Clustering: how much bias do we need?

What characteristics are most important in stratifying patients into groups with different risk of diabetic foot ulceration?

AIMS/INTRODUCTION

This study aimed to assess if patients can be divided into different strata, and to explore if these correspond to the risk of diabetic foot complications.

MATERIALS AND METHODS

A set of 28 demographic, vascular, neurological and biomechanical measures from 2,284 (1,310 men, 974 women) patients were included in this study. A two-step cluster analysis technique  was utilised to divide the patients into groups, each with similar characteristics.

RESULTS

Only two distinct groups: group 1 (n = 1,199; 669 men, 530 women) and group 2 (n = 1,072; 636 men, 436 women) were identified. From continuous variables, the most important predictors of grouping were: ankle vibration perception threshold (16.9 ± 4.1 V vs 31.9 ± 7.4 V); hallux vibration perception threshold (16.1 ± 4.7 V vs 33.1 ± 7.9 V); knee vibration perception threshold (18.2 ± 5.1 V vs 30.1 ± 6.5 V); average temperature sensation threshold to cold (29.2 ± 1.1°C vs 26.7 ± 0.7°C) and hot (35.4 ± 1.8°C vs 39.5 ± 1.0°C) stimuli, and average temperature tolerance threshold to hot stimuli at the foot (43.4 ± 0.9°C vs 46.6 ± 1.3°C). From categorical variables, only impaired sensation to touch was found to have importance at the highest levels: 87.4% of those with normal sensation were in group 1; whereas group 2 comprised 95.1%, 99.3% and 90.5% of those with decreased, highly-decreased and absent sensation to touch, respectively. In addition, neuropathy (monofilament) was a moderately important predictor (importance level 0.52) of grouping with 26.2% of participants with neuropathy in group 1 versus 73.5% of participants with neuropathy in group 2. Ulceration during follow up was almost fivefold higher in group 2 versus group 1.

CONCLUSIONS

Impaired sensations to temperature, vibration and touch were shown to be the strongest factors in stratifying patients into two groups with one group having almost 5-fold risk of future foot ulceration compared to the other.

Assessment of Automated Flow Cytometry Data Analysis Tools within Cell and Gene Therapy Manufacturing

Flow cytometry is widely used within the manufacturing of cell and gene therapies to measure and characterise cells. Conventional manual data analysis relies heavily on operator judgement, presenting a major source of variation that can adversely impact the quality and predictive potential of therapies given to patients. Computational tools have the capacity to minimise operator variation and bias in flow cytometry data analysis; however, in many cases, confidence in these technologies has yet to be fully established mirrored by aspects of regulatory concern. Here, we employed synthetic flow cytometry datasets containing controlled population characteristics of separation, and normal/skew distributions to investigate the accuracy and reproducibility of six cell population identification tools, each of which implement different unsupervised clustering algorithms: Flock2, flowMeans, FlowSOM, PhenoGraph, SPADE3 and SWIFT (density-based, k-means, self-organising map, k-nearest neighbour, deterministic k-means, and model-based clustering, respectively). We found that outputs from software analysing the same reference synthetic dataset vary considerably and accuracy deteriorates as the cluster separation index falls below zero. Consequently, as clusters begin to merge, the flowMeans and Flock2 software platforms struggle to identify target clusters more than other platforms. Moreover, the presence of skewed cell populations resulted in poor performance from SWIFT, though FlowSOM, PhenoGraph and SPADE3 were relatively unaffected in comparison. These findings illustrate how novel flow cytometry synthetic datasets can be utilised to validate a range of automated cell identification methods, leading to enhanced confidence in the data quality of automated cell characterisations and enumerations.

Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban Trips

Millions of individuals rely on urban transportation every day to travel inside cities. However, it is not clear how route parameters (e.g., traffic conditions, waiting times) influence users when selecting a particular route option for their trips. These parameters play an important role in route recommendation systems, and most of the currently available applications omit them. This work introduces a new hybrid-multimodal routing algorithm that evaluates different routes that combine different transportation modes. Hybrid-multimodal routes are route options that might consist of more than one transportation mode. The motivation to use different transportation modes is to avoid unpleasant trip segments (e.g., traffic jams, long walks) by switching to another mode. We show that the possibility of planning a trip with different transportation modes can lead to improvement of cost, duration, and quality of experience urban trips. We outline the main research contributions of this work, as (i) an user experience model that considers time, price, active transportation (i.e., non-motorized transport) acceptability, and traffic conditions to evaluate the hybrid routes; and, (ii) a flow clustering technique to identify relevant mobility flows in low-sampled datasets for reducing the data volume and allow the execution of the analytical evaluation. (i) uses a Discrete Choice Analyses framework to model different variables and estimate a value for user experience in the trip. (ii) is a methodology to aggregate mobility flows by using Spatio-temporal Clustering and identify the most relevant of these flows using Curvature Analysis. We evaluate the proposed hybrid-multimodal routing algorithm with data from the Green and Yellow Taxis of New York, Citi Bike NYC data, and other publicly available datasets; and, different APIs, such as Uber and Google Directions. The results reveal that selecting hybrid routes can benefit passengers by saving time or reducing costs, and sometimes both, when compared to routes using a single transportation mode.

A brief guide for the measurement and interpretation of microbial functional diversity

The importance of functional diversity for the functioning and behaviour of microbial communities is clear, yet the widespread incorporation of functional diversity measurements into environmental microbiology study designs remains surprisingly limited. This may, at least to some extent, be a consequence of the unique conceptual and methodological challenges to measuring functional diversity in microbial communities. To facilitate the increased incorporation of functional diversity measurements into environmental microbiology study designs, we review here the process and some key caveats for measuring functional diversity and provide specific examples. We highlight three main decision points and provide guidance to making these decisions based on the underlying mechanisms for how functional diversity relates to an ecosystem process or property of interest. We discuss the selection of an appropriate type of functional trait, selection of the specificity at which functional diversity will be measured, and selection of an appropriate metric for estimating functional diversity from quantitative measures of those traits. We further discuss decisions regarding the use of one- or multi-dimensional measures of functional diversity and how advances in the field of trait-based community ecology could be applied or adapted to address questions in environmental microbiology.

Subclustering in Skeletal Class III Phenotypes of Different Ethnic Origins: A Systematic Review

OBJECTIVE

We aimed to systematically review articles investigating the efficiency of the clustering of skeletal class III malocclusion phenotypic subtypes of different ethnic origins as a diagnostic tool.

METHODS

The review protocol was structured in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement and registered in Prospero (CRD42016053865). A survey of articles published up to March 2018 investigating the identification of different subgroups of skeletal class III malocclusion via cluster analysis was performed using 11 electronic databases. Any type of study design that addressed the classification of subclusters of class III malocclusion was considered. The Newcastle-Ottawa scale for cohort and cross-sectional (modified) studies was used for quality assessment.

RESULTS

The final selection included 7 studies that met all the criteria for eligibility (% overall agreement 0.889, free marginal kappa 0.778). All studies identified at least 3 different types of class III clusters (ranging from 3 to 14 clusters; the total variation of the prevalence of each cluster ranged from 0.2% to 36.0%). The main shared variables used to describe the more prevalent clusters in the studies included were vertical measurements (Ar-Go-Me: 117.51°-135.8°); sagittal measurements: maxilla (SNA: 75.3°-82.95°), mandible (SNB: 77.03°-85.0°). With regard to ethnicity, a mean number of 8.5 and 3.5 clusters of class III were retrieved for Asian and Caucasian population, respectively.

CONCLUSIONS

The total number of clusters identified varied from 3 to 14 to explain all the variability in the phenotype class III malocclusions. Although each extreme may be too simple or complex to facilitate an exhaustive but useful classification for clinical use, a classification system including 4 to 7 clusters may prove to be efficient for clinical use in conjunction with complete and meticulous subgrouping.

CLINICAL SIGNIFICANCE

The identification and description of a subclustering classification system may constitute an additional step toward more precise orthodontic/orthopedic diagnosis and treatment of skeletal class III malocclusion.

Mathematical methods in medicine: neuroscience, cardiology and pathology

The application of mathematics, natural sciences and engineering to medicine is gaining momentum as the mutual benefits of this collaboration become increasingly obvious. This theme issue is intended to highlight the trend in the case of mathematics. Specifically, the scope of this theme issue is to give a general view of the current research in the application of mathematical methods to medicine, as well as to show how mathematics can help in such important aspects as understanding, prediction, treatment and data processing. To this end, three representative specialties have been selected: neuroscience, cardiology and pathology. Concerning the topics, the 12 research papers and one review included in this issue cover biofluids, cardiac and virus dynamics, computational neuroscience, functional magnetic resonance imaging data processing, neural networks, optimization of treatment strategies, time-series analysis and tumour growth. In conclusion, this theme issue contains a collection of fine contributions at the intersection of mathematics and medicine, not as an exercise in applied mathematics but as a multidisciplinary research effort that interests both communities and our society in general.This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'.