Phenotype similarities in automatically grouped T2D patients by variation-based clustering of IL-1β gene expression

Background

Analyzing longitudinal gene expression data is extremely challenging due to limited prior information, high dimensionality, and heterogeneity. Similar difficulties arise in research of multifactorial diseases such as Type 2 Diabetes. Clustering methods can be applied to automatically group similar observations. Common clinical values within the resulting groups suggest potential associations. However, applying traditional clustering methods to gene expression over time fails to capture variations in the response. Therefore, shape-based clustering could be applied to identify patient groups by gene expression variation in a large time metabolic compensatory intervention.

Objectives

To search for clinical grouping patterns between subjects that showed similar structure in the variation of IL-1β gene expression over time.

Methods

A new approach for shape-based clustering by IL-1β expression behavior was applied to a real longitudinal database of Type 2 Diabetes patients. In order to capture correctly variations in the response, we applied traditional clustering methods to slopes between measurements.

Results

In this setting, the application of K-Medoids using the Manhattan distance yielded the best results for the corresponding database. Among the resulting groups, one of the clusters presented significant differences in many key clinical values regarding the metabolic syndrome in comparison to the rest of the data.

Conclusions

The proposed method can be used to group patients according to variation patterns in gene expression (or other applications) and thus, provide clinical insights even when there is no previous knowledge on the subject clinical profile and few timepoints for each individual.

Unsupervised monitoring of vegetation in a surface coal mining region based on NDVI time series

Surface coal mining causes vegetation disturbance while providing an energy source. Thus, much attention is given to monitoring the vegetation of surface coal mining regions. Multitemporal satellite imagery, such as Landsat time-series imagery, is an operational environment monitoring service widely used to access vegetation traits and ensure vegetation surveillance across large areas. However, most of the previous studies have been conducted with change detection models or threshold-based methods that require multiple parameter settings or sample training. In this paper, we tried to analyze the change traits of vegetation in surface coal mining regions using shape-based clustering based on Normalized Difference Vegetation Index (NDVI) time series without multiple parameter settings and sample training. The shape-based clustering used in this paper applied shape-based distance (SBD) to obtain the distance between time series and used Dynamic Time Warping Barycenter Averaging (DBA) to generate cluster centroids. We applied the method to a stack of 19 NDVI images from 2000 to 2018 for a surface coal mining region located in North China. The results showed that the shape-based clustering used in this paper was appropriate for monitoring vegetation change in the region and achieved 79.0% overall accuracy in detecting disturbance-recovery trajectory types.

Associations of hamstring and triceps surae muscle spasticity and stance phase gait kinematics in children with spastic diplegic cerebral palsy

Clinical decisions on interventions to improve function in children with cerebral palsy (CP) are based, in part, on hypothesized interactions amongst physical signs of CP and functional deficits. However, a knowledge gap exists regarding associations between spasticity and gait function. This study quantified associations of hamstring and triceps surae spasticity with hip, knee and ankle CP gait patterns. This is a cohort study of children and adolescents [n = 51; 31 male; 20 female; spastic diplegia; Gross Motor Function Classification System I (n = 23) and II (n = 28)] who participated in a clinical consult including gait (Motion Analysis, USA) and modified Tardieu scale (MTS) testing (hamstrings, triceps surae). Shape-based clustering was performed on stance phase sagittal hip, knee and ankle patterns using z-normalized and non-normalized data. Linear regression (R, v3.5.0, R Core Team, Austria) was conducted to assess associations between MTS measures and data clusters (α = 0.05). Shape-clustering revealed two hip and three knee and ankle clusters for z-normalized and non-normalized data. Significant associations of hamstring spasticity and joint patterns were observed for z-normalized knee clusters (C_{Knee A} p = 0.002; C_{Knee B} p = 0.006) and interactions amongst non-normalized hip and knee clusters (C_HipA:C_{Knee B} p = 0.033). Trends were observed for soleus spasticity and gastrocnemius range of motion angle and non-normalized ankle clusters (C_{Ankle B} p = 0.051; C_{Ankle B} p = 0.053 respectively). Significant associations of early knee extension and hamstring spasticity, observed using shape-clustering of z-normalized data, provide unique information that may inform the identification of individuals most likely to benefit from spasticity management and targets for spasticity management assessment.