Response surface methodology for process optimization in livestock wastewater treatment: A review

With increasing demand for meat and dairy products, the volume of wastewater generated from the livestock industry has become a significant environmental concern. The treatment of livestock wastewater (LWW) is a challenging process that involves removing nutrients, organic matter, pathogens, and other pollutants from livestock manure and urine. In response to this challenge, researchers have developed and investigated different biological, physical, and chemical treatment technologies that perform better upon optimization. Optimization of LWW handling processes can help improve the efficacy and sustainability of treatment systems as well as minimize environmental impacts and associated costs. Response surface methodology (RSM) as an optimization approach can effectively optimize operational parameters that affect process performance. This review article summarizes the main steps of RSM, recent applications of RSM in LWW treatment, highlights the advantages and limitations of this technique, and provides recommendations for future research and practice, including its cost-effectiveness, accuracy, and ability to improve treatment efficiency.

A novel nanocomposite for photocatalytic rhodamine B dye removal from wastewater using visible light

Providing safe access to water and addressing the impact of waterborne diseases, which claim over two million lives annually, is a major contribution to water purification. The study introduces a novel nanocomposite, Ch/Fe3O4/α-MoO3, which exhibits outstanding photocatalytic efficacy under visible light. An in-depth investigation of the nanocomposite's synthesis, characterization, and photodegradation mechanisms reveals its outstanding capabilities. Photocatalytic activity is influenced by the catalytic dose, pH, dye concentration, and reaction time, according to the study. A response surface method is used to determine the optimal conditions for Rhodamine B degradation, which results in 96.3% removal efficiency at pH 8.5, dye concentration 25 mg/L, nanocomposite dose at 22 mg/L, and reaction time 50 min. As a result of its high surface area, biocompatibility, availability, and magnetization with iron compounds, Chitosan is an excellent substrate for enhancing the photocatalytic properties of MoO3 nanoparticles. A nanocomposite with an energy band of 3.18 eV exhibits improved visible light absorption. This study confirms the nanocomposite's recyclability and stability, affirming its practicality. Besides dye removal, it offers hope for the global quest for clean water sources by addressing a broader range of waterborne contaminants. By combining molybdenum and magnetite, nanocomposite materials facilitate the degradation of pollutant and bacteria, contributing positively to society's quest for clean and safe water. It emphasizes the role nanotechnology plays in preserving human health and well-being in combating waterborne diseases.

Advanced oxidation processes for water and wastewater treatment - Guidance for systematic future research

Advanced oxidation processes (AOPs) are a growing research field with a large variety of different process variants and materials being tested at laboratory scale. However, despite extensive research in recent years and decades, many variants have not been transitioned to pilot- and full-scale operation. One major concern are the inconsistent experimental approaches applied across different studies that impede identification, comparison, and upscaling of the most promising AOPs. The aim of this tutorial review is to streamline future studies on the development of new solutions and materials for advanced oxidation by providing guidance for comparable and scalable oxidation experiments. We discuss recent developments in catalytic, ozone-based, radiation-driven, and other AOPs, and outline future perspectives and research needs. Since standardized experimental procedures are not available for most AOPs, we propose basic rules and key parameters for lab-scale evaluation of new AOPs including selection of suitable probe compounds and scavengers for the measurement of (major) reactive species. A two-phase approach to assess new AOP concepts is proposed, consisting of (i) basic research and proof-of-concept (technology readiness levels (TRL) 1-3), followed by (ii) process development in the intended water matrix including a cost comparison with an established process, applying comparable and scalable parameters such as UV fluence or ozone consumption (TRL 3-5). Subsequent demonstration of the new process (TRL 6-7) is briefly discussed, too. Finally, we highlight important research tools for a thorough mechanistic process evaluation and risk assessment including screening for transformation products that should be based on chemical logic and combined with complementary tools (mass balance, chemical calculations).

Utilizing experimental design and desirability function in optimizing RP-HPLC method for simultaneous determination of some skeletal muscle relaxants and analgesics

An experimental design and response surface methodologies using Plackett-Burman and Box-Behnken designs were applied for selecting and optimizing the most appropriate parameters which significantly affect the separation and quantitative estimation of five skeletal muscle relaxants and four analgesic drugs (baclofen, methocarbamol, dantrolene sodium, orphenadrine citrate, cyclobenzaprine hydrochloride, ketoprofen, etoricoxib, ibuprofen, and mefenamic acid) with a relatively short duration of analysis in a single run. For the separation of the nine drugs, an INERTSIL ODS-V3-5 µm C18 column (250 × 4.6 mm I.D.) was used with the optimum mobile phase conditions (45.15 mM ammonium acetate buffer pH 5.56 adjusted with acetic acid, acetonitrile, and methanol in a ratio of 30.5:29.5:40, v/v/v with a flow rate of 1.5 mL/min) and UV-detection at 220 nm. The optimized method was successfully subjected to the validation steps as described in ICH guidelines for linearity, precision, accuracy, robustness, and sensitivity. The optimized and validated method was effectively applied to determine the content of the studied drugs in their pharmaceutical preparations and to expand its applicability to the counterfeit estimation of etoricoxib in different brands of tablet dosage forms.

Dental plaque microbiota sequence counts for microbial profiling and resistance genes detection

Shotgun metagenomics sequencing experiments are finding a wide range of applications. Nonetheless, there are still limited guidelines regarding the number of sequences needed to acquire meaningful information for taxonomic profiling and antimicrobial resistance gene (ARG) identification. In this study, we explored this issue in the context of oral microbiota by sequencing with a very high number of sequences (~ 100 million), four human plaque samples, and one microbial community standard and by evaluating the performance of microbial identification and ARGs detection through a downsampling procedure. When investigating the impact of a decreasing number of sequences on quantitative taxonomic profiling in the microbial community standard datasets, we found some discrepancies in the identified microbial species and their abundances when compared to the expected ones. Such differences were consistent throughout downsampling, suggesting their link to taxonomic profiling methods limitations. Overall, results showed that the number of sequences has a great impact on metagenomic samples at the qualitative (i.e., presence/absence) level in terms of loss of information, especially in experiments having less than 40 million reads, whereas abundance estimation was minimally affected, with only slight variations observed in low-abundance species. The presence of ARGs was also assessed: a total of 133 ARGs were identified. Notably, 23% of them inconsistently resulted as present or absent across downsampling datasets of the same sample. Moreover, over half of ARGs were lost in datasets having less than 20 million reads. This study highlights the importance of carefully considering sequencing aspects and suggests some guidelines for designing shotgun metagenomics experiments with the final goal of maximizing oral microbiome analyses. Our findings suggest varying optimized sequence numbers according to different study aims: 40 million for microbiota profiling, 50 million for low-abundance species detection, and 20 million for ARG identification. KEY POINTS: • Forty million sequences are a cost-efficient solution for microbiota profiling • Fifty million sequences allow low-abundance species detection • Twenty million sequences are recommended for ARG identification.

Adapting vector surveillance using Bayesian experimental design: An application to an ongoing tick monitoring program in the southeastern United States

Maps of the distribution of medically-important ticks throughout the US remain lacking in spatial and temporal resolution in many areas, leading to holes in our understanding of where and when people are at risk of tick encounters, an important baseline for informing public health response. In this work, we demonstrate the use of Bayesian Experimental Design (BED) in planning spatiotemporal surveillance of disease vectors. We frame survey planning as an optimization problem with the objective of identifying a calendar of sampling locations that maximizes the expected information regarding some goal. Here we consider the goals of understanding associations between environmental factors and tick presence and minimizing uncertainty in high risk areas. We illustrate our proposed BED workflow using an ongoing tick surveillance study in South Carolina parks. Following a model comparison study based on two years of initial data, several techniques for finding optimal surveys were compared to random sampling. Two optimization algorithms found surveys better than all replications of random sampling, while a space-filling heuristic performed favorably as well. Further, optimal surveys of just 20 visits were more effective than repeating the schedule of 111 visits used in 2021. We conclude that BED shows promise as a flexible and rigorous means of survey design for vector control, and could help alleviate pressure on local agencies by limiting the resources necessary for accurate information on arthropod distributions. We have made the code for our BED workflow publicly available on Zenodo to help promote the application of these methods to future surveillance efforts.

Optimization of lignin precipitation from black liquor using organic acids and its valorization by preparing lignin nanoparticles

This work focuses on the precipitation of lignin from kraft black liquor (BL) along with its valorization into lignin nanoparticles (LNP). Two organic acids namely, acetic acid, and lactic acid were used for the precipitation of lignin as an alternative to sulfuric acid. An optimization study was carried out to determine the effect of three key variables, namely acid type, temperature, and pH, on the isolation yield and purity of lignin. The study showed that all factors primarily influenced the lignin yield, while the purity of precipitated lignin varied only around 1 % between minimum to maximum purity. Further, the acid precipitation method was selected for the preparation of LNP. The study aimed to observe the effect of pH, lignin concentration, and surfactant concentration over the properties of the prepared nanoparticles. The results showed that a smaller nanoparticle size and maximization of phenolic content was achieved with a lignin concentration of 35 mg/mL, a surfactant concentration of 10 % (w/w lignin), and a pH of 5. Additionally, the antibacterial activity of LNPs against Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa bacteria was evaluated. The results showed only minor activity against Staphylococcus aureus. Overall, the study demonstrates the potential method for precipitation and valorization of lignin through the production of LNP with desirable properties.

Optimisation of electrochemical sensors based on molecularly imprinted polymers: from OFAT to machine learning

Molecularly imprinted polymers (MIPs) rely on synthetic engineered materials able to selectively bind and intimately recognise a target molecule through its size and functionalities. The way in which MIPs interact with their targets, and the magnitude of this interaction, is closely linked to the chemical properties derived during the polymerisation stages, which tailor them to their specific target. Hence, MIPs are in-deep studied in terms of their sensitivity and cross-reactivity, further being used for monitoring purposes of analytes in complex analytical samples. As MIPs are involved in sensor development within different approaches, a systematic optimisation and rational data-driven sensing is fundamental to obtaining a best-performant MIP sensor. In addition, the closer integration of MIPs in sensor development requires that the inner properties of the materials in terms of sensitivity and selectivity are maintained in the presence of competitive molecules, which focus is currently opened. Identifying computational models capable of predicting and reporting the best-performant configuration of electrochemical sensors based on MIPs is of immense importance. The application of chemometrics using design of experiments (DoE) is nowadays increasingly adopted during optimisation problems, which largely reduce the number of experimental trials. These approaches, together with the emergent machine learning (ML) tool in sensor data processing, represent the future trend in design and management of point-of-care configurations based on MIP sensing. This review provides an overview on the recent application of chemometrics tools in optimisation problems during development and analytical assessment of electrochemical sensors based on MIP receptors. A comprehensive discussion is first presented to cover the recent advancements on response surface methodologies (RSM) in optimisation studies of MIPs design. Therefore, the recent advent of machine learning in sensor data processing will be focused on MIPs development and analytical detection in sensors.

Enhanced benzene vapor adsorption through microwave-assisted fabrication of activated carbon from peanut shells using ZnCl2 as an activating agent

Herein, microwave-assisted activated carbon (MW-AC) was fabricated from peanut shells using a ZnCl2 activator and utilized for the first time to eliminate benzene vapor as a volatile organic compound (VOC). During the MW-AC production process, which involved two steps-microwave treatment and muffle furnace heating-we investigated the effects of various factors and achieved the highest iodine number of 1250 mg/g. This was achieved under optimal operating conditions, which included a 100% impregnation ratio, CO2 as the gas in the microwave environment, a microwave power set at 500 W, a microwave duration of 10 min, an activation temperature of 500 °C and an activation time of 45 min. The structural and morphological properties of the optimized MW-AC were assessed through SEM, FTIR, and BET analysis. The dynamic adsorption process of benzene on the optimized MW-AC adsorbent, which has a significant BET surface area of 1204.90 m2/g, was designed using the Box-Behnken approach within the response surface methodology. Under optimal experimental conditions, including a contact duration of 80 min, an inlet concentration of 18 ppm, and a temperature of 26 °C, the maximum adsorption capacity reached was 568.34 mg/g. The experimental data are better described by the pseudo-second-order kinetic model, while it is concluded that the equilibrium data are better described by the Langmuir isotherm model. MW-AC exhibited a reuse efficiency of 86.54% for benzene vapor after five consecutive recycling processes. The motivation of the study highlights the high adsorption capacity and superior reuse efficiency of MW-AC adsorbent with high BET surface area against benzene pollutant. According to our results, the developed MW-AC presents itself as a promising adsorbent candidate for the treatment of VOCs in various industrial applications.

Application of a magnetically separable Zr-MOF for fast extraction of palladium before its spectrophotometric detection

In this research, a novel magnetic zirconium-based metal-organic framework (Fe3O4@SiO2@MIP-202, MMOF), was fabricated, fully characterized, and applied for the batch-mode solid phase extraction of trace amounts of Pd2+ ions from water and wastewater samples before its spectrophotometric detection. Pd2+ ions were desorbed from MMOF by nitric acid and were complexed by treating with KI solution to have a maximum absorbance at 410 nm. The synthesized MMOF composite showed a very large surface area (65 m2.g- 1), good magnetization (1.7 emu.g- 1) and a large pore volume (0.059 cm3.g- 1) with adsorption capacity of 194.5 mg of Pd2+ ions/g of the adsorbent. This nanosorbent boasts chemo-mechanical stability, high adsorption capacity due to its vast active sites, and facile recovery facilitated by its magnetic properties. Parameters affecting the extraction efficiency of the method were optimized as pH of the sample 7.4, volume of the sample 25 mL, 15 mg adsorbent, 1 mL of 0.1 M HNO3 eluent, with 10 and 15 min as the extraction and desorption times, respectively. The calibration curve was found to be linear across the 10.0-1500.0 µg.L- 1 range with a limit of detection of 1.05 µg.L- 1. The obtained extraction efficiency and enrichment were 98% and 245, respectively. The total analysis time was less than 30 min. This MMOF has never been used for the extraction of Pd2+ ions before.

Highly efficient ceramic membrane synthesized from sugar scum and fly ash as sustainable precursors for dyes removal

Recycling solid industrial wastes into valuable materials is always the priority solution in waste management. In this perspective, sugar scum and fly ash were used to produce an effective low-cost porous ceramic membrane. The impacts of the sintering temperature, amount of sugar scum, and sintering time on the properties of the prepared ceramic membrane were investigated and optimized using experimental design. A simultaneous rise in both the sintering temperature and the amount of sugar scum leads to a notable increase in porosity. Moreover, the simultaneous increase or decrease in the time and the amount of sugar scum causes a significant decrease in the compressive strength. The optimal conditions have been determined as a sintering temperature of 1197 °C, a sugar scum amount of 12.06 %, and a sintering time of 253 min. Under these conditions, the density, porosity, and compressive strength were found to be 2.16 g/cm³, 34.66 %, and 28.24 MPa, respectively. In addition, the obtained ceramic membrane has a water permeability of 2356.68 L/h m2 bar, a pore size in the range 0-4.5 μm, and excellent chemical resistance in both acidic and basic media. Finally, the performance of the prepared ceramic membrane was evaluated by the filtration of methylene blue. The results indicate that sugar scum and fly ash are suitable precursors to manufacture an effective ceramic membrane for the treatment of wastewater.

UV-light-responsive Ag/TiO2/PVA nanocomposite for photocatalytic degradation of Cr, Ni, Zn, and Cu heavy metal ions

The rapid growth of industrialization has led to the uncontrolled pollution of the environment, and rapid action is needed. This study synthesized Ag/TiO2/polyvinyl alcohol (PVA) nano photocatalyst for promising light-derived photocatalytic removal of heavy metal ions. The design of experiment (DOE) was used to study the effect of important factors (pH, reaction time, and photocatalyst dosage) to maximize the final performance of the photocatalyst. In the optimized condition, the Ag/TiO2/PVA nano-photocatalyst removed more than 94% of Cr6+ in 180 min, and the efficiency was more than 70% for Cu2+, Zn2+, and Ni2+ metal ions. The adsorption of the heavy metal ions on the photocatalyst was described well with the Langmuir isotherm, while the pseudo-second-order linear kinetic model fitted with the experimental data. The nano-photocatalyst's stability was confirmed after maintaining its performance for five successive runs. The enhanced photocatalytic activity for the heavy metal ions removal can be attributed to the presence of metallic silver nanoparticles (electron transfer and plasmonic fields mechanisms) and PVA, which delayed the recombination of electron-hole. The synthesized ternary Ag/TiO2/PVA nano-photocatalyst showed promising performance for the elimination of heavy metal ions and can be used for environmental remediation purposes.

Chronic brain functional ultrasound imaging in freely moving rodents performing cognitive tasks

BACKGROUND

Functional ultrasound imaging (fUS) is an emerging imaging technique that indirectly measures neural activity via changes in blood volume. Chronic fUS imaging during cognitive tasks in freely moving animals faces multiple exceptional challenges: performing large durable craniotomies with chronic implants, designing behavioral experiments matching the hemodynamic timescale, stabilizing the ultrasound probe during freely moving behavior, accurately assessing motion artifacts, and validating that the animal can perform cognitive tasks while tethered.

NEW METHOD

We provide validated solutions for those technical challenges. In addition, we present standardized step-by-step reproducible protocols, procedures, and data processing pipelines. Finally, we present proof-of-concept analysis of brain dynamics during a decision making task.

RESULTS

We obtain stable recordings from which we can robustly decode task variables from fUS data over multiple months. Moreover, we find that brain wide imaging through hemodynamic response is nonlinearly related to cognitive variables, such as task difficulty, as compared to sensory responses previously explored.

COMPARISON WITH EXISTING METHODS

Computational pipelines in fUS are nascent and we present an initial development of a full processing pathway to correct and segment fUS data.

CONCLUSIONS

Our methods provide stable imaging and analysis of behavior with fUS that will enable new experimental paradigms in understanding brain-wide dynamics in naturalistic behaviors.

Beyond single paradigms, pipelines, and outcomes: Embracing multiverse analyses in psychophysiology

Psychophysiological research is an inherently complex undertaking due to the nature of the data, and its analysis is characterized by many decision points that shape the final dataset and a study's findings. These decisions create a "multiverse" of possible outcomes, and each decision from study conceptualization to statistical analysis can lead to different results and interpretations. This review describes the concept of multiverse analyses, a methodological approach designed to understand the impact of different decisions on the robustness of a study's findings and interpretation. The emphasis is on transparently showcasing different reasonable approaches for constructing a final dataset and on highlighting the influence of various decision points, from experimental design to data processing and outcome selection. For example, the choice of an experimental task can significantly impact event-related brain potential (ERP) scores or skin conductance responses (SCRs), and different tasks might elicit unique variances in each measure. This review underscores the importance of transparently embracing the flexibility inherent in psychophysiological research and the potential consequences of not understanding the fragility or robustness of experimental findings. By navigating the intricate terrain of the psychophysiological multiverse, this review serves as an introduction, helping researchers to make informed decisions, improve the collective understanding of psychophysiological findings, and push the boundaries of the field.

Science of forests and floods: The quantum leap forward needed, literally and metaphorically

A century of research has generated considerable disagreement on the effect of forests on floods. Here we call for a causal inference framework to advance the science and management of the effect of any forest or its removal on flood severity and frequency. The causes of floods are multiple and chancy and, hence, can only be investigated via a probabilistic approach. We use the stochastic hydrology literature to infer a blueprint framework which could guide future research on the understanding and prediction of the effects of forests on floods in environments where rain is the dominant form of precipitation. Drawing parallels from other disciplines, we show that the introduction of probability in forest hydrology could stimulate a gestalt switch in the science of forests and floods. In light of increasing flood risk caused by climate change, this probabilistic framework can help policymakers develop robust forest and water management plans based on a defensible and clear understanding of floods.

Assessment of metal extraction from e-waste using supported IL membrane with reliable comparison between RSM regression and ANN framework

Recently, efficient techniques to remove indium ions from e-waste have been described due to their critical application. This paper illustrates the recovery of indium ions from an aqueous solution using a liquid membrane. CyphosIL 104 described the excellent potential for the extraction of indium ions. Evaluation of the five process parameters, such as indium concentration (10-100 mg/L), carrier concentration (0.05-0.2 mol/L), feed phase acidity (0.01-3 mol/L), chloride ion concentration (0.5-4 mol/L) and the stripping agent concentration (0.1-5 mol/L) were conducted. The interactive impacts of the various parameters on the extraction efficiency were investigated. The response surface methodology (RSM) and artificial neural network (ANN) were employed to model and compare the FS-SLM process results. RSM model with a quadratic equation (R2 = 0.9589) was the most suitable model for describing the efficiency. ANN model with six neurons showed a prediction of extraction efficiency with R2 = 0.9860. The best-optimized data were: 73.92 mg/L, 0.157 mol/L, 1.386 mol/L, 2.99 mol/L, and 3.06 mol/L for indium concentration, carrier concentration, feed phase acidity, chloride ion concentration, and stripping agent concentration. The results achieved by RSM and ANN led to an experimentally determined extraction efficiency of 93.91%, and 94.85%, respectively. It was close to the experimental data in the optimization condition (95.77%). Also, the evaluation shows that the ANN model has a better prediction and fitting ability to reach outcomes than the RSM model.

Optimization of adaptive neuro-fuzzy inference system (ANFIS) parameters via Box-Behnken experimental design approach: The prediction of chromium adsorption

Prediction of adsorption via Adaptive Neuro-Fuzzy Inference System (ANFIS) can save the cost and time in practical applications. Chromium (VI) adsorption data obtained at different temperature, activated carbon dosage and pH values were evaluated by using MATLAB ANFIS. In order to achieve prediction of adsorption via ANFIS with acceptable error values, optimum membership function (MF) and optimum number of MF were determined by using Box-Behnken experimental design (BBD) method. In order to determine the optimum number of MF for each input, all combinations given in BBD matrix were examined via ANFIS, then, regression models for each MFs were developed between the root mean square error (RMSE) and MF numbers of each input. The most used five membership functions (triangular, trapezoidal, generalized bell shaped, Gaussian, Gaussian 2) were investigated. According to the analysis of variance (ANOVA), regression models developed for the test data with triangular and trapezoidal membership functions were significant in the 95 % confidence level. Predictions were employed via ANFIS by using optimum MF numbers of each inputs (6, 6, 3 for triangular MF and 8, 8, 2 for trapezoidal MF). Consequently, the best Cr(VI) adsorption percentage prediction (RMSE = 1.9084 and R2 = 0.992) was obtained by using triangular membership function with optimum MF numbers. Response surface plots, which gives the relationship between MF numbers and RMSE values for triangular MF were also evaluated. In this study, it was demonstrated that MF type and numbers, which are crucial for good prediction via ANFIS grid partition method, can be determined optimally by applying experimental design methodology.

Optimization of the Erythrosine-mediated photodynamic therapy against Escherichia coli using response surface methodology

BACKGROUND

The efficacy of photodynamic therapy (PDT) depends on the combination of light and a photosensitizer for inactivation of microorganisms. However, finding the ideal conditions for the factors involved in this technique is time and cost-consuming. The rotational composite central design (RCCD) is a tool that can be allied with PDT to achieve precise results within a shorter working time.

METHODS

This study used the response surface methodology to optimize the parameters of PDT mediated by Erythrosine (ERY) and green light-emitting diodes (LED) in different Escherichia coli strains by applying RCCD.

RESULTS

The RCCD predicted optimum values of ERY and light exposure on PDT. According to the experimental results, the light exposure time showed the most significant influence on the inactivation of the evaluated bacteria. The optimized operating conditions were validated in laboratory tests, and no viable cells were recovered with ERY at 116 µmol L-1 and 30 min of light (33.34 J cm2) for E. coli ATCC 25922, 108 µmol L-1 and 40 min (44.38 J cm2) for E. coli ATCC 35218, and 108 µmol L-1 and 29.3 min (32.5 J cm2) for E. coli O157:H7 EDL 933.

CONCLUSION

The adjusted polynomial models provided accurate information on the combined effects of ERY and lighting time with green LED on PDT. The application of the RCCD, in addition to reducing the number of experiments, also allows for increased quantity and quality of the results. Therefore, surface response methodology combined with PDT is a promising approach to inactivate E. coli.

"Synthesis and characterization of mesoporous V-Mo-MCM-41 nanocatalysts: Enhancing efficiency in oxalic acid synthesis"

Mesoporous V-Mo-MCM-41 nano molecular sieves were synthesized via the direct hydrothermal method, employing tetraethyl orthosilicate (TEOS) as a silica source and cetyltrimethylammonium bromide (CTAB) as a surfactant template. Comprehensive characterization through N2-adsorption (BET), Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and scanning electron microscopy-energy-dispersive X-ray spectroscopy (SEM-EDX) confirmed the mesoporous nature of the catalysts, revealing variations in specific surface area and a significant pore diameter of 6.3 nm, enhancing their versatility for various chemical transformations. The nanoscale structure was further validated through XRD analysis and SEM images. The catalytic efficiency of V-Mo-MCM-41 was demonstrated by synthesizing oxalic acid from molasses, and a response surface methodology (RSM) study on four key variables revealed a maximum yield of 83 % within 1 h using minimal sulfuric acid, showcasing the effectiveness of the prepared catalysts.

Optimal design of radial tire section layout based on thermal fatigue life improving

The design of tire fatigue life was optimized by combining approximate model and finite element simulation, and compared with the tire endurance test results. The design variables are selected through sensitivity analysis of materials in various regions of the tire, and the thermal fatigue life of the tire is used as the objective function, and the approximate relationship between the design variables and the objective function is fitted based on the approximate model method, and the approximate model is optimized using genetic algorithm to find the optimal solution. The fatigue life of the tire is improved by about 25 %.

Microwave-assisted extraction based on emulsion breaking with natural deep eutectic solvent for vegetable oil sample preparation prior to elemental determination by ICP OES

This study presents a novel and efficient method for the extraction of Al, Ca, Cr, Cu, K, Mg, Mn, and Zn in vegetable oil samples using a Natural Deep Eutectic Solvent (NADES) as an extractor combined with microwave radiation (MW) in an emulsion system. The NADES prepared with choline chloride:oxalic acid:water (1:1:4 molar ratio) provided a high extraction rate using 5.0 mL of the sample, 1.7 mL of NADES, and 1.3 mL of Triton X-100. The optimum conditions were obtained with 36 s of vortexing, 5 min of extraction, and 10 s for emulsion-breaking in MW. Under these conditions, recoveries ranged from 91% to 110% and relative standard deviations <9.0% were obtained. The limit of quantification (mg kg-1) was: 0.018 (Al), 0.032 (Ca), 0.007 (Cr), 0.006 (Cu), 0.013 (K), 0.027 (Mg), 0.002 (Mn), and 0.019 (Zn). The proposed method showed comparable results to reference methods and advantages, such as speed, low cost, and simplicity. The combination of NADES and MW represents a sustainable and innovative approach to the elemental determination composition of vegetable oils and contributes to advances in sample preparation methods.

Systematic heterogenization revisited: Increasing variation in animal experiments to improve reproducibility?

Life sciences are currently facing a reproducibility crisis. Originally, the crisis was born out of single alarming failures to reproduce findings at different times and locations. Nowadays, systematic studies indicate that the prevalence of irreproducible research does in fact exceed 50%. Viewed from a rather cynical perspective, Fett's law of the lab "Never replicate a successful experiment" has thus taken on a completely new meaning. In this respect, animal research has come under particular scrutiny, as the stakes are high in terms of both research ethics and societal impact. To counteract this, it is essential to identify sources of poor reproducibility as well as to iron out these failures. We here review the current debate, briefly discuss potential reasons, and summarize steps that have already been undertaken to improve reproducibility in animal research. By the example of classical behavioural phenotyping studies, we particularly highlight the role strict standardization plays in exacerbating the crisis, and review the concept of systematic heterogenization as an alternative strategy to deal with variation in animal studies. Briefly, we argue that systematic variation rather than strict homogenization of experimental conditions benefits the robustness of research findings, and hence their reproducibility. To this end, we will present concrete examples for systematically heterogenized experiments and provide a practical guide on how to apply systematic heterogenization in experimental practice.

VOIS: A framework for recording Voice Over Internet Surveys

Verbal data provide researchers insight beyond that offered by text-based responses, including tone, reasoning elaboration, and experienced difficulty, among other processes. Additionally, it offers a less cognitively taxing way for participants to provide long responses. Verbal data collection methods are found in a variety of fields, mostly conducted in lab-based settings or requiring specialized hardware. Restricting verbal protocols to lab-based settings can have several drawbacks, including smaller sample sizes, biased populations, reduced adoption, and incompatibility with potential social distancing requirements. No method currently exists for researchers to collect verbal data within major online survey collection platforms. The current paper offers a user-friendly approach for collecting verbal data online, where a researcher can copy and paste JavaScript code into the desired survey platform. By providing a framework that does not require any advanced programming ability, researchers can collect verbal data in a scalable way using familiar modalities.

Mind the gap - Relevant design for laboratory oil exposure of fish as informed by a numerical impact assessment model

Laboratory experiments provide knowledge of species-specific effects thresholds that are used to parameterize impact assessment models of oil contamination on marine ecosystems. Such experiments typically place individuals of species and life stages in tanks with different contaminant concentrations. Exposure concentrations are usually fixed, and the individuals experience a shock treatment being moved from clean water directly into contaminated water and then back to clean water. In this study, we use a coupled numerical model that simulates ocean currents and state, oil dispersal and fate, and early life stages of fish to quantify oil exposure histories, specifically addressing oil spill scenarios of high rates and long durations. By including uptake modelling we also investigate the potential of buffering transient high peaks in exposure. Our simulation results are the basis for a recommendation on the design of laboratory experiments to improve impact assessment model development and parameterization. We recommend an exposure profile with three main phases: i) a gradual increase in concentration, ii) a transient peak that is well above the subsequent level, and iii) a plateau of fixed concentration lasting ∼3 days. In addition, a fourth phase with a slow decrease may be added.

Using meta-analysis to explore the roles of global upwelling exposure and experimental design in bivalve responses to low pH

Low pH conditions, associated with ocean acidification, represent threats to many commercially and ecologically important organisms, including bivalves. However, there are knowledge gaps regarding factors explaining observed differences in biological responses to low pH in laboratory experiments. Specific sources of local adaptation such as upwelling exposure and the role of experimental design, such as carbonate chemistry parameter changes, should be considered. Linking upwelling exposure, as an individual oceanographic phenomenon, to responses measured in laboratory experiments may further our understanding of local adaptation to global change. Here, meta-analysis is used to test the hypotheses that upwelling exposure and experimental design affect outcomes of individual, laboratory-based studies that assess bivalve metabolic (clearance and respiration rate) responses to low pH. Results show that while bivalves generally decrease metabolic activity in response to low pH, upwelling exposure and experimental design can significantly impact outcomes. Bivalves from downwelling or weak upwelling areas decrease metabolic activity in response to low pH, but bivalves from strong upwelling areas increase or do not change metabolic activity in response to low pH. Furthermore, experimental temperature, exposure time and magnitude of the change in carbonate chemistry parameters all significantly affect outcomes. These results suggest that bivalves from strong upwelling areas may be less sensitive to low pH. This furthers our understanding of local adaptation to global change by demonstrating that upwelling alone can explain up to 49 % of the variability associated with bivalve metabolic responses to low pH. Furthermore, when interpreting outcomes of individual, laboratory experiments, scientists should be aware that higher temperatures, shorter exposure times and larger changes in carbonate chemistry parameters may increase the chance of suppressed metabolic activity.

Can cognitive neuroscience solve the lab-dilemma by going wild?

Reproducibility, measurability, and refutability are the foundation of the scientific method applied to empirical work. In the study of animal and human behavior, experimental protocols conducted in the lab are the most reliable means by which scientists can operationalize behaviors using controlled and parameterized setups. However, whether observations in the lab fully generalize in the real world remain legitimately disputed. The notion of "experimental design" was originally intended to ensure the generalizability of experimental findings to real-world situations. Experiments in the wild are more frequently explored and significant technological advances have been made allowing mobile neuroimaging. Yet some methodological limitations remain when testing scientific hypotheses in ecological conditions. Herein, we discuss the limitations of inferential processes derive from empirical observations in the wild. The multi-causal property of an ecological situation often lacks controls, and this major concern may prevent the replication and the reliability of behavioral observations. We discuss the epistemological and historical grounds of the induction process for behavioral and cognitive neurosciences and provide some possible heuristics for In situ experimental designs compatible with psychophysics in the wild.

Development and evaluation of quercetin enriched bentonite-reinforced starch-gelatin based bioplastic with antimicrobial property

Nowadays novel bio-based materials have been widely employed in food and pharmaceutical industry because of their wide acceptability by the consumers rather than the synthetic materials nevertheless, they possess poor mechanical properties. Reinforcement of biopolymers with intercalation of mineral clays can improve their physicochemical properties; so that such biocomposites possess superior barrier and mechanical properties as well as stability and drug loading efficacy. Thus, this research aimed at formulating quercetin loaded bentonite-reinforced starch-gelatin based novel bioplastic with diverse applicability. The methodology of the study included Box Behnken optimization as well as physical, structural, mechanical and antimicrobial properties evaluation of the proposed reinforced bioplastics. Amount of starch, bentonite and glycerin were the independent variables while the tensile strength, swelling index and elongation percentage were studied as dependent variables. The optimized bioplastic film showed excellent physicochemical and morphological characteristics and also for efficient percentage drug content. The antimicrobial activity showed the highest activity against Escherichia coli followed by Pseudomonas aeruginosa and Staphylococcus aureus. Scanning electron microscopy (SEM) revealed the non-homogenous nature of the film. Generally, the results revealed that quercetin loaded bentonite-reinforced starch-gelatin based could be used as ecological friendly active food packaging as well as pharmaceutical application with significant antimicrobial properties.

Transforming a volcanic rock powder waste into an efficient adsorbent to remove dyes (acid green 16 and acid red 97) and metals (Ag+, Co2+, and Cu2+) from water

This study searched for the best synthesis route for producing an adsorbent from the alkaline fusion of volcanic rock powder waste. The samples synthesized under different conditions of temperature and alkalizing ratio/precursor material, nine in total (NP.F, NP.F1, NP.F2, ...NP.F8 ), were used in the adsorption of acid green 16 (AG 16) and acid red 97 (AR 97) dyes and Ag+, Co2+, and Cu2+ ions. Subsequently, the 22 central composite rotational design (CCRD) was applied, and the effects of the alkalizing ratio (NaOH)/volcanic rock (VR) and temperature (T) on the synthesis process were analyzed in terms of their influence on the physical properties of the materials and in the process of adsorption of contaminants. From the experimental design, it can be seen that the independent variables alkalizing ratio/volcanic rock and temperature greatly influence the characteristic and synthesis of adsorbent materials by alkaline fusion, which in turn reflects on the results achieved in the adsorption of contaminants. Therefore, the temperature of 550 °C and NaOH/VR ratio equal to 1 was the most satisfactory synthesis route to obtain high values of adsorption capacity (q, mg g-1) and removal (R, %) for all studied contaminants, as well as the optimization of the physical characteristics of the material. For example, the adsorption capacity of dye AG 16 was 49.1 mg g-1, and for Ag+ was 66.2 mg g-1, while the removal percentages were 97.6% and 93.4%, respectively. This approach made it possible to transform volcanic rock powder wastes into an efficient adsorbent to treat contaminated waters with dyes and metals.

Empirical Quantification of Predictive Uncertainty Due to Model Discrepancy by Training with an Ensemble of Experimental Designs: An Application to Ion Channel Kinetics

When using mathematical models to make quantitative predictions for clinical or industrial use, it is important that predictions come with a reliable estimate of their accuracy (uncertainty quantification). Because models of complex biological systems are always large simplifications, model discrepancy arises-models fail to perfectly recapitulate the true data generating process. This presents a particular challenge for making accurate predictions, and especially for accurately quantifying uncertainty in these predictions. Experimentalists and modellers must choose which experimental procedures (protocols) are used to produce data used to train models. We propose to characterise uncertainty owing to model discrepancy with an ensemble of parameter sets, each of which results from training to data from a different protocol. The variability in predictions from this ensemble provides an empirical estimate of predictive uncertainty owing to model discrepancy, even for unseen protocols. We use the example of electrophysiology experiments that investigate the properties of hERG potassium channels. Here, 'information-rich' protocols allow mathematical models to be trained using numerous short experiments performed on the same cell. In this case, we simulate data with one model and fit it with a different (discrepant) one. For any individual experimental protocol, parameter estimates vary little under repeated samples from the assumed additive independent Gaussian noise model. Yet parameter sets arising from the same model applied to different experiments conflict-highlighting model discrepancy. Our methods will help select more suitable ion channel models for future studies, and will be widely applicable to a range of biological modelling problems.

Stressor fluctuations alter mechanisms of seagrass community responses relative to static stressors

Ecosystems are increasingly affected by multiple anthropogenic stressors that contribute to habitat degradation and loss. Natural ecosystems are highly dynamic, yet multiple stressor experiments often ignore variability in stressor intensity and do not consider how effects could be mediated across trophic levels, with implications for models that underpin stressor management. Here, we investigated the in situ effects of changes in stressor intensity (i.e., fluctuations) and synchronicity (i.e., timing of fluctuations) on a seagrass community, applying the stressors reduced light and physical disturbance to the sediment. We used structural equation models (SEMs) to identify causal effects of dynamic multiple stressors on seagrass shoot density and leaf surface area, and abundance of associated crustaceans. Responses depended on whether stressor intensities fluctuated or remained static. Relative to static stressor exposure at the end of the experiment, shoot density, leaf surface area, and crustacean abundance all declined under in-phase (synchronous; 17, 33, and 30 % less, respectively) and out-of-phase (asynchronous; 11, 28, and 39 % less, respectively) fluctuating treatments. Static treatment increased seagrass leaf surface area and crustacean abundance relative to the control group. We hypothesised that crustacean responses are mediated by changes in seagrass; however, causal analysis found only weak evidence for a mediation effect via leaf surface area. Changes in crustacean abundance, therefore, were primarily a direct response to stressors. Our results suggest that the mechanisms underpinning stress responses change when stressors fluctuate. For instance, increased leaf surface area under static stress could be caused by seagrass acclimating to low light, whereas no response under fluctuating stressors suggests an acclimation response was not triggered. The SEMs also revealed that community responses to the stressors can be independent of one another. Therefore, models based on static experiments may be representing ecological mechanisms not observed in natural ecosystems, and underestimating the impacts of stressors on ecosystems.

Aggregation in experimental studies with microparticles: Experimental settings change particle size distribution during exposure

The ubiquitous occurrence of microplastics is raising broad concerns and motivating effect studies. In these studies, however, particle behaviour in the water and aggregation are rarely considered leading to contradictory results reported by different studies. Using an environmentally relevant experimental setup with Daphnia magna as a test organism, we investigated how experimental conditions affect particle aggregation and the aggregate heterogeneity in terms of the particle size distribution. The experimental factors considered were (1) exposure duration (48 h vs 120 h), (2) the total mass of suspended solids (0-10 mg/l) composed of natural mineral particles (kaolin) and microplastics, (3) the proportion of the microplastics in the particle suspension (0-10% by mass), (4) dissolved organic matter (DOM; 0 vs 20 mg agarose/l), and (5) presence of the test organism (0 and 5 daphnids/vial). We found that particle aggregation occurs within the first 48 h of incubation in all treatments, no substantial change in the aggregate heterogeneity is observed afterwards. The median aggregate size was ∼2-fold higher than the nominal average particle size of clay and microplastics in the stock suspensions used to prepare the experimental mixtures. The strongest positive driver of the aggregate size and heterogeneity was DOM, followed by the presence of daphnids and the concentration of the suspended solids in the system. Also, microplastics were found to facilitate aggregation, albeit they were the weakest contributor. Moreover, besides directly increasing the aggregation, DOM relaxed the effects of the total solids and daphnids on the aggregate size. Thus, the particle size distribution was established early during the exposure and shaped by all experimental factors and their interactions. These findings improve our understanding of the processes occurring in the exposure systems when conducting effect studies with microplastics and other particulates and demonstrate the necessity to access the particle size distribution to characterise the exposure. Aslo, relevant experimental designs with microplastics must include relevant natural particulates and DOM to ensure environmentally realistic particle behaviour and adequate particle-biota interactions.

Optimization of enhanced oil recovery using ASP solution

Many studies have been conducted to focused on developing an optimal alkali/surfactant/polymer (ASP) composition to increase the recovered fraction of oil in reservoirs that have already undergone water injection. To analyze the effect of alkali (Na2CO3), surfactant (lauryl sodium sulfate), and polymer (commercial xanthan gum) concentration on oil recovery, a complete factorial experimental design was performed with combinations of three variables (alkali, surfactant, and polymer) and three central point replications (2³ + 3). The experiments were carried out on a core holder using rock samples from the Botucatu formation. The simulated oil reservoirs have an average permeability of 348 mD and a temperature of 60 °C. The crude oil was acquired from the Carmópolis field, with 25.72 °API. Synthetic production water containing 40,000 mg L-1 of NaCl and 13,000 mg L-1 of Na2SO4 was injected through an HPLC pump to saturate the rock samples and to recover the oil in the secondary step. From the experimental results, it was verified that the surfactant and polymer concentrations are the most statistically significant independent variables and that first-order interactions are not statistically significant for the process. The oil recovery factors in the secondary stage ranged between 30 and 36 % of the OOIP, which are within the range reported in the literature. The optimal composition of the ASP fluid obtained a recovered fraction of oil of 62 % in the advanced step. Other combinations reported in the literature used higher concentrations of alkali, surfactant, and polymer with lower recoveries and higher cost in the injection design. Thus, the present study highlights the necessity to investigate the performance of each component of the ASP solution. In addition, the results obtained in this study are very attractive for possible full-scale applications.

Tailored synthesis of iron oxide nanoparticles for specific applications using a statistical experimental design

For this work, iron oxide nanoparticles are synthesized by the co-precipitation method with stoichiometric amounts of Fe2+ and Fe3+ salts in a 1:2 ratio in distilled water and the pH is raised by adding an aqueous ammonia solution by controlled dripping. Nanoparticles precipitating after the reaction time are magnetically filtered and stored in ethanol for further analysis. Superparamagnetic Fe3O4 nanoparticles with a slight deviation from the stoichiometry are obtained, with sizes between 7.4 and 12.8 nm and saturation magnetization between 40 and 78 emu/gr. At pH 6, rod-shaped nanoparticles are obtained in addition to spherical ones. With a statistical design, it is shown how the morphological, structural and magnetic properties of the resulting nanoparticles can be manipulated by the synthesis parameters, offering many possibilities to tailor the materials to a wide range of applications.

Effect of nutrient, processing and hedonic claims on food-related perceptions: An experimental online study in Brazil and Germany

Prior studies on perceived healthiness of foods have often compared nutrient and hedonic claims, neglecting comparisons to a control condition. The effect of food claims focusing on the food processing level has received considerably less research attention, although food processing has been included in dietary guidelines in Brazil. Thus, the present study aimed to explore the effect of a control and processing claim, additionally to the effects of a nutrient. and hedonic claim, on perceptions related to a food item typically considered "less healthy" (chocolate cake). We further compared these effects between Brazil and Germany, a country where food processing is currently not included in dietary guidelines. A total of 634 lay adults were recruited in a cross-sectional online study and randomised to see the photo of a piece of cake with one of the four different claim conditions and to report their health-related perceptions of the cake. The main analyses included two-way ANCOVAs (4 claims x 2 countries) for each dependent variable controlled for gender, age and level of hunger; followed by post hoc tests. Overall, results revealed that in both countries, the claims highlighting nutrients or processing aspects rendered the perceptions of the cake healthier compared to the control claim. These effects were more pronounced among Brazilian than among German participants. Food-related perceptions mostly did not differ between the hedonic and control claim and did not differ at all between the nutrient and processing claims. In conclusion, it is noteworthy that, even for an inherently considered "less healthy food item" (chocolate cake) nutrient and processing claims increase healthiness perceptions. While our findings may imply that in both countries lay people are highly susceptible to nutrient claims, food processing aspects seem to be similarly relevant.

Automated splitting into batches for observational biomedical studies with sequential processing

Experimental design usually focuses on the setting where treatments and/or other aspects of interest can be manipulated. However, in observational biomedical studies with sequential processing, the set of available samples is often fixed, and the problem is thus rather the ordering and allocation of samples to batches such that comparisons between different treatments can be made with similar precision. In certain situations, this allocation can be done by hand, but this rapidly becomes impractical with more challenging cohort setups. Here, we present a fast and intuitive algorithm to generate balanced allocations of samples to batches for any single-variable model where the treatment variable is nominal. This greatly simplifies the grouping of samples into batches, makes the process reproducible, and provides a marked improvement over completely random allocations. The general challenges of allocation and why good solutions can be hard to find are also discussed, as well as potential extensions to multivariable settings.

Alternative ground covers and strip-tilling in CBD hemp production

OBJECTIVE

Little research has been done on managing soil health for large-scale, outdoor hemp production, contributing to the possible overuse of black plastic for weed suppression. Our experiment aimed to understand the performance of alternative ground covers including forage crops and hay as well as a less disruptive tilling method called strip-tilling compared to black plastic.

RESULTS

Yield and soil health data were taken from three experimental plantings from two different outdoor CBD hemp farms in Vermont, USA. We find that hay may be a competitive alternative to black plastic in terms of producing heavier plants. Our research also found that clover seed and hay are both more cost-effective options than black plastic which may sway some farmers to adopt these alternative ground cover options.

Signal recovery in single cell batch integration

Data integration to align cells across batches has become a cornerstone of single cell data analysis, critically affecting downstream results. Yet, how much biological signal is erased during integration? Currently, there are no guidelines for when the biological differences between samples are separable from batch effects, and thus, data integration usually involve a lot of guesswork: Cells across batches should be aligned to be "appropriately" mixed, while preserving "main cell type clusters". We show evidence that current paradigms for single cell data integration are unnecessarily aggressive, removing biologically meaningful variation. To remedy this, we present a novel statistical model and computationally scalable algorithm, CellANOVA, to recover biological signal that is lost during single cell data integration. CellANOVA utilizes a "pool-of-controls" design concept, applicable across diverse settings, to separate unwanted variation from biological variation of interest. When applied with existing integration methods, CellANOVA allows the recovery of subtle biological signals and corrects, to a large extent, the data distortion introduced by integration. Further, CellANOVA explicitly estimates cell- and gene-specific batch effect terms which can be used to identify the cell types and pathways exhibiting the largest batch variations, providing clarity as to which biological signals can be recovered. These concepts are illustrated on studies of diverse designs, where the biological signals that are recovered by CellANOVA are shown to be validated by orthogonal assays. In particular, we show that CellANOVA is effective in the challenging case of single-cell and single-nuclei data integration, where the recovered biological signals are replicated in an independent study.

Correctly establishing evidence for cue combination via gains in sensory precision: Why the choice of comparator matters

Studying how sensory signals from different sources (sensory cues) are integrated within or across multiple senses allows us to better understand the perceptual computations that lie at the foundation of adaptive behaviour. As such, determining the presence of precision gains - the classic hallmark of cue combination - is important for characterising perceptual systems, their development and functioning in clinical conditions. However, empirically measuring precision gains to distinguish cue combination from alternative perceptual strategies requires careful methodological considerations. Here, we note that the majority of existing studies that tested for cue combination either omitted this important contrast, or used an analysis approach that, unknowingly, strongly inflated false positives. Using simulations, we demonstrate that this approach enhances the chances of finding significant cue combination effects in up to 100% of cases, even when cues are not combined. We establish how this error arises when the wrong cue comparator is chosen and recommend an alternative analysis that is easy to implement but has only been adopted by relatively few studies. By comparing combined-cue perceptual precision with the best single-cue precision, determined for each observer individually rather than at the group level, researchers can enhance the credibility of their reported effects. We also note that testing for deviations from optimal predictions alone is not sufficient to ascertain whether cues are combined. Taken together, to correctly test for perceptual precision gains, we advocate for a careful comparator selection and task design to ensure that cue combination is tested with maximum power, while reducing the inflation of false positives.

Invasive biomass algae valorization: Assessment of the viability of Sargassum seaweed as pozzolanic material

The present study aims to develop the operating conditions to produce ash from marine biomass i.e. Sargassum seaweed in order to consider their ash as pozzolanic materials. An experimental design is used to determine the most significant parameters of the ash elaboration. The parameters of the experimental design are calcination temperature (600 and 700 °C), granulometry of raw biomass (diameter D < 0.4 mm and 0.4 mm < D < 1 mm) and content by mass of algae (67 wt% of Sargassum fluitans and 100 wt% of Sargassum fluitans). The influence of these parameters on the yield of calcination, specific density, loss on ignition of ash and pozzolanic activity of ash are studied. At the same time, texture and a number of oxides in ash are observed by scanning electron microscopy. The first results show that in order to obtain light ash, a mixture of Sargassum (67% by mass of Sargassum fluitans + 33% by mass of Sargassum natans) of diameter (0.4 mm < diameter <1 mm) should be burnt at 600 °C for 3 h. In the second part, it appears that the morphological and thermal degradation characteristics of the Sargassum algae ash are similar to pozzolanic materials ones. Nevertheless, Chapelle tests, chemical composition and structural surface, crystallinity show that the Sargassum algae ash is not a pozzolanic-like material.

Optimization and synthesis of a novel sorbent composite based on magnetic chitosan-amine-functionalized bimetallic MOF for the simultaneous dispersive solid-phase microextraction of four aflatoxins in real water, herbal distillate, and food samples

Aflatoxins (AFs), an important category of pollutants, are formed in many foods and adversely affect human health. Therefore, their determination is critical to ensuring human food health. An efficient dispersive solid-phase microextraction technique was developed as a simple and straightforward sample preparation technique for determination of four aflatoxins using a high-performance liquid chromatography (HPLC) fluorescence detector. A novel efficient, green sorbent for extracting AFs was synthesized based on hydrothermal and chemical strategies. The amounts of three sorbent components were optimized using a mixture design (simplex lattice design), including 14 experiments. The optimal amount of amino-bimetallic Fe/Ni-MIL-53 nanospheres, chitosan, and magnetic Fe3O4 nanoparticles as sorbent components was 0.87, 0.67, and 0.47 g, respectively. Also, various factors affecting the process of AF determination were studied and optimized in two successive experimental designs, including the definitive screening design and the Box-Behnken design. Under optimal conditions, the linear ranges for measuring aflatoxin B1, aflatoxin B2, aflatoxin G1, and aflatoxin G2 were 0.05-82.6, 0.07-86.4, 0.08-85.7, and 0.07-89.5 ng mL-1, respectively. The relative standard deviations under inter-day and intra-day conditions for measuring AFs at three analyte concentrations were determined in triplicate analysis and were in the ranges of 3.7-4.6% and 4.9-6.1% for water sample analysis, respectively. The qualitative detection limits for determining AFs were between 0.01 and 0.05 ng mL-1. The pre-concentration factor of the method for measuring AFs ranged from 739.7 to 802.1. The proposed method was used for determining AFs in several real samples, including herbal distillate, black tea, corn, and real water samples. The relative recovery and standard deviation were 87.8-97.8% and 4.10-6.82%, respectively.

Improving performance of human action intent recognition: Analysis of gait recognition machine learning algorithms and optimal combination with inertial measurement units

Human action intent recognition has become increasingly dependent on computational accuracy, real-time responsiveness, and model lightness. Model selection, data filtering, and experimental design are three critical factors for the recognition of human intention in research. However, the performance of machine learning algorithms can vary depending on factors such as sensor location, the number of sensors used, channel selection, and dimensional combinations. Moreover, the collection of adequate and balanced data in such scenarios can be challenging. To address this issue, we present a comparative analysis of 12 commonly used machine learning algorithms for human action intention recognition. The synthetic minority oversampling technique is applied to fill in missing data. Traversing all possible combinations would require conducting 686 experiments, which is a daunting task in terms of both cost and efficiency. To tackle this challenge, we employ an orthogonal experiment design based on the Quasi-horizontal method. Our analysis indicates that lightGBM outperforms other algorithms in recognizing eight human daily activities. Furthermore, we conduct a polar difference and variance analysis based on a comprehensive balanced multi-metric orthogonal experiment for lightGBM using various sensor combinations and dimensions. The optimal combinations of different sensor numbers in terms of position, channel, and dimension are derived using this approach. Notably, our experimental design reduces the number of experiments required to only 49 times.

How venom pore placement may influence puncture performance in snake fangs

When designing experimental studies, it is important to understand the biological context of the question being asked. For example, many biological puncture experiments embed the puncture tool to a standardized depth based on a percentage of the total tool length, to compare the performance between tools. However, this may not always be biologically relevant to the question being asked. To understand how definitions of penetration depth may influence comparative results, we performed puncture experiments on a series of venomous snake fangs using the venom pore location as a functionally relevant depth standard. After exploring variation in pore placement across snake phylogeny, we compared the work expended during puncture experiments across a set of snake fangs using various depth standards: puncture initiation, penetration to a series of depths defined by the venom pore and penetration to 15% of fang length. Contrary to our hypothesis, we found almost no pattern in pore placement between clades, dietary groups or venom toxicity. Rank correlation statistics of our experimental energetics results showed no difference in the broad comparison of fangs when different puncture depth standards were used. However, pairwise comparisons between fangs showed major shifts in significance patterns between the different depth standards used. These results imply that the interpretation of experimental puncture data will heavily depend upon which depth standard is used during the experiments. Our results illustrate the importance of understanding the biological context of the question being addressed when designing comparative experiments.

Migration study of phthalates from non-food plastic containers used in food preservation

Phthalate acid esters (PAE) are used as additives in the formulation of plastics, to increase their flexibility and transparency. They can migrate from plastic packaging to food, then cause endocrine disruption in consumers. This migration depends on the conditions of use defined for each plastic. Non-food plastics are likely to release more PAE than food-grade plastics. In Cameroon, non-food grade plastics such as old paint buckets are used by people to preserve liquid food. The present work aimed at studying the conditions and mechanism of migration of total PAE from paint buckets to pap. For this purpose, the effects of seven factors were determined through Plackett-Burman experimental design. The interactions of the most influential factors were determined through a full factorial design. The conditions of the migration of total PAE were obtained via face-centered composite design. Then experimental results of migration kinetics were modelled according to equations of pseudo-first order, pseudo-second order and intra-particle diffusion. The results revealed that the most influential factors were pH, temperature and contact time. The effects of these factors are non-linear, and their interactions have to be considered. When pap is preserved in paint buckets according to the conditions: temperature of pap >70 °C, pH of pap ≤4 or ≥10 and contact time > 2 h, as is the case in donut shops in Cameroon, the amount of total PAE released is greater than 50 μg/L. Migration of total PAE from paint buckets to pap is best described by the pseudo-second order model.

Methods in cognitive pupillometry: Design, preprocessing, and statistical analysis

Cognitive pupillometry is the measurement of pupil size to investigate cognitive processes such as attention, mental effort, working memory, and many others. Currently, there is no commonly agreed-upon methodology for conducting cognitive-pupillometry experiments, and approaches vary widely between research groups and even between different experiments from the same group. This lack of consensus makes it difficult to know which factors to consider when conducting a cognitive-pupillometry experiment. Here we provide a comprehensive, hands-on guide to methods in cognitive pupillometry, with a focus on trial-based experiments in which the measure of interest is the task-evoked pupil response to a stimulus. We cover all methodological aspects of cognitive pupillometry: experimental design, preprocessing of pupil-size data, and statistical techniques to deal with multiple comparisons when testing pupil-size data. In addition, we provide code and toolboxes (in Python) for preprocessing and statistical analysis, and we illustrate all aspects of the proposed workflow through an example experiment and example scripts.

A Continuation Technique for Maximum Likelihood Estimators in Biological Models

Estimating model parameters is a crucial step in mathematical modelling and typically involves minimizing the disagreement between model predictions and experimental data. This calibration data can change throughout a study, particularly if modelling is performed simultaneously with the calibration experiments, or during an on-going public health crisis as in the case of the COVID-19 pandemic. Consequently, the optimal parameter set, or maximal likelihood estimator (MLE), is a function of the experimental data set. Here, we develop a numerical technique to predict the evolution of the MLE as a function of the experimental data. We show that, when considering perturbations from an initial data set, our approach is significantly more computationally efficient that re-fitting model parameters while producing acceptable model fits to the updated data. We use the continuation technique to develop an explicit functional relationship between fit model parameters and experimental data that can be used to measure the sensitivity of the MLE to experimental data. We then leverage this technique to select between model fits with similar information criteria, a priori determine the experimental measurements to which the MLE is most sensitive, and suggest additional experiment measurements that can resolve parameter uncertainty.

Culture Optimization to Produce High Yields of Mycosporine-Like Amino Acids by Fischerella sp. F5

Fischerella sp. is a valuable source of active metabolites, including UV-protecting compounds, among which mycosporin-like amino acids (MAAs) can be mentioned. Mycosporine-like amino acids are attractive secondary metabolites of a wide range of microorganisms, including microalgae and cyanobacteria. Enhanced production of MAAs has been studied in different sources. This study aimed to optimize the phosphate and nitrate concentrations of the culture medium on BG11 to maximize MAAs production from Fischerella sp. F5, using response surface methodology. The extraction process from the cultures, grown in adjusted conditions, was also optimized. The results confirmed that increasing both, nitrate and phosphate concentration, in the culture medium had a positive effect on the MAAs production by Fischerella sp. F5. While, optimization of the extraction process was not led to a highly accurate predictive model; temperature, sonication time, methanol ratio, and solvent/biomass ratio exhibited significant effects on the final MAAs' concentration in partially purified extracts. In general, more optimization cultures studies need to complete these findings in reference to MAAs production and extraction from Fischerella sp. F5, for commercial-scale applications.

Training doctoral students in critical thinking and experimental design using problem-based learning

BACKGROUND

Traditionally, doctoral student education in the biomedical sciences relies on didactic coursework to build a foundation of scientific knowledge and an apprenticeship model of training in the laboratory of an established investigator. Recent recommendations for revision of graduate training include the utilization of graduate student competencies to assess progress and the introduction of novel curricula focused on development of skills, rather than accumulation of facts. Evidence demonstrates that active learning approaches are effective. Several facets of active learning are components of problem-based learning (PBL), which is a teaching modality where student learning is self-directed toward solving problems in a relevant context. These concepts were combined and incorporated in creating a new introductory graduate course designed to develop scientific skills (student competencies) in matriculating doctoral students using a PBL format.

METHODS

Evaluation of course effectiveness was measured using the principals of the Kirkpatrick Four Level Model of Evaluation. At the end of each course offering, students completed evaluation surveys on the course and instructors to assess their perceptions of training effectiveness. Pre- and post-tests assessing students' proficiency in experimental design were used to measure student learning.

RESULTS

The analysis of the outcomes of the course suggests the training is effective in improving experimental design. The course was well received by the students as measured by student evaluations (Kirkpatrick Model Level 1). Improved scores on post-tests indicate that the students learned from the experience (Kirkpatrick Model Level 2). A template is provided for the implementation of similar courses at other institutions.

CONCLUSIONS

This problem-based learning course appears effective in training newly matriculated graduate students in the required skills for designing experiments to test specific hypotheses, enhancing student preparation prior to initiation of their dissertation research.

Evaluation of combinatorial optimisation algorithms for c-optimal experimental designs with correlated observations

We show how combinatorial optimisation algorithms can be applied to the problem of identifying c-optimal experimental designs when there may be correlation between and within experimental units and evaluate the performance of relevant algorithms. We assume the data generating process is a generalised linear mixed model and show that the c-optimal design criterion is a monotone supermodular function amenable to a set of simple minimisation algorithms. We evaluate the performance of three relevant algorithms: the local search, the greedy search, and the reverse greedy search. We show that the local and reverse greedy searches provide comparable performance with the worst design outputs having variance < 10 % greater than the best design, across a range of covariance structures. We show that these algorithms perform as well or better than multiplicative methods that generate weights to place on experimental units. We extend these algorithms to identifying modle-robust c-optimal designs.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11222-023-10280-w.

Adsorption of chromium from electroplating wastewater using activated carbon developed from water hyacinth

Industrial wastewater polluted with high concentrations of Cr is commonly discharged into water resources without proper treatment. This gives rise to the deterioration of water quality and imposes adverse effects on public health. Therefore, this study is aimed at removing Cr from electroplating wastewater using activated carbon produced from water hyacinth under a full factorial experimental design with three factors and three levels (pH,2,5 and 8, adsorbent dose 0.5,1and1.5 in 100 mL and contact time 30, 60 and120 min). A phosphoric acid solution of 37% was used to activate the carbon, which was then subjected to thermal decomposition for 15 min at 500 °C. The activated carbon was characterized by the presence of a high surface area (203.83 m²/g) of BET, cracking of adsorbent beads of SEM morphology, amorphous nature of XRD, and many functional groups of FTIR such as hydroxyl (3283 cm^-1), alkane (2920 cm^-1), nitrile (2114 cm^-1) and aromatics (1613 cm^-1). The minimum Cr adsorption performance of 15.6% was obtained whereas maximum removal of 90.4% was recorded at the experimental condition of pH 2, adsorbent dose of 1.5 g/100 mL, and contact time of 120 min at a fixed value of initial Cr concentration of 100 mg/L. Similarly, the maximum Cr removal from real electroplating wastewater was 81.2% at this optimum point. Langmuir's model best described the experimental value at R² 0.96 which implies the adsorption is chemically bonded, homogeneous, and monolayer. Pseudo-second-order model best fits with the experimental data with R² value of 0.99. The adsorbent was regenerated for seven cycles and the removal efficiency decreased from 93.25% to 21.35%. Finally, this technology is promising to be scaled up to an industrial level.

Use of research electronic data capture (REDCap) in a sequential multiple assignment randomized trial (SMART): a practical example of automating double randomization

BACKGROUND

Adaptive interventions are often used in individualized health care to meet the unique needs of clients. Recently, more researchers have adopted the Sequential Multiple Assignment Randomized Trial (SMART), a type of research design, to build optimal adaptive interventions. SMART requires research participants to be randomized multiple times over time, depending upon their response to earlier interventions. Despite the increasing popularity of SMART designs, conducting a successful SMART study poses unique technological and logistical challenges (e.g., effectively concealing and masking allocation sequence to investigators, involved health care providers, and subjects) in addition to other challenges common to all study designs (e.g., study invitations, eligibility screening, consenting procedures, and data confidentiality protocols). Research Electronic Data Capture (REDCap) is a secure, browser-based web application widely used by researchers for data collection. REDCap offers unique features that support researchers' ability to conduct rigorous SMARTs. This manuscript provides an effective strategy for performing automatic double randomization for SMARTs using REDCap.

METHODS

Between January and March 2022, we conducted a SMART using a sample of adult (age 18 and older) New Jersey residents to optimize an adaptive intervention to increase COVID-19 testing uptake. In the current report, we discuss how we used REDCap for our SMART, which required double randomization. Further, we share our REDCap project XML file for future investigators to use when designing and conducting SMARTs.

RESULTS

We report on the randomization feature that REDCap offers and describe how the study team automated an additional randomization that was required for our SMART. An application programming interface was used to automate the double randomizations in conjunction with the randomization feature provided by REDCap.

CONCLUSIONS

REDCap offers powerful tools to facilitate the implementation of longitudinal data collection and SMARTs. Investigators can make use of this electronic data capturing system to reduce errors and bias in the implementation of their SMARTs by automating double randomization.

TRIAL REGISTRATION

The SMART study was prospectively registered at Clinicaltrials.gov; registration number: NCT04757298, date of registration: 17/02/2021.