Eucalyptus Oils Phytochemical Composition in Correlation with Their Newly Explored Anti-SARS-CoV-2 Potential: in Vitro and in Silico Approaches

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the latest arisen contagious respiratory pathogen related to the global outbreak of atypical pneumonia pandemic (COVID-19). The essential oils (EOs) of Eucalyptus camaldulensis, E. ficifolia F. Muell., E. citriodora Hook, E. globulus Labill, E. sideroxylon Cunn. ex Woolls, and E. torquata Luehm. were investigated for its antiviral activity against SARS-CoV-2. The EOs phytochemical composition was determined using GC/MS analysis. Correlation with the explored antiviral activity was also studied using multi-variate data analysis and Pearson's correlation. The antiviral MTT and cytopathic effect inhibition assays revealed very potent and promising anti SARS-CoV-2 potential for E. citriodora EO (IC50 = 0.00019 µg/mL and SI = 26.27). The multivariate analysis revealed α-pinene, α-terpinyl acetate, globulol, γ -terpinene, and pinocarvone were the main biomarkers for E. citriodora oil. Pearson's correlation revealed that globulol is the top positively correlated compound in E. citriodora oil to its newly explored potent anti SARS-CoV-2 potential. A molecular simulation was performed on globulol via docking in the main active sites of both SARS-CoV-2 viral main protease (Mpro) and spike protein (S). In silico predictive ADMET study was also developed to investigate the pharmacokinetic profile and predict globulol toxicity. The obtained in silico, in vitro and Pearson's correlation results were aligned showing promising SARS-CoV-2 inhibitory activity of E. citriodora and globulol. This study is a first record for E. citriodora EO as a novel lead exhibiting potent in vitro, and in silico anti SARS-CoV-2 potential and suggesting its component globulol as a promising candidate for further extensive in silico, in vitro and in vivo anti-COVID studies.

Towards an accurate autism spectrum disorder diagnosis: multiple connectome views from fMRI data

Functional connectome has revealed remarkable potential in the diagnosis of neurological disorders, e.g. autism spectrum disorder. However, existing studies have primarily focused on a single connectivity pattern, such as full correlation, partial correlation, or causality. Such an approach fails in discovering the potential complementary topology information of FCNs at different connection patterns, resulting in lower diagnostic performance. Consequently, toward an accurate autism spectrum disorder diagnosis, a straightforward ambition is to combine the multiple connectivity patterns for the diagnosis of neurological disorders. To this end, we conduct functional magnetic resonance imaging data to construct multiple brain networks with different connectivity patterns and employ kernel combination techniques to fuse information from different brain connectivity patterns for autism diagnosis. To verify the effectiveness of our approach, we assess the performance of the proposed method on the Autism Brain Imaging Data Exchange dataset for diagnosing autism spectrum disorder. The experimental findings demonstrate that our method achieves precise autism spectrum disorder diagnosis with exceptional accuracy (91.30%), sensitivity (91.48%), and specificity (91.11%).

Exploring vegetation trends and restoration possibilities in Pakistan by using Hurst exponent

Vegetation cover change and its interaction with climate are significant to study as it has impact on ecosystem stability. We used the Normalized Difference Vegetation Index (NDVI) and climatic factors (temperature and rainfall) for investigating the relationship between vegetation and climate. We also traced spatiotemporal changes in the vegetation in Pakistan from 2000 to 2020; we used the Hurst exponent to estimate future vegetation trends in Pakistan. Our results show an increase in vegetation throughout Pakistan, and the Punjab Province is showing the highest significant vegetation trend at 88.51%. Our findings reveal that the response of vegetation to climate change varies by region and is influenced by local climatic conditions. However, the relationship between rainfall and annual NDVI is stronger than the temperature in the study area-Pakistan. The Hurst exponent value is above 0.5 in all four provinces, that is, the indication of consistent vegetation trends in the future. The highest values are observed in Punjab and Khyber Pakhtunkhwa (KPK). In the Punjab Province, 88.41% of the area showed positive development, with forests in particular showing a significant positive effect on land use classes. On the other hand, the Sindh Province has the highest negative result at 2.87%, with urban areas showing the highest negative development. To sum up, the NDVI pattern and change attribute suggest vegetation restoration in Pakistan.

Pooling of intra-site measurements inflates variability of the correlation between environmental DNA concentration and organism abundance

Environmental DNA (eDNA) analysis can promote efficient ecosystem monitoring and resource management. However, limited knowledge of the factors affecting the relationship between eDNA concentration and organism abundance causes uncertainty in relative abundance estimates based on eDNA concentration. Pooling of data points obtained from multiple locations within a site has been used to mitigate intra-site variation in eDNA and abundance estimates, but decreases the sample size used for estimating the relationship. I here assessed how the pooling of intra-site measurements of eDNA concentration and organism abundance impacted the reliability of the correlative relationship between eDNA concentration and organism abundance. Mathematical models were developed to simulate measurements of eDNA concentrations and organism abundances from multiple locations in a given survey site, and the CVs (coefficient of variability) of the correlations were compared depending on whether data points from different locations were individually treated or pooled. Although the mean and median values of the correlation coefficients were similar between the scenarios, the CVs of the simulated correlations were substantially higher under the pooled scenario than the individual scenario. Additionally, I re-analyzed two empirical studies conducted in lakes, both showing higher CVs of the correlations by pooling intra-site measurements. This study suggests that it would make eDNA-based abundance estimation more reliable and reproducible to individually analyze target eDNA concentrations and organism abundance estimates.

PPIGCF: A Protein-Protein Interaction-Based Gene Correlation Filter for Optimal Gene Selection

Biological data at the omics level are highly complex, requiring powerful computational approaches to identifying significant intrinsic characteristics to further search for informative markers involved in the studied phenotype. In this paper, we propose a novel dimension reduction technique, protein-protein interaction-based gene correlation filtration (PPIGCF), which builds on gene ontology (GO) and protein-protein interaction (PPI) structures to analyze microarray gene expression data. PPIGCF first extracts the gene symbols with their expression from the experimental dataset, and then, classifies them based on GO biological process (BP) and cellular component (CC) annotations. Every classification group inherits all the information on its CCs, corresponding to the BPs, to establish a PPI network. Then, the gene correlation filter (regarding gene rank and the proposed correlation coefficient) is computed on every network and eradicates a few weakly correlated genes connected with their corresponding networks. PPIGCF finds the information content (IC) of the other genes related to the PPI network and takes only the genes with the highest IC values. The satisfactory results of PPIGCF are used to prioritize significant genes. We performed a comparison with current methods to demonstrate our technique's efficiency. From the experiment, it can be concluded that PPIGCF needs fewer genes to reach reasonable accuracy (~99%) for cancer classification. This paper reduces the computational complexity and enhances the time complexity of biomarker discovery from datasets.

Effects of rainfall and land use on nutrient responses in rivers in the Brazilian semiarid region

This paper investigated whether rainfall promotes dilution or increase in nutrient concentrations and which land use indicators are the main predictors of nutrients in intermittent rivers in a large Brazilian semiarid region. The total phosphorus (TP) and total inorganic nitrogen (TIN) were monitored between 2013 and 2018 at 92 river water quality monitoring sites. The monthly rainfall (Rn) was obtained from 575 rain gauges. Pearson's correlation (R) between Rn and nutrient concentration was performed. The correlation patterns were also analysed based on land use data: urban area (%), agricultural field area (%), demographic density (inhabitants/km2), sewer system coverage (%), and reservoir density (reservoir/km2). Backward stepwise regression was performed to identify predictors of nutrient concentrations. The results revealed a marginal effect of rainfall on nutrients when the effects of urbanisation outweigh all other aspects. However, in regions with greater accumulated rainfall and lower reservoir density, the rainfall was related to a linear increase in nutrient concentrations (R > 0.8). Contrastingly, in the basins with less accumulated rainfall and greater inter-basin hydrological disconnection, there was a linear reduction in nutrient concentration (R < - 0.5). In the backward stepwise regression, sewer system coverage and Rn had the greatest influence for TP, and the urban area was the strongest predictor for TIN. Importantly, our results demonstrated that in semiarid rivers in densely populated regions, there is no single pattern of variability in nutrient concentration, on a wide scale of assessment. Therefore, adaptative and decentralised management can be more effective in improving water quality in these regions.

A Data-Driven Preprocessing Framework for Atrial Fibrillation Intracardiac Electrocardiogram Analysis

Atrial Fibrillation (AF) is the most common cardiac arrhythmia. Signal-processing approaches are widely used for the analysis of intracardiac electrograms (iEGMs), which are collected during catheter ablation from patients with AF. In order to identify possible targets for ablation therapy, dominant frequency (DF) is widely used and incorporated in electroanatomical mapping systems. Recently, a more robust measure, multiscale frequency (MSF), for iEGM data analysis was adopted and validated. However, before completing any iEGM analysis, a suitable bandpass (BP) filter must be applied to remove noise. Currently, no clear guidelines for BP filter characteristics exist. The lower bound of the BP filter is usually set to 3-5 Hz, while the upper bound (BP¯th) of the BP filter varies from 15 Hz to 50 Hz according to many researchers. This large range of BP¯th subsequently affects the efficiency of further analysis. In this paper, we aimed to develop a data-driven preprocessing framework for iEGM analysis, and validate it based on DF and MSF techniques. To achieve this goal, we optimized the BP¯th using a data-driven approach (DBSCAN clustering) and demonstrated the effects of different BP¯th on subsequent DF and MSF analysis of clinically recorded iEGMs from patients with AF. Our results demonstrated that our preprocessing framework with BP¯th = 15 Hz has the best performance in terms of the highest Dunn index. We further demonstrated that the removal of noisy and contact-loss leads is necessary for performing correct data iEGMs data analysis.

Technology Acceptance Model for Exoskeletons for Rehabilitation of the Upper Limbs from Therapists' Perspectives

Over the last few years, exoskeletons have been demonstrated to be useful tools for supporting the execution of neuromotor rehabilitation sessions. However, they are still not very present in hospitals. Therapists tend to be wary of this type of technology, thus reducing its acceptability and, therefore, its everyday use in clinical practice. The work presented in this paper investigates a novel point of view that is different from that of patients, which is normally what is considered for similar analyses. Through the realization of a technology acceptance model, we investigate the factors that influence the acceptability level of exoskeletons for rehabilitation of the upper limbs from therapists' perspectives. We analyzed the data collected from a pool of 55 physiotherapists and physiatrists through the distribution of a questionnaire. Pearson's correlation and multiple linear regression were used for the analysis. The relations between the variables of interest were also investigated depending on participants' age and experience with technology. The model built from these data demonstrated that the perceived usefulness of a robotic system, in terms of time and effort savings, was the first factor influencing therapists' willingness to use it. Physiotherapists' perception of the importance of interacting with an exoskeleton when carrying out an enhanced therapy session increased if survey participants already had experience with this type of rehabilitation technology, while their distrust and the consideration of others' opinions decreased. The conclusions drawn from our analyses show that we need to invest in making this technology better known to the public-in terms of education and training-if we aim to make exoskeletons genuinely accepted and usable by therapists. In addition, integrating exoskeletons with multi-sensor feedback systems would help provide comprehensive information about the patients' condition and progress. This can help overcome the gap that a robot creates between a therapist and the patient's human body, reducing the fear that specialists have of this technology, and this can demonstrate exoskeletons' utility, thus increasing their perceived level of usefulness.

A Combination of Metabolomics and Machine Learning Results in the Identification of a New Cyst Nematode Hatching Factor

Potato Cyst Nematodes (PCNs) are an economically important pest for potato growers. A crucial event in the life cycle of the nematode is hatching, after which the juvenile will move toward the host root and infect it. The hatching of PCNs is induced by known and unknown compounds in the root exudates of host plant species, called hatching factors (HFs, induce hatching independently), such as solanoeclepin A (solA), or hatching stimulants (HSs, enhance hatching activity of HFs). Unraveling the identity of unknown HSs and HFs and their natural variation is important for the selection of cultivars that produce low amounts of HFs and HSs, thus contributing to more sustainable agriculture. In this study, we used a new approach aimed at the identification of new HFs and HSs for PCNs in potato. Hereto, root exudates of a series of different potato cultivars were analyzed for their PCN hatch-inducing activity and their solA content. The exudates were also analyzed using untargeted metabolomics, and subsequently the data were integrated using machine learning, specifically random forest feature selection, and Pearson’s correlation testing. As expected, solA highly correlates with hatching. Furthermore, this resulted in the discovery of a number of metabolite features present in the root exudate that correlate with hatching and solA content, and one of these is a compound of m/z 526.18 that predicts hatching even better than solA with both data methods. This compound’s involvement in hatch stimulation was confirmed by the fractionation of three representative root exudates and hatching assays with the resulting fractions. Moreover, the compound shares mass fragmentation similarity with solA, and we therefore assume it has a similar structure. With this work, we show that potato likely produces a solA analogue, and we contribute to unraveling the hatch-inducing cocktail exuded by plant roots.

Unbiased and robust analysis of co-localization in super-resolution images

Spatial data from high-resolution images abound in many scientific disciplines. For example, single-molecule localization microscopy, such as stochastic optical reconstruction microscopy, provides super-resolution images to help scientists investigate co-localization of proteins and hence their interactions inside cells, which are key events in living cells. However, there are few accurate methods for analyzing co-localization in super-resolution images. The current methods and software are prone to produce false-positive errors and are restricted to only 2-dimensional images. In this paper, we develop a novel statistical method to effectively address the problems of unbiased and robust quantification and comparison of protein co-localization for multiple 2- and 3-dimensional image datasets. This method significantly improves the analysis of protein co-localization using super-resolution image data, as shown by its excellent performance in simulation studies and an analysis of co-localization of protein light chain 3 and lysosomal-associated membrane protein 1 in cell autophagy. Moreover, this method is directly applicable to co-localization analyses in other disciplines, such as diagnostic imaging, epidemiology, environmental science, and ecology.

Two Statistical Tools for Assessing Functionality and Protein Characteristics of Different Fava Bean (Vicia faba L.) Ingredients

Fava bean (Vicia faba L.) is a promising source of proteins that can be potentially used as nutritional and/or functional agents for industrial food applications. Fava ingredients are industrially produced, modified, and utilized for food applications. Their processing conditions influence physico-chemical protein properties that further impact ingredient functionality. To design a functionally suitable ingredient, an understanding of the interrelationships between different properties is essential. Hence, this work aimed to assess two statistical analytical tools, Pearson’s correlation and Principal Component Analysis (PCA), for investigating the role of the process conditions of fava ingredients on their functional and protein properties. Fava concentrates were processed by pH (2, 4, 6.4 and 11), temperature (55, 75 and 95 °C) and treatment duration (30 and 360 min) into different modified ingredients. These were utilized under two application conditions (pH 4 and 7), and their foam and emulsion properties as well as their ingredient characteristics (charge, solubility, and intrinsic fluorescence) were measured. The results show that foam and emulsion properties are not correlated to each other. They are associated with different protein and non-protein attributes as fava concentrate is a multi-component matrix. Importantly, it is found that the results from the two statistical tools are not fully comparable but do complement each other. This highlights that both statistical analytical tools are equally important for a comprehensive understanding of the impact of process conditions on different properties and the interrelationships between them. Therefore, it is recommended to use Pearson’s correlation and principal component analysis in future investigations of new plant-based proteins.

Cerebral CT Perfusion in Acute Stroke: The Effect of Lowering the Tube Load and Sampling Rate on the Reproducibility of Parametric Maps

The aim of this study was to define lower dose parameters (tube load and temporal sampling) for CT perfusion that still preserve the diagnostic efficiency of the derived parametric maps. Ninety stroke CT examinations from four clinical sites with 1 s temporal sampling and a range of tube loads (mAs) (100–180) were studied. Realistic CT noise was retrospectively added to simulate a CT perfusion protocol, with a maximum reduction of 40% tube load (mAs) combined with increased sampling intervals (up to 3 s). Perfusion maps from the original and simulated protocols were compared by: (a) similarity using a voxel-wise Pearson’s correlation coefficient r with in-house software; (b) volumetric analysis of the infarcted and hypoperfused volumes using commercial software. Pearson’s r values varied for the different perfusion metrics from 0.1 to 0.85. The mean slope of increase and cerebral blood volume present the highest r values, remaining consistently above 0.7 for all protocol versions with 2 s sampling interval. Reduction of the sampling rate from 2 s to 1 s had only modest impacts on a TMAX volume of 0.4 mL (IQR −1–3) (p = 0.04) and core volume of −1.1 mL (IQR −4–0) (p < 0.001), indicating dose savings of 50%, with no practical loss of diagnostic accuracy. The lowest possible dose protocol was 2 s temporal sampling and a tube load of 100 mAs.

Edible mushrooms dietary fibre and antioxidants: Effects on glycaemic load manipulation and their correlations pre-and post-simulated in vitro digestion

In this study, mushroom stems were separated from the fruiting body of two edible mushrooms, white button mushroom (WB, Agaricus bisporus) and oyster mushroom (OY, Pleurotus ostreatus), and their functionalities were compared in wheat flour noodles at fortification levels of 5, 10, 15%. The inclusion of WB led to higher protein content than OY, which had more dietary fibre, especially insoluble dietary fibre. The fortification of mushrooms decreased the area under the curve (AUC) of reducing sugars released during in vitro digestion significantly (p < 0.05). WB fortified noodles yielded higher antioxidant capacities than OY fortification, whereas the digesta following digestion of WB and OY groups shared similar free accessible weighted average antioxidants. Mushrooms derived insoluble dietary fibre was negatively correlated with AUC and positively correlated with antioxidants (p < 0.05), suggesting the efficacy of mushroom stems over post-prandial glucose release of foods and providing the antioxidant environment to the intestine.

Characterization and Optimization of the Tyrosinase Inhibitory Activity of Vitis amurensis Root Using LC-Q-TOF-MS Coupled with a Bioassay and Response Surface Methodology

Vitis amurensis roots have been reported to have the potential for skin whitening through the evaluation of melanogenesis and tyrosinase inhibitory activities. In this study, V. amurensis roots were utilized to quickly select whitening ingredients using LC-Q-TOF-MS coupled with tyrosinase inhibitory assay, and to optimize the extraction process for use as a skin whitening functional material by response surface methodology. Results showed that V. amurensis roots exhibited tyrosinase inhibitory effects by two stilbene oligomers, ε-viniferin (1) and vitisin B (2), as predicted by LC-Q-TOF-MS coupled with bioassay. The optimal extraction conditions (methanol concentration 66%, solvent volume 140 mL, and extraction time 100 min) for skin whitening ingredients were established with the yields 6.20%, and tyrosinase inhibitory activity was 87.27%. The relationship between each factor and its corresponding response was confirmed by Pearson’s correlation analysis. The solvent volume showed clear linear relationship with yields, and methanol concentration had a strong linear relationship with tyrosinase inhibitory activity for compounds 1 and 2, as well as their combination. Overall, LC-Q-TOF-MS coupled with bioassay was proved to have the potential to effectively find new active constituents, as well as known active constituents; vitisin B can be proposed as a new natural potential whitening agent.

Correlating Capsaicinoid Levels and Physicochemical Proper-ties of Kimchi and Its Perceived Spiciness

Capsaicinoid content, among other factors, affects the perception of spiciness of commercial kimchi. Here, we investigated whether the physicochemical properties of kimchi affect the spicy taste of capsaicinoids perceived by the tasting. High-performance liquid chromatography (HPLC) was used to evaluate the capsaicinoid content (mg/kg) of thirteen types of commercial kimchi. The physicochemical properties such as pH, titratable acidity, salinity, free sugar content, and free amino acid content were evaluated, and the spicy strength grade was determined by selected panel to analyze the correlation between these properties. Panels were trained for 48 h prior to actual evaluation by panel leaders trained for over 1000 h according to the Spectrum^TM method. Partial correlation analysis was performed to examine other candidate parameters that interfere with the sensory evaluation of spiciness and capsaicinoid content. To express the specific variance after eliminating the effects of other variables, partial correlations were used to estimate the relationships between two variables. We observed a strong correlation between spiciness intensity ratings and capsaicinoid content, with a Pearson’s correlation coefficient of 0.78 at p ≤ 0.001. However, other specific variables may have influenced the relationship between spiciness intensity and total capsaicinoid content. Partial correlation analysis indicated that the free sugar content most strongly affected the relationship between spiciness intensity and capsaicinoid content, showing the largest first-order partial correlation coefficient (r_xy/z: 0.091, p ≤ 0.01).

Functional connectivity network estimation with an inter-similarity prior for mild cognitive impairment classification

Functional connectivity network (FCN) analysis is an effective technique for modeling human brain patterns and diagnosing neurological disorders such as Alzheimer's disease (AD) and its early stage, Mild Cognitive Impairment. However, accurately estimating biologically meaningful and discriminative FCNs remains challenging due to the poor quality of functional magnetic resonance imaging (fMRI) data and our limited understanding of the human brain. Inspired by the inter-similarity nature of FCNs, similar regions of interest tend to share similar connection patterns. Here, we propose a functional brain network modeling scheme by encoding Inter-similarity prior into a graph-regularization term, which can be easily solved with an efficient optimization algorithm. To illustrate its effectiveness, we conducted experiments to distinguish Mild Cognitive Impairment from normal controls based on their respective FCNs. Our method outperformed the baseline and state-of-the-art methods by achieving an 88.19% classification accuracy. Furthermore, post hoc inspection of the informative features showed that our method yielded more biologically meaningful functional brain connectivity.

Association Factor for Identifying Linear and Nonlinear Correlations in Noisy Conditions

Background: In data analysis and machine learning, we often need to identify and quantify the correlation between variables. Although Pearson’s correlation coefficient has been widely used, its value is reliable only for linear relationships and Distance correlation was introduced to address this shortcoming. Methods: Distance correlation can identify linear and nonlinear correlations. However, its performance drops in noisy conditions. In this paper, we introduce the Association Factor (AF) as a robust method for identification and quantification of linear and nonlinear associations in noisy conditions. Results: To test the performance of the proposed Association Factor, we modeled several simulations of linear and nonlinear relationships in different noise conditions and computed Pearson’s correlation, Distance correlation, and the proposed Association Factor. Conclusion: Our results show that the proposed method is robust in two ways. First, it can identify both linear and nonlinear associations. Second, the proposed Association Factor is reliable in both noiseless and noisy conditions.

Group Similarity Constraint Functional Brain Network Estimation for Mild Cognitive Impairment Classification

Functional brain network (FBN) provides an effective biomarker for understanding brain activation patterns and a diagnostic criterion for neurodegenerative diseases detections. Unfortunately, it remains challenges to estimate the biologically meaningful or discriminative FBNs accurately, because of the poor quality of functional magnetic resonance imaging data or our limited understanding of human brain. In this study, a novel FBN estimation model based on group similarity prior was proposed. In particular, we extended the FBN estimation model to tensor form and incorporated the tensor trace-norm regularizer to formulate the group similarity constraint. To verify the proposed method, we conducted experiments on identifying mild cognitive impairments (MCIs) from normal controls (NCs) based on the estimated FBNs. Experimental results illustrated that our method is effective in modeling FBNs. Consequently, we achieved 91.97% classification accuracy, outperforming the state-of-the-art methods. The post hoc analysis further demonstrated that more biologically meaningful functional brain connections were obtained using our proposed method.

Electroencephalogram Profiles for Emotion Identification over the Brain Regions Using Spectral, Entropy and Temporal Biomarkers

Identifying emotions has become essential for comprehending varied human behavior during our daily lives. The electroencephalogram (EEG) has been adopted for eliciting information in terms of waveform distribution over the scalp. The rationale behind this work is twofold. First, it aims to propose spectral, entropy and temporal biomarkers for emotion identification. Second, it aims to integrate the spectral, entropy and temporal biomarkers as a means of developing spectro-spatial ( S S ) , entropy-spatial ( E S ) and temporo-spatial ( T S ) emotional profiles over the brain regions. The EEGs of 40 healthy volunteer students from the University of Vienna were recorded while they viewed seven brief emotional video clips. Features using spectral analysis, entropy method and temporal feature were computed. Three stages of two-way analysis of variance (ANOVA) were undertaken so as to identify the emotional biomarkers and Pearson's correlations were employed to determine the optimal explanatory profiles for emotional detection. The results evidence that the combination of applied spectral, entropy and temporal sets of features may provide and convey reliable biomarkers for identifying S S , E S and T S profiles relating to different emotional states over the brain areas. EEG biomarkers and profiles enable more comprehensive insights into various human behavior effects as an intervention on the brain.

Identification of jointly correlated gene sets

Associations between expressions of genes play a key role in deciphering their functions. Correlation score between pairs of genes is often utilized to associate two genes. However, the relationship between genes is often more complex; multiple genes might collaborate to control the transcription of a gene. In this paper, we introduce the problem of searching pairs of genes, which collectively correlate with another gene. This problem is computationally much harder than the classical problem of identifying pairwise gene associations. Exhaustive search is infeasible for transcriptomic datasets also; since for [Formula: see text] genes, there are [Formula: see text] possible gene combinations. Our method builds three filters to avoid computing the association for a large fraction of the gene combinations, which do not produce high correlation. Our experiments on a synthetic dataset and a prostate cancer dataset demonstrate that our method produces accurate results at the transcriptome level in practical time. Moreover, our method identifies biologically novel results which classical pairwise gene association studies are unlikely to discover.

Phenolic Profile and Bioactive Properties of Carissa macrocarpa (Eckl.) A.DC.: An In Vitro Comparative Study between Leaves, Stems, and Flowers

The present work aimed to characterize leaves, stems, and flowers of Carissa macrocarpa (Eckl.) A.DC., by performing an analysis of the phenolic compounds by HPLC-DAD/ESI-MS, correlating them with bioactive properties, such as antioxidant, anti-inflammatory, cytotoxic, and antimicrobial activities. Thirty polyphenols were identified in the hydroethanolic extract, including phenolic acids, flavan-3-ols, and flavonol glycosides derivatives (which presented the highest number of identified compounds). However, flavan-3-ols showed the highest concentration in stems (mainly owing to the presence of dimers, trimmers, and tetramers of type B (epi)catechin). Leaves were distinguished by their high antioxidant and anti-inflammatory activities, as well as their bactericidal effect against E. coli, while stems presented a higher cytotoxic activity and bactericidal effect against Gram-positive bacteria. Moreover, a high correlation between the studied bioactivities and the presence of phenolic compounds was also verified. The obtained results bring added value to the studied plant species.

Trichoderma-Inoculation and Mowing Synergistically Altered Soil Available Nutrients, Rhizosphere Chemical Compounds and Soil Microbial Community, Potentially Driving Alfalfa Growth

Trichoderma spp. are proposed as major plant growth-promoting fungi (PGPF) to increase plants growth and productivity. Mowing can stimulate aboveground regrowth to improve plant biomass and nutritional quality. However, the synergistic effects of Trichoderma and mowing on plants growth, particularly the underlying microbial mechanisms mediated by rhizosphere soil chemical compounds, have rarely been reported. In the present study, we employed Trichoderma harzianum T-63 and conducted a pot experiment to investigate the synergistic effect of Trichoderma-inoculation and mowing on alfalfa growth, and the potential soil microbial ecological mechanisms were also explored. Alfalfa treated with Trichoderma-inoculation and/or mowing (T, M, and TM) had significant (P < 0.05) increases in plant shoot and root dry weights and soil available nutrients (N, P, and K), compared with those of the control (CK). Non-metric multidimensional scaling (NMDS) demonstrated that the rhizosphere chemical compounds and soil bacterial and fungal communities were, respectively, separated according to different treatments. There was a clear significant (P < 0.05) positive correlation between alfalfa biomass and the relative abundance of Trichoderma (R² = 0.3451, P = 0.045). However, Pseudomonas, Flavobacterium, Arthrobacter, Bacillus, Agrobacterium, and Actinoplanes were not significantly correlated with alfalfa biomass. According to structure equation modeling (SEM), Trichoderma abundance and available P served as primary contributors to alfalfa growth promotion. Additionally, Trichoderma-inoculation and mowing altered rhizosphere soil chemical compounds to drive the soil microbial community, indirectly influencing alfalfa growth. Our research provides a basis for promoting alfalfa growth from a soil microbial ecology perspective and may provide a scientific foundation for guiding the farming of alfalfa.

Influence of Grape Berry Maturity on Juice and Base Wine Composition and Foaming Properties of Sparkling Wines from the Champagne Region

In sparkling wine cool-climate regions like Champagne, it is sometimes necessary to pick the healthy grape clusters that have a relatively low maturity level to avoid the deleterious effects of Botrytis cinerea. In such conditions, we know that classical oenological parameters (sugars, pH, total acidity) may change but there is little information concerning the impact of grape berry maturity on wine proteins and foaming properties. Therefore, healthy grapes (Chardonnay and Pinot meunier) in 2015 and 2016 were picked at different maturity levels within the range of common industrial maturity for potential alcohol content 8–11% v/v in the Champagne region. Base wine protein content and foamability, and oenological parameters in grape juice and their corresponding base wines, were investigated. The results showed that base wine protein contents (analyzed by the Bradford method and by electrophoresis) and foamability were higher when the grapes were riper. The Pearson’s correlation test found significant positive correlations (r = 0.890–0.997, p < 0.05) between Chardonnay grape berry maturity degree (MD) and base wine foamability in both vintages. Strong correlations between MD and most of the oenological parameters in grape juice and base wine were also found for the two cultivars. Under the premise of guaranteed grape health, delaying harvest date is an oenological decision capable of improving base wine protein content and foamability.

Multiple mini-interview as a predictor of performance in the objective structured clinical examination among Physician Associates in the United Kingdom: a cohort study

INTRODUCTION

Patient satisfaction and health care outcomes are directly linked to useful communication skills. Therefore, excellent interpersonal skills are imperative for health care professionals. Multiple mini-interview (MMI) is designed as a selection tool to assess the communication skills of applicants in medical schools during the admission process. However, objective structured clinical examination (OSCE) assesses students' communication and clinical skills at the end of their academic terms. Recently, Anglia Ruskin University, Chelmsford, UK, adopted MMI in the selection process for the first cohort of MSc Physician Associate trainees for the academic year 2015-2016. This study aimed to determine the likelihood of MMI as a predictor of future performance of communication skills in the OSCE.

MATERIALS AND METHODS

The anonymous data of the average scores of communication skills attained in MMI and OSCE at the end of year 1 were collected for 30 students from the Physician Associate program team. Subsequently, Pearson's correlation was computed to determine the relationship between the average scores of communication skills attained in MMI, and OSCE during trimester 2 and trimester 3 by the Physician Associate trainees.

RESULTS

The study showed positive correlation between the scores of communication skills attained in MMI and OSCE during trimester 2 (r=0.956, n=30, p<0.001) and trimester 3 (r=0.966, n=30, p<0.001).

CONCLUSION

The study provides empirical evidence for the validity of MMI as a predictor of future performance of Physician Associate trainees' communication skills during subsequent OSCEs.

Genetic analysis of persistency in HF crossbred cattle at an organized farm of northern India

The present study was undertaken to estimate effect of various genetic and non-genetic factors on persistency of milk production and to identify the most appropriate persistency method that fits best in our environment. In the present study, effects of different non-genetic factors, viz. year, season, days to attain peak yield, and genetic group based on the level of exotic inheritance on persistency of milk yield in crossbred cattle were studied. Data comprised of 686 first lactation daily milk yield records of crossbred cattle that were maintained at GADVASU dairy farm over a period of 25 years from 1991 to 2015 were utilized to calculate persistency coefficients by four methods, viz., Ludwick and Peterson method (P1), Mahadevan method (P2), ratio method (P3), and Prasad et al. method (P4). Overall least squares means for persistency by Ludwick and Peterson method (P1), Mahadevan method (P2), ratio method (P3), and Prasad et al. method (P4) were 0.896 ± 0.096, 1.385 ± 0.224, 187.207 ± 26.398, and 0.621 ± 0.098, respectively. Effect of sires was significant (P < 0.05) on P2 and P4 methods. Effect of genetic group on all four methods was non-significant. Period of calving had significant (P < 0.01) effect on persistency of milk yield (P2, P3, and P4 methods). Effect of season of calving on persistency of milk yield was found to be significant in all estimates obtained by the four methods. Summer and autumn calvers were most persistent whereas spring and winter calvers were least persistent for (P2, P3, and P4 methods). Persistency of milk yield was significantly (P < 0.05) affected by days to attain peak yield in P1 and P2 methods. Maximum persistency was obtained in animals attaining peak at 41-57 days of lactation and minimum in < 41 days for Mahadevan method and ratio method. The highest heritability of persistency and minimum value of standard error was estimated as 0.275 ± 0.11 for the Mahadevan method followed by the Prasad method (0.197 ± 0.10) by half sib correlation method. The maximum coefficient of variation which indicates available variability was estimated as 20.788% for persistency by the Mahadevan method followed by 18.969% for the Prasad method. The highest correlation was also observed between P1 and P3 methods by Spearman's and Pearson's correlation for least squares breeding value of the sires. On the basis of heritability, standard error of heritability, and coefficient of variation, it can be concluded that the Mahadevan method followed by the Prasad method suits best to our environment for animals in first lactation as well as they can be utilized for effective selection for higher persistency in crossbred animals of Punjab.

From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data

The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.

A new method to detect event-related potentials based on Pearson's correlation

Event-related potentials (ERPs) are widely used in brain-computer interface applications and in neuroscience.  Normal EEG activity is rich in background noise, and therefore, in order to detect ERPs, it is usually necessary to take the average from multiple trials to reduce the effects of this noise.  The noise produced by EEG activity itself is not correlated with the ERP waveform and so, by calculating the average, the noise is decreased by a factor inversely proportional to the square root of N, where N is the number of averaged epochs. This is the easiest strategy currently used to detect ERPs, which is based on calculating the average of all ERP’s waveform, these waveforms being time- and phase-locked.  In this paper, a new method called GW6 is proposed, which calculates the ERP using a mathematical method based only on Pearson’s correlation. The result is a graph with the same time resolution as the classical ERP and which shows only positive peaks representing the increase—in consonance with the stimuli—in EEG signal correlation over all channels.  This new method is also useful for selectively identifying and highlighting some hidden components of the ERP response that are not phase-locked, and that are usually hidden in the standard and simple method based on the averaging of all the epochs.  These hidden components seem to be caused by variations (between each successive stimulus) of the ERP’s inherent phase latency period (jitter), although the same stimulus across all EEG channels produces a reasonably constant phase. For this reason, this new method could be very helpful to investigate these hidden components of the ERP response and to develop applications for scientific and medical purposes. Moreover, this new method is more resistant to EEG artifacts than the standard calculations of the average and could be very useful in research and neurology.  The method we are proposing can be directly used in the form of a process written in the well-known Matlab programming language and can be easily and quickly written in any other software language.

Time-Domain Nuclear Magnetic Resonance Investigation of Water Dynamics in Different Ginger Cultivars

Different ginger cultivars may contain different nutritional and medicinal values. In this study, a time-domain nuclear magnetic resonance method was employed to study water dynamics in different ginger cultivars. Significant differences in transverse relaxation time T2 values assigned to the distribution of water in different parts of the plant were observed between Henan ginger and four other ginger cultivars. Ion concentration and metabolic analysis showed similar differences in Mn ion concentrations and organic solutes among the different ginger cultivars, respectively. On the basis of Pearson's correlation analysis, many organic solutes and 6-gingerol, the main active substance of ginger, exhibited significant correlations with water distribution as determined by NMR T2 relaxation, suggesting that the organic solute differences may impact water distribution. Our work demonstrates that low-field NMR relaxometry provides useful information about water dynamics in different ginger cultivars as affected by the presence of different organic solutes.