Computational Fluid Dynamics Analysis and Empirical Evaluation of Carboxymethylcellulose/Alginate 3D Bioprinting Inks for Screw-Based Microextrusion.

Three-dimensional microextrusion bioprinting technology uses pneumatics, pistons, or screws to transfer and extrude bioinks containing biomaterials and cells to print biological tissues and organs. Computational fluid dynamics (CFD) analysis can simulate the flow characteristics of bioinks in a control volume, and the effect on cell viability can be predicted by calculating the physical quantities. In this study, we developed an analysis system to predict the effect of a screw-based dispenser system (SDS) on cell viability in bioinks through rheological and CFD analyses. Furthermore, carboxymethylcellulose/alginate-based bioinks were used for the empirical evaluation of high-viscous bioinks. The viscosity of bioinks was determined by rheological measurement, and the viscosity coefficient for the CFD analysis was derived from a correlation equation by non-linear regression analysis. The mass flow rate derived from the analysis was successfully validated by comparison with that from the empirical evaluation. Finally, the cell viability was confirmed after bioprinting with bioinks containing C2C12 cells, suggesting that the developed SDS may be suitable for application in the field of bioengineering. Consequently, the developed bioink analysis system is applicable to a wide range of systems and materials, contributing to time and cost savings in the bioengineering industry.

A comparative study of 11 non-linear regression models highlighting autoencoder, DBN, and SVR, enhanced by SHAP importance analysis in soybean branching prediction.

To explore a robust tool for advancing digital breeding practices through an artificial intelligence-driven phenotype prediction expert system, we undertook a thorough analysis of 11 non-linear regression models. Our investigation specifically emphasized the significance of Support Vector Regression (SVR) and SHapley Additive exPlanations (SHAP) in predicting soybean branching. By using branching data (phenotype) of 1918 soybean accessions and 42 k SNP (Single Nucleotide Polymorphism) polymorphic data (genotype), this study systematically compared 11 non-linear regression AI models, including four deep learning models (DBN (deep belief network) regression, ANN (artificial neural network) regression, Autoencoders regression, and MLP (multilayer perceptron) regression) and seven machine learning models (e.g., SVR (support vector regression), XGBoost (eXtreme Gradient Boosting) regression, Random Forest regression, LightGBM regression, GPs (Gaussian processes) regression, Decision Tree regression, and Polynomial regression). After being evaluated by four valuation metrics: R2 (R-squared), MAE (Mean Absolute Error), MSE (Mean Squared Error), and MAPE (Mean Absolute Percentage Error), it was found that the SVR, Polynomial Regression, DBN, and Autoencoder outperformed other models and could obtain a better prediction accuracy when they were used for phenotype prediction. In the assessment of deep learning approaches, we exemplified the SVR model, conducting analyses on feature importance and gene ontology (GO) enrichment to provide comprehensive support. After comprehensively comparing four feature importance algorithms, no notable distinction was observed in the feature importance ranking scores across the four algorithms, namely Variable Ranking, Permutation, SHAP, and Correlation Matrix, but the SHAP value could provide rich information on genes with negative contributions, and SHAP importance was chosen for feature selection. The results of this study offer valuable insights into AI-mediated plant breeding, addressing challenges faced by traditional breeding programs. The method developed has broad applicability in phenotype prediction, minor QTL (quantitative trait loci) mining, and plant smart-breeding systems, contributing significantly to the advancement of AI-based breeding practices and transitioning from experience-based to data-based breeding.

Gasoline quality prediction using the alternating conditional expectation (ACE) algorithm and ATR-FTIR.

Accurate estimation of oxygenates is a critical issue in the quality evaluation of gasoline samples. This work aims to examine the nonparametric robust principal component analysis-alternating conditional expectation (rPCA-ACE) algorithm combined with FTIR spectroscopy as a rapid and accurate analytical method for predicting the quality of gasoline samples based on oxygenates content (methanol, methyl tert-butyl ether, and isobutanol). In the ACE algorithm, a set of optimal transformations is estimated for both the independent and dependent variables. These transformations reveal their non-linear relationships and generate a maximum linear effect between the transformed independent variables and the transformed response variable. In this study, the ACE algorithm was applied to an empirical gasoline dataset and considered a series of possible transformations of the independent and dependent variables to find the best transformations. Among all possible transformations, the ACE algorithm identified a series of polynomials and a nearly linear transformation as the best transformations for the independent and dependent variables, respectively. The regression statistics for calibration and prediction, including the correlation coefficient (Rcal2 = 0.9692), root mean square error of calibration (RMSEC = 2.8638), and root mean square error of prediction (RMSEP = 4.0498) (%v/v) for oxygenates content, were calculated. The ACE model showed improved regression results compared to the linear PLS model (Rcal2 = 0.9550, RMSEC = 3.9052, RMSEP = 5.1342) and PCR model (Rcal2 = 0.9160, RMSEC = 6.5330, RMSEP = 7.0270). By applying the ACE technique to the synthetic fully non-linear dataset obtained from the equation y'=exp(y) for the response variable, we demonstrated the power of the ACE algorithm in multivariate analysis and its ability to identify the exact functional relationship between independent and dependent variables to solve fully non-linear problems.

The impact of burial depth on Centaurea diluta emergence and modelling of its growth using a nonlinear regression and artificial neural network.

BACKGROUND: Centaurea diluta Aiton (North African knapweed) is a major weed concern in Spain as a result of the limited herbicides capable of controlling it, and the limited knowledge of its biology hinders the development of integrated weed management strategies. RESULTS: The current study presents results from two experiments that aimed to: (i) determine the effect of seed burial on seedling emergence; and (ii) model its phenology progression using sigmoidal (SRM) and artificial neural network models (ANN) based on different cohort emergence times. In the first experiment, burial at 2 cm and 5 cm decreased C. diluta emergence by 54% and 90%, respectively, compared to the emergence at 0 cm. In the second experiment, without crop-weed competition conditions, the emergence delay led to reductions in leaf number, rosette diameter, plant height and dry biomass by 63%, 50%, 59% and 93%, respectively. Seed production per plant exceeded 21 469. According to the growth model, leaf number was the most consistent morphological trait and critical for timing weed control actions, so it was used to compare SRMs and ANNs. On average, ANNs increased the precision in 5.72% (± 2.4 leaves) compared to SRMs. This slight performance of ANNs may be valuable for controlling C. diluta because control methods must be applied at the 4-leaf stage to achieve good efficacy. CONCLUSION: Seed burial at 5 cm depth is an effective method reducing C. diluta emergence. ANNs accurately predicted the leaf number employing environmental variables can help increase the efficiency of C. diluta control actions and reduce the risk of escapes. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Computation of a probabilistic uranium concentration map of Norway: A digital expert elicitation approach employing random forests and artificial neural networks.

We compute the first probabilistic uranium concentration map of Norway. Such a map can support mineral exploration, geochemical mapping, or the assessment of the health risk to the human population. We employ multiple non-linear regression to fill the information gaps in sparse airborne and ground-borne uranium data sets. We mimic an expert elicitation by employing Random Forests and Multi-layer Perceptrons as digital agents equally qualified to find regression models. In addition to the regression, we use supervised classification to produce conservative and alarmistic classified maps outlining regions with different potential for the local occurrence of uranium concentration extremes. Embedding the introduced digital expert elicitation in a Monte Carlo approach we compute an ensemble of plausible uranium concentrations maps of Norway discretely quantifying the uncertainty resulting from the choice of the regression algorithm and the chosen parametrization of the used regression algorithms. We introduce digitated glyphs to visually integrate all computed maps and their associated uncertainties in a loss-free manner to fully communicate our probabilistic results to map perceivers. A strong correlation between mapped geology and uranium concentration is found, which could be used to optimize future sparse uranium concentration sampling to lower extrapolation components in future map updates.

Performance evaluation of artificial intelligence with particle swarm optimization (PSO) to predict treatment water plant DBPs (haloacetic acids).

The prevention of water-borne diseases requires the disinfection of water consumed. Disinfection by-products, however, are an increasing concern, and they require advanced knowledge of water treatment plants before their release for human consumption. In this study, multivariate non-linear regression (MNR) and adaptive neuro-fuzzy inference system (ANFIS: Grid partition - GP and Sub-clustering - SC) integrated with particle swarm optimization (PSO) were proposed for the prediction of haloacetic acids (HAAs) in actual distribution systems. PSO-ANFIS-GP and PSO-ANFIS-SC were trained and verified for a total of 64 sets of data with eight parameters (pH, Temperature, UVA254, DOC, Br-; NH4+-N; NO2--N, residual free chlorine). With MNR, R2 is 0.5184

Determination of the effect of the dam age, birth type, and sex on growth curve traits of hair goat kids.

This study was conducted to investigate the best-fit growth curve and dam age, sex, and birth type effect on growth curve traits of hair goat kids. Monthly 3858 test day body weight (BW) records of 643 hair goat kids from birth to 150 days of age were used to determine the best-fit growth curve and estimate growth curve parameters with Gompertz and Von Bertalanffy models. The BW records were assigned to three groups: dam age (3, 4, 5, 6, 7 years), sex (female, male), and birth type (single, twin). The Gompertz model gave more consistent results than the Von Bertalanffy model according to the goodness of fit criteria. Dam age had no significant effect on any of the growth curve traits. Sex of kids showed a significant effect on maturity index (parameter K) (P < 0.001), estimated mature body weight (parameter A), and weight at point of inflection (IPW) (P < 0.01). Also, birth type had a significant effect on initial/birth weight (parameter B) and parameter K (P < 0.001). Age at point of inflection (IPT) was not affected by any of the factors. Twin kids had a higher maturity index than singles while females higher than males. In conclusion, the Gompertz model was the most suitable model for hair goat kids for selection strategies. For proper selection, the effect of sex and birth type on growth curve traits should be considered by hair goat breeders.

The Impact of the Macroeconomic Environment on Social Preferences: Evidence from the Global Preference Survey.

The effect of social preferences, such as altruism and trust, on economic development is widely recognized. However, the reciprocal impact, i.e., how individuals experience the economic environment and how this shapes their social preferences, has remained largely under-explored. This study sheds light on this reciprocal effect, revealing an intriguing macroeconomic impact on individuals' social preferences. By harnessing the Global Preference Survey data and a non-linear regression model, our findings highlight an interesting trend: there is a discernible decrease in individuals' social preference as they experience enhanced economic conditions, and this effect is more pronounced for males. This crucial revelation underscores the importance for researchers and policymakers to take into account the prospective attenuation of social preferences in the pursuit of economic well-being.

Investigating surrogate safety measures at midblock pedestrian crossings using multivariate models with roadside camera data.

This study aims to evaluate and compare Surrogate Safety Measures (SSMs) at five midblock Rectangular Rapid Flashing Beacons (RRFB) and two midblock Pedestrian Hybrid Beacons (PHB) sites in Florida using extensive video data collected over the study period of July to November 2021. Computer vision and data processing resulted in four pedestrian SSMs, namely spatial gap, temporal gap, relative time to collision (RTTC) and Post Encroachment Time (PET). An initial investigation of the SSMs using Mann-Whitney-Wilcoxon tests revealed significant differences in the SSM values across different treatment types and hours of the day. Additionally, univariate regression of spatial gap, and multivariate regression of temporal gap, RTTC and PET revealed significant differences of SSMs across RRFB and PHB sites. The study considered both linear and non-linear (gamma, inverse Gaussian and lognormal) regression models. After considering various traffic and operational parameters, the data were aggregated for each pedestrian-vehicle interaction on each lane to create a total of 395 observations. The SSMs included average spatial gap, temporal gap, RTTC and PET for each interaction of pedestrian and vehicle on each lane. The results indicated that non-linear models performed better than the linear models. Moreover, the presence of the PHB, weekday, signal activation, lane count, pedestrian speed, vehicle speed, land use mix, morning period and pedestrian starting position from the sidewalk have been found to be significant determinants of the SSMs. Results also suggest temporal SSMs increase at the PHB sites compared to the RRFB sites, indicating an improvement of traffic safety at PHB sites. However, the spatial gap decreased for PHB sites compared to the RRFB sites, which suggests that pedestrians tend to start to cross the RRFB sites when they perceive vehicles to be further away than at the PHB sites.

Production, characterization, and kinetic modeling of biosurfactant synthesis by Pseudomonas aeruginosa gi |KP 163922|: a mechanism perspective.

Kinetic studies and modeling of production parameters are essential for developing economical biosurfactant production processes. This study will provide a perspective on mechanistic reaction pathways to metabolize Waste Engine Oil (WEO). The results will provide relevant information on (i) WEO concentration above which growth inhibition occurs, (ii) chemical changes in WEO during biodegradation, and (iii) understanding of growth kinetics for the strain utilizing complex substrates. Laboratory scale experiments were conducted to study the kinetics and biodegradation potential of the strain Pseudomonas aeruginosa gi |KP 163922| over a range (0.5-8% (v/v)) of initial WEO concentration for 168 h. The kinetic models, such as Monod, Powell, Edward, Luong, and Haldane, were evaluated by fitting the experimental results in respective model equations. An unprecedented characterization of the substrate before and after degradation is presented, along with biosurfactant characterization. The secretion of biosurfactant during the growth, validated by surface tension reduction (72.07 ± 1.14 to 29.32 ± 1.08 mN/m), facilitated the biodegradation of WEO to less harmful components. The strain showed an increase in maximum specific growth rate (µmax) from 0.0185 to 0.1415 h-1 upto 49.92 mg/L WEO concentration. Maximum WEO degradation was found to be ~ 94% gravimetrically. The Luong model (adj. R2 = 0.97) adapted the experimental data using a non-linear regression method. Biochemical, 1H NMR, and FTIR analysis of the produced biosurfactant revealed a mixture of mono- and di- rhamnolipid. The degradation compounds in WEO were identified using FTIR, 1H NMR, and GC-MS analysis to deduce the mechanism.

Insights into the degradation of high-density polyethylene microplastics using microbial strains: Effect of process parameters, degradation kinetics and modeling.

The extensive distribution of microplastics and their abundance around the world has raised a global concern because of the lack of proper disposal channels as well as poor knowledge of their implications on human health. Sustainable remediation techniques are required owing to the absence of proper disposal methods. The present study explores the deterioration process of high-density polyethylene (HDPE) microplastics using various microbes along with the kinetics and modeling of the process using multiple non-linear regression models. Ten different microbial strains were used for the degradation of microplastics for a period of 30 days. Effect of process parameters on the degradation process was studied with the selected five microbial strains that presented the best degradation results. The reproducibility and efficacy of the process were tested for an extended period of 90 days. Fourier-transform infrared spectroscopy (FTIR) and field emission-scanning electron microscopy (FE-SEM) were used for the analysis of microplastics. Polymer reduction and half-life were evaluated. Pseudomonas putida achieved the maximum degradation efficiency of 12.07% followed by Rhodococcus ruber (11.36%), Pseudomonas stutzeri (8.28%), Bacillus cereus (8.26%), and Brevibacillus borstelensis (8.02%) after 90 days. Out of 14 models tested, 5 were found capable of modeling the process kinetics and based on simplicity and statistical data, Modified Michaelis-Menten model (F8; R2 = 0.97) was selected as superior to others. This study successfully establishes the potential of bioremediation of microplastics as the viable process.

Greece since the 1960s: the mortality transition revisited: a joinpoint regression analysis.

Mortality transition in Greece is a well-studied phenomenon in several of its aspects. It is characterised by an almost constant increase in life expectancy at birth and other ages and a parallel decrease in death probabilities. The scope of this paper is a comprehensive assessment of the mortality transition in Greece since 1961, in the light of holistic analysis. Within this paper, life tables by gender were calculated and the temporal trends of life expectancy at several ages were examined. Moreover, a cluster analysis was used in order to verify the temporal changes in the mortality patterns. The probabilities of death in large age classes are presented. Furthermore, the death distribution was analysed in relation to various parameters: the modal age at death, mode, left and right inflexion points and the length of the old age heap. Before that, a non-linear regression method, originating from the stochastic analysis, was applied. Additionally, the Gini coefficient, average inter-individual differences, and interquartile range of survival curves were examined. Finally, the standardised rates of the major causes of death are presented. All the analysis variables were scholastically examined for their temporal trends with the method of Joinpoint Regression analysis. Mortality transition in Greece after the year 1961 is asymmetrical with a gender and an age-specific component, leading to the elevation of life expectancy at birth over time. During this period, the older ages' mortality decreases, but at a slower pace than that of the younger ones. The modal age at death, mode, the left and right inflexion points and the width of the old age heap denote the compression of mortality in the country. The old age death heap shifts towards older ages, while at the same time, the variability of ages at death decreases, being verified by the Gini Coefficient and average inter-individual differences. As a result, the rectangularization of survival curves is evident. These changes have a different pace of transition over time, especially after the emergence of the economic crisis. Finally, the major causes of death were the diseases of the circulatory system, neoplasms, diseases of the respiratory system and others. The temporal trends of these diseases differ according to the diseases and gender. Greece's mortality transition is an asymmetrical stepwise process characterised by its gender and age-specific characteristics. This process, despite being a continuous one, is not linear. Instead, a combination of serious developments over time governs the country's modern mortality regime. The evaluation of Greece's mortality transition through the lens of more advanced analytical methods may provide new insights and methodological alternatives for assessing mortality transition in other countries of the world.

Principal factors affecting the yield of dilute acid pretreatment of lignocellulosic biomass: A critical review.

This review provides a critical analysis of the state of the art of dilute acid pretreatment applied to lignocellulosic biomass. Data from 63 publications were extracted and analysed. The majority of the papers used residence times of<30 min, temperature ranges from 100 °C to 200 °C, and acid levels between 0 % and 2 %. Yields are quantified directly after pretreatment (xylose content) or after enzymatic hydrolysis (glucose content). Statistical analyses allowed the time-temperature equivalence to be quantified for three types of biomass: they were formulated by non-linear expressions. In further works, investigating less explored areas, for example moderate temperature levels with longer residence times, is recommended. Pretreatment material (time-temperature kinetics, reactor type) and analytical methods should be standardized and better described. It becomes mandatory to promote the development of an open, findable, accessible, interoperable, and reusable data approach for pretreatments research.

Prediction of carbon-dioxide activity coefficient for solubility in ionic liquids using multi-non-linear regression analysis.

Activity coefficient values offer insight into the intermolecular interactions between the solute and the solvent and the deviation from the ideal behavior. CO2 capture from different industrial processes is a globally pertinent issue and the search for suitable chemicals is required. To address the issue, knowledge of activity coefficient values is crucial for CO2 separation-based process. In this regard, a correlation is developed that predicts the coefficient of CO2 activity in ionic liquids by multi-nonlinear regression analysis. The correlation is developed between the pressure range of 1-50 bar and the temperature range of 298.15-33.15 K for mole fractions of 0.3, 0.5, and 0.7. Outliers' analysis is performed using the boxplot method to determine the suitability of ranges of the selected input parameters. The preceding literature does not predict the activity coefficient in relatively lower to higher temperature and pressure ranges for CO2 solubility in ionic liquids. Initially, the activity coefficient values from COSMO-RS were obtained and compared with the correlation results. The COSMO-RS and the correlation predicted results were subsequently validated with the experimental data. The average absolute error (AAE%) of the predicted correlation values is 19.53% while the root mean square error (RMSE) value is 0.465. The correlation can be used in the future to predict the CO2 activity coefficient values in ionic liquids to facilitate qualitative analyses of their CO2 capture efficiency.

Greedy knot selection algorithm for restricted cubic spline regression.

Non-linear regression modeling is common in epidemiology for prediction purposes or estimating relationships between predictor and response variables. Restricted cubic spline (RCS) regression is one such method, for example, highly relevant to Cox proportional hazard regression model analysis. RCS regression uses third-order polynomials joined at knot points to model non-linear relationships. The standard approach is to place knots by a regular sequence of quantiles between the outer boundaries. A regression curve can easily be fitted to the sample using a relatively high number of knots. The problem is then overfitting, where a regression model has a good fit to the given sample but does not generalize well to other samples. A low knot count is thus preferred. However, the standard knot selection process can lead to underperformance in the sparser regions of the predictor variable, especially when using a low number of knots. It can also lead to overfitting in the denser regions. We present a simple greedy search algorithm using a backward method for knot selection that shows reduced prediction error and Bayesian information criterion scores compared to the standard knot selection process in simulation experiments. We have implemented the algorithm as part of an open-source R-package, knutar.

Reconsidering vitamin D optimal values based on parathyroid hormone levels in a North Algerian cohort: stratification by gender and season.

"Health-based threshold value" is used to define the optimal cutoff of vitamin D. This approach is based on the hypothesis of a secondary hyperparathyroidism associated with hypovitaminosis D. We define the optimal values in a North Algerian population. The optimal value is 25.0 ng/ml in men and 30.0 ng/ml in women. PURPOSE/INTRODUCTION: There is no consensus defining the vitamin D optimal values. The aim of this study is to establish vitamin D optimal values in the Northern Algerian population, based on its skeletal effects as represented by the inverse relationship between 25-hydroxy vitamin D (25(OH) D) and parathyroid hormone (PTH). METHODS: 451 healthy volunteers of both genders, aged 19 to 79 years, were enrolled in a cross-sectional study conducted at the medical analysis laboratory of the University Hospital of Blida, Algeria. 25(OH) D was assessed by a sequential competitive immuno-fluoroassay technique. Determination of vitamin D optimal values was performed based on the kinetic relationship between 25(OH) D and PTH, as explored by inverse nonlinear regression on a spline plots curve. The optimal value represents the 25(OH) D level at which PTH ceases to increase and reaches a virtual plateau. RESULTS: In men and women, respectively, the 25 (OH) D thresholds are estimated at 25.0 ng/ml and 30 ng/ml, above this value, PTH stabilizes in a virtual plateau, estimated at 22.3 pg/ml and 26.8 pg/ml. In warm and cold seasons, respectively, the 25 (OH) D cut-offs are estimated at 30.0 ng/ml and 25.0 ng/ml, from these values, the PTH stabilizes in a virtual plateau, estimated at 21.5 pg/ml and 27.7 pg/ml. CONCLUSION: In this study, the optimal values of 25(OH) D were defined for the first time in a North Algerian adult population. The optimal value is 25.0 ng/ml in men and 30.0 ng/ml in women.

Impact of the first national COVID-19 lockdown on referral of women experiencing domestic violence and abuse in England and Wales.

BACKGROUND: The lockdown periods to curb COVID-19 transmission have made it harder for survivors of domestic violence and abuse (DVA) to disclose abuse and access support services. Our study describes the impact of the first COVID-19 wave and the associated national lockdown in England and Wales on the referrals from general practice to the Identification and Referral to Improve Safety (IRIS) DVA programme. We compare this to the change in referrals in the same months in the previous year, during the school holidays in the 3 years preceding the pandemic and the period just after the first COVID-19 wave. School holiday periods were chosen as a comparator, since families, including the perpetrator, are together, affecting access to services. METHODS: We used anonymised data on daily referrals received by the IRIS DVA service in 33 areas from general practices over the period April 2017-September 2020. Interrupted-time series and non-linear regression were used to quantify the impact of the first national lockdown in March-June 2020 comparing analogous months the year before, and the impact of school holidays (01/04/2017-30/09/2020) on number of referrals, reporting Incidence Rate Ratio (IRR), 95% confidence intervals and p-values. RESULTS: The first national lockdown in 2020 led to reduced number of referrals to DVA services (27%, 95%CI = (21,34%)) compared to the period before and after, and 19% fewer referrals compared to the same period in the year before. A reduction in the number of referrals was also evident during the school holidays with the highest reduction in referrals during the winter 2019 pre-pandemic school holiday (44%, 95%CI = (32,54%)) followed by the effect from the summer of 2020 school holidays (20%, 95%CI = (10,30%)). There was also a smaller reduction (13-15%) in referrals during the longer summer holidays 2017-2019; and some reduction (5-16%) during the shorter spring holidays 2017-2019. CONCLUSIONS: We show that the COVID-19 lockdown in 2020 led to decline in referrals to DVA services. Our findings suggest an association between decline in referrals to DVA services for women experiencing DVA and prolonged periods of systemic closure proxied here by both the first COVID-19 national lockdown or school holidays. This highlights the need for future planning to provide adequate access and support for people experiencing DVA during future national lockdowns and during the school holidays.

Occipital condyle width (OCW) is a highly accurate predictor of body mass in therian mammals.

BACKGROUND: Body mass estimation is of paramount importance for paleobiological studies, as body size influences numerous other biological parameters. In mammals, body mass has been traditionally estimated using regression equations based on measurements of the dentition or limb bones, but for many species teeth are unreliable estimators of body mass and postcranial elements are unknown. This issue is exemplified in several groups of extinct mammals that have disproportionately large heads relative to their body size and for which postcranial remains are rare. In these taxa, previous authors have noted that the occiput is unusually small relative to the skull, suggesting that occiput dimensions may be a more accurate predictor of body mass. RESULTS: The relationship between occipital condyle width (OCW) and body mass was tested using a large dataset (2127 specimens and 404 species) of mammals with associated in vivo body mass. OCW was found to be a strong predictor of body mass across therian mammals, with regression models of Mammalia as a whole producing error values (~ 31.1% error) comparable to within-order regression equations of other skeletal variables in previous studies. Some clades (e.g., monotremes, lagomorphs) exhibited specialized occiput morphology but followed the same allometric relationship as the majority of mammals. Compared to two traditional metrics of body mass estimation, skull length, and head-body length, OCW outperformed both in terms of model accuracy. CONCLUSIONS: OCW-based regression models provide an alternative method of estimating body mass to traditional craniodental and postcranial metrics and are highly accurate despite the broad taxonomic scope of the dataset. Because OCW accurately predicts body mass in most therian mammals, it can be used to estimate body mass in taxa with no close living analogues without concerns of insufficient phylogenetic bracketing or extrapolating beyond the bounds of the data. This, in turn, provides a robust method for estimating body mass in groups for which body mass estimation has previously been problematic (e.g., "creodonts" and other extinct Paleogene mammals).

Influence of pH, Temperature and Protease Inhibitors on Kinetics and Mechanism of Thermally Induced Aggregation of Potato Proteins.

Understanding aggregation in food protein systems is essential to control processes ranging from the stabilization of colloidal dispersions to the formation of macroscopic gels. Patatin rich potato protein isolates (PPI) have promising techno-functionality as alternatives to established proteins from egg white or milk. In this work, the influence of pH and temperature on the kinetics of PPI denaturation and aggregation was investigated as an option for targeted functionalization. At a slightly acidic pH, rates of denaturation and aggregation of the globular patatin in PPI were fast. These aggregates were shown to possess a low amount of disulfide bonds and a high amount of exposed hydrophobic amino acids (S0). Gradually increasing the pH slowed down the rate of denaturation and aggregation and alkaline pH levels led to an increased formation of disulfide bonds within these aggregates, whereas S0 was reduced. Aggregation below denaturation temperature (Td) favored aggregation driven by disulfide bridge formation. Aggregation above Td led to fast unfolding, and initial aggregation was less determined by disulfide bridge formation. Inter-molecular disulfide formation occurred during extended heating times. Blocking different protein interactions revealed that the formation of disulfide bond linked aggregation is preceded by the formation of non-covalent bonds. Overall, the results help to control the kinetics, morphology, and interactions of potato protein aggregation for potential applications in food systems.

On the estimation of sugars concentrations using Raman spectroscopy and artificial neural networks.

In this paper, we present an analysis of the performance of Raman spectroscopy, combined with feed-forward neural networks (FFNN), for the estimation of concentration percentages of glucose, sucrose, and fructose in water solutions. Indeed, we analysed our method for the estimation of sucrose in three solid industrialized food products: donuts, cereal, and cookies. Concentrations were estimated in two ways: using a non-linear fitting system, and using a classifier. Our experiments showed that both the classifier and the fitting systems performed better than a Support Vector Machine (SVM), a Linear Discriminant Analysis (LDA), a Linear Regression (LR), and interval Partial Least Squares (iPLS). The best-case obtained by an FFNN for water solutions was 93.33% of classification and 3.51% of Root Mean Square Error in Prediction (RMSEP), compared with 82.22% obtained by a LDA. Our proposed method got an RMSEP of 1% for the best-case obtained with the food products.