Mapping Actuarial Criteria for Parkinson’s Disease-Mild Cognitive Impairment onto Data-Driven Cognitive Phenotypes

Prevalence rates for mild cognitive impairment in Parkinson’s disease (PD-MCI) remain variable, obscuring the diagnosis’ predictive utility of greater dementia risk. A primary factor of this variability is inconsistent operationalization of normative cutoffs for cognitive impairment. We aimed to determine which cutoff was optimal for classifying individuals as PD-MCI by comparing classifications against data-driven PD cognitive phenotypes. Participants with idiopathic PD (n = 494; mean age 64.7 ± 9) completed comprehensive neuropsychological testing. Cluster analyses (K-means, Hierarchical) identified cognitive phenotypes using domain-specific composites. PD-MCI criteria were assessed using separate cutoffs (−1, −1.5, −2 SD) on ≥2 tests in a domain. Cutoffs were compared using PD-MCI prevalence rates, MCI subtype frequencies (single/multi-domain, executive function (EF)/non-EF impairment), and validity against the cluster-derived cognitive phenotypes (using chi-square tests/binary logistic regressions). Cluster analyses resulted in similar three-cluster solutions: Cognitively Average (n = 154), Low EF (n = 227), and Prominent EF/Memory Impairment (n = 113). The −1.5 SD cutoff produced the best model of cluster membership (PD-MCI classification accuracy = 87.9%) and resulted in the best alignment between PD-MCI classification and the empirical cognitive profile containing impairments associated with greater dementia risk. Similar to previous Alzheimer’s work, these findings highlight the utility of comparing empirical and actuarial approaches to establish concurrent validity of cognitive impairment in PD.

Diversity of Ruderal Communities in Urban Environments—A Case Study from Serbia (SE Europe)

The high diversity of ruderal vegetation in urban environments is well known. Although it has been a subject of numerous studies in Serbia, in recent years it has been slightly overlooked, although, due to the dynamics of ruderal habitats, constant research is required. We investigated ruderal vegetation in 20 cities across Serbia during a period of 5 years. Most of the relevés were collected during the summer months, and within 712 relevés, 422 taxa were recorded. Results of the cluster analyses and identified diagnostic species revealed 26 plant communities, of which nine are dominated or co-dominated by aliens. The relevés can be grouped into six ecologically well-differentiated major vegetation groups. Our study revealed the ruderal communities which are the most widespread in urban environments in Serbia. Additionally, some communities were registered for the first time in the country.

Number of Experiments that Should be Considered in the Cluster Analysis of Common Bean Genotypes for Plant Architecture and Grain Yield Traits

The number of experiments that allows the choice of parents to be used in controlled crossings in a more assertive way in cluster analysis is unknown for plant architecture and grain yield traits in common bean. Therefore, the objective of this work was to determine the number of experiments that should be considered in Tocher's and the unweighted pair group method with arithmetic mean (UPGMA) cluster analyses to identify promising common bean parents for several plant architecture and grain yield traits. Four experiments were carried out in different years and growing seasons, in the same site. The randomized block design was used and 17 common bean genotypes with carioca (beige seed coat with brown streaks) and black grains were evaluated in relation to 12 traits related to plant architecture and five traits related to grain yield. Statistical analyses were performed with data obtained from individual and combined experiments. Significant genotype × experiment interaction was observed for most of the evaluated traits. When Tocher's and UPGMA cluster analyses was performed from data obtained in individual experiments different groups were formed. The use from data obtained in two, three ou four experiments allowed greather reliability in the formation of groups. Three and two experiments are sufficient in the Tocher's and the UPGMA cluster analyses, respectively, to identify promising carioca and black common bean parents for several plant architecture and grain yield traits in a more assertive way.

PATH-26. EVALUATING THE DISTRIBUTION AND PROGNOSIS OF IMMUNE CELL SIGNATURES OF THE TUMOR MICRO-ENVIRONMENT IN DIFFUSE GLIOMAS

The tumor micro-environment (TME) plays an important role in the biology of cancer, including gliomas. Single cell studies have highlighted the role of specific TME components in gliomas, and the methods to deconvolve bulk profiling data may serve to complement these studies on clinically annotated tumors. In this study, we estimated cell type proportions in 3 large glioma datasets (TCGA, CGGA-325, CGGA-693) using CIBERSORTx. Using a signature matrix comprising 22 immune cell types, we identified IDH mutation status-specific immune cell distributions and found that the proportions of 10 cell types were significantly different between IDHmut and IDHwt tumors across the 3 datasets. Looking further within IDHmut tumors, we found that monocytes were enriched in 1p/19q non-co-deleted tumors across the 3 glioma datasets, consistent with prior single cell studies. We then examined estimated gene expression among immune cell types relative to IDH mutation status and found clear separation of gene expression in 15 of 22 cell types in all 3 datasets. When we applied these 22 gene expression signatures in each tumor sample onto cluster-of-cluster analyses to identify tumor groups with distinct immune signature patterns, we found that samples were distributed largely according to the IDH status in all 3 datasets, confirming that immune cell expression is distinct based on IDH status. Among IDH-specific groups, cluster-of-cluster analyses showed that immune cell-based cluster groups had distinct survival outcomes, and that IDHwt samples were distributed significantly based on tumor grades as well as based on EGFR overexpression. Among IDHmut tumors, the distributions of tumor grade and 1p/19q co-deletion status were significantly different in the immune-based clusters in 2 of the 3 datasets examined. Overall, these results highlight the biological and clinical significance of the immune cell environment in gliomas, including distinctions based on IDH mutation status as well as prognosis within IDH-specific groups.

Identification of clinical phenotypes of peripheral involvement in patients with spondyloarthritis, including psoriatic arthritis: a cluster analysis in the worldwide ASAS-PerSpA study

ObjectiveTo identify clusters of peripheral involvement according to the specific location of peripheral manifestations (ie, arthritis, enthesitis and dactylitis) in patients with spondyloarthritis (SpA) including psoriatic arthritis (PsA), and to evaluate whether these clusters correspond with the clinical diagnosis of a rheumatologist.MethodsCross-sectional study with 24 participating countries. Consecutive patients diagnosed by their rheumatologist as PsA, axial SpA or peripheral SpA were enrolled. Four different cluster analyses were conducted: one using information on the specific location from all the peripheral manifestations, and a cluster analysis for each peripheral manifestation, separately. Multiple correspondence analyses and k-means clustering methods were used. Distribution of peripheral manifestations and clinical characteristics were compared across the different clusters.ResultsThe different cluster analyses performed in the 4465 patients clearly distinguished a predominantly axial phenotype (cluster 1) and a predominantly peripheral phenotype (cluster 2). In the predominantly axial phenotype, hip involvement and lower limb large joint arthritis, heel enthesitis and lack of dactylitis were more prevalent. In the predominantly peripheral phenotype, different subgroups were distinguished based on the type and location of peripheral involvement: a predominantly involvement of upper versus lower limbs joints, a predominantly axial enthesitis versus peripheral enthesitis, and predominantly finger versus toe involvement in dactylitis. A poor agreement between the clusters and the rheumatologist‘s diagnosis as well as with the classification criteria was found.ConclusionThese results suggest the presence of two main phenotypes (predominantly axial and predominantly peripheral) based on the presence and location of the peripheral manifestations.

African Swine Fever Re-Emerging in Estonia: The Role of Seropositive Wild Boar from an Epidemiological Perspective

African swine fever (ASF) emerged in Estonia in 2014. From February 2019 to August 2020, no pigs or wild boar tested positive for ASF virus (ASFV), only ASFV-specific antibodies could be detected in shot wild boar. However, ASF recently re-emerged in wild boar. We tested three hypotheses that might explain the current situation: (i) ASFV may have been present throughout, but at a prevalence below the detection limit; (ii) seropositive wild boar may have remained infectious (i.e., virus-carriers) and kept the epidemic going; or (iii) ASF was gone for 1.5 years, but was recently re-introduced. Using Estonian surveillance data, the sensitivity of the surveillance system and the confidence in freedom from ASF were estimated. Furthermore, the detection probability was determined and cluster analyses were performed to investigate the role of serological positive wild boar. The results suggest that the surveillance system was not able to detect virus circulation at a design prevalence below 1%. With respect to the confidence in freedom from ASF, the results indicate that circulating virus should have been detected over time, if the prevalence was ≥2%. However, the decreasing wild boar population density and ongoing surveillance activities made ASFV circulation at a low prevalence unlikely. Cluster analyses provided no evidence for a significant accumulation of serologically positive wild boar in temporal connection to the re-emergence of ASFV. Further targeted research, such as long-term experimental studies and molecular epidemiology, is necessary to improve our knowledge on the epidemiology of ASF and to control the disease more effectively.

Improving enterprise’s competitiveness on the basis of combined ABC/XYZ and cluster analyses of the commodity nomenclature

Subject. This article examines the relationship between marketing activities and the development of entrepreneurial structures in Russia. Objectives. The article aims to develop a methodological approach that helps optimize the product nomenclature and develop differentiated marketing strategies for managing sales in different segments. Methods. For the study, I used the methods of ABC/XYZ and cluster analyses. Results. The article offers a methodological approach to the analysis of the nomenclature of goods, tested on the data of one of the business structures of the Crimean wine industry. It identifies goods nomenclature clusters that have similar dynamic characteristics for changes in sales volumes and demand predictability, and provides recommendations for increasing sales, margins, and improving enterprise competitiveness. Conclusions. The developed methodological approach can be considered as an element of information and methodological support for the enterprise competitiveness management system.

Prognostic Factors in Neurorehabilitation of Stroke: A Comparison among Regression, Neural Network, and Cluster Analyses

There is a large body of literature reporting the prognostic factors for a positive outcome of neurorehabilitation performed in the subacute phase of stroke. Despite the recent development of algorithms based on neural networks or cluster analysis for the identification of these prognostic factors, the literature lacks a rigorous comparison among classical regression, neural network, and cluster analysis. Moreover, the three methods have rarely been tested on a sample independent from that in which prognostic factors have been identified. This study aims at providing this comparison on a wide sample of data (1522 patients) and testing the results on an independent sample (1000 patients) using 30 variables. The accuracy was similar among regression, neural network, and cluster analyses on the analyzed sample (76.6%, 74%, and 76.1%, respectively), but on the test sample, the accuracy of neural network decreased (70.1%). The three models agreed in identifying older age, severe impairment, unilateral spatial neglect, and total anterior circulation infarcts as important prognostic factors. The binary regression analysis also provided solid results in the test sample, especially in terms of specificity (81.8%). Cluster analysis also showed a high sensitivity in the test sample (82.6%) and allowed a meaningful easy-to-use classification tree to be obtained.