Model Validation Tests Research Articles

IntLIM 2.0: identifying multi-omic relationships dependent on discrete or continuous phenotypic measurements.

IntLIM uncovers phenotype-dependent linear associations between two types of analytes (e.g. genes and metabolites) in a multi-omic dataset, which may reflect chemically or biologically relevant relationships. The new IntLIM R package includes newly added support for generalized data types, covariate correction, continuous phenotypic measurements, model validation and unit testing. IntLIM analysis uncovered biologically relevant gene-metabolite associations in two separate datasets, and the run time is improved over baseline R functions by multiple orders of magnitude. IntLIM is available as an R package with a detailed vignette (https://github.com/ncats/IntLIM) and as an R Shiny app (see Supplementary Figs S1-S6) (https://intlim.ncats.io/). Supplementary data are available at Bioinformatics Advances online.

Identification of microscale fracture models for mortar with in-situ tests

The microscale fracture modeling of concrete requires explicit descriptions of the microstructure and the fracture properties of its constituents. The identification of fracture properties of mortar was carried out by numerical analyses of two in situ (via X-ray tomography) mesoflexural tests on small-scale specimens. Realistic meshes were created for mechanical simulations to be run. The experimental kinematic fields were measured by processing the reconstructed volumes via Digital Volume Correlation. The measured displacements were used as kinematic boundary conditions to simulate three-point flexural tests. The full dataset consisting of realistic geometry and experimental boundary conditions, allowed for full simulations of the performed tests. A phase-field model for brittle materials was selected to describe damage of the cement paste, and debonding at the matrix-aggregate interfaces was modeled with a cohesive zone model. The fracture paths and ultimate forces simulated with the phase-field model were consistent with the experiment thanks to representative microstructure and boundary conditions. In both tests, the predicted crack path changed because of interface debonding. The introduction of cohesive interface elements did not significantly improve the faithfulness of the path prediction. This study demonstrates the potential of microscale simulations of in situ tests for model identification, validation and development at the microscale of mortar.

PyBanshee version (1.0): A Python implementation of the MATLAB toolbox BANSHEE for Non-Parametric Bayesian Networks with updated features

In this paper we discuss PyBanshee, which is a Python-based open-source implementation of the MATLAB toolbox BANSHEE. PyBanshee constitutes the first fully open-source package to quantify, visualize and validate Non-Parametric Bayesian Networks (NPBNs). The architecture of PyBanshee is heavily based on its MATLAB predecessor. It presents the full implementation of existing tools and introduces new modules. Specifically, PyBanshee allows for: (i) choosing fully parametric one-dimensional margins, (ii) choosing different sample sizes for the model-validation tests based on the Hellinger distance, (iii) drawing user-defined sample sizes of the NPBN, (iv) sample-based conditioning sampling (similarly to the closed-source proprietary package UNINET by LightTwist Software) and (v) visualizing the comparison between the histograms of the unconditional and conditional marginal distributions. New detailed examples demonstrating new features are provided.

OP59 The Tunisian Guidelines For Pharmacoeconomic Analysis: What We Need To Know

IntroductionAs a new milestone in health technology assessment (HTA) implementation in Tunisia, L’Instance Nationale de l’Evaluation et de l’Accréditation en Santé (INEAS)—the Tunisian HTA body—published a set of methodological guidelines to support HTA dossier submission by the pharmaceutical industry. Including, ‘guide for submitting clinical data for an HTA at INEAS’, ‘methodological choices guide for pharmacoeconomic analysis at INEAS’, and ‘methodological choices guide for budget impact analysis at INEAS’. We aim to report the major methodological recommendations of the pharmacoeconomic analysis guideline.MethodsThe ‘methodological choices for pharmacoeconomic analysis at INEAS’ guideline was reviewed and the major recommendations were retrieved and reported.ResultsThe reference analysis required by INEAS is the cost-utility analysis systematically combined with a cost-effectiveness analysis (cost per life-year gained) from the public payers’ perspective. The choice of any other type of analysis must be duly justified. Comparators should include alternative treatments which are considered to be ‘the standard of care’ (i.e., interventions routinely used in Tunisia for the same indication) and in which public resources are invested. The time horizon should be sufficiently long to reflect all differences in costs and outcomes. Additionally, a discount rate of 5 percent per year is recommended. The best available evidence for efficacy, safety and quality of life is required. An indirect measure of patient preference, through a validated measurement instrument is preferred for utility calculation. Cost inputs should be identified from Tunisian sources. Health resource utilization should reflect the care pathway in Tunisia. INEAS favors the use of a recognized model. Uncertainty and impact of the input parameters on the results should be assessed and reported through probabilistic and deterministic sensitivity analyses. Model validation tests to assess face validity and internal validity should be performed, and a discussion of the methods used provided. Demonstration of external validity is required. Results should be presented in incremental cost-utility and cost-effectiveness ratios.ConclusionsThe recommendations of ‘methodological choices for pharmacoeconomic evaluation at INEAS’ is an important step to facilitate and harmonize pharmaceutical companies’ submissions and to enhance the use of these analyses in decision-making.

Quadrotor Model for Energy Consumption Analysis

In this paper, a quadrotor dynamic model’s energy efficiency was investigated. A method for the design of the dynamic model which assures energy consumption estimation was presented. This model was developed to analyze the energy efficiency of the quadrotor during each maneuver. A medium-class quadrotor (4.689 kg) was used as a test platform. Thrust force correction factors obtained with FLIGHTLAB software were used to predict object behavior in forward flight. Model validation and long-duration flight tests in outdoor windy conditions are also presented. Monte-Carlo simulation was used to study the influence of uncertainties in model parameters on the simulation reliability. The developed model might be used for practical purposes (for example, energy-efficient coverage path planning).

Nomograms for prediction of breast cancer in breast imaging reporting and data system (BI-RADS) ultrasound category 4 or 5 lesions: A single-center retrospective study based on radiomics features.

PurposeTo develop nomograms for predicting breast malignancy in BI-RADS ultrasound (US) category 4 or 5 lesions based on radiomics features.MethodsBetween January 2020 and January 2022, we prospectively collected and retrospectively analyzed the medical records of 496 patients pathologically proven breast lesions in our hospital. The data set was divided into model training group and validation testing group with a 75/25 split. Radiomics features were obtained using the PyRadiomics package, and the radiomics score was established by least absolute shrinkage and selection operator regression. A nomogram was developed for BI-RADS US category 4 or 5 lesions according to the results of multivariate regression analysis from the training group.ResultThe AUCs of radiomics score consisting of 31 US features was 0.886. The AUC of the model constructed with radiomics score, patient age, lesion diameter identified by US and BI-RADS category involved was 0.956 (95% CI, 0.910–0.972) for the training group and 0.937 (95% CI, 0.893–0.965) for the validation cohort. The calibration curves showed good agreement between the predictions and observations.ConclusionsBoth nomogram and radiomics score can be used as methods to assist radiologists and clinicians in predicting breast malignancy in BI-RADS US category 4 or 5 lesions.

CFD modelling to analyze the droplets deposition behavior on vibrating rice leaves

The external airflow and the auxiliary airflow of air-assisted application make the leaves vibrate during operation. Such vibration affects the deposition behavior of spray droplets on the leaves. In this study, A multiphase flow computational fluid dynamics (CFD) simulation model was developed and validated. Dynamic process simulations were performed using a validated CFD simulation model, which set spray droplet particle size, impact velocity, leaf tilt angle, and rotational angular velocity as variables. The simulation test results revealed that: the droplet velocity and leaf tilt angle were crucial factors affecting the deposition behavior of droplet adhesion, bouncing, and splitting; compared to static leaves, leaf vibration did not impact the deposition behavior selection of droplets under the influence of 5–10 m/s air velocity. However, the increase of leaves rotation angular velocity increased the droplet spreading to a certain degree. The CFD model validation test results conform with the simulation results. More specifically, the relative error of the longitudinal spreading factor was within 10% and the relative error of the spray droplet spreading diameter was within 20%. This study provides CFD models that can be used for the study of spray droplet deposition behavior on the surface of vibrating rice leaves, thereby providing a reference for rice plant protection and the selection of pesticide application parameters.

The Effect of Layer Batch Normalization and Droupout of CNN model Performance on Facial Expression Classification

One of the implementations of face recognition is facial expression recognition in which a machine can recognize facial expression patterns from the observed data. This study used two models of convolutional neural network, model A and model B. The first model A was without batch normalization and dropout layers, while the second model B used batch normalization and dropout layers. It used an arrangement of 4 layer models with activation of ReLU and Softmax layers as well as 2 fully connected layers for 5 different classes of facial expressions of angry, happy, normal, sad, and shock faces. Research Metodology are 1). Data Analysis, 2). Preprocessing grayscaling, 3). Convolutional Neural Network (CNN), 4). Model validation Testing, Obtained an accuracy of 64.8% for training data and accuracy of 63.3% for validation data. The use of dropout layers and batch normalization could maintain the stability of both training data and validation data so that there was no overfitting. By dividing the batch size on the training data into 50% with 200 iterations, aiming to make the load on each training model lighter, by using the learning rate to be 0.001 which works to improve the weight value, thus making the training model work to be fast without crossing the minimum error limit. Accuracy results in the classification of ekp facial receipts from the distance of the camera to the face object about 30 cm in the room with the use of bright enough lighting by 78%.

VLA-SMILES: Variable-Length-Array SMILES Descriptors in Neural Network-Based QSAR Modeling

Machine learning represents a milestone in data-driven research, including material informatics, robotics, and computer-aided drug discovery. With the continuously growing virtual and synthetically available chemical space, efficient and robust quantitative structure–activity relationship (QSAR) methods are required to uncover molecules with desired properties. Herein, we propose variable-length-array SMILES-based (VLA-SMILES) structural descriptors that expand conventional SMILES descriptors widely used in machine learning. This structural representation extends the family of numerically coded SMILES, particularly binary SMILES, to expedite the discovery of new deep learning QSAR models with high predictive ability. VLA-SMILES descriptors were shown to speed up the training of QSAR models based on multilayer perceptron (MLP) with optimized backpropagation (ATransformedBP), resilient propagation (iRPROP‒), and Adam optimization learning algorithms featuring rational train–test splitting, while improving the predictive ability toward the more compute-intensive binary SMILES representation format. All the tested MLPs under the same length-array-based SMILES descriptors showed similar predictive ability and convergence rate of training in combination with the considered learning procedures. Validation with the Kennard–Stone train–test splitting based on the structural descriptor similarity metrics was found more effective than the partitioning with the ranking by activity based on biological activity values metrics for the entire set of VLA-SMILES featured QSAR. Robustness and the predictive ability of MLP models based on VLA-SMILES were assessed via the method of QSAR parametric model validation. In addition, the method of the statistical H0 hypothesis testing of the linear regression between real and observed activities based on the F2,n−2 -criteria was used for predictability estimation among VLA-SMILES featured QSAR-MLPs (with n being the volume of the testing set). Both approaches of QSAR parametric model validation and statistical hypothesis testing were found to correlate when used for the quantitative evaluation of predictabilities of the designed QSAR models with VLA-SMILES descriptors.

Comparing the Soil Conservation Service model with new machine learning algorithms for predicting cumulative infiltration in semi-arid regions

Water infiltration into soil is an important process in hydrologic cycle; however, its measurement is difficult, time-consuming and costly. Empirical and physical models have been developed to predict cumulative infiltration (CI), but are often inaccurate. In this study, several novel standalone machine learning algorithms (M5Prime (M5P), decision stump (DS), and sequential minimal optimization (SMO)) and hybrid algorithms based on additive regression (AR) ( i.e ., AR-M5P, AR-DS, and AR-SMO) and weighted instance handler wrapper (WIHW) ( i.e ., WIHW-M5P, WIHW-DS, and WIHW-SMO) were developed for CI prediction. The Soil Conservation Service (SCS) model developed by the United States Department of Agriculture (USDA), one of the most popular empirical models to predict CI, was considered as a benchmark. Overall, 154 measurements of CI (explanatory/input variables) were taken from 16 sites in a semi-arid region of Iran (Illam and Lorestan provinces). Six input variable combinations were considered based on Pearson correlations between candidate model inputs (time of measuring and soil bulk density, moisture content, and sand, clay, and silt percentages) and CI. The dataset was divided into two subgroups at random: 70% of the data were used for model building (training dataset) and the remaining 30% were used for model validation (testing dataset). The various models were evaluated using different graphical approaches (bar charts, scatter plots, violin plots, and Taylor diagrams) and quantitative measures (root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and percent bias (PBIAS)). Time of measuring had the highest correlation with CI in the study area. The best input combinations were different for different algorithms. The results showed that all hybrid algorithms enhanced the CI prediction accuracy compared to the standalone models. The AR-M5P model provided the most accurate CI predictions (RMSE = 0.75 cm, MAE = 0.59 cm, NSE = 0.98), while the SCS model had the lowest performance (RMSE = 4.77 cm, MAE = 2.64 cm, NSE = 0.23). The differences in RMSE between the best model (AR-M5P) and the second-best (WIHW-M5P) and worst (SCS) were 40% and 84%, respectively.

Analysis and experiment of feeding material process of Hermetia illucens L. frass bucket wheel based on DEM

• The parameters of bucket wheel operating for Hermetia illucens sand are analyzed. • EDEM is used during the study of bucket wheel shoveling process. • Computer simulation is used in the analysis of the bucket wheel shoveling and unloading Hermetia illucens sand. • The optimal operation parameters affecting the bucket wheel working performance are obtained through the DOE. This study aims to improve the load factor and reduce the residual rate of the Hermetia illucens L. feeding bucket. Based on the sand bucket feeding conveyor designed in the early stage, the technological characteristics of biotransformation of pig manure with Hermetia illucens L., and the mechanism of the bucket shape feeding and transport, the 4-day-old Hermetia illucens L. larvae was firstly inoculated in the pig manure environment with a water content of 75% at 26℃. Then, the single factor experiment was conducted to compare the effect of pig-manure bulking thickness on the biotransformation efficiency of Hermetia illucens L. The results demonstrated that when the thickness of pig manure was 0.10–0.15 m, the biotransformation efficiency of Hermetia illucens L. was relatively high, and the weight loss rate of pig manure was more than 50%. Hence, the thickness of pig manure was an essential factor influencing the efficiency of bucket feeding transportation. On this basis, the physical parameters such as shape size, particle size distribution, and water content of the Hermetia illucens L. sand particles after biotransformation of pig manure were determined. Furthermore, the discrete element method was adopted to establish the numerical simulation model of the bucket shape feeding conveyer under the combination of discrete element simulation and bench test, so as to simulate the bucket shape feeding transport process with insect sands after cleaning the Hermetia illucens L. larva as the test object. Meanwhile, a mechanics and kinematics analysis was conducted for the stress of the horizontal and vertical directions, the mass of materials in the bucket, and the leakage area of the bucket to reveal the mechanism of feeding filling conveying and leakage bucket. Additionally, the main factors influencing the bucket material feeding transport efficiency were comprehensively determined, including feed speed, bucket rotating speed, and feeding depth. The test indicators such as bucket load factor, variation coefficient, and residual rate were established. Besides, a three-factor three-level orthogonal combination experiment was conducted by combining with the Box-Behnken test method. Then, the regression mathematical model between the indexes and factors was set up to analyze the influence law of various factors on the evaluation indicators. The parameters were optimized with the regression model. The model validation test results suggested that the bucket rotating speed was 6.5r·min-1 when the feed speed was 45 mm·s −1 ; the experimental values of the load factor, variation coefficient, and residual rate were consistent with the theoretical optimization values when the feeding depth was 15 cm, with the relative errors of 2.14%, 5.84%, and 6.40%, respectively. Thus, the model optimization is reliable. Under the condition of the optimization parameters, the bench test equipment was established, and the experiment was performed. With the mixture of Hermetia illucens L. insect sand as the experimental material, its load factor, variation coefficient, sand retention rate, and larvae loss rate were 81.23%, 1.48%, 1.85%, and 3.75%, respectively, which met the experimental and actual production requirements. The research results provided a theoretical reference for the design and optimization of the bucket shape feeding conveying machinery of Hermetia illucens L. insect sand.

An intelligent framework for prediction and forecasting of dissolved oxygen level and biofloc amount in a shrimp culture system using machine learning techniques

The present study approaches towards the feasibility of prediction and forecasting of dissolved oxygen (DO) and biofloc amount using the state of art machine learning algorithms in a shrimp culture system. The study was carried out considering; average DO and biofloc amount as target parameters in a shrimp culture system. There were seventeen numbers of culture and meteorological parameters considered and three different feature selection techniques used to create twelve different data subsets for model development. The model development was carried out using three popular machine learning algorithms viz., Random Forest, Adaboost and Deep neural network. The totals of thirty-six different models were obtained and their accuracies were evaluated with seven model validation tests and results were obtained and discussed. Out of thirty-six models, Random Forest technique applied model for prediction of dissolved oxygen with combined culture and meteorological parameters (R2value – 0.709, prediction accuracy – 98.26%, score – 0.7381) was found to be the best one for predictive model development. Moreover, exploratory data analysis was carried out for prediction and a framework for prediction of DO and biofloc amount in a shrimp based biofloc culture system was developed. The dissolved oxygen was found to be more robust in the predictive model development. The Intelligent Framework was developed based on the study conducted to understand and carryout prediction in farming system in a scientific manner. The developed framework can help the literate farmers and new entrants of shrimp farming to devise their own prediction models suitable for the farming conditions.

Enhanced Lithium-ion battery model considering critical surface charge behavior

Battery model is the basis of battery efficient and safe management. The widely used equivalent circuit model (ECM) generally shows poor behavior in predicting battery terminal voltage at low sate of charge (SOC), increasing the risk in the urgent use of a battery at low voltage greatly. To model strong nonlinearity of battery open circuit voltage (OCV), a solid-phase diffusion equation based surface SOC is proposed to characterize OCV behavior and establish the new structure of the enhanced ECM to describe low SOC behavior more precisely. Finally, a battery test platform was built to conduct battery tests for model validation. The results show that the root mean square error (RMSE) of the battery terminal voltage obtained from the proposed model at low SOC has been reduced to 8 mV compared with the RMSE of 17 mV from the traditional ECM model. It is expected that the proposed model can be employed in battery management systems to effectively improve the reliability and safety of emergency use of a battery at low SOC.

Bisindo-based rational emotive behaviour therapy model: study preliminary prevention of sexual harassment in women with deafness

PurposeWomen with deafness are one of the vulnerable groups who experience sexual harassment. As a result, they experience psychological disorders such as stress, anxiety, fear and trauma. This problem is made worse because women with deafness have language and speech disorders. This paper aims to explore sexual harassment of women with deafness and develop Bisindo-based rational emotive behaviour therapy (REBT) to deal with sexual harassment against women with deafness.Design/methodology/approachUsing the Analysis, Design, Development, Implementation and Evaluation model of the R&D approach, this preliminary research study was carried out in four stages. The research respondents for the need assessment were 166 people with ten professional backgrounds spread throughout Indonesia. Respondents for model validation tests are ten people: REBT experts, psychologists, Bisindo experts, teachers, lecturers, Head of Women's Organization (WCC) and Head of the Deaf Community (Gerkatin) in West Sumatra. Data were collected by questionnaire.FindingsThe results showed that many women with deafness experienced sexual harassment. The handling model using REBT mediated by Bisindo is needed in further research to test its effectiveness. The validity test results show that the Bisindo-based REBT model has been declared feasible, helpful, accurate and practical to be used as a response model for sexual harassment in women with deafness.Originality/valueThe results of this study imply the need for the application of therapies that follow the characteristics of victims, such as Bisindo-based REBT for female sexual victims.

A nonlinear hyper-viscoelastic model for predicting recoverable behavior of largely-deformed asphalt mortar

For predicting the recoverable behavior of largely-deformed asphalt mortar with geometrical-material nonlinearity and viscoelastic-viscoplastic coupling, a nonlinear hyper-viscoelastic constitutive model is established in the local intermediate configuration based upon the Clausius-Duhem inequality. Although with a stress-dependent parameter to represent the viscoelastic nonlinearity, the proposed model follows the linear superposition principle in a creep-recovery (C-R) process. Based on this feature, recovery curves in C-R tests can be transformed to creep curves for model calibration. It is applied to two types of asphalt mortar labeled AM-1 and AM-2. AM-1 comes from dense-graded asphalt concrete and contains filler, while AM-2 has an idealized gradation and a lower bitumen content. The increasing-stress repeated C-R tests under uniaxial compression are performed for determining model parameters, and the other C-R tests for model validation. A numerical algorithm of the constitutive model is presented and implemented in the ABAQUS finite element (FE) software, in order to validate the constitutive model and the obtained model parameters by comparing the measurements with the FE predictions. Reasonable agreements are found in the comparisons, and the R-squared values are more than 0.7. The determined model parameters show that AM-2 has higher initial moduli and better deformation resistance, but its long-term relative moduli are lower than those of AM-1.

Comprehensive Counseling Guidance Model In Providing Pre-Marriage Services For Students

Premarital education for students is considered necessary because, at this time, a person must be prepared to enter married life. Premarital education is one of the preparations that can be done by early adults in marriage and has a goal in preventing divorce. This study aims to formulate a hypothetical comprehensive guidance and counselling program to improve students' self-readiness in facing marriage and family life. This type of research is development research or Research and Development methods, namely research strategies used to produce specific products. This research will be conducted at Padang State University, namely to students of Special Education, Faculty of Education. The results of this study are (1) In general, special education department’s students of the Faculty of Education, Padang State University, as early adults, have less interest in preparing for their marriage. (2) The comprehensive premarital guidance and counselling model for students developed in this study is formulated from a theoretical framework based on field findings, consisting of 7 rational components, key concepts, vision & mission, model objectives, the content of guidance and counselling, control, and organization & personnel. (3) Based on the model validation test conducted by three counsellors on the General Guidelines for the Development of Comprehensive Premarital Guidance and Models for Students, it shows that the contents of the general guidelines are categorized as suitable or adequate.

Virtual Detection of Mechanically Induced Short Circuits in a Cylindrical Lithium-Ion Battery Cell Based on Finite Element Simulation

Lithium-ion batteries (LIBs) are commonly used in today’s electric vehicles. Studying their behaviour under mechanical loading, including short circuits, is vital for vehicle safety. This paper covers three major topics, (1) a general literature review for the state-of-the-art of LIBs, (2) physical cell tests for model validation are performed, wherein the occurrence of short circuits is detected and (3) creating a finite element model (FEM) of an 18650 cylindrical LIB using the most recent testing and simulation techniques. A variety of short-circuit criteria based on stresses, strains and geometric parameters have been implemented in the simulation and compared to the test results. It will be demonstrated that a combination of two geometric criteria, in the radial and axial directions of the cell, is best suited for virtual short-circuit detection in the simulation. Finally, the short-circuit criteria are implemented in a post-processing tool that allows fast short-circuit analysis of cells of different loadings. In the future, this method of short-circuit detection will be used to analyse an assembly of several battery cells such as, for instance, an automotive or maritime battery pack. Furthermore, the developed method enables mechanical integration with respect to crash safety in vehicles.

Using the International Tree-Ring Data Bank (ITRDB) records as century-long benchmarks for global land-surface models

Abstract. The search for a long-term benchmark for land-surface models (LSMs) has brought tree-ring data to the attention of the land-surface modelling community, as tree-ring data have recorded growth well before human-induced environmental changes became important. We propose and evaluate an improved conceptual framework of when and how tree-ring data may, despite their sampling biases, be used as century-long hindcasting targets for evaluating LSMs. Four complementary benchmarks – size-related diameter growth, diameter increment of mature trees, diameter increment of young trees, and the response of tree growth to extreme events – were simulated using the ORCHIDEE version r5698 LSM and were verified against observations from 11 sites in the independent, unbiased European biomass network datasets. The potential for big-tree selection bias in the International Tree-Ring Data Bank (ITRDB) was investigated by subsampling the 11 sites from European biomass network. We find that in about 95 % of the test cases, using ITRDB data would result in the same conclusions as using the European biomass network when the LSM is benchmarked against the annual radial growth during extreme climate years. The ITRDB data can be used with 70 % confidence when benchmarked against the annual radial growth of mature trees or the size-related trend in annual radial growth. Care should be taken when using the ITRDB data to benchmark the annual radial growth of young trees, as only 50 % of the test cases were consistent with the results from the European biomass network. The proposed maximum tree diameter and annual growth increment benchmarks may enable the use of ITRDB data for large-scale validation of the LSM-simulated response of forest ecosystems to the transition from pre-industrial to present-day environmental conditions over the past century. The results also suggest ways in which tree-ring width observations may be collected and/or reprocessed to provide long-term validation tests for land-surface models.

Modeling of wastewater treatment by Azolla filiculoides using response surface methodology.

Due to the population growth and reduction of water resources, wastewater treatment and reuse vital. As the secondary wastewater treatment processes enable removes a significant amount of P and N, nutrient-rich effluents can cause eutrophication in water bodies. On the other hand, nutrients removal in sewage treatment using mechanical methods is costly and complex. The aquaculture method using Azolla filiculoides could be an appropriate option for removing total phosphorus (TP), total nitrogen (TN), and chemical oxygen demand (COD) from wastewater. Synthetic wastewater has been prepared in the typical range of municipal wastewater. Two g fresh weight of an acclimatized A.filiculoides was floated in sample bowls each one containing 500 CC prepared wastewater. Total nitrogen, TP, and COD removal by Azolla filiculoides for 21-days were optimized and investigated using the response surface methodology (RSM). For this aim, the D-optimal method was used to optimize the three independent variables (TP concentration (10.8-84.6mg l- 1), TN concentration (20-99mg l- 1), and COD concentration (66.26-415mg l- 1)) for their maximum removal efficiency of them. Experiments were performed on 28 runs in which independent variables were measured using a HACH DR 5000 spectrometer. Predicted R-squared for COD, TP, TN removal, and Azolla mass (responses) have been equal to -0.0897, 0.8514, 0.7779, and 0.5645, respectively. The model was used to maximize Azolla growth and maximize removal efficiency of nitrogen, phosphorus, and COD that occurred in minimum concentrations of TN (20mg l- 1), TP (10.8mg l- 1), and COD (66.26mg l- 1). The removal efficiency of Azolla was obtained 77.5 % for COD, 66.8 % for TP, and 78.1 % for TN in the optimum condition of independent variables. Also, increase of Azolla mass was 239 %, with desirability of 0.66. The difference between model prediction and model validation testing for Azolla mass increase, COD, TN, and TP removal was equal to ± 11.6 %, ± 7.9 %, ± 0.0 %, and ± 1.9 %, respectively. Azolla could remove phosphorus in nitrogen deficiency or even lack of nitrogen. Results indicate that removal efficiency has an upward trend as the Azolla growth increases. This kind of fern has a significant effect on removing nitrogen, phosphorus, and COD from an aqueous solution. The removal efficiency of TN, TP, and COD at optimum operating conditions showed good agreement with model-predicted removal efficiency.

Effects of Chronic Administration of P-Cymene in an Animal Model of LPS-Induced Autism

Abstract p-Cymene is a monoterpene found in over 100 plant species. It shows a range of biological activity, including anti-inflammatory and antimicrobial effects. It is possibly a new therapeutic alternative for autism spectrum disorder characterized by deficits in interaction and behavioral abnormalities. These symptoms can occur in response to maternal immune activation through prenatal exposure to lipopolysaccharide. Thus, this study aimed to evaluate the behavioral, memory, and biochemical effects of chronic administration of p-cymene in an animal model of autism by prenatal maternal exposure to lipopolysaccharide. Twenty-four pregnant Wistar rats were used, who received 100 μg/kg of lipopolysaccharide or saline intraperitoneally (i.p.) on the 9.5 gestational day. After birth, the male offspring remained with the mothers until weaning and underwent model validation tests on postnatal day 30. From postnatal day 31 on, chronic administration, via i.p., of saline (1 mL/kg), risperidone (0.2 mg/kg), or p-cymene (100 mg/kg) for 22 days was performed. The animals were submitted to behavioral (postnatal day 52) and memory tests (postnatal days 52–53) and subsequently sacrificed (postnatal day 54) when their brain structures were removed for quantification of proinflammatory cytokines (TNF-α, interleukin 1β, and interleukin 6). Prenatal exposure to lipopolysaccharide significantly increased episodes of stereotyped movement (p=0.0001) and decreased parameters of social interaction in offspring, including sniffing, following, mounting, and allowing mounting (p=0.0043, p<0.0001, p=0.0009, and p=0.0200, respectively). Chronic p-cymene treatment was not significant for behavioral, memory, and biochemical tests. However, due to their pharmacokinetic characteristics, p-cymene nanomaterials’ formulation may be an exciting alternative to be tested for further results.