Hierarchical Agglomerative Cluster Analysis Research Articles

P.118 Near infrared spectroscopy based indices of cerebrovascular reactivity cluster with intracranial pressure based indices in moderate to severe TBI patients

Background: Cerebrovascular reactivity has been identified as an important contributor to secondary injury following moderate to severe traumatic brain injury (TBI). “Gold-standard” intracranial pressure (ICP) based indies of cerebrovascular reactivity are limited by their invasive nature poor spatial resolution. Near infrared spectroscopy (NIRS) based indices of cerebrovascular reactivity are minimally invasive and have improved spatial resolution. In this study, classical machine-learning algorithms are leveraged to better characterize the relationship between these indices. Methods: High-resolution physiologic data was collected in a cohort of adult moderate to severe TBI patients. From this data both ICP and NIRS based indices of cerebrovascular reactivity were derived. Utilizing Agglomerative Hierarchical Clustering (AHC) and Principal Component Analysis, the relationship between these indices in higher dimensional physiologic space was examined. Results: A total of 83 patients with 314,395 minutes of unique and complete physiologic data was obtained. Through AHC and PCA there was higher order clustering between NIRS and ICP based indices, separate from other physiologic parameters. Conclusions: NIRS and ICP based indices of cerebrovascular reactivity relate to one another in higher dimensional physiologic space. NIRS based indices of cerebrovascular reactivity may be a viable alternative to ICP based indices.

Phenotypes and outcomes in non-anticoagulated patients with atrial fibrillation: An unsupervised cluster analysis

BackgroundPatients with atrial fibrillation are characterized by great clinical heterogeneity and complexity. The usual classifications may not adequately characterize this population. Data-driven cluster analysis reveals different possible patient classifications. AimsTo identify different clusters of patients with atrial fibrillation who share similar clinical phenotypes, and to evaluate the association between identified clusters and clinical outcomes, using cluster analysis. MethodsAn agglomerative hierarchical cluster analysis was performed in non-anticoagulated patients from the Loire Valley Atrial Fibrillation cohort. Associations between clusters and a composite outcome comprising stroke/systemic embolism/death and all-cause death, stroke and major bleeding were evaluated using Cox regression analyses. ResultsThe study included 3434 non-anticoagulated patients with atrial fibrillation (mean age 70.3±17 years; 42.8% female). Three clusters were identified: cluster 1 was composed of younger patients, with a low prevalence of co-morbidities; cluster 2 included old patients with permanent atrial fibrillation, cardiac pathologies and a high burden of cardiovascular co-morbidities; cluster 3 identified old female patients with a high burden of cardiovascular co-morbidities. Compared with cluster 1, clusters 2 and 3 were independently associated with an increased risk of the composite outcome (hazard ratio 2.85, 95% confidence interval 1.32–6.16 and hazard ratio 1.52, 95% confidence interval 1.09–2.11, respectively) and all-cause death (hazard ratio 3.54, 95% confidence interval 1.49–8.43 and hazard ratio 1.88, 95% confidence interval 1.26–2.79, respectively). Cluster 3 was independently associated with an increased risk of major bleeding (hazard ratio 1.72, 95% confidence interval 1.06–2.78). ConclusionCluster analysis identified three statistically driven groups of patients with atrial fibrillation, with distinct phenotype characteristics and associated with different risks for major clinical adverse events.

A principal component model to identify Turkish soundscapes’ affective attributes based on a Corpus-Driven Approach

This study focused on achieving linguistic and culturally appropriate equivalents of Turkish soundscape attributes present in ISO 12913–3 by incorporating a Corpus-Driven Approach (CDA). A two-phase experiment was set up to find Turkish equivalents of affective quality attributes. The first phase consisted of the formation of a Corpus. An online questionnaire was prepared and sent to 196 native Turkish speakers from all around Türkiye to define adjectives. The second phase of the experiment was performed in a listening room. For this purpose, twenty-four binaural sound recordings were collected from seven public spaces. Afterward, forty individuals evaluated the recordings by using the attributes from Phase 1. The perceptual dimensions were obtained from the generated corpus in Turkish based on a rating scale by applying the Principal Component Analysis (PCA). Results indicated a two-dimensional model with two main components, Pleasantness and Eventfulness. Each component is associated with a main orthogonal axis denoted by ‘annoying-comfortable’ and ‘dynamic-uneventful,’ respectively. This circular organization of soundscape attributes is supported by two derived axes, namely ‘chaotic-calm’ and ‘monotonous-enjoyable’, rotated 45°on the same plane. Additionally, by using Spearman’s rank correlation coefficient, sixty-four different bipolar adjective pairs were found. The adjective pairs showed that the highest correlations are mainly on the pleasant-unpleasant continuum, namely Component 1 of PCA. The collected data were also analyzed using Agglomerative Hierarchical Cluster analysis with the Ward method in R programming language to cluster the adjectives. The results inferred that there are four top-level categories. From the first to the fourth level, categories consisted of pleasant, uneventful, eventful, and annoying adjectives, respectively. Moreover, the terms grouped on the first cluster found their dichotomous on the fourth cluster, while maintaining the same relationship in the pleasant-unpleasant continuum.

Composition of essential oil from the bark and leaves of two morphotypes of Salmea scandens (Asteraceae), a medicinal plant in Oaxaca, Mexico

The chemical composition of the essential oils (EOs) from leaves and bark of two morphotypes, white (WM) and black (BM) of Salmea scandens obtained by microwave-assisted extraction (MAE) were analyzed by GC-MS. The major components of the bark EOs were aliphatic hydrocarbons (38.0% in WM, 48.6% in BM) and oxygenated sesquiterpenes (27.6% in WM, 11.3% in BM); in leaves EOs major components were oxygenated sesquiterpenes (43.9% in WM, 45.7% in BM) and oxygenated aliphatics (13.7% in WM, 1.1% in BM) stood out. Nine components have been reported to have antimicrobial and anti-inflammatory potential. The agglomerative hierarchical clustering and principal component analysis confirmed the variability of the EOs. These results suggest that WM may have a better therapeutic effect for treating infectious and inflammatory processes in traditional medicine.

Forest soil inoculation with Bacillus subtilus reduces soil detachment rate to mitigate rill erosion

Rill erosion is one of the most important types of soil erosion that has caused the destruction of many lands in recent decades, and its management has become an essential issue in world research. This study has evaluated the effects of soil physical properties induced by Bacillus subtilis OSU 142 (B. subtilis) on the soil detachment rate (Dc) and rill erodibility (Kr) in the inoculated soil, compared to the uninoculated soil in rill erosion-prone areas of Northern Iran. For measuring Dc, a hydraulic flume was used at 5 slopes (from 4.3% to 34.2%) and 5 water discharges (from 0.31 l m−1 s−1 to 0.67 l m−1 s−1) with 7 replications. Moreover, three physical properties of soils including the bulk density, mean weight diameter and total porosity of soils were measured before and after inoculation. Dc was found to be significantly lower (on average - 52% for the average slopes) in the inoculated soil with the B. subtilis (IB) compared to the uninoculated soil (UB). Rill erodibility (Kr), also was notably reduced in the IB soil (−70%) relative to the UB soil. The variability of Dc in the two studied soils showed a positive correlation to bulk density (R2 = 0.87), and a negative correlation to mean weight diameter (P < 0.01) (R2 = 0.89). The clustering of soils in two distinct classes (inoculated and uninoculated) by agglomerative hierarchical cluster analysis (AHCA) revealed a clear difference based on the physical properties of the treated and control soils. Based on the findings of present study, direct inoculation of B. subtilis can be proposed as a new and suitable technique for the improvement of soil physical properties and the reduction of soil detachment rate, especially in rill erosion-prone areas.

Exploring color metaphor with Behavioral Profiles: A usage-based analysis on the metaphorical meanings of the Chinese color term bái “white”

This paper conducts a usage-based, cognitive-semantic study on semantic relations between the twelve metaphorical meanings of the Chinese color term, bái 白 “white”, with the Behavioral Profiles approach. The contextual variations of the twelve metaphorical meanings of bái are captured by 51 contextual features, including collocational patterns, morphosyntactic and semantic properties, and discourse information. With a dataset consisting of 1200 instances, two statistical methods are adopted to explore the semantic relations of these meanings, namely Hierarchical Agglomerative Clustering and Multiple Correspondence Analysis. The results show that the twelve metaphorical senses of bái fall into four clusters, and their semantic relations can be empirically distinguished based on the proposed contextual features. Furthermore, we found that the identified semantic relations can be explained by mutually related conceptual metaphors based on the perceptual experiences of the white color regarding its three perceptual properties – absence of hue, highest brightness, and zero colorfulness. The present study ultimately demonstrates how the semantic relations among the twelve metaphorical meanings of bái can be empirically distinguished based on their linguistic encodings and explained by cognitive principles in line with the Conceptual Metaphor Theory.

Hydrochemical characterization of the Ramsar-listed Koshi Tappu Wetland, Nepal

Nepal abounds a vast array of freshwater bodies, from sub-tropical lowlands to glacier-fed highlands with varying water quality. This study evaluated the spatial variations in water quality at the Koshi Tappu Wetland, the first Ramsar site of Nepal, located in the eastern Tarai region within the Koshi Tappu Wildlife Reserve. Nineteen water quality parameters were chosen and analyzed from twenty-one different sampling points within the wetland. Parameters including pH, EC, TDS, turbidity, and DO were analyzed on-site, whereas HCO3-, Cl-, NH4+, NO3-, SO42- PO43-, K+, TH, Ca2+, Mg2+, CaH, MgH, Na+, and Fe3+ were analyzed in the laboratory. Multivariate methods such as hierarchical agglomerative cluster analysis (CA) and principal component analysis (PCA), and geochemical indices such as piper and mixing diagrams were applied to assess the spatial variation in water quality. Findings indicated Ca2+ as the principal cation and HCO3- as the principal anion regulating the hydrochemistry of the wetland. Based on CA, three spatial clusters were observed, which depicted variations in chemical composition with the PCA results highlighting the primary contamination sources and controlling factors of the sampling locations with 84.13% of the total variance. Findings from the PCA and ionic relationship analyses elucidated that the hydrochemistry of the Koshi Tappu wetland is mainly controlled by carbonate weathering processes with a minor contribution of silicate weathering and anthropogenic activities.

Comparison of rainwater quality before and during the MCO using chemometric analyses.

This study aimed to classify the spatiotemporal analysis of rainwater quality before and during the Movement Control Order (MCO) implementation due to the COVID-19 pandemic. Chemometric analysis was carried out on rainwater samples collected from 24-gauge stations throughout Malaysia to determine the samples' chemical content, pH, and conductivity. Other than that, hierarchical agglomerative cluster analysis (HACA) and discriminant analysis (DA) were used to classify the quality of rainwater at each location into four clusters, namely good, satisfactory, moderate, and bad clusters. Note that DA was carried out on the predefined clusters. The reduction in acidity levels occurred in 11 stations (46% of overall stations) after the MCO was implemented. Chemical content and ion abundance followed a downward trend, indicating that Cl- and Na+ were the most dominant among the anions and cations. Apart from that, NH4+, Ca2+, NO3-, and SO42- concentrations were evident in areas with significant anthropogenic activity, as there was a difference in the total chemical content in rainwater when compared before and during the MCO. Based on the dataset before the MCO, 75% of gauge stations were in the good cluster, 8.3% in the satisfactory cluster, 12.5% in the moderate cluster, and 4.2% in the bad cluster. Meanwhile, the dataset during the MCO shows that 72.7% of gauge stations were in the good cluster, 9.1% in the satisfactory cluster, 9.1% in the moderate, and 4.5% in the bad cluster. From this study, the chemometric analysis of the year 2020 rainwater chemical composite dataset strongly indicates that reduction of human activities during MCO affected the quality of rainwater.

Which congestion presentation pattern on the physical findings is associated with future adverse events? A cluster analysis in the multicenter acute heart failure registry.

Clinical congestion is the most frequent reason for hospital admission in patients with acute heart failure (AHF). However, few studies have investigated the patterns and prognostic implication of the physical congestion using unbiased and robust statistical methods. A hierarchical agglomerative clustering analysis was performed in the multicenter Japanese AHF registry (N = 3151) with the distance calculated by Jaccard's distance for jugular vein distention (JVD), leg edema, S3, crackles, and orthopnea. The primary outcome was a composite of cardiac death and heart failure readmission within 1-year. At the time of admission, the median number of prevalent congestive signs was 2. We identified three phenogroups: 'no physical congestions' (N = 251); 'congestion without JVD' (N = 1415); and 'congestion with JVD' (N = 1495). Patients in 'no physical congestion' were the youngest (median 75 [62, 83] years) with the lowest systolic blood pressure (122 [106, 142] mmHg). Patients in 'congestion without JVD', and 'congestion with JVD' were similar in terms of age (77 [67, 84] vs. 78 [69, 84] years) and systolic blood pressure (138 [118, 160] vs. 137 [118, 158] mmHg). While 30-day mortality was similar (4.0%, 3.7%, and 4.3% in 'no physical congestion,' 'congestion without JVD,' and 'congestion with JVD', respectively), the patients in 'congestion with JVD' were at the highest risk for the primary outcome (adjusted hazard ratio 1.79, 95% CI 1.26-2.55 when 'no physical congestion' was a reference). Our clustering analysis demonstrated that congestion signs, particularly JVD, allowed identification of AHF phenogroups with distinct clinical characteristics and long-term outcomes.

Effects of Environmental and Disturbance Factors on Plant Community Distribution in Tropical Moist Afromontane Forests, South-West Ethiopia

This study was carried out to investigate the effects of environmental and disturbance factors on plant community distribution in the Majang Forest Biosphere Reserve (MFBR) in south-west Ethiopia. A systematic sample design was conducted to collect vegetation and environmental factors in four study sites. In a nested plot design, the vegetation data were collected from 140 main plots, i.e., 400 m2 (trees), 25 m2 subplots (shrubs, lianas, seedlings, and saplings), and 1 m2 (herbs), respectively. The plant community classification was performed using agglomerative hierarchical cluster analysis (Ward’s Linkage method) in R software (version 4.0.1). The distribution of plant communities along an environmental gradient was computed using canonical correspondence analysis (CCA). In this study, a total of 15 (9.5%) endemic plant species were recorded in MFBR. Four plant community types were identified, and these were Celtis zenkeri-Blighia unijugata, Pouteria altissima-Lecaniodiscus fraxinifolius, Antiaris toxicaria-Celtis toka, and Dracaena afromontana-Cyathea manniana. Environmental and disturbance factors, such as elevations, slopes, harvesting indexes, soil pH, silt, and herbaceous cover, were the most important for determining plant community distribution in the area. Elevation and slope were found to have a significant (P &lt; 0.05) negative and positive relationship with species diversity and richness, respectively. Therefore, the finding of this study provides baseline information that could be necessary for making further conservation and management in MFBR.

Clusters based on demography, disease phenotype, and autoantibody status predicts mortality in lupus: data from Indian lupus cohort (INSPIRE).

SLE is associated with significant mortality, and data from South Asia is limited. Thus, we analysed the causes and predictors of mortality and hierarchical cluster-based survival in the Indian SLE Inception cohort for Research (INSPIRE). Data for patients with SLE was extracted from the INSPIRE database. Univariate analyses of associations between mortality and a number of disease variables were conducted. Agglomerative unsupervised hierarchical cluster analysis was undertaken using 25 variables defining the SLE phenotype. Survival rates across clusters were assessed using non-adjusted and adjusted Cox proportional-hazards models. Among 2072 patients (with a median follow-up of 18 months), there were 170 deaths (49.2 deaths per 1000 patient-years) of which cause could be determined in 155 patients. 47.1% occurred in the first 6 months. Most of the mortality (n = 87) were due to SLE disease activity followed by coexisting disease activity and infection (n = 24), infections (n = 23), and 21 to other causes. Among the deaths in which infection played a role, 24 had pneumonia. Clustering identified four clusters, and the mean survival estimates were 39.26, 39.78, 37.69 and 35.86 months in clusters 1, 2, 3 and 4, respectively (P < 0.001). The adjusted hazard ratios (HRs) (95% CI) were significant for cluster 4 [2.19 (1.44, 3.31)], low socio-economic-status [1.69 (1.22, 2.35)], number of BILAG-A [1.5 (1.29, 1.73)] and BILAG-B [1.15 (1.01, 1.3)], and need for haemodialysis [4.63 (1.87,11.48)]. SLE in India has high early mortality, and the majority of deaths occur outside the health-care setting. Clustering using the clinically relevant variables at baseline may help identify individuals at high risk of mortality in SLE, even after adjusting for high disease activity.

Symptom Cluster Experiences of Patients Operated for Oral Cancer: A Mixed Methods Study

ObjectiveThis convergent mixed methods study aimed to obtain a comprehensive understanding of symptom cluster experiences in patients with oral cancer. Survey and phenomenological interviews were conducted in parallel to identify distinct patient subgroups based on symptom cluster experiences along with their predictors and explore experiences of living with symptom clusters, respectively. Data SourcesA convenience sample of 300 patients with oral cancer who had completed surgery provided the quantitative data, and a maximum variation purposive subsample of 20 participants, drawn from the survey sample, provided the qualitative data. Agglomerative hierarchical cluster analysis was used to identify subgroups, multivariate analyses were done to identify predictors, and thematic analysis was used for patient narratives. ConclusionAlmost 94% of the survey participants had two or more co-occurring symptoms. The four most severe and prevalent symptoms were dysphagia, problems with teeth or gums, speech difficulty, and dry mouth. A distinct subgroup consisting of 61% of patients reported severe dysphagia and teeth problems, which was associated with age, oral cancer stage and site. Interviews revealed the causes and the context influencing the perception and response to these symptoms. Thus, the quantitative data provided information on severity and patient subgroups based on symptom cluster experiences, while the qualitative data validated these conclusions and additionally provided in-depth details and meaningful insight on perceived causes and contextual influences of their experiences. This comprehensive picture of symptom cluster experiences can aid in the development of patient-centered interventions for people with oral cancer. Implications for Nursing PracticeAn interdisciplinary approach to targeting concurrent symptoms incorporating psychological and physical interventions is necessary. Older patients treated for Stage IV cancers and for buccal mucosa tumors are at high-risk of having severe dysphagia postoperatively, and these patients should be targeted for dysphagia interventions. The contextual factors play an important role in developing patient-centered interventions.

FINANCIAL PERFORMANCE OF TOURISM BUSINESSES UNDER THE INFLUENCE OF MEASURES RELATED TO COVID-19

We all know that the spread of the COVID-19 disease has brought significant restrictions for the whole world, which are also closely related to the business activities of companies. Due to the measures taken by the government of the Slovak Republic, some businesses were limited in the performance of their activities. In order to achieve the goal, in addition to general logical methods, we used hierarchical agglomerative cluster analysis. We chose the ward method as the clustering method. Ward's method of minimum variance is an agglomerative (merging) hierarchical method. Slovak businesses in the field of tourism, the most businesses contained cluster 3, which contained 16 businesses. This most numerous and dominant cluster has an average asset indicator value of 8546978 €. In Slovakia, as part of the aid to tourism enterprises at the government level, a call was launched within the framework of the scheme, during which it was possible to draw aid for the months of April to October 2020. This aid was supposed to cover the loss of 10% of their sales to entrepreneurs in the gastronomy and tourism industry.

Chromatin Remodeling Enzyme Cluster Predicts Prognosis and Clinical Benefit of Therapeutic Strategy in Breast Cancer.

The treatment provided for breast cancer depends on the expression of hormone receptors, human epidermal growth factor receptor-2 (HER2), and cancer staging. Surgical intervention, along with chemotherapy or radiation therapy, is the mainstay of treatment. Currently, precision medicine has led to personalized treatment using reliable biomarkers for the heterogeneity of breast cancer. Recent studies have shown that epigenetic modifications contribute to tumorigenesis through alterations in the expression of tumor suppressor genes. Our aim was to investigate the role of epigenetic modifications in genes involved in breast cancer. A total of 486 patients from The Cancer Genome Atlas Pan-cancer BRCA project were enrolled in our study. Hierarchical agglomerative clustering analysis further divided the 31 candidate genes into 2 clusters according to the optimal number. Kaplan-Meier plots showed worse progression-free survival (PFS) in the high-risk group of gene cluster 1 (GC1). In addition, the high-risk group showed worse PFS in GC1 with lymph node invasion, which also presented a trend of better PFS when chemotherapy was combined with radiotherapy than when chemotherapy was administered alone. In conclusion, we developed a novel panel using hierarchical clustering that high-risk groups of GC1 may be promising predictive biomarkers in the clinical treatment of patients with breast cancer.

Tree species composition along environmental and disturbance gradients in tropical sub-montane forests, Tanzania.

Understanding the environmental and disturbance determinants of tree species dominance and community composition in an ecosystem, is important for informing management and conservation decisions, through maintaining or improving the existing forest composition and structure. This study was carried out to quantify the relationship between forest tree composition structure and environmental and disturbance gradients, in a tropical sub-montane forest of Eastern Usambara. Vegetation, environmental, and anthropogenic disturbance data for 58 plots across Amani and Nilo nature forest reserves were obtained. Agglomerative hierarchical cluster analysis and canonical correspondence analysis (CCA) were used to identify plant communities and analyze the influence of environmental variables and anthropogenic disturbances on tree species and community composition respectively. Four communities were identified and CCA results showed that the variation was significantly related to elevation, pH, Annual mean temperature, temperature seasonality, phosphorus nutrients and pressures from adjacent villages and roads. Likewise, environmental factors (climate, soil and topography) explained the most variation (14.5%) of tree and community composition in relation to disturbance pressure (2.5%). The large and significant variation in tree species and community patterns explained by environmental factors suggests a need for site-specific assessment of environmental properties for biodiversity conservation plans. Similarly, the intensification of human activities and associated impacts on natural environment should be minimized to maintain forest species composition patterns and communities. The findings are useful in guiding in policy interventions that focus on minimizing human disturbances in the forests and could aid in preserving and restoring the functional organization and tree species composition of the sub-tropical montane forests.

Instrumental classification of beer based on mouthfeel

Beer is one of the most consumed alcoholic beverages in the world. Classification of beer helps the consumer to find a preferred beer. Sensory assessments of taste are commonly done by sensory panels and therefore susceptible to subjectivity. Mouthfeel is an important parameter for the total perception of beer flavor. Three dimensions of mouthfeel are distinguished: contracting, coating, and drying. In this study 24 beer samples were evaluated chemically. The data were matched with sensorial data obtained from a trained panel. Different chemical analyses were performed; total acidity (TA), total flavonoids (TF), total polyphenols (TPC), total sugars (TS), color, pH, carbon dioxide content, ethanol, bitterness units (BU) and total iso-α-acids (TIA). The data were analyzed by performing several statistical techniques such as analysis of variance, principal component analysis, agglomerative hierarchical cluster analysis and multiple factor analysis. Sensory data obtained from trained panelists on the different mouthfeel attributes correlated with the data found instrumentally. The drying dimension could be expressed using the TPC, BU, TIA and pH. Contracting compounds correlated positively with TA and negatively with pH. As expected, ethanol was strongly associated with burning sensations and carbon dioxide with carbonation. The results of this experiment indicate that commercial beers can be classified into three mouthfeel attributes: drying, coating and contracting. The Principal Component Analysis (PCA) in this study confirmed the dimensions of the mouthfeel model and identified drying and coating as opposites, contracting forces interact on these dimensions. Moreover, these attributes were shown to be quantifiable by instrumental analysis which suggests that a data-driven approach based on mouthfeel could reduce subjectivity in the analysis of taste.

Study of genetic variability and diversity analysis in maize (Zea mays L.) by agglomerative hierarchical clustering and principal component analysis

Study of genetic variability and diversity analysis in maize (Zea mays L.) by agglomerative hierarchical clustering and principal component analysis

The Essential Oils of Rubber Rabbitbrush (Ericameria nauseosa) from North-Central Utah and Southwestern Idaho

Background: Rabbitbrush ( Ericameria nauseosa) is a conspicuous member of plant communities in the deserts of western North America. The plant is an important winter forage for several ungulates in the region and has been used in Native American ethnopharmacology. Methods: Several specimens were collected from North Central Utah (8 samples) and Southwestern Idaho (6 samples). The essential oils were obtained by hydrodistillation and analyzed by gas chromatographic techniques. The data were analyzed using agglomerative hierarchical cluster analysis (HCA), principal component analysis (PCA), and Pearson correlation analysis. Results: Essential oil yields ranged from 0.030% to 2.011%. The major components in the essential oils were β-phellandrene (1.8%-56.5%), β-pinene (0.%3-23.3%), limonene (0.7%-22.3%), and ( Z)-β-ocimene (0.0%-29.3%). With the exception of 1 sample from Utah, the essential oils showed greater than 50% similarity in composition. There were significant differences in percentages of some components, however. Sabinene, β-phellandrene, cosmene, 1,3,8- p-menthatriene, terpinen-4-ol, and β-eudesmol concentrations were higher in the Idaho samples while myrcene, limonene, geranyl acetate, ( E)-β-caryophyllene, γ-curcumene, germacrene D, α-muurolene, γ-cadinene, and δ-cadinene concentrations were significantly higher in the Utah samples. Chiral gas chromatography–mass spectrometry showed the chiral monoterpenoids to be dominated by the (−)-enantiomers, but there were some differences in enantiomeric distribution in the essential oils from Utah compared to those from Idaho. Conclusion: The essential oil compositions in this work are qualitatively similar to those reported previously. However, there were quantitative differences in chemical composition and enantiomeric distributions between the Utah samples and the Idaho samples. Additional research is needed to compare essential oil composition from other geographical locations in western North America.

Effects of riparian buffer and stream channel widths on ecological indicators in the upper and lower Indus River basins in Pakistan

Riparian buffers and stream channel widths along river networks have extremely significant ecological influences on parameters and stressors associated with riparian health indicators (RHIs). It is imperative for countries that rely heavily on rivers for irrigation to protect RHIs such as habitat, plant cover, regeneration, exotics, and erosion. It is unclear which protection methods are most effective for RHIs in less developed countries, such as Pakistan. This study fills this gap by using a quick field-based technique that includes 273 transects and examines the response of RHIs in the upper and lower Indus River basins (IRB). In the lower Indus basin (LIB), riparian buffer and stream channel widths had the most considerable influence on RHIs using Pearson’s correlations, ranging from ̶ 0.47 &amp;lt; r &amp;lt; 0.71 and ̶ 0.41 &amp;lt; r &amp;lt; 0.32, respectively. There was a significant relationship between stressors and RHIs in the LIB when these widths were changed, and stressors had a significant influence on habitat ̶ 0.37 &amp;lt; r &amp;lt; 0.41, plant cover ̶ 0.32 &amp;lt; r &amp;lt; 0.38, regeneration ̶ 0.29 &amp;lt; r &amp;lt; 0.25, erosion ̶ 0.34 &amp;lt; r &amp;lt; 0.49, and exotics ̶ 0.39 &amp;lt; r &amp;lt; 0.24. In contrast, these stressors in the upper Indus basin (UIB) also adversely affected habitat ̶ 0.28 &amp;lt; r &amp;lt; 0.27, plant cover ̶ 0.34 &amp;lt; r &amp;lt; 0.26, regeneration ̶ 0.19 &amp;lt; r &amp;lt; 0.26, erosion ̶ 0.38 &amp;lt; r &amp;lt; 0.23, and exotics ̶ 0.31 &amp;lt; r &amp;lt; 0.30. It was found from the principal component analysis that the responses of RHIs and stressors varied considerably between the UIB and LIB. Additionally, the agglomerative hierarchical cluster analysis of the RHIs and stressor indices revealed dissimilarities in the UIB and LIB. This study supports the need to examine riparian regions along long rivers, which are subject to the same administrative strategies. Large river ecosystems need revised standards to prevent further degradation based on ecological indicators.

Clustering of Health and Oral Health-Compromising Behaviours in Army Personnel in Central Peninsular Malaysia.

Health- and oral health-compromising behaviours (HOHCBs) impact the health readiness of military personnel, resulting in decreased fitness performance, thus affecting combat readiness. This study aimed to identify the clustering patterns and number of HOHCBs in army personnel in Central Peninsular Malaysia. Thus, a cross-sectional study using a multistage sampling technique and a validated 42-item online questionnaire was conducted to assess ten health (medical screening, physical activity, sedentary lifestyle, smoking status, alcohol consumption, substance abuse, aggressive behaviours, sleep, and road safety habits) and five oral health behaviour domains (tooth brushing, fluoridated toothpaste use, flossing, dental visits, and bruxism). Each HOHCB was dichotomised into healthy and health-compromising behaviour and analysed using hierarchical agglomerative cluster analysis (HACA). With the majority being males (92.5%), of other ranks (96.8%), and healthy (83.9%), 2435 army members of a mean age of 30.3 years (SD = 5.9) participated, with a response rate of 100%. HACA identified two clustering patterns: (i) 'high-risk behaviours' (30 HOHCBs) and (ii) 'most common risk behaviours' (12 HOHCBs) with a mean clustering number of 14.1 (SD = 4.1). In conclusion, army personnel in Central Peninsular Malaysia displayed 2 broad HOHCB clustering patterns, 'high-risk' and 'most common risk', with an average of 14 HOHCB clusters per person.