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.

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.