Wine quality analysis by the structural causal model (SCM)

Bayes network modelling for structural causal analysis between wine physicochemical data and quantitative human quality blind assessments is applied. The large dataset of white and red "Vinho Verde\u27\u27 wine samples from Portugal, which was available from an open data repository for machine learning at the University of California at Irving, was analysed. The dataset contains 4898 white and 1599 red samples evaluated by blind tastes by a minimum of 3 sensory assessors and 12 physicochemical properties. The casual effects of wine analytic data on human quality evaluations are evaluated numerically by Bayes neural networks for adjusted sets of the covariates as marginal distributions and presented graphically as partial dependence plots. Structural causal analysis revealed important differences between the most important variables for quality predictions and the individual causal effects. Bayes neural network models of the partial dependencies show more pronounced nonlinear effects for red wines compared to white wine quality. The artificial intelligence models with boosted random decision tree forests for untrained wine samples yield a 5% relative standard error of predictions compared to 12% for the linear models and ordinary least squares estimation. For red wine, the most important direct causal quality effects are caused by alcohol, volatile acidity, and sulphates. Alcohol improves quality with a maximum plateau at 14%, while volatile acidity has a strong proportional negative effect. The effect of sulphates is highly nonlinear with maximum positive effect at a concentration of 1 g/L of (K_{2}SO_{4}). For the white wine samples causal effects are linear with positive effects of alcohol and negative effects of volatile and fixed acidity. The developed structural causal model enables evaluation of targeted wine production interventions, named as “doing x, do(x) models”, as restructured adjusted Bayes networks. It leads to potential applications of artificial intelligence in wine production technology and process quality control

Analysis of synergism in biochemical networks

Probabilistic approach to analysis of synergism in mathematical models of biochemical networks is introduced. It is based on system analysis concept when information on the importance of a parameter of a complex biochemical model is evaluated as part of joint interaction with a complete set of model parameters. For example, this approach accounts for uncertainties in the estimates of enzyme activities and kinetic parameters involved in kinetic modelling of the networks and/or concentration of metabolite or cofactors involved in the interaction of a pathway with perturbations on a cellular level. The parameters are considered as random variables with assumed corresponding probability distribution functions, and total effects of their variability on the network fluxes are evaluated. A numerical measure of synergism of an individual parameter with respect to interaction with model parameters is defined as the difference between the ensemble expected value of conditional variance for the complementary parameters and the variance of conditional expected value of the particular parameter relative to the total parameter ensemble dispersion. In order to demonstrate the concept, the proposed method is applied to two simple cases and to a complex model. The first case is the analysis of synergism between activator and substrate in uni-uni type I mechanism. In the second example, synergism between enzymes involved in flux through a serial pathway is evaluated. As an example of a complex system, synergism between glycogenolytic flux in a skeletal muscle and involved cellular level cofactors is analyzed

The Causal Ecological Model Based on EU Project Data “LTER Northern Adriatic Sea”

Cilj ovog rada je pokazati mogućnosti primijene metodologije umjetne inteligencije i strukturnog kauzalnog modeliranja (engl. Structural Causal Model, SCM) s ciljem postizanja znanstvenog doprinosa utvrđivanjem kauzalne funkcionalne zakonitosti bioloških značajki o abiotičkim parametrima. Temeljna zadaća rada je istražiti model SCM za određivanje zavisnosti koncentracije klorofila o fizikalnim značajkama u području sjevernog Jadrana tijekom razdoblja od 1965. do 2015. godine. Eksperimentalni podatci rezultat su dugotrajnog i ekstenzivnog istraživanja u okviru EU projekta “LTER Northern Adriatic Sea” i dostupni su (putem EU znanstvene politike “Open Science”) u velikoj bazi podataka (engl. Big Data), koja sadrži 10 8687 uzoraka s 43 značajke. Predložen je matematički model Bayesove mreže (engl. Bayes Network, BN) kao usmjereni neciklički graf (engl. Directed Acyclic Graph, DAG). Struktura grafa određena je primjenom testa uvjetne nezavisnosti (Hamilton-Schmidtova Conditional Indepedence test, HSCI) s razinom signifikantnosti α = 0,05. SCM model pokazuje da su neposredni kauzalni utjecaji na koncentraciju klorofila: temperatura, salinitet, pH, dušik, fosfor i silicij. Primijenjena je metodologija d-razdvajanja BN grafa sa svrhom blokiranja interferencije (engl. confounding) za procjenu kauzalne funkcionalne zavisnosti bioloških značajki o abiotičkim parametrima. Funkcije kauzalnosti određene su kao rubne razdiobe (engl. marginal distributions) modeliranjem Bayesovom neuronskom mrežom (engl. Bayes Neural Network, BNN). Najveći neposredni negativni kauzalni učinak na klorofil A (Chlorophyll A) ima temperatura (−0,07 μg klorofila A/°C). Utvrđena je pozitivna kauzalna zavisnost između klorofila-A i otopljenog kisika (0,2 mg otopljenog kisika DO2/μg klorofila A). Također je provedena neparametarska usporedna analiza klorofila A i fizikalnih parametara hrvatskog dijela i podataka za cjelokupni sjeverni Jadran. Medijan koncentracije otopljenog kisika u hrvatskom dijelu Jadrana je 5,8 mg O2/l a u sjevernom je 5,5 mg O2/l, dok je medijan temperature u hrvatskom dijelu T = 14,6 °C u odnosu na T = 15,1 °C za sjeverni Jadran. Medijan broja stanica bičaša (Dinoflagellate) je u hrvatskom dijelu Jadrana 3 stanice/l, u odnosu na cijeli sjeverni Jadran, gdje je on od 5 stanica/l. Značajna je razlika u učestalosti i iznosu visokog broja bičaša. Medijani koncentracija klorofila A ne pokazuju značajnu razliku (0,65 i 0,90 μg l–1), ali u sjevernom Jadranu je znatno veći broj uzoraka koji po iznosu signifikantno odstupaju od normalne razdiobe (engl. outliers or hotspots). Utvrđena je i značajna razlika u razdiobi koncentracije silicija s velikim brojem uzoraka s visokim koncentracijama u zapadnom dijelu Jadrana. Primijenjeni su modeli “šume” stabala odlučivanja (engl. random forest) predikcije bioloških značajki na osnovi abiotičkih veličina. Validacije modela provedene su određivanjem relativne postotne pogreške predikcije primjenom simulacije “novih” podataka peterostrukom podjelom baze podataka. Postignute su sljedeće pogreške modela predikcije: za klorofil (engl. chlorophyll) 6,5 %; feopigment (Pheeopigment) 17,4 %; diatomeje (Diatom) 18,8 %; dinoflagelat (Dinoflagellate) 17,4 %; i kokolitifore (Coccolithoophores) 12,1 %. Za svaki od modela utvrđeni su ključni abiotički faktori za procjenu predikcija.The aim of this work was to show possibilities of applied artificial intelligence methodologies and structural causal modelling (“Structural Causal Model”, SCM) with the object of gaining a scientific level contribution to the determination of functional causal dependencies in complex ecological systems. In this work, applied was SCM for the determination of dependencies of chlorophyll concentration on physical and chemical parameters in the northern Adriatic Sea during the period 1965 to 2015. The experimental data are the outcome of the long-term and extensive investigation as a part of the EU project “LTER Northern Adriatic Sea”, and are freely available within the EU Open Science policy. The data are a “Big Data” base with 108 687 samples and 43 descriptors. Proposed is a mathematical model with Bayes network (BN) as a directed acyclic graph (DAG). The model structure was determined by the Hamilton-Schmidt conditional independence test with a significance level of α = 0.05. The SCM model shows that the direct causal variables for chlorophyll concentration are: temperature, salinity, pH, and concentrations of nitrogen, phosphor, and silica. The BN model was adjusted according to d-separation with the objective to block confounding and contra-causal back door interference. The functions of causal dependencies were determined as the marginal distributions with Bayes network models with a single interior layer for interpolation. The most important causal effect was due to temperature (−0.07 μg chlorophyll A/°C). The model predicted reversed positive causality between chlorophyll concentration and dissolved oxygen (0.2 mg DO2/μg chlorophyll A). Also evaluated was nonparametric comparative analysis of chlorophyll and abiotic parameters between Croatian and northern Adriatic Sea (Slovenia and Italy). The comparison was based on median metrics to avoid the pronounced influence of outliers due to hydrodynamic effects. The median concentration of dissolved oxygen in Croatian Adriatic was 5.8 mg O2/l, while in Slovenian and Italian 5.5 mg O2/l, and the median temperature was T = 14.6 °C compared to T = 15.1 °C. There is a significant difference in the abundance of dinoflagellates in Croatia 3 cell/l, while in Slovenia and Italian 5 cells/l. The difference is more pronounced by the number and values of “hot spots” outliers. The difference between chlorophyll concentrations is not significant (0.65 and 0.90 μg l–1); however, the difference in the distribution of the outliers is significant with more frequent and bigger outliers in Italian and Slovenian Adriatic. Also observed was a significant difference in SiO4 distribution, with higher concentrations in the western Adriatic. The random forest RF decision tree models are applied for the development of the predictive models of biological parameters based on abiotic data. The RF models are validated by 5-fold cross-validation. The models have out-of-box mean relative errors of 6.5 % for chlorophyll, photopigment 17.4 %; diatoms 18.8 %; dinoflagellate 17.4 %; and 12.1 % for coccolithophores. For each predictive model determined are the first five most important predictors accounting for 95 % of importance

Systems Biology Markup Language: Case Study of T-Cell Signal Transduction Network

Abstract. Applicatio

Proportion of milk and milk products in boarding school meals in Zagreb area

Zbog velikog udjela proteina, minerala i vitamina, mlijeko spada u skupinu vrlo važnih namirnica tijekom rasta i razvoja mladih. Osobito su važne prehrambene navike pojedinaca. Kako bi se dobio što bolji uvid u prehrambeni status izabrane skupine (djevojke i mladići od 14-18 godina) obavljena je analiza obroka u učeničkim domovima. Iz analiziranih obroka razvidno je što se može poboljšati u obrocima društvene prehrane. Analizom obroka te anketom, utvrđeno je da 62% djevojaka i 66% mladića konzumira mlijeko i mliječne proizvode ako su uvršteni u dnevne obroke koji se nude u učeničkom domu. Samo 29% djevojaka i 27% mladića svakodnevno konzumira mlijeko i mliječne proizvode. Prosječni dnevni unos kalcija nije u skladu sa RDA preporukama (zadovoljava svega 81 % potreba djevojaka i 82,5 % potreba mladića). Cilj rada je utvrditi nutritivni i energijski sadržaj obroka te linearnim programiranjem optimirati sastav obroka i time poboljšati energetski i nutritivni unos ovisno o spolu i dobi.For young people that are still in the stage of growing and developing the quality of nutritive intakes is of great importance as well as the nutritional habits of each individual. The high values of proteins, minerals and vitamins in milk make this product highly valuable for children and young people. To gain a precise insight into nutritive status of young people in Croatian boarding schools a \u27\u27closed type group" was selected. For such groups it is possible to measure the content of daily meals (breakfast, dinner and supper). The examined groups include teenagers aged 14-18 and accommodated in boarding schools. From results of meal analysis a necessary improvement can be maid. Weekly analyses demonstrated that 62% girls and 66% boys consume milk and milk products only if are served in the boarding school meal Only 29% girls and 27% hoys consume milk or milk products on daily basis. Besides, very important is the daily intake of calcium. For girls the daily intake is 81 % of the RDA recommendation and for the boys 82,5%? The aim of this work was to assess energy and nutrient intake and to use linear programming in order to make improvements in planning of teenager\u27s nutrition