Scatterplot Matrix Research Articles

Prevalence of flatfoot among young Korean males and the correlation among flatfoot angles measured in weight-bearing lateral radiographs.

Flatfoot causes significant fatigue and pain while walking, and even asymptomatic flatfoot may increase the risk of metatarsal stress fracture during long-distance walking. While most studies have used physical examination or plantar footprints to diagnose flatfoot, a weight-bearing radiograph of the foot provides more objective data. However, data on the prevalence of flatfoot in Asian populations gathered in a nationwide cohort of a specific age group is lacking. We examined the prevalence of flatfoot among 19-year-old male Korean army recruits using a weight-bearing lateral radiograph and evaluated the correlation among flatfoot angles. A total of 560,141 19-year-old Korean males were examined at the regional Military Manpower Administration offices between April 2018 and April 2020. Weight-bearing lateral radiographs of the foot were obtained using an X-ray system while the subjects were standing on a table with their feet in a neutral position. Based on these radiographs, military orthopedic surgeons and radiologists measured the talo-first metatarsal angle (TMA) and calcaneal pitch angle (CPA) for flatfoot diagnosis. Mild flatfoot was diagnosed when the TMA ranged from 6 to 15° or the CPA was <17°, and moderate-to-severe flatfoot was diagnosed when the TMA was 15° or greater or the CPA was <10°. Pearson correlation coefficients and scatter plot matrix were used to evaluate the correlation among the flatfoot angles. Finally, we evaluated the relationship between body mass index (BMI) and flatfoot angles and compared the BMI in subjects with or without self-checked foot deformities including flatfoot and pes cavus. Of the 560,141 subjects, 16,102 (2.9%) were diagnosed as flatfoot, and 5265 (0.9%) were diagnosed with moderate-to-severe flatfoot. The coefficients between TMA and CPA ranged from 0.342 to 0.449 (all P values < 0.001), and those between the 2 sides of TMA and CPA were 0.709 and 0.746 (all P values < 0.001), respectively. BMI had a significant correlation with both TMA and CPA in subjects with flatfoot, and those with self-checked foot deformities had a significantly higher BMI than the group without foot deformities. The prevalence of total flatfoot and moderate-to-severe flatfoot in 19-year-old Korean males based on a weight-bearing lateral radiograph was 2.9% and 0.9%, respectively. The correlation coefficients between TMAs and CPAs showed a low degree of positive correlation. Higher BMI was associated with the likelihood of the presence of flatfoot.

Improved strategy of screening tolerant genotypes in drought stress based on a new program in R-language: a practical triticale breeding program

In order to improve the efficiency of breeding programs related to abiotic stresses, a new package in R-based-language, “PBTolindex,” was introduced to distinguish tolerant genotypes to drought stress. Accordingly, a dataset of a practical breeding program on 30 triticale genotypes cultivated under drought stress and normal irrigation conditions in six different environments was evaluated. Correlation plot, scatter plot matrix, 3 D plot, and biplot along with indices’ values and their correlation coefficients were automatically produced as output files for considering the tested genotypes. Additionally, heatmap, a novel data mining method, was successfully applied for the first time in tolerance analysis. Our results indicated that no single suitable tolerance index could be suggested as the best one, and for any other study, different indices should be considered. Furthermore, the outputs of testing triticale genotypes indicated no suitable genotypes for both conditions. However, genotype ELTTCL15 for normal condition and genotypes ET-90-7, ELTTCL21, and ELTTCL18 for stress conditions were recommended by the program. Testing and identifying genotypes by heatmap and principal component analyses showed that the output of our program was accurate. Therefore, using this source-code in future plant breeding projects based on stress indices in any plant species is recommended.

Geothermal water in Bakreshwar-Tantoli region in West Bengal, India: Implications on water quality for irrigation and drinking purposes

Groundwater becomes more critical for drinking and irrigation purposes due to reduced surface water supplies and contamination of these sources. Knowing the quality of underground water is vital to the protection of public health and sustainable water resource development. The goal of this study is, therefore, to assess the suitability of groundwater for drinking and irrigation purposes in the Bakreshwar-Tantaloi hot springs in West Bengal, India. This study evaluates the quality of water produced from 7 different geothermal hot springs by investigating its chemical concentration. The physicochemical parameters were examined for the water samples. The suitability of groundwater for drinking purposes was measured by contrasting the chemical concentrations with the WHO standards and also computing and comparing the Water Quality Index (WQI) value with recommended norms. The concentrations of Ca2+, Mg2+, Cl−, SO42−, and NO3− are within the WHO (2011) and BIS (2012) tolerable limit. Statistical analyses are used to reveal patterns and groups from the physiochemical dataset of groundwater samples. Principal Component Analysis (PCA) and factor analysis identify the source for groundwater parameters. The correlation matrix and scatter plot matrix give the relationship among major chemical ions of water samples. Dendrogram defines the relationship between physicochemical variables and groups them into strong to weak correlations among water components.

Taxonomic significance of palynological studies for identification of two morphologically similar Malva species.

Pollen studies can assist in distinguishing different plant taxa on the basis of pollen diameter, exine thickness, spines length, spine shape, number of rows of spines between colpi, shape of pollen, P/E ratio, pollen class and aperture type. Light microscopy (LM) and scanning electron microscopy (SEM) was used to compare and differentiate two Malva species on the basis of different pollen characteristics. Pollen in Malva neglecta and Malva parviflora had punctate and subpsilate, and, granulate and verrucate sculpturing between spines respectively. Both species pollen had bulbous spine base, but were more swollen in M.parviflora. M.neglecta had more pollen size, spine length and distance between spine base and spine apex, except spine base and pore diameter. The scatterplot matrix showed a wide variation among sculpturing types and other pollen attributes of both species. In PCA biplot, pore diameter and spine length were linked to M.parviflora while other pollen attributes were clearly linked to M.neglecta, indicating the taxonomic significance of the observed characters. It was concluded that the palynological studies can act as an identification tool and are of great significance in delimiting Malva and different plant taxa. RESEARCH HIGHLIGHTS: Taxonomic identifications is a big problem in morphologically identical Malva neglecta and Malva parviflora. The usefulness of pollen morphology (pollen diameter and exine thickness) and wall ornamentation in identification through light microscopy (LM) and scanning electron microscopy (SEM) was confirmed. Pollen of M.neglecta can be differentiated from M.parviflora by its sculpturing patterns. Spine base and pore diameter of M.parviflora is a taxonomically important feature.

Application of multivariate data analysis to biogeochemical exploration at the Twin Lakes Deposit, Monument Bay Gold Project, Manitoba, Canada

The Twin Lakes Deposit, located in the northwest of the Superior Province, is a promising Archean Greenstone-hosted orogenic gold deposit. Biogeochemical and statistical techniques were used for the first time in this area to assess the geochemical dispersion patterns of Au, As, Bi, Se, Sb, Tl, Fe, Co, Ni, Cr, Mo, Cd, Pb, Zn, Mn, K, P, B, Mg, Ca, Ba, and Cu in black spruce samples collected perpendicularly to the overall trend of shear-hosted gold mineralization. ICP-MS analysis of ashed black spruce bark, twigs, and needles has outlined a biogeochemical response of black spruce to the concealed mineralization. Most elements are accumulated preferentially in twigs and bark, while B, K, Mg, Mn, and P are highly concentrated in needles. High Au and Tl enrichments were identified in all sample types compared to the background level. The scatter plot matrix demonstrated a positive bivariate relationship between Au and Fe, Mg, Cu, Mo, and Zn; and horizontal to slightly negative correlation with Ca, K, Mn, P, and B. Investigations on the spatial distribution pattern of Au and its pathfinder elements using a robust log-transformed inverse distance weighted (IDW) interpolation method revealed the presence of zones of gold enrichment in the north to the northwest of the survey, where Au is accompanied by Tl-As-Cd > Bi, Pb, Se > Sb. According to the Robust RQ-mode principal component analysis (PCA), two main components control the distribution of elements in black spruce, including (1) a geological factor that mostly reflects bedrock composition; and (2) a physiological factor that reflects biological processing of essential elements. Of importance is that Au and its pathfinder elements, including As, Se, Tl, and to a lesser extent, Bi, Cd, and Pb were well-separated as a subgroup of the geological factor with a clear spatial relationship to gold mineralization. This study demonstrates a biogeochemical response in black spruce is associated with gold mineralization at the Monument Bay Gold Project using multivariate statistical methods. The results of this study are promising and can be extrapolated to wider surveys across the Monument Bay Gold Project to vector toward new gold mineralized zones or extensions of the Twin Lakes gold deposit. The results also provide a suite of gold pathfinder elements that support exploration efforts and regional target generation.

Landing Gear Leg Fatigue Life Analysis for Light Aircraft

Light aircraft, designed for training and recreational purposes, have vital components subject to hard time replacement intervals. Given the wide spectrum of operational conditions those aircraft are exposed to, such a maintenance strategy is not optimal since it is not uncommon for the light aircraft owner to also be the pilot and person in charge of maintenance. Fatigue damage is known to be the primary cause of component failure. Component replacement is a common way of avoiding fatigue damage. Accumulated fatigue damage can be expressed through a quantitative variable dependent on many factors; besides component geometry and material properties, aircraft takeoff mass and component load profile play a key role in fatigue life determination. This research aims at providing insight into the interdependence and relationship between fatigue relevant parameters and the fatigue life of a light aircraft landing gear leg to enhance the understanding of input variables and results enabling the improvement of the method for fatigue life prognosis of a light aircraft landing gear leg. Correlation analysis is performed first. The covariance matrix, Pearson's and Spearman's correlation coefficients, correlation statistics, and scatter plot matrices are observed and discussed to establish joint variability of dependent and independent variables. Regression analysis is applied next. Analysis of variance, R-squared statistics, and residual assessment are performed to evaluate the impact on the dependent variable, gaining insight into how well the regression model explains data variability. Shortcomings of the applied methodology and consequent analysis results are identified and discussed. Lastly, a suggestion based on the findings of this research aimed at validating and improving the method for light aircraft landing gear leg fatigue life calculation is stated.

Prioritization of food – pathogen pairs in export refusals of fishery commodities from Indonesia

A wide variety of Indonesian fishery commodities faced refusal during 2017–2019 due to food safety issues, especially microbial contamination. This is concerning because it contributes to a loss in the national income. This study aims to prioritize the combination of food and microbial issues in refused commodities in Indonesia. The study was initiated by developing food – microbial contamination pairs; then a risk scatterplot and risk matrix were used to carry out prioritization based on economic and health aspects. Several metrics representing economic impacts (such as the total value of export and potential loss due to rejections) and health impacts (such as the burden of diseases, number of hospitalizations, and number of deaths) were used in the risk matrix development. This prioritization suggests that shrimp – Salmonella spp. and tuna – Salmonella spp. were the food – pathogen pairs with the highest risk in terms of both economic and health impacts. Tuna – Listeria monocytogenes was placed as a third priority in the majority of metrics representing health impact. Different priorities were considered for other pairs, except for anchovy – Salmonella spp., which is considered the last priority for all matrixes. The metrics used in this prioritization are considered useful in assisting risk managers in determining which commodities should receive attention first based on economic and health considerations. • A structured prioritization based on economic and health impacts was developed. • A combination of risk scatterplot and risk matrix was applied for prioritization. • Salmonella spp. pairing with shrimp and tuna were the top priorities for mitigation.

Morpho-molecular Characterization of Maize Inbred Lines Accelerating Parental Selection for Hybridization

Characterization of genetic diversity is the foundation step for crop improvement, which provides a basis for analyzing combining ability and heterosis of inbred genotypes during a hybridization program. An investigation was carried out at the field laboratory of the Genetics and Plant Breeding department in Bangabandhu Sheikh Mujibur Rahman Agricultural University, to elucidate the genetic architecture by evaluating 12 morphological and 4 molecular (SSR) markers within 52 diverse S1 genotypes, and to assess the relationship of molecular and morphological GD. An almost equal amount of PCV and GCV coupled with high heritability and genetic advance for the traits cob weight (gm), NKPC, and NKPR lead to the selection of promising genotypes based on these characters. Correlation coefficient and scatter plot matrix established a positive and strong relationship of KL (mm), KW (mm), and KT (mm) with 100 kernel weight (gm) suggesting the importance of kernel morphology. Mahalanobis D2 statistics revealed the highest inter-cluster distance between I and II. The percentages of molecular variance within the population and among the population were 76% and 14 %, respectively. The optimum K-value was 5. Heatmap relying on molecular GD exposed MMIL-28, MMIL-54, and MMIL-96 as the most diverse lines. SHE analysis hypothesized the increase of richness and diversity over time. Less correlation between the divergence generated from morphological traits and molecular markers suggested that the morphological variation may be determined by environmental factors and also by genetic factors. A strategy for the effective selection of predicting parental lines for a future hybridization program was developed.

Tamoxifen offers long-term neuroprotection after hippocampal silent infarct in male rats

Silent infarcts (SI) are a cerebral small vessel disease characterized by small subcortical infarcts. These occur in the absence of typical ischemia symptoms but are linked to cognitive decline and dementia. While there are no approved treatments for SI, recent results from our laboratory suggest that tamoxifen, a selective estrogen receptor modulator, is a viable candidate. In the present study, we induced SI in the dorsal hippocampal CA1 region of rats and assessed the effects of systemic administration of tamoxifen (5 mg/kg, twice) 21 days after injury on cognitive and pathophysiological measures, including cell loss, apoptosis, gliosis and estrogen receptors (ERs). We found that tamoxifen protected against the SI-induced cognitive dysfunction on the hippocampal-dependent, place recognition task, cell and ER loss, and increased apoptosis and gliosis in the CA1. Exploratory data analyses using a scatterplot matrix and principal component analysis indicated that SI-tamoxifen rats were indistinguishable from sham controls while they differed from SI rats, who were characterized by enhanced cell loss, apoptosis and gliosis, lower ERs, and recognition memory deficit. Supervised machine learning using support vector machine (SVM) determined predictors of progression from the early ischemic state to the dementia-like state. It showed that caspase-3 and ERα in the CA1 and exploration proportion were reliable and accurate predictors of this progression. Importantly, tamoxifen ameliorated SI-induced effects on all three of these variables, providing further evidence for its viability as a candidate treatment for SI and prevention of associated dementia.

The selective estrogen receptor modulator tamoxifen protects against subtle cognitive decline and early markers of injury 24 h after hippocampal silent infarct in male Sprague-Dawley rats

Silent infarcts (SI) are subcortical cerebral infarcts occurring in the absence of typical ischemia symptoms and are linked to cognitive decline and dementia development. There are no approved treatments for SI. One potential treatment is tamoxifen, a selective estrogen receptor modulator. It is critical to establish whether treatments effectively target the early consequences of SI to avoid progression to complete injury. We induced SI in the dorsal hippocampal CA1 of rats and assessed whether tamoxifen is protective 24 h later against cognitive deficits and injury responses including gliosis, apoptosis, inflammation and changes in estrogen receptors (ERs). SI led to subtle cognitive impairment on the object place task, an effect ameliorated by tamoxifen administration. SI did not lead to detectable hippocampal cell loss but increased apoptosis, astrogliosis, microgliosis and inflammation. Tamoxifen protected against the effects of SI on all measures except microgliosis. SI increased ERα and decreased ERβ in the hippocampus, which were mitigated by tamoxifen. Exploratory data analyses using scatterplot matrices and principal component analysis indicated that SI rats given tamoxifen were indistinguishable from controls. Further, SI rats were significantly different from all other groups, an effect associated with low levels of ERα and increased apoptosis, gliosis, inflammation, ERβ, and time spent with the unmoved object. The results demonstrate that tamoxifen is protective against the early cellular and cognitive consequences of hippocampal SI 24 h after injury. Tamoxifen mitigates apoptosis, gliosis, and inflammation and normalization of ER levels in the CA1, leading to improved cognitive outcomes after hippocampal SI.

Does visual saliency affect decision-making?

In the present study, we explore potential effects of visual saliency on decision quality in context of multi-criteria decision-making (MCDM). We compare two visualization techniques: parallel coordinates (PC) and scatterplot matrices (SPM). We investigate the impact of saliency facilitated by means of either color or size. The saliency and visualization techniques were factors in our analysis, and effects were evaluated in terms of decision quality, attention, time on task, and confidence. Results show that the quality of choice and attention were comparable for all saliency conditions when SPM was used. For PC, we found a positive effect of color saliency both on the quality of choice and on attention. Different forms of saliency led to varying times on task in both PC and SPM; however, those variations were not significant. A comparison of PC and SPM shows, users spent less time on the task, obtained better decision quality, and were more confident with their decision when using PC. To summarize, our findings suggest that saliency can increase attention and decision quality in MCDM for certain visualization techniques and forms of saliency. Another contribution of this work is the novel suggestion of the method to elicit of users’ preferences; its potential benefits are discussed in the end of the paper.Graphic abstract

Modeling Eects of Climatic Variables on Tea Production in Kenya Using Linear Regression Model with Serially Correlated Errors

Aims/ Objectives: To formulated a linear regression model to capture the relationship between tea production and climatic variables in terms of ARIMA.Place and Duration of Study: Department of Mathematics and Actuarial Science, Catholic University of Eastern Africa, Nairobi, Kenya, between June 2019 and April 2021.Methodology: The study used time-series data for mean annual temperature, mean annual rainfall, humidity, solar radiation, and NDVI, collected from six counties, namely Embu, Kakamega, Kisii, Kericho, Meru, and Nyeri.Results: The study ndings noted that there is a presence of trend and seasonality for all the data. The scatter plot matrix for all the climatic variables for all the counties under the study indicated that tea production has a linear relationship with most climatic variables. Model t of the data indicated statistical signicance when tea production data is dierenced. A second linear model with tea production data deseasoned has mixed results in terms of a signicancetest. The variation of independent variables with tea production yielded very low values, suggesting that the data used has many variabilities.Conclusion: The study ndings show the climatic variables can be used to forecast tea production.Recommendation: Future studies may combine the analysis with other statistical modeling procedures such as the GARCH models.

The Network Analysis Profiler (NAP v2.0): a web tool for visual topological comparison between multiple networks

In this article we present the Network Analysis Profiler (NAP v2.0), a web tool to directly compare the topological features of multiple networks simultaneously. NAP is written in R and Shiny and currently offers both 2D and 3D network visualisation, as well as simultaneous visual comparisons of node- and edge-based topological features as bar charts or scatterplot matrix. NAP is fully interactive, and users can easily export and visualise the intersection between any pair of networks using Venn diagrams or a 2D and a 3D multi-layer graph-based visualisation. NAP supports weighted, unweighted, directed, undirected and bipartite graphs.

The Network Analysis Profiler (NAP v2.0): a web tool for visual topological comparison between multiple networks

In this article we present the Network Analysis Profiler (NAP v2.0), a web tool to directly compare the topological features of multiple networks simultaneously. NAP is written in R and Shiny and currently offers both 2D and 3D network visualisation, as well as simultaneous visual comparisons of node- and edge-based topological features as bar charts or scatterplot matrix. NAP is fully interactive, and users can easily export and visualise the intersection between any pair of networks using Venn diagrams or a 2D and a 3D multi-layer graph-based visualisation. NAP supports weighted, unweighted, directed, undirected and bipartite graphs.

Improving Visualization Design for Effective Multi-Objective Decision Making.

Decision-makers across many professions are often required to make multi-objective decisions over increasingly larger volumes of data with several competing criteria. Data visualization is a powerful tool for exploring these complex 'solution spaces', but there is limited research on its ability to support multi-objective decisions. In this article, we explore the effects of chart complexity and data volume on decision quality in multi-objective scenarios with complex trade-offs. We look at the impact of four common multidimensional chart types (scatter plot matrices, parallel coordinates plots, heat maps, radar charts), the number of options and dimensions and participant chart usage experience on decision time and accuracy when selecting the 'optimal option'. As objectively evaluating the quality of multi-objective decisions and the trade-offs involved is challenging, we employ rank- and score-based accuracy metrics. While heat maps demonstrate a time advantage, our findings show no strong performance benefit for one chart type over another for accuracy. We find mixed evidence for the impact of chart complexity on performance, with our results suggesting the existence of a 'ceiling' in the number of dimensions considered by participants. This points to a potential limit to data complexity that is useful for decision making. Lastly, participants who use charts frequently performed better, suggesting that users can potentially be trained to effectively use complex visualizations in their decision-making.

Assessment of heavy metal pollution in Yamuna River, Delhi-NCR, using heavy metal pollution index and GIS

The present study was conducted on the river Yamuna, which passes through Delhi-NCR from Baghpat to Chhainssa, a distance of about 125 km, at six sampling locations to evaluate the concentrations of heavy metals in surface water using heavy metal pollution index (HPI) approach. The river serves both urban-industrial and rural areas in the study area; hence, domestic, industrial, and agricultural wastes are being contributed greatly in the contamination of river water. The Yamuna River is one of the major tributaries of the river Ganga originated in the Himalayas and is flowing through a varied geological terrain. Metals such as iron (Fe), copper (Cu), cobalt (Co), zinc (Zn), lead (Pb), cyanide (CN), nickel (Ni), and chromium (Cr) in selected sites of Yamuna River water were determined by using atomic absorption spectrophotometer. The concentrations of Fe, Cu, Co, Zn, Pb, CN, Ni, and Cr in the river water were found to be in the range of 40–190, 50–120, 4–66, 840–1800, 2–40, 100–600, 88–253, and 35–52 μg/L, respectively. The results show that the maximum heavy metal content was found at sampling site S3 (Nizamuddin) followed by S6 (Chhainssa), S4 (Okhla), S1 (Baghpat), S5 (Manjhawali), and S2 (Pachahira). The heavy metal data was integrated in GIS environment for preparing spatial distribution maps of sampling sites. A scatter plot matrix was created to assess the pattern and interrelationships between heavy metals. The average concentration of heavy metals was recorded high, often exceeding the permissible limits for drinking of surface water prescribed by the Bureau of Indian Standards (BIS) and World Health Organization (WHO). Based on HPI (varies from 98.2 to 555.1), about 85% of the river water was classified as highly polluted; hence, it is not recommended for drinking. Overall, significant variations were observed in concentrations of heavy metals from one location to the other which may be because of toxic industrial effluents and domestic sewage wastes being added to the river water by various anthropogenic activities in the study area. The present work highlights the pollution load of heavy metals in the river Yamuna and also advocates an urgent attention towards minimizing the health risk of people residing not only along the river banks and surrounding regions but also for city population.

Evaluation on interactive visualization data with scatterplots

Scatterplots and scatterplot matrix methods have been popularly used for showing statistical graphics and for exposing patterns in multivariate data. A recent technique, called Linkable Scatterplots, provides an interesting idea for interactive visual exploration which provides a set of necessary plot panels on demand together with interaction, linking and brushing. This article presents a controlled study with a mixed-model design to evaluate the effectiveness and user experience on the visual exploration when using a Sequential-Scatterplots who a single plot is shown at a time, Multiple-Scatterplots who number of plots can be specified and shown, and Simultaneous-Scatterplots who all plots are shown as a scatterplot matrix. Results from the study demonstrated higher accuracy using the Multiple-Scatterplots visualization, particularly in comparison with the Simultaneous-Scatterplots.​ While the time taken to complete tasks was longer in the Multiple-Scatterplots technique, compared with the simpler Sequential-Scatterplots, Multiple-Scatterplots is inherently more accurate. Moreover, the Multiple-Scatterplots technique is the most highly preferred and positively experienced technique in this study. Overall, results support the strength of Multiple-Scatterplots and highlight its potential as an effective data visualization technique for exploring multivariate data.

Combining visual analytics and case-based reasoning for rupture risk assessment of intracranial aneurysms

PurposeMedical case-based reasoning solves problems by applying experience gained from the outcome of previous treatments of the same kind. Particularly for complex treatment decisions, for example, incidentally found intracranial aneurysms (IAs), it can support the medical expert. IAs bear the risk of rupture and may lead to subarachnoidal hemorrhages. Treatment needs to be considered carefully, since it may entail unnecessary complications for IAs with low rupture risk. With a rupture risk prediction based on previous cases, the treatment decision can be supported.MethodsWe present an interactive visual exploration tool for the case-based reasoning of IAs. In presence of a new aneurysm of interest, our application provides visual analytics techniques to identify the most similar cases with respect to morphology. The clinical expert can obtain the treatment, including the treatment outcome, for these cases and transfer it to the aneurysm of interest. Our application comprises a heatmap visualization, an adapted scatterplot matrix and fully or partially directed graphs with a circle- or force-directed layout to guide the interactive selection process. To fit the demands of clinical applications, we further integrated an interactive identification of outlier cases as well as an interactive attribute selection for the similarity calculation. A questionnaire evaluation with six trained physicians was used.ResultOur application allows for case-based reasoning of IAs based on a reference data set. Three classifiers summarize the rupture state of the most similar cases. Medical experts positively evaluated the application.ConclusionOur case-based reasoning application combined with visual analytic techniques allows for representation of similar IAs to support the clinician. The graphical representation was rated very useful and provides visual information of the similarity of the k most similar cases.

BigPint: A Bioconductor visualization package that makes big data pint-sized.

Interactive data visualization is imperative in the biological sciences. The development of independent layers of interactivity has been in pursuit in the visualization community. We developed bigPint, a data visualization package available on Bioconductor under the GPL-3 license (https://bioconductor.org/packages/release/bioc/html/bigPint.html). Our software introduces new visualization technology that enables independent layers of interactivity using Plotly in R, which aids in the exploration of large biological datasets. The bigPint package presents modernized versions of scatterplot matrices, volcano plots, and litre plots through the implementation of layered interactivity. These graphics have detected normalization issues, differential expression designation problems, and common analysis errors in public RNA-sequencing datasets. Researchers can apply bigPint graphics to their data by following recommended pipelines written in reproducible code in the user manual. In this paper, we explain how we achieved the independent layers of interactivity that are behind bigPint graphics. Pseudocode and source code are provided. Computational scientists can leverage our open-source code to expand upon our layered interactive technology and/or apply it in new ways toward other computational biology tasks.

Short‐Contact Touch‐Manipulation of Scatterplot Matrices on Wall Displays

AbstractThis paper presents a short‐contact multitouch vocabulary for interacting with scatterplot matrices (SPLOMs) on wall‐sized displays. Fling‐based gestures overcome central interaction challenges of such large displays by avoiding long swipes on the typically blunt surfaces, frequent physical navigation by walking for accessing screen areas beyond arm's reach in the horizontal direction and uncomfortable postures for accessing screen areas in the vertical direction. Furthermore, we make use of the display's high resolution and large size by supporting the efficient specification of two‐tiered focus + context regions which are consistently propagated across the SPLOM. These techniques are complemented by axis‐centered and lasso‐based selection techniques for specifying subsets of the data. An expert review as well as a user study confirmed the potential and general usability of our seamlessly integrated multitouch interaction techniques for SPLOMs on large vertical displays.