Multi-Block Data Integration Analysis for Identifying and Validating Targeted N-Glycans as Biomarkers for Type II Diabetes Mellitus

Plasma N-glycan profiles have been shown to be defective in type II diabetes Mellitus (T2DM) and holds a promise to discovering biomarkers. The study comprised 232 T2DM patients and 219 healthy individuals. N-glycans were analysed by high-performance liquid chromatography. Principal component analysis (PCA), discriminatory analysis and agglomerative hierarchical cluster analysis (HCA) were performed. N-glycan groups (GPs 34, 32, 26, 31, 36 and 30) were significantly expressed in T2DM in component 1 and GPs 38 and 20 were related to T2DM in component 2. Four clusters based on the correlation of the expressive signatures of the 39 N-glycans across T2DM and controls. Cluster A, B, C and D had 16, 16, 4 and 3 N-glycans respectively, of which 11, 8, 1 and 1 were found to express differently between controls and T2DM in a univariate analysis P. Multi-block analysis revealed that trigalactosylated (G3), triantennary (TRIA), high branching (HB) and trisialylated (S3) expressed significantly highly in T2DM than healthy controls. A bipartite relevance network revealed that HB, monogalactosylated (G1) and G3 were central in the network and observed more connections, highlighting their importance in discriminating between T2DM and healthy controls. Investigation of these N-glycans can enhance the understanding of T2DM.

Stem Anatomical Descriptors of Four Sesbania Scop. Species And Their Systematic Implication

Stem anatomical features of four Sesbania Scop. species viz. S. bispinosa (Jacq.) W. Wight, S. cannabina (Retz.) Poir., S. sesban (L.) Merr., and S. rostrata Bremek. & Oberm., were examined to add some insights for identification of these species using quantitative anatomical descriptors. Sesbania stem is composed of epidermis, cortex, vascular tissues – phloem, cambium zone and xylem, and pith, which exhibit significant variations among the species in terms of their area and thickness. Sesbania sesban showed the largest area and widest epidermal cells. The close relationship between S. bispinosa and S. rostrata was found in the stem anatomical descriptors. Moreover, S. rostrata and S. cannabina were closer to some extent according to some anatomical descriptors; also rationalizing the external morphological similarities of these species. A dichotomous key of the studied Sesbania species was made. Dendrograms based on Agglomerative Hierarchical Cluster analysis of stem anatomical descriptors also confirmed close relationships identified in previous phylogenetic analyses. Bangladesh J. Plant Taxon. 28(2): 441-449, 2021 (December)

Clustering of health behaviours in Canadians: A multiple behaviour analysis of data from the Canadian Longitudinal Study on Aging

Health risk behaviours such as physical inactivity, unhealthy eating, smoking tobacco, and alcohol use are each leading risk factors for non-communicable chronic disease and each play a central role in limiting health and life satisfaction. However, much less is known about how co-occurring behaviours are associated with health outcomes. Understanding which behaviours tend to co-occur (i.e., cluster together), and how such clusters are associated with physical and mental health, life satisfaction, and health care utilization may provide novel opportunities to leverage this co-occurrence to develop and evaluate interventions to promote multiple health behaviour change. Using cross-sectional baseline data (N=40,268) from the Canadian Longitudinal Study of Aging, we performed a pre-defined set of analyses to examine the co-occurrence of health behaviours. We used agglomerative hierarchical cluster analysis to cluster individuals based on their behavioural tendencies and multinomial logistic regression to examine how these clusters are associated with demographic characteristics, healthcare utilization, and general health and life satisfaction, and assess whether sex and age moderate these relationships. Seven clusters were identified with clusters differentiated by six of the seven health behaviours included in the analysis. Variability between clusters was observed in frequencies of weekly walking, strenuous exercise, and alcohol consumption. Sociodemographic characteristics varied across several clusters while self-reported physical/mental health showed less variation across clusters. The seven identified clusters of health behaviours allow for contrasts to be made with comparable analyses in other countries and will help inform the development of future health behaviour change interventions tailored to sub-populations and their sociodemographic profiles.

Modelling Cabernet-Sauvignon wine sensory traits from spectrofluorometric data

Understanding how wine compositional traits can be related to sensory profiles is an important and ongoing challenge. Enhancing knowledge in this area could assist producers to select practices that deliver wines of the desired style and sensory specifications. This work reports the use of spectrofluorometry in conjunction with chemometrics for prediction, correlation, and classification based on sensory descriptors obtained using a rate-all-that-apply sensory assessment of Cabernet-Sauvignon wines (n = 26). Sensory results were first subjected to agglomerative hierarchical cluster analysis, which separated the wines into five clusters represented by different sensory profiles. The clusters were modelled in conjunction with excitation-emission matrix (EEM) data from fluorescence measurements using extreme gradient boosting discriminant analysis. This machine learning technique was able to classify the wines into the pre-defined sensory clusters with 100 % accuracy. Parallel factor analysis of the EEMs identified four main fluorophore components that were tentatively assigned as catechins, phenolic aldehydes, anthocyanins, and resveratrol (C1, C2, C3, and C4, respectively). Association of these four components with different sensory descriptors was possible through multiple factor analysis, with C1 relating to ‘dark fruits’ and ‘savoury’, C2 with ‘barnyard’, C3 with ‘cooked vegetables’ and ‘vanilla/chocolate’, and C4 with ‘barnyard’ and a lack of C1 descriptors. Partial least squares regression modelling was undertaken with EEM data and sensory results, with a model for perceived astringency being able to predict the panel scores with 68.1 % accuracy. These encouraging outcomes pave the way for further studies that relate sensory traits to fluorescence data and move research closer to the ultimate goal of predicting wine sensory expression from a small number of compositional factors.

Source rock characterization and oil grouping in the NW Java, Central Java and NE Java Basins, Indonesia

This study reveals the detailed organic geochemistry from crude oils (acquired from wells and seepages) and rock extracts from NW Java and NE Java Basin that have been gathered and compiled from previous publications. The interpretation was conducted from geochemical data value and plot, GC-MS fingerprints, and agglomerative-hierarchical cluster analysis using the Euclidean algorithm. Various source rocks from those basins were deposited under fluvio-lacustrine to the marine environment. Six groups of crude oils are also distinguished. Groups 1, 2, and 6 are oils from deltaic source rocks, Groups 3 and 4 are oils from marine source rocks, and Group 5 is from lacustrine and/or fluvio-lacustrine source rocks. Groups 1, 2, and 6 could be distinguished from the pristane/phytane (Pr/Ph) ratio and C29 sterane composition, while Groups 3 and 4 differ from the distribution of C27 sterane. The schematic depositional environment of source rocks is also generated from this study and suggests that Group 5 is deposited during early syn-rift non-marine settings, while the remaining groups are deposited in the deltaic (Group 1,2 and 6) and marine settings (Groups 3 and 4). The main differences between those groups are including the distributions of C27-C28-C29 steranes.

Socio-Ecological Systems (SESs)—Identification and Spatial Mapping in the Central Himalaya

The Himalaya is a mosaic of complex socio-ecological systems (SESs) characterized by a wide diversity of altitude, climate, landform, biodiversity, ethnicity, culture, and agriculture systems, among other things. Identifying the distribution of SESs is crucial for integrating and formulating effective programs and policies to ensure human well-being while protecting and conserving natural systems. This work aims to identify and spatially map the boundaries of SESs to address the questions of how SESs can be delineated and what the characteristics of these systems are. The study was carried out for the state of Uttarakhand, India, a part of the Central Himalaya. The presented approach for mapping and delineation of SESs merges socio-economic and ecological data. It also includes validation of delineated system boundaries. We used 32 variables to form socio-economic units and 14 biophysical variables for ecological units. Principal component analysis followed by sequential agglomerative hierarchical cluster analysis was used to delineate the units. The geospatial statistical analysis identified 6 socio-economic and 3 ecological units, together resulting in 18 SESs for the entire state. The major characteristics for SESs were identified as forest types and agricultural practices, indicating the influence and dependency of SESs on these two features. The database would facilitate diverse application studies in vulnerability assessment, climate change adaptation and mitigation, and other socio-ecological studies. Such a detailed database addresses particularly site-specific characteristics to reduce risks and impacts. Overall, the identified SESs will help in recognizing local needs and gaps in existing policies and institutional arrangements, and the given methodological framework can be applied for the entire Himalayan region and for other mountain systems across the world.

Implications of COVID-19 vaccination and public health countermeasures on SARS-CoV-2 variants of concern in Canada: evidence from a spatial hierarchical cluster analysis

Background: The influence of coronavirus disease-2019 (COVID-19) containment measures on variants of concern (VOC) has been understudied in Canada. Our objective was to identify provinces with disproportionate prevalence of VOC relative to COVID-19 mitigation efforts in provinces and territories in Canada. Methods: We analyzed publicly available provincial- and territorial-level data on the prevalence of VOCs in relation to mitigating factors (summarized in three measures: 1. strength of public health countermeasures: stringency index, 2. how much people moved about outside their homes: mobility index, and 3. vaccine intervention: proportion of Canadian population fully vaccinated). Using spatial agglomerative hierarchical cluster analysis (unsupervised machine learning), the provinces and territories were grouped into clusters by stringency index, mobility index and full vaccine coverage. Kruskal-Wallis test was used to determine the differences in the prevalence of VOC (Alpha, or B.1.1.7, Beta, or B.1.351, Gamma, or P.1, and Delta, or B.1.617.2 variants) between the clusters. Results: Three clusters of vaccine uptake and countermeasures were identified. Cluster 1 consisted of the three Canadian territories, and characterized by higher degree of vaccine deployment and lesser degree of countermeasures. Cluster 2 (located in Central Canada and Atlantic region) was typified by lesser implementation of vaccine deployment and moderate countermeasures. The third cluster was formed by provinces inthe Pacific region, Central Canada, and Prairie region, with moderate vaccine deployment but stronger countermeasures. The overall and variant-specific prevalence were significantly different across the clusters. Interpretation: This study found that implementation of COVID-19 public health measures varied across the provinces and territories. Considering the high prevalence of VOCs in Canada, completing the second dose of COVID-19 vaccine in a timely manner is crucial.

Relationships between topographic factors, soil and plant communities in a dry Afromontane forest patches of Northwestern Ethiopia

Plant community types are influenced by topographic factors, the physical and chemical properties of soil. Therefore, the study was carried out to investigate the relationships of soil and topographic factors on the distribution of species and plant community formation of the Dega Damot district in Northwestern Ethiopia. Vegetation and environmental data were collected from 86 plots (900 m2). Agglomerative hierarchical cluster analysis and redundancy analysis (RDA) with R software were used to identify plant communities and analyze the relationship between plant community types and environmental variables. Five plant community types were identified: Erica arborea-Osyris quadripartita, Discopodium penninervium-Echinops pappii, Olea europaea -Scolopia theifolia, Euphorbia abyssinica-Prunus africana, Dodonaea anguistifolia-Acokanthera schimperi. The RDA result showed that the variation of species distribution and plant community formation were significantly related to altitude, organic matter, moisture content, slope, sand, pH, EC, total nitrogen and phosphorus. Our results suggest that the variation of plant communities (Community 1, 2, 3, and 4) were closely related to environmental factors, including altitude, moisture content, OM, slope, sand, pH, EC, soil nitrogen, and phosphorus, among which altitude was the most important one. However, all the measured environmental variables are not correlated to Dodonaea anguistifolia-Acokanthera schimperi community type. Therefore, it can be concluded that some other environmental variables may influence the species composition, which is needed to be further investigated.

Diversity Assessment of Indian Sunnhemp (Crotalaria juncea L.) Accessions for Enhanced Biomass and Fibre Yield using Geographic Information System Approach

Background: Sunnhemp, a rapid growing, high biomass yielding bast fibre crop has a tremendous potentiality in biofuels sector as a lignocellulosic substrate. In order to capitalize the new found area there is a need to identify high biomass and fibre yielding sunnhemp genotypes. The present study provides details of morphological diversity and geographical distribution pattern of Indian sunnhemp accessions. Methods: A total of 42 germplasm accessions collected from ten different states were evaluated for fibre yield and attributing traits in April-June cropping season. Based on phenotypic data agglomerative hierarchical cluster analysis was performed. Geographical coordinates of germplasm collection site were utilized to derive the spatial genetic diversity pattern for green biomass yield and fibre yield.Result: Phenotypic evaluation revealed significant genetic variability among the genotypes for biomass and fibre yield leading to identification of several promising accessions. Cluster analysis and PCA grouped the 42 sunnhemp accessions into three clusters. Cluster II and III are highly divergent harboring contrasting phenotypes. DIVA-GIS approach identified eastern Rajasthan, western Jharkhand and border area between Bihar and Jharkhand as sites of highest sunnhemp diversity.