scholarly journals Evidence for differences in DNA methylation between Germans and Japanese

2021 ◽  
Author(s):  
J. Becker ◽  
P. Böhme ◽  
A. Reckert ◽  
S. B. Eickhoff ◽  
B. E. Koop ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Age Estimation ◽  
Prediction Models ◽  
Total Sample ◽  
Training Data ◽  
Research Strategies ◽  
Different Populations ◽  
The Impact ◽  
Age Prediction

AbstractAs a contribution to the discussion about the possible effects of ethnicity/ancestry on age estimation based on DNA methylation (DNAm) patterns, we directly compared age-associated DNAm in German and Japanese donors in one laboratory under identical conditions. DNAm was analyzed by pyrosequencing for 22 CpG sites (CpGs) in the genes PDE4C, RPA2, ELOVL2, DDO, and EDARADD in buccal mucosa samples from German and Japanese donors (N = 368 and N = 89, respectively).Twenty of these CpGs revealed a very high correlation with age and were subsequently tested for differences between German and Japanese donors aged between 10 and 65 years (N = 287 and N = 83, respectively). ANCOVA was performed by testing the Japanese samples against age- and sex-matched German subsamples (N = 83 each; extracted 500 times from the German total sample). The median p values suggest a strong evidence for significant differences (p < 0.05) at least for two CpGs (EDARADD, CpG 2, and PDE4C, CpG 2) and no differences for 11 CpGs (p > 0.3).Age prediction models based on DNAm data from all 20 CpGs from German training data did not reveal relevant differences between the Japanese test samples and German subsamples. Obviously, the high number of included “robust CpGs” prevented relevant effects of differences in DNAm at two CpGs.Nevertheless, the presented data demonstrates the need for further research regarding the impact of confounding factors on DNAm in the context of ethnicity/ancestry to ensure a high quality of age estimation. One approach may be the search for “robust” CpG markers—which requires the targeted investigation of different populations, at best by collaborative research with coordinated research strategies.

scholarly journals Combined effects of genotype and childhood adversity shape variability of DNA methylation across age

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Darina Czamara ◽  
Elleke Tissink ◽  
Johanna Tuhkanen ◽  
Jade Martins ◽  
Yvonne Awaloff ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Social Services ◽  
Disease Risk ◽  
Childhood Adversity ◽  
Total Sample ◽  
Synaptic Function ◽  
Combined Effects ◽  
Self Reports ◽  
Main Effects ◽  
The Impact

AbstractLasting effects of adversity, such as exposure to childhood adversity (CA) on disease risk, may be embedded via epigenetic mechanisms but findings from human studies investigating the main effects of such exposure on epigenetic measures, including DNA methylation (DNAm), are inconsistent. Studies in perinatal tissues indicate that variability of DNAm at birth is best explained by the joint effects of genotype and prenatal environment. Here, we extend these analyses to postnatal stressors. We investigated the contribution of CA, cis genotype (G), and their additive (G + CA) and interactive (G × CA) effects to DNAm variability in blood or saliva from five independent cohorts with a total sample size of 1074 ranging in age from childhood to late adulthood. Of these, 541 were exposed to CA, which was assessed retrospectively using self-reports or verified through social services and registries. For the majority of sites (over 50%) in the adult cohorts, variability in DNAm was best explained by G + CA or G × CA but almost never by CA alone. Across ages and tissues, 1672 DNAm sites showed consistency of the best model in all five cohorts, with G × CA interactions explaining most variance. The consistent G × CA sites mapped to genes enriched in brain-specific transcripts and Gene Ontology terms related to development and synaptic function. Interaction of CA with genotypes showed the strongest contribution to DNAm variability, with stable effects across cohorts in functionally relevant genes. This underscores the importance of including genotype in studies investigating the impact of environmental factors on epigenetic marks.

scholarly journals Impact of a Series of Educational Talks Taught by Health Professionals to Promote Healthy Snack Choices among Children

Children ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 203
Author(s):  
Víctor Arufe Giráldez ◽  
Javier Puñal Abelenda ◽  
Rubén Navarro-Patón ◽  
Alberto Sanmiguel-Rodríguez
Keyword(s):  
Nutritional Quality ◽  
Positive Impact ◽  
Total Sample ◽  
Overweight And Obesity ◽  
Healthy Habits ◽  
Quasi Experimental ◽  
Nutrition And Dietetics ◽  
Future Work ◽  
The Impact

Background: One of the great challenges facing today’s society is the need to combat overweight and obesity in schoolchildren. This study aimed to analyze the impact of a cycle of didactic talks—given to families by a specialist in pediatrics, a specialist in nutrition and dietetics and a specialist in physical exercise—on childrens’ snack choices and nutrition quality. Methods: A longitudinal, quasi-experimental and quantitative investigation was designed, working with a total sample of 50 students divided into control and experimental groups. The nutritional quality of daily snacks was recorded during the month before and the month after the cycle of talks given by health experts. Results: An increase in the nutritional quality of the snacks was observed in the days after the talk—but, after a week, values returned to normal. Conclusions: The giving of educational talks to promote healthy habits may have a positive impact on the nutritional quality of school snacks in the days immediately following the talks. However, some forgetfulness was detected over time, which reduced the nutritional quality of the snacks once more. For future work, it is recommended that researchers measure the impact produced by giving regular talks.

scholarly journals Pulsed Light treatment below a Critical Fluence (3.82 J/cm2) minimizes photo-degradation and browning of a model Phenolic (Gallic Acid) Solution

Foods ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 380 ◽  
Author(s):  
Wiktor ◽  
Mandal ◽  
Pratap Singh
Keyword(s):  
Acid Solution ◽  
Gallic Acid ◽  
Prediction Models ◽  
Pulse Frequency ◽  
Food Preservation ◽  
Nutrient Loss ◽  
Pulsed Light ◽  
Cumulative Energy ◽  
The Impact

Pulsed light (PL) is one of the most promising non-thermal technologies used in food preservation and processing. Its application results in reduction of microbial load as well as influences the quality of food. The data about the impact of PL on bioactive compounds is ambiguous, therefore the aim of this study was to analyze the effect of PL treatment of a gallic acid aqueous solution—as a model system of phenolic abundant liquid food matrices. The effect of PL treatment was evaluated based on colour, phenolic content concentration and antioxidant activity measured by DPPH assay using a design of experiments approach. The PL fluence (which is the cumulative energy input) was varied by varying the pulse frequency and time. Using Response Surface Methodology, prediction models were developed for the effect of fluence on gallic acid properties. It was demonstrated that PL can modify the optical properties of gallic acid and cause reactions and degradation of gallic acid. However, application of PL did not significantly alter the overall quality of the model gallic acid solution at low fluence levels. Cluster analysis revealed that below 3.82 J/cm2, changes in gallic acid were minimal, and this fluence level could be used as the critical level for food process design aiming to minimize nutrient loss.

scholarly journals The impact of COVID-19 pandemic in the quality of sleep by Pittsburgh Sleep Quality Index: A systematic review

2021 ◽  
Vol 26 (4) ◽  
pp. 1457-1466
Author(s):  
Luiz Felipe Ferreira de Souza ◽  
Laisa Liane Paineiras-Domingos ◽  
Maria Eduarda de Souza Melo-Oliveira ◽  
Juliana Pessanha-Freitas ◽  
Eloá Moreira-Marconi ◽  
...  
Keyword(s):  
Sleep Quality ◽  
Health Professionals ◽  
Quality Index ◽  
Pittsburgh Sleep Quality Index ◽  
Poor Sleep ◽  
Poor Sleep Quality ◽  
Eligibility Criteria ◽  
Different Populations ◽  
The Impact

Abstract This article aims to evaluate the sleep quality in individuals during the COVID-19 pandemic by Pittsburgh Sleep Quality Index (PSQI). Searches were conducted in the PubMed, Embase, Web of Science, and PEDro databases, on May 22, 2020. In the publications, 208 articles were found and, considering the eligibility criteria, 10 articles were included at the end, showing the effects on sleep quality during the pandemic, in populations hospitalized, quarantined, and in frontline health professionals. The PSQI measured sleep disorders and a higher score indicated poor sleep quality. Nine articles were classified with evidence level IV and one as level III-2. Eight studies present a “serious” risk of bias and two in “moderate”. The studies investigated different populations and described the results as “poor” sleep quality, considering the PSQI on quarantined individuals and frontline health professionals as the most committed. A poor sleep quality was found in the populations evaluated in the selected publications, probably, due to the COVID-19 to contribute as a risk factor for mental health. Psychological interventions must be made to minimize the consequences through social support and social capital.

scholarly journals Analysis of the Impact of Oral Health on Adolescent Quality of Life Using Standard Statistical Methods and Artificial Intelligence Algorithms

Children ◽  
2021 ◽  
Vol 8 (12) ◽  
pp. 1156
Author(s):  
Milica Gajic ◽  
Jovan Vojinovic ◽  
Katarina Kalevski ◽  
Maja Pavlovic ◽  
Veljko Kolak ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Artificial Intelligence ◽  
Oral Health ◽  
Statistical Methods ◽  
Life Quality ◽  
Total Sample ◽  
Serbian Version ◽  
Value Decomposition ◽  
The Impact

The aim of this study was to determine the impact of oral health on adolescent quality of life and to compare the results obtained using standard statistical methods and artificial intelligence algorithms. In order to measure the impact of oral health on adolescent quality of life, a validated Serbian version of the Oral Impacts on Daily Performance (OIDP) scale was used. The total sample comprised 374 respondents. The obtained results were processed using standard statistical methods and machine learning, i.e., artificial intelligence algorithms—singular value decomposition. OIDP score was dichotomized into two categories depending on whether the respondents had or did not have oral or teeth problems affecting their life quality. Human intuition and machine algorithms came to the same conclusion on how the respondents should be divided. As such, method quality and the need to perform analyses of this type in dentistry studies were demonstrated. Using artificial intelligence algorithms, the respondents can be clustered into characteristic groups that allow the discovery of details not possible with the intuitive division of respondents by gender.

scholarly journals Circular RNA as a potential biomarker for forensic age prediction using multiple machine learning models: A preliminary study

2020 ◽  
Author(s):  
Junyan Wang ◽  
Chunyan Wang ◽  
Lihong Fu ◽  
Qian Wang ◽  
Guangping Fu ◽  
...  
Keyword(s):  
Random Forest ◽  
Age Estimation ◽  
Prediction Models ◽  
Circular Rna ◽  
Support Vector ◽  
Random Forest Regression ◽  
Age Related ◽  
Potential Biomarker ◽  
Preliminary Study ◽  
Age Prediction

AbstractIn forensic science, accurate estimation of the age of a victim or suspect can facilitate the investigators to narrow a search and aid in solving a crime. Aging is a complex process associated with various molecular regulation on DNA or RNA levels. Recent studies have shown that circular RNAs (circRNAs) upregulate globally during aging in multiple organisms such as mice and elegans because of their ability to resist degradation by exoribonucleases. In the current study, we attempted to investigate circRNAs’ potential capability of age prediction. Here, we identified more than 40,000 circRNAs in the blood of thirteen Chinese unrelated healthy individuals with ages of 20-62 years according to their circRNA-seq profiles. Three methods were applied to select age-related circRNAs candidates including false discovery rate, lasso regression, and support vector machine. The analysis uncovered a strong bias for circRNA upregulation during aging in human blood. A total of 28 circRNAs were chosen for further validation in 50 healthy unrelated subjects aged between 19 and 72 years by RT-qPCR and finally, 7 age-related circRNAs were chosen for final age prediction models. Several different algorithms including multivariate linear regression (MLR), regression tree, bagging regression, random forest regression (RFR), and support vector regression (SVR) were compared based on root mean square error (RMSE) and mean average error (MAE) values. Among five modeling methods, random forest regression (RFR) performed better than the others with an RMSE value of 5.072 years and an MAE value of 4.065 years (R2 = 0.902). In this preliminary study, we firstly used circRNAs as additional novel age-related biomarkers for developing forensic age estimation models. We propose that the use of circRNAs to obtain additional clues for forensic investigations and serve as aging indicators for age prediction would become a promising field of interest.Author summaryIn forensic investigations, estimation of the age of biological evidence recovered from crime scenes can provide additional information such as chronological age or the appearance of a culprit, which could give valuable investigative leads especially when there is no eyewitness available. Hence, generating an accurate model for age prediction using body fluids such as blood commonly seen at a crime scene can be of vital importance. Various molecular changes on DNA or RNA levels were discovered that they upregulated or downregulated during a person’s lifetime. Although some biomarkers have been proved to be associated with aging and used to predict age, several disadvantages such as low sensitivity, prediction accuracy, instability and susceptibility of diseases or immune states, thus limiting their applicability in the field of age estimation. Here, we utilized a novel biomarker namely circular RNA (circRNA) to generate highly accurate age prediction models. We propose that circRNA is more suitable for forensic degradation samples because of its unique molecular structure. This preliminary research offers a new thought for exploring potential biomarker for age prediction.

scholarly journals Assessing the Impact of Expert Labelling of Training Data on the Quality of Automatic Classification of Lithological Groups Using Artificial Neural Networks

2020 ◽  
Vol 25 (2) ◽  
pp. 145-152
Author(s):  
Yan Kuchin ◽  
Ravil Mukhamediev ◽  
Kirill Yakunin ◽  
Janis Grundspenkis ◽  
Adilkhan Symagulov
Keyword(s):  
Neural Network ◽  
Automatic Classification ◽  
Training Data ◽  
Classification Problems ◽  
Expert Opinions ◽  
Input Parameters ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Impact

AbstractMachine learning (ML) methods are nowadays widely used to automate geophysical study. Some of ML algorithms are used to solve lithological classification problems during uranium mining process. One of the key aspects of using classical ML methods is causing data features and estimating their influence on the classification. This paper presents a quantitative assessment of the impact of expert opinions on the classification process. In other words, we have prepared the data, identified the experts and performed a series of experiments with and without taking into account the fact that the expert identifier is supplied to the input of the automatic classifier during training and testing. Feedforward artificial neural network (ANN) has been used as a classifier. The results of the experiments show that the “knowledge” of the ANN of which expert interpreted the data improves the quality of the automatic classification in terms of accuracy (by 5 %) and recall (by 20 %). However, due to the fact that the input parameters of the model may depend on each other, the SHapley Additive exPlanations (SHAP) method has been used to further assess the impact of expert identifier. SHAP has allowed assessing the degree of parameter influence. It has revealed that the expert ID is at least two times more influential than any of the other input parameters of the neural network. This circumstance imposes significant restrictions on the application of ANNs to solve the task of lithological classification at the uranium deposits.

scholarly journals A Comparison of Forensic Age Prediction Models Using Data From Four DNA Methylation Technologies

2020 ◽  
Vol 11 ◽  
Author(s):  
A. Freire-Aradas ◽  
E. Pośpiech ◽  
A. Aliferi ◽  
L. Girón-Santamaría ◽  
A. Mosquera-Miguel ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Prediction Models ◽  
Using Data ◽  
Age Prediction

scholarly journals Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5518 ◽  
Author(s):  
Tomislav Hengl ◽  
Madlene Nussbaum ◽  
Marvin N. Wright ◽  
Gerard B.M. Heuvelink ◽  
Benedikt Gräler
Keyword(s):  
Random Forest ◽  
Data Quality ◽  
Cross Validation ◽  
Prediction Models ◽  
Proximity Effects ◽  
Training Data ◽  
Calibration Data ◽  
Temporal Prediction ◽  
Spatio Temporal

Random forest and similar Machine Learning techniques are already used to generate spatial predictions, but spatial location of points (geography) is often ignored in the modeling process. Spatial auto-correlation, especially if still existent in the cross-validation residuals, indicates that the predictions are maybe biased, and this is suboptimal. This paper presents a random forest for spatial predictions framework (RFsp) where buffer distances from observation points are used as explanatory variables, thus incorporating geographical proximity effects into the prediction process. The RFsp framework is illustrated with examples that use textbook datasets and apply spatial and spatio-temporal prediction to numeric, binary, categorical, multivariate and spatiotemporal variables. Performance of the RFsp framework is compared with the state-of-the-art kriging techniques using fivefold cross-validation with refitting. The results show that RFsp can obtain equally accurate and unbiased predictions as different versions of kriging. Advantages of using RFsp over kriging are that it needs no rigid statistical assumptions about the distribution and stationarity of the target variable, it is more flexible towards incorporating, combining and extending covariates of different types, and it possibly yields more informative maps characterizing the prediction error. RFsp appears to be especially attractive for building multivariate spatial prediction models that can be used as “knowledge engines” in various geoscience fields. Some disadvantages of RFsp are the exponentially growing computational intensity with increase of calibration data and covariates and the high sensitivity of predictions to input data quality. The key to the success of the RFsp framework might be the training data quality—especially quality of spatial sampling (to minimize extrapolation problems and any type of bias in data), and quality of model validation (to ensure that accuracy is not effected by overfitting). For many data sets, especially those with lower number of points and covariates and close-to-linear relationships, model-based geostatistics can still lead to more accurate predictions than RFsp.

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