Discovering hidden relationships between renal diseases and regulated genes through 3D network visualizations

Abstract Background In a recent study, two-dimensional (2D) network layouts were used to visualize and quantitatively analyze the relationship between chronic renal diseases and regulated genes. The results revealed complex relationships between disease type, gene specificity, and gene regulation type, which led to important insights about the underlying biological pathways. Here we describe an attempt to extend our understanding of these complex relationships by reanalyzing the data using three-dimensional (3D) network layouts, displayed through 2D and 3D viewing methods. Findings The 3D network layout (displayed through the 3D viewing method) revealed that genes implicated in many diseases (non-specific genes) tended to be predominantly down-regulated, whereas genes regulated in a few diseases (disease-specific genes) tended to be up-regulated. This new global relationship was quantitatively validated through comparison to 1000 random permutations of networks of the same size and distribution. Our new finding appeared to be the result of using specific features of the 3D viewing method to analyze the 3D renal network. Conclusions The global relationship between gene regulation and gene specificity is the first clue from human studies that there exist common mechanisms across several renal diseases, which suggest hypotheses for the underlying mechanisms. Furthermore, the study suggests hypotheses for why the 3D visualization helped to make salient a new regularity that was difficult to detect in 2D. Future research that tests these hypotheses should enable a more systematic understanding of when and how to use 3D network visualizations to reveal complex regularities in biological networks.http://deepblue.lib.umich.edu/bitstream/2027.42/112972/1/13104_2010_Article_700.pd

Quantifying Object- and Command-Oriented Interaction

International audienceIn spite of previous work showing the importance of understanding users’ strategies when performing tasks, i.e. the order in which users perform actions on objects using commands, HCI researchers evaluating and comparing interaction techniques remain mainly focused on performance (e.g. time, error rate). This can be explained to some extent by the difficulty to characterize such strategies.We propose metrics to quantify if an interaction technique introduces a rather object- or command-oriented task strategy, depending if users favor completing the actions on an object before moving to the next one or in contrast if they are reluctant to switch between commands. On an interactive surface, we compared Fixed Palette and Toolglass with two novel techniques that take advantage of finger identification technology, Fixed Palette using Finger Identification and Finger Palette. We evaluated our metrics with previous results on both existing techniques. With the novel techniques we found that (1) minimizing the required physical movement to switch tools does not necessarily lead to more object-oriented strategies and (2) increased cognitive load to access commands can lead to command-oriented strategies

Levofloxacin pharmacokinetics/pharmacodynamics, dosing, susceptibility breakpoints, and artificial intelligence in the treatment of multidrug-resistant tuberculosis

Background Levofloxacin is used for the treatment of multidrug-resistant tuberculosis; however the optimal dose is unknown. Methods We used the hollow fiber system model of tuberculosis (HFS-TB) to identify 0–24 hour area under the concentration-time curve (AUC0-24) to minimum inhibitory concentration (MIC) ratios associated with maximal microbial kill and suppression of acquired drug resistance (ADR) of Mycobacterium tuberculosis (Mtb). Levofloxacin-resistant isolates underwent whole-genome sequencing. Ten thousands patient Monte Carlo experiments (MCEs) were used to identify doses best able to achieve the HFS-TB–derived target exposures in cavitary tuberculosis and tuberculous meningitis. Next, we used an ensemble of artificial intelligence (AI) algorithms to identify the most important predictors of sputum conversion, ADR, and death in Tanzanian patients with pulmonary multidrug-resistant tuberculosis treated with a levofloxacin-containing regimen. We also performed probit regression to identify optimal levofloxacin doses in Vietnamese tuberculous meningitis patients. Results In the HFS-TB, the AUC0-24/MIC associated with maximal Mtb kill was 146, while that associated with suppression of resistance was 360. The most common gyrA mutations in resistant Mtb were Asp94Gly, Asp94Asn, and Asp94Tyr. The minimum dose to achieve target exposures in MCEs was 1500 mg/day. AI algorithms identified an AUC0-24/MIC of 160 as predictive of microbiologic cure, followed by levofloxacin 2-hour peak concentration and body weight. Probit regression identified an optimal dose of 25 mg/kg as associated with >90% favorable response in adults with pulmonary tuberculosis. Conclusions The levofloxacin dose of 25 mg/kg or 1500 mg/day was adequate for replacement of high-dose moxifloxacin in treatment of multidrug-resistant tuberculosis.</p