Oil Spills or Look-Alikes? Classification Rank of Surface Ocean Slick Signatures in Satellite Data

Linear discriminant analysis (LDA) is a mathematically robust multivariate data analysis approach that is sometimes used for surface oil slick signature classification. Our goal is to rank the effectiveness of LDAs to differentiate oil spills from look-alike slicks. We explored multiple combinations of (i) variables (size information, Meteorological-Oceanographic (metoc), geo-location parameters) and (ii) data transformations (non-transformed, cube root, log10). Active and passive satellite-based measurements of RADARSAT, QuikSCAT, AVHRR, SeaWiFS, and MODIS were used. Results from two experiments are reported and discussed: (i) an investigation of 60 combinations of several attributes subjected to the same data transformation and (ii) a survey of 54 other data combinations of three selected variables subjected to different data transformations. In Experiment 1, the best discrimination was reached using ten cube-transformed attributes: ~85% overall accuracy using six pieces of size information, three metoc variables, and one geo-location parameter. In Experiment 2, two combinations of three variables tied as the most effective: ~81% of overall accuracy using area (log transformed), length-to-width ratio (log- or cube-transformed), and number of feature parts (non-transformed). After verifying the classification accuracy of 114 algorithms by comparing with expert interpretations, we concluded that applying different data transformations and accounting for metoc and geo-location attributes optimizes the accuracies of binary classifiers (oil spill vs. look-alike slicks) using the simple LDA technique.

Identification of multi-defects in an arched composite structure by the corrected probabilistic diagnostic imaging with the fused damage index

Due to the complicacy of geometry and structure in the arched composite structure, it is difficult to monitor various kinds of defects accurately. The developed damage probabilistic diagnostic imaging approach based on ultrasonic guided wave energy signal characteristics is very feasible for the structural health monitoring in the arched composite structures. However, the conventional probabilistic diagnostic imaging (PDI) approaches united with the signal energy damage indices ( DIs) have some limitations in the identification of the number, location and specific size information of multi-defects. Thus, the damage shape factor from the single damage-impaired path imminently demands to be majorized to raise the precision and stability of PDI approach in the damage recognition. A corrected probabilistic diagnostic imaging (CPDI) approach integrated with the damage shape factor [Formula: see text] needs to be recommended to precisely inspect the expansion of defect zones and different multi-defects in the arched composite structure. The availability and feasibility of the proposed methods has been validated by the experiments in the tested specimen. The results show that the fused frequency-domain energy DIs can be applied to indicate the expansion of defect zones quantitatively. It is proved that the defect identification accuracy of multi-defects from the CPDI approach can be improved by the majorization of damage shape factor, effectively. It is also clearly observed that the number, location and specific size information of different conditions of multi-defects can be distinguished by using the CPDI algorithm, availably.

On a modified auction dynamics technique for semi-supervised multiclass data classification

Multiclass data classification, where the goal is to segment data into classes, is an important task in machine learning. However, the task is challenging due to reasons including the scarcity of labeled training data; in fact, most machine learning algorithms require a large amount of labeled examples to perform well. Moreover, the accuracy of a classifier can be dependent on the accuracy of the training labels which can be corrupted. In this paper, we present an efficient and unconditionally stable semi-supervised graph-based method for multiclass data classification which requires considerably less labeled training data to accurately classify a data set compared to current techniques, due to properties such as the embedding of data into a similarity graph. In particular, it performs very well and more accurately than current approaches in the common scenario of few labeled training elements. Morever, we show that the algorithm performs with good accuracy even with a large number of mislabeled examples and is also able to incorporate class size information. The proposed method uses a modified auction dynamics technique. Extensive experiments on benchmark datasets are performed and the results are compared to other methods.

CDK control pathways integrate cell size and ploidy information to control cell division

Maintenance of cell size homeostasis is a property that is conserved throughout eukaryotes. Cell size homeostasis is brought about by the co-ordination of cell division with cell growth, and requires restriction of smaller cells from undergoing mitosis and cell division, whilst allowing larger cells to do so. Cyclin-CDK is the fundamental driver of mitosis and therefore ultimately ensures size homeostasis. Here we dissect determinants of CDK activity in vivo to investigate how cell size information is processed by the cell cycle network in fission yeast. We develop a high-throughput single-cell assay system of CDK activity in vivo and show that inhibitory tyrosine phosphorylation of CDK encodes cell size information, with the phosphatase PP2A aiding to set a size threshold for division. CDK inhibitory phosphorylation works synergistically with PP2A to prevent mitosis in smaller cells. Finally, we find that diploid cells of equivalent size to haploid cells exhibit lower CDK activity in response to equal cyclin-CDK enzyme concentrations, suggesting that CDK activity is reduced by increased DNA levels. Therefore, scaling of cyclin-CDK levels with cell size, CDK inhibitory phosphorylation, PP2A, and DNA-dependent inhibition of CDK activity, all inform the cell cycle network of cell size, thus contributing to cell-size homeostasis.

Factors Affecting Auditory Estimates of Virtual Room Size: Effects of Stimulus, Level, and Reverberation

When vision is unavailable, auditory level and reverberation cues provide important spatial information regarding the environment, such as the size of a room. We investigated how room-size estimates were affected by stimulus type, level, and reverberation. In Experiment 1, 15 blindfolded participants estimated room size after performing a distance bisection task in virtual rooms that were either anechoic (with level cues only) or reverberant (with level and reverberation cues) with a relatively short reverberation time of T60 = 400 milliseconds. Speech, noise, or clicks were presented at distances between 1.9 and 7.1 m. The reverberant room was judged to be significantly larger than the anechoic room ( p < .05) for all stimuli. In Experiment 2, only the reverberant room was used and the overall level of all sounds was equalized, so only reverberation cues were available. Ten blindfolded participants took part. Room-size estimates were significantly larger for speech than for clicks or noise. The results show that when level and reverberation cues are present, reverberation increases judged room size. Even relatively weak reverberation cues provide room-size information, which could potentially be used by blind or visually impaired individuals encountering novel rooms.

SIns: A Novel Insertion Detection Approach Based on Soft-Clipped Reads

As a common type of structural variation, an insertion refers to the addition of a DNA sequence into an individual genome and is usually associated with some inherited diseases. In recent years, many methods have been proposed for detecting insertions. However, the accurate calling of insertions is also a challenging task. In this study, we propose a novel insertion detection approach based on soft-clipped reads, which is called SIns. First, based on the alignments between paired reads and the reference genome, SIns extracts breakpoints from soft-clipped reads and determines insertion locations. The insert size information about paired reads is then further clustered to determine the genotype, and SIns subsequently adopts Minia to assemble the insertion sequences. Experimental results show that SIns can achieve better performance than other methods in terms of the F-score value for simulated and true datasets.

Retrospectively Estimating Energy Intake and Misreporting From a Qualitative Food Frequency Questionnaire: An Example Using Australian Cohort and National Survey Data

Qualitative food frequency questionnaires (Q-FFQ) omit portion size information from dietary assessment. This restricts researchers to consumption frequency data, limiting investigations of dietary composition (i.e., energy-adjusted intakes) and misreporting. To support such researchers, we provide an instructive example of Q-FFQ energy intake estimation that derives typical portion size information from a reference survey population and evaluates misreporting. A sample of 1,919 Childhood Determinants of Adult Health Study (CDAH) participants aged 26–36 years completed a 127-item Q-FFQ. We assumed sex-specific portion sizes for Q-FFQ items using 24-h dietary recall data from the 2011–2012 Australian National Nutrition and Physical Activity Survey (NNPAS) and compiled energy density values primarily using the Australian Food Composition Database. Total energy intake estimation was daily equivalent frequency × portion size (g) × energy density (kJ/g) for each Q-FFQ item, summed. We benchmarked energy intake estimates against a weighted sample of age-matched NNPAS respondents (n = 1,383). Median (interquartile range) energy intake was 9,400 (7,580–11,969) kJ/day in CDAH and 9,055 (6,916–11,825) kJ/day in weighted NNPAS. Median energy intake to basal metabolic rate ratios were 1.43 (1.15–1.78) in CDAH and 1.35 (1.03–1.74) in weighted NNPAS, indicating notable underreporting in both samples, with increased levels of underreporting among the overweight and obese. Using the Goldberg and predicted total energy expenditure methods for classifying misreporting, 65 and 41% of CDAH participants had acceptable/plausible energy intake estimates, respectively. Excluding suspected CDAH misreporters improved the plausibility of energy intake estimates, concordant with expected body weight associations. This process can assist researchers wanting an estimate of energy intake from a Q-FFQ and to evaluate misreporting, broadening the scope of diet–disease investigations that depend on consumption frequency data.

Method for optimizing the errors of the wavefront of recorded radiation for large-size information-measuring optical systems

The article proposes a new method for increasing the information content of the image by minimizing the errors of the optical system of a large telescope using the redistribution of errors over the image field. Variation of the parameters of the secondary mirror is carried out to solve the optimization problem of uniform distribution of aberrations over the image field of a large telescope using the example of a cryotelescope with a diameter of 12 m in the Zemax-EE package. The results of calculations of optical schemes by the traditional method and by the method of optimization of the errors of the radiation wavefront are presented. The advantages and disadvantages are considered.