modified to employ this new measure, outperforms the baseline program in both retrieval and statistical accuracy on ASTRAL, a SCOP-based test set.     

Using neural networks for prediction of the subcellular location of proteins.

Neural networks have been trained to predict the subcellular location of proteins in prokaryotic or eukaryotic cells from their amino acid composition. For three possible subcellular locations in prokaryotic organisms a prediction accuracy of 81% can be achieved. Assigning a reliability index, 33% of the predictions can be made with an accuracy of 91%. For eukaryotic proteins (excluding plant sequences) an overall prediction accuracy of 66% for four locations was achieved, with 33% of the sequences being predicted with an accuracy of 82% or better. With the subcellular location restricting a protein's possible function, this method should be a useful tool for the systematic analysis of genome data and is available via a server on the world wide web.     

Increasing the accuracy of the global alignment of amino acid sequences by constructing a set of alignment candidates

The accuracy of the global Smith-Waterman alignments and Pareto-optimal alignments depending on the degree of sequence similarity (percent of coincidence, % id, and the number of remote fragments NGap) has been examined. An algorithm for constructing a set of three to six alignments has been developed of which the accuracy of the best alignment exceeds on the average the accuracy of the best alignment that can be constructed using the Smith-Waterman algorithm. For weakly homologous sequences (% id 15, NGap 20), the increase in the accuracy is on the average about 8%, with the average accuracy of the global Smith-Waterman alignments being about 38% (the accuracy was estimated on model test sets).     

More genes or more taxa? The relative contribution of gene number and taxon number to phylogenetic accuracy

The relative contribution of taxon number and gene number to accuracy in phylogenetic inference is a major issue in phylogenetics and of central importance to the choice of experimental strategies for the successful reconstruction of a broad sketch of the tree of life. Maximization of the number of taxa sampled is the strategy favored by most phylogeneticists, although its necessity remains the subject of debate. Vast increases in gene number are now possible due to advances in genomics, but large numbers of genes will be available for only modest numbers of taxa, raising the question of whether such genome-scale phylogenies will be robust to the addition of taxa. To examine the relative benefit of increasing taxon number or gene number to phylogenetic accuracy, we have developed an assay that utilizes the symmetric difference tree distance as a measure of phylogenetic accuracy. We have applied this assay to a genome-scale data matrix containing 106 genes from 14 yeast species. Our results show that increasing taxon number correlates with a slight decrease in phylogenetic accuracy. In contrast, increasing gene number has a significant positive effect on phylogenetic accuracy. Analyses of an additional taxon-rich data matrix from the same yeast clade show that taxon number does not have a significant effect on phylogenetic accuracy. The positive effect of gene number and the lack of effect of taxon number on phylogenetic accuracy are also corroborated by analyses of two data matrices from mammals and angiosperm plants, respectively. We conclude that, for typical data sets, the number of genes utilized may be a more important determinant of phylogenetic accuracy than taxon number.     

Optimal parameter settings for information processing in gene regulatory networks

Gene networks can often be interpreted as computational circuits. This article investigates the computational properties of gene regulatory networks defined in terms of the speed and the accuracy of the output of a gene network. It will be shown that there is no single optimal set of parameters, but instead, there is a trade-off between speed and accuracy. Using the trade-off it will also be shown how systems with various parameters can be ranked with respect to their computational efficiency. Numerical analysis suggests that the trade-off can be improved when the output gene is repressing itself, even though the accuracy or the speed of the auto-regulated system may be worse than the unregulated system.     

Development of head docking device for linac-based radiosurgery with a Neptun 10 PC linac

Stereotactic radiosurgery is a method for focused irradiation of intracranial lesions. Linac-based radiosurgery is currently performed by two techniques: couch mounted and pedestal mounted. In the first technique a device is required to affix the patient's head to the couch and neoreover to translate it accurately. Structure of such a device constructed by the authors plus acceptance test performed for evaluation is described in the article.A head docking device has been designed and constructed according to geometry of linac's couch and also desired functions. The device is cornpletely made from aluminum and consists of four major components: attachment bar, lower structure with four moveing accuracy mechanical stability and isocentric accuracy were assessed in the frame of acceptance test.Translating accuracy, mechanical stability and isocentric accuracy were found to be respectively: 1 mm, 1.64 mm and 3.2 mm with accuracy of 95%.According to AAPM report no. 54, a head docking device should translate head with an accuracy of 1 mm; this recommendation has been met. Moreover, we have demonstrated that the isocentric accuracy and mechanical stability of the device are sufficient that the device on confidently be used in stereotactic treatment.     

Radiochemical determination of the optical purity of L-amino acids

This technique gives accurate, reproducible results in a short time. It is particularly suitable for studying alanine, aspartic acid, glutamic acid, isoleucine, leucine, methionine, phenylalanine, selenomethionine, serine, tyrosine, and valine. A major factor contributing to the accuracy and the precision of the method is the statistical accuracy when scanning. The value obtained for the “blank” will be crucial to the accuracy of the determination. When scanning, the same portion of the tlc plate is examined to determine the α-keto acid and to determine the value of the “blank” to be subtracted to give the true d-isomer value. Tritium samples were not determined with the same accuracy or precision as the other isotopes due to the inferior single detector scanner in use. The method cannot be used for tritiated samples labeled in the 2-position due to the loss of activity.     

On the proper use of mass accuracy in proteomics

Mass measurement is the main outcome of mass spectrometry-based proteomics yet the potential of recent advances in accurate mass measurements remains largely unexploited. There is not even a clear definition of mass accuracy in the proteomics literature, and we identify at least three uses of this term: anecdotal mass accuracy, statistical mass accuracy, and the maximum mass deviation (MMD) allowed in a database search. We suggest using the second of these terms as the generic one. To make the best use of the mass precision offered by modern instruments we propose a series of simple steps involving recalibration of the data on "internal standards" contained in every proteomics data set. Each data set should be accompanied by a plot of mass errors from which the appropriate MMD can be chosen. More advanced uses of high mass accuracy include an MMD that depends on the signal abundance of each peptide. Adapting search engines to high mass accuracy in the MS/MS data is also a high priority. Proper use of high mass accuracy data can make MS-based proteomics one of the most "digital" and accurate post-genomics disciplines.     

How a GNSS Receiver Is Held May Affect Static Horizontal Position Accuracy

The static horizontal position accuracy of a mapping-grade GNSS receiver was tested in two forest types over two seasons, and subsequently was tested in one forest type against open sky conditions in the winter season. The main objective was to determine whether the holding position during data collection would result in significantly different static horizontal position accuracy. Additionally, we wanted to determine whether the time of year (season), forest type, or environmental variables had an influence on accuracy. In general, the F4Devices Flint GNSS receiver was found to have mean static horizontal position accuracy levels within the ranges typically expected for this general type of receiver (3 to 5 m) when differential correction was not employed. When used under forest cover, in some cases the GNSS receiver provided a higher level of static horizontal position accuracy when held vertically, as opposed to held at an angle or horizontally (the more natural positions), perhaps due to the orientation of the antenna within the receiver, or in part due to multipath or the inability to use certain satellite signals. Therefore, due to the fact that numerous variables may affect static horizontal position accuracy, we only conclude that there is weak to moderate evidence that the results of holding position are significant. Statistical test results also suggest that the season of data collection had no significant effect on static horizontal position accuracy, and results suggest that atmospheric variables had weak correlation with horizontal position accuracy. Forest type was found to have a significant effect on static horizontal position accuracy in one aspect of one test, yet otherwise there was little evidence that forest type affected horizontal position accuracy. Since the holding position was found in some cases to be significant with regard to the static horizontal position accuracy of positions collected in forests, it may be beneficial to have an understanding of antenna positioning within the receiver to achieve the greatest accuracy during data collection.     

Second-Order Conditioning and Conditioned Inhibition: Influences of Speed versus Accuracy on Human Causal Learning

In human causal learning, excitatory and inhibitory learning effects can sometimes be found in the same paradigm by altering the learning conditions. This study aims to explore whether learning in the feature negative paradigm can be dissociated by emphasising speed over accuracy. In two causal learning experiments, participants were given a feature negative discrimination in which the outcome caused by one cue was prevented by the addition of another. Participants completed training trials either in a self-paced fashion with instructions emphasising accuracy, or under strict time constraints with instructions emphasising speed. Using summation tests in which the preventative cue was paired with another causal cue, participants in the accuracy groups correctly rated the preventative cue as if it reduced the probability of the outcome. However, participants in the speed groups rated the preventative cue as if it increased the probability of the outcome. In Experiment 1, both speed and accuracy groups later judged the same cue to be preventative in a reasoned inference task. Experiment 2 failed to find evidence of similar dissociations in retrospective revaluation (release from overshadowing vs. mediated extinction) or learning about a redundant cue (blocking vs. augmentation). However in the same experiment, the tendency for the accuracy group to show conditioned inhibition and the speed group to show second-order conditioning was consistent even across sub-sets of the speed and accuracy groups with equivalent accuracy in training, suggesting that second-order conditioning is not merely a consequence of poorer acquisition. This dissociation mirrors the trade-off between second-order conditioning and conditioned inhibition observed in animal conditioning when training is extended.     

A coalescent model for genotype imputation

The potential for imputed genotypes to enhance an analysis of genetic data depends largely on the accuracy of imputation, which in turn depends on properties of the reference panel of template haplotypes used to perform the imputation. To provide a basis for exploring how properties of the reference panel affect imputation accuracy theoretically rather than with computationally intensive imputation experiments, we introduce a coalescent model that considers imputation accuracy in terms of population-genetic parameters. Our model allows us to investigate sampling designs in the frequently occurring scenario in which imputation targets and templates are sampled from different populations. In particular, we derive expressions for expected imputation accuracy as a function of reference panel size and divergence time between the reference and target populations. We find that a modestly sized "internal" reference panel from the same population as a target haplotype yields, on average, greater imputation accuracy than a larger "external" panel from a different population, even if the divergence time between the two populations is small. The improvement in accuracy for the internal panel increases with increasing divergence time between the target and reference populations. Thus, in humans, our model predicts that imputation accuracy can be improved by generating small population-specific custom reference panels to augment existing collections such as those of the HapMap or 1000 Genomes Projects. Our approach can be extended to understand additional factors that affect imputation accuracy in complex population-genetic settings, and the results can ultimately facilitate improvements in imputation study designs.     

A refined accuracy index to evaluate algorithms of protein secondary structure prediction

Nowadays even a 1% increase of the accuracy for the secondary structure prediction is considered remarkable progress. In this case, we have to consider the reasonableness of the accuracy index Q3, which is used widely. A refined accuracy index, called Q8, is proposed to evaluate algorithms of secondary structure prediction. It is shown that Q8 is superior to the widely used index Q3 in that the former carries more information of the predictive accuracy matrix than does the latter. Therefore, algorithms are evaluated more objectively by Q8 than Q3. Based on 396 nonhomologous proteins, five currently available algorithms of secondary structure prediction were evaluated and compared using the new index Q8. Of the five algorithms, PHD turned out to be the unique algorithm, with Q8 accuracy better than 70%. It is suggested that Q3 should be replaced by Q8 in evaluating secondary structure prediction in future studies.     

Drive counts as a method of estimating ungulate density in forests: mission impossible?

Although drive counts are frequently used to estimate the size of deer populations in forests, little is known about how counting methods or the density and social organization of the deer species concerned influence the accuracy of the estimates obtained, and hence their suitability for informing management decisions. As these issues cannot readily be examined for real populations, we conducted a series of ‘virtual experiments’ in a computer simulation model to evaluate the effects of block size, proportion of forest counted, deer density, social aggregation and spatial auto-correlation on the accuracy of drive counts. Simulated populations of red and roe deer were generated on the basis of drive count data obtained from Polish commercial forests. For both deer species, count accuracy increased with increasing density, and decreased as the degree of aggregation, either demographic or spatial, within the population increased. However, the effect of density on accuracy was substantially greater than the effect of aggregation. Although improvements in accuracy could be made by reducing the size of counting blocks for low-density, aggregated populations, these were limited. Increasing the proportion of the forest counted led to greater improvements in accuracy, but the gains were limited compared with the increase in effort required. If it is necessary to estimate the deer population with a high degree of accuracy (e.g. within 10% of the true value), drive counts are likely to be inadequate whatever the deer density. However, if a lower level of accuracy (within 20% or more) is acceptable, our study suggests that at higher deer densities (more than ca. five to seven deer/100 ha) drive counts can provide reliable information on population size.