Be careful with your principal components

Drivers of avian genomic change revealed by evolutionary rate decomposition

Modern birds have diversified into a striking array of forms, behaviours and ecological roles. Analyses of molecular evolutionary rates can reveal the links between genomic and phenotypic change1-4, but disentangling the drivers of rate variation at the whole-genome scale has been difficult. Using comprehensive estimates of traits and evolutionary rates across a family-level phylogeny of birds5,6, we find that genome-wide mutation rates across lineages are predominantly explained by clutch size and generation length, whereas rate variation across genes is driven by the content of guanine and cytosine. Here, to find the subsets of genes and lineages that dominate evolutionary rate variation in birds, we estimated the influence of individual lineages on decomposed axes of gene-specific evolutionary rates. We find that most of the rate variation occurs along recent branches of the tree, associated with present-day families of birds. Additional tests on axes of rate variation show rapid changes in microchromosomes immediately after the Cretaceous-Palaeogene transition. These apparent pulses of evolution are consistent with major changes in the genetic machineries for meiosis, heart performance, and RNA splicing, surveillance and translation, and correlate with the ecological diversity reflected in increased tarsus length. Collectively, our analyses paint a nuanced picture of avian evolution, revealing that the ancestors of the most diverse lineages of birds underwent major genomic changes related to mutation, gene usage and niche expansion in the early Palaeogene period.

Optimized phenotyping of complex morphological traits: enhancing discovery of common and rare genetic variants

Genotype-phenotype (G-P) analyses for complex morphological traits typically utilize simple, predetermined anatomical measures or features derived via unsupervised dimension reduction techniques (e.g. principal component analysis (PCA) or eigen-shapes). Despite the popularity of these approaches, they do not necessarily reveal axes of phenotypic variation that are genetically relevant. Therefore, we introduce a framework to optimize phenotyping for G-P analyses, such as genome-wide association studies (GWAS) of common variants or rare variant association studies (RVAS) of rare variants. Our strategy is two-fold: (i) we construct a multidimensional feature space spanning a wide range of phenotypic variation, and (ii) within this feature space, we use an optimization algorithm to search for directions or feature combinations that are genetically enriched. To test our approach, we examine human facial shape in the context of GWAS and RVAS. In GWAS, we optimize for phenotypes exhibiting high heritability, estimated from either family data or genomic relatedness measured in unrelated individuals. In RVAS, we optimize for the skewness of phenotype distributions, aiming to detect commingled distributions that suggest single or few genomic loci with major effects. We compare our approach with eigen-shapes as baseline in GWAS involving 8246 individuals of European ancestry and in gene-based tests of rare variants with a subset of 1906 individuals. After applying linkage disequilibrium score regression to our GWAS results, heritability-enriched phenotypes yielded the highest SNP heritability, followed by eigen-shapes, while commingling-based traits displayed the lowest SNP heritability. Heritability-enriched phenotypes also exhibited higher discovery rates, identifying the same number of independent genomic loci as eigen-shapes with a smaller effective number of traits. For RVAS, commingling-based traits resulted in more genes passing the exome-wide significance threshold than eigen-shapes, while heritability-enriched phenotypes lead to only a few associations. Overall, our results demonstrate that optimized phenotyping allows for the extraction of genetically relevant traits that can specifically enhance discovery efforts of common and rare variants, as evidenced by their increased power in facial GWAS and RVAS.

Allometry and phylogenetic divergence: Correspondence or incongruence?

The potential connection between trends of within species variation, such as those of allometric change in morphology, and phylogenetic divergence has been a central topic in evolutionary biology for more than a century, including in the context of human evolution. In this study, I focus on size-related shape change in craniofacial proportions using a sample of more than 3200 adult Old World monkeys belonging to 78 species, of which 2942 specimens of 51 species are selected for the analysis. Using geometric morphometrics, I assess whether the divergence in the direction of static allometries increases in relation to phyletic differences. Because both small samples and taxonomic sampling may bias the results, I explore the sensitivity of the main analyses to the inclusion of more or less taxa depending on the choice of a threshold for the minimum sample size of a species. To better understand the impact of sampling error, I also use randomized subsampling experiments in the largest species samples. The study shows that static allometries vary broadly in directions without any evident phylogenetic signal. This variation is much larger than previously found in ontogenetic trajectories of Old World monkeys, but the conclusion of no congruence with phylogenetic divergence is the same. Yet, the effect of sampling error clearly contributes to inaccuracies and tends to magnify the differences in allometric change. Thus, morphometric research at the boundary between micro- and macro-evolution in primates, and more generally in mammals, critically needs very large and representative samples. Besides sampling error, I suggest other non-mutually exclusive explanations for the lack of correspondence between allometric and phylogenetic divergence in Old World monkeys, and also discuss why directions might be more variable in static compared to ontogenetic trajectories. Even if allometric variation may be a poor source of information in relation to phylogeny, the evolution of allometry is a fascinating subject and the study of size-related shape changes remains a fundamental piece of the puzzle to understand morphological variation within and between species in primates and other animals.

Multi-assay assessment of cytotoxicity reveals multiple mechanisms of action in 3D microtissues

Cell viability assays are an integral component of toxicology and high-throughput drug screening studies; however, many assays rely on a single biomarker of cell death which provides an incomplete assessment of cell viability. Here, we introduce an innovative approach that combines data from multiple assays using a linear mixed effects regression model and principal component analysis. We explored the cytotoxic response of various assay-treatment combinations using four assays with distinct mechanisms of action and seven different treatments across three types of microtissue cultures. The multi-assay data revealed the presence of multifaceted cellular injuries which highlight the need for multimodal approaches to better understand complex disruptions to viability. By incorporating outputs from the four assays, we introduced a new lethal concentration threshold that captures changes from different cellular injuries to provide a more comprehensive evaluation of cytotoxicity. Overall, the proposed approach provides a unique opportunity to analyze data from multiple assays in a holistic manner to improve the predictive power of drug screening and toxicology studies.

The relationship between the three-factor eating questionnaire (TFEQ) subscales (dietary restraint, disinhibition, and hunger) and the body mass index: A cross-sectional study among female students

Background

Identification of an individuals vulnerability to specific eating behaviours could explain weight variations, which could help develop tailored interventional programs to prevent obesity and other pathological eating behaviours. However, there is no baseline data available on the associations between the subscales of the Three-Factor Eating Questionnaire (TFEQ) (dietary restraint, disinhibition, and susceptibility to hunger) and the body mass index (BMI) in a sample of the Saudi population. Thus, this study examines the relationship between the BMI and eating behaviours of Saudi female students, using the scores of the TFEQ subscales.

Methods

This cross-sectional study was conducted at King Saud University (KSU) with 200 female students who completed the Arabic version of TFEQ. The mean age of the participants was 22.88 ± 3.60 years. A Pearson correlation test determined the association between the TFEQ subscales and the BMI.

Results

More than 50 % of the participants had a mean BMI of 23.57 ± 5.15 kg/m2 and a majority showed medium-to-high restraint scores (n = 150). Moreover, 70 % (n = 140) of the particpants showed significantly low disinhibition traits, and a significant positive correlation between BMI and eating behaviour (P ≤ 0.05). Among all TEFQ subscales, disinhibition significantly correlated with the BMI (r = 0.38, P < 0.000) the most, followed by dietary restraint (r = 0.18, P < 0.007). In contrast, susceptibility to hunger was the least associated with the BMI (r = 15, P < 0.029).

Conclusions

The study indicated that all TFEQ subscales play an important role in weight variability, increasing the risk of eating disorders among college-aged Saudi female students. The BMI positively correlated to the students' eating behavioural profiles of high restrain, high disinhibition, and high hunger, and the hidden risk for eating disorders and obesity in normal-weight female students was high. Thus, interventional programs directed at Saudi female students, based on the eating behaviour traits that affect their BMIs, are recommended for managing weight and preventing eating disorder pathologies.

Artificial intelligence tools for engagement prediction in neuromotor disorder patients during rehabilitation

BACKGROUND

Robot-Assisted Gait Rehabilitation (RAGR) is an established clinical practice to encourage neuroplasticity in patients with neuromotor disorders. Nevertheless, tasks repetition imposed by robots may induce boredom, affecting clinical outcomes. Thus, quantitative assessment of engagement towards rehabilitation using physiological data and subjective evaluations is increasingly becoming vital. This study aimed at methodologically exploring the performance of artificial intelligence (AI) algorithms applied to structured datasets made of heart rate variability (HRV) and electrodermal activity (EDA) features to predict the level of patient engagement during RAGR.

METHODS

The study recruited 46 subjects (38 underage, 10.3 ± 4.0 years old; 8 adults, 43.0 ± 19.0 years old) with neuromotor impairments, who underwent 15 to 20 RAGR sessions with Lokomat. During 2 or 3 of these sessions, ad hoc questionnaires were administered to both patients and therapists to investigate their perception of a patient's engagement state. Their outcomes were used to build two engagement classification targets: self-perceived and therapist-perceived, both composed of three levels: "Underchallenged", "Minimally Challenged", and "Challenged". Patient's HRV and EDA physiological signals were processed from raw data collected with the Empatica E4 wristband, and 33 features were extracted from the conditioned signals. Performance outcomes of five different AI classifiers were compared for both classification targets. Nested k-fold cross-validation was used to deal with model selection and optimization. Finally, the effects on classifiers performance of three dataset preparation techniques, such as unimodal or bimodal approach, feature reduction, and data augmentation, were also tested.

RESULTS

The study found that combining HRV and EDA features into a comprehensive dataset improved the synergistic representation of engagement compared to unimodal datasets. Additionally, feature reduction did not yield any advantages, while data augmentation consistently enhanced classifiers performance. Support Vector Machine and Extreme Gradient Boosting models were found to be the most effective architectures for predicting self-perceived engagement and therapist-perceived engagement, with a macro-averaged F1 score of 95.6% and 95.4%, respectively.

CONCLUSION

The study displayed the effectiveness of psychophysiology-based AI models in predicting rehabilitation engagement, thus promoting their practical application for personalized care and improved clinical health outcomes.

Unveiling pepper immunity's robustness to temperature shifts: insights for empowering future crops

Boosting plant immunity is an effective alternative to pesticides. However, environmental variations, accentuated by climate change, can compromise immunity. The robustness of a trait corresponds to the absence (or low level) of variation in that trait in the face of an environmental change. Here, we examined two types of robustness, robustness of immunity mean and robustness of immunity variation, and proposed nine quantitative robustness estimators. We characterized the immunity of a set of accessions representative of the natural diversity of pepper (Capsicum annuum L.), to two major pathogens: the oomycete Phytophthora capsici Leon. and potato virus Y. For each pathogen, we measured the immunity of accessions in two contrasting environments in terms of temperature. For each type of robustness and each pathogen, the impact of temperature change on immunity varied between accessions. The robustness estimators proved to be complementary and differed in terms of heritability and ability to discriminate accessions. A positive and significant correlation was observed between immunity and robustness. There was no significant relationship between the robustness of immunity to the two pathogens, but some accessions showed high immunity and robustness against both pathogens. These results justify the need to consider both immunity and robustness to environmental variations in order to select varieties adapted to current and future climate conditions. Phenotypic robustness should also be considered when assessing the "value of sustainable cultivation and use" of future plant varieties, particularly during the application process for protection rights granted from the European Community Plant Variety Office.

Mapping genes for human face shape: exploration of univariate phenotyping strategies

Human facial shape, while strongly heritable, involves both genetic and structural complexity, necessitating precise phenotyping for accurate assessment. Common phenotyping strategies include simplifying 3D facial features into univariate traits such as anthropometric measurements (e.g., inter-landmark distances), unsupervised dimensionality reductions (e.g., principal component analysis (PCA) and auto-encoder (AE) approaches), and assessing resemblance to particular facial gestalts (e.g., syndromic facial archetypes). This study provides a comparative assessment of these strategies in genome-wide association studies (GWASs) of 3D facial shape. Specifically, we investigated inter-landmark distances, PCA and AE-derived latent dimensions, and facial resemblance to random, extreme, and syndromic gestalts within a GWAS of 8,426 individuals of recent European ancestry. Inter-landmark distances exhibit the highest SNP-based heritability as estimated via LD score regression, followed by AE dimensions. Conversely, resemblance scores to extreme and syndromic facial gestalts display the lowest heritability, in line with expectations. Notably, the aggregation of multiple GWASs on facial resemblance to random gestalts reveals the highest number of independent genetic loci. This novel, easy-to-implement phenotyping approach holds significant promise for capturing genetically relevant morphological traits derived from complex biomedical imaging datasets, and its applications extend beyond faces. Nevertheless, these different phenotyping strategies capture different genetic influences on craniofacial shape. Thus, it remains valuable to explore these strategies individually and in combination to gain a more comprehensive understanding of the genetic factors underlying craniofacial shape and related traits.

Author Summary

Advancements linking variation in the human genome to phenotypes have rapidly evolved in recent decades and have revealed that most human traits are influenced by genetic variants to at least some degree. While many traits, such as stature, are straightforward to acquire and investigate, the multivariate and multipartite nature of facial shape makes quantification more challenging. In this study, we compared the impact of different facial phenotyping approaches on gene mapping outcomes. Our findings suggest that the choice of facial phenotyping method has an impact on apparent trait heritability and the ability to detect genetic association signals. These results offer valuable insights into the importance of phenotyping in genetic investigations, especially when dealing with highly complex morphological traits.

Social network and related factors in older people with sensory impairment in the community: Using principal component analysis

AIM

Older people with sensory impairment are more likely to have smaller and weaker social network due to their reduced ability, which lowers their quality of life. However, there is little research on the social network in older people with sensory impairment, especially the related factors. The aim of the study was to explore the related factors of social network and to provide evidence for the improvement of social network to promote successful aging in older people with sensory impairment.

METHODS

A cross-sectional study was conducted among 374 participants for hearing and vision assessment and questionnaire survey in a community, Beijing. Data were collected and analyzed by principal component analysis (PCA) and multiple logistic regression using IBM SPSS 25.0 software.

RESULTS

PCA showed that there were six risk factors whose eigenvalues >1 were extracted, with a total variance of 56.555%. Multiple logistic regression analysis of principal component indicated that five factors including physical health factor, social interaction factor, psychological status factor, lifestyle factor, and family condition factor, were statistically significant (p < 0.05).

CONCLUSIONS

The social network of older people with sensory impairment is relatively poor. Physical health factor, social interaction factor, psychological status factor, lifestyle factor, and family condition factor may be related factors. Medical staff should pay attention to physical, psychological and social characteristics of older people, especially with sensory impairment, to carry out necessary measures to improve social network and avoid social isolation.

ClockstaRX: Testing Molecular Clock Hypotheses With Genomic Data

Phylogenomic data provide valuable opportunities for studying evolutionary rates and timescales. These analyses require theoretical and statistical tools based on molecular clocks. We present ClockstaRX, a flexible platform for exploring and testing evolutionary rate signals in phylogenomic data. Here, information about evolutionary rates in branches across gene trees is placed in Euclidean space, allowing data transformation, visualization, and hypothesis testing. ClockstaRX implements formal tests for identifying groups of loci and branches that make a large contribution to patterns of rate variation. This information can then be used to test for drivers of genomic evolutionary rates or to inform models for molecular dating. Drawing on the results of a simulation study, we recommend forms of data exploration and filtering that might be useful prior to molecular-clock analyses.

Impact of low light intensity on biomass partitioning and genetic diversity in a chickpea mapping population

With recent climatic changes, the reduced access to solar radiation has become an emerging threat to chickpeas' drought tolerance capacity under rainfed conditions. This study was conducted to assess, and understand the effects of reduced light intensity and quality on plant morphology, root development, and identifying resistant sources from a Sonali/PBA Slasher mapping population. We evaluated 180 genotypes, including recombinant inbred lines (RILs), parents, and commercial checks, using a split-block design with natural and low light treatments. Low light conditions, created by covering one of the two benches inside two growth chambers with a mosquito net, reduced natural light availability by approximately 70%. Light measurements encompassed photosynthetic photon flux density, as well as red, and far-red light readings taken at various stages of the experiment. The data, collected from plumule emergence to anthesis initiation, encompassed various indices relevant to root, shoot, and carbon gain (biomass). Statistical analysis examined variance, treatment effects, heritability, correlations, and principal components (PCs). Results demonstrated significant reductions in root biomass, shoot biomass, root/shoot ratio, and plant total dry biomass under suboptimal light conditions by 52.8%, 28.2%, 36.3%, and 38.4%, respectively. Plants also exhibited delayed progress, taking 9.2% longer to produce their first floral buds, and 19.2% longer to commence anthesis, accompanied by a 33.4% increase in internodal lengths. A significant genotype-by-environment interaction highlighted differing genotypic responses, particularly in traits with high heritability (> 77.0%), such as days to anthesis, days to first floral bud, plant height, and nodes per plant. These traits showed significant associations with drought tolerance indicators, like root, shoot, and plant total dry biomass. Genetic diversity, as depicted in a genotype-by-trait biplot, revealed contributions to PC1 and PC2 coefficients, allowing discrimination of low-light-tolerant RILs, such as 1_52, 1_73, 1_64, 1_245, 1_103, 1_248, and 1_269, with valuable variations in traits of interest. These RILs could be used to breed desirable chickpea cultivars for sustainable production under water-limited conditions. This study concludes that low light stress disrupts the balance between root and shoot morphology, diverting photosynthates to vegetative structures at the expense of root development. Our findings contribute to a better understanding of biomass partitioning under limited-light conditions, and inform breeding strategies for improved drought tolerance in chickpeas.

Predicting Neuroblastoma Patient Risk Groups, Outcomes, and Treatment Response Using Machine Learning Methods: A Review

Neuroblastoma, a paediatric malignancy with high rates of cancer-related morbidity and mortality, is of significant interest to the field of paediatric cancers. High-risk NB tumours are usually metastatic and result in survival rates of less than 50%. Machine learning approaches have been applied to various neuroblastoma patient data to retrieve relevant clinical and biological information and develop predictive models. Given this background, this study will catalogue and summarise the literature that has used machine learning and statistical methods to analyse data such as multi-omics, histological sections, and medical images to make clinical predictions. Furthermore, the question will be turned on its head, and the use of machine learning to accurately stratify NB patients by risk groups and to predict outcomes, including survival and treatment response, will be summarised. Overall, this study aims to catalogue and summarise the important work conducted to date on the subject of expression-based predictor models and machine learning in neuroblastoma for risk stratification and patient outcomes including survival, and treatment response which may assist and direct future diagnostic and therapeutic efforts.

Estimating the number of principal components via Split-Half Eigenvector Matching (SHEM)

Estimating the number of principal components to retain for dimension reduction is a critical step in many applications of principal component analysis. Common methods may not be optimal, however. The current paper presents an alternative procedure that aims to recover the true number of principal components, in the sense of the number of independent vectors involved in the generation of the data.•Data are split into random halves repeatedly.•For each split, the eigenvectors in one half are compared to those in the other.•The split between high and low similarities is used to estimate the number of principal components. The method is a proof of principle that similarity over split-halves of the data may provide a useful approach to estimating the number of components in dimension reduction, or of similar dimensions in other models.

The role of intraspecific variation in bumblebee body size and behavior on buzz pollination of a tropical legume species

PREMISE

The outcomes of generalized mutualisms rely on partner trait variation. In pollination mutualisms, although less explored, intraspecific variation in pollinator traits can be pivotal for successful pollination. We investigated the role of intraspecific body size and behavioral trait variations of bumblebee Bombus morio on the pollination of a buzz-pollinated legume species, Chamaecrista latistipula.

METHODS

To explore the impact of body size and behavior of B. morio on the pollination of C. latistipula, we observed visits to virgin flowers and quantified the pollen removal and deposition (pollination performance) and fruit and seed production (reproductive fitness). By analyzing video and sound recordings, we measured B. morio body size and behavior on each flower, including bee vibration descriptors.

RESULTS

We observed intraspecific behavioral differences among B. morio bumblebees associated with different body sizes. Larger bumblebees had half the handling time and vibrational pulses, less angular displacement within flowers, and larger relative peak amplitudes during vibrations than smaller bumblebees did. Regardless of their large variation in size and behavior, bumblebees were equally effective in removing pollen and pollinating flowers. The high female reproductive fitness was independent of bumblebee body size and behavior, likely due to the interaction between both. On the other hand, larger bumblebees visited flowers for shorter periods, probably promoting higher male reproductive fitness.

CONCLUSIONS

This study is the first to highlight the large intraspecific variation of bumblebee body size and behavior in buzz-pollinated flowers in the field. Together, body size and behavior effects unexpectedly cancel each other, generating a high buzz pollination efficiency.

Morphological and genetic variability in cosmopolitan tardigrade species-Paramacrobiotus fairbanksi Schill, Förster, Dandekar & Wolf, 2010

Paramacrobiotus fairbanksi was described from Alaska (USA) based on integrative taxonomy and later reported from various geographical localities making it a true cosmopolitan species. The 'Everything is Everywhere' (EiE) hypothesis assumes that the geographic distribution of microscopic organisms is not limited by dispersal but by local environmental conditions, making them potentially cosmopolitan. In the present work we report four new populations of P. fairbanksi from the Northern Hemisphere which suggests that the 'EiE' hypothesis is true, at least for some tardigrade species. We also compared all known populations of P. fairbanksi at the genetic and morphological levels. The p-distances between COI haplotypes of all sequenced P. fairbanksi populations from Albania, Antarctica, Canada, Italy, Madeira, Mongolia, Spain, USA and Poland ranged from 0.002 to 0.005%. In total, twelve haplotypes (H1-H12) of COI gene fragments were identified. We also report statistically significant morphometrical differences of species even though they were cultured and bred in the same laboratory conditions. Furthermore, we also discuss differences in the potential distribution of two Paramacrobiotus species.

Principal component analysis of the biopsychosocial features relevant to temporomandibular disorders

OBJECTIVES

This study aimed to explore the dimensionality of three biopsychosocial constructs with multiple subdimensions, including the Symptom Checklist-90-Revised (SCL-90R), pain catastrophizing scale (PCS) and Pittsburgh Sleep Quality Index (PSQI), and to identify the latent dimensions of five biopsychosocial constructs (SCL-90R, PCS, PSQI, pain severity, and pain interference) using the principal component analysis (PCA) in patients with temporomandibular disorder (TMD).

SUBJECTS AND METHODS

A secondary analysis of a previous cross-sectional study comprising 1488 patients with painful TMD was conducted using multiple questionnaires.

RESULTS

PCA of the SCL-90R and PCS identified one factor, which explained 60.8% and 80.2% of the total variance, respectively. For the PSQI, three factors explained 61.3% of the variance. PCA resulted in two main orthogonal components: factor 1, which comprised a combination of scores for pain severity, pain interference and global scores of PCS, and PSQI; and factor 2, which comprised one measure of the SCL-90R. Factors 1 (46.5%) and 2 (20.0%) explained 66.5% of the total variance.

CONCLUSION

The findings of this study revealed that five measures can be primarily categorised into two latent constructs of the psychological (affective) and pain-related (sensory-cognitive) dimensions. These core components could be applied in clinical settings and for research purposes.

BACE1 and SCD1 are associated with neurodegeneration

Introduction

Proteolytic processing of amyloid protein precursor by β-site secretase enzyme (BACE1) is dependent on the cellular lipid composition and is affected by endomembrane trafficking in dementia and Alzheimer's disease (AD). Stearoyl-CoA desaturase 1 (SCD1) is responsible for the synthesis of fatty acid monounsaturation (MUFAs), whose accumulation is strongly associated with cognitive dysfunction.

Methods

In this study, we analyzed the relationship between BACE1 and SCD1 in vivo and in vitro neurodegenerative models and their association in familial AD (FAD), sporadic AD (SAD), and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) using microscopy, biochemical, and mass SPECT approach.

Results

Our findings showed that BACE1 and SCD1 immunoreactivities were increased and colocalized in astrocytes of the hippocampus in a rat model of global cerebral ischemia (2-VO). A synergistic effect of double BACE1/SCD1 silencing on the recovery of motor and cognitive functions was obtained. This neuroprotective regulation involved the segregation of phospholipids (PLs) associated with polyunsaturated fatty acids in the hippocampus, cerebrospinal fluid, and serum. The double silencing in the sham and ischemic groups was stronger in the serum, inducing an inverse ratio between total phosphatydilcholine (PC) and lysophosphatidylcholine (LPC), represented mainly by the reduction of PC 38:4 and PC 36:4 and an increase in LPC 16:0 and LPC 18:0. Furthermore, PC 38:4 and PC:36:4 levels augmented in pathological conditions in in vitro AD models. BACE1 and SCD1 increases were confirmed in the hippocampus of FAD, SAD, and CADASIL.

Conclusion

Therefore, the findings suggest a novel convergence of BACE-1 and SCD1 in neurodegeneration, related to pro-inflammatory phospholipids.

Social Environment Influences the Temporal Dynamics of Sneak-Spawning in a Fish with Alternative Reproductive Tactics

AbstractSeveral predictions of sperm competition theory are not well supported empirically. One potential reason is that most current theory and empirical research ignore how the social environment influence the temporal dynamics of mating. We propose that understanding these dynamics is key to understanding sexual selection and improving the predictive power of theory. To demonstrate the importance of these dynamics, we quantify how males' social role, interactions among males, and current social environment influence the timing of mating in Symphodus ocellatus, a species with three alternative male reproductive tactics. Nesting males spawn synchronously with females; sneakers and satellites sneak-spawn with some time delay. Satellites also cooperate with nesting males. We found that satellites have shorter sneak-spawning delays than sneakers, a benefit of their cooperation with nesting males. Sneak-spawning delays decreased with increasing nest activity for sneakers but not for satellites, suggesting that sneakers may benefit from increased sperm competition intensity. Current sperm competition models ignore this potential benefit, which may be why the prediction that males should decrease investment when sperm competition involves more than two males is not well supported. Our study provides insight into mechanisms that drive variation in the timing of spawning, which could explain mismatches between theoretical and empirical results.

Effects of Ginger and Garlic Powders on the Physicochemical and Microbiological Characteristics of Fruit Juices during Storage

Natural preservatives such as garlic and ginger can be added to the formulation of fresh fruit juices to encourage the consumption of health-promoting foods. In this study, the influence of garlic and ginger and the storage conditions on physicochemical and microbiological characteristics of fruit juices were investigated. The fruit juice assortments were produced from apple, apple and pumpkin, and apple and pomegranate and were treated with 0.5 g garlic powder, 0.5 g ginger powder, and 0.25 g mix of garlic and ginger powders. A total of 12 unpasteurized samples were produced, of which 3 were control samples. Samples stored at 20 and 4 °C were analyzed at 0, 3, 6, and 9 days for water activity (aw), pH, titratable acidity (TA), total soluble solids (TSS), electrical conductivity (EC), vitamin C, color parameters, total number of germs, yeasts, and molds, Listeria, Enterobacteriaceae, and Escherichia coli. Results showed that aw, pH, TSS, and vitamin C content decreased during storage of fruit juice samples, while TA increased. The lowest increase in total number of aerobic mesophilic germs was determined for the apple and pumpkin juice with garlic and ginger and apple juice with garlic.

Seawater intrusion pattern recognition supported by unsupervised learning: A systematic review and application

Seawater intrusion is among the world's leading causes of groundwater contamination, as salty water can affect potable water access, food production, and ecosystem functions. To explore such contamination sources, multivariate analysis supported by unsupervised learning tools has been used for decades to aid in water resource pattern recognition, clustering, and water quality data variability characterization. This study proposes a systematic review of these techniques applied for supporting seawater intrusion identification based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and subsequent bibliometric analysis of 102 coastal hydrogeological studies. The most relevant identified methods, including principal components analysis (PCA), hierarchical clustering analysis, K-means clustering, and self-organizing maps, are explained and applied to a case study. Although 74 % of the studies that applied dimensional reduction methods, such as PCA, associated most of the database variance with the salinization process, 77 % of the studies that applied clustering methods associated at least one water sample cluster with the influence of seawater intrusion. Based on the review and a practical demonstration using the open-source R software platform, recommendations are made regarding data preprocessing, research opportunities, and publishing information necessary to replicate and validate the studies.

Living with chronic infection: Persistent immunomodulation during avirulent haemoparasitic infection in a wild rodent

Apicomplexans are a protozoan phylum of obligate parasites which may be highly virulent during acute infections, but may also persist as chronic infections which appear to have little fitness cost. Babesia microti is an apicomplexan haemoparasite that, in immunocompromised individuals, can cause severe, potentially fatal disease. However, in its natural host, wild field voles (Microtus agrestis), it exhibits chronic infections that have no detectable impact on survival or female fecundity. How is damage minimized, and what is the impact on the host's immune state and health? We examine the differences in immune state (here represented by expression of immune-related genes in multiple tissues) associated with several common chronic infections in a population of wild field voles. While some infections show little impact on immune state, we find strong associations between immune state and B. microti. These include indications of clearance of infected erythrocytes (increased macrophage activity in the spleen) and activity likely associated with minimizing damage from the infection (anti-inflammatory and antioxidant activity in the blood). By analysing gene expression from the same individuals at multiple time points, we show that the observed changes are a response to infection, rather than a risk factor. Our results point towards continual investment to minimize the damage caused by the infection. Thus, we shed light on how wild animals can tolerate some chronic infections, but emphasize that this tolerance does not come without a cost.

Food Intake of Macro and Trace Elements from Different Fresh Vegetables Taken from Timisoara Market, Romania-Chemometric Analysis of the Results

Vegetable consumption is recommended and encouraged by all nutritionists and doctors across the planet. However, in addition to minerals which are beneficial to the body, certain minerals with a negative influence on human health can sneak in. It is very important that in the case of some minerals their content in vegetables is known, so that the recommended limits are not exceeded. The purpose of this study was to evaluate the macro elements (Na, K, Ca, Mg) and trace elements (Cu, Mn, Fe, Cd, Pb, Zn, Co) in 24 samples of vegetables from four botanical families (Solanaceae, Brassicaceae, Apiaceae and Amaryllidaceae), purchased from the market in Timișoara, Romania, both imported products as well as local products. The atomic-absorption-spectrometry technique (FAAS) was used to evaluate the macro elements and trace elements. The values obtained for the macro elements and trace elements were used as input data for the analysis of multivariate data, the principal component analysis (PCA) in which the vegetable samples were grouped according to their contribution of certain mineral elements, as well as according to some of the botanical families to which they belong. At the same time, based on the values obtained for trace elements, an assessment of the risk to human health in terms of consumption of the vegetables studied was carried out. The risk assessment for human health was determined on the basis of the estimated daily dose (EDI), the values of the target hazard coefficient (THQ), the values of the total target hazard coefficient (TTHQ) and the carcinogenic risk (CR). Following the determination of THQ, the values obtained followed the order THQ_With > THQ_Cd > THQ_Pb > THQ_Co > THQ_Mn > THQ_Zn > THQ_Fe. The results on the content of macro elements and trace elements, as well as the assessment of the risk to human health when consuming the assessed vegetables, were within the limits of European Union (EU) and World Health Organization and Food and Agriculture Organization (WHO/FAO)legislation.

Response to Comment on "Feasibility of Raman spectroscopy as a potential in vivo tool to screen for pre-diabetes and diabetes"

This letter aims to reply to Bratchenko and Bratchenko's comment on our paper "Feasibility of Raman spectroscopy as a potential in vivo tool to screen for pre-diabetes and diabetes." Our paper analyzed the feasibility of using in vivo Raman measurements combined with machine learning techniques to screen diabetic and prediabetic patients. We argued that this approach yields high overall accuracy (94.3%) while retaining a good capacity to distinguish between diabetic (area under the receiver-operating curve [AUC] = 0.86) and control classes (AUC = 0.97) and a moderate performance for the prediabetic class (AUC = 0.76). Bratchenko and Bratchenko's comment focuses on the possible overestimation of the proposed classification models and the absence of information on the age of participants. In this reply, we address their main concerns regarding our previous manuscript.

Whole-exome sequencing and bioinformatic analyses revealed differences in gene mutation profiles in papillary thyroid cancer patients with and without benign thyroid goitre background

Background

Papillary thyroid cancer (PTC) is the most common thyroid malignancy. Concurrent presence of cytomorphological benign thyroid goitre (BTG) and PTC lesion is often detected. Aberrant protein profiles were previously reported in patients with and without BTG cytomorphological background. This study aimed to evaluate gene mutation profiles to further understand the molecular mechanism underlying BTG, PTC without BTG background and PTC with BTG background.

Methods

Patients were grouped according to the histopathological examination results: (i) BTG patients (n = 9), (ii) PTC patients without BTG background (PTCa, n = 8), and (iii) PTC patients with BTG background (PTCb, n = 5). Whole-exome sequencing (WES) was performed on genomic DNA extracted from thyroid tissue specimens. Nonsynonymous and splice-site variants with MAF of ≤ 1% in the 1000 Genomes Project were subjected to principal component analysis (PCA). PTC-specific SNVs were filtered against OncoKB and COSMIC while novel SNVs were screened through dbSNP and COSMIC databases. Functional impacts of the SNVs were predicted using PolyPhen-2 and SIFT. Protein-protein interaction (PPI) enrichment of the tumour-related genes was analysed using Metascape and MCODE algorithm.

Results

PCA plots showed distinctive SNV profiles among the three groups. OncoKB and COSMIC database screening identified 36 tumour-related genes including BRCA2 and FANCD2 in all groups. BRAF and 19 additional genes were found only in PTCa and PTCb. "Pathways in cancer", "DNA repair" and "Fanconi anaemia pathway" were among the top networks shared by all groups. However, signalling pathways related to tyrosine kinases were the most significantly enriched in PTCa while "Jak-STAT signalling pathway" and "Notch signalling pathway" were the only significantly enriched in PTCb. Ten SNVs were PTC-specific of which two were novel; DCTN1 c.2786C>G (p.Ala929Gly) and TRRAP c.8735G>C (p.Ser2912Thr). Four out of the ten SNVs were unique to PTCa.

Conclusion

Distinctive gene mutation patterns detected in this study corroborated the previous protein profile findings. We hypothesised that the PTCa and PTCb subtypes differed in the underlying molecular mechanisms involving tyrosine kinase, Jak-STAT and Notch signalling pathways. The potential applications of the SNVs in differentiating the benign from the PTC subtypes requires further validation in a larger sample size.

Biochar Stimulated Actual Evapotranspiration and Wheat Productivity under Water Deficit Conditions in Sandy Soil Based on Non-Weighing Lysimeter

The major climate-related hazard to worldwide agricultural productivity is drought, which is becoming more common because of ongoing climate change, especially in the arid and semi-arid regions. Herein, we investigated the influence of biochar soil application at 0, (B1), 7.5 ha^-1 (B2), and 15 t ha^-1 (B3) on the productivity and drought-tolerance indices of wheat (Triticum aestivum L., cv. Sakha 93) grown in sandy soil under irrigation levels of 100 (I1), 80 (I2), and 60% (I3) of crop evapotranspiration (ETc), as well as soil properties based on non-weighing lysimeter units. Increasing water deficiency significantly decreased the actual evapotranspiration (ETa) values. A growing biochar rate caused a significant increase in ETa values, water use efficiency, and wheat productivity compared to the untreated control. Additionally, biochar supplementation revealed an improvement in soil quality as measured by the reduction in the bulk density and hydraulic conductivity with an increase in the total porosity and void ratio of the experimental soil. The correlation analysis exhibited a highly significant and positive correlation (0.98 **) between biological yield and grain yield traits. Therefore, it may be stated that these traits are the most significant components of the evaluated grain yield in wheat plants. The productivity of I1 plants was not significantly different and slightly higher than that of I2 plants. Therefore, it can be recommended that exposed wheat plants cultivated in sandy soil with I2 × B3 treatment significantly provide the highest yield while saving 20% of the irrigation water.

Abiotic and biotic correlates of the occurrence, extent and cover of invasive aquatic Elodea nuttallii

Biological invasions, especially invasive alien aquatic plants, are a major and growing ecological and socioeconomic problem worldwide. Freshwater systems are particularly vulnerable to invasion, where impacts of invasive alien species can damage ecological structure and function. Identifying abiotic and biotic factors that mediate successful invasions is a management priority. Our aim was to determine the environmental correlates of Elodea nuttallii; a globally significant invasive aquatic species. Elodea nuttallii presence/absence (occurrence), extent (patch area) and percentage cover (density) was visually assessed from a boat throughout Lough Erne (approximately 144 km2), County Fermanagh, Northern Ireland during the active summer growth season (July-September). In addition, substrate type and zebra mussel Dreissena polymorpha occurrence was recorded. Fourteen water chemistry variables were collected monthly from 12 recording stations throughout the lake during the 9 years before the survey to spatially interpolate values and establish temporal trajectories in their change. Shoreline land use was derived from CORINE land cover maps. Environmental associations between E. nuttallii, substrate, D. polymorpha, water chemistry and land use were assessed. Elodea nuttallii occurrence was positively associated with water conductivity, alkalinity, suspended solids, phosphorus (both total and soluble) and chlorophyll-a concentrations, but negatively associated with pH and total oxidised nitrogen. E. nuttallii patch extent and proportional cover were positively associated, to varying degrees, with the presence of D. polymorpha, biological oxygen demand, water clarity and soft substrate, but negatively associated with urban development and ammonium. Elodea nuttallii displayed high levels of phenotypic plasticity in response to environmental variation, allowing it to adapt to a wide range of conditions and potentially gain competitive advantage over native or other invasive macrophytes.It is evident that multiple abiotic and biotic factors, including facilitation by co-occurring invasive dreissenid mussels, interact to influence the distribution and abundance of E. nuttallii. Thus, it is necessary to consider a more comprehensive environmental context when planning Elodea management strategies.

Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated

Principal Component Analysis (PCA) is a multivariate analysis that reduces the complexity of datasets while preserving data covariance. The outcome can be visualized on colorful scatterplots, ideally with only a minimal loss of information. PCA applications, implemented in well-cited packages like EIGENSOFT and PLINK, are extensively used as the foremost analyses in population genetics and related fields (e.g., animal and plant or medical genetics). PCA outcomes are used to shape study design, identify, and characterize individuals and populations, and draw historical and ethnobiological conclusions on origins, evolution, dispersion, and relatedness. The replicability crisis in science has prompted us to evaluate whether PCA results are reliable, robust, and replicable. We analyzed twelve common test cases using an intuitive color-based model alongside human population data. We demonstrate that PCA results can be artifacts of the data and can be easily manipulated to generate desired outcomes. PCA adjustment also yielded unfavorable outcomes in association studies. PCA results may not be reliable, robust, or replicable as the field assumes. Our findings raise concerns about the validity of results reported in the population genetics literature and related fields that place a disproportionate reliance upon PCA outcomes and the insights derived from them. We conclude that PCA may have a biasing role in genetic investigations and that 32,000-216,000 genetic studies should be reevaluated. An alternative mixed-admixture population genetic model is discussed.

Validation of the Fear of Missing Out Scale for Use with African Americans in the United States

Social media connects people in a myriad of ways, yet when prevented from staying connected, an experience of missing out on information and events perceived to be integral to one's well-being may ensue. Relatedness, a core construct of self-determination theory, is a primary influencer of motivation, and therefore being cut off from others has a negative impact on one's quality of life. Across diverse groups of people, social media is utilized for a variety of purposes directly related to connectedness, which implies inherent differences in how one's fear of missing out (FoMO) manifests in everyday life. This study employed the previously validated Fear of Missing Out Scale (FoMOS) with a nonclinical sample of African American and Caucasian college students in the United States, with a particular focus on validity of the measure with an African American cohort due to a lack of empirical evidence pertaining to with this demographic. Factor analyses yielded inconsistent findings from the FoMOS initial validation study, and results indicated differences in self-reported FoMO between the two racial groups. We speculate that observed group-based differences are at least partially the result of how individuals from each group understood and internalized the conceptual meaning of FoMO, supporting a hypothesis of practical differences in how anxieties related to missing out manifest. This may be due to individual or group-based differences in motivations for, or general purpose of, using social media.

Determining the attributes that influence students' online learning satisfaction during COVID-19 pandemic

The inevitable disruptions caused by COVID-19 in the hospitality and tourism education sector have made online learning a necessity rather than an option. This study employs the user experience questionnaire (UEQ) to examine students' online learning experiences specifically in the context of COVID-19. Data collection involved a Qualtrics online survey with a convenience sample of 216 tourism and hospitality students in Macau. Overall, results point to a generally positive appraisal of online attributes, but satisfaction is marginal. Initial principal component factor analysis generated three orthogonal factors of online learning attributes: "Perspicuity and dependability"; "Stimulation and attractiveness"; and "Usability and innovation". Further regression analysis reveals that "Stimulation and attractiveness" is the strongest predictor of the students' satisfaction regarding online learning during the COVID-19 disruptions. This novel finding points to the need for hospitality and tourism education institutions to develop an attractive and motivating visual environment for online course delivery since a stimulating online learning atmosphere is crucial in the context of the pedagogical disruptions caused by COVID-19. Nonetheless, these findings are specific to Chinese students and reflect their learning satisfaction which may differ in other contexts.

Exceptional evolutionary lability of flower-like inflorescences (pseudanthia) in Apiaceae subfamily Apioideae

PREMISE

Pseudanthia are widespread and have long been postulated to be a key innovation responsible for some of the angiosperm radiations. The aim of our study was to analyze macroevolutionary patterns of these flower-like inflorescences and their potential correlation with diversification rates in Apiaceae subfamily Apioideae. In particular, we were interested to investigate evolvability of pseudanthia and evaluate their potential association with changes in the size of floral display.

METHODS

The framework for our analyses consisted of a time-calibrated phylogeny of 1734 representatives of Apioideae and a morphological matrix of inflorescence traits encoded for 847 species. Macroevolutionary patterns in pseudanthia were inferred using Markov models of discrete character evolution and stochastic character mapping, and a principal component analysis was used to visualize correlations in inflorescence architecture. The interdependence between net diversification rates and the occurrence of pseudocorollas was analyzed with trait-independent and trait-dependent approaches.

RESULTS

Pseudanthia evolved in 10 major clades of Apioideae with at least 36 independent origins and 46 reversals. The morphospace analysis recovered differences in color and compactness between floral and hyperfloral pseudanthia. A correlation between pseudocorollas and size of inflorescence was also strongly supported. Contrary to our predictions, pseudanthia are not responsible for variation in diversification rates identified in this subfamily.

CONCLUSIONS

Our results suggest that pseudocorollas evolve as an answer to the trade-off between enlargement of floral display and costs associated with production of additional flowers. The high evolvability and architectural differences in apioid pseudanthia may be explained on the basis of adaptive wandering and evolutionary developmental biology.

PCAtest: testing the statistical significance of Principal Component Analysis in R

Principal Component Analysis (PCA) is one of the most broadly used statistical methods for the ordination and dimensionality-reduction of multivariate datasets across many scientific disciplines. Trivial PCs can be estimated from data sets without any correlational structure among the original variables, and traditional criteria for selecting non-trivial PC axes are difficult to implement, partially subjective or based on ad hoc thresholds. PCAtest is an R package that implements permutation-based statistical tests to evaluate the overall significance of a PCA, the significance of each PC axis, and of contributions of each observed variable to the significant axes. Based on simulation and empirical results, I encourage R users to routinely apply PCAtest to test the significance of their PCA before proceeding with the direct interpretation of PC axes and/or the utilization of PC scores in subsequent evolutionary and ecological analyses.

Identification of a Quinone Derivative as a YAP/TEAD Activity Modulator from a Repurposing Library

The transcriptional regulators YAP (Yes-associated protein) and TAZ (transcriptional co-activator with PDZ-binding motif) are the major downstream effectors in the Hippo pathway and are involved in cancer progression through modulation of the activity of TEAD (transcriptional enhanced associate domain) transcription factors. To exploit the advantages of drug repurposing in the search of new drugs, we developed a similar approach for the identification of new hits interfering with TEAD target gene expression. In our study, a 27-member in-house library was assembled, characterized, and screened for its cancer cell growth inhibition effect. In a secondary luciferase-based assay, only seven compounds confirmed their specific involvement in TEAD activity. IA5 bearing a p-quinoid structure reduced the cytoplasmic level of phosphorylated YAP and the YAP–TEAD complex transcriptional activity and reduced cancer cell growth. IA5 is a promising hit compound for TEAD activity modulator development.

Advanced Operationalization Framework for Climate-Resilient Urban Public Health Care Services: Composite Indicators-Based Scenario Assessment of Khon Kaen City, Thailand

Conventional local public health planning and monitoring are insufficiently addressing the conjugated impact of urban development change and climate change in the future. The existing checklist and index often ignore the spatial-network interaction determining urban public health services in forward-looking aspects. This study offers and demonstrates a climate-resilient operationalization framework for urban public health services considering the interaction between urban development change and climate change across scales. A combination of collaborative scenario planning and tailor-made composite indicators were applied based on the IPCC Fifth Assessment Report (AR5)'s climate risk concept to adhere to local realities and diverse sets of scenarios. The framework was contested in a medium-sized city with a universal health care coverage setting, Khon Kaen city, Thailand. The results show that the coupling of collaborative scenario planning and composite indicators allows local public health care to operationalize their potential impact and climate-resilient targets in the future(s) in multiple service operation aspects. The scenarios assessment outcomes prove that although public health devotion can be fail-safe, achieving climate-resilient targets requires sectoral integration with urban development and health determining domains. Further exploration and disputation of the framework with a wider scale and diversified settings are recommended to enhance their robustness and universality.

The Shapes of Birds' Eggs: Evolutionary Constraints and Adaptations

AbstractWe studied the shapes of eggs from 955 extant bird species across the avian phylogeny, including 39 of 40 orders and 78% of 249 families. We show that the elongation component of egg shape (length relative to width) is largely the result of constraints imposed by the female's anatomy during egg formation, whereas asymmetry (pointedness) is mainly an adaptation to conditions during the incubation period. Thus, egg elongation is associated with the size of the egg in relation to both the size of the female's oviduct and her general body conformation and mode of locomotion correlated with pelvis shape. Egg asymmetry is related mainly to clutch size and the structure of the incubation site, factors that influence thermal efficiency during incubation and the risk of breakage. Importantly, general patterns across the avian phylogeny do not always reflect the trends within lower taxonomic levels. We argue that the analysis of avian egg shape is most profitably conducted within taxa where all species share similar life histories and ecologies, as there is no single factor that influences egg shape in the same way in all bird species.

Statistics of eigenvalue dispersion indices: Quantifying the magnitude of phenotypic integration

Analysis of trait covariation plays a pivotal role in the study of phenotypic evolution. The magnitude of covariation is often quantified with statistics based on dispersion of eigenvalues of a covariance or correlation matrix-eigenvalue dispersion indices. This study clarifies the statistical justifications of these statistics and elaborates on their sampling properties. The relative eigenvalue variance of a covariance matrix is known in the statistical literature a test statistic for sphericity, and thus is an appropriate measure of eccentricity of variation. The same of a correlation matrix is equal to the average squared correlation, which has a straightforward interpretation as a measure of integration. Here, expressions for the mean and variance of these statistics are analytically derived under multivariate normality, clarifying the effects of sample size N, number of variables p, and parameters on sampling bias and error. Simulations confirm that approximations involved are reasonably accurate with a moderate sample size (N ≥ 16-64). Importantly, sampling properties of these indices are not adversely affected by a high p:N ratio, promising their utility in high-dimensional phenotypic analyses. They can furthermore be applied to shape variables and phylogenetically structured data with appropriate modifications.

The characterization and pollution status of the surface sediment in the Boka Kotorska Bay, Montenegro

Surface sediments collected from twelve stations in the Boka Kotorska Bay were analyzed for the level and distribution of twenty-six elements and ten oxides, grain sizes, organic matter, and carbonate content. Potentially toxic elements (Al, Fe, Mn, Cr, Zn, Ni, Cu, Pb, As, Co, U) were determined to assess the contamination status and potential environmental risk according to the single-element indices (enrichment factor (EF), geoaccumulation index (Igeo), contamination factor (CF)), and combined index (pollution load index (PLI)). The single-element indices EF and CF revealed that the surface marine sediment was moderately polluted with Pb, Cu, and Cr, while Igeo indicated moderate pollution with Ni > Cr > Zn > Cu > As and moderate to heavy pollution with Pb, as a result of the anthropogenic factors. The method of the combined effect of toxic elements, PLI, showed the highest pollution rate at the shipyard location in the Bay of Tivat. Pearson's correlation coefficient (r), principal component analysis (PCA), and cluster analysis (CA) were applied to highlight similarities and differences in the distribution of the investigated elements in the Bay, confirming the claim obtained by the pollution indices. The sediment contamination with most heavy metals, such as Cr, Zn, Ni, Cu, Pb, and As, has been identified in the Tivat Bay area.

Learning Target Class Feature Subspace (LTC-FS) Using Eigenspace Analysis and N-ary Search-Based Autonomous Hyperparameter Tuning for OCSVM

Existing dimensionality reduction (DR) techniques such as principal component analysis (PCA) and its variants are not suitable for target class mining due to the negligence of unique statistical properties of class-of-interest (CoI) samples. Conventionally, these approaches utilize higher or lower eigenvalued principal components (PCs) for data transformation; but the higher eigenvalued PCs may split the target class, whereas lower eigenvalued PCs do not contribute significant information and wrong selection of PCs leads to performance degradation. Considering these facts, the present research offers a novel target class-guided feature extraction method. In this approach, initially, the eigendecomposition is performed on variance–covariance matrix of only the target class samples, where the higher- and lower-valued eigenvectors are rejected via statistical analysis, and the selected eigenvectors are utilized to extract the most promising feature subspace. The extracted feature-subset gives a more tighter description of the CoI with enhanced associativity among target class samples and ensures the strong separation from nontarget class samples. One-class support vector machine (OCSVM) is evaluated to validate the performance of learned features. To obtain optimized values of hyperparameters of OCSVM a novel [Formula: see text]-ary search-based autonomous method is also proposed. Exhaustive experiments with a wide variety of datasets are performed in feature-space (original and reduced) and eigenspace (obtained from original and reduced features) to validate the performance of the proposed approach in terms of accuracy, precision, specificity and sensitivity.

Response of aquatic macroinvertebrates communities to multiple anthropogenic stressors in a lowland tributary river

In the current study the response of aquatic macroinvertebrate communities to multiple anthropogenic stressors in a typical lowland river that crosses pristine sectors situated toward headwaters, as well as densely populated urban areas was assessed. We wished to develop an effective bioassay procedure for assessing water and sediment quality in lotic ecosystems from Romania with the aid of macroinvertebrate organisms correlated with physico-chemical parameters and pollutants in both dissolved fractions and material bonded to sediment. A fast scanning approach of the river, from springs to the mouth, was employed. We found significant changes in physico-chemical parameters along a longitudinal gradient, the highest values being registered within the urban area and heavily agriculturally developed areas. The macroinvertebrates showed affinities for certain abiotic factors, emphasising their potential use for future studies as reliable ecological indicators, shaped by a synergic combination of urban effects and magnitude of type of land use.

Moderate to severe OSA screening based on support vector machine of the Chinese population faciocervical measurements dataset: a cross-sectional study

OBJECTIVES

Obstructive sleep apnoea (OSA) has received much attention as a risk factor for perioperative complications and 68.5% of OSA patients remain undiagnosed before surgery. Faciocervical characteristics may screen OSA for Asians due to smaller upper airways compared with Caucasians. Thus, our study aimed to explore a machine-learning model to screen moderate to severe OSA based on faciocervical and anthropometric measurements.

DESIGN

A cross-sectional study.

SETTING

Data were collected from the Shanghai Jiao Tong University School of Medicine affiliated Ruijin Hospital between February 2019 and August 2020.

PARTICIPANTS

A total of 481 Chinese participants were included in the study. PRIMARY AND SECONDARY OUTCOME: (1) Identification of moderate to severe OSA with apnoea-hypopnoea index 15 events/hour and (2) Verification of the machine-learning model.

RESULTS

Sex-Age-Body mass index (BMI)-maximum Interincisal distance-ratio of Height to thyrosternum distance-neck Circumference-waist Circumference (SABIHC2) model was set up. The SABIHC2 model could screen moderate to severe OSA with an area under the curve (AUC)=0.832, the sensitivity of 0.916 and specificity of 0.749, and performed better than the STOP-BANG (snoring, tiredness, observed apnea, high blood pressure, BMI, age, neck circumference, and male gender) questionnaire, which showed AUC=0.631, the sensitivity of 0.487 and specificity of 0.772. Especially for asymptomatic patients (Epworth Sleepiness Scale <10), the SABIHC2 model demonstrated better predictive ability compared with the STOP-BANG questionnaire, with AUC (0.824 vs 0.530), sensitivity (0.892 vs 0.348) and specificity (0.755 vs 0.809).

CONCLUSION

The SABIHC2 machine-learning model provides a simple and accurate assessment of moderate to severe OSA in the Chinese population, especially for those without significant daytime sleepiness.

Spatial and temporal distribution of pollution indices in marine surface sediments-a chemometric approach

Concentrations of As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Zn, and Fe measured at forty locations in the Boka Kotorska Bay were used to monitor the spatial and temporal quality of sediment and to assess surface sediment contamination over the last 15 years. This ecological geochemistry assessment was made using two classes of pollution indices: single indices concerning the investigated elements (contamination factor (Cf)) and integrated indices concerning the locations (pollution load index (PLI), potential ecological risk index (RI), mean effects range median quotient (MERMQ), toxic risk index (TRI), contamination severity index (CSI)). The distribution of all indices was geostatistically mapped and several hotspots were identified. Based on the indices applied in the risk assessment, the mean contribution of individual metal species to the total risk was determined and presented in the following order: Cd > Hg > As > Pb > Ni > Cu > Cr > Zn. This study revealed that Cd and Hg were at the top of the risk list among the examined elements. The temporal distribution of Hg has shown a decreasing trend during the period 2005-2019, while the presence of Cd in the Bay environment is of major concern. The results revealed that the most polluted part of the investigated area was Tivat bay within the Boka Kotorska Bay. Multivariate statistical analysis of pollution indices resulted in multicollinearity, which enabled the use of a reduced number of indices with an acceptable risk estimation.

Assessment and prioritization of cultural ecosystem services in the Sahara-Sahelian region

Desert environments remain largely neglected by the society and their potential to provide benefits to people remain understudied. Hotspots of cultural ecosystem services have been identified in some deserts; yet, knowing which countries need to strengthen efforts to satisfy people's demand for those services is timely needed. Here, we show the performance of countries within the Earth's largest warm region - the Sahara-Sahel - in managing cultural ecosystem services. Using the most-advanced decision-support tools and updated databases on biodiversity features and constrains to ecosystem services and on socioeconomic indicators, we identified national priorities for cultural services management. We also identified countries that are missing opportunities for local sustainable development. About 34% of Sahara-Sahel is prioritized for cultural ecosystem services, particularly in the main mountains and waterbodies of the region and along the Western and Eastern coastal limits. Algeria, Egypt, Libya, Morocco, Senegal, and Tunisia are performing better in managing their cultural services given the availability of such services in their territories. Burkina Faso, Cameroon, Chad, Egypt, Libya, Mali, Niger, Nigeria, Sudan, and South Sudan need to urgently improve their ease of mobility, governance, safety, socioeconomic and health systems to foster ecosystem services demand. Cameroon, Eritrea, and Senegal are receiving far less tourists than what their ecosystems can handle and need to improve their local conditions for better marketing international tourists able to economically contribute to sustainable development through ecotourism programs. The approach developed here serves as a framework for conserving the last world wild ecosystems and is replicable to other contexts where regional planning for ecosystem management is compulsory.

Pool choice in a vertical landscape: Tadpole-rearing site flexibility in phytotelm-breeding frogs

Many species of Neotropical frogs have evolved to deposit their tadpoles in small water bodies inside plant structures called phytotelmata. These pools are small enough to exclude large predators but have limited nutrients and high desiccation risk. Here, we explore phytotelm use by three common Neotropical species: Osteocephalus oophagus, an arboreal frog that periodically feeds eggs to its tadpoles; Dendrobates tinctorius, a tadpole-transporting poison frog with cannibalistic tadpoles; and Allobates femoralis, a terrestrial tadpole-transporting poison frog with omnivorous tadpoles. We found that D. tinctorius occupies pools across the chemical and vertical gradient, whereas A. femoralis and O. oophagus appear to have narrower deposition options that are restricted primarily by pool height, water capacity, alkalinity, and salinity. Dendrobates tinctorius tadpoles are particularly flexible and can survive in a wide range of chemical, physical, and biological conditions, whereas O. oophagus seems to prefer small, clear pools and A. femoralis occupies medium-sized pools with abundant leaf litter and low salinity. Together, these results show the possible niche partitioning of phytotelmata among frogs and provide insight into stressors and resilience of phytotelm breeders.

Integrative taxonomy resolves species identities within the Macrobiotus pallarii complex (Eutardigrada: Macrobiotidae)

The taxonomy of many groups of meiofauna is challenging due to their low number of diagnostic morphological characters and their small body size. Therefore, with the advent of molecular techniques that provide a new source of traits, many cryptic species have started to be discovered. Tardigrades are not an exception, and many once thought to be cosmopolitan taxa are being found to be complexes of phenotypically similar species. Macrobiotus pallarii Maucci, 1954 was originally described in South Italy and has been subsequently recorded in Europe, America, and Asia. This allegedly wide geographic range suggests that multiple species may be hidden under this name. Moreover, recently, genetic evidence to support this was put forward, and the Macrobiotus pallarii complex has been proposed to accommodate putative species related to M. pallarii. Here, we describe three new pseudocryptic species based on populations that would have been all classified as Macrobiotus pallarii if molecular methods were not employed. Using an integrative taxonomy approach, we analyzed animals and eggs from the topotypic population of Macrobiotus pallarii, together with four other populations of the complex. We recovered four distinct phylogenetic lineages that, despite the overlap of morphometric traits, can be separated phenotypically by subtle but discrete morphological characters. One lineage corresponds to Macrobiotus pallarii, whereas the other three are newly described as Macrobiotus margoae Stec, Vecchi & Bartels, sp. nov. from the USA, Macrobiotus ripperi Stec, Vecchi & Michalczyk, sp. nov. from Poland and Finland, and Macrobiotus pseudopallarii Stec, Vecchi & Michalczyk, sp. nov. from Montenegro. To facilitate species identification, we provide a dichotomous key for species of the M. pallarii complex. Delimitation of these pseudocryptic taxa highlights the need for an integrative approach to uncover the phylum's diversity in full.

Comparative bioacoustics: a roadmap for quantifying and comparing animal sounds across diverse taxa

Animals produce a wide array of sounds with highly variable acoustic structures. It is possible to understand the causes and consequences of this variation across taxa with phylogenetic comparative analyses. Acoustic and evolutionary analyses are rapidly increasing in sophistication such that choosing appropriate acoustic and evolutionary approaches is increasingly difficult. However, the correct choice of analysis can have profound effects on output and evolutionary inferences. Here, we identify and address some of the challenges for this growing field by providing a roadmap for quantifying and comparing sound in a phylogenetic context for researchers with a broad range of scientific backgrounds. Sound, as a continuous, multidimensional trait can be particularly challenging to measure because it can be hard to identify variables that can be compared across taxa and it is also no small feat to process and analyse the resulting high-dimensional acoustic data using approaches that are appropriate for subsequent evolutionary analysis. Additionally, terminological inconsistencies and the role of learning in the development of acoustic traits need to be considered. Phylogenetic comparative analyses also have their own sets of caveats to consider. We provide a set of recommendations for delimiting acoustic signals into discrete, comparable acoustic units. We also present a three-stage workflow for extracting relevant acoustic data, including options for multivariate analyses and dimensionality reduction that is compatible with phylogenetic comparative analysis. We then summarize available phylogenetic comparative approaches and how they have been used in comparative bioacoustics, and address the limitations of comparative analyses with behavioural data. Lastly, we recommend how to apply these methods to acoustic data across a range of study systems. In this way, we provide an integrated framework to aid in quantitative analysis of cross-taxa variation in animal sounds for comparative phylogenetic analysis. In addition, we advocate the standardization of acoustic terminology across disciplines and taxa, adoption of automated methods for acoustic feature extraction, and establishment of strong data archival practices for acoustic recordings and data analyses. Combining such practices with our proposed workflow will greatly advance the reproducibility, biological interpretation, and longevity of comparative bioacoustic studies.

Comparison of Correction Factors and Sample Size Required to Test the Equality of the Smallest Eigenvalues in Principal Component Analysis

In the inferential process of Principal Component Analysis (PCA), one of the main challenges for researchers is establishing the correct number of components to represent the sample. For that purpose, heuristic and statistical strategies have been proposed. One statistical approach consists in testing the hypothesis of the equality of the smallest eigenvalues in the covariance or correlation matrix using a Likelihood-Ratio Test (LRT) that follows a χ2 limit distribution. Different correction factors have been proposed to improve the approximation of the sampling distribution of the statistic. We use simulation to study the significance level and power of the test under the use of these different factors and analyze the sample size required for an dequate approximation. The results indicate that for covariance matrix, the factor proposed by Bartlett offers the best balance between the objectives of low probability of Type I Error and high Power.             If the correlation matrix is used, the factors W ∗             and cχ2             are the most               recommended. Empirically, we can observe that most factors require sample sizes 10 or 20 times the number of variables if covariance or correlationmatrices, respectively, are implemented.

How general is cognitive ability in non-human animals? A meta-analytical and multi-level reanalysis approach

General intelligence has been a topic of high interest for over a century. Traditionally, research on general intelligence was based on principal component analyses and other dimensionality reduction approaches. The advent of high-speed computing has provided alternative statistical tools that have been used to test predictions of human general intelligence. In comparison, research on general intelligence in non-human animals is in its infancy and still relies mostly on factor-analytical procedures. Here, we argue that dimensionality reduction, when incorrectly applied, can lead to spurious results and limit our understanding of ecological and evolutionary causes of variation in animal cognition. Using a meta-analytical approach, we show, based on 555 bivariate correlations, that the average correlation among cognitive abilities is low (r = 0.185; 95% CI: 0.087-0.287), suggesting relatively weak support for general intelligence in animals. We then use a case study with relatedness (genetic) data to demonstrate how analysing traits using mixed models, without dimensionality reduction, provides new insights into the structure of phenotypic variance among cognitive traits, and uncovers genetic associations that would be hidden otherwise. We hope this article will stimulate the use of alternative tools in the study of cognition and its evolution in animals.

Detecting European Aspen (Populus tremula L.) in Boreal Forests Using Airborne Hyperspectral and Airborne Laser Scanning Data

Sustainable forest management increasingly highlights the maintenance of biological diversity and requires up-to-date information on the occurrence and distribution of key ecological features in forest environments. European aspen (Populus tremula L.) is one key feature in boreal forests contributing significantly to the biological diversity of boreal forest landscapes. However, due to their sparse and scattered occurrence in northern Europe, the explicit spatial data on aspen remain scarce and incomprehensive, which hampers biodiversity management and conservation efforts. Our objective was to study tree-level discrimination of aspen from other common species in northern boreal forests using airborne high-resolution hyperspectral and airborne laser scanning (ALS) data. The study contained multiple spatial analyses: First, we assessed the role of different spectral wavelengths (455–2500 nm), principal component analysis, and vegetation indices (VI) in tree species classification using two machine learning classifiers—support vector machine (SVM) and random forest (RF). Second, we tested the effect of feature selection for best classification accuracy achievable and third, we identified the most important spectral features to discriminate aspen from the other common tree species. SVM outperformed the RF model, resulting in the highest overall accuracy (OA) of 84% and Kappa value (0.74). The used feature set affected SVM performance little, but for RF, principal component analysis was the best. The most important common VI for deciduous trees contained Conifer Index (CI), Cellulose Absorption Index (CAI), Plant Stress Index 3 (PSI3), and Vogelmann Index 1 (VOG1), whereas Green Ratio (GR), Red Edge Inflection Point (REIP), and Red Well Position (RWP) were specific for aspen. Normalized Difference Red Edge Index (NDRE) and Modified Normalized Difference Index (MND705) were important for coniferous trees. The most important wavelengths for discriminating aspen from other species included reflectance bands of red edge range (724–727 nm) and shortwave infrared (1520–1564 nm and 1684–1706 nm). The highest classification accuracy of 92% (F1-score) for aspen was achieved using the SVM model with mean reflectance values combined with VI, which provides a possibility to produce a spatially explicit map of aspen occurrence that can contribute to biodiversity management and conservation efforts in boreal forests.

Recent introgression between Taiga Bean Goose and Tundra Bean Goose results in a largely homogeneous landscape of genetic differentiation

Several studies have uncovered a highly heterogeneous landscape of genetic differentiation across the genomes of closely related species. Specifically, genetic differentiation is often concentrated in particular genomic regions (“islands of differentiation”) that might contain barrier loci contributing to reproductive isolation, whereas the rest of the genome is homogenized by introgression. Alternatively, linked selection can produce differentiation islands in allopatry without introgression. We explored the influence of introgression on the landscape of genetic differentiation in two hybridizing goose taxa: the Taiga Bean Goose (Anser fabalis) and the Tundra Bean Goose (A. serrirostris). We re-sequenced the whole genomes of 18 individuals (9 of each taxon) and, using a combination of population genomic summary statistics and demographic modeling, we reconstructed the evolutionary history of these birds. Next, we quantified the impact of introgression on the build-up and maintenance of genetic differentiation. We found evidence for a scenario of allopatric divergence (about 2.5 million years ago) followed by recent secondary contact (about 60,000 years ago). Subsequent introgression events led to high levels of gene flow, mainly from the Tundra Bean Goose into the Taiga Bean Goose. This scenario resulted in a largely undifferentiated genomic landscape (genome-wide F_ST = 0.033) with a few notable differentiation peaks that were scattered across chromosomes. The summary statistics indicated that some peaks might contain barrier loci while others arose in allopatry through linked selection. Finally, based on the low genetic differentiation, considerable morphological variation and incomplete reproductive isolation, we argue that the Taiga and the Tundra Bean Goose should be treated as subspecies.