Seeing beyond reading: a survey on visual text analytics

We review recent visualization techniques aimed at supporting tasks that require the analysis of text documents, from approaches targeted at visually summarizing the relevant content of a single document to those aimed at assisting exploratory investigation of whole collections of documents.Techniques are organized considering their target input materialeither single texts or collections of textsand their focus, which may be at displaying content, emphasizing relevant relationships, highlighting the temporal evolution of a document or collection, or helping users to handle results from a query posed to a search engine.We describe the approaches adopted by distinct techniques and briefly review the strategies they employ to obtain meaningful text models, discuss how they extract the information required to produce representative visualizations, the tasks they intend to support and the interaction issues involved, and strengths and limitations. Finally, we show a summary of techniques, highlighting their goals and distinguishing characteristics. We also briefly discuss some open problems and research directions in the fields of visual text mining and text analytics.

OPTIMUM RATIO OF CALCIUM AND BORON IN THE NUTRIENT SOLUTION OR IN CASTOR BEAN SHOOT FOR FRUIT YIELD AND SEED OIL CONTENT

Calcium (Ca) and boron (B) have been reported as the major macro-and micronutrient required for castor bean plant yield. The objective of this study was to determine the Ca: B ratios (in the growth media and plant tissue) for fruit yield and shoot dry weight of the castor bean (Ricinus communis L.), grown in a nutrient solution, and to evaluate Ca and B supply on concentration and total uptake of Ca, potassium (K), magnesium (Mg), and B, as well on the seed oil content. The treatments were arranged in a 3 x 3 factorial fashion, consisting of three rates of Ca (40, 80, and 160 mg L-1) and three of B (0.32, 0.96, and 1.60 mg L-1). Calcium and B rates increased the shoot and root dry weight and fruit yield at a Ca: B ratio in the nutrient solution of 166 and 100, respectively. Symptoms of B deficiency were observed in plants supplied with 0.32 mg B L-1, regardless of the Ca concentration in the nutrient solution. Plants which showed visual symptoms of Ca deficiency cultivated with 40 mg Ca L-1 presented concentration of Ca in plant tissue up to 10 g kg(-1). The concentration and total Ca and B uptake increased with the rates of them. Notwithstanding, the shoot Ca accumulation was improved by B rates. In addition, there were no decreases in K and Mg uptake due to Ca rates. Furthermore, addition of 80 mg L-1 of Ca and 1.60 mg L-1 of B in the growth media increased the seed oil content. The Ca: B ratio in the diagnostic leaf associated with the highest plant dry weight (shoot and root) and fruit yield, was 500 (16 to 20 g kg(-1) of Ca, and for 30 to 40 mg kg(-1) of B).

Semi-supervised dimensionality reduction based on partial least squares for visual analysis of high dimensional data

Dimensionality reduction is employed for visual data analysis as a way to obtaining reduced spaces for high dimensional data or to mapping data directly into 2D or 3D spaces. Although techniques have evolved to improve data segregation on reduced or visual spaces, they have limited capabilities for adjusting the results according to user's knowledge. In this paper, we propose a novel approach to handling both dimensionality reduction and visualization of high dimensional data, taking into account user's input. It employs Partial Least Squares (PLS), a statistical tool to perform retrieval of latent spaces focusing on the discriminability of the data. The method employs a training set for building a highly precise model that can then be applied to a much larger data set very effectively. The reduced data set can be exhibited using various existing visualization techniques. The training data is important to code user's knowledge into the loop. However, this work also devises a strategy for calculating PLS reduced spaces when no training data is available. The approach produces increasingly precise visual mappings as the user feeds back his or her knowledge and is capable of working with small and unbalanced training sets.

Visual impairment in children with spastic cerebral palsy measured by psychophysical and electrophysiological grating acuity tests

Background: This study measured grating visual acuity in 173 children between 6-48 months of age who had different types of spastic cerebral palsy (CP). Method: Behavioural acuity was measured with the Teller Acuity Cards (TAC) using a staircase psychophysical procedure. Electrophysiological visual acuity was estimated using the sweep VEP (sVEP). Results: The percentage of children outside the superior tolerance limits was 44 of 63 (69%) and 50 of 55 (91%) of tetraplegic, 36 of 56 (64%) and 42 of 53 (79%) of diplegic, 10 of 48 (21%) and 12 of 40 (30%) of hemiplegic for sVEP and TAC, respectively. For the sVEP, the greater visual acuity deficit found in the tetraplegic group was significantly different from that of the hemiplegic group (p < 0.001). In the TAC procedure the mean visual acuity deficits of the tetraplegic and diplegic groups were significantly different from that of hemiplegic group (p < 0.001). The differences between sVEP and TAC means of visual acuity difference were statistically significant for the tetraplegic (p < 0.001), diplegic (p < 0.001), and hemiplegic group (p = 0.004). Discussion: Better visual acuities were obtained in both procedures for hemiplegic children compared to diplegic or tetraplegic. Tetraplegic and diplegic children showed greater discrepancies between the TAC and sVEP results. Inter-ocular acuity difference was more frequent in sVEP measurements. Conclusions: Electrophysiologically measured visual acuity is better than behavioural visual acuity in children with CP.

Decreasing the number of false positives in sequence classification

Abstract Background A large number of probabilistic models used in sequence analysis assign non-zero probability values to most input sequences. To decide when a given probability is sufficient the most common way is bayesian binary classification, where the probability of the model characterizing the sequence family of interest is compared to that of an alternative probability model. We can use as alternative model a null model. This is the scoring technique used by sequence analysis tools such as HMMER, SAM and INFERNAL. The most prevalent null models are position-independent residue distributions that include: the uniform distribution, genomic distribution, family-specific distribution and the target sequence distribution. This paper presents a study to evaluate the impact of the choice of a null model in the final result of classifications. In particular, we are interested in minimizing the number of false predictions in a classification. This is a crucial issue to reduce costs of biological validation. Results For all the tests, the target null model presented the lowest number of false positives, when using random sequences as a test. The study was performed in DNA sequences using GC content as the measure of content bias, but the results should be valid also for protein sequences. To broaden the application of the results, the study was performed using randomly generated sequences. Previous studies were performed on aminoacid sequences, using only one probabilistic model (HMM) and on a specific benchmark, and lack more general conclusions about the performance of null models. Finally, a benchmark test with P. falciparum confirmed these results. Conclusions Of the evaluated models the best suited for classification are the uniform model and the target model. However, the use of the uniform model presents a GC bias that can cause more false positives for candidate sequences with extreme compositional bias, a characteristic not described in previous studies. In these cases the target model is more dependable for biological validation due to its higher specificity.

The nature of extremely red galaxies in the local universe

We investigate the nature of extremely red galaxies (ERGs), objects whose colours are redder than those found in the red sequence present in colour–magnitude diagrams of galaxies. We selected from the Sloan Digital Sky Survey Data Release 7 a volume-limited sample of such galaxies in the redshift interval 0.010 < z < 0.030, brighter than Mr = −17.8 (magnitudes dereddened, corrected for the Milky Way extinction) and with (g − r) colours larger than those of galaxies in the red sequence. This sample contains 416 ERGs, which were classified visually. Our classification was cross-checked with other classifications available in the literature. We found from our visual classification that the majority of objects in our sample are edge-on spirals (73 per cent). Other spirals correspond to 13 per cent, whereas elliptical galaxies comprise only 11 per cent of the objects. After comparing the morphological mix and the distributions of Hα/Hβ and axial ratios of ERGs and objects in the red sequence, we suggest that dust, more than stellar population effects, is the driver of the red colours found in these extremely red galaxies.

Verbal and visual stimulation effects on rectus femoris and biceps femoris muscles during isometric and concentric

Abstract Background: Coactivation may be both desirable (injury prevention) or undesirable (strength measurement). In this context, different styles of muscle strength stimulus have being investigated. In this study we evaluated the effects of verbal and visual stimulation on rectus femoris and biceps femoris muscles contraction during isometric and concentric. Methods: We investigated 13 men (age =23.1 ± 3.8 years old; body mass =75.6 ± 9.1 kg; height =1.8 ± 0.07 m). We used the isokinetic dynamometer BIODEX device and an electromyographic (EMG) system. We evaluated the maximum isometric and isokinetic knee extension and flexion at 60°/s. The following conditions were evaluated: without visual nor verbal command (control); verbal command; visual command and; verbal and visual command. In relation to the concentric contraction, the volunteers performed five reciprocal and continuous contractions at 60°/s. With respect to isometric contractions it was made three contractions of five seconds for flexion and extension in a period of one minute. Results: We found that the peak torque during isometric flexion was higher in the subjects in the VVC condition (p > 0.05). In relation to muscle coactivation, the subjects presented higher values at the control condition (p > 0.05). Conclusion We suggest that this type of stimulus is effective for the lower limbs.

A system for classification of time-series data from industrial non-destructive device

This work proposes a system for classification of industrial steel pieces by means of magnetic nondestructive device. The proposed classification system presents two main stages, online system stage and off-line system stage. In online stage, the system classifies inputs and saves misclassification information in order to perform posterior analyses. In the off-line optimization stage, the topology of a Probabilistic Neural Network is optimized by a Feature Selection algorithm combined with the Probabilistic Neural Network to increase the classification rate. The proposed Feature Selection algorithm searches for the signal spectrogram by combining three basic elements: a Sequential Forward Selection algorithm, a Feature Cluster Grow algorithm with classification rate gradient analysis and a Sequential Backward Selection. Also, a trash-data recycling algorithm is proposed to obtain the optimal feedback samples selected from the misclassified ones.

Supervised classification of basaltic aggregate particles based on texture properties

The strength and durability of materials produced from aggregates (e.g., concrete bricks, concrete, and ballast) are critically affected by the weathering of the particles, which is closely related to their mineral composition. It is possible to infer the degree of weathering from visual features derived from the surface of the aggregates. By using sound pattern recognition methods, this study shows that the characterization of the visual texture of particles, performed by using texture-related features of gray scale images, allows the effective differentiation between weathered and nonweathered aggregates. The selection of the most discriminative features is also performed by taking into account a feature ranking method. The evaluation of the methodology in the presence of noise suggests that it can be used in stone quarries for automatic detection of weathered materials.