Mathematical modeling of the epidemic diseases

Biology and mathematics are key lessons in our curriculum from elementary to high school. Biology science studies the structure and evolution of living organisms and is directly linked to human life on the planet. Many of us have been wondering, "Why do we learn Math?" And "Where will they serve us?" In this work, by linking the courses of Biology and Mathematics, we highlight the role and value of mathematical science in analyzing and explaining real-world situations and phenomena. The experiment we conducted in three classrooms of our school, enabled us to mathematical modeling of infectious diseases, to draw conclusions, to motivate us to further research mathematical models and simulations that would interpret real-world conditions and contemporary ones. Therefore, it will help in making decisions and preventive measures

Quantifying meat spoilage with an array of biochemical indicators

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. It is crucial to validate and establish new rapid methods for the accurate detection of microbial spoilage of meats. In the current thesis, the microbial association of meat was monitored in parallel with the chemical changes, pH measurements and sensory analysis. Several chemical analytical techniques were applied to explore their dynamics on quantifying spoilage indicators and evaluate the shelf life of meat products. The applied analytical methods used were Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy, image analysis, high performance liquid chromatography (HPLC) and gas chromatography/mass spectroscopy (GC/MS). The first component of the study was designed to evaluate the potential of FTIR spectroscopy as a rapid, reagent-less and non-destructive analytical technique in estimating the freshness and shelf life of beef. For this reason, minced beef samples survey from the Greek market), beef fillet samples stored aerobically (0, 5, 10, 15 and 20ºC) and minced beef samples stored aerobically, under modified atmosphere packaging (MAP) and active packaging (0, 5, 10, and 15ºC), were analysed with FTIR. The statistical analysis from the survey revealed that the impact of the market type, the packaging type, the day and the season of purchase had a significant effect on the microbial association of mince. Furthermore, the Principal Components Analysis (PCA) and Factorial Discriminant Analysis (FDA), applied to the FTIR spectral data, showed discrimination of the samples based on freshness, packaging type, the day and season of purchase. The validated overall classification accuracies VCA) were 61.7% for the freshness, 79.2% for the packaging 80.5% for the season and 61.7% for the day of purchase. The shelf life of beef fillets and minced beef was evaluated and correlated with FTIR spectral data. This analysis revealed discrimination of the samples regarding their freshness (VCA 81.6% for the fillets, 76.34% for the mince), their storage temperature (VCA 55.3% and 88.1% for the fillets and mince, respectively) and the packaging type (VCA 92.5% for the mince). Moreover, estimations of the different microbial populations using Partial Least Squares Regression (PLS-R) were demonstrated (e.g. Total viable counts-TVC: RMSE 1.34 for the beef fillets and 0.72 for the mince). Cont/d.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Children and Adolescents' Ingroup Biases and Developmental Differences in Evaluations of Peers Who Misinform

This is the final version. Available from Frontiers Media via the DOI in this record. The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.Previous developmental research shows that young children display a preference for ingroup members when it comes to who they accept information from - even when that information is false. However, it is not clear how this ingroup bias develops into adolescence, and how it affects responses about peers who misinform in intergroup contexts, which is important to explore with growing numbers of young people on online platforms. Given that the developmental span from childhood to adolescence is when social groups and group norms are particularly important, the present study took a Social Reasoning Developmental Approach. This study explored whether children and adolescents respond differently to a misinformer spreading false claims about a peer breaking COVID-19 rules, depending on (a) the group membership of the misinformer and their target and (b) whether the ingroup had a "critical" norm that values questioning information before believing it. 354 United Kingdom-based children (8-11 years old) and adolescents (12-16 years old) read about an intergroup scenario in which a peer spreads misinformation on WhatsApp about a competitor. Participants first made moral evaluations, which asked them to judge and decide whether or not to include the misinformer, with follow-up "Why?" questions to capture their reasoning. This was followed by asking them to attribute intentions to the misinformer. Results showed that ingroup preferences emerged both when participants morally evaluated the misinformer, and when they justified those responses. Participants were more likely to evaluate an ingroup compared to an outgroup misinformer positively, and more likely to accuse an outgroup misinformer of dishonesty. Adolescents attributed more positive intentions to the misinformer compared with children, with children more likely to believe an outgroup misinformer was deliberately misinforming. The critical norm condition resulted in children making more positive intentionality attributions toward an ingroup misinformer, but not an outgroup misinformer. This study's findings highlight the importance of shared group identity with a misinformer when morally evaluating and reasoning about their actions, and the key role age plays in intentionality attributions surrounding a misinformer when their intentions are ambiguous.University of Exete

Adherence to dietary recommendations, nutrient intake adequacy and diet quality among pediatric cystic fibrosis patients: results from the greecf study

Nutrition is an important component of cystic fibrosis (CF) therapy, with a high-fat diet being the cornerstone of treatment. However, adherence to the dietary recommendations for CF appears suboptimal and burdensome for most children and adolescents with CF, leading to malnutrition, inadequate growth, compromised lung function and increased risk for respiratory infections. A cross-sectional approach was deployed to examine the degree of adherence to the nutrition recommendations and diet quality among children with CF. A total of 76 children were recruited from Aghia Sophia’s Children Hospital, in Athens, Greece. In their majority, participants attained their ideal body weight, met the recommendations for energy and fat intake, exceeding the goal for saturated fatty acids consumption. Carbohydrate and fiber intake were suboptimal and most participants exhibited low or mediocre adherence to the Mediterranean diet prototype. It appears that despite the optimal adherence to the energy and fat recommendations, there is still room for improvement concerning diet quality and fiber intake.info:eu-repo/semantics/publishedVersio

Linguistic foundations of heritage language development from the perspective of romance languages in Germany

This paper discusses the role of different factors determining the linguistic competence of heritage speakers (HSs) based on examples from speakers who speak a Romance language (French, Italian, Portuguese, or Spanish) as heritage language (HL) and German as the environmental language. Since the relative amount of contact with the HL and the environmental language may vary during the acquisition process, the role of language dominance (in terms of relative language proficiency) is of particular interest for HL development. In addition to dominance (and related to it), cross-linguistic influence (CLI) may have an influence on the outcome of HL acquisition. Finally, quality and quantity of input also determine HL acquisition and will be discussed in connection with heritage language education.info:eu-repo/semantics/publishedVersio

Feel4Diabetes healthy diet score: Development and evaluation of clinical validity

Background: The aim of this paper is to present the development of the Feel4Diabetes Healthy Diet Score and to evaluate its clinical validity. Methods: Study population consisted of 3268 adults (63% women) from high diabetes risk families living in 6 European countries. Participants filled in questionnaires at baseline and after 1 year, reflecting the dietary goals of the Feel4Diabetes intervention. Based on these questions the Healthy Diet Score was constructed, consisting of the following components: breakfast, vegetables, fruit and berries, sugary drinks, whole-grain cereals, nuts and seeds, low-fat dairy products, oils and fats, red meat, sweet snacks, salty snacks, and family meals. Maximum score for each component was set based on its estimated relative importance regarding T2DM risk, higher score indicating better quality of diet. Clinical measurements included height, weight, waist circumference, heart rate, blood pressure, and fasting blood sampling, with analyses of glucose, total cholesterol, HDL-cholesterol, LDL-cholesterol, and triglycerides. Analysis of (co) variance was used to compare the Healthy Diet Score and its components between countries and sexes using baseline data, and to test differences in clinical characteristics between score categories, adjusted for age, sex and country. Pearson''s correlations were used to study the association between changes from baseline to year 1 in the Healthy Diet Score and clinical markers. To estimate reproducibility, Pearson''s correlations were studied between baseline and 1 year score, within the control group only. Results: The mean total score was 52.8 ± 12.8 among women and 46.6 ± 12.8 among men (p < 0.001). The total score and its components differed between countries. The change in the Healthy Diet Score was significantly correlated with changes in BMI, waist circumference, and total and LDL cholesterol. The Healthy Diet Score as well as its components at baseline were significantly correlated with the values at year 1, in the control group participants. Conclusion: The Feel4Diabetes Healthy Diet Score is a reproducible method to capture the dietary information collected with the Feel4Diabetes questionnaire and measure the level of and changes in the adherence to the dietary goals of the intervention. It gives a simple parameter that associates with clinical risk factors in a meaningful manner

Input effects across domains:The case of Greek subjects in child heritage language

A recurring question in the literature of heritage language acquisition, and more generally of bilingual acquisition, is whether all linguistic domains are sensitive to input reduction and to cross-linguistic influence and to what extent. According to the Interface Hypothesis, morphosyntactic phenomena regulated by discourse–pragmatic conditions are more likely to lead to non-native outcomes than strictly syntactic aspects of the language (Sorace, 2011). To test this hypothesis, we examined subject realization and placement in Greek–English bilingual children learning Greek as a heritage language in North America and investigated whether the amount of heritage language use can predict their performance in syntax–discourse and narrow syntactic contexts. Results indicated two deviations from the Interface Hypothesis: First, subject realization (a syntax–discourse phenomenon) was found to be largely unproblematic. Second, subject placement was affected not only in syntax–discourse structures but also in narrow syntactic structures, though to a lesser degree, suggesting that the association between the interface status of subject placement and its sensitivity to heritage language use among children heritage speakers is gradient rather than categorical

Rapid qualitative and quantitative detection of beef fillets spoilage based on Fourier transform infrared spectroscopy data and artificial neural networks

A machine learning strategy in the form of a multilayer perceptron (MLP) neural network was employed to correlate Fourier transform infrared (FTIR) spectral data with beef spoilage during aerobic storage at chill and abuse temperatures. Fresh beef fillets were packaged under aerobic conditions and left to spoil at 0, 5, 10, 15, and 20 °C for up to 350 hours. FTIR spectra were collected directly from the surface of meat samples, whereas total viable counts of bacteria were obtained with standard plating methods. Sensory evaluation was performed during storage and samples were attributed into three quality classes namely fresh, semi-fresh, and spoiled. A neural network was designed to classify beef samples to one of the three quality classes based on the biochemical profile provided by the FTIR spectra, and in parallel to predict the microbial load (as total viable counts) on meat surface. The results obtained demonstrated that the developed neural network was able to classify with high accuracy the beef samples in the corresponding quality class using their FTIR spectra. The network was able to classify correctly 22 out of 24 fresh samples (91.7%), 32 out of 34 spoiled samples (94.1%), and 13 out of 16 semi-fresh samples (81.2%). No fresh sample was misclassified as spoiled and vice versa. The performance of the network in the prediction of microbial counts was based on graphical plots and statistical indices (bias and accuracy factors, standard error of prediction, mean relative and mean absolute percentage residuals). Results demonstrated good correlation of microbial load on beef surface with spectral data. The results of this work indicated that the biochemical fingerprints during beef spoilage obtained by FTIR spectroscopy in combination with the appropriate machine learning strategy have significant potential for rapid assessment of meat spoilage

A comparison of artificial neural networks and partial least squares modelling for the rapid detection of the microbial spoilage of beef fillets based on Fourier transform infrared spectral fingerprints

A series of partial least squares (PLS) models were employed to correlate spectral data from FTIR analysis with beef fillet spoilage during aerobic storage at different temperatures (0, 5, 10, 15, and 20°C) using the dataset presented by Argyri etal. (2010). The performance of the PLS models was compared with a three-layer feed-forward artificial neural network (ANN) developed using the same dataset. FTIR spectra were collected from the surface of meat samples in parallel with microbiological analyses to enumerate total viable counts. Sensory evaluation was based on a three-point hedonic scale classifying meat samples as fresh, semi-fresh, and spoiled. The purpose of the modelling approach employed in this work was to classify beef samples in the respective quality class as well as to predict their total viable counts directly from FTIR spectra. The results obtained demonstrated that both approaches showed good performance in discriminating meat samples in one of the three predefined sensory classes. The PLS classification models showed performances ranging from 72.0 to 98.2% using the training dataset, and from 63.1 to 94.7% using independent testing dataset. The ANN classification model performed equally well in discriminating meat samples, with correct classification rates from 98.2 to 100% and 63.1 to 73.7% in the train and test sessions, respectively. PLS and ANN approaches were also applied to create models for the prediction of microbial counts. The performance of these was based on graphical plots and statistical indices (bias factor, accuracy factor, root mean square error). Furthermore, results demonstrated reasonably good correlation of total viable counts on meat surface with FTIR spectral data with PLS models presenting better performance indices compared to ANN