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.

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.

Background Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates. Methods Twenty healthy older adults, 20 people with Parkinson’s disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated. Results We identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture). Algorithms’ performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms. Conclusions Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms’ performances. Trial registration ISRCTN – 12246987.

While missing data is a commonly occurring issue in many domains, it is a topic that has been greatly overlooked by visualization scientists. Missing data values reduce the reliability of analysis results. A range of methods exist to replace the missing values with estimated values, but their appropriateness often depend on the patterns of missingness. Increased understanding of the missingness patterns and the distribution of missing values in data may greatly improve reliability, as well as provide valuable insight into potential problems in data gathering and analyses processes, and better understanding of the data as a whole. Visualization methods have a unique possibility to support investigation and understanding of missingness patterns by making the missing values and their relationship to recorded values visible. This article provides an overview of visualization of missing data values and defines a set of three missingness patterns of relevance for understanding missingness in data. It also contributes a usability evaluation which compares visualization methods representing missing values and how well they help users identify missingness patterns. The results indicate differences in performance depending on the visualization method as well as missingness pattern. Recommendations for future design of missing data visualization are provided based on the outcome of the study.

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