Biology subjects

Bach, B.

Publications and source records attributed to Bach, B..

3 recordsLinked to original sources

bioRxiv2025

SAPID: a Strategy to Analyze Plant Extracts Taste In Depth. Application to the complex tasteof Swertia chirayita (Roxb.) H.Karst.

Bitterness is challenging to analyze due to the diversity of bitter compounds, variability in sensory perception, and its interplay with other tastes. To address this, we developed an untargeted approach to deconvolute the taste and molecular composition of complex plant extracts. We applied our methodology to an ethanolic extract of Swertia chirayita (Roxb.) H.Karst., a plant known for its unique bitterness. Chemical characterization was performed through nuclear magnetic resonance spectroscopy experiments together with untargeted liquid chromatography-high resolution tandem mass spectrometry analysis coupled to a charged aerosol detector. After clustering the fractions based on chemical similarity, we performed free sensory analysis and classical descriptive analysis on each cluster. Our results confirmed the attribution of bitterness to iridoids and highlighted the role of other important compounds in the overall taste. This method offers a systematic approach to analyzing and enhancing the taste profiles of plant-based beverages. HighlightsO_LIAn untargeted method to analyze in depth plant extracts taste and chemical composition has been developed. C_LIO_LIThe attribution of the bitterness of an ethanolic extract of Swertia chirayita (Roxb.) H.Karst. to well-known bitter major and minor iridoids was confirmed using untargeted methods. C_LIO_LIChemically informed tasting allowed to highlight other less pronounced tastes within the extract, contributing to its overall complexity. C_LIO_LIChemically informed tasting led to interesting insights into the sub-threshold impact on taste and taste modulating properties. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=154 SRC="FIGDIR/small/628830v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@1b0c4a3org.highwire.dtl.DTLVardef@3db05aorg.highwire.dtl.DTLVardef@50ddeorg.highwire.dtl.DTLVardef@1d16adb_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2018

Pattern-Driven Navigation in 2D Multiscale Visual Spaces with Scalable Insets

We present Scalable Insets, a technique for interactively exploring and navigating large numbers of annotated patterns in multiscale visualizations such as gigapixel images, matrices, or maps. Exploration of many but sparsely-distributed patterns in multiscale visualizations is challenging as visual representations change across zoom levels, context and navigational cues get lost upon zooming, and navigation is time consuming. Our technique visualizes annotated patterns too small to be identifiable at certain zoom levels using insets, i.e., magnified thumbnail views of the annotated patterns. Insets support users in searching, comparing, and contextualizing patterns while reducing the amount of navigation needed. They are dynamically placed either within the viewport or along the boundary of the viewport to offer a compromise between locality and context preservation. Annotated patterns are interactively clustered by location and type. They are visually represented as an aggregated inset to provide scalable exploration within a single viewport. In a controlled user study with 18 participants, we found that Scalable Insets can speed up visual search and improve the accuracy of pattern comparison at the cost of slower frequency estimation compared to a baseline technique. A second study with 6 experts in the field of genomics showed that Scalable Insets is easy to learn and provides first insights into how Scalable Insets can be applied in an open-ended data exploration scenario.

bioinformatics↗

bioRxiv2017

HiPiler: Visual Exploration Of Large Genome Interaction Matrices With Interactive Small Multiples

This paper presents an interactive visualization interface--HiPiler--for the exploration and visualization of regions-of-interest in large genome interaction matrices. Genome interaction matrices approximate the physical distance of pairs of regions on the genome to each other and can contain up to 3 million rows and columns with many sparse regions. Regions of interest (ROIs) can be defined, e.g., by sets of adjacent rows and columns, or by specific visual patterns in the matrix. However, traditional matrix aggregation or pan-and-zoom interfaces fail in supporting search, inspection, and comparison of ROIs in such large matrices. In HiPiler, ROIs are first-class objects, represented as thumbnail-like \"snippets\". Snippets can be interactively explored and grouped or laid out automatically in scatterplots, or through dimension reduction methods. Snippets are linked to the entire navigable genome interaction matrix through brushing and linking. The design of HiPiler is based on a series of semi-structured interviews with 10 domain experts involved in the analysis and interpretation of genome interaction matrices. We describe six exploration tasks that are crucial for analysis of interaction matrices and demonstrate how HiPiler supports these tasks. We report on a user study with a series of data exploration sessions with domain experts to assess the usability of HiPiler as well as to demonstrate respective findings in the data.

bioinformatics↗