Data analysis of the influence of microstructure, composition, and loading conditions on stress corrosion cracking behavior of Mg alloys

Stress corrosion cracking (SCC) can be a crucial problem in applying rare earth (RE) Magnesium alloys in environments where mechanical loads and electrochemical driven degradation processes interact. It has been proven already that the SCC behavior is associated with microstructural features, compositions, loading conditions, and corrosive media, especially in-vivo. However, it is still unclear when and how mechanisms acting on multiple scales and respective system descriptors predictable contribute to SCC for the wide set of existing Mg alloys. In the present work, suitable literature data along SCC of Mg alloys has been analyzed to enable the development of a reliable SCC model for MgGd binary alloys. Pearson correlation coefficient and linear fitting are utilized to describe the contribution of selected parameters to corrosion and mechanical properties. Based on our data analysis, a parameter ranking is obtained, providing information on the SCC impact with regard to ultimate tensile strength (UTS) and fracture elongation of respective materials. According to the analyzed data, SCC susceptibility can be grouped and mapped onto Ashby type diagrams for UTS and elongation of respective base materials tested in air and in corrosive media. The analysis reveals the effect of secondary phase content as a crucial materials descriptor for our analyzed materials and enables better understanding towards SCC model development for Mg-5Gd alloy based implant.

An integrative framework for clinical diagnosis and knowledge discovery from exome sequencing data

Non-silent single nucleotide genetic variants, like nonsense changes and insertion-deletion variants, that affect protein function and length substantially are prevalent and are frequently misclassified. The low sensitivity and specificity of existing variant effect predictors for nonsense and indel variations restrict their use in clinical applications. We propose the Pathogenic Mutation Prediction (PMPred) method to predict the pathogenicity of single nucleotide variations, which impair protein function by prematurely terminating a protein's elongation during its synthesis. The prediction starts by monitoring functional effects (Gene Ontology annotation changes) of the change in sequence, using an existing ensemble machine learning model (UniGOPred). This, in turn, reveals the mutations that significantly deviate functionally from the wild-type sequence. We have identified novel harmful mutations in patient data and present them as motivating case studies. We also show that our method has increased sensitivity and specificity compared to state-of-the-art, especially in single nucleotide variations that produce large functional changes in the final protein. As further validation, we have done a comparative docking study on such a variation that is misclassified by existing methods and, using the altered binding affinities, show how PMPred can correctly predict the pathogenicity when other tools miss it. PMPred is freely accessible as a web service at https://pmpred.kansil.org/, and the related code is available at https://github.com/kansil/PMPred.

Effect of thermal shock and sustained heat treatment on mainstream partial nitrification and microbial community in sequencing batch reactors

In order to develop a promising means of achieving mainstream short-cut nitrification, this study evaluated the effect of thermal shock on nitrite accumulation using intermittent offline and continuous inline heat treatment of biomass in sequencing batch reactors (SBRs). The SBRs fed with municipal wastewater were operated at a solid retention time of 7 days and nitrogen loading rate of 0.04 gN/L·d to 0.08 gN/L·d without the application of pre-treatment. Contrary to literature studies that showed suppression of nitrite-oxidizing bacteria at temperature 60 to 80 °C, nitrite accumulation was achieved temporarily when 20% of the biomass was heated for 2 h at 47 °C, as well as in continuously heated SBRs at 37 °C and 42 °C. The continuously heated reactors at 37 °C and 42 °C produced a maximum nitrite accumulation ratio (NAR) of 0.59 and 0.79, respectively, whereas the intermittent offline heating at 47 °C-2 h produced a NAR of 0.37. Although nitrite accumulation was stable only for 10-12 days in all heated reactors, this study demonstrates the achievement of mainstream partial nitrification (PN) at lower temperature (42 °C) than that reported in literature and also highlights the potential for achieving PN by implementing heat treatment of a portion of the return activated sludge (RAS) in biological nitrogen removal (BNR) systems. During the time when full nitrification was achieved, Nitrospira was more dominant than Nitrosomonas in all reactors at ratios of 1.4:1, 2.4:1, 2.4:1, and 3.7:1 for the control SBR (22 °C), 47 °C -2 h offline heating SBR, 37 °C SBR, and 42 °C SBR, respectively, suggesting that it may have played a role as a comammox bacteria capable of degrading ammonia to nitrates at elevated temperature.

Ecological and human health risk assessment of potentially toxic elements in water and soils within a crude oil waste management facility, Southwestern Ghana

Crude oil waste management is challenging due to the diverse constituents of the waste and its consequent impact on valued environmental receptors (water and soil). Characterization of the potentially toxic elements (PTEs) in soils and water within the surroundings of crude oil waste management facility is imperative, to aid evaluation of potential risks. The study assessed the potential environmental and human health risks posed by PTEs in soil and water from surroundings and adjoining settlement communities. A total of forty-four (44) samples were analyzed for PTEs (Cr, Pb, Zn, Co, Mn, Ni, Hg, Fe, As, Cu, Hg, and Cd) and physicochemical properties in both matrices. The total carcinogenic risk (TCR) for adults and children in the neighbouring community was 4.73 × 10-6 and 1.2 × 10-4, respectively, which was due to the high carcinogenic slope factor of arsenic. A strong correlation was observed between the PTEs and physicochemical properties, and their health risk was attributed to both geogenic and anthropogenic factors. The study indicated that the human health and ecological risk values obtained were within acceptable limits, with the waste management facility posing a higher risk in comparison to the nearby community. These risks may be attributed to the specific nature and intensity of the activities conducted at the facility. Hence, there is the need for continuous promotion of occupational and public awareness on the health and environmental impact of crude oil waste management.

The Prediction of Cu(II) Adsorption Capacity of Modified Pomelo Peels Using the PSO-ANN Model

It is very well known that traditional artificial neural networks (ANNs) are prone to falling into local extremes when optimizing model parameters. Herein, to enhance the prediction performance of Cu(II) adsorption capacity, a particle swarm optimized artificial neural network (PSO-ANN) model was developed. Prior to predicting the Cu(II) adsorption capacity of modified pomelo peels (MPP), experimental data collected by our research group were used to build a consistent database. Then, a PSO-ANN model was established to enhance the model performance by optimizing the ANN's weights and biases. Finally, the performances of the developed ANN and PSO-ANN models were deeply evaluated. The results of this investigation revealed that the proposed hybrid method did increase both the generalization ability and the accuracy of the predicted data of the Cu(II) adsorption capacity of MPPs when compared to the conventional ANN model. This PSO-ANN model thus offers an alternative methodology for optimizing the adsorption capacity prediction of heavy metals using agricultural waste biosorbents.

Exact inference around ordinal measures of association is often not exact

In this paper, we build upon the work of DiCiccio and Romano (2017) by extending their permutation test approach, based on the Pearson correlation coefficient in the continuous case, to ordinal measures of association. We investigate commonly used ordinal measures such as the Spearman correlation, Kendall's tau-b, and gamma, which are widely implemented in commercial and open-source software packages for exact testing routines based on generalized hypergeometric probabilities. Similar to DiCiccio and Romano's method, we apply studentization to correct the test statistic, which yields asymptotically valid inference for testing no ordinal association. We present a comprehensive theoretical framework for our approach, followed by a simulation study. Furthermore, we use toy examples to highlight the differences between the exact tests and the asymptotically valid tests. Our findings align with those of DiCiccio and Romano, indicating that exact permutation tests based on ordinal measures of association are often not exact, whereas the asymptotically correct tests perform well for moderate to large sample sizes.

Key factors influencing pollution of heavy metals and phenolic compounds in mangrove sediments, South China

Concentrations of heavy metals (HMs) and phenolic compounds with factors which potentially affected their spatial distribution were investigated in mangrove sediments, South China. Compared to Qi'ao, Futian sediments exhibited higher levels of Pb and nonylphenol (NP), but lower levels of Co and Ni. Seasonal variation showed higher concentrations of Pb, Cr, Co, NP and bisphenol A (BPA), while lower concentration of methylparaben (MP) in wet than dry season. Contaminant levels in sediments collected at different tidal heights showed insignificant variations, except for Zn and NP. MP was found negatively correlated with nearly all HMs and BPA, whereas the latter exhibited positive correlations with each other. Sedimentary total carbon, total nitrogen, C/N and N/P ratios were screened as the most influential factors affecting the distribution of these contaminants. Additionally, both salinity and total phosphate exhibited positive, while both pH and sedimentary particle size registered negative correlation, with one or more contaminants.

Distance Correlation-Based Feature Selection in Random Forest

The Pearson correlation coefficient (ρ) is a commonly used measure of correlation, but it has limitations as it only measures the linear relationship between two numerical variables. The distance correlation measures all types of dependencies between random vectors X and Y in arbitrary dimensions, not just the linear ones. In this paper, we propose a filter method that utilizes distance correlation as a criterion for feature selection in Random Forest regression. We conduct extensive simulation studies to evaluate its performance compared to existing methods under various data settings, in terms of the prediction mean squared error. The results show that our proposed method is competitive with existing methods and outperforms all other methods in high-dimensional (p≥300) nonlinearly related data sets. The applicability of the proposed method is also illustrated by two real data applications.

Adsorption-desorption characteristics of atrazine on soil and vermicompost prepared with different ratios of raw materials

In this work, vermicompost was prepared with maize stover and cattle dung in ratios of 60:40 (VC1), 50:50 (VC2) and 40:60 (VC3), and the physicochemical properties of the vermicompost were related to the ratio of the raw materials used. The effect of the vermicomposts on the adsorption kinetics, adsorption isotherms and desorption of atrazine were investigated in unamended soil (S) and soil amended with 4% (w/w) of VC1(S-VC1), VC2(S-VC2) and VC3(S-VC3). The total organic carbon (TOC) content of VC1, VC2 and VC3 was 38.46, 37.33 and 34.47%, the HA content was 43.50, 42.22 and 39.28 g/kg, and the HA/FA ratios was 1.47, 0.44 and 0.83, respectively. The adsorption of atrazine on the soil, on the vermicompost and on soils amended with vermicompost followed a pseudo-second-order kinetic model. The Freundlich equation better fitted the adsorption isotherm of atrazine. The vermicomposts enhanced atrazine adsorption and decreased atrazine desorption. Correlation analysis showed that the TOC and HA were significantly positively correlated with Kf, which indicated that TOC and HA of the vermicomposts contributed significantly to the adsorption and desorption of atrazine. This study demonstrated that vermicomposts have great potential in the bioremediation of atrazine pollution and that their role is related to the raw materials used to prepare them.

GeCoNet-Tool: a software package for gene co-expression network construction and analysis

BACKGROUND

Network analysis is a powerful tool for studying gene regulation and identifying biological processes associated with gene function. However, constructing gene co-expression networks can be a challenging task, particularly when dealing with a large number of missing values.

RESULTS

We introduce GeCoNet-Tool, an integrated gene co-expression network construction and analysis tool. The tool comprises two main parts: network construction and network analysis. In the network construction part, GeCoNet-Tool offers users various options for processing gene co-expression data derived from diverse technologies. The output of the tool is an edge list with the option of weights associated with each link. In network analysis part, the user can produce a table that includes several network properties such as communities, cores, and centrality measures. With GeCoNet-Tool, users can explore and gain insights into the complex interactions between genes.

Physicochemical Aspects, Bioactive Compounds, Phenolic Profile and In Vitro Antioxidant Activity of Tropical Red Fruits and Their Blend

The combination of fruit pulps from different species, in addition to multiplying the offer of flavors, aromas and textures, favors the nutritional spectrum and the diversity of bioactive principles. The objective was to evaluate and compare the physicochemical characteristics, bioactive compounds, profile of phenolic compounds and in vitro antioxidant activity of pulps of three species of tropical red fruits (acerola, guava and pitanga) and of the blend produced from the combination. The pulps showed significant values of bioactive compounds, with emphasis on acerola, which had the highest levels in all parameters, except for lycopene, with the highest content in pitanga pulp. Nineteen phenolic compounds were identified, being phenolic acids, flavanols, anthocyanin and stilbene; of these, eighteen were quantified in acerola, nine in guava, twelve in pitanga and fourteen in the blend. The blend combined positive characteristics conferred by the individual pulps, with low pH favorable for conservation, high levels of total soluble solids and sugars, greater diversity of phenolic compounds and antioxidant activity close to that of acerola pulp. Pearson's correlation between antioxidant activity and ascorbic acid content, total phenolic compounds, flavonoids, anthocyanins and carotenoids for the samples were positive, indicating their use as a source of bioactive compounds.

Characterization of municipal solid waste: Measures towards management strategies using statistical analysis

Implementing unified municipal solid waste management (MSWM) is often difficult due to socio-economic variables. However, spatial GIS models and statistical analysis of solid waste characterized by the weekdays, weekends, and festivals can somewhat mitigate the variance and assist with selecting suitable waste management methods. This paper presents the example of Rajouri, India, to propose a suitable MSWM based on Inverse Distance Weighted (IDW) intensity maps and statistical findings. The considered region was divided into different sample sites based on the local population density, and Municipal Solid Waste (MSW) was collected from four locations in each site on weekdays, weekends, and festivals. Compositional analysis of the MSW was then used to generate spatial IDW models in QGIS 3.22.7 to interpolate MSW generation over the entire area. Finally, statistical analysis was conducted to gain insight into the waste generation and accumulation trends. The results show that Rajouri produces 245 tonnes of waste daily (per capita: 0.382 kg/day) with a large organic fraction compared to other waste categories. Besides, waste generation is observed to increase over weekends and festivals due to increased consumption of material goods. Composting could serve as a vector for municipal solid waste because of its increased organic component and cost constraints. However, further research on the potential segregation techniques for the organic fraction of solid waste is needed.

Variability of PM10 level with gaseous pollutants and meteorological parameters during episodic haze event in Malaysia: Domestic or solely transboundary factor?

Haze has become a seasonal phenomenon affecting Southeast Asia, including Malaysia, and has occurred almost every year within the last few decades. Air pollutants, specifically particulate matter, have drawn a lot of attention due to their adverse impact on human health. In this study, the spatial and temporal variability of the PM₁₀ concentration at Kelang, Melaka, Pasir Gudang, and Petaling Jaya during historic haze events were analysed. An hourly dataset consisting of PM₁₀, gaseous pollutants and weather parameters were obtained from Department of Environment Malaysia. The mean PM₁₀ concentrations exceeded the stipulated Recommended Malaysia Ambient Air Quality Guideline for the yearly average of 150 μg/m³ except for Pasir Gudang in 1997 and 2005, and Petaling Jaya in 2013. The PM₁₀ concentrations exhibit greater variability in the southwest monsoon and inter-monsoon periods at the studied year. The air masses are found to be originating from the region of Sumatra during the haze episodes. Strong to moderate correlation of PM₁₀ concentrations was found between CO during the years that recorded episodic haze, meanwhile, the relationship of PM₁₀ level with SO₂ was found to be significant in 2013 with significant negatively correlated relative humidity. Weak correlation of PM₁₀-NOx was measured in all study areas probably due to less contribution of domestic anthropogenic sources towards haze events in Malaysia.

Physicochemical characteristics and gel-forming properties of mandarin fish (Siniperca chuatsi) protein during the fish fermentation with Lactobacillus sake SMF-L5: The formation of garlic-cloves shaped protein gel

Mandarin fish (Siniperca chuatsi) during fermentation presents a unique elastic texture. In this investigation, the physicochemical and gel-forming properties of fish proteins were evaluated to explain the formation of elastic characteristics. During fermentation, the combined effects of acidification by Lactobacillus sake SMF-L5, increased sodium chloride, and decreased moisture content in the fish protein generated a suitable microenvironment for gelation. The mass transfer of sodium chloride was accompanied by NMR relaxation of the immobilized water. The ripening fermented fish had a functionally available MHC, a higher fractal dimension, and a stable α-helical structure. Also, it exhibited excellent gel-forming performances, mainly including garlic-cloves shaped protein gel, stronger springiness, and enhanced L* and whiteness. Correlation analysis showed that the gel's physical properties were differently related to the protein's physicochemical characteristics except for total free amino acids. These results could lay a theoretical foundation for the gel formation mechanism of fermented mandarin fish.

Distinguishing Patients with MRI-Negative Temporal Lobe Epilepsy from Normal Controls Based on Individual Morphological Brain Network

Temporal Lobe Epilepsy (TLE) is the most common subtype of focal epilepsy and the most refractory to drug treatment. Roughly 30% of patients do not have easily identifiable structural abnormalities. In other words, MRI-negative TLE has normal MRI scans on visual inspection. Thus, MRI-negative TLE is a diagnostic and therapeutic challenge. In this study, we investigate the cortical morphological brain network to identify MRI-negative TLE. The 210 cortical ROIs based on the Brainnetome atlas were used to define the network nodes. The least absolute shrinkage and selection operator (LASSO) algorithm and Pearson correlation methods were used to calculate the inter-regional morphometric features vector correlation respectively. As a result, two types of networks were constructed. The topological characteristics of networks were calculated by graph theory. Then after, a two-stage feature selection strategy, including a two-sample t-test and support vector machine-based recursive feature elimination (SVM-RFE), was performed in feature selection. Finally, classification with support vector machine (SVM) and leave-one-out cross-validation (LOOCV) was employed for the training and evaluation of the classifiers. The performance of two constructed brain networks was compared in MRI-negative TLE classification. The results indicated that the LASSO algorithm achieved better performance than the Pearson pairwise correlation method. The LASSO algorithm provides a robust method of individual morphological network construction for distinguishing patients with MRI-negative TLE from normal controls.

NASA POWER satellite meteorological system is a good tool for obtaining estimates of the temperature-humidity index under Brazilian conditions compared to INMET weather stations data

Heat stress negatively affects livestock, with undesirable effects on animals' production and reproduction. Temperature and humidity index (THI) is a climatic variable used worldwide to study the effect of heat stress on farm animals. Temperature and humidity data can be obtained in Brazil through the National Institute of Meteorology (INMET), but complete data may not be available due to temporary failures on weather stations. An alternative to obtaining meteorological data is the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) satellite-based weather system. We aimed to compare THI estimates obtained from INMET weather stations and NASA POWER meteorological information sources using Pearson correlation and linear regression. After quality check, data from 489 INMET weather stations were used. The hourly, average daily and maximum daily THI were evaluated. We found greater correlations and better regression evaluation metrics when average daily THI values were considered, followed by maximum daily THI, and hourly THI. NASA POWER satellite-based weather system is a suitable tool for obtaining the average and maximum THI values using information collected from Brazil, showing high correlations with THI estimates from INMET and good regression evaluation metrics, and can assist studies that aim to analyze the impact of heat stress on livestock production in Brazil, providing additional data to complement the existing information available in the INMET database.

A retrospective multi-center feasibility study of a new PTV margin estimation approach for moving targets using CyberKnife log files

PURPOSE

This study investigates a new approach for estimating the planning target volume (PTV) margin for moving tumors treated with robotic stereotactic body radiotherapy (SBRT).

METHODS

In this new approach, the covariance of modeling and prediction errors was estimated using error propagation and implemented in the Van Herk formula to form a Modified Van Herk formula (MVHF). To perform a retrospective multi-center analysis, the MVHF was studied using 163 patients treated with different system versions of robotic SBRT (G3 version 6.2.3, VSI version 8.5, and VSI version 9.5) and compared with two established PTV margins estimation methods: The original Van Herk Formula (VHF) and the Uncertainty Estimation Method (UEM).

RESULTS

Overall, the PTV margins provided by the three formalisms are similar with 4-5 mm in the lung region and 4 mm in abdomen region to the PTV margins used in clinical. Furthermore, when analyzing individual patients, a difference of up to 1 mm was found between the PTV margin estimations using MVHF and VHF. The corresponding average discrepancies for the superior-inferior (SI) direction ranged between -0.19 mm to 0.38 mm in CK G3 version 6.2.3, -0.36 mm to 0.33 mm in CK VSI version 8.5, and -0.34 mm to 0.40 mm in CK VSI version 9.5.

CONCLUSIONS

It was found that for the lower left lung, upper left lung, lower right lung, upper right lung, central liver, and upper liver, the effect of covariance between model and prediction errors in SI direction was around 20%, 30%, 25%, 25%, 25%, and 30%, respectively. Notable covariance effects between model and prediction errors can be considered in PTV margin estimation using a modified VHF, which allowed for more precise target localization in robotic SBRT for moving tumors. Overall, in each of the three directions, the difference between MVHF and utilized clinical margins is 0.65 mm in the lung and abdominal region. Therefore, to improve the clinical PTV margins with the new approach, it is suggested to use the adaptive PTV margins in the next fractions.

Equilibrium-based COVID-19 diagnosis from routine blood tests: A sparse deep convolutional model

SARS-CoV2 (COVID-19) is the virus that causes the pandemic that has severely impacted human society with a massive death toll worldwide. Hence, there is a persistent need for fast and reliable automatic tools to help health teams in making clinical decisions. Predictive models could potentially ease the strain on healthcare systems by early and reliable screening of COVID-19 patients which helps to combat the spread of the disease. Recent studies have reported some key advantages of employing routine blood tests for initial screening of COVID-19 patients. Thus, in this paper, we propose a novel COVID-19 prediction model based on routine blood tests. In this model, we depend on exploiting the real dependency among the employed feature pool by a sparsification procedure. In this sparse domain, a hybrid feature selection mechanism is proposed. This mechanism fuses the selected features from two perspectives, the first is Pearson correlation and the second is a new Minkowski-based equilibrium optimizer (MEO). Then, the selected features are fed into a new 1D Convolutional Neural Network (1DCNN) for a final diagnosis decision. The proposed prediction model is tested with a new public dataset from San Raphael Hospital, Milan, Italy, i.e., OSR dataset which has two sub-datasets. According to the experimental results, the proposed model outperforms the state-of-the-art techniques with an average testing accuracy of 98.5% while we employ only less than half the size of the feature pool, i.e., we need only less than half the given blood tests in the employed dataset to get a final diagnosis decision.

Pearson Correlation in Determination of Quality of Current Transformers

The article elaborates on the accuracy of current transformers (CT) in interaction with temperature and frequency using Pearson's correlation. The first part of the analysis compares the accuracy of the mathematical model of the current transformer and the result of the measurement on the real CT using the Pearson correlation calculation. The mathematical model of CT is determined by deriving the formula of the functional error with the display of the accuracy of the measured value. The accuracy of the mathematical model is affected by the accuracy of current transformer model parameters and the calibration characteristic of the ammeter used to measure the CT current. Variables that cause deviation in the accuracy of CT are temperature and frequency. The calculation shows the effects on accuracy in both cases. The second part of the analysis refers to the calculation of the partial correlation of three quantities: (1) CT accuracy, (2) temperature, and (3) frequency on a set of 160 measurements. First, the influence of temperature on the correlation of CT accuracy and frequency is proven, following the proof of the influence of frequency on the correlation of CT accuracy and temperature. In the end, the analysis is combined by comparing the measured results of the first and second part of the analysis.

The spectral color of natural and anthropogenic time series and its impact on the statistical significance of cross correlation

The cross-correlation between time series is a common tool to study and quantify the impact of climatic and anthropogenic changes on ecosystems. The traditional method for estimating the statistical significance of correlation relies on the assumption that the data are independent, but time series found in nature are often strongly auto-correlated because of low-frequency environmental variability and ecosystem inertia. Previous authors have used Monte Carlo simulations to study the impact of serial auto-correlation on the significance of cross-correlations. Most studies have used random time series that are often a poor representation of those found in nature, e.g., low-order auto-regressive models with normally distributed noise. Moreover, we are not aware of any tests of the applicability of those methods to anthropogenic time series. Here, we study the effect of serial auto-correlation on the performance of two methods for estimating the significance of cross-correlations determined from Monte Carlo simulations with time series that are generated synthetically based on power-law specification of spectral characteristics. Such time series have an auto-correlation structure defined by a single parameter, their spectral "color", and are generally more convenient representations of natural time series than the autoregressive models. Our results show that one of the two methods considered here accurately reproduces prescribed error rates for the wide range of spectral colors representative of climatic, ecological and anthropogenic time series. For this, we characterized roughly 1800 observational records in different categories of spectral colors, including climate variability, abundance of vertebrate species, and pollution. We specifically focus on time series with annual sampling over data records of at least 40 years, which are particularly relevant for climate studies. The methodology advocated in this study provides a simple and realistic assessment of the significance of sample estimates of cross correlation for time series with any sample interval and record length.

Comparison and Intercorrelation of Various Bentonite Products for Oenological Properties, Elemental Compositions, Volatile Compounds and Organoleptic Attributes of White Wine

Bentonite fining is one of the generally applied wine-making technological elements that may seriously affect wine components. The aim of this study was (i) to investigate the effect of 21 bentonite products on eight oenological parameters, 19 elements, 21 volatile organic compounds (VOCs) and 10 organoleptic properties of white wine; and (ii) to quantify intercorrelations among the parameters separately for each of the four quality attributes. Among oenological parameters, sugar, acidity, malic-, lactic-, citric acid and total phenol contents were significant among several bentonite products. The amounts of elements were the lowest in the control wine treatments (with exceptions of, e.g., Ni and Cu); and these values were significantly different from several bentonite products. The relative presence of the VOCs was above 100% for most VOCs, but it was below 100% for 1-propanol, 4-amino-1,5-pentandioic acid and butane-dioic acid, and diethyl ester in all treatments. For organoleptic parameters, the values of clearness, colour, flavour intensity and taste persistency was the lowest in the control wine treatment, while the values of flavour character, flavour quality, taste intensity, taste character, and overall harmony were the highest for the bentonite products of AP, EBE, M-SA, EBE, EBE, respectively. Results of correlation and factor analyses showed strong intercorrelative effects of bentonite fining on the four quality attributes. In conclusion, this study can help in the proper choice of a specific bentonite product in relation to complexity effects of bentonite fining.

Characterization of Indigenous Non-Saccharomyces Yeast Strains with Potential Use in Winemaking

BACKGROUND

The vineyard is a great reservoir of autochthonous yeast strains whose composition is defined by different regional (edaphology, orography or climatology) and anthropological factors (cultivation systems or cultural practices). Most of this yeast diversity corresponds to non-Saccharomyces strains, some of which have potential use in winemaking.

METHODS

The oenological potential of 29 different native non-Saccharomyces strains belonging to 4 species (Lachancea thermotolerans, Torulaspora delbrueckii, Starmerella bacillaris and Metschnikowia spp.) was evaluated, using the autochthonous Saccharomyces cerevisiae XG3 strain as a control. Microfermentations with pure culture of each strain were performed in duplicate and the basic parameters and major volatiles of wines were analysed following official methodology. The best strain within each species was selected using a quantification matrix including the relevant oenological characteristics.

RESULTS

The fermentative ability of non-Saccharomyces was lower than S. cerevisiae in all cases, but with differences among species. L. thermotolerans and T. delbrueckii showed higher fermentation rates than Starm. bacillaris, whereas Metschnikowia spp. presented a low fermentative power. At chemical level all non-Saccharomyces strains reduced the alcoholic content, the higher alcohols and the volatile acidity of wines and increased the content of glycerol, with differences among strains within a given species. T. delbrueckii and L. thermotolerans increased the total acidity of wines. The latter and Metschnikowia spp. strains produced lactic acid, which decreased the wine pH in the case of L. thermotolerans. According to their oenological traits the best rated strains of each species were Lt93, Td315, Mf278 and Sb474. In addition, the data obtained in pure fermentations were correlated to those chemical and aromatic compounds obtained with these non-Saccharomyces strains in sequential fermentations.

CONCLUSIONS

Autochthonous strains of non-Saccharomyces yeast species contribute distinctive chemical characteristics to the wines. The correlations observed between wines fermented with the different non-Saccharomyces indigenous strains in pure and sequential fermentations suggest that their contribution to wine properties remains stable regardless of must composition or winemaking techniques.

Meteorological driving forces of reference evapotranspiration and their trends in California

Reference evapotranspiration (ETo) is a variable that helps determine atmospheric pressure on living (reference) grass to release water into the atmosphere. For this purpose, four main driving forces: air temperature, air humidity, solar radiation, and wind speed need to be measured over the well-watered reference grass. The relative influence of these driving forces is region and climate-specific, with daily and seasonal variations. A clear understanding of the dynamic interactions of ETo's driving factors can illuminate the water and energy cycles of the earth and assist modelers with more accurate predictions of ETo. In this study, Pearson correlation, mutual information, and random forest feature importance analyses have been used to evaluate the relative importance of meteorological driving forces of ETo in California. To better understand the interrelations of these variables, 1,365,823 daily data samples from 237 standardized weather stations for 36 years have been clustered into homogeneous climatic zones and analyzed. To compensate for the effects of seasonality, feature importance analysis is also conducted on seasonal and monthly clustered data. Moreover, seasonal and annual trends of ETo and its driving factors are investigated for California and homogeneous zones using the Mann-Kendall test. Our findings reveal that for annually clustered data, solar radiation is the most influential driving factor of ETo in California. However, analysis of seasonal and monthly clustered data shows that vapor pressure deficit is the most informative factor during the summer and spring, while solar radiation is more important during the colder seasons. Results of trend analysis don't suggest a consistent monotonic trend for ETo and other variables for different seasons and zones. However, it is shown that agricultural regions with heavy irrigation dependence like the Central Valley are getting warmer and drier, especially during the irrigation season. This can adversely affect the water resources, agriculture industry, and food production of California, and modeling efforts like this can be very informative for future water resources management.

Mutual Relations between Texture and Aroma of Cooked Rice-A Pilot Study

Texture and aroma are two important attributes for the eating quality of cooked rice, but their mutual relations are not clear. Cooked rice with a desirable texture might suffer from a deteriorated aroma property. To better understand the relations between texture and aroma, six different rice varieties with desirable eating qualities have been selected, with their texture and aroma profile characterized by a texture analyzer and gas chromatography-ion mobility spectrometry, respectively. A large variance of textural attributes and a total number of 39 major volatile organic components were observed for these cooked rice varieties. Pearson correlation showed that the hardness of cooked rice was positively correlated with the content of E-2-hexenal, 2-hexanol-monomer, 1-propanol, and E-2-pentenal, while stickiness was positively correlated with 5-methyl-2-furanmethanol and dimethyl trisulfide. Possible underneath mechanisms were discussed for these relations. These results could help the rice industry to develop rice products with both desirable texture and aroma property.

Deciphering environmental factors and defense response of rice genotypes against sheath blight disease

Sheath blight (ShB) is one of the most serious diseases in rice, leading to severe yield losses globally. In our study, we evaluated a total of 63 rice genotypes for resistance against sheath blight disease by artificial inoculation over two seasons under field conditions and studied the weather parameters associated with disease incidence. Based on two years of testing, 23 genotypes were found moderately resistant, 38 were moderately susceptible, and 2 exhibited a susceptible reaction to sheath blight disease. Among the specific four genotypes (IC283139, IC283041, IC283038, and IC283023) of the moderately resistant group exhibited less disease reaction in comparison with check variety Tetep. Further, the correlation of percent disease index (PDI) with weather parameters revealed negative associations between PDI and maximum temperature, minimum temperature, low rainfall and the positive association with maximum relative humidity (RH) suggest that very low temperature or high precipitation might have a negative impact on pathogen establishment. In addition, the sheath blight-linked SSRs were assessed using distance and model-based approaches, results of both the models revealed that genotypes distinguished the resistant population from the susceptible one. From the output of two years of principal component analysis, two genotypes from each group of moderately resistant, moderately susceptible and susceptible were studied for their biochemical reaction against the sheath blight pathogen. The biochemical study revealed that the accumulation of defense and antioxidant enzymes, namely, polyphenol oxidase, peroxidase, total phenol, phenylalanine ammonia-lyase, catalase, and superoxide dismutase, were higher in moderately resistant genotypes, but was observed to be lower in moderately susceptible and susceptible genotypes. The statistical analysis revealed the enzyme activities (defense and antioxidant) exhibited a strong negative correlation with area under the disease progress curve (AUDPC) and influence of weather parameter RH. This demonstrates that the environment factor RH plays a major role in imparting the resistance mechanism by decreasing the enzymes activities and increasing PDI. This study found that the identified novel resistant genotype (IC283139) with purple stem base demonstrated improved resistance against sheath blight infection through a defense response and the use of antioxidant machinery.

Time Series Analysis of SARS-CoV-2 Genomes and Correlations among Highly Prevalent Mutations

The efforts of the scientific community to tame the recent pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seem to have been diluted by the emergence of new viral strains. Therefore, it is imperative to understand the effect of mutations on viral evolution. We performed a time series analysis on 59,541 SARS-CoV-2 genomic sequences from around the world to gain insights into the kinetics of the mutations arising in the viral genomes. These 59,541 genomes were grouped according to month (January 2020 to March 2021) based on the collection date. Meta-analysis of these data led us to identify significant mutations in viral genomes. Pearson correlation of these mutations led us to the identification of 16 comutations. Among these comutations, some of the individual mutations have been shown to contribute to viral replication and fitness, suggesting a possible role of other unexplored mutations in viral evolution. We observed that the mutations 241C>T in the 5' untranslated region (UTR), 3037C>T in nsp3, 14408C>T in the RNA-dependent RNA polymerase (RdRp), and 23403A>G in spike are correlated with each other and were grouped in a single cluster by hierarchical clustering. These mutations have replaced the wild-type nucleotides in SARS-CoV-2 sequences. Additionally, we employed a suite of computational tools to investigate the effects of T85I (1059C>T), P323L (14408C>T), and Q57H (25563G>T) mutations in nsp2, RdRp, and the ORF3a protein of SARS-CoV-2, respectively. We observed that the mutations T85I and Q57H tend to be deleterious and destabilize the respective wild-type protein, whereas P323L in RdRp tends to be neutral and has a stabilizing effect. IMPORTANCE We performed a meta-analysis on SARS-CoV-2 genomes categorized by collection month and identified several significant mutations. Pearson correlation analysis of these significant mutations identified 16 comutations having absolute correlation coefficients of >0.4 and a frequency of >30% in the genomes used in this study. The correlation results were further validated by another statistical tool called hierarchical clustering, where mutations were grouped in clusters on the basis of their similarity. We identified several positive and negative correlations among comutations in SARS-CoV-2 isolates from around the world which might contribute to viral pathogenesis. The negative correlations among some of the mutations in SARS-CoV-2 identified in this study warrant further investigations. Further analysis of mutations such as T85I in nsp2 and Q57H in ORF3a protein revealed that these mutations tend to destabilize the protein relative to the wild type, whereas P323L in RdRp is neutral and has a stabilizing effect. Thus, we have identified several comutations which can be further characterized to gain insights into SARS-CoV-2 evolution.

Putting the Mantel test back together again

The Mantel test has been widely used in ecology and evolution, but over the last two decades it has been frequently critiqued because results were inconsistent with expectations and there were issues with Type I (false-positive) and Type II (false-negative) error rates. Three-matrix extensions of the Mantel test have been challenged for similar reasons. Even the null hypotheses underlying the Mantel test have been questioned. As a result, use of the Mantel test and its variants has been discouraged or limited to special situations. Here, we examine Mantel test criticisms including the lack of agreement between traditional variable-based Pearson correlations (r) and observation-based Mantel correlations (r_m ), and the unusual Type I and Type II error rates. We propose an alternate proximity measure that resolves these issues. We use simulations and examples to contrast Mantel results based on Euclidean distance, squared Euclidean distance, and the simple difference (Diff) with traditional bivariate Pearson correlations. We demonstrate that use of the simple difference in Mantel tests can resolve the underlying problems with poor agreement between bivariate Pearson and Mantel correlations, as well as appropriate Type I and Type II errors (i.e., where r = cor(x,y) and r_m  = cor(d_x ,d_y ), if d_x  = Diff(x) and d_y  = Diff(y), r = r_m ). We also show that the simple difference can provide solutions to issues with partial Mantel tests and distance-based MANOVA. Because our results resolve many of the issues with Mantel tests, we hope that these findings will restore the popularity of the Mantel test.

Objective Signal Analysis for Investigating Feasibility of Active Noise Cancellation in Hearing Screening

With the development of active noise cancellation (ANC) technology, ANC has been used to mitigate the effects of environmental noise on audiometric results. However, objective evaluation methods supporting the accuracy of audiometry for ANC exposure to different levels of noise have not been reported. Accordingly, the audio characteristics of three different ANC headphone models were quantified under different noise conditions and the feasibility of ANC in noisy environments was investigated. Steady (pink noise) and non-steady noise (cafeteria babble noise) were used to simulate noisy environments. We compared the integrity of pure-tone signals obtained from three different ANC headphone models after processing under different noise scenarios and analyzed the degree of ANC signal correlation based on the Pearson correlation coefficient compared to pure-tone signals in quiet. The objective signal correlation results were compared with audiometric screening results to confirm the correspondence. Results revealed that ANC helped mitigate the effects of environmental noise on the measured signal and the combined ANC headset model retained the highest signal integrity. The degree of signal correlation was used as a confidence indicator for the accuracy of hearing screening in noise results. It was found that the ANC technique can be further improved for more complex noisy environments.

Machine Learning Assisted Prediction of Power Conversion Efficiency of All-Small Molecule Organic Solar Cells: A Data Visualization and Statistical Analysis

Organic solar cells are famous for their cheap solution processing. Their industrialization needs fast designing of efficient materials. For this purpose, testing of large number of materials is necessary. Machine learning is a better option due to cheaper prediction of power conversion efficiencies. In the present work, machine learning was used to predict power conversion efficiencies. Experimental data were collected from the literature to feed the machine learning models. A detailed data visualization analysis was performed to study the trends of the dataset. The relationship between descriptors and power conversion efficiency was quantitatively determined by Pearson correlations. The importance of features was also determined using feature importance analysis. More than 10 machine learning models were tried to find better models. Only the two best models (random forest regressor and bagging regressor) were selected for further analysis. The prediction ability of these models was high. The coefficient of determination (R2) values for the random forest regressor and bagging regressor models were 0.892 and 0.887, respectively. The Shapley additive explanation (SHAP) method was used to identify the impact of descriptors on the output of models.

Nanoscale Visualization of the Electron Conduction Channel in the SiO/Graphite Composite Anode

Conductive atomic force microscopy (C-AFM) is widely used to determine the electronic conductivity of a sample surface with nanoscale spatial resolution. However, the origin of possible artifacts has not been widely researched, hindering the accurate and reliable interpretation of C-AFM imaging results. Herein, artifact-free C-AFM is used to observe the electron conduction channels in Si-based composite anodes. The origin of a typical C-AFM artifact induced by surface morphology is investigated using a relevant statistical method that enables visualization of the contribution of artifacts in each C-AFM image. The artifact is suppressed by polishing the sample surface using a cooling cross-section polisher, which is confirmed by Pearson correlation analysis. The artifact-free C-AFM image was used to compare the current signals (before and after cycling) from two different composite anodes comprising single-walled carbon nanotubes (SWCNTs) and carbon black as conductive additives. The relationship between the electrical degradation and morphological evolution of the active materials depending on the conductive additive is discussed to explain the improved electrical and electrochemical properties of the electrode containing SWCNTs.

Mapping analysis to predict the associated EuroQol five-dimension three-level utility values from the Oxford Knee Score : a prediction and validation study

Aims

The aims of this study were to assess mapping models to predict the three-level version of EuroQoL five-dimension utility index (EQ-5D-3L) from the Oxford Knee Score (OKS) and validate these before and after total knee arthroplasty (TKA).

Methods

A retrospective cohort of 5,857 patients was used to create the prediction models, and a second cohort of 721 patients from a different centre was used to validate the models, all of whom underwent TKA. Patient characteristics, BMI, OKS, and EQ-5D-3L were collected preoperatively and one year postoperatively. Generalized linear regression was used to formulate the prediction models.

Results

There were significant correlations between the OKS and EQ-5D-3L preoperatively (r = 0.68; p < 0.001) and postoperatively (r = 0.77; p < 0.001) and for the change in the scores (r = 0.61; p < 0.001). Three different models (preoperative, postoperative, and change) were created. There were no significant differences between the actual and predicted mean EQ-5D-3L utilities at any timepoint or for change in the scores (p > 0.090) in the validation cohort. There was a significant correlation between the actual and predicted EQ-5D-3L utilities preoperatively (r = 0.63; p < 0.001) and postoperatively (r = 0.77; p < 0.001) and for the change in the scores (r = 0.56; p < 0.001). Bland-Altman plots demonstrated that a lower utility was overestimated, and higher utility was underestimated. The individual predicted EQ-5D-3L that was within ± 0.05 and ± 0.010 (minimal clinically important difference (MCID)) of the actual EQ-5D-3L varied between 13% to 35% and 26% to 64%, respectively, according to timepoint assessed and change in the scores, but was not significantly different between the modelling and validation cohorts (p ≥ 0.148).

Conclusion

The OKS can be used to estimate EQ-5D-3L. Predicted individual patient utility error beyond the MCID varied from one-third to two-thirds depending on timepoint assessed, but the mean for a cohort did not differ and could be employed for this purpose.

Cite this article: Bone Jt Open 2022;3(7):573–581.

Abundance & distribution of aquatic benthic macro-invertebrate families of river Ganga and correlation with environmental parameters

Bio-monitoring freshwater bodies using macro-invertebrates is an excellent way to detect biological water quality. Organic contamination in aquatic settings is well indicated by benthic macro-invertebrates. The use of macro-invertebrates to bio-monitor freshwater bodies is an effective method for determining biological water quality. Benthic macro-invertebrates are excellent indicators of organic pollution in aquatic environments. In the present study, the distribution of pollution-sensitive and pollution-tolerant families of benthic macro-invertebrates from 33 different locations along the Ganga River in Uttarakhand, Uttar Pradesh, Bihar, and West Bengal was studied. Benthic macro-invertebrates collected from different studied locations were identified up to family level and it was observed that a total of 15 pollution-sensitive families belong to four taxonomic orders, while eight pollution-tolerant families come from two taxonomic orders. Several moderately tolerant families have also been observed, but in this paper the distribution of only pollution-sensitive and pollution-tolerant families is presented as they reflect the extreme states of organic pollution. In the majority of locations, the pollution-sensitive Ephemeroptera family Ameletidae predominated. Likewise, the pollution-tolerant families Chironomidae (order-Diptera) and Naididae (order-Oligochaeta) dominated the Ganga River locations. Besides, the relationship between macro-invertebrate diversity and physicochemical factors (pH, water temperature, and dissolved oxygen) was investigated, and 3D surface distribution maps were displayed for qualitative interpretation. The correlation coefficients for all parameters were found to be positive. Macro-invertebrate pollution indices for bio-monitoring are based on community impacts and assist in evaluating the success of action plans to prevent industrial and anthropogenic pollution that contributes to the Ganga.

Ecological risks of heavy metals on surficial sediment of Nijhum Dweep (Island), an important biodiversity area of Bangladesh

Ten surficial sediment samples (up to a depth of 3 cm) were randomly collected during the pre-monsoon and analyzed for nine heavy metals using an atomic absorption spectrophotometer (AAS) to assess the distribution, contamination levels, and potential sources of heavy metals. The particle size distribution of the sediments was also investigated using a laser particle size analyzer. The mean concentration (mg/kg) of the analyzed metals followed a decreasing order of Fe (4706.24) > Mn (95.20) > Cu (36.97) > Zn (20.65) > Ni (9.26) > Cr (7.20) > Pb (5.63) > Co (5.52) > Cd (0.29). Surficial sediment of the Island was low to moderately contaminated by Cd and, to a laser extent by Cu in terms of enrichment factor (EF), geo-accumulation index (Igeo), and contamination factor. Ecologically, the Nijhum Dweep area was at low to moderate risk, where Cd was the most potential ecological risk factor. The Pearson correlation analysis revealed a significant positive correlation among Cr, Pb, Zn, Mn, Cd, Fe, Co, Ni, and silt particles, whereas a negative correlation with sand particles. No correlation was found with Cu and other metals except clay particles.

Biomechanical effect of anatomical tibial component design on load distribution of medial proximal tibial bone in total knee arthroplasty : finite element analysis indicating anatomical design prevents stress-shielding

Aims

This study aimed to identify the effect of anatomical tibial component (ATC) design on load distribution in the periprosthetic tibial bone of Koreans using finite element analysis (FEA).

Methods

3D finite element models of 30 tibiae in Korean women were created. A symmetric tibial component (STC, NexGen LPS-Flex) and an ATC (Persona) were used in surgical simulation. We compared the FEA measurements (von Mises stress and principal strains) around the stem tip and in the medial half of the proximal tibial bone, as well as the distance from the distal stem tip to the shortest anteromedial cortical bone. Correlations between this distance and FEA measurements were then analyzed.

Results

The distance from the distal stem tip to the shortest cortical bone showed no statistically significant difference between implants. However, the peak von Mises stress around the distal stem tip was higher with STC than with ATC. In the medial half of the proximal tibial bone: 1) the mean von Mises stress, maximum principal strain, and minimum principal strain were higher with ATC; 2) ATC showed a positive correlation between the distance and mean von Mises stress; 3) ATC showed a negative correlation between the distance and mean minimum principal strain; and 4) STC showed no correlation between the distance and mean measurements.

Conclusion

Implant design affects the load distribution on the periprosthetic tibial bone, and ATC can be more advantageous in preventing stress-shielding than STC. However, under certain circumstances with short distances, the advantage of ATC may be offset.

Cite this article: Bone Joint Res 2022;11(5):252–259.

Wavelet-Based Fractal Analysis of rs-fMRI for Classification of Alzheimer's Disease

The resting-state functional magnetic resonance imaging (rs-fMRI) modality has gained widespread acceptance as a promising method for analyzing a variety of neurological and psychiatric diseases. It is established that resting-state neuroimaging data exhibit fractal behavior, manifested in the form of slow-decaying auto-correlation and power-law scaling of the power spectrum across low-frequency components. With this property, the rs-fMRI signal can be broken down into fractal and nonfractal components. The fractal nature originates from several sources, such as cardiac fluctuations, respiration and system noise, and carries no information on the brain’s neuronal activities. As a result, the conventional correlation of rs-fMRI signals may not accurately reflect the functional dynamic of spontaneous neuronal activities. This problem can be solved by using a better representation of neuronal activities provided by the connectivity of nonfractal components. In this work, the nonfractal connectivity of rs-fMRI is used to distinguish Alzheimer’s patients from healthy controls. The automated anatomical labeling (AAL) atlas is used to extract the blood-oxygenation-level-dependent time series signals from 116 brain regions, yielding a 116 × 116 nonfractal connectivity matrix. From this matrix, significant connections evaluated using the p-value are selected as an input to a classifier for the classification of Alzheimer’s vs. normal controls. The nonfractal-based approach provides a good representation of the brain’s neuronal activity. It outperformed the fractal and Pearson-based connectivity approaches by 16.4% and 17.2%, respectively. The classification algorithm developed based on the nonfractal connectivity feature and support vector machine classifier has shown an excellent performance, with an accuracy of 90.3% and 83.3% for the XHSLF dataset and ADNI dataset, respectively. For further validation of our proposed work, we combined the two datasets (XHSLF+ADNI) and still received an accuracy of 90.2%. The proposed work outperformed the recently published work by a margin of 8.18% and 11.2%, respectively.

Lipid oxidation in sorted herring (Clupea harengus) filleting co-products from two seasons and its relationship to composition

Lipid oxidation in ice-stored sorted herring fractions (head, backbone, viscera + belly flap, tail, fillet) from spring and fall, and its association with endogenous prooxidants, antioxidants and lipid substrates were investigated. Peroxide value (PV) and thiobarbituric acid reactive substances (TBARS) had increased significantly in all fractions after 1 day, but for both seasons, the most rapid PV and TBARS development occurred in head, which also had highest hemoglobin (Hb) levels and lipoxygenases (LOX) activity. Viscera + belly flap was overall the most stable part, and also had the highest α-tocopherol content. Pearson correlation analyses across all five fractions confirmed a significant impact of Hb, LOX and α-tocopherol on the lipid oxidation susceptibility, while content of total iron, copper, lipids or polyunsaturated fatty acids provided no significant correlation. Overall, the study showed which pro-oxidants that should be inhibited or removed to succeed with value adding of herring filleting co-products and the fillet itself.

Distinctive correlations between cell concentration and cell size to microalgae biomass under increasing carbon dioxide

Carbon capture and storage (CCS) via microalgae cultivations is getting renewed interest as climate change mitigation effort, owing to its excellent photosynthetic and CO₂ fixation capability. Microalgae growth is monitored based on their biomass, cell concentrations and cell sizes. The key parametric relationships on microalgae growth under CO₂ are absent in previous studies and this inadequacy hampers the design and scale-up of microalgae-based CCS. In this study, three representative microalgae species, Chlorella, Nostoc and Chlamydomonas, were investigated for establishing key correlations of cell concentrations and sizes towards their biomass fluctuations under CO₂ influences of 0% to 20% volume ratios (v/v). This revealed that Chlorella and Chlamydomonas cell concentrations significantly contributed towards increasing biomass concentration under CO₂ elevations. Chlorella and Nostoc cell sizes were enhanced at 20% (v/v). These findings provided new perspectives on growth responses under increasing CO₂ treatment, opening new avenues on CCS schemes engineering designs and biochemical production.

Predicting COVID-19 future trends for different European countries using Pearson correlation

The ability to accurately forecast the number of COVID-19 cases and future case trends would certainly assist governments and various organisations in strategising and preparing for the newly infected cases well in advance. Many predictions have failed to foresee future COVID-19 cases due to the lack of reliable data; however, such data are now widely available for predicting future trends in COVID-19 after more than one and a half years of the pandemic. Also, various countries are closely monitoring other countries that are experiencing a surge in COVID-19 cases in the expectation of similar scenarios, but this does not always produce correct results, as no research has identified specific correlations between different countries in terms of COVID-19 cases. During the past 18 months, many nations have watched countries whose COVID-19 cases have risen sharply, in anticipation of handling the situation themselves. However, this did not provide accurate results, as no research was conducted that compared countries to determine if their COVID-19 case trends were correlated. As official data on COVID-19 cases has become increasingly available, using the Pearson correlation technique to pinpoint the countries that should be closely monitored will help governments plan and prepare for the number of infections that are expected in the future at an early stage. In this study, a simple and real-time prediction of COVID-19 cases incorporating existing variables of coronavirus variants was used to explore the correlation among different European countries in terms of the number of COVID-19 cases officially recorded on a daily basis. Data from selected countries over the past 76 weeks were analysed using a Pearson correlation technique to determine if there were correlations between case trends and geographical position. The correlation coefficient (r) was employed for identifying whether the different countries in Europe were interrelated, with r > 0.85 indicating they were very strongly correlated, 0.85 > r > 0.8 indicating that they were strongly correlated, 0.8 > r > 0.7 indicating that they were moderately correlated, and r < 0.7 indicating that the examined countries were either weakly correlated or that a correlation did not exist. The results showed that although some neighbouring countries are strongly correlated, other countries that are not geographically close are also correlated. In addition, some countries on opposite sides of Europe (Belgium and Armenia) are also correlated. Other countries (France, Iceland, Israel, Kosovo, San Marino, Spain, Sweden and Turkey) were either weakly correlated or had no relationship at all.

Correlation and mechanism of extracellular polymeric substances (EPS) on the effect of sewage sludge electro-dewatering

Electro-dewatering (ED) is an attractive technology for enhancing dewaterability of waste activated sludge (WAS), and the distribution and composition of EPS and secondary structure of extracellular protein in sludge has a great influence on the sludge dewaterability. Therefore, in order to optimize and regulate sludge ED process, it is necessary to study the influence of EPS components and composition on the efficiency of sludge ED. In this study, the effects of distribution and composition of EPS and the structure of extracellular proteins on the ED rates and performance were analyzed using eight sludges from different sewage treatment process. The results showed that ED rates at both electrodes were significantly negatively correlated with the concentration of soluble EPS (S-EPS) and loosely bound EPS (LB-EPS), and this correlation was gradually weakened with the in-depth structure of layered EPS structure. High concentration of S-EPS and LB-EPS decreased the initial current and the pH gradient, deteriorating the anodic oxidation and acidification, which affect the release and degradation of EPS. Additionally, the proteins secondary structure can be destroyed and transformed by anodic oxidation and acidification, which can impact the protein water-holding capacity and ED performance.

Impact of muscle mass on blood glucose level

OBJECTIVES

At present, diabetes is one of the leading causes of mortality across the world. It was hypothesized that muscle mass could have a significant influence on blood glucose level and this corelation if established successfully could pave way for novel treatment modalities for type 2 diabetes mellitus (T2DM). In the present study, the association between muscle mass and blood glucose level was examined in a healthy population who was not having T2DM at baseline and was undergoing a regular course of exercise.

METHODS

The clinical study was performed involving 53 healthy male populations between 10 and 60 years of age. The participants were sampled in accordance with the quantitative experimental study design, using nonprobability sampling techniques. The independent variable measured among the subjects included muscle mass and blood glucose level, using bioelectrical impedance and a simple glucometer respectively. Subgroup analysis amongst different substantial parameters including body mass index (BMI), myostatin inhibitor usage, and age factor that could affect the muscle mass and glucose level correlation were also studied simultaneously.

RESULTS

The study findings demonstrated a negative correlation between muscle mass and glucose utilization levels. There was a significant difference in the mean muscle mass of the participants which was 36.2453, and the mean glucose utilization level which was 15.1493%. Pearson correlation between the muscle mass and percentage of glucose utilization of the participants indicated a significant difference (since p-value <0.05) between these two studied parameters.

CONCLUSIONS

The study finding suggests an inverse association of the skeletal muscle mass with blood glucose level which encourages the implication of muscle-building exercises as the preventive measure for T2DM.

Estimation of stature and body weight from static and dynamic footprints - Forensic implications and validity of non-colouring cream method

In the present study, the metric properties of dynamic footprints were analysed using non-colouring method in relation with body parameters and compared with static footprint measurements. The results of the study provide a better understanding of the relationship between static and dynamic footprints, which may be useful for biological profiling that allows more accurate identification. Stature, body weight, five length and two width parameters of dynamic (walking) footprints of young Slovak adults (65 females and 68 males) were analysed. Pearson correlation coefficients were calculated and equations for prediction of stature and body weight by linear regression analysis and multiple regression analysis were developed. Intersex differences were confirmed for all parameters and bilateral for some. Statistically significant differences were found in all measurements (p-value>0.05), except for the width of the standing and walking footprint in the mixed group. A positive correlation was found between the selected footprint diameters with stature (max - r = 0.82) and body weight (max - r = 0.70). Stature could be calculated with an accuracy of up to 4.40 cm and body weight with an accuracy of up to 9.56 kg. The results of the present study show that selected measurements of dynamic footprints correlate with stature and body weight. These results could be used in biological profiling in the medical and forensic fields.

Clustering of countries for COVID-19 cases based on disease prevalence, health systems and environmental indicators

The coronavirus has a high basic reproduction number ( R 0 ) and has caused the global COVID-19 pandemic. Governments are implementing lockdowns that are leading to economic fallout in many countries. Policy makers can take better decisions if provided with the indicators connected with the disease spread. This study is aimed to cluster the countries using social, economic, health and environmental related metrics affecting the disease spread so as to implement the policies to control the widespread of disease. Thus, countries with similar factors can take proactive steps to fight against the pandemic. The data is acquired for 79 countries and 18 different feature variables (the factors that are associated with COVID-19 spread) are selected. Pearson Product Moment Correlation Analysis is performed between all the feature variables with cumulative death cases and cumulative confirmed cases individually to get an insight of relation of these factors with the spread of COVID-19. Unsupervised k-means algorithm is used and the feature set includes economic, environmental indicators and disease prevalence along with COVID-19 variables. The learning model is able to group the countries into 4 clusters on the basis of relation with all 18 feature variables. We also present an analysis of correlation between the selected feature variables, and COVID-19 confirmed cases and deaths. Prevalence of underlying diseases shows strong correlation with COVID-19 whereas environmental health indicators are weakly correlated with COVID-19.

[Analysis of spectrum-activity relationship among antioxidant parts of Lycii Fructus using three different chemometrics methods]

The chemical components of Lycii Fructus were analyzed by liquid chromatography( LC) and mass spectrometry( MS for the establishment of spectrum-activity relationship,on the basis of which its antioxidant active ingredients were determined. In this experiment,Lycii Fructus was extracted with different solvents and then separated into 80 samples by macroporous adsorption resin and reversed-phase chromatography,respectively. The antioxidant components were enriched into 11 samples and their scavenging abilities against DPPH free radical and ferric ion reducing antioxidant power( FRAP) were significantly stronger than those before the treatment( P&lt;0. 05). The spectrum-activity relationship regarding the antioxidant activity in vitro of Lycii Fructus was established by Pearson correlation analysis,orthogonal partial least squares( OPLS) and elastic net regression. Six chromatographic peaks greatly contributing to the antioxidant activity in vitro of Lycii Fructus were identified as rutin( P6),quercetin( P35),scopoletin( P14),N-cis-feruloyl-4-O-β-D-glucopyranosyl-tyramine or N-( 4-O-β-D-glucopyranosyl-trans-feruloyl)-tyramine( P8), ferulic acid( P13) and1,3,5-dihydroxy-2-isoprenyl-3-xanthone( P23). The active components associated with free radical scavenging were rutin and quercetin both belonging to flavonoids. The reduction of Fe3+was based on phenylpropanoids such as ferulic acid,scopoletin,xanthone and phenolic amides. These results indicated that the antioxidant activity of Lycii Fructus was ascribed to the synergistic action of different products through different ways. Besides,the data analysis model should be chosen carefully for the establishment of spectrum-activity relationship,thus ensuring the reliability of results.

A study on water quality monitoring of Buddha Nullah, Ludhiana, Punjab (India)

Diverse genotoxic agents, entering the aquatic environment through natural and anthropogenic events, pose serious threats to its biotic components. The present study involves the monitoring of water quality by assessing the genotoxic effects and physico-chemical parameters including heavy metals of 10 surface water samples collected from different locations of Buddha Nullah, a tributary of Sutlej flowing through Ludhiana, Punjab (India). Genotoxicity was evaluated following Allium cepa root chromosomal aberration assay and DNA nicking assay using plasmid (pBR322) whilst the metal (cadmium, chromium, cobalt, copper, lead, nickel and zinc) analysis was conducted using atomic absorption spectrophotometer. All water samples collected from the study area had cobalt and lead content more than the permissible limits (0.04 and 0.01, respectively) recommended by the Bureau of Indian Standards and the World Health Organization. The samples also induced genotoxicity following both bioassays. The water samples collected from Gaunspur (GP), a site approx. 75.53 km upstream of the Sutlej-Buddha Nullah joining point, has shown the maximum genotoxic effect, i.e. 38.62% in terms of per cent total aberrant cells during A. cepa assay and 100% DNA damage during DNA nicking assay. The Pearson correlation indicated that genotoxicity had a significant positive correlation with the content of cobalt (at p ≤ 0.5). During cluster analysis, the samples from 10 sites formed four statistically significant clusters based on the level of pollution that was dependent on two factors like similarity in physico-chemical characteristics and source of pollution at a specific site.

Detection of Genes in Arabidopsis thaliana L. Responding to DNA Damage from Radiation and Other Stressors in Spaceflight

Ionizing radiation present in extraterrestrial environment is an important factor that affects plants grown in spaceflight. Pearson correlation-based gene regulatory network inferencing from transcriptional responses of the plant Arabidopsis thaliana L. grown in real and simulated spaceflight conditions acquired by GeneLab, followed by topological and spectral analysis of the networks is performed. Gene regulatory subnetworks are extracted for DNA damage response processes. Analysis of radiation-induced ATR/ATM protein-protein interactions in Arabidopsis reveals interaction profile similarities under low radiation doses suggesting novel mechanisms of DNA damage response involving non-radiation-induced genes regulating other stress responses in spaceflight. The Jaccard similarity index shows that the genes AT2G31320, AT4G21070, AT2G46610, and AT3G27060 perform similar functions under low doses of radiation. The incremental association Markov blanket method reveals non-radiation-induced genes linking DNA damage response to root growth and plant development. Eighteen radiation-induced genes and sixteen non-radiation-induced gene players have been identified from the ATR/ATM protein interaction complexes involved in heat, salt, water, osmotic stress responses, and plant organogenesis. Network analysis and logistic regression ranking detected AT3G27060, AT1G07500, AT5G66140, and AT3G21280 as key gene players involved in DNA repair processes. High atomic weight, high energy, and gamma photon radiation result in higher intensity of DNA damage response in the plant resulting in elevated values for several network measures such as spectral gap and girth. Nineteen flavonoid and carotenoid pigment activations involved in pigment biosynthesis processes are identified in low radiation dose total light spaceflight environment but are not found to have significant regulations under very high radiation dose environment.

Analysis of microbial community diversity of muscadine grape skins

Microorganisms in grape skins play vital roles in grapevine health, productivity, wine quality and organoleptic properties. To investigate microbial diversity of muscadine grape skins, 16S and ITS sequences of 30 samples from six muscadine (Muscadinia rotundifolia Michx.) cultivars grown in Guangxi, China, were sequenced using Illumina Novaseq platform. A total of 7,317 bacterial operational taxonomic units (OTUs) and 1,611 fungal OTUs were obtained, and clustered into 38 bacterial and 7 known fungal phyla. The dominant bacterial phyla were Proteobacteria, Firmicutes, Bacteroidetes, Planctomycetes, Actinobacteria, Verrucomicrobia, Acidobacteria, and Patescibacteria, and the dominant genera were Lelliottia, Prevotella_9, Escherichia-Shigella, Lactobacillus, Pseudomonas, Akkermansia, Faecalibacterium, Rahnella, and Acinetobacter. For fungi, the dominant phyla were Ascomycota, Basidiomycota, and Mortierellomycota, and the dominant genera were Acaromyces, Uwebraunia, Penicillium, Zygosporium, Ilyonectria, Aspergillus, Neodevriesia, Strelitziana, Mortierella, and Fusarium. Alpha diversity analysis and Kruskal-Wallis H test demonstrated that microbial diversity and composition were affected by the cultivar. The Pearson correlation analysis of species revealed complex interactions among microbes. PICRUSt2 predicted that the metabolism of carbohydrates, cofactors, vitamins, amino acids, terpenoids, polyketides, lipids and biosynthesis of other secondary metabolites were abundant. These results contribute to understanding the uniqueness of muscadine grapes and the links among microorganisms in grape skins.

Spatiotemporal distribution of boron in the groundwater and human health risk assessment from the coastal region of Bangladesh

High concentrations of naturally-occurring and man-accentuated boron in groundwater possess a potential threat to the health and well-being of humans worldwide. In Bangladesh, only a few studies focused on the health risks of boron-containing groundwater. Therefore, the present study investigated the spatiotemporal distribution of boron concentrations in groundwater and its associated health risks in the coastal districts of Bangladesh. A total of 268 samples from the shallow and deep wells during the wet and dry season (137 and 131 samples, respectively) were collected to determine the level of boron concentrations and health risk status. The groundwater boron concentrations ranged from 0 to 4.10 mg/L with an average concentration of 0.68 mg/L, which was much lower than the WHO and the values of Bangladesh drinking water standard guideline. The boron concentrations in the shallow wells override the deep ones with insignificant seasonal variation. Boron contamination affected 10% of the shallow wells in the wet season and only 6% of them in the dry season, whereas only 1% of deep wells exceeds the WHO guideline values of 2.40 mg/L during the wet and dry season, respectively. The human health risk of boron was determined by computing estimated daily intake (EDI) and hazard quotient (HQ) values for infants, children, teenagers, and adults. The average HQ value delimitated that children have high risk followed by teenagers, adults, and infants. About 90-95% of the studied samples were free from boron contamination because of having HQ values < 1 and the rest of the samples possess a high risk for children. For the overall study area, the deep wells were found safer than the shallow wells, which were more susceptible to boron contamination aided by localized freshwater inputs.

Effect of recovery technique, antioxidant addition and compositional features on lipid oxidation in protein enriched products from cod- salmon and herring backbones

The influence of recovery technique (pH-shift processing vs mechanical separation), antioxidant addition and endogenous factors on lipid oxidation in protein-enriched products from herring, salmon and cod backbones was investigated. Salmon-derived products were very stable during both ice and -20 °C storage. Contrary, peroxide value and TBA-reactive substances in herring- and cod-derived products increased rapidly during ice storage, with the pH-shift-produced protein isolates (PI) being most susceptible to oxidation in case of cod. Duralox MANC (0.5%) however largely increased the oxidation lag phase in both PI and mechanically separated meat (MSM); from <1 day to >15 days. At -20 °C, mainly the herring products oxidized, and particularly the MSM. Pearson correlation tests showed that endogenous levels of Hb, total Fe, ascorbic acid and α-tocopherol correlated significantly (p < 0.05) with lipid oxidation development. Evaluating the role of pre-processing storage indicated that fish co-products should be processed immediately after the filleting process unless antioxidants are added.

Temporal Patterns and Inter-Correlations among Physical and Antioxidant Attributes and Enzyme Activities of Apricot Fruit Inoculated with Monilinia laxa under Salicylic Acid and Methyl Jasmonate Treatments under Shelf-Life Conditions

Monilinia laxa causes serious postharvest damage on apricot fruits under shelf-life storage conditions. Plant elicitors of methyl jasmonate (MeJA) and salicylic acid (SA) can reduce this damage, and their research can explain the background of the plant defense physiological processes in M. laxa-infected fruits. The aims of this study were: (i) to evaluate the effect of various concentrations of MeJA and SA on brown rot incidence (BRI) and lesion diameter (LD) of apricot fruits; (ii) to measure the temporal patterns for the effect of 0.4 mmol L-1 MeJA and 2 mmol L-1 SA treatments on BRI, LD and seven fruit measures (fruit firmness (FF), lignin content (LC), total soluble phenol content (TSPC), total antioxidant capacity (TAC) and enzyme activities of PAL, POD and SOD) in treatments of M. laxa-inoculated versus (vs.) non-inoculated fruits over an eight-day shelf-life storage period; and (iii) to determine inter-correlations among the seven fruit measures for MeJA and SA treatments. Both MeJA and SA significantly reduced BRI and LD. LC, FF, TAC, TSPC, as well as SOD and PAL activities in the MeJA and SA treatments were higher than the water-treated control in most assessment days and both inoculation treatments. In both inoculation treatments, the activity of POD in the SA-treated fruits was higher than MeJA-treated and control fruits at all dates. In MeJA vs. SA and inoculated vs. non-inoculated treatments, six variable pairs (FF vs. TSPC, FF vs. TAC, TAC vs. PAL, PAL vs. POD, PAL vs. SOD, and POD vs. SOD) showed significant inter-correlation values. Principal component analyses explained 96% and 93% of the total variance for inoculated and non-inoculated treatments, respectively. In inoculated treatments, both PC1 and PC2 explained 41% of the total variance and correlated with FF, TSPC and TAC and with PAL, SOD and POD, respectively. In non-inoculated treatments, PC1 and PC2 explained 49% and 44% of the total variance and correlated with LC, PAL, POD and SOD and with FF, TSPC and TAC, respectively. It can be concluded that MeJA and SA are useful in the practice to enhance the plant defense system against brown rot by reducing fungal growth and by improving physical and antioxidant attributes (FF, LC, TAC and TSPC) and the activity of defense-related enzymes (PAL, POD and SOD) in apricot fruits during shelf-life storage conditions.

A tail of two pandas- whole genome k-mer signature analysis of the red panda (Ailurus fulgens) and the Giant panda (Ailuropoda melanoleuca)

Background

The red panda (Ailurus fulgens) is a riddle of morphology, making it hard to tell whether it is an ursid, a procyonid, a mustelid, or a member of its own family. Previous genetic studies have given quite contradictory results as to its phylogenetic placement.

Results

A recently developed whole genome-based algorithm, the Whole Genome K-mer Signature algorithm was used to analyze the genomes of 28 species of Carnivora, including A. fulgens and several felid, ursid, mustelid, one mephitid species. This algorithm has the advantage of holistically using all the information in the genomes of these species. Being a genomics-based algorithm, it also reduces stochastic error to a minimum. Besides the whole genome, the mitochondrial DNA from 52 mustelids, mephitids, ursids, procyonids and A. fulgens were aligned to draw further phylogenetic inferences.

The results from the whole genome study suggested that A. fulgens is a member of the mustelid clade (p = 9·10^− 97). A. fulgens also separates from the mephitid Spilogala gracilis. The giant panda, Ailuropoda melanoleuca also clusters away from A. fulgens, together with other ursids (p = 1.2·10^− 62). This could be due to the geographic isolation of A. fulgens from other mustelid species. However, results from the mitochondrial study as well as neighbor-joining methods based on the sequence identity matrix suggests that A. fulgens forms a monophyletic group. A Maximum Likelihood tree suggests that A. fulgens and Ursidae form a monophyletic group, although the bootstrap value is weak.

Conclusions

The main conclusion that we can draw from this study is that on a whole genome level A. fulgens possibly belongs to the mustelid clade, and not an ursid or a mephitid. This despite the fact that previously some researchers classified A. fulgens and A. melanoleuca as relatives. Since the genotype determines the phenotype, molecular-based classification takes precedence over morphological classifications. This affirms the results of some previous studies, which studied smaller portions of the genome. However, mitochondrial analyses based on neighbor-joining and maximum likelihood methods suggest otherwise.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07531-3.