Introduction The assessment and treatment of Hypersomnolence Disorder (HD) is burdened by patient heterogeneity. Data-driven subtyping has resolved problematic heterogeneity across various medical ...conditions. Clustering exists as a preferred technique for establishing homogeneous subdivisions within clinical disorders. Thus, this investigation employed clustering analysis utilizing the symptoms of excessive sleep duration, daytime sleepiness, and sleep inertia to determine whether distinct subtypes exist within a clinical HD sample. Methods A sample of 62 patients participating in a larger study evaluating novel hypersomnolence assessments underwent polysomnography (PSG) and multiple sleep latency test (MSLT). Participants were subsequently diagnosed with HD via post hoc chart review. A comprehensive clustering process was performed using self-reported habitual sleep duration (SR TST), Epworth Sleepiness Scale (ESS) score, and Sleep Inertia Questionnaire (SIQ) score. Ward’s D hierarchical clustering technique was determined most appropriate. Resulting clusters were compared across a variety of subjective characteristics and objective measurements. Results The sample was young-to-middle aged (Age = 31.2±10.2), and predominantly female (90.3%). Two subgroups, HYPA (N=32) and HYPB (N=30), emerged from the clustering process. Across clustering variables, HYPA endorsed significantly worse daytime sleepiness (ESS mean difference = 3.36±0.98; p=0.001) and sleep inertia (SIQ mean difference = 28.2±2.31; p<0.0001), yet the clusters did not statistically differ in SR TST. Furthermore, HYPA endorsed significantly greater depressive symptoms (Inventory of Depressive Symptomatology - Self Report mean difference = 14.6±2.84; p<0.0001) and functional impairment (Functional Outcomes of Sleep Questionnaire-10 mean difference = -2.68±0.73; p=0.0006), while displaying longer sleep duration (PSG Total Sleep Time mean difference = 98.8±24.9 minutes; p=0.0002) and worse vigilance (Psychomotor Vigilance Task Lapses Transformed mean difference = 1.11±0.50; p=0.03). Age, body mass index, and MSLT sleep onset latency were not different between clusters. Conclusion This investigation demonstrates two distinct clusters in HD, delineated by depressive and hypersomnolence symptoms whose severity parallels one another. These results highlight the complex relationships between mood and hypersomnolence symptoms, and the need for improved classification of non-cataplectic disorders of hypersomnolence. Support (If Any) This research was supported by a Strategic Research Award to DTP from the American Sleep Medicine Foundation.
Abstract
Kulon Progo is one of the regencies in Daerah Istimewa Yogyakarta which has diverse of plant genetic resources, one is durian
(Durio zibethinus).
Exploration and characterization of durian ...genetic resources have been carried out. Durian diversity can be determined by characterization and it is also an effort to complete information in registering process of local varieties. Registration and releasing varieties are an effort that must be made to provide legal protection for the status of variety ownership. Characterization was done using descriptor for durian. The aim of this research was to determine some of Kulon Progo local durian diversity based on their morphology characteristic and then analysed it to determine similarity among cultivars by cluster analysis. Method research was exploration. The results showed that there were six local durian from Kulon Progo : Menoreh Kuning, Menoreh Jambon, Promasan, Banjar, Kendil and Cempli. Based on morphological characteristic, there were three groups of Kulon Progo local durian, namely group I consisting of Banjar, Kendil, Cempli and Menoreh Jambon, group II (Promasan) and group III (Menoreh Kuning).
Many cellular processes are controlled by sleep. Therefore, alterations in sleep might be expected to stress biological systems that could influence malignancy risk.
What is the association between ...polysomnographic measures of sleep disturbances and incident cancer, and what is the validity of cluster analysis in identifying polysomnography phenotypes?
We conducted a retrospective multicenter cohort study using linked clinical and provincial health administrative data on consecutive adults free of cancer at baseline with polysomnography data collected between 1994 and 2017 in four academic hospitals in Ontario, Canada. Cancer status was derived from registry records. Polysomnography phenotypes were identified by k-means cluster analysis. A combination of validation statistics and distinguishing polysomnographic features was used to select clusters. Cox cause-specific regressions were used to assess the relationship between identified clusters and incident cancer.
Among 29,907 individuals, 2,514 (8.4%) received a diagnosis of cancer over a median of 8.0 years (interquartile range, 4.2-13.5 years). Five clusters were identified: mild (mildly abnormal polysomnography findings), poor sleep, severe OSA or sleep fragmentation, severe desaturations, and periodic limb movements of sleep (PLMS). The associations between cancer and all clusters compared with the mild cluster were significant while controlling for clinic and year of polysomnography. When additionally controlling for age and sex, the effect remained significant only for PLMS (adjusted hazard ratio aHR, 1.26; 95% CI, 1.06-1.50) and severe desaturations (aHR, 1.32; 95% CI, 1.04-1.66). Further controlling for confounders, the effect remained significant for PLMS, but was attenuated for severe desaturations.
In a large cohort, we confirmed the importance of polysomnographic phenotypes and highlighted the role that PLMS and oxygenation desaturation may play in cancer. Using this study’s findings, we also developed an Excel (Microsoft) spreadsheet (polysomnography cluster classifier) that can be used to validate the identified clusters on new data or to identify which cluster a patient belongs to.
ClinicalTrials.gov; Nos.: NCT03383354 and NCT03834792; URL: www.clinicaltrials.gov
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To detect landslides by object-based image analysis using criteria based on shape, color, texture, and, in particular, contextual information and process knowledge, candidate segments must be ...delineated properly. This has proved challenging in the past, since segments are mainly created using spectral and size criteria that are not consistent for landslides. This paper presents an approach to select objectively parameters for a region growing segmentation technique to outline landslides as individual segments and also addresses the scale dependence of landslides and false positives occurring in a natural landscape. Multiple scale parameters were determined using a plateau objective function derived from the spatial autocorrelation and intrasegment variance analysis, allowing for differently sized features to be identified. While a high-resolution Resourcesat-1 Linear Imaging and Self Scanning Sensor IV (5.8 m) multispectral image was used to create segments for landslide recognition, terrain curvature derived from a digital terrain model based on Cartosat-1 (2.5 m) data was used to create segments for subsequent landslide classification. Here, optimal segments were used in a knowledge-based classification approach with the thresholds of diagnostic parameters derived from If-means cluster analysis, to detect landslides of five different types, with an overall recognition accuracy of 76.9%. The approach, when tested in a geomorphologically dissimilar area, recognized landslides with an overall accuracy of 77.7%, without modification to the methodology. The multiscale classification-based segment optimization procedure was also able to reduce the error of commission significantly in comparison to a single-optimal-scale approach.
Most groundwater geochemical studies associated with earthquakes have focused on changes in major ions and radon concentration. However, trace elements have also shown a dramatic response to ...earthquakes and attracted increasing attention. In this study, hydrochemical and water level changes were detected by an artesian well (DZ well) in Yunnan, China, during the 2018 Ms 5.9 Mojiang earthquake. The results show that the major and trace element concentrations changed considerably before and after the earthquake, which were attributed to the mixing of water from different aquifers due to the earthquake-induced alteration of permeability through wavelet analysis of continuous monitoring data and coseismic static strain. In addition, Self-Organizing Map (SOM) and K-means clustering algorithms were used to classify all water samples with 10 trace elements as variables, and the results showed that samples collected in pre- and post-earthquake can be categorized as two groups, showing a significant difference. We argue that the response of trace elements remains unaffected by their initial concentration levels. Notably, both high- and low-concentration trace elements (e.g., Cs, Rb, V, etc.) exhibit discernible changes in response to the Ms 5.9 Mojiang earthquake. These observations corroborate the efficacy of clustering methodologies in delineating seismic-induced concentration shifts and furnish fresh insights into the anomalous behavior of trace elements during the earthquake.
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•Major and trace elements in well water showed obvious changes during the earthquake.•Response amplitudes of tracer elements were not affected by their concentration levels.•Hydrochemical changes were attributed to the mixing of water from different aquifers.•The results could be useful for groundwater monitoring in seismic active areas.
The cluster robust variance estimator (CRVE) relies on the number of clusters being sufficiently large. Monte Carlo evidence suggests that the ‘rule of 42’ is not true for unbalanced clusters. ...Rejection frequencies are higher for datasets with 50 clusters proportional to US state populations than with 50 balanced clusters. Using critical values based on the wild cluster bootstrap performs much better. However, this procedure fails when a small number of clusters is treated. We explain why CRVE t statistics and the wild bootstrap fail in this case, study the ‘effective number’ of clusters and simulate placebo laws with dummy variable regressors.
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