Traumatic Head Injury and the Diagnosis of Abuse: A Cluster Analysis

OBJECTIVES: Data guiding abusive head trauma (AHT) diagnosis rest on case-control studies that have been criticized for circularity. We wished to sort children with neurologic injury using mathematical algorithms, without reference to physicians’ diagnoses or predetermined diagnostic criteria, and to compare the results to existing AHT data, physicians’ diagnoses, and a proposed triad of findings. METHODS: Unsupervised cluster analysis of an existing data set regarding 500 young patients with acute head injury hospitalized for intensive care. Three cluster algorithms were used to sort (partition) patients into subpopulations (clusters) on the basis of 32 reliable (κ > 0.6) clinical and radiologic variables. P values and odds ratios (ORs) identified variables most predictive of partitioning. RESULTS: The full cohort partitioned into 2 clusters. Variables substantially (P < .001 and OR > 10 in all 3 cluster algorithms) more prevalent in cluster 1 were imaging indications of brain hypoxemia, ischemia, and/or swelling; acute encephalopathy, particularly when lasting >24 hours; respiratory compromise; subdural hemorrhage or fluid collection; and ophthalmologist-confirmed retinoschisis. Variables substantially (P < .001 and OR < 0.10 in any cluster algorithm) more prevalent in cluster 2 were linear parietal skull fracture and epidural hematoma. Postpartitioning analysis revealed that cluster 1 had a high prevalence of physician-diagnosed abuse. CONCLUSIONS: Three cluster algorithms partitioned the population into 2 clusters without reference to predetermined diagnostic criteria or clinical opinion about the nature of AHT. Clinical difference between clusters replicated differences previously described in comparisons of AHT with non-AHT. Algorithmic partition was predictive of physician diagnosis and of the triad of findings heavily discussed in AHT literature.

Separating Phages From Other Virus Families and Classifying the Different Phage Families By GI-Clusters

Background: Phages are the most abundant biological entities, but the commonly used clustering techniques are difficult to separate them from other virus families and classify the different phage families together.Results: This work uses GI-clusters to separate phages from other virus families and classify the different phage families, where GI-clusters are constructed by GI-features, GI-features are constructed by the togetherness with F-features, training data, MG-Euclidean and Icc-cluster algorithms, F-features are the frequencies of multiple-nucleotides that are generated from genomes of viruses, MG-Euclidean algorithm is able to put the nearest neighbors in the same mini-groups, and Icc-cluster algorithm put the distant samples to the different mini-clusters. For these viruses that the maximum element of their GI-features are in the same locations, they are put to the same GI-clusters, where the families of viruses in test data are identified by GI-clusters, and the families of GI-clusters are defined by viruses of training data.Conclusions: From analysis of 4 data sets that are constructed by the different family viruses, we demonstrate that GI-clusters are able to separate phages from other virus families, correctly classify the different phage families, and correctly predict the families of these unknown phages also.

An Efficient GUI-Based Clustering Software for Simulation and Bayesian Cluster Analysis of Single-Molecule Localization Microscopy Data

Ligand binding of membrane proteins triggers many important cellular signaling events by the lateral aggregation of ligand-bound and other membrane proteins in the plane of the plasma membrane. This local clustering can lead to the co-enrichment of molecules that create an intracellular signal or bring sufficient amounts of activity together to shift an existing equilibrium towards the execution of a signaling event. In this way, clustering can serve as a cellular switch. The underlying uneven distribution and local enrichment of the signaling cluster’s constituting membrane proteins can be used as a functional readout. This information is obtained by combining single-molecule fluorescence microscopy with cluster algorithms that can reliably and reproducibly distinguish clusters from fluctuations in the background noise to generate quantitative data on this complex process. Cluster analysis of single-molecule fluorescence microscopy data has emerged as a proliferative field, and several algorithms and software solutions have been put forward. However, in most cases, such cluster algorithms require multiple analysis parameters to be defined by the user, which may lead to biased results. Furthermore, most cluster algorithms neglect the individual localization precision connected to every localized molecule, leading to imprecise results. Bayesian cluster analysis has been put forward to overcome these problems, but so far, it has entailed high computational cost, increasing runtime drastically. Finally, most software is challenging to use as they require advanced technical knowledge to operate. Here we combined three advanced cluster algorithms with the Bayesian approach and parallelization in a user-friendly GUI and achieved up to an order of magnitude faster processing than for previous approaches. Our work will simplify access to a well-controlled analysis of clustering data generated by SMLM and significantly accelerate data processing. The inclusion of a simulation mode aids in the design of well-controlled experimental assays.

Relative Performance of Cluster Algorithms and Validation Indices in Maize Genome-wide Structure Patterns

A number of clustering algorithms are available to depict population genetic structure (PGS) with genomic data; however, there is no consensus on which methods are the best performing ones. We conducted a simulation study of three PGS scenarios with subpopulations k=2, 5 and 10, recreating several maize genomes as a model to: (i) compare three well-known clustering methods: UPGMA, k-means and, Bayesian method (BM), (ii) asses four internal validation indices: CH, Connectivity, Dunn and Silhouette, to determine the reliable number of groups defining a PGS, and (iii) estimate the misclassification rate for each validation index. Moreover, a publicly available maize dataset was used to illustrate the outcomes of our simulation. BM was the best method to classify individuals in all tested scenarios, without assignment errors. Conversely, UPGMA was the method with the highest misclassification rate. In scenarios with 5 and 10 subpopulations, CH and Connectivity indices had the maximum underestimation of group number for all cluster algorithms. Dunn and Silhouette indices showed the best performance with BM. Nevertheless, since Silhouette measures the degree of confidence in cluster assignment, and BM measures the probability of cluster membership, these results should be considered with caution. In this study we found that BM showed to be efficient to depict the PGS in both simulated and real maize datasets. This study offers a robust alternative to unveil the existing PGS, thereby facilitating population studies and breeding strategies in maize programs. Moreover, the present findings may have implications for other crop species.

An efficient GUI-based clustering software for simulation and Bayesian cluster analysis of single-molecule localization microscopy data

Ligand binding of membrane proteins triggers many important cellular signaling events by the lateral aggregation of ligand-bound and other membrane proteins in the plane of the plasma membrane. This local clustering can lead to the co-enrichment of molecules that create an intracellular signal or bring sufficient amounts of activity together to shift an existing equilibrium towards the execution of a signaling event. In this way, clustering can serve as a cellular switch. The underlying uneven distribution and local enrichment of the signaling cluster's constituting membrane proteins can be used as a functional readout. This information is obtained by combining single-molecule fluorescence microscopy with cluster algorithms that can reliably and reproducibly distinguish clusters from fluctuations in the background noise to generate quantitative data on this complex process. Cluster analysis of single-molecule fluorescence microscopy data has emerged as a proliferative field, and several algorithms and software solutions have been put forward. However, in most cases, such cluster algorithms require multiple analysis parameters to be defined by the user, which may lead to biased results. Furthermore, most cluster algorithms neglect the individual localization precision connected to every localized molecule, leading to imprecise results. Bayesian cluster analysis has been put forward to overcome these problems, but so far, it has entailed high computational cost, increasing runtime drastically. Finally, most software is challenging to use as they require advanced technical knowledge to operate. Here we combined three advanced cluster algorithms with the Bayesian approach and parallelization in a user-friendly GUI and achieved up to an order of magnitude faster processing than for previous approaches. Our work will simplify access to a well-controlled analysis of clustering data generated by SMLM and significantly accelerate data processing. The inclusion of a simulation mode aids in the design of well-controlled experimental assays.

Advances in Atypical FT-IR Milk Screening: Combining Untargeted Spectra Screening and Cluster Algorithms

Fourier-transform mid-infrared spectrometry is an attractive technology for screening adulterated liquid milk products. So far, studies on how infrared spectroscopy can be used to screen spectra for atypical milk composition have either used targeted methods to test for specific adulterants, or have used untargeted screening methods that do not reveal in what way the spectra are atypical. In this study, we evaluate the potential of combining untargeted screening methods with cluster algorithms to indicate in what way a spectrum is atypical and, if possible, why. We found that a combination of untargeted screening methods and cluster algorithms can reveal meaningful and generalizable categories of atypical milk spectra. We demonstrate that spectral information (e.g., the compositional milk profile) and meta-data associated with their acquisition (e.g., at what date and which instrument) can be used to understand in what way the milk is atypical and how it can be used to form hypotheses about the underlying causes. Thereby, it was indicated that atypical milk screening can serve as a valuable complementary quality assurance tool in routine FTIR milk analysis.