Feature-expression heat maps--a new visual method to explore complex associations between two variable sets

A coping self-insight scale for adults: development and preliminary psychometric properties

BACKGROUND

Self-insights focused on the coping process are implicated in the refinement of capacities for resilience. To advance this research, we must identify key coping self-insights and develop a concise measurement tool.

OBJECTIVE

The purpose of this paper is to develop evidence for the construct dimensionality and validity of a measure of coping self-insight.

METHODS

Items measuring 13 coping self-insight dimensions were generated via consultation with theoretical work, subject matter experts, and pre-testing items for clarity. Thereafter, the dimensionality of items was assessed with undergraduate students (N = 232) and an online sample (N = 800) via exploratory and confirmatory analyses. Finally, a multi-trait, multi-method approach was used to test discriminant validity in a further sample of students (N = 228).

RESULTS

The initial item list was reduced to five key dimensions that balanced data-driven and conceptual considerations. Confirmatory Factor Analysis revealed moderate-to-strong correlations (r = .47-.80) among dimensions. We also demonstrated evidence of internal reliability, convergent, criterion, and discriminant validity. Invariance tests for sub-groups of interest (e.g., sex, sample type) frequently demonstrated metric or scalar invariance, except for age sub-groups.

CONCLUSIONS

Findings offer a starting point regarding the types of coping self-insights important for the emergence of resilience and a validated tool for future research.

Seasonal Variability of Volatile Components in Calypogeia integristipula

Liverworts contain a large number of biologically active compounds that are synthesised and stored in their oil bodies. However, knowledge about the chemical composition of individual species is still incomplete. The subject of the study was Calypogeia integristipula, a species representing leafy liverworts. Plant material for chemotaxonomic studies was collected from various locations in Poland. The chemical composition was determined in 74 samples collected from the natural environment in 2021 and 2022 in three growing seasons: spring, summer and autumn, and for comparison with samples originating from in vitro culture. The plants were classified as Calypogeia integristipula on the basis of morphological characteristics, oil bodies, and DNA markers. The volatile organic compounds (VOCs) from the biological material were extracted by headspace solid phase microextraction (HS-SPME). The samples were then analysed by gas chromatography-mass spectrometry (GC-MS). A total of 79 compounds were detected, of which 44 compounds were identified. The remaining compounds were described using the MS fragmentation spectrum. Cyclical changes in the composition of compounds associated with the growing season of Calypogeia integristipula were observed. Moreover, samples from in vitro culture and samples taken from the natural environment were shown to differ in the composition of chemical compounds. In terms of quantity, among the volatile compounds, compounds belonging to the sesquiterpene group (46.54-71.19%) and sesqiuterpenoid (8.12-22.11%) dominate. A smaller number of compounds belong to aromatic compounds (2.30-10.96%), monoterpenes (0.01-0.07%), monoterpenoids (0.02-0.33%), and aliphatic hydrocarbons (1.11-6.12%). The dominant compounds in the analysed liverworts were: anastreptene (15.27-31.14%); bicyclogermacrene (6.99-18.09%), 4,5,9,10-dehydro-isolongifolene (2.00-8.72%), palustrol (4.95-9.94%), spathulenol (0.44-5.11%).

Reinterpretation of anthocyanins biosynthesis in developing black rice seeds through gene expression analysis

The biosynthesis of anthocyanins is still questionable in regulating the quantities of anthocyanins biosynthesized in rice seeds and the expression levels of transcription factors and the structural genes involved in the biosynthetic pathway of anthocyanins. We herein investigated the relationship between the accumulated anthocyanin contents and the expression levels of genes related to the biosynthesis of anthocyanins in rice seeds. Liquid chromatography/mass spectrometry-mass spectrometry analysis of cyanidin 3-glucoside (C3G) in rice seeds showed no accumulation of C3G in white and red rice cultivars, and the differential accumulation of C3G among black rice cultivars. RNA-seq analysis in rice seeds, including white, red, and black rice cultivars, at twenty days after heading (DAH) further exhibited that the genes involved in the biosynthesis of anthocyanins were differentially upregulated in developing seeds of black rice. We further verified these RNA-seq results through gene expression analysis by a quantitative real-time polymerase chain reaction in developing seeds of white, red, and black rice cultivars at 20 DAH. Of these genes related to the biosynthesis of anthocyanins, bHLHs, MYBs, and WD40, which are regulators, and the structural genes, including chalcone synthase (CHS), flavanone 3-hydroxylase (F3H), flavonoid 3´-hydroxylase (F3´H), dihydroflavonol 4-reductase (DFR), and anthocyanidin synthase (ANS), were differentially upregulated in black rice seeds. The correlation analysis revealed that the quantities of C3G biosynthesized in black rice seeds were positively correlated to the expression levels of bHLHs, MYBs and WD40, CHS, F3H, F3´H, DFR, and ANS. In addition, we present bHLH2 (LOC_Os04g47040) and MYBs (LOC_Os01g49160, LOC_Os01g74410, and LOC_Os03g29614) as new putative transcription factor genes for the biosynthesis of anthocyanins in black rice seeds. It is expected that this study will help to improve the understanding of the molecular levels involved in the biosynthesis of anthocyanins in black rice seeds.

Increasing Complexity of the N-Glycome During Caenorhabditis Development

Caenorhabditis elegans is a frequently employed genetic model organism and has been the object of a wide range of developmental, genetic, proteomic, and glycomic studies. Here, using an off-line MALDI-TOF-MS approach, we have analyzed the N-glycans of mixed embryos and liquid- or plate-grown L4 larvae. Of the over 200 different annotatable N-glycan structures, variations between the stages as well as the mode of cultivation were observed. While the embryonal N-glycome appears less complicated overall, the liquid- and plate-grown larvae differ especially in terms of methylation of bisecting fucose, α-galactosylation of mannose, and di-β-galactosylation of core α1,6-fucose. Furthermore, we analyzed the O-glycans by LC-electrospray ionization-MS following β-elimination; especially the embryonal O-glycomes included a set of phosphorylcholine-modified structures, previously not shown to exist in nematodes. However, the set of glycan structures cannot be clearly correlated with levels of glycosyltransferase transcripts in developmental RNA-Seq datasets, but there is an indication for coordinated expression of clusters of potential glycosylation-relevant genes. Thus, there are still questions to be answered in terms of how and why a simple nematode synthesizes such a diverse glycome.

Investigating phenotypic relationships in persimmon accessions through integrated proteomic and metabolomic analysis of corresponding fruits

Together with phenological and genomic approaches, gel-based and label-free proteomic as well metabolomic procedures were separately applied to plants to highlight differences between ecotypes, to estimate genetic variability within/between organism populations, or to characterize specific mutants/genetically modified lines at metabolic level. To investigate the possible use of tandem mass tag (TMT)-based quantitative proteomics in the above-mentioned contexts and based on the absence of combined proteo-metabolomic studies on Diospyros kaki cultivars, we here applied integrated proteomic and metabolomic approaches to fruits from Italian persimmon ecotypes with the aim to characterize plant phenotypic diversity at molecular level. We identified 2255 proteins in fruits, assigning 102 differentially represented components between cultivars, including some related to pomological, nutritional and allergenic characteristics. Thirty-three polyphenols were also identified and quantified, which belong to hydroxybenzoic acid, flavanol, hydroxycinnamic acid, flavonol, flavanone and dihydrochalcone sub-classes. Heat-map representation of quantitative proteomic and metabolomic results highlighted compound representation differences in various accessions, whose elaboration through Euclidean distance functions and other linkage methods defined dendrograms establishing phenotypic relationships between cultivars. Principal component analysis of proteomic and metabolomic data provided clear information on phenotypic differences/similarities between persimmon accessions. Coherent cultivar association results were observed between proteomic and metabolomic data, emphasizing the utility of integrating combined omic approaches to identify and validate phenotypic relationships between ecotypes, and to estimate corresponding variability and distance. Accordingly, this study describes an original, combined approach to outline phenotypic signatures in persimmon cultivars, which may be used for a further characterization of other ecotypes of the same species and an improved description of nutritional characteristics of corresponding fruits.

Augmented Behavioral Annotation Tools, with Application to Multimodal Datasets and Models: A Systematic Review

Annotation tools are an essential component in the creation of datasets for machine learning purposes. Annotation tools have evolved greatly since the turn of the century, and now commonly include collaborative features to divide labor efficiently, as well as automation employed to amplify human efforts. Recent developments in machine learning models, such as Transformers, allow for training upon very large and sophisticated multimodal datasets and enable generalization across domains of knowledge. These models also herald an increasing emphasis on prompt engineering to provide qualitative fine-tuning upon the model itself, adding a novel emerging layer of direct machine learning annotation. These capabilities enable machine intelligence to recognize, predict, and emulate human behavior with much greater accuracy and nuance, a noted shortfall of which have contributed to algorithmic injustice in previous techniques. However, the scale and complexity of training data required for multimodal models presents engineering challenges. Best practices for conducting annotation for large multimodal models in the most safe and ethical, yet efficient, manner have not been established. This paper presents a systematic literature review of crowd and machine learning augmented behavioral annotation methods to distill practices that may have value in multimodal implementations, cross-correlated across disciplines. Research questions were defined to provide an overview of the evolution of augmented behavioral annotation tools in the past, in relation to the present state of the art. (Contains five figures and four tables).

A Proteomics Data Mining Strategy for the Identification of Quinoa Grain Proteins with Potential Immunonutritional Bioactivities

Quinoa proteins are attracting global interest for their wide amino acid profile and as a promising source for the development of biomedical treatments, including those against immune-mediated diseases. However, information about the bioactivity of quinoa proteins is scarce. In this study, a quinoa grain proteome map obtained by label-free mass spectrometry-based shotgun proteomics was investigated for the identification of quinoa grain proteins with potential immunonutritional bioactivities, including those related to cancer. After carefully examining the sequence similarities of the 1211 identified quinoa grain proteins against already described bioactive proteins from other plant organisms, 71, 48, and 3 of them were classified as antimicrobial peptides (AMPs), oxidative stress induced peptides (OSIPs), and serine-type protease inhibitors (STPIs), respectively, suggesting their potential as immunomodulatory, anti-inflammatory, and anticancer agents. In addition, data interpretation using Venn diagrams, heat maps, and scatterplots revealed proteome similarities and differences with respect to the AMPs, OSIPs, and STPIs, and the most relevant bioactive proteins in the predominant commercial quinoa grains (i.e., black, red, white (from Peru), and royal (white from Bolivia)). The presented proteomics data mining strategy allows easy screening for potentially relevant quinoa grain proteins and commercial classes for immunonutrition, as a basis for future bioactivity testing.

Support vector machine deep mining of electronic medical records to predict the prognosis of severe acute myocardial infarction

Cardiovascular disease is currently one of the most important diseases causing death in China and the world, and acute myocardial infarction is a major cause of cardiovascular disease. This study provides an analytical technique for predicting the prognosis of patients with severe acute myocardial infarction using a support vector machine (SVM) technique based on information gleaned from electronic medical records in the Medical Information Marketplace for Intensive Care (MIMIC)-III database. The MIMIC-III database provided 4785 electronic medical records data for inclusion in the model development after screening 7070 electronic medical records of patients admitted to the intensive care unit for treatment of acute myocardial infarction. Adopting the APS-III score as the criterion for identifying anticipated risk, the dimensions of data information incorporated into the mathematical model design were found using correlation coefficient matrix heatmaps and ordered logistic analysis. An automated prognostic risk-prediction model was developed using SVM, and the fit was evaluated by 5× cross-validation. We used a grid search method to further optimize the parameters and improve the model fit. The excellent generalization ability of SVM was fully verified by calculating the 95% confidence interval of the area under the receiver operating characteristic curve (AUC) for six algorithms (linear discriminant, tree, Kernel Naive Bayes, RUSBoost, KNN, and SVM). Compared to the remaining five models, its confidence interval was the narrowest with higher fitting accuracy and better performance. The patient prognostic risk prediction model constructed using SVM had a relatively impressive accuracy (92.2%) and AUC value (0.98). In this study, a model was designed for fitting that can maximize the potential information to be gleaned in the electronic medical records data. It was demonstrated that SVM models based on electronic medical records data can offer an effective solution for clinical disease prognostic risk assessment and improved clinical outcomes and have great potential for clinical application in the clinical treatment of myocardial infarction.

Fecal Bile Acids and Neutral Sterols Are Associated with Latent Microbial Subgroups in the Human Gut

Bile acids, neutral sterols, and the gut microbiome are intricately intertwined and each affects human health and metabolism. However, much is still unknown about this relationship. This analysis included 1280 participants of the KORA FF4 study. Fecal metabolites (primary and secondary bile acids, plant and animal sterols) were analyzed using a metabolomics approach. Dirichlet regression models were used to evaluate associations between the metabolites and twenty microbial subgroups that were previously identified using latent Dirichlet allocation. Significant associations were identified between 12 of 17 primary and secondary bile acids and several of the microbial subgroups. Three subgroups showed largely positive significant associations with bile acids, and six subgroups showed mostly inverse associations with fecal bile acids. We identified a trend where microbial subgroups that were previously associated with "healthy" factors were here inversely associated with fecal bile acid levels. Conversely, subgroups that were previously associated with "unhealthy" factors were positively associated with fecal bile acid levels. These results indicate that further research is necessary regarding bile acids and microbiota composition, particularly in relation to metabolic health.

Measures of Daily Activities Associated With Mental Health (Things You Do Questionnaire): Development of a Preliminary Psychometric Study and Replication Study

Background

A large body of research has identified modifiable cognitions and behaviors (actions) associated with psychological health. However, little is known regarding the actions that are most strongly associated with psychological health or the frequency with which they should be performed.

Objective

This paper described 2 studies that used survey methodology to create the Things You Do Questionnaire (TYDQ), which aims to identify and rank actions (items) and domains of actions (factors) most strongly associated with psychological health.

Methods

We used digital marketing strategies to recruit Australian adult participants, who were asked to complete 2 web-based surveys comprising versions of the TYDQ; validated measures of depression, anxiety, and satisfaction with life; and demographic questions. In study 1, a total of 3040 participants rated how often they performed each of the 96 items comprising the TYDQ. This design was replicated in study 2, in which a 59-item version of the TYDQ was completed by 3160 participants. In both studies, the factor structure and validity were examined, as were the associations between individual TYDQ items and 3 mental health outcomes: depression, anxiety, and satisfaction with life.

Results

In study 1, factor analyses revealed that a 5-factor model comprising 27 items achieved an optimum balance between brevity and variance and accounted for 38.1%, 31.4%, and 33.2% of the variance in scores on measures of depression, anxiety, and satisfaction with life, respectively. The factors were interpreted as realistic thinking, meaningful activities, goals and plans, healthy habits, and social connections. These 5 factors were more strongly associated with psychological health than those such as practicing kindness, exercising gratitude, and practicing spirituality. This pattern of results was replicated across gender, age groups, and depression severity. The 5-factor solution found in study 1 was replicated in study 2. Analyses revealed that a 21-item version accounted for 46.8%, 38.2%, and 38.1% of the variance in scores on measures of depression, anxiety, and satisfaction with life, respectively.

Conclusions

These findings indicate that some actions are more strongly associated with psychological health than others and that these activities fall within 5 broad domains, which represent skills often taught in psychological treatments. Subsequent studies are planned to explore the reliability of these items and results in other samples and to examine patterns of change in scores during treatment for anxiety and depression. If replicated, these efforts will assist in the development of new psychological interventions and provide an evidence base for public mental health campaigns designed to promote good mental health and prevent the emergence of common mental disorders.

Machine-learning-assisted molecular design of phenylnaphthylamine-type antioxidants

In this study, a total of 302 molecular structures of phenylnaphthylamine antioxidants based on N-phenyl-1-naphthylamine and N-phenyl-2-naphthylamine skeletons with various substituents were modeled by exhaustive methods. Antioxidant parameters, including the hydrogen dissociation energy, solubility parameter, and binding energy, were calculated through molecular simulations. Then, a group decomposition scheme was determined to decompose 302 antioxidants. The antioxidant parameters and decomposition results constituted machine-learning data sets. Using an artificial neural network model, a correlation coefficient between the predicted and true values above 0.88 and an average relative error within 6% were achieved. Random forest models were used to analyze the factors affecting antioxidant activity from chemical and physical perspectives; the results showed that amino and alkyl groups were conducive to improving antioxidant performance. Moreover, substituent positions 1, 7, and 10 of N-phenyl-1-naphthylamine and 3, 7, and 10 of N-phenyl-2-naphthylamine were found to be the optimal positions for modifications to improve antioxidant activity. Two potentially efficient phenylnaphthylamine antioxidant structures were proposed and their antioxidant parameters were also calculated; the hydrogen dissociation energy and solubility parameter decreased by more than 9% and 7%, respectively, whereas the binding energy increased by more than 16% compared with the benchmark of N-phenyl-1-naphthylamine. These results indicate that molecular simulation and machine learning could provide alternative tools for the molecular design of new antioxidants.

Point-specific interactions of isovitexin with the neighboring amino acid residues of the hACE2 receptor as a targeted therapeutic agent in suppressing the SARS-CoV-2 influx mechanism

Objective:

Despite the development of several vaccines against severe acute respiratory syndrome coronavirus-2, the need for an additional prophylactic agent is evident. In recent in silico studies, isovitexin exhibited a higher binding affinity against the human angiotensin converting-enzyme 2 (hACE2) receptor than existing antiviral drugs. The research aimed to find out the point specificity of isovitexin for the hACE2 receptor and to assess its therapeutic potential, depending on the stability of the isovitexin–hACE2 complex.

Materials and Methods:

The pharmacokinetic profile of isovitexin was analyzed. The crystal structure of the hACE2 receptor and the ligand isovitexin were docked to form a ligand–protein complex following molecular optimization. To determine the isovitexin–hACE2 complex stability, their binding affinity, hydrogen bonding, and hydrophobic interactions were studied. Lastly, the root mean square deviation (RMSD), root mean square fluctuation, solvent accessible surface area, molecular surface area, radius of gyration (Rg), polar surface area, and principal component analysis values were found by simulating the complex with molecular dynamic (MD).

Results:

The predicted Lethal dose₅₀ for isovitexin was 2.56 mol/kg, with an acceptable maximum tolerated dose and no hepatotoxicity or AMES toxicity. Interactions with the amino acid residues Thr371, Asp367, Glu406, Pro346, His345, Phe274, Tyr515, Glu375, Thr347, Glu402, and His374 of the hACE2 protein were required for the high binding affinity and specificity of isovitexin. Based on what was learned from the MD simulation, the hACE2 receptor-blocking properties of isovitexin were looked at.

Conclusions:

Isovitexin is a phytochemical with a reasonable bioactivity and safety profile for use in humans, and it can potentially be used as a hACE2-specific therapeutic to inhibit COVID-19 infection.

FlexDotPlot: a universal and modular dot plot visualization tool for complex multifaceted data

Motivation

Dot plots are heatmap-like charts that provide a compact way to simultaneously display two quantitative information by means of dots of different sizes and colors. Despite the popularity of this visualization method, particularly in single-cell RNA-sequencing (scRNA-seq) studies, existing tools used to make dot plots are limited in terms of functionality and usability.

Results

We developed FlexDotPlot, an R package for generating dot plots from multifaceted data, including scRNA-seq data. It provides a universal and easy-to-use solution with a high versatility. An interactive R Shiny application is also available allowing non-R users to easily generate dot plots with several tunable parameters.

Availability and implementation

Source code and detailed manual are available on CRAN (stable version) and at https://github.com/Simon-Leonard/FlexDotPlot (development version). Code to reproduce figures is available at https://github.com/Simon-Leonard/FlexDotPlot_paper. A Shiny app is available as a stand-alone application within the package.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Online Environment as a Tool to Push Forward the Research: An Example for Landscape Disservices

Due to the COVID-19 pandemic, researchers have had to find different resources in order to continue their research and the use of online information can represent a temporary solution. Our research is mainly focusing on a landscape which offers services and disservices. Recently, numerous studies that rely on landscape disservices have appeared. We associate wildlife-human-interactions (WHI) and human-wildlife-interactions (HWI) as part of landscape disservices. More precisely, in the first category (WHI) we have included the interaction of the wild animals with human and in the second category (HWI) we have created a database with animals attacked or/and killed by human. In order to sustain this analysis, we have selected data from local newspapers and Facebook groups, which supports our hypothesis that online resources could provide valuable data. The study area is represented by the Southern and Eastern Carpathians. The most affected mammals for this type of interactions (HWI) are bears, followed by wild boars and red deer, while WHI has intensified in the last five years. Based on the analysed data we can conclude that the animals who generate the most disservices to humans are bears and wild boars. The solutions we have identified, which also include online sources, for both HWI and WHI are relocation, rescue, capturing of the animals in reservations or, as a last resort, euthanasia. In order to reduce these types of interactions it is important to promote ecological education, development and promoting of certain attitudes and behaviour that have a visible impact upon HWI and WHI.

Optogenetics stimulates nerve reorganization in the contralesional anterolateral primary motor cortex in a mouse model of ischemic stroke

The anterolateral motor cortex of rodents is an important motor auxiliary area, and its function is similar to that of the premotor area in humans. Activation and inhibition of the contralesional anterolateral motor cortex (cALM) have been shown to have direct effects on motor behavior. However, the significance of cALM activation and inhibition in the treatment of stroke remains unclear. This study investigated the role of optogenetic cALM stimulation in a mouse model of cerebral stroke. The results showed that 21-day optogenetic cALM inhibition, but not activation, improved neurological function. In addition, optogenetic cALM stimulation substantially altered dendritic structural reorganization and dendritic spine plasticity, as optogenetic cALM inhibition resulted in increased dendritic length, number of dendritic spines, and number of perforated synapses, whereas optogenetic activation led to an increase in the number of multiple synapse boutons and the number of dendritic intersections. Furthermore, RNA-seq analysis showed that multiple biological processes regulated by the cALM were upregulated immediately after optogenetic cALM inhibition, and that several immediate-early genes (including cFOS, Erg1, and Sema3f) were expressed at higher levels after optogenetic inhibition than after optogenetic activation. These results were confirmed by quantitative reverse transcription-polymerase chain reaction. Finally, immunofluorescence analysis showed that the c-FOS signal in layer V of the primary motor cortex in the ischemic hemisphere was higher after optogenetic cALM activation than it was after optogenetic cALM inhibition. Taken together, these findings suggest that optogenetic cALM stimulation promotes neural reorganization in the primary motor cortex of the ischemic hemisphere, and that optogenetic cALM inhibition and activation have different effects on neural plasticity. The study was approved by the Experimental Animal Ethics Committee of Fudan University (approval No. 201802173S) on March 3, 2018.

The Association Between Inflammatory and Oxidative Stress Biomarkers and Plasma Metabolites in a Longitudinal Study of Healthy Male Welders

INTRODUCTION

Human metabolism and inflammation are closely related modulators of homeostasis and immunity. Metabolic profiling is a useful tool to understand the association between metabolism and inflammation at a systemic level.

OBJECTIVE

To investigate the longitudinal associations between the concentration of plasma metabolites and biomarkers related to inflammation and oxidative stress.

METHODS

We conducted a repeated cross-sectional analysis consisting of 8 short-term panels that included 88 healthy adult male welders in Massachusetts, USA. In each panel, we collected 1-6 repeated measurements of blood and urine. We used a human vascular injury panel assay and custom cytokine/chemokine assay to quantify inflammatory biomarker plasma levels, liquid chromatography-mass spectrometry to quantify the concentrations of 665 plasma metabolites, and a competitive enzyme-linked immunoassay to quantify urinary 8-OHdG and 8-isoprostane levels. We used linear mixed effects models to estimate the longitudinal association between each inflammatory and oxidative stress biomarker and each metabolite.

RESULTS

At a 5% FDR threshold, we detected ≥1metabolite association for 8 unique inflammatory and oxidative stress biomarkers: urinary 8-isoprostane, plasma C-reactive protein (CRP), serum amyloid A (SAA), intercellular adhesion molecule 1, circulating vascular cell adhesion molecule-1, interleukin 8 (IL-8), interleukin 10 (IL-10) and vascular endothelial growth factor. Specifically, 3 metabolites in the androgenic steroids pathway were negatively associated with SAA; 3 dihydrosphingomyelins metabolites were positively associated with 1 or more of CRP, SAA, IL-8 and IL-10; 4 metabolites in acyl choline metabolism pathways were negatively associated with IL-8; 7 lysophospholipid metabolites were negatively associated with 1 or more of CRP, SAA and IL-8; 4 sphingomyelins were positively associated with CRP and/or SAA; and 10 metabolites in the xanthine pathway were positively associated with urinary 8-isoprostane.

CONCLUSION

We found that metabolites in phospholipid groups had strong associations with multiple inflammatory biomarkers, especially CRP, SAA and IL-8. The mechanism of these associations warrants further investigation.

Combined Effect of Cultivar and Peel Chromaticity on Figs' Primary and Secondary Metabolites: Preliminary Study Using Biochemical and FTIR Fingerprinting Coupled to Chemometrics

Simple Summary

Primary and secondary metabolites are among the markers for addressing fig chemotypic variability. These compounds are mainly driven by the cultivar factor besides chromatic coordinates color as they are highly correlated to these biomarkers. Combined use of chemical analytical procedures and vibrational spectroscopy is of great importance for a better understanding of network connections within the dataset. In this study, we screened 11 fig tree cultivars for their biochemical and spectral fingerprints in a comparative scheme for high resolution discrimination. Analytical methods herein used were also evaluated for their greenness aspects using GAPI eco-scale tool.

Abstract

Figs are a traditional pantry staple for healthy eating in Middle Eastern and North African countries as fig trees grow abundantly in such hot and dry climates. Despite the importance of this species, chemotypic diversity has gone unheeded and therefore its valorization pathways remain poorly documented. For this reason, high-pressure liquid chromatography (HPLC) alongside vibrational spectroscopy were used to investigate the changes of antiradical potency and primary and secondary metabolites in fresh figs with regard to the combined effect of the cultivar factor and the fruit peel chromatic coordinates. Fourier-transform infrared spectroscopy (FTIR) fingerprinting displayed six major peaks assigned to functional groups of the investigated samples with significant differences in their vibration intensities. Biochemical screening revealed highly significant variability (p < 0.05) among the investigated cultivars. Antioxidant activity was found to be higher in free radical scavenging using 2,2-diphenyl-1-picrylhydrazyl (DPPH) compared to ferric reducing ability (FRAP). Chemometric investigations of both biochemical and FTIR fingerprinting showed satisfactory resolutions, and the total phenol contents and chromatic coordinates had the highest scores in the dataset. However, the cultivars’ geographical origin seemed not to have a clear impact on the clustering results. The aforementioned analytical procedures were found to be equally important and can be jointly used for high-resolution screening and discrimination of fig trees.

Characterization and differentiation of quinoa seed proteomes by label-free mass spectrometry-based shotgun proteomics

Quinoa seed proteins are of prime importance in human nutrition and in plant breeding for cultivar identification and improvement. In this study, proteins from seeds of black, red, white quinoa from Peru and white quinoa from Bolivia (also known as royal) were extracted, digested and analyzed by nano-liquid chromatography coupled to Orbitrap tandem mass spectrometry (LC-MS/MS). The raw mass spectra data were processed for identification and label-free quantification (LFQ) using MaxQuant/Andromeda against a specific quinoa database from The National Center for Biotechnology Information (NCBI). In total, 1,211 quinoa proteins (85 were uncharacterized) were identified. Inspection and visualization using Venn diagrams, heat maps and Gene Ontology (GO) graphs revealed proteome similarities and differences between the four varieties. The presented data provides the most comprehensive experimental quinoa seed proteome map existing to date in the literature, as a starting point for more specific characterization and nutritional studies of quinoa and quinoa-containing foodstuff.

Survey of Phenolic Acids, Flavonoids and In Vitro Antioxidant Potency Between Fig Peels and Pulps: Chemical and Chemometric Approach

In the present study, chromatic coordinates, phenolic acids, flavonoids and antioxidant capacity assessed by 1,1-diphenyl-2-picrylhydrazyl (DPPH), 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonate (ABTS) and lipid peroxidation inhibition capacity (LPIC) essays and their relative IC50 were investigated in 25 fig cultivars growing in Morocco. The aims of this study were to determine (i) the variation in these compounds among light and dark-colored cultivars, (ii) their partitioning between fruit peel and pulp and (iii) to display network connections among these variables. Twelve phenolic compounds (PCs) were isolated in peel extract versus eight in pulp samples. Anthocyanins, mainly cyanidin-3,5-diglucoside and cyanidin-3-O-rutinoside, were the predominant compounds in peels, where the mean concentrations were 75.90 ± 18.76 and 77.97 ± 18.95 µg/g dw, respectively. On the other hand, (−)-epicatechin and cyanidin-3-O-rutinoside were the major compounds in the pulp extracts, where the mean values were 5.23 ± 4.03 and 9.01 ± 5.67 µg/g dw, respectively. A two-dimensional hierarchically clustered heatmap was applied to the dataset to explore correlations in the dataset and similarities between cultivars, without dimensionality reduction. Results showed that anthocyanins, particularly pelargonidin-3-O-rutinoside, cyanidin-3,5-diglucoside and cyanidin-3-O-rutinoside, were the main contributors to the peels’ free radical scavenging capacity. This capacity was particularly higher in the peel of dark-colored figs compared to the fruit pulp. The local cultivar “INRA 1301” showed the most promising phenolic profile due to its very high levels of almost all detected PCs, especially (−)-epicatechin, quercetin-3-O-rutinoside, quercetin-3-O-glucoside, cyanidine-3,5-diglucoside, cyanidine-3-O-rutinoside and pelargonidin-3-O-rutinoside (54.66, 141.08, 35.48, 494.08, 478.66, 12.56 µg/g dw, respectively). Having the darkest figs in the collection (L* = 25.72, c* = 22.09 and h° = 20.99), this cultivar has also combined promising IC50 values, which were of 19.85, 40.58 and 124.78 µg/mL for DPPH, ABTS and LPIC essays, respectively.

Associations between habitual diet, metabolic disease, and the gut microbiota using latent Dirichlet allocation

BACKGROUND

The gut microbiome impacts human health through various mechanisms and is involved in the development of a range of non-communicable diseases. Diet is a well-known factor influencing microbe-host interaction in health and disease. However, very few findings are based on large-scale analysis using population-based studies. Our aim was to investigate the cross-sectional relationship between habitual dietary intake and gut microbiota structure in the Cooperative Health Research in the Region of Augsburg (KORA) FF4 study.

RESULTS

Fecal microbiota was analyzed using 16S rRNA gene amplicon sequencing. Latent Dirichlet allocation (LDA) was applied to samples from 1992 participants to identify 20 microbial subgroups within the study population. Each participant's gut microbiota was subsequently described by a unique composition of these 20 subgroups. Associations between habitual dietary intake, assessed via repeated 24-h food lists and a Food Frequency Questionnaire, and the 20 subgroups, as well as between prevalence of metabolic diseases/risk factors and the subgroups, were assessed with multivariate-adjusted Dirichlet regression models. After adjustment for multiple testing, eight of 20 microbial subgroups were significantly associated with habitual diet, while nine of 20 microbial subgroups were associated with the prevalence of one or more metabolic diseases/risk factors. Subgroups 5 (Faecalibacterium, Lachnospiracea incertae sedis, Gemmiger, Roseburia) and 14 (Coprococcus, Bacteroides, Faecalibacterium, Ruminococcus) were particularly strongly associated with diet. For example, participants with a high probability for subgroup 5 were characterized by a higher Alternate Healthy Eating Index and Mediterranean Diet Score and a higher intake of food items such as fruits, vegetables, legumes, and whole grains, while participants with prevalent type 2 diabetes mellitus were characterized by a lower probability for subgroup 5.

CONCLUSIONS

The associations between habitual diet, metabolic diseases, and microbial subgroups identified in this analysis not only expand upon current knowledge of diet-microbiota-disease relationships, but also indicate the possibility of certain microbial groups to be modulated by dietary intervention, with the potential of impacting human health. Additionally, LDA appears to be a powerful tool for interpreting latent structures of the human gut microbiota. However, the subgroups and associations observed in this analysis need to be replicated in further studies. Video abstract.

Exploring Antioxidant Activity, Organic Acid, and Phenolic Composition in Strawberry Tree Fruits (Arbutus unedo L.) Growing in Morocco

This study aimed to explore the main biochemical components and the antioxidant capacity of five strawberry tree fruits using three antioxidant essays within the ecotypic comparison scheme, to find out the most valuable fruit presenting disease-preventing properties. Total phenols, total flavonoids, total anthocyanins, antioxidant activity (DPPH, ABTS, and β-Carotene bleaching assays), pH, titratable acidity, soluble solids, and moisture content were investigated in five strawberry tree genotypes belonging to several areas in Morocco. Phenolic compounds were also identified using high performance chromatography (HPLC), with a diode array detector (DAD). High significant differences (p ˂ 0.05) were revealed among the examined genotypes regarding their total phenols (25.37–39.06 mg gallic acid equivalents (GAE)/g Dry weight (DW), total flavonoids (3.30–7.07 mg RE/g Dry weight (DW), total anthocyanins (0.15–0.64 mg cya-3-glu/100g Dry weight (DW), pH (2.44–3.92), titratable acidity (0.65–1.01 g malic acid/100g Fresh weight (FW), and soluble solids (14.83–18.53%). The average radical scavenging capacity, assessed using three methods, exhibited the following concentration ranges: 3.33–21.08, 2.25–19.58, and 1.08–13 mg Ascorbic Equivalent (AAE/g Dry weight(DW) for the DPPH scavenging test, ABTS, and β-carotene bleaching, respectively. Seventeen phenolic compounds were identified in sampled cultivars. Gallocatechol and catechin were found to be the major phenolic compounds. The correlation matrix revealed significant correlations among investigated variables, particularly ABTS and DPPH. The principal component analysis showed that the first three components formed 90.25% of the total variance. The following variables: chlorogenic acid, ellagic acid derivative, ellagic acid, rutin, and cyanidin−30.5-diglucoside, were the most involved in the total variance. The results revealed highly promising physico-biochemical profiles within the studied strawberry tree genotypes.

Performance comparison of nonlinear and linear regression algorithms coupled with different attribute selection methods for quantitative structure - retention relationships modelling in micellar liquid chromatography

In micellar liquid chromatography (MLC), the addition of a surfactant to the mobile phase in excess is accompanied by an alteration of its solubilising capacity and a change in the stationary phase's properties. As an implication, the prediction of the analytes' retention in MLC mode becomes a challenging task. Mixed Quantitative Structure - Retention Relationships (QSRR) modelling represents a powerful tool for estimating the analytes' retention. This study compares 48 successfully developed mixed QSRR models with respect to their ability to predict retention of aripiprazole and its five impurities from molecular structures and factors that describe the Brij - acetonitrile system. The development of the models was based on an automatic combining of six attribute (feature) selection methods with eight predictive algorithms and the optimization of hyper-parameters. The feature selection methods included Principal Component Analysis (PCA), Non-negative Matrix Factorization (NMF), ReliefF, Multiple Linear Regression (MLR), Mutual Info and F-Regression. The series of investigated predictive algorithms comprised Linear Regressions (LR), Ridge Regression, Lasso Regression, Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest (RF), Gradient Boosted Trees (GBT) and K-Nearest neighbourhood (k-NN). A sufficient amount of data for building the model (78 cases in total) was provided by conducting 13 experiments for each of the 6 analytes and collecting the target responses afterwards. Different experimental settings were established by varying the values of the concentration of Brij L23, pH of the aqueous phase and acetonitrile content in the mobile phase according to the Box-Behnken design. In addition to the chromatographic parameters, the pool of independent variables was expanded by 27 molecular descriptors from all major groups (physicochemical, quantum chemical, topological and spatial structural descriptors). The best model was chosen by taking into consideration the Root Mean Square Error (RMSE) and cross-validation (CV) correlation coefficient (Q²) values. Interestingly, the comparative analysis indicated that a change in the set of input variables had a minor impact on the performance of the final models. On the other hand, different regression algorithms showed great diversity in the ability to learn patterns conserved in the data. In this regard, testing many regression algorithms is necessary in order to find the most suitable technique for model building. In the specific case, GBT-based models have demonstrated the best ability to predict the retention factor in the MLC mode. Steric factors and dipole-dipole interactions have proven to be relevant to the observed retention behaviour. This study, although being of a smaller scale, is a most promising starting point for comprehensive MLC retention prediction.

Dietary characteristics associated with plasma concentrations of per- and polyfluoroalkyl substances among adults with pre-diabetes: Cross-sectional results from the Diabetes Prevention Program Trial

Diet is assumed to be the main source of exposure to per- and polyfluoroalkyl substances (PFAS) in non-occupationally exposed populations, but studies on the diet-PFAS relationship in the United States are scarce. We extracted multiple dietary variables, including daily intakes of food group, diet scores, and dietary patterns, from self-reported dietary data collected at baseline (1996-1999) from adults with pre-diabetes enrolled in the Diabetes Prevention Program, and used linear regression models to evaluate relationships of each dietary variable with plasma concentrations of six PFAS (perfluorooctane sulfonic acid (PFOS), perfluorooctanoic acid (PFOA), perfluorohexane sulfonic acid (PFHxS), 2-(N-ethyl-perfluorooctane sulfonamido) acetic acid (EtFOSAA), 2-(N-methyl-perfluorooctane sulfonamido) acetic acid (MeFOSAA), perfluorononanoic acid (PFNA) adjusting for covariates. Participants (N = 941, 65% female, 58% Caucasian, 68% married, 75% with higher education, 95% nonsmoker) had similar PFAS concentrations compared to the general U.S. population during 1999-2000. Using a single food group approach, fried fish, other fish/shellfish, meat and poultry had positive associations with most PFAS plasma concentrations. The strongest effect estimate detected was between fried fish and PFNA [13.6% (95% CI: 7.7, 19.9) increase in median concentration per SD increase]. Low-carbohydrate and high protein diet score had positive association with plasma PFHxS. Some food groups, mostly vegetables and fruits, and the Dietary Approaches to Stop Hypertension diet score had inverse associations with PFOS and MeFOSAA. A vegetable diet pattern was associated with lower plasma concentrations of MeFOSAA, while high-fat meat and low-fiber and high-fat grains diet patterns were associated with higher plasma concentrations of PFOS, PFHxS, MeFOSAA and PFNA. We summarized four major dietary characteristics associated with variations in PFAS plasma concentrations in this population. Specifically, consuming more meat/fish/shellfish (especially fried fish, and excluding Omega3-rich fish), low-fiber and high-fat bread/cereal/rice/pasta, and coffee/tea was associated with higher plasma concentrations while dietary patterns of vegetables, fruits and Omega-3 rich fish were associated with lower plasma concentrations of some PFAS.

Using measures of intrinsic homeostasis and extrinsic modulation to evaluate mental health in adolescents: Preliminary results from the longitudinal adolescent brain study (LABS)

Adolescence is a period when complex interactions occur between mental health risk factors. The Longitudinal Adolescent Brain Study (LABS) commenced in 2018, to monitor environmental and psychosocial factors thought to influence mental health in 500 young people. Participants commence at 12 years of age, via a community-based recruitment model, and data is collected at 15 time-points over five years. This study examines demographic and psychosocial self-report data from time-point 1, for the first 50 participants. Here we investigate associations between environmental and psychosocial factors, considered as measures of intrinsic homeostasis and extrinsic modulation. Numerous strong correlations were found. Findings indicate that sleep dysfunction and social connectedness were strongly associated external modulators of intrinsic homeostasis in this sample of 12-year old participants. To successfully address the increase in mental health problems in young people, comprehensive evaluation of lifestyle and environmental risk factors is recommended in addition to medicalised approaches. Interventions to promote mental health wellbeing in young adolescents should include a focus on sleep quality and patterns and the positive and negative aspects of social connectedness.

Hepatic gene expression explains primary drug toxicity in bipolar disorder

In bipolar disorder (BPD), long-term psychotropic drug treatment is often necessary to prevent relapse or recurrence. Nevertheless, adverse drug effects including disturbances in hepatic metabolism are observed and still poorly understood. Here, the association between hepatic gene expression and histopathological changes of the liver was investigated. By the use of microarrays (Affymetrix U133 plus2.0), a genome-wide expression study was performed on BPD patients with psychotropic drug treatment (n = 29) compared to unaffected controls (n = 20) and validated by quantitative real-time PCR. WebGestalt was used to identify over-represented functional pathways of the Reactome database. Association analyses between histopathological changes and differentially expressed genes comprised in the over-represented functional pathways were performed using regression analyses, from which feature-expression heatmaps were drawn. The majority of identified genes were underexpressed and involved in energy supply, metabolism of lipids and proteins, and the innate immune system. Positive associations were found for genes involved in all pathways and degenerative changes. The strongest negative association was observed between genes involved in energy supply and hepatic activity, as well as inflammation. In summary, we found a possible association between gene expression involved in various biological pathways and histopathological changes of the liver in BPD. Further, we found support for the probable primary toxic effect of psychotropic drugs on hepatic injury in BPD. Even if the safety of psychotropic drugs improves, adverse effects especially on hepatic function should not be underestimated.

Immunomodulatory effects of antipsychotic treatment on gene expression in first-episode psychosis

Previous studies suggest immunological alterations in patients with first-episode psychosis (FEP). Some studies show that antipsychotic compounds may cause immunomodulatory effects. To evaluate the immunological changes and the possible immunomodulatory effects in FEP, we recruited patients with FEP (n = 67) and matched controls (n = 38), aged 18-40 years, from the catchment area of the Helsinki University Hospital and the City of Helsinki, Finland. Fasting peripheral blood samples were collected between 8 and 10 a.m. in 10 ml PAXgene tubes. We applied the NanoString nCounter in-solution hybridization technology to determine gene expression levels of 147 candidate genes reflecting activation of the immune system. Cases had higher gene expression levels of BDKRB1 and SPP1/osteopontin compared with controls. Of the individual medications used as monotherapy, risperidone was associated with a statistically significant upregulation of 11 immune system genes, including cytokines and cytokine receptors (SPP1, IL1R1, IL1R2), pattern recognition molecules (TLR1, TLR2 and TLR6, dectin-1/CLEC7A), molecules involved in apoptosis (FAS), and some other molecules with functions in immune activation (BDKRB1, IGF1R, CR1). In conclusion, risperidone possessed strong immunomodulatory properties affecting mainly innate immune response in FEP patients, whereas the observed effects of quetiapine and olanzapine were only marginal. Our results further emphasize the importance of understanding the immunomodulatory mechanisms of antipsychotic treatment, especially in terms of specific compounds, doses and duration of medication in patients with severe mental illness. Future studies should evaluate the response pre- and post-treatment, and the possible role of this inflammatory activation for the progression of psychiatric and metabolic symptoms.

Associations between Diet and Toenail Arsenic Concentration among Pregnant Women in Bangladesh: A Prospective Study

This prospective study evaluated the relationship between long-term dietary habits and total arsenic (As) concentration in toenail clippings in a cohort of 1616 pregnant women in the Bangladeshi administrative regions of Sirajdikhan and Pabna Sadar. Diet was assessed at Gestation Week 28 and at Postpartum Month 1, using a locally-validated dish-based semi-quantitative food-frequency questionnaire. Toenail As concentration was analyzed by microwave-assisted acid digestion and inductively coupled plasma mass spectrometry. Associations between natural log-transformed consumption of individual food items and temporally matched natural log-transformed toenail As concentration were quantified using general linear models that accounted for As concentration in the primary drinking water source and other potential confounders. The analysis was stratified by As in drinking water (≤50 μg/L versus >50 μg/L) and the time of dietary assessment (Gestation Week 28 versus Postpartum Week 1). Interestingly, toenail As was not significantly associated with consumption of plain rice as hypothesized. However, toenail As was positively associated with consumption of several vegetable, fish and meat items and was negatively associated with consumption of rice, cereal, fruits, and milk based food items. Further studies in pregnant women are needed to compare As metabolism at different levels of As exposure and the interaction between dietary composition and As absorption.

Phenotypic and Genotypic Analysis of Newly Obtained Interspecific Hybrids in the Campanula Genus

Interspecific hybridisation creates new phenotypes within several ornamental plant species including the Campanula genus. We have employed phenotypic and genotypic methods to analyse and evaluate interspecific hybridisation among cultivars of four Campanula species, i.e. C. cochleariifolia, C. isophylla, C. medium and C. formanekiana. Hybrids were analysed using amplified fragment length polymorphism (AFLP), flow cytometry and biometrical measurements. Results of correlation matrices demonstrated heterogeneous phenotypes for the parental species, which confirmed our basic premise for new phenotypes of interspecific hybrids. AFLP assays confirmed the hybridity and identified self-pollinated plants. Limitation of flow cytometry analysis detection was observed while detecting the hybridity status of two closely related parents, e.g. C. cochleariiafolia × C. isophylla. Phenotypic characteristics such as shoot habitus and flower colour were strongly influenced by one of the parental species in most crosses. Rooting analysis revealed that inferior rooting quality occurred more often in interspecific hybrids than in the parental species. Only interspecific hybrid lines of C. formanekiana ‘White’ × C. medium ‘Pink’ showed a high rooting level. Phenotype analyses demonstrated a separation from the interspecific hybrid lines of C. formanekiana ‘White’ × C. medium ‘Pink’ to the other clustered hybrids of C. formanekiana and C. medium. In our study we demonstrated that the use of correlation matrices is a suitable tool for identifying suitable cross material. This study presents a comprehensive overview for analysing newly obtained interspecific hybrids. The chosen methods can be used as guidance for analyses for further interspecific hybrids in Campanula, as well as in other ornamental species.