Principal Component Loadings Research Articles

South Western Ghats of India: A niche of clove (Syzygium aromaticum (L.) Merr. & Perry) diversity

The survey conducted across major clove (Syzygium aromaticum (L.) Merr. & Perry) growing regions of the South Western Ghats of India, especially in the states of Kerala and Tamil Nadu, identified thirty accessions with distinct characteristics and superior yield. Clove accessions were characterized based on twenty-one qualitative and twelve quantitative traits. Significant variations were observed among fifteen qualitative characters with predominant traits including elliptical canopy shape, semi-erect branching pattern, leaf lamina and its apex, mid-bud forming season, clustering pattern of flower, bud size, fruit, and seed shape. The dendrogram constructed using the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) grouped thirty accessions into five major clusters at a genetic similarity of 73%, with Accession 19 identified as unique. Principal component analysis of twelve quantitative characters revealed three component groups, explaining 70.85% of the total variance. The score plot generated from the principal component loading grouped the accessions into eighteen clusters. A minimal dataset comprising four characters, namely plant height, canopy spread in East-West direction, number of inflorescences per square meter, and mature bud length, was generated. Observations on both qualitative traits and the minimum data set facilitated the identification of ideotypes. The geographical location was not found to influence genetic diversity significantly.

Mapping dominant tree species of miombo woodlands in Western Tanzania using PlanetScope imagery

Mapping dominant tree species in miombo woodlands is essential for enhancing their monitoring and management. We evaluated PlanetScope imagery to map Julbernardia globiflora, Brachystegia spiciformis, and Pterocarpus tinctorius in Tongwe Forest Reserve, Tanzania. The study assessed the effectiveness of PlanetScope bands in discriminating tree species and investigated how different months/seasons influenced tree species classification. Optimal months (seasons) and spectral bands were selected using Principal Component loading, temporal pattern analysis, mean decrease in accuracy, and mean decrease Gini techniques. Random forest classification was employed for tree species classification, and accuracy was assessed using an error matrix. The study identified March, July, and September as optimal months for acquiring imagery, with effective bands including blue, green-1, green, yellow, red, and red-edge. July and September imagery in the dry season achieved higher overall accuracies of 65% and 69%, respectively, while March imagery in the wet season reached 55%. The highest overall accuracy of 72% was achieved using images from different seasons. Producer’s accuracy was highest for Brachystegia spiciformis (79%) and Julbernardia globiflora (95%), whereas Pterocarpus tinctorius had lower accuracy (25%). User’s accuracy varied with 74% for Brachystegia spiciformis, 70% for Julbernardia globiflora, and 67% for Pterocarpus tinctorius. Mapping accuracy was notably high for Brachystegia spiciformis and Julbernardia globiflora, reflecting their high sample size (dominance) and distinct phenology. The yellow and red bands were particularly effective in distinguishing miombo tree species demonstrating PlanetScope’s capability. Future research should focus on scaling up PlanetScope’s application for broad miombo tree species mapping.

Potential association between allergenic food exposure and skin disease in Bangladesh: An application of principal component logistic regression (PCLR).

Allergenic foods can trigger skin conditions, yet their impact in Bangladesh remains underexplored. This study aims to investigate the potential association between exposure to allergenic foods and the development of skin disease across Bangladesh. We conducted a cross-sectional survey with 970 respondents from the divisions of Dhaka, Sylhet, Rangpur, and Chattogram who self-reported skin disease triggered by food allergens. We analyzed the data using multiple response analysis and principal component logistic regression. Approximately 64% of respondents had skin disease attributable to allergenic foods, with a relatively higher proportion among females (52%) than males (48%). Beef (67%), Brinjal (47.1%), Hilsa (45.8%), and Shrimp (23.7%) were the most frequently cited allergenic foods. Principal component logistic regression identified two highly significant principal components: the first representing Hilsa, Beef, and Brinjal (odds ratio = 2.256), and the sixth reflecting Corn (odds ratio = 1.342). Individuals who consumed foods with high loadings of the first principal component were 125.6% more likely to develop skin disease than nonconsumers, while those exposed to Corn had a 34.2% higher risk. The findings highlight prevalent allergenic foods in Bangladesh and their substantial impact on skin health, underscoring the need for dietary awareness and tailored interventions to mitigate allergic skin conditions in this population.

Impacts of unregulated dumpsites: a study on toxic soil contamination, associated risks, and call for sustainable environmental protection in Nnewi, Nigeria

The accumulation of leachates from unlined dumpsites laden soil-water environment with toxic elements poses imminent threats to both the environment and humans. This study evaluated the seasonal levels of potentially toxic elements (PTEs), their health-related risks, contamination factors (CF), pollution load index, geo-accumulation index (Igeo), ecological risk index, polymetallic contaminant index and Nemerow's synthetic pollution index of soils near a waste dump in Nnewi, Anambra State, Nigeria. The PTE results indicated that levels of Cd (1.54–3.40 mg/kg) were above the World Health Organization's threshold limit for soil. The studied soil samples had PTE levels higher than the control samples due to their proximity to the dumpsite. The CF of the PTE in the soil indicated moderate to considerable contamination, while the control areas showed moderate contamination for both seasons. The pollution load indices of the soil indicated heavy and moderate pollution for the study sample (2.207–2.517) and control samples (1.445–1.659). However, the ecological risk indices ranged from 85.72 to 164.84, indicating a low ecological risk. The Igeo of the PTE in the soil ranged from unpolluted, moderately polluted to very strongly polluted. The Nemerow's synthetic pollution index values ranged from 3.560 to 3.564 for the study samples and 2.070–2.502 for the control samples, indicating heavy and moderate pollution of the soil respectively. The polymetallic contaminant index values for the study samples (23.22–26.43) and control samples (15.02–17.23) showed high and considerable contamination respectively. Health risk examination highlighted that both adults and children have a low risk for non-cancer health threat via ingestion route, while a high risk was obtained for children for cancer health dangers via ingestion routes. For the dermal pathway, children and adults have minimal chances of exposure to cancer risks, while a high exposure to non-cancer risk was observed for children. Pearson's correlation and principal component loadings revealed the sources of pollution to be of similar origins linked to anthropogenic activities. Awareness programs are necessary to educate the populace about the dangers of using contaminated soil for farming or recreational activities. The use of sanitary dumpsites should be encouraged and adopted as a waste disposal method by the government to mitigate the spread of PTE into the environment.

Development and evaluation of the reliability and validity of the Japanese Version of the Positive Solitude Scale

Objectives Solitude is a state of being without social contact; it occurs when a person chooses not to interact with others, even when they are in the company of others. It is not necessarily accompanied by negative feelings. In contrast, loneliness, is a psychological state characterized by the feeling of needing the company of others. Most Japanese studies have regarded loneliness as an unpleasant and painful experience. In this study, focusing on solitude, we developed the Japanese version of the Positive Solitude Scale (JPSS) developed by Palgi et al. and evaluated its reliability and validity. This scale assesses the "conscious and voluntary decision to be alone as a positive experience."Methods A self-administered, anonymous questionnaire survey was conducted between May and August 2023, with 700 men and women participants aged 20 years or older living in Ward A, Sapporo. The survey items comprised basic attributes, the JPSS, subjective sense of health, subjective sense of well-being, and depression to verify convergent validity and social network and loneliness to verify discriminant validity. Additionally, principal component and correlation analyses were performed.Results A total of 245 questionnaires were collected, and 237 valid responses were obtained (valid response rate: 33.9%). The participants' mean age was 58.5±1.2 years and 111 (46.8%) were men; the Cronbach's alpha coefficient for the JPSS was 0.92. The principal component analysis revealed that all nine items had principal component loadings above 0.6, with a cumulative contribution of 62.3% to the overall scale. The total scale score was significantly positively correlated with subjective health (ρ=0.210, P=0.001), positive affect (ρ=0.302, P<0.001), and life satisfaction (ρ=0.241, P<0.001). There were no significant correlations among depression, negative affect, social networks, and loneliness.Conclusions The JPSS is a reliable and valid instrument unaffected by social networks and other social relationships. It is expected to be a promising new scale that can measure perceptions of time as a positive emotion, distinct from loneliness.

Association of Integrated Proteomic and Metabolomic Modules with Risk of Kidney Disease Progression.

Proteins and metabolites play crucial roles in various biological functions and are frequently interconnected through enzymatic or transport processes. We present an integrated analysis of 4,091 proteins and 630 metabolites in the Chronic Renal Insufficiency Cohort Study (N=1,708; average follow-up for kidney failure [KF], 9.5 years, with 537 events). Proteins and metabolites were integrated using an unsupervised clustering method and we assessed associations between clusters and CKD progression and kidney failure using Cox proportional hazards models. Analyses were adjusted for demographics and risk factors including the estimated glomerular filtration rate (eGFR) and urine protein-creatinine ratio. Associations were identified in a discovery sample (random two-thirds, N=1139) and then evaluated in a replication sample (one-third, N=569). We identified 139 modules of correlated proteins and metabolites, which were represented by their principal components (PC). Modules and PC loadings were projected onto the replication sample which demonstrated a consistent network structure. Two modules, representing a total of 236 proteins and 82 metabolites, were robustly associated with both CKD progression and kidney failure in both discovery and validation samples. Using gene set enrichment, several transmembrane related terms were identified as over-represented in these modules. Transmembrane-ephrin receptor activity displayed the largest odds (OR = 13.2, P-value = 5.5×10 -5 ). A module containing CRIM1 and NPNT expressed in podocytes demonstrated particularly strong associations with kidney failure (P-value = 2.6×10 -5 ). This study demonstrates that integration of the proteome and metabolome can identify functions of pathophysiologic importance in kidney disease.

Cell type evolution reconstruction across species through cell phylogenies of single-cell RNA sequencing data.

The origin and evolution of cell types has emerged as a key topic in evolutionary biology. Driven by rapidly accumulating single-cell datasets, recent attempts to infer cell type evolution have largely been limited to pairwise comparisons because we lack approaches to build cell phylogenies using model-based approaches. Here we approach the challenges of applying explicit phylogenetic methods to single-cell data by using principal components as phylogenetic characters. We infer a cell phylogeny from a large, comparative single-cell dataset of eye cells from five distantly related mammals. Robust cell type clades enable us to provide a phylogenetic, rather than phenetic, definition of cell type, allowing us to forgo marker genes and phylogenetically classify cells by topology. We further observe evolutionary relationships between diverse vessel endothelia and identify the myelinating and non-myelinating Schwann cells as sister cell types. Finally, we examine principal component loadings and describe the gene expression dynamics underlying the function and identity of cell type clades that have been conserved across the five species. A cell phylogeny provides a rigorous framework towards investigating the evolutionary history of cells and will be critical to interpret comparative single-cell datasets that aim to ask fundamental evolutionary questions.

In situ monitoring of sapphire nanostructure etching using optical emission spectroscopy

Fabrication of nanostructures on sapphire surfaces can enable unique applications in nanophotonics, optoelectronics, and functional transparent ceramics. However, the high chemical stability and mechanical hardness of sapphire make the fabrication of high density, high aspect ratio structures in sapphire challenging. In this study, we propose the use of optical emission spectroscopy (OES) to investigate the sapphire etching mechanism and for endpoint detection. The proposed process employs nanopillars composed of polymer and polysilicon as an etch mask, which allows the fabrication of large-area sapphire nanostructures. The results show that one can identify the emission wavelengths of key elements Al, O, Br, Cl, and H using squared loadings of the primary principal component obtained from principal component analysis of OES readings without the need of domain knowledge or user experience. By further examining the OES signal of Al and O at 395.6 nm, an empirical first-order model can be used to find a predicted endpoint at around 170 s, indicating the moment when the mask is completely removed, and the sapphire substrate is fully exposed. The fabrication results show that the highest aspect ratio of sapphire nanostructures that can be achieved is 2.07, with a width of 242 nm and a height of 500 nm. The demonstrated fabrication approach can create high sapphire nanostructures without using a metal mask to enhance the sapphire etch selectivity.

A principal component analysis assisted machine learning modeling and validation of methanol formation over Cu-based catalysts in direct CO2 hydrogenation

With the advent of powerful machine learning algorithms strongly supporting complex non-linear regression modeling, catalyst design features for a wide and customized set of catalysts used for a specific reaction have been made easy. Herein, we use these techniques in the methanol synthesis by CO2 reduction over Cu-based binary and ternary catalysts with the help of three machine learning algorithms: artificial neural network, support vector machine regression, and gaussian process regression. 227 catalytic performance dataset points from existing literature on CO2 hydrogenation to methanol were compiled and initially accessed by Principal Component Analysis (PCA) for training and preliminary evaluation of the algorithms, which was further guided using a 10-fold cross-validation method. The predictive model and its insights were validated experimentally over 30 datasets derived from experimental runs of this reaction over a ternary Cu/ZnO/ZrO2 laboratory-synthesized catalyst at varying conditions of temperature, pressure, and space velocity in a continuous mode fixed-bed plug-flow reactor. The assessment of the space of input and laboratory data was aided by Principal Component Analysis (PCA), scores, and loadings plot. This work shows how experimentalists can predict typical heterogeneous catalytic reaction outputs (R2 greater than 0.9 for three variables) with fair accuracy using a combination of machine learning and PCA.

Proteomics-based milk whey proteome profiling of Indian Jersey crossbreed cows followed by chromosomal mapping.

Milk contains a massive class of minor proteins that are known for their various biological and molecular functions. Many whey proteins transfer the host defense mechanism to the human body. In this assay, electrophoresis followed by a high-resolution mass spectrometry-based proteomic approach has been applied to identify the whey proteome of Indian Jersey crossbreed bovines. Two search engines, MS Amanda and Sequest HT, have shown more than 29 minor proteins. Chromosomal mapping revealed that chromosomes 5 and 9 are expressing maximum proteins in the whey proteome. The principal component analysis, outlier plots, scree plots, score plots, and loading plots were generated to further assess the results. The majorly expressed ones are glycosylation-dependent cell adhesion molecule-1, ubiquitin, desmoglein, annexin, glycoprotein, arginase, histones, peroxiredoxin, vimentin, desmin, catenin, peripherin, and 70 kDa heat shock protein. © 2023 Society of Chemical Industry.

User satisfaction with Tokyo Metropolitan Government ward office counter services

This study clarifies the main factors affecting user satisfaction in public facilities using a two-part questionnaire, specifically, the determinants of service satisfaction among users of a ward office in Tokyo. The questionnaire comprised three main categories of multiple-choice questions: A) facility equipment (physical elements in the government office), B) staff responses, and C) service delivery (e.g., promptness of services, whether visitors completed their scheduled errands). In addition, the questionnaire investigated the users’ personalities of each facility. During a 1-day survey, responses were collected from 400 women aged 30–59 years who had visited a ward office in the Tokyo Metropolitan Area within the previous month. This age segment was studied because it is the major segment of the panel of the internet research firm used. First, a factor analysis was used to check if the question groupings were appropriate based on eigenvalues and scree plots, and it was confirmed. Next, principal component and multiple regression analyses were conducted for each question category to examine the determinants of counter-service user satisfaction. The regression analysis, using the three main question categories and three other respondents’ personality-related question categories, shows that category C (service delivery quality) strongly influenced the dependent variable, followed by categories B and A. The adjusted R2 value was .70. This is consistent with the author’s surveys of government offices in urban areas conducted for Higashihiroshima City Hall and Osaka ward offices. Concerning the hardware aspect, the principal component loadings, the analysis results show that 1) indoor atmosphere, 2) ease of following the layout of the floors and offices inside the building, and 3) ease of understanding the building’s locations and entrances at the site of the ward office, are important factors for overall satisfaction at the ward office.

Word and color impressions measured with normal and simulated deutan color stimulus sets in color vision normal and deuteranopic observers.

Impressions of nine semantic words expressing abstract meanings were measured by a color selection process using twelve hues from vivid tone in the Practical Color Coordinate System (PCCS) as well as White, Grey and Black (as a normal color stimulus set) in a paired comparison method (Experiment 1). Color impressions were rated using 35 paired words in a semantic differential (SD) method (Experiment 2). The data of ten color vision normal (CVN) and four deuteranopic observers were analyzed separately by principal component analysis (PCA). Our previous study [J. Opt. Soc. Am. A37, A181 (2020)JOAOD60740-323210.1364/JOSAA.382518] using these methods indicated that deuteranopes could understand the impression of all colors if the color names can be recognized, even if deuteranopes cannot perceive redness and greenness. In this study, we also used a simulated deutan color stimulus set in which colors were modified to simulate the color appearance in deuteranopes by using Brettel-Viénot-Mollon's model to investigate how these simulated deutan colors would be treated by the deutan observers. In the CVN and deutan observers, color distributions of the principal component (PC) loading values in Experiment 1 were close to the PCCS hue circle in the normal colors, and those of the simulated deutan colors could be fitted by ellipses, but there were wide gaps of 73.7° (CVNs) and 89.5° (deutan) where only "White" existed. The word distributions as the PC score values could also be fitted by ellipses and are moderately similar between stimulus sets, but the fitting ellipses were considerably compressed in the minor axis directions in the deutan observers, although categories of words were similar between observer groups. The word distributions in Experiment 2 were not statistically different between the observer groups and the stimulus sets. The color distribution of the PC score values were statistically different, but tendencies of the color distributions were similar between observers. The color distributions of the normal colors could be fitted by ellipses, similar to the hue circle, and those of the simulated deutan colors could be fitted by cubic function curves. These results suggest that both of the stimulus sets were perceived as one dimensional and as a monotonic series of colors by a deuteranope, but the deuteranope can recognize the difference of the stimulus sets and recall the color distributions of each set, similar to those in the CVN observers.

Clustering climate and management practices to define environmental challenges affecting gastrointestinal parasitism in Katahdin sheep.

Gastrointestinal nematodes (GIN) negatively affect the performance and well-being of sheep. Due to anthelmintic resistance, GIN are difficult to control leading producers to choose breeds that can exhibit resistance to parasitism. An example is Katahdin sheep. Katahdins are raised in various climates and management systems in the United States. These environmental factors can be combined to form eco-management groupings or clusters. We hypothesized that GIN challenge varies predictably based on the characteristics of these environmental clusters. Forty Katahdin producers from across the United States were surveyed for management information, with body weights (BW), fecal egg counts (FEC), and FAMACHA scores (FAM) available from 17 of the 40 flocks. The performance data included 3,426 lambs evaluated around 90 d of age. Management and climate data were combined into clusters using multiple correspondence and principal component (PC) analysis. Performance data were aligned with their corresponding cluster. Depending on the trait, eco-management cluster, birth-rearing type, sex, and, as a covariate, dam age, were fitted as systematic effects with ANOVA. Clusters also were formed based on climate or management data alone. When compared with fitting the eco-management clusters, they defined less variation in each of the traits based on Akaike and Bayesian information criterion, and adjusted r2 values. To further examine variation defined by eco-management clusters, residuals from an ANOVA model excluding eco-management cluster were retained, and their correlation with PC loadings calculated. All PC loadings were included as potential independent variables and tested for significance using backward stepwise regression. The PC loadings with a correlation |≥0.49| explained significant variation in each trait and were included in the final models chosen; adjusted r2 values for BW, FEC, and FAM were 0.90, 0.81, and 0.97, respectively. When analyzing GIN challenge, eco-management clusters corresponding with hotter temperatures and greater rainfall, and with pasture-born lambs, suffered greater parasitism. Conversely, the eco-management clusters with lambs turned out to pasture at older ages benefited from reduced parasitism. Through the formation of eco-management clusters, an environmental variable can be defined to study interactions of genotypes to their environment, providing a potentially useful tool for identifying parasite-resistant sheep.

An Emerging Strategy for Muscle Evanescent Trauma Discrimination by Spectroscopy and Chemometrics.

Trauma is one of the most common conditions in the biomedical field. It is important to identify it quickly and accurately. However, when evanescent trauma occurs, it presents a great challenge to professionals. There are few reports on the establishment of a rapid and accurate trauma identification and prediction model. In this study, Fourier transform infrared spectroscopy (FTIR) and microscopic spectroscopy (micro-IR) combined with chemometrics were used to establish prediction models for the rapid identification of muscle trauma in humans and rats. The results of the average spectrum, principal component analysis (PCA) and loading maps showed that the differences between the rat muscle trauma group and the rat control group were mainly related to biological macromolecules, such as proteins, nucleic acids and carbohydrates. The differences between the human muscle trauma group and the human control group were mainly related to proteins, polysaccharides, phospholipids and phosphates. Then, a partial least squares discriminant analysis (PLS-DA) was used to evaluate the classification ability of the training and test datasets. The classification accuracies were 99.10% and 93.69%, respectively. Moreover, a trauma classification and recognition model of human muscle tissue was constructed, and a good classification effect was obtained. The classification accuracies were 99.52% and 91.95%. In conclusion, spectroscopy and stoichiometry have the advantages of being rapid, accurate and objective and of having high resolution and a strong recognition ability, and they are emerging strategies for the identification of evanescent trauma. In addition, the combination of spectroscopy and stoichiometry has great potential in the application of medicine and criminal law under practical conditions.

Evaluation of a Raman Chemometric Method for Detecting Protein Structural Conformational Changes in Solution

Raman scattering shows promise as a powerful routine tool, to determine both secondary and the smaller tertiary structural changes that precede aggregation in both solutions and solids. A method was developed utilizing principal component analysis (PCA) of Raman spectra for detection of small, but meaningful, pH induced changes in tertiary protein structure linked to aggregate formation using α-lactalbumin solutions as a model. The sample preparation and spectral parameters, were optimized for a bulk Raman probe. Analysis of large regions (600–1850 cm−1) yielded principal component (PC) scores useful for semi-quantitative comparison of protein conformation between formulations. PC loadings corresponded to specific structural peaks known to change with solution pH. PCA of circular dichroism (CD) spectra of dilute solutions yielded similar results. Sucrose is a common formulation excipient with a Raman spectrum that overlaps many protein peaks. With sucrose in the protein solution, the ability of PCA to discern protein structural changes from the Raman spectra was somewhat reduced. Analysis of a more limited spectral region (1530–1780 cm−1) with negligible sucrose spectral contribution improved the discrimination of protein conformational states. The new Raman method accurately distinguished differences in protein structure in concentrated solutions. The long-term goal is to explore Raman characterization as a routine monitoring tool of protein stability in both solution and solid states.

Minimizing features while maintaining performance in data classification problems.

High dimensional classification problems have gained increasing attention in machine learning, and feature selection has become essential in executing machine learning algorithms. In general, most feature selection methods compare the scores of several feature subsets and select the one that gives the maximum score. There may be other selections of a lower number of features with a lower score, yet the difference is negligible. This article proposes and applies an extended version of such feature selection methods, which selects a smaller feature subset with similar performance to the original subset under a pre-defined threshold. It further validates the suggested extended version of the Principal Component Loading Feature Selection (PCLFS-ext) results by simulating data for several practical scenarios with different numbers of features and different imbalance rates on several classification methods. Our simulated results show that the proposed method outperforms the original PCLFS and existing Recursive Feature Elimination (RFE) by giving reasonable feature reduction on various data sets, which is important in some applications.

Physicochemical characterization and fatty acid profiles of testa oils from various coconut (Cocos nucifera L.) genotypes.

Cocos nucifera (L.) is an important plantation crop with immense but untapped nutraceutical potential. Despite its bioactive potential, the biochemical features of testa oils of various coconut genotypes are poorly understood. Hence, in this study, the physicochemical characteristics of testa oils extracted from six coconut genotypes - namely West Coast Tall (WCT), Federated Malay States Tall (FMST), Chowghat Orange Dwarf (COD), Malayan Yellow Dwarf (MYD), and two Dwarf × Dwarf (D × D hybrids) viz., Cameroon Red Dwarf (CRD) × Ganga Bondam Green Dwarf (GBGD) and MYD × Chowghat Green Dwarf (CGD) - were analyzed. The proportion of testa in the nuts (fruits) (1.29-3.42%), the proportion of oil in the testa (40.97-50.56%), and biochemical components in testa oils - namely proxidant elements Fe (34.17-62.48 ppm) and Cu (1.63-2.77 ppm), and the total phenolic content (6.84-8.67 mg GAE/100 g), and phytosterol content (54.66-137.73 mg CE/100 g) varied depending on the coconut genotypes. The saturated fatty acid content of testa oils (67.75 to 78.78%) was lower in comparison with that of coconut kernel oils. Similarly, the lauric acid (26.66-32.04%), myristic (18.31-19.60%), and palmitic acid (13.43-15.71%,) content of testa oils varied significantly in comparison with the coconut kernel oils (32-51%, 17-21% and 6.9-14%, respectively). Liquid chromatography-mass spectrometry (LC-MS) analysis revealed the presence of 18 phenolic acids in coconut testa oil. Multivariate analysis revealed the biochemical attributes that defined the principal components loadings. Hierarchical clustering analysis of the genotypes showed two distinct clusters. This study reveals the genotypic variations in the nutritionally important biochemical components of coconut testa oils. The relatively high concentration of polyunsaturated fatty acids (PUFA) and polyphenol content in testa oils warrant further investigation to explore their nutraceutical potential. © 2022 Society of Chemical Industry.

Dimensions of Morphological Integration

Over several generations of evolutionary and developmental biologists, ever since Olson and Miller’s pioneering work of the 1950’s, the concept of “morphological integration” as applied to Gaussian representations N(mu ,Sigma ) of morphometric data has been a focus equally of methodological innovation and methodological perplexity. Reanalysis of a century-old example from Sewall Wright shows how some fallacies of distance analysis by correlations can be avoided by careful matching of the distance rosters involved to a different multivariate approach, factor analysis. I reinterpret his example by restoring the information (means and variances) ignored by the correlation matrix, while confirming what Wright called “special size factors” by a different technique, inspection of the concentration matrix Sigma ^{-1}. In geometric morphometrics (GMM), data accrue instead as Cartesian coordinates of labelled points; nevertheless, just as in the Wright example, statistical manipulations do better when they reconsider the normalizations that went into the generation of those coordinates. Here information about both mu and Sigma , the means and the variances/covariances, can be preserved via the Boas coordinates (Procrustes shape coordinates without the size adjustment) that protect the role of size per se as an essential explanatory factor while permitting the analyst to acknowledge the realities of animal anatomy and its trajectories over time or size in the course of an analysis. A descriptive quantity for this purpose is suggested, the correlation of vectorized mu against the first eigenvector of Sigma for the Boas coordinates. The paper reanalyzes two GMM data sets from this point of view. In one, the classic Vilmann rodent neurocranial growth data, a description of integration can be aligned with the purposes of evolutionary and developmental biology by a graphical exegesis based mainly in the loadings of the first Boas principal component. There results a multiplicity of morphometric patterns, some homogeneous and others characterized by gradients. In the other, a Vienna data set comprising human midsagittal skull sections mostly sampled along curves, a further integrated feature emerges, thickening of the calvaria, that requires a reparametrization and a modified thin plate spline graphic distinct from the digitized configurations per se. This new GMM protocol fulfills the original thrust of Olson & Miller’s (Evolution 5:325–338, 1951) “rho F-groups,” the alignment of statistical and biological explanatory guidance, while respecting the enormously greater range of morphological descriptors afforded by well-designed landmark/semilandmark configurations.

Verification of an Environmental Impact Assessment Using a Multivariate Statistical Model

Environmental impact assessment is a means of preventing and mitigating the adverse effects of economic development activities on the natural environment. It is meant to ensure that decision-makers have sufficient information to consider environmental impacts before proceeding with new projects. Despite their important role in public policy, verification of environmental impact assessments has seldom been conducted. In this study, we used principal component analysis (PCA) to identify the major sources of influence on the coastal waters adjacent to a major tourist facility (an aquarium) in southern Taiwan, followed by the construction of a structural equation model (SEM) to determine the direct and indirect effects of the abiotic factors on phytoplankton and zooplankton density and diversity. Based on the loadings of principal components 1–3, we identified that river input, suspended matter, and seasonal changes were the major factors affecting the coastal area. The SEM further suggested that phytoplankton density and diversity were affected directly by seasonal changes and suspended matter, but only indirectly by river input, owing to the latter’s effect on suspended matter. In contrast, the SEM suggested that zooplankton density and diversity were affected directly by seasonal changes, but indirectly by both river input and suspended matter owing to their effects on phytoplankton density and diversity. Q2 was the season with the highest number of visitors to the aquarium, but none of the abiotic or biotic parameters showed particular differences, implying that the variations in those parameters in the adjacent coastal waters were not related to the visitors. We suggest that PCA and SEM be used in the future in other contexts to verify environmental impact assessments.

Inflammation subtypes in psychosis and their relationships with genetic risk for psychiatric and cardiometabolic disorders

Cardiometabolic disorders have known inflammatory implications, and peripheral measures of inflammation and cardiometabolic disorders are common in persons with psychotic disorders. Inflammatory signatures are also related to neurobiological and behavioral changes in psychosis. Relationships between systemic inflammation and cardiometabolic genetic risk in persons with psychosis have not been examined. Thirteen peripheral inflammatory markers and genome-wide genotyping were assessed in 122 participants (n ​= ​86 psychosis, n ​= ​36 healthy controls) of European ancestry. Cluster analyses of inflammatory markers classified higher and lower inflammation subgroups. Single-trait genetic risk scores (GRS) were constructed for each participant using previously reported GWAS summary statistics for the following traits: schizophrenia, bipolar disorder, major depressive disorder, coronary artery disease, type-2 diabetes, low-density lipoprotein, high-density lipoprotein, triglycerides, and waist-to-hip ratio. Genetic correlations across traits were quantified. Principal component (PC) analysis of the cardiometabolic GRSs generated six PC loadings used in regression models to examine associations with inflammation markers. Functional module discovery explored biological mechanisms of the inflammation association of cardiometabolic GRS genes. A subgroup of 38% persons with psychotic disorders was characterized with higher inflammation status. These higher inflammation individuals had lower BACS scores (p ​= ​0.038) compared to those with lower inflammation. The first PC of the cardiometabolic GRS matrix was related to higher inflammation status in persons with psychotic disorders (OR ​= ​2.037, p ​= ​0.001). Two of eight modules within the functional interaction network of cardiometabolic GRS genes were enriched for immune processes. Cardiometabolic genetic risk may predispose some individuals with psychosis to elevated inflammation which adversely impacts cognition associated with illness.