Should I use fixed effects or random effects when I have fewer than five levels of a grouping factor in a mixed-effects model?

As linear mixed-effects models (LMMs) have become a widespread tool in ecology, the need to guide the use of such tools is increasingly important. One common guideline is that one needs at least five levels of the grouping variable associated with a random effect. Having so few levels makes the estimation of the variance of random effects terms (such as ecological sites, individuals, or populations) difficult, but it need not muddy one's ability to estimate fixed effects terms-which are often of primary interest in ecology. Here, I simulate datasets and fit simple models to show that having few random effects levels does not strongly influence the parameter estimates or uncertainty around those estimates for fixed effects terms-at least in the case presented here. Instead, the coverage probability of fixed effects estimates is sample size dependent. LMMs including low-level random effects terms may come at the expense of increased singular fits, but this did not appear to influence coverage probability or RMSE, except in low sample size (N = 30) scenarios. Thus, it may be acceptable to use fewer than five levels of random effects if one is not interested in making inferences about the random effects terms (i.e. when they are 'nuisance' parameters used to group non-independent data), but further work is needed to explore alternative scenarios. Given the widespread accessibility of LMMs in ecology and evolution, future simulation studies and further assessments of these statistical methods are necessary to understand the consequences both of violating and of routinely following simple guidelines.

Microarray Data Preprocessing: From Experimental Design to Differential Analysis

DNA microarray data preprocessing is of utmost importance in the analytical path starting from the experimental design and leading to a reliable biological interpretation. In fact, when all relevant aspects regarding the experimental plan have been considered, the following steps from data quality check to differential analysis will lead to robust, trustworthy results. In this chapter, all the relevant aspects and considerations about microarray preprocessing will be discussed. Preprocessing steps are organized in an orderly manner, from experimental design to quality check and batch effect removal, including the most common visualization methods. Furthermore, we will discuss data representation and differential testing methods with a focus on the most common microarray technologies, such as gene expression and DNA methylation.

Preparation and Curation of Multiyear, Multilocation, Multitrait Datasets

Genome-wide association studies (GWAS) are a powerful approach to dissect genotype-phenotype associations and identify causative regions. However, this power is highly influenced by the accuracy of the phenotypic data. To obtain accurate phenotypic values, the phenotyping should be achieved through multienvironment trials (METs). In order to avoid any technical errors, the required time needs to be spent on exploring, understanding, curating and adjusting the phenotypic data in each trial before combining them using an appropriate linear mixed model (LMM). The LMM is chosen to minimize as much as possible any effect that can lead to misestimation of the phenotypic values. The purpose of this chapter is to explain a series of important steps to explore and analyze data from METs used to characterize an association panel. Two datasets are used to illustrate two different scenarios.

Designing Experiments for Physiological Phenomics

Phenomics has emerged as the technology of choice for understanding quantitative genetic variation in plant physiology and plant breeding. Phenomics has allowed for unmatched precision in exploring plant life cycles and physiological patterns. As new technologies are developed, it is still vital to follow best practices for designing and planning to be able to fully exploit any experimental results. Here we describe the basic - but sometimes overlooked - considerations of a phenomics experiment to help you maximize the value from the data collected: choosing population and location, accounting for sources of variation, establishing a timeline, and leveraging ground-truth measurements.

Plant-wide investigation of sulfur flows in a water resource recovery facility (WRRF)

Even though sulfur compounds and their transformations may strongly affect wastewater treatment processes, their importance in water resource recovery facilities (WRRF) operation remains quite unexplored, notably when it comes to full-scale and plant-wide characterization. This contribution presents a first-of-a-kind, plant-wide quantification of total sulfur mass flows for all water and sludge streams in a full-scale WRRF. Because of its important impact on (post-treatment) process operation, the gaseous emission of sulfur as hydrogen sulfide (H₂S) was also included, thus enabling a comprehensive evaluation of sulfur flows. Data availability and quality were optimized by experimental design and data reconciliation, which were applied for the first time to total sulfur flows. Total sulfur flows were successfully balanced over individual process treatment units as well as the plant-wide system with only minor variation to their original values, confirming that total sulfur is a conservative quantity. The two-stage anaerobic digestion with intermediate thermal hydrolysis led to a decreased sulfur content of dewatered sludge (by 36%). Higher (gaseous) H₂S emissions were observed in the second-stage digester (42% of total emission) than in the first one, suggesting an impact of thermal treatment on the production of H₂S. While the majority of sulfur mass flow from the influent left the plant through the treated effluent (> 95%), the sulfur discharge through dewatered sludge and gaseous emissions are critical. The latter are indeed responsible for odour nuisance, lower biogas quality, SO₂ emissions upon sludge combustion and corrosion effects.

The gap between atmospheric nitrogen deposition experiments and reality

Anthropogenic activities have dramatically altered the global nitrogen (N) cycle. Atmospheric N deposition, primarily from combustion of biomass and fossil fuels, has caused acidification of precipitation and freshwater, and triggered intense research into ecosystem responses to this pollutant. Experimental simulations of N deposition have been the main scientific tool to understand ecosystem responses, revealing dramatic impacts on soil microbes, plants, and higher trophic levels. However, comparison of the experimental treatments applied in the vast majority of studies with observational and modelled N deposition reveals a wide gulf between research and reality. While the majority of experimental treatments exceed 100 kg N ha^-1 y^-1, global median land surface deposition rates are around 1 kg N ha^-1 y^-1 and only exceed 10 kg N ha^-1 y^-1 in certain regions, primarily in industrialized areas of Europe and Asia and particularly in forests. Experimental N deposition treatments are in fact similar to mineral fertilizer application rates in agriculture. Some ecological guilds, such as saprotrophic fungi, are highly sensitive to N and respond differently to low and high N availability. In addition, very high levels of N application cause changes in soil chemistry, such as acidification, meaning that unrealistic experimental treatments are unlikely to reveal true ecosystem responses to N. Hence, despite decades of research, past experiments can tell us little about how the biosphere has responded to anthropogenic N deposition. A new approach is required to improve our understanding of this important phenomenon. First, characterization of N response functions using observed N deposition gradients. Second, application of experimental N addition gradients at realistic levels over long periods to detect cumulative effects. Third, application of non-linear meta-regressions to detect non-linear responses in meta-analyses of experimental studies.

Key aroma compounds of Canastra cheese: HS-SPME optimization assisted by olfactometry and chemometrics

An analytical method was developed to determine volatile compounds (VC) that contribute to the aroma of cheese from Serra da Canastra (Brazil) and evaluate them in three ripening stages (fresh, short-ripened, and ripened) via headspace solid-phase microextraction (HS-SPME) combined with gas chromatography (GC). Proximate and fatty acid compositions were determined to observe whether there would be changes during ripening. Multivariate designs were applied to optimize the extraction parameters of volatile compounds and assisted by GC olfactometry (GC-O) and chemometrics. The adopted strategy revealed that the best extraction condition requires 10 min of equilibration, 75.2 min of fiber exposure at 40 °C, and 1 g of sample. The data obtained evidenced the alteration of the abundance of volatile compounds, fatty acids, and proximate composition of Canastra cheese during ripening. The fatty acid profile of the samples was mainly composed of palmitic, oleic, and stearic acids. This dairy product is rich in volatile compounds and formed primarily by alcohols (n = 14), acids (n = 13), and esters (n = 11). Olfactometry indicated that the VCs that most affected the aroma of ripened Canastra cheese were acetic acid, isobutyric acid, butanoic acid, and ethyl hexanoate. The method developed effectively discriminated against Canastra cheeses at their different ripening stages.

Bayesian optimization of comprehensive two-dimensional liquid chromatography separations

Comprehensive two-dimensional liquid chromatography (LC×LC), is a powerful, emerging separation technique in analytical chemistry. However, as many instrumental parameters need to be tuned, the technique is troubled by lengthy method development. To speed up this process, we applied a Bayesian optimization algorithm. The algorithm can optimize LC×LC method parameters by maximizing a novel chromatographic response function based on the concept of connected components of a graph. The algorithm was benchmarked against a grid search (11,664 experiments) and a random search algorithm on the optimization of eight gradient parameters for four different samples of 50 compounds. The worst-case performance of the algorithm was investigated by repeating the optimization loop for 100 experiments with random starting experiments and seeds. Given an optimization budget of 100 experiments, the Bayesian optimization algorithm generally outperformed the random search and often improved upon the grid search. Moreover, the Bayesian optimization algorithm offered a considerably more sample-efficient alternative to grid searches, as it found similar optima to the grid search in far fewer experiments (a factor of 16-100 times less). This could likely be further improved by a more informed choice of the initialization experiments, which could be provided by the analyst's experience or smarter selection procedures. The algorithm allows for expansion to other method parameters (e.g., temperature, flow rate, etc.) and unlocks closed-loop automated method development.

Introduction of nitrogen doped graphene nanosheets as efficient adsorbents for nitrate removal from aqueous samples

PURPOSE

Introducing and developing new kinds of adsorbents are always a significant challenge in water treatments. In this work, for the first time, graphene oxide (GO), nitrogen-doped graphene oxide (ND-GO), highly nitrogen-doped graphene oxide (HND-GO), and 3D high nitrogen-doped graphene oxide (3D-HND-GO) were synthesized and comparatively evaluated in the removal of nitrate content of tap and underground waters.

METHODS

The removal of the target analyte was performed through a batch adsorption approach, and the factors influencing its removal efficiency (i.e., initial pH of the sample, primary concentrations of nitrate, amount of adsorbent, and contact time) were evaluated through a central composite design (CCD) and response surface methodology (RSM).

RESULTS

Based on the results, 3D-HND-GO showed the highest removal efficiency in comparison with the other mentioned nanoparticles. The nitrate removal using this adsorbent was modeled successfully so that R 2, adjusted R 2, and predicted R 2 values were 0.9717, 0.9508, and 0.9010, respectively. In addition, the optimal removal condition was achieved using the Nelder-Mead non-linear optimization algorithm as follow: the initial concentrations of nitrate (expressed as nitrogen): 15.0 mg/mL, the amount of the adsorbent: 2.0 mg/mL; pH of the sample: 3.0; and the contact time: 20.0 min. Under this optimal condition, the actual removal result (92.5 ± 4.0%) was in good agreement with the expected value (94.8 ± 5.1%). Additional studies were also performed to comprehensibly evaluate the adsorption activity of the adsorbent (e.g., kinetic, isotherm, and desorption parameters). The adsorption isotherm complied with the Langmuir model illustrating the considerable mono-layer adsorption capacities for the target ions with qm of 8.7 mg/g. The adsorption process was indicated to obey a pseudo 2nd order kinetic model, with the rate-limiting step for the adsorption phase.

CONCLUSIONS

This study revealed which 3D-HND-G leads to improved yield in the nitrate ions elimination, particularly at acidic media, which was related to the enhanced dispersibility and larger surface area. The adsorbent was further successfully used for treating tap and underground water samples. At the present moment, research as grown to modify 3D-HND-G in orders to increase the potentiality for industrial applications.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40201-021-00741-7.

The preparation phase in the multiphase optimization strategy (MOST): a systematic review and introduction of a reporting checklist

Multicomponent behavioral interventions developed using the multiphase optimization strategy (MOST) framework offer important advantages over alternative intervention development models by focusing on outcomes within constraints relevant for effective dissemination. MOST consists of three phases: preparation, optimization, and evaluation. The preparation phase is critical to establishing the foundation for the optimization and evaluation phases; thus, detailed reporting is critical to enhancing rigor and reproducibility. A systematic review of published research using the MOST framework was conducted. A structured framework was used to describe and summarize the use of MOST terminology (i.e., preparation phase and optimization objective) and the presentation of preparation work, the conceptual model, and the optimization. Fifty-eight articles were reviewed and the majority focused on either describing the methodology or presenting results of an optimization trial (n = 38, 66%). Although almost all articles identified intervention components (96%), there was considerable variability in the degree to which authors fully described other elements of MOST. In particular, there was less consistency in use of MOST terminology. Reporting on the MOST preparation phase is varied, and there is a need for increased focus on explicit articulation of key design elements and rationale of the preparation phase. The proposed checklist for reporting MOST studies would significantly advance the use of this emerging methodology and improve implementation and dissemination of MOST. Accurate reporting is essential to reproducibility and rigor of scientific trials as it ensures future research fully understands not only the methodology, but the rationale for intervention and optimization decisions.

Rapid screening of chemical warfare agents (nerve agents) using dimethyl methylphosphonate as simulant substances in beverages by hollow fiber membrane-protected solid phase microextraction followed by corona discharge ion mobility spectrometry

The following work presents a new, rapid, potential to be portable, convenient, and low-cost method using hollow fiber membrane-protected solid phase microextraction followed by corona discharge ion mobility spectrometry which was used for determining dimethyl methylphosphonate in beverages. Response surface methodology based on the design of Box-Behnken was implemented for optimizing the different factors influencing the proposed method for obtaining the best results. Optimal extractions were calculated with 65 µm polydimethylsiloxane-divinylbenzene fiber, fiber equilibration time of 10 min, stirring rate of the sample solution at 750 rpm, and extraction temperature of 50 °C. The proposed technique provided linear range (0.5-50 µg mL^-1), good linearity (>0.991), and repeatability (the relative standard deviations of 5.42% and 8.37% of intra- and inter-day analyses, respectively) under the optimized extraction conditions. Finally, the developed method was successfully used for determining dimethyl methylphosphonate in beverages such as coffee mix, fruit juice, tap water, milk, and tea.

Sampling the fish gill microbiome: a comparison of tissue biopsies and swabs

Background

Understanding the influence of methodology on results is an essential consideration in experimental design. In the expanding field of fish microbiology, many best practices and targeted techniques remain to be refined. This study aimed to compare microbial assemblages obtained from Atlantic salmon (Salmo salar) gills by swabbing versus biopsy excision. Results demonstrate the variation introduced by altered sampling strategies and enhance the available knowledge of the fish gill microbiome.

Results

The microbiome was sampled using swabs and biopsies from fish gills, with identical treatment of samples for 16S next generation Illumina sequencing. Results show a clear divergence in microbial communities obtained through the different sampling strategies, with swabbing consistently isolating a more diverse microbial consortia, and suffering less from the technical issue of host DNA contamination associated with biopsy use. Sequencing results from biopsy-derived extractions, however, hint at the potential for more cryptic localisation of some community members.

Conclusions

Overall, results demonstrate a divergence in the obtained microbial community when different sampling methodology is used. Swabbing appears a superior method for sampling the microbiota of mucosal surfaces for broad ecological research in fish, whilst biopsies might be best applied in exploration of communities beyond the reach of swabs, such as sub-surface and intracellular microbes, as well as in pathogen diagnosis. Most studies on the external microbial communities of aquatic organisms utilise swabbing for sample collection, likely due to convenience. Much of the ultrastructure of gill tissue in live fish is, however, potentially inaccessible to swabbing, meaning swabbing might fail to capture the full diversity of gill microbiota. This work therefore also provides valuable insight into partitioning of the gill microbiota, informing varied applications of different sampling methods in experimental design for future research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02374-0.

Application of chemometric tools in the development and sensory evaluation of gluten-free cracknel biscuits with the addition of chia seeds and turmeric powder

Gluten-free biscuits were developed with the addition of chia seeds (Salvia hispanica L.) and turmeric powder. An experimental design 2² were employed in the formulation optimization that promotes better sensory acceptance through acceptance test with a hedonic scale of 9 points. For characterization purposes, the centesimal composition from chia seeds and the turmeric powder were determined. The biscuits were produced from an optimized formulation with and without chia seeds and turmeric powder with microbiological results safe for consumption by evaluation Salmonella sp., positive coagulase Staphylococcus, and Coliforms to 45 °C. The Principal Components Analysis (PCA) was used in the investigation of sensory results (color, flavor, texture, smell, appearance, overall impression). It was also considered the habits of consuming food with/without gluten, purchase intentions, including age and gender. The results show that there is no distinction between the biscuits with the addition of chia seeds and turmeric powder. A statistical test using the confidence ellipse confirms that there no significant difference, at a 95% confidence level, among the sensory results for the biscuits with and without chia seeds and turmeric powder.

Skin absorption of mixed halide anions from concentrated aqueous solutions

Non-ideal behaviour of mixed ions is disclosed in skin absorption experiments of mixed halide anions in excised pig skin. Comparison of skin absorption of pure and mixed ions shows enhanced penetration of chaotropic ions from mixed solutions. An experimental design and statistical analysis using a Scheffé {3,2} simplex-lattice allows investigating the full ternary diagram of anion mixtures of fluoride, bromide and iodide. Synergism in mixed absorption is observed for chaotropic bromide and iodide anions. A refined analysis highlighting specific interactions is made by considering the ratio of the absorbed amount to the ion activity instead of the directly measured absorbed amount. Statistical analysis discards non-significant effects and discloses specific interactions. Such interactions between bromide and iodide cause an absorption enhancement of their partner by a factor of 2-3 with respect to the case of ideal mixing. It is proposed that enhanced absorption from mixed solution involves the formation of neutral complex species of mixed bromide and iodide with endogenous magnesium or calcium inside stratum corneum.

Experimental Design for Time-Series RNA-Seq Analysis of Gene Expression and Alternative Splicing

RNA-sequencing (RNA-seq) is currently the method of choice for analysis of differential gene expression. To fully exploit the wealth of data generated from genome-wide transcriptomic approaches, the initial design of the experiment is of paramount importance. Biological rhythms in nature are pervasive and are driven by endogenous gene networks collectively known as circadian clocks. Measuring circadian gene expression requires time-course experiments which take into account time-of-day factors influencing variability in expression levels. We describe here an approach for characterizing diurnal changes in expression and alternative splicing for plants undergoing cooling. The method uses inexpensive everyday laboratory equipment and utilizes an RNA-seq application (3D RNA-seq) that can handle complex experimental designs and requires little or no prior bioinformatics expertise.

Removal of Hydroxychloroquine Using Engineered Biochar from Algal Biodiesel Industry Waste: Characterization and Design of Experiment (DoE)

Adsorption of hydroxychloroquine (HCQ) onto H₃PO₄-activated Cystoseira barbata (Stackhouse) C. Agardh (derived from algal biodiesel industry waste) biochar was investigated via batch experiments and mathematical models. The activated biochar (BC-H) was produced in a single step by using the microwave irradiation method. Thus, it was obtained with a low cost, energy efficiency and by promoting clean production processes. BC-H exhibited a remarkable adsorption efficiency (98.9%) and large surface area (1088.806 m² g⁻¹) for removal of HCQ. The Langmuir isotherm and the pseudo-second-order kinetic models were the best fit for the equilibrium adsorption and kinetics experiments, and the maximum monolayer adsorption capacity (qmax) was found to be 353.58 µg g⁻¹. Additionally, the experiments with real wastewater showed that BC-H's ability to adsorb HCQ was not affected by competitive ions in the water. The Taguchi orthogonal array (L16 OA) experimental design was applied for the effective cost optimization analyses of the adsorption process by considering four levels and four controllable factors (initial pH, HCQ concentration, amount of adsorbent and contact time). Scanning electron microscopy, Fourier transform infrared spectroscopy and Brunauer–Emmett–Teller analyses were used for characterizing the adsorbent. The findings showed that BC-H can be used as an effective and low-cost adsorbent in the removal of HCQ from water.

Guidelines for reporting methods to estimate metabolic rates by aquatic intermittent-flow respirometry

Interest in the measurement of metabolic rates is growing rapidly, because of the importance of metabolism in advancing our understanding of organismal physiology, behaviour, evolution and responses to environmental change. The study of metabolism in aquatic animals is undergoing an especially pronounced expansion, with more researchers utilising intermittent-flow respirometry as a research tool than ever before. Aquatic respirometry measures the rate of oxygen uptake as a proxy for metabolic rate, and the intermittent-flow technique has numerous strengths for use with aquatic animals, allowing metabolic rate to be repeatedly estimated on individual animals over several hours or days and during exposure to various conditions or stimuli. There are, however, no published guidelines for the reporting of methodological details when using this method. Here, we provide the first guidelines for reporting intermittent-flow respirometry methods, in the form of a checklist of criteria that we consider to be the minimum required for the interpretation, evaluation and replication of experiments using intermittent-flow respirometry. Furthermore, using a survey of the existing literature, we show that there has been incomplete and inconsistent reporting of methods for intermittent-flow respirometry over the past few decades. Use of the provided checklist of required criteria by researchers when publishing their work should increase consistency of the reporting of methods for studies that use intermittent-flow respirometry. With the steep increase in studies using intermittent-flow respirometry, now is the ideal time to standardise reporting of methods, so that - in the future - data can be properly assessed by other scientists and conservationists.

An innovative green protocol for the quantification of benzothiazoles, benzotriazoles and benzosulfonamides in PM10 using microwave-assisted extraction coupled with solid-phase microextraction gas chromatography tandem-mass spectrometry

Benzothiazoles (BTHs), benzotriazoles (BTRs), and benzenesulfonamides (BSAs) are chemicals used in several industrial and household applications. Despite these compounds are emerging pollutants, there is still a lack of information about their presence in outdoor air samples. In this paper, we developed a new method for the quantification of BTHs, BTRs, and BSAs in airborne particulate matter (PM₁₀). The extraction of fourteen analytes from PM₁₀ was accomplished by microwave-assisted extraction (MAE) using an environmentally friendly mixture of water and ethanol. SPME was used to analyze the target compounds from the MAE extract by gas chromatography-tandem mass spectrometry (SPME-GC-MS/MS), eliminating additional sample clean-up steps. The best working conditions for MAE and SPME were examined multivariately by experimental design techniques. The target compounds were quantified in selected reaction monitoring acquisition mode. The proposed method was carefully validated, and the achieved results were satisfactory in terms of linearity, lower limit of quantification (picograms per cubic meter), intra- and inter-day accuracy (81-118% and 82-114%, respectively), and precision (repeatability and reproducibility in the range 2.3-17% and 7.4-19%, respectively). The application in a real monitoring campaign showed that the developed protocol is a valuable and eco-friendly alternative to the methods proposed so far.

Predictive processing models and affective neuroscience

The neural bases of affective experience remain elusive. Early neuroscience models of affect searched for specific brain regions that uniquely carried out the computations that underlie dimensions of valence and arousal. However, a growing body of work has failed to identify these circuits. Research turned to multivariate analyses, but these strategies, too, have made limited progress. Predictive processing models offer exciting new directions to address this problem. Here, we use predictive processing models as a lens to critique prevailing functional neuroimaging research practices in affective neuroscience. Our review highlights how much work relies on rigid assumptions that are inconsistent with a predictive processing approach. We outline the central aspects of a predictive processing model and draw out their implications for research in affective and cognitive neuroscience. Predictive models motivate a reformulation of "reverse inference" in cognitive neuroscience, and placing a greater emphasis on external validity in experimental design.

Modelling conditions of storing quality commercial eggs

Egg storage has been a problem due to ineffective methods subjecting many farmers and egg retailers to losses. These techniques include various models involving statistical analysis of the storage conditions on the egg quality. Apparent deficiencies of the information from the randomized complete block design model prompted this study. The study evaluated the effect of storage temperature at three levels (5 ^°C, 19.5 ^°C, 30 ^°C) and duration at four levels (2^nd, 12^th, 22^nd, 32^nd) on egg quality using a fixed and mixed-effect model. We used a total of 618 fresh and unfertilized eggs from ISA (Institut de Sélection Animale) brown layers. We determined egg quality by the changes of physical characterization under storage conditions. The study used Restricted maximum likelihood and analysis of variance methods to assess the efficiency of fixed and mixed effect models. Results showed that the physical components of the egg were significantly affected at 5 ^°C, 19.5 ^°C, and 30 ^°C(P<0.05). The effect was more adverse on eggs stored at 30 ^°C for 32 days. However, storage temperatures of 5 ^°C and 19.5 ^°C led to an extensive reduction in the Haugh unit, yolk index, and egg white height. On the other hand, it increased the weight loss and albumen diameter under storage for 2^nd, 12^th, 22^nd and 32^nd-time intervals. Based on these findings, the study recommends 5 ^°C for egg quality preservation. The eggs should be refrigerated for 32 days, stored at 19.5 ^°C for 14 days, and lastly kept at 30 ^°C for a maximum of 7 days. The fixed-effect models exhibited more minor variances in diameter and height of albumen, yolk index, weight loss, and Haugh unit. This overlapped instances where the fixed-effect models were significantly the same as the mixed-effect models. This study proposes that the fixed effect model is the most appropriate for randomized completely block design experiments.

Paradoxical facilitation alongside interhemispheric inhibition

Neurophysiological experiments using transcranial magnetic stimulation (TMS) have sought to probe the function of the motor division of the corpus callosum. Primary motor cortex sends projections via the corpus callosum with a net inhibitory influence on the homologous region of the opposite hemisphere. Interhemispheric inhibition (IHI) experiments probe this inhibitory pathway. A test stimulus (TS) delivered to the motor cortex in one hemisphere elicits motor evoked potentials (MEPs) in a target muscle, while a conditioning stimulus (CS) applied to the homologous region of the opposite hemisphere modulates the effect of the TS. We predicted that large CS MEPs would be associated with increased IHI since they should be a reliable index of how effectively contralateral motor cortex was stimulated and therefore of the magnitude of interhemispheric inhibition. However, we observed a strong tendency for larger CS MEPs to be associated with reduced interhemispheric inhibition which in the extreme lead to a net effect of facilitation. This surprising effect was large, systematic, and observed in nearly all participants. We outline several hypotheses for mechanisms which may underlie this phenomenon to guide future research.

Determination of benzotriazole and benzothiazole derivatives in human urine by eco-friendly deep eutectic solvent-based ultrasound-assisted liquid-liquid microextraction followed by ultrahigh performance liquid chromatography quadrupole-time-of-flight mass spectrometry

Benzotriazole (BTRs) and benzothiazole (BTHs) derivatives have been classified as high production volume pollutants of emerging concern. The present work describes a rapid and simple process using an eco-friendly deep eutectic solvent (DES) based-ultrasound-assisted liquid-liquid microextraction (DES-UALLME) technique to effectively extract five BTRs and four BTHs in human urine samples, and then applying ultrahigh-performance liquid chromatography and electrospray ionization (+)-quadrupole time-of-flight mass spectrometry (UHPLC-ESI(+)-QTOF-MS) for their detection and quantification. DESs are a group of novel "green" solvents, and their applications in sample pretreatment are appropriate for the requirements for green chemistry, environmental protection and sustainable development. Furthermore, to overcome the challenges related to different experimental conditions, multivariate experimental design approaches conducted by means of a multilevel categorical design and a Box-Behnken Design were applied to screen and optimize parameters that have significant influences on the extraction efficiency of DES-UALLME. After optimization, the method was validated and shown to possess low limits of quantitation (LOQs; 0.4 - 9 ng mL^-1), high precision (3-12%), and high accuracy (mean spiked recoveries; 80-101%). The developed method was then successfully applied for the analysis of BTRs and BTHs in human urine samples. Interestingly, 5,6-dimethyl-1H-benzotriazole (XTR) was detected in almost all of the urine samples, which correlates with its high production and widely applications in industry processes and consumer products in Taiwan. These target analytes could potentially be used as biomarkers to assess exposure of BTRs and BTHs in biomonitoring programs and studies.

Methodological and institutional considerations for the use of 360-degree video and pet animals in human subject research: An experimental case study from the United States

Head-mounted virtual-reality headsets and virtual-reality content have experienced large technological advances and rapid proliferation over the last years. These immersive technologies bear great potential for the facilitation of the study of human decision-making and behavior in safe, perceptually realistic virtual environments. Best practices and guidelines for the effective and efficient use of 360-degree video in experimental research is also evolving. In this paper, we summarize our research group's experiences with a sizable experimental case study on virtual-reality technology, 360-degree video, pet animals, and human participants. Specifically, we discuss the institutional, methodological, and technological challenges encountered during the implementation of our 18-month-long research project on human emotional response to short-duration 360-degree videos of human-pet interactions. Our objective in this paper is to contribute to the growing body of research on 360-degree video and to lower barriers related to the conceptualization and practice of research at the intersection of virtual-reality experiences, 360-degree video, live animals, and human behavior. Practical suggestions for human-subject researchers interested in utilizing virtual-reality technology, 360-degree videos, and pet animals as a part of their research are discussed.

mfapy: An open-source Python package for 13C-based metabolic flux analysis

¹³C-based metabolic flux analysis (¹³C-MFA) is an essential tool for estimating intracellular metabolic flux levels in metabolic engineering and biology. In ¹³C-MFA, a metabolic flux distribution that explains the observed isotope labeling data was computationally estimated using a non-linear optimization method. Herein, we report the development of mfapy, an open-source Python package developed for more flexibility and extensibility for ¹³C-MFA. mfapy compels users to write a customized Python code by describing each step in the data analysis procedures of the isotope labeling experiments. The flexibility and extensibility provided by mfapy can support trial-and-error performance in the routine estimation of metabolic flux distributions, experimental design by computer simulations of ¹³C-MFA experiments, and development of new data analysis techniques for stable isotope labeling experiments. mfapy is available to the public from the Github repository (https://github.com/fumiomatsuda/mfapy).

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An open-source Python package, mfapy, is developed for ¹³C-MFA.

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mfapy enables users to write Python codes for data analysis procedures of ¹³C-MFA.

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mfapy has a flexibility and extensibility to support various data analysis procedures.

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Computer simulations of ¹³C-MFA experiments is supported for experimental design.

Comprehending the complexity of microplastic organismal exposures and effects, to improve testing frameworks

Microplastics (MPs) have been identified as a threat to global ecosystems. Current projections indicate that the negative impacts of MPs will increase in the environment. Traditional toxicity testing does not account for the diversity of MP particles, the inherent diversity in potential exposure routes, and complex impacts in exposed organisms. Here we present and discuss factors influencing organismal exposure to MPs driven by fate and behavior of MPs in different environmental matrices and organisms behavioral niches. We then provide a structured classification of potential effects of MPs, chemical or particulate, generic or specific to MPs. Using these analyses, we discuss appropriateness and limitations of applying traditional, chemical-based ecotoxicity testing for the study of MPs, and propose practical recommendations and guidelines. Future laboratory based studies can be improved to increase understanding of potential real world effects of MPs by careful selection of appropriate exposure systems and conditions, test organism, MP characteristics, endpoints and required controls. We build upon recommendations provided in previous publications and complement them with a list of parameters and practical information that should be checked and/or reported in MP studies.