Preterm gut microbiota and metabolome following discharge from intensive care

The development of the preterm gut microbiome is important for immediate and longer-term health following birth. We aimed to determine if modifications to the preterm gut on the neonatal intensive care unit (NICU) impacted the gut microbiota and metabolome long-term. Stool samples were collected from 29 infants ages 1–3 years post discharge (PD) from a single NICU. Additional NICU samples were included from 14/29 infants. Being diagnosed with disease or receiving increased antibiotics while on the NICU did not significantly impact the microbiome PD. Significant decreases in common NICU organisms including K. oxytoca and E. faecalis and increases in common adult organisms including Akkermansia sp., Blautia sp., and Bacteroides sp. and significantly different Shannon diversity was shown between NICU and PD samples. The metabolome increased in complexity, but while PD samples had unique bacterial profiles we observed comparable metabolomic profiles. The preterm gut microbiome is able to develop complexity comparable to healthy term infants despite limited environmental exposures, high levels of antibiotic administration, and of the presence of serious disease. Further work is needed to establish the direct effect of weaning as a key event in promoting future gut health

Algoritmiskt vägledd informationsvisualisering för högdimensionell och kategorisk data

Facilitated by the technological advances of the last decades, increasing amounts of complex data are being collected within fields such as biology, chemistry and social sciences. The major challenge today is not to gather data, but to extract useful information and gain insights from it. Information visualization provides methods for visual analysis of complex data but, as the amounts of gathered data increase, the challenges of visual analysis become more complex. This thesis presents work utilizing algorithmically extracted patterns as guidance during interactive data exploration processes, employing information visualization techniques. It provides efficient analysis by taking advantage of fast pattern identification techniques as well as making use of the domain expertise of the analyst. In particular, the presented research is concerned with the issues of analysing categorical data, where the values are names without any inherent order or distance; mixed data, including a combination of categorical and numerical data; and high dimensional data, including hundreds or even thousands of variables. The contributions of the thesis include a quantification method, assigning numerical values to categorical data, which utilizes an automated method to define category similarities based on underlying data structures, and integrates relationships within numerical variables into the quantification when dealing with mixed data sets. The quantification is incorporated in an interactive analysis pipeline where it provides suggestions for numerical representations, which may interactively be adjusted by the analyst. The interactive quantification enables exploration using commonly available visualization methods for numerical data. Within the context of categorical data analysis, this thesis also contributes the first user study evaluating the performance of what are currently the two main visualization approaches for categorical data analysis. Furthermore, this thesis contributes two dimensionality reduction approaches, which aim at preserving structure while reducing dimensionality, and provide flexible and user-controlled dimensionality reduction. Through algorithmic quality metric analysis, where each metric represents a structure of interest, potentially interesting variables are extracted from the high dimensional data. The automatically identified structures are visually displayed, using various visualization methods, and act as guidance in the selection of interesting variable subsets for further analysis. The visual representations furthermore provide overview of structures within the high dimensional data set and may, through this, aid in focusing subsequent analysis, as well as enabling interactive exploration of the full high dimensional data set and selected variable subsets. The thesis also contributes the application of algorithmically guided approaches for high dimensional data exploration in the rapidly growing field of microbiology, through the design and development of a quality-guided interactive system in collaboration with microbiologists

Algoritmiskt vägledd informationsvisualisering för högdimensionell och kategorisk data

Facilitated by the technological advances of the last decades, increasing amounts of complex data are being collected within fields such as biology, chemistry and social sciences. The major challenge today is not to gather data, but to extract useful information and gain insights from it. Information visualization provides methods for visual analysis of complex data but, as the amounts of gathered data increase, the challenges of visual analysis become more complex. This thesis presents work utilizing algorithmically extracted patterns as guidance during interactive data exploration processes, employing information visualization techniques. It provides efficient analysis by taking advantage of fast pattern identification techniques as well as making use of the domain expertise of the analyst. In particular, the presented research is concerned with the issues of analysing categorical data, where the values are names without any inherent order or distance; mixed data, including a combination of categorical and numerical data; and high dimensional data, including hundreds or even thousands of variables. The contributions of the thesis include a quantification method, assigning numerical values to categorical data, which utilizes an automated method to define category similarities based on underlying data structures, and integrates relationships within numerical variables into the quantification when dealing with mixed data sets. The quantification is incorporated in an interactive analysis pipeline where it provides suggestions for numerical representations, which may interactively be adjusted by the analyst. The interactive quantification enables exploration using commonly available visualization methods for numerical data. Within the context of categorical data analysis, this thesis also contributes the first user study evaluating the performance of what are currently the two main visualization approaches for categorical data analysis. Furthermore, this thesis contributes two dimensionality reduction approaches, which aim at preserving structure while reducing dimensionality, and provide flexible and user-controlled dimensionality reduction. Through algorithmic quality metric analysis, where each metric represents a structure of interest, potentially interesting variables are extracted from the high dimensional data. The automatically identified structures are visually displayed, using various visualization methods, and act as guidance in the selection of interesting variable subsets for further analysis. The visual representations furthermore provide overview of structures within the high dimensional data set and may, through this, aid in focusing subsequent analysis, as well as enabling interactive exploration of the full high dimensional data set and selected variable subsets. The thesis also contributes the application of algorithmically guided approaches for high dimensional data exploration in the rapidly growing field of microbiology, through the design and development of a quality-guided interactive system in collaboration with microbiologists

Visual analysis of missing data - To see what isn't there

Missing data are records that are absent from a data set. They are data that were intended to be recorded, but for some reason were not. Missing values are common in data analysis and occur in almost any domain, causing problems such as biased results and reduced statistical rigour. Visual analytics has great potential to provide invaluable support for the investigation of missing data. This poster aims to highlight the importance of analysing missing data and the challenges involved, as well as to emphasize the lack of visualization support in the area and through this encourage visualization scientists to discuss and address this highly relevant issue

UKM_datastructure – Supplemental material for To identify what is not there: A definition of missingness patterns and evaluation of missing value visualization

<p>Supplemental material, UKM_datastructure for To identify what is not there: A definition of missingness patterns and evaluation of missing value visualization by Sara Johansson Fernstad in Information Visualization</p

Iris_Datastructure – Supplemental material for To identify what is not there: A definition of missingness patterns and evaluation of missing value visualization

<p>Supplemental material, Iris_Datastructure for To identify what is not there: A definition of missingness patterns and evaluation of missing value visualization by Sara Johansson Fernstad in Information Visualization</p

Quality-based guidance for exploratory dimensionality reduction

High-dimensional data sets containing hundreds of variables are difficult to explore, as traditional visualization methods often are unable to represent such data effectively. This is commonly addressed by employing dimensionality reduction prior to visualization. Numerous dimensionality reduction methods are available. However, few reduction approaches take the importance of several structures into account and few provide an overview of structures existing in the full high-dimensional data set. For exploratory analysis, as well as for many other tasks, several structures may be of interest. Exploration of the full high-dimensional data set without reduction may also be desirable. This paper presents flexible methods for exploratory analysis and interactive dimensionality reduction. Automated methods are employed to analyse the variables, using a range of quality metrics, providing one or more measures of ‘interestingness’ for individual variables. Through ranking, a single value of interestingness is obtained, based on several quality metrics, that is usable as a threshold for the most interesting variables. An interactive environment is presented in which the user is provided with many possibilities to explore and gain understanding of the high-dimensional data set. Guided by this, the analyst can explore the high-dimensional data set and interactively select a subset of the potentially most interesting variables, employing various methods for dimensionality reduction. The system is demonstrated through a use-case analysing data from a DNA sequence-based study of bacterial populations

Spatial variations in the microbial community structure and diversity of the human foot is associated with the production of odorous volatiles

The human foot provides an ideal environment for the colonization and growth of bacteria and subsequently is a body site associated with the liberation of odour. This study aimed to enumerate and spatially map bacterial populations' resident across the foot to understand any association with odour production. Culture-based analysis confirmed that Staphylococci were present in higher numbers than aerobic corynebacteria and Gram-positive aerobic cocci, with all species being present at much higher levels on the plantar sites compared to dorsal sites. Microbiomic analysis supported these findings demonstrating that Staphylococcus spp. were dominant across different foot sites and comprised almost the entire bacterial population on the plantar surface. The levels of volatile fatty acids, including the key foot odour compound isovaleric acid, that contribute to foot odour were significantly increased at the plantar skin site compared to the dorsal surface. The fact that isovaleric acid was not detected on the dorsal surface but was present on the plantar surface is probably attributable to the high numbers of Staphylococcus spp. residing at this site. Variations in the spatial distribution of these microbes appear to be responsible for the localized production of odour across the foot