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.

Mapping strategies towards improved external validity in preclinical translational research

INTRODUCTION

Translation is about successfully bringing findings from preclinical contexts into the clinic. This transfer is challenging as clinical trials frequently fail despite positive preclinical results. Limited robustness of preclinical research has been marked as one of the drivers of such failures. One suggested solution is to improve the external validity of in vitro and in vivo experiments via a suite of complementary strategies.

AREAS COVERED

In this review, the authors summarize the literature available on different strategies to improve external validity in in vivo, in vitro, or ex vivo experiments; systematic heterogenization; generalizability tests; and multi-batch and multicenter experiments. Articles that tested or discussed sources of variability in systematically heterogenized experiments were identified, and the most prevalent sources of variability are reviewed further. Special considerations in sample size planning, analysis options, and practical feasibility associated with each strategy are also reviewed.

EXPERT OPINION

The strategies reviewed differentially influence variation in experiments. Different research projects, with their unique goals, can leverage the strengths and limitations of each strategy. Applying a combination of these approaches in confirmatory stages of preclinical research putatively increases the chances of success in clinical studies.

Systematic review of the performance evaluation of clinicians with or without the aid of machine learning clinical decision support system

Background

For the adoption of machine learning clinical decision support systems (ML-CDSS) it is critical to understand the performance aid of the ML-CDSS. However, it is not trivial, how the performance aid should be evaluated. To design reliable performance evaluation study, both the knowledge from the practical framework of experimental study design and the understanding of domain specific design factors are required.

Objective

The aim of this review study was to form a practical framework and identify key design factors for experimental design in evaluating the performance of clinicians with or without the aid of ML-CDSS.

Methods

The study was based on published ML-CDSS performance evaluation studies. We systematically searched articles published between January 2016 and December 2022. From the articles we collected a set of design factors. Only the articles comparing the performance of clinicians with or without the aid of ML-CDSS using experimental study methods were considered.

Results

The identified key design factors for the practical framework of ML-CDSS experimental study design were performance measures, user interface, ground truth data and the selection of samples and participants. In addition, we identified the importance of randomization, crossover design and training and practice rounds. Previous studies had shortcomings in the rationale and documentation of choices regarding the number of participants and the duration of the experiment.

Conclusion

The design factors of ML-CDSS experimental study are interdependent and all factors must be considered in individual choices.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12553-023-00763-1.

Revisiting the Design of the Long-Term Evolution Experiment with Escherichia coli

The long-term evolution experiment (LTEE) with Escherichia coli began in 1988 and it continues to this day, with its 12 populations having recently reached 75,000 generations of evolution in a simple, well-controlled environment. The LTEE was designed to explore open-ended questions about the dynamics and repeatability of phenotypic and genetic evolution. Here I discuss various aspects of the LTEE's experimental design that have enabled its stability and success, including the choices of the culture regime, growth medium, ancestral strain, and statistical replication. I also discuss some of the challenges associated with a long-running project, such as handling procedural errors (e.g., cross-contamination) and managing the expanding collection of frozen samples. The simplicity of the experimental design and procedures have supported the long-term stability of the LTEE. That stability-along with the inherent creativity of the evolutionary process and the emergence of new genomic technologies-provides a platform that has allowed talented students and collaborators to pose questions, collect data, and make discoveries that go far beyond anything I could have imagined at the start of the LTEE.

Enhanced UV-light driven photocatalytic performance of magnetic CoFe2O4/TiO2 nanohybrid for environmental applications

In this work, CoFe₂O₄/TiO₂ nanostructure was prepared through a facile and effective solvothermal route for efficient use in the degradation of the Erionyl Red A-3G model pollutant under ultraviolet irradiation. Characterization analysis indicated the successful heterojunction among the precursors. The composite presented band gap value of 2.75 eV, being smaller than that of the pristine TiO₂, as well as mesoporous structure. The catalytic activity of nanostructure was investigated by employing a 2² factorial experimental design with 3 central points. The optimized reaction conditions were set as pH = 2 and catalyst dosage = 1.0 g L^-1 for an initial pollutant concentration of 20 mg L^-1. The prepared nanohybrid presented remarkable catalytic activity, reaching color removal efficiency of 95.39% after 15 min, as well as total organic carbon (TOC) removal of 69.4% after 120 min. The kinetic studies of TOC removal followed the pseudo-first order model, with a rate constant of 0.10 min^-1. Moreover, the nanostructure presented magnetic behavior, being easily separated from the aqueous medium through the use of a simple external magnetic field.

Optimization of kappa-carrageenan cationization using experimental design for model-drug release and investigation of biological properties

Cationization is a promising chemical modification technique that improves properties by attaching permanent positive charges to the backbone of biopolymers. Carrageenan is a widely available and non-toxic polysaccharide, commonly used in food industry but with low solubility in cold water. We performed a central composite design experiment to check the parameters that most influence the degree of cationic substitution and the film solubility. Hydrophilic quaternary ammonium groups on the carrageenan backbone enhance interaction in drug delivery systems and create active surfaces. Statistical analysis indicated that within the studied range, only the molar ratio between the cationizing reagent and the repeating disaccharide unit of carrageenan had a significant effect. Optimized parameters using 0.086 g of sodium hydroxide and glycidyltrimethylammonium/disaccharide repeating unit of 6.83 achieved 65.47 % degree of substitution and 4.03 % solubility. Characterizations confirmed the effective incorporation of cationic groups into the commercial structure of carrageenan and thermal stability improvement of the derivatives.

Feelings toward lying flatism and attitudes toward singlehood: the mediating role of happiness belief

BACKGROUND

Lying flatism, a new emerging lifestyle by refusing to participate in consumerist lifestyles, is anticipated to be related to singlehood. Based on the Theory of Reasoned Action, we proposed a mediation model to examine the indirect relationship between feelings towards lying flatism and attitudes toward singlehood via individuals' belief in that happiness can be achieved without romantic relationships (happiness belief).

METHODS

Using purposive and snowball sampling methods, 232 single Malaysian young adults participated in an online experiment consisting of a writing task (to manipulate feelings toward lying flatism), single-item measures of manipulation checking and happiness belief, Attitudes toward Singlehood Scale, Negative Stereotyping of Single Persons Scale (a measure of singlism) and Fear of Being Single Scale.

RESULTS

T-Test results support the writing task that successfully induced positive feelings toward lying flatism. Mediation analysis showed that the feelings toward lying flatism measured before the writing task, but not the manipulation of lying flatism, has an indirect relationship with attitudes toward singlehood via happiness belief, after controlling for gender, singlism, and fear of being single.

CONCLUSIONS

The findings offer preliminary support to the hypothetical relationships among feelings towards lying flatism, happiness belief, and attitudes toward singlehood. Implications of the findings are discussed.

Unintentional Discrimination Against Patients with a Migration Background by General Practitioners in Mental Health Management: An Experimental Study

Populations with a migration background have a higher prevalence of mental health problems than their native counterparts. They are also more likely to have unmet medical needs and are less frequently referred to mental health services. One potential explanation for this is that physicians, such as general practitioners (GPs), may unintentionally discriminate against migrant patients, particularly when they lack humanization. To date, no experimental study has investigated this hypothesis. This paper assesses the influence of humanization on GPs' discriminatory decisions regarding migrant patients with depression. A balanced 2 × 2 factorial experiment was carried out with Belgian GPs (N = 797) who received video-vignettes depicting either a native patient or a migrant patient with depression. Half of the respondents were exposed to a text that humanized the patient by providing more details about the patient's life story. Decisions related to diagnosis, treatment and referral were collected, as well as the time spent on each video and text, and were analysed using ANOVA. Migrant patients' symptoms were judged to be less severe than those of native patients (F = 7.71, p < 0.05). For almost all treatments, the decision was less favourable for the migrant patient. Humanization had little effect on medical decisions. We observed that GPs spent significantly more time on the vignette with the humanization intervention, especially for the migrant patients. The results indicate that ethnic differences in the management of depression persist in primary care. Humanization, however, does not mitigate those differences in medical decisions.

Consensus design of a calibration experiment for human fear conditioning

Fear conditioning is a widely used laboratory model to investigate learning, memory, and psychopathology across species. The quantification of learning in this paradigm is heterogeneous in humans and psychometric properties of different quantification methods can be difficult to establish. To overcome this obstacle, calibration is a standard metrological procedure in which well-defined values of a latent variable are generated in an established experimental paradigm. These intended values then serve as validity criterion to rank methods. Here, we develop a calibration protocol for human fear conditioning. Based on a literature review, series of workshops, and survey of N = 96 experts, we propose a calibration experiment and settings for 25 design variables to calibrate the measurement of fear conditioning. Design variables were chosen to be as theory-free as possible and allow wide applicability in different experimental contexts. Besides establishing a specific calibration procedure, the general calibration process we outline may serve as a blueprint for calibration efforts in other subfields of behavioral neuroscience that need measurement refinement.

Green analytical chemistry and experimental design: a combined approach for the analysis of zonisamide

Green analytical chemistry principles, as well as experimental design, are a combined approach adopted to develop sensitive reproducible stability indicating HPLC method for Zonisamide (ZNS) determination. The optimal conditions for three chromatographic parameters were determined using a central composite design of the response surface. Kromasil C₁₈ column (150 mm × 4.6 mm, 5 µm) was utilized with ethanol, H₂O (30:70 v/v) as a mobile phase at a flow rate of 1 mL/min at 35 °C. Good reproducibility and high sensitivity were achieved along (0.5-10 µg/mL) concentration range. In contrast, the TLC-densitometric method was performed on aluminum plates precoated with silica gel 60F₂₅₄ as a stationary phase and chloroform: methanol: acetic acid (8:1.5:0.5 by volume) as a developing system. Reproducible results were obtained in the range of (2-10 μg/band). The chromatograms of HPLC and TLC were scanned at 280 nm and 240 nm, respectively. The suggested methods have been validated following ICH guidelines, and no statistically significant differences were detected between the results of the current study and the official USP method. It was also found that using experimental design implements the green concept by reducing the environmental impact. Finally, Eco-Scale, GAPI and AGREE were used to assess the environmental impacts of the suggested methods.

Improving fMRI in Parkinson's disease by accounting for brain region-specific activity patterns

In functional magnetic imaging (fMRI) in Parkinson's disease (PD), a paradigm consisting of blocks of finger tapping and rest along with a corresponding general linear model (GLM) is often used to assess motor activity. However, this method has three limitations: (i) Due to the strong magnetic field and the confined environment of the cylindrical bore, it is troublesome to accurately monitor motor output and, therefore, variability in the performed movement is typically ignored. (ii) Given the loss of dopaminergic neurons and ongoing compensatory brain mechanisms, motor control is abnormal in PD. Therefore, modeling of patients' tapping with a constant amplitude (using a boxcar function) and the expected Parkinsonian motor output are prone to mismatch. (iii) The motor loop involves structures with distinct hemodynamic responses, for which only one type of modeling (e.g., modeling the whole block of finger tapping) may not suffice to capture these structure's temporal activation. The first two limitations call for considering results from online recordings of the real motor output that may lead to significant sensitivity improvements. This was shown in previous work using a non-magnetic glove to capture details of the patients' finger movements in a so-called kinematic approach. For the third limitation, modeling motion initiation instead of the whole tapping block has been suggested to account for different temporal activation signatures of the motor loop's structures. In the present study we propose improvements to the GLM as a tool to study motor disorders. For this, we test the robustness of the kinematic approach in an expanded cohort (n = 31), apply more conservative statistics than in previous work, and evaluate the benefits of an event-related model function. Our findings suggest that the integration of the kinematic approach offers a general improvement in detecting activations in subcortical structures, such as the basal ganglia. Additionally, modeling motion initiation using an event-related design yielded superior performance in capturing medication-related effects in the putamen. Our results may guide adaptations in analysis strategies for functional motor studies related to PD and also in more general applications.

Battle of the sexes: analysis of sex bias in host use and reporting practices in parasitological experiments

Experimental approaches are among the most powerful tools available to biologists, yet in many disciplines their results have been questioned due to an underrepresentation of female animal subjects. In parasitology, experiments are crucial to understand host-parasite interactions, parasite development, host immune responses, as well as the efficacy of different control methods. However, distinguishing between species-wide and sex-specific effects requires the balanced inclusion of both male and female hosts in experiments and the reporting of results for each sex separately. Here, using data from over 3600 parasitological experiments on helminth-mammal interactions published in the past four decades, we investigate patterns of male versus female subject use and result reporting practices in experimental parasitology. We uncover multiple effects of the parasite taxon used, the type of host used (rats and mice for which subject selection is fully under researcher control versus farm animals), the research subject area and the year of publication, on whether host sex is even specified, whether one or both host sexes have been used (and if only one then which one), and whether the results are presented separately for each host sex. We discuss possible reasons for biases and unjustifiable selection of host subjects, and for poor experimental design and reporting of results. Finally, we make some simple recommendations for increased rigour in experimental design and to reset experimental approaches as a cornerstone of parasitological research.

Green solid-liquid extraction of cactus (Opuntia ficus-indica) cladode dietary fibers. I- optimization, pilot-scale production, and characterization

In this research work, an optimization of an environment friendly extraction method of cactus (Opuntia ficus indica) cladode dietary fibers was conducted. For this purpose, a central composite experimental design with two factors (temperature and time) and five levels was established. The basic objective of this optimization was to maximize fiber yield using hot water as an extraction eco-solvent. The optimum extraction time (330 min) and temperature (100 °C) were determined with a constant medium agitation rate. Additionally, this study also aimed at establishing the validation of the statistical model to carry out the extrapolation of the extraction process at the pilot scale. The fibers extracted at the pilot scale showed yields (45.2 ± 0.01%) in agreement with those obtained through the optimization and validation lab-scale steps (44.97 ± 0.02). Fourier Transform Infrared (FTIR) spectroscopy, X-ray Diffraction (XRD) and Scanning electron microscopy (SEM) analysis were conducted to investigate the structure and microstructure of pilot scale-produced fibers. FTIR spectrum and XRD pattern were typical to lignocellulosic fibers results. Sharp and thin peaks characteristic of cellulose were detected. Pure and crystallized phases were recorded with a 45% crystallinity index. SEM analysis presented elongated and organized cells with a uniform structure comparable to cellulosic fibers microstructure.

Impact of Organism Allocations on Potency Estimates from Ceriodaphnia dubia Reproduction Tests

In aquatic toxicology experiments, organisms are randomly assigned to an exposure group that receives a particular concentration level of a toxicant (including a control group with no exposure), and their survival, growth, or reproduction outcomes are recorded. Standard experiments use equal numbers of organisms in each exposure group. In the present study, we explored the potential benefits of modifying the current design of aquatic toxicology experiments when it is of interest to estimate the concentration associated with a specific level of decrease from control reproduction responses. A function of the parameter estimates from fitting a generalized linear regression model used to describe the relationship between individual responses and the toxicant concentration provides an estimate of the potency of the toxicant. After comparing different allocations of organisms to concentration groups, we observed that a reallocation of organisms among these concentration groups could provide more precise estimates of toxicity endpoints than the standard experimental design that uses equal number of organisms in each concentration group; this provides greater precision without the added cost of conducting the experiment. More specifically, assigning more observations to the control zero-concentration condition may result in more precise interval estimates of potency. Environ Toxicol Chem 2023;00:1-10. © 2023 SETAC.

Digital phenotyping of student mental health during COVID-19: an observational study of 100 college students

Objective: This study assessed the feasibility of capturing smartphone based digital phenotyping data in college students during the COVID-19 pandemic with the goal of understanding how digital biomarkers of behavior correlate with mental health. Participants: Participants were 100 students enrolled in 4-year universities. Methods: Each participant attended a virtual visit to complete a series of gold-standard mental health assessments, and then used a mobile app for 28 days to complete mood assessments and allow for passive collection of GPS, accelerometer, phone call, and screen time data. Students completed another virtual visit at the end of the study to collect a second round of mental health assessments. Results: In-app daily mood assessments were strongly correlated with their corresponding gold standard clinical assessment. Sleep variance among students was correlated to depression scores (ρ = .28) and stress scores (ρ = .27). Conclusions: Digital Phenotyping among college students is feasible on both an individual and a sample level. Studies with larger sample sizes are necessary to understand population trends, but there are practical applications of the data today.

Modeling COVID-19 disease processes by remote elicitation of causal Bayesian networks from medical experts

BACKGROUND

COVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are involved, their exact causal relationships remain opaque. Better understanding is needed for predicting their progression, targeting therapeutic approaches, and improving patient outcomes. While many mathematical causal models describe COVID-19 epidemiology, none have described its pathophysiology.

METHODS

In early 2020, we began developing such causal models. The SARS-CoV-2 virus's rapid and extensive spread made this particularly difficult: no large patient datasets were publicly available; the medical literature was flooded with sometimes conflicting pre-review reports; and clinicians in many countries had little time for academic consultations. We used Bayesian network (BN) models, which provide powerful calculation tools and directed acyclic graphs (DAGs) as comprehensible causal maps. Hence, they can incorporate both expert opinion and numerical data, and produce explainable, updatable results. To obtain the DAGs, we used extensive expert elicitation (exploiting Australia's exceptionally low COVID-19 burden) in structured online sessions. Groups of clinical and other specialists were enlisted to filter, interpret and discuss the literature and develop a current consensus. We encouraged inclusion of theoretically salient latent (unobservable) variables, likely mechanisms by extrapolation from other diseases, and documented supporting literature while noting controversies. Our method was iterative and incremental: systematically refining and validating the group output using one-on-one follow-up meetings with original and new experts. 35 experts contributed 126 hours face-to-face, and could review our products.

RESULTS

We present two key models, for the initial infection of the respiratory tract and the possible progression to complications, as causal DAGs and BNs with corresponding verbal descriptions, dictionaries and sources. These are the first published causal models of COVID-19 pathophysiology.

CONCLUSIONS

Our method demonstrates an improved procedure for developing BNs via expert elicitation, which other teams can implement to model emergent complex phenomena. Our results have three anticipated applications: (i) freely disseminating updatable expert knowledge; (ii) guiding design and analysis of observational and clinical studies; (iii) developing and validating automated tools for causal reasoning and decision support. We are developing such tools for the initial diagnosis, resource management, and prognosis of COVID-19, parameterized using the ISARIC and LEOSS databases.

Development of molecular and antigenic-based rapid tests for the identification of African swine fever virus in different tissues

African swine fever (ASF) is a severe haemorrhagic infectious disease affecting suids, thus representing a great economic concern. Considering the importance of the early diagnosis, rapid point of care testing (POCT) for ASF is highly demanded. In this work, we developed two strategies for the rapid onsite diagnosis of ASF, based on Lateral Flow Immunoassay (LFIA) and Recombinase Polymerase Amplification (RPA) techniques. The LFIA was a sandwich-type immunoassay exploiting a monoclonal antibody directed towards the p30 protein of the virus (Mab). The Mab was anchored onto the LFIA membrane to capture the ASFV and was also labelled with gold nanoparticles for staining the antibody-p30 complex. However, the use of the same antibody for capturing and as detector ligand showed a significant competitive effect for antigen binding, so required an experimental design to minimize reciprocal interference and maximize the response. The RPA assay, employing primers to the capsid protein p72 gene and an exonuclease III probe, was performed at 39 °C. The limit of detection of the method was assessed using a plasmid encoding the target gene and resulted in 5 copy/μL. The new LFIA and RPA were applied for ASFV detection in the animal tissues usually analysed by conventional assays (i.e., real-time PCR), such as kidney, spleen, and lymph nodes. A simple and universal virus extraction protocol was applied for sample preparation, followed by DNA extraction and purification for the RPA. The LFIA only required the addition of 3% H2O2 to limit matrix interference and prevent false positive results. The two rapid methods (25 min and 15 min were needed to complete the analysis for RPA and LFIA, respectively) showed high diagnostic specificity (100%) and sensitivity (93% and 87% for LFIA and RPA, respectively) for samples with high viral load (Ct < 27). False negative results were observed for samples with low viral load (Ct > 28) and/or also containing specific antibodies to ASFV, which decreased antigen availability and were indicative of a chronic, poorly transmissible infection. The simple and rapid sample preparation and the diagnostic performance of the LFIA suggested its large practical applicability for POC diagnosis of ASF.

Measuring food-related inhibition with go/no-go tasks: Critical considerations for experimental design

The use of go/no-go tasks to assess inhibitory control over food stimuli is becoming increasingly popular. However, the wide variability in the design of these tasks makes it difficult to fully leverage their results. The goal of this commentary was to provide researchers with crucial aspects to consider when designing food-related go/no-go experiments. We examined 76 studies that used food-themed go/no-go tasks and extracted characteristics related to participant population, methodology, and analysis. Based on our observations of common issues that can influence study conclusions, we stress the importance for researchers to design an appropriate control condition and match stimuli between experimental conditions in terms of emotional and physical properties. We also emphasize that stimuli should be tailored to the participants under study, whether at the individual or group level. To ensure that the task truly measures inhibitory abilities, researchers should promote the establishment of a prepotent response pattern by presenting more go than no-go trials and by using short trials. Researchers should also pre-specify the criteria used to identify potentially invalid data. While go/no-go tasks represent valuable tools for studying food cognition, researchers should choose task parameters carefully and justify their methodological and analytical decisions in order to ensure the validity of results and promote best practices in food-related inhibition research.

Assessment of benzothiazoles, benzotriazoles and benzenesulfonamides in environmental waters using an optimized combination of microextraction by packed sorbent with programmed temperature vaporization-gas chromatography tandem-mass spectrometry

This work proposes a new method for the quantification of benzothiazoles (BTs), benzotriazoles (BTRs), and benzenesulfonamides (BSAs) in tap water, river water, and wastewater. The protocol involved the use of microextraction by packed sorbent (MEPS), applied for the first time for the extraction of the target analytes, combined with programmed temperature vaporization-gas chromatography-triple quadrupole mass spectrometry (PTV-GC-QqQ-MS). Considering the synergism between MEPS extraction and PTV injection, the experimental variables affecting their performance were simultaneously optimized by "experimental design", while principal component analysis (PCA) was used to find the overall optimal working conditions. Response surface methodology was used to gain a comprehensive understanding of the effects of working variables on method performance. The developed method achieved very good linearities and satisfactory intra- and inter-day accuracies and precisions. The protocol permitted the detection of the target molecules with limit of detection (LODs) values between 0.005 and 0.85 μg/L. The green character of the procedure was evaluated using three metrics: "Analytical Eco-Scale", "Green Analytical Procedure Index" (GAPI), and "Analytical Greenness metric for sample preparation (AGREEprep). The satisfactory results obtained with real water samples demonstrate the applicability of the method for monitoring campaigns and exposome studies.

Preparation of alumina-supported Fe-Al-La composite for fluoride removal from an aqueous matrix

Using groundwater for human consumption is an alternative for places with no nearby surface water resources. Fluoride is commonly found in groundwater, and the consumption of this water for a prolonged time in concentrations that exceed established limits by WHO and Brazilian legislation on water potability (1.5 mg L^-1) can cause harmful problems to human health. For this reason, fluoride removal is an important step before water consumption. In this work, activated alumina was impregnated with Fe-Al-La composite and employed for the first time as an adsorbent for fluoride removal from an aqueous environment. XRD, SEM/EDS, FT-IR, and point of zero charge were used to characterize the prepared adsorbent. The adsorptive performance of adsorbent material was investigated by employing a 2³-central composite design (CCD), and the obtained experimental conditions were pH = 6.5 and adsorbent dosage = 3.0 g L^-1. A maximum adsorption capacity of 8.17 mg g^-1 at 298 K and pH = 6.5 was achieved by Langmuir isotherm to describe the adsorption. The kinetic model that better described experimental data was Avrami, with the k_av parameter increasing with the initial concentration from 0.076 to 0.231 (min^-1)ⁿ_av. The nature of adsorption was found to be homogeneous, and it occurs in a monolayer. The fluoride removal performance for the prepared adsorbent was higher than granular activated alumina, showing that supporting Fe-Al-La at the alumina surface increased its fluoride adsorption capacity from 16 to 42% at the same experimental conditions. Finally, the influence of co-existing ions Cl^-, SO₄^2-, and NO₃^- was evaluated in fluoride adsorption, and the material presented great selectivity to fluoride. Thus, Fe-Al-La/AA adsorbent is a promising material for fluoride removal from water.

Next generation forensic taphonomy: Automation for experimental, field-based research

Determining the post-mortem interval (PMI) is often a critical goal in forensic casework. Consequently, the discipline of forensic taphonomy has involved considerable research efforts towards achieving this goal, with substantial strides made in the past 40 years. Importantly, quantification of decompositional data (and the models derived from them) and standardisation in experimental protocols are being increasingly recognised as key components of this drive. However, despite the discipline's best efforts, significant challenges remain. Still lacking are standardisation of many core components of experimental design, forensic realism in experimental design, true quantitative measures of the progression of decay, and high-resolution data. Without these critical elements, large-scale, synthesised multi-biogeographically representative datasets - necessary for building comprehensive models of decay to precisely estimate PMI - remain elusive. To address these limitations, we propose the automation of taphonomic data collection. We present the world's first reported fully automated, remotely operable forensic taphonomic data collection system, inclusive of technical design details. Through laboratory testing and field deployments, the apparatus substantially reduced the cost of actualistic (field-based) forensic taphonomic data collection, improved data resolution, and provided for more forensically realistic experimental deployments and simultaneous multi-biogeographic experiments. We argue that this device represents a quantum leap in experimental methodology in this field, paving the way for the next generation of forensic taphonomic research and, we hope, attainment of the elusive goal of precise estimation of PMI.

Designing experimental conditions to use the Lotka-Volterra model to infer tumor cell line interaction types

The Lotka-Volterra model is widely used to model interactions between two species. Here, we generate synthetic data mimicking competitive, mutualistic and antagonistic interactions between two tumor cell lines, and then use the Lotka-Volterra model to infer the interaction type. Structural identifiability of the Lotka-Volterra model is confirmed, and practical identifiability is assessed for three experimental designs: (a) use of a single data set, with a mixture of both cell lines observed over time, (b) a sequential design where growth rates and carrying capacities are estimated using data from experiments in which each cell line is grown in isolation, and then interaction parameters are estimated from an experiment involving a mixture of both cell lines, and (c) a parallel experimental design where all model parameters are fitted to data from two mixtures (containing both cell lines but with different initial ratios) simultaneously. Each design is tested on data generated from the Lotka-Volterra model with noise added, to determine efficacy in an ideal sense. In addition to assessing each design for practical identifiability, we investigate how the predictive power of the model - i.e., its ability to fit data for initial ratios other than those to which it was calibrated - is affected by the choice of experimental design. The parallel calibration procedure is found to be optimal and is further tested on in silico data generated from a spatially-resolved cellular automaton model, which accounts for oxygen consumption and allows for variation in the intensity level of the interaction between the two cell lines. We use this study to highlight the care that must be taken when interpreting parameter estimates for the spatially-averaged Lotka-Volterra model when it is calibrated against data produced by the spatially-resolved cellular automaton model, since baseline competition for space and resources in the CA model may contribute to a discrepancy between the type of interaction used to generate the CA data and the type of interaction inferred by the LV model.

Closed-loop automatic gradient design for liquid chromatography using Bayesian optimization

Contemporary complex samples require sophisticated methods for full analysis. This work describes the development of a Bayesian optimization algorithm for automated and unsupervised development of gradient programs. The algorithm was tailored to LC using a Gaussian process model with a novel covariance kernel. To facilitate unsupervised learning, the algorithm was designed to interface directly with the chromatographic system. Single-objective and multi-objective Bayesian optimization strategies were investigated for the separation of two complex (n>18, and n>80) dye mixtures. Both approaches found satisfactory optima in under 35 measurements. The multi-objective strategy was found to be powerful and flexible in terms of exploring the Pareto front. The performance difference between the single-objective and multi-objective strategy was further investigated using a retention modeling example. One additional advantage of the multi-objective approach was that it allows for a trade-off to be made between multiple objectives without prior knowledge. In general, the Bayesian optimization strategy was found to be particularly suitable, but not limited to, cases where retention modelling is not possible, although its scalability might be limited in terms of the number of parameters that can be simultaneously optimized.

Case-based teaching of experimental design - contributions for meaningful learning

This article argues the need for education and training of researchers carrying out animal studies on the fundamentals of experimental design (ED), as a key means of improving the reliability and reproducibility of preclinical results. The current landscape in ED education in Europe is presented, and we make the case for dedicated tutor-guided teaching of ED. With less than a day dedicated to it in many courses effective techniques for communicating key issues are needed. We have developed two approaches that transfer to experimental design teaching the case-study, problem-solving techniques known to be effective in other fields. They use realistic research scenarios to provoke discussion and engage learning. In one the scenario is for group discussion or informal or formal assessment with subsequent tutor-led discussion of key points. For this each scenario needs a clear statement of the purpose of the research study, simplified text outlining the comparisons and procedures, and a statement of the outcome measure. In the other approach, the scenario is used with freely-available software with a good graphical output to explore the sizing of experiments and the use of both sexes. Trainee feedback and informal assessment show that these approaches can make for interesting and memorable sessions and offer a useful contribution to improvement in experimental design teaching so that it produces meaningful learning that can translate into better practice.