Probing the effect of perceptual (dis)fluency on metacognitive judgments

Despite research showing that perceptually fluent stimuli (i.e., stimuli that are easier to process) are given higher judgment of learning (JOL) ratings than perceptually disfluent stimuli, it remains unknown whether the influence of perceptual fluency on JOLs is driven by the fluent or disfluent items. Moreover, it is unclear whether this difference hinges on relative differences in fluency. The current study addressed these unanswered questions by employing (Fiacconi et al., Journal of Experimental Psychology: Learning, Memory, and Cognition 46:926-944, 2020), Journal of Experimental Psychology: Learning, Memory, and Cognition, 46[5], 926-944) letter set priming procedure. In this procedure, participants are first exposed to words containing only a subset of letters. Following this exposure, JOLs to new words composed of the same letters (i.e., fluent), and new words composed of nonexposed letters (i.e., disfluent) are compared with isolate the contribution of perceptual fluency. Because this procedure does not rely on parametric variations in perceptual features, we can directly assess the potential benefit and/or cost of fluent and disfluent items, respectively, by including neutral baseline conditions. Moreover, implementing both a mixed- and pure-list design allowed us to assess the comparative nature of perceptual fluency on JOLs. Counter to previous assumptions, our results are the first to demonstrate that perceptual disfluency decreases JOLs. Moreover, we found that the influence of perceptual disfluency on JOLs hinges on the relative differences in fluency between items even in the absence of a belief about the mnemonic impact of the fluency manipulation. These findings have important implications as they provide evidence that the difficulty, rather than ease, of information form the basis of individuals metacognitive judgments.

Volatolomic approach by HS-SPME/GC-MS and chemometric evaluations for the discrimination of X-ray irradiated mozzarella cheese

In this work, an untargeted screening of the volatile profile of X-ray irradiated mozzarella cheese was carried out to study the possible radio-induced modifications. A Central Composite Design (CCD) for Response Surface Methodology (RSM) was employed to optimise the HS-SPME analysis of volatile organic compounds (VOCs). The optimised HS-SPME conditions, in terms of sample amount (5.0 g), extraction temperature (50 °C) and extraction time (75 min), were used to analyse non-irradiated and irradiated samples at three dose levels, 1.0, 2.0, 3.0 kGy. Partial Least Squares-Discriminant Analysis (PLS-DA) and Linear Discriminant Analysis (LDA) were applied to explore the variation of volatile profile with respect to the X-ray irradiation treatment. Both methods highlighted a high discriminant capability with excellent values of accuracy, specificity and sensitivity, demonstrating the effectiveness of the volatolomic approach to evaluate the variations induced by the treatment and allowing to select a total of 35 VOCs as potential irradiation markers.

Optimization of Ultrasound-Assisted Extraction of Chlorogenic Acid from Potato Sprout Waste and Enhancement of the In Vitro Total Antioxidant Capacity

Potato sprouts, an underutilized by-product of potato processing, could be exploited for the recovery of caffeoyl-quinic acids (CQAs), a family of polyphenols with well-recognized biological activities. In this work, the predominant compound of this class, 5-CQA, was extracted by Ultrasound-Assisted Extraction (UAE) under conditions optimized by an Experimental Design. The investigated variables solid/solvent ratio (1:10-1:50 g/mL), water content in ethanol (30-100% v/v) and UAE time (5-20 min) highlighted a critical influence of the last two factors on the extraction efficiency: extracts richer in 5-CQA were obtained with lower water content (30%) and time (5 min). The addition of ascorbic acid (1.7 mM) as anti-browning agent to the extraction solvent improved the extraction efficiency of 5-CQA compared to acetic and citric acids (3158.71 μg/mL, 1766.71 μg/mL, 1468.20 μg/mL, respectively). A parallel trend for the three acids and an increase in 5-CQA recovery was obtained with the use of freeze-dried sprouts (4980.05 μg/mL, 4795.62, 4211.25 μg/mL, respectively). Total antioxidant capacity (TAC) in vitro demonstrated UAE being a more valuable technique than conventional maceration. Furthermore, three-times-higher values of TPC (7.89 mg GAE/g) and TAC (FRAP: 24.01 mg TE/g; DPPH: 26.20 mg TE/g; ABTS 26.72 mg TE/g) were measured for the optimized extract compared to the initial one. An HPLC-DAD method was applied to monitor 5-CQA recovery, while an LC-HRMS/MS investigation allowed us to perform analyte identity confirmation along with detection of the glycoalkaloids α-solanine and α-chaconine. This evidence underlines the necessity to develop purification strategies in order to maximize the potential of potato sprout waste as a source of 5-CQA.

A Design-assisted Spectrofluorometric Method Utilizing a One-pot Fluorescent Probe for the Quantitation of some Calcium Channel Blockers

Based on their reaction with highly fluorescent carbon quantum dots (CQDts), a precise and reliable spectrofluorometric approach was developed for the determination of three calcium channel blockers. The studied drugs are: lercanidipine, nimodipine and nifedipine. (CQDts) were produced using a one-step hydrothermal method with ascorbic acid as the carbon source. The produced CQDts were capped by alcohol to create yellow emitters displaying a high fluorescence emission at 524 nm when excited at 325 nm. The fluorescence intensity of CQDts was noticeably quenched by each of the three calcium channel blockers. The relation between their concentrations and fluorescence quenching is linear over the concentration range of 0.5-20 µg/mL for each of the three drugs. A full factorial design was used to optimize the effect of variable factors. Therefore, under optimum experimental design conditions, the detection limits for lercanidipine, nimodipine, and nifedipine were 0.11 ± 1.09, 0.10 ± 0.25 and 0.12 ± 0.71 µg/mL, respectively. The LOQ was 0.33, 0.30, and 0.37 µg/mL respectively. The quenching of fluorescent CQDts occurred through the inner filter effect (IFE) for nimodipine, while it was mixed with dynamic quenching for lercanidipine and nifedipine. The proposed method was effectively used to determine the cited drugs in their pharmaceutical products and had an acceptable level of precision. The selectivity of the CQDts system towards the studied drugs was examined indicating no interference from interfering species.

ExploreModelMatrix: Interactive exploration for improved understanding of design matrices and linear models in R

Linear and generalized linear models are used extensively in many scientific fields, to model observed data and as the basis for hypothesis tests. The use of such models requires specification of a design matrix, and subsequent formulation of contrasts representing scientific hypotheses of interest. Proper execution of these steps requires a thorough understanding of the meaning of the individual coefficients, and is a frequent source of uncertainty for end users. Here, we present an R/Bioconductor package, ExploreModelMatrix, which enables interactive exploration of design matrices and linear model diagnostics. Given a sample data table and a desired design formula, the package displays how the model coefficients are combined to give the fitted values for each combination of predictor variables, which allows users to both extract the interpretation of each individual coefficient, and formulate desired linear contrasts. In addition, the interactive interface displays informative characteristics for the regular linear model corresponding to the provided design, such as variance inflation factors and the pseudoinverse of the design matrix. We envision the package and the built-in collection of common types of linear model designs to be useful for teaching and self-learning purposes, as well as for assisting more experienced users in the interpretation of complex model designs.

Using Differential Evolution to Design Optimal Experiments

Differential Evolution (DE) has become one of the leading metaheuristics in the class of Evolutionary Algorithms, which consists of methods that operate off of survival-of-the-fittest principles. This general purpose optimization algorithm is viewed as an improvement over Genetic Algorithms, which are widely used to find solutions to chemometric problems. Using straightforward vector operations and random draws, DE can provide fast, efficient optimization of any real, vector-valued function. This article reviews the basic algorithm and a few of its modifications with various enhancements. We provide guidance for practitioners, discuss implementation issues and give illustrative applications of DE with the corresponding R codes to find different types of optimal designs for various statistical models in chemometrics that involve the Arrhenius equation, reaction rates, concentration measures and chemical mixtures.

A Figure One Web Tool for Visualization of Experimental Designs

This manuscript introduces a user-friendly, point and click open source and platform-independent software tool that aids the graphical representation of experimental studies. A graphical summary can give a high-level view of a study and represent in one illustration the important features of the data. Examples include sample collections, the time of each data collection, perturbations, and analysis performed. Graphical summaries can be useful in clarifying and documenting the complex relationships within an experiment by breaking down the component parts and expressing them visually. Commonly used cases for this tool include generating summary figures for presentation and publications. This tool was used either alone or in conjunction with other tools to generate schematic diagrams for talks and publications on several different on-going research projects.

Statistical Contributions to Bioinformatics: Design, Modeling, Structure Learning, and Integration

The advent of high-throughput multi-platform genomics technologies providing whole-genome molecular summaries of biological samples has revolutionalized biomedical research. These technologiees yield highly structured big data, whose analysis poses significant quantitative challenges. The field of Bioinformatics has emerged to deal with these challenges, and is comprised of many quantitative and biological scientists working together to effectively process these data and extract the treasure trove of information they contain. Statisticians, with their deep understanding of variability and uncertainty quantification, play a key role in these efforts. In this article, we attempt to summarize some of the key contributions of statisticians to bioinformatics, focusing on four areas: (1) experimental design and reproducibility, (2) preprocessing and feature extraction, (3) unified modeling, and (4) structure learning and integration. In each of these areas, we highlight some key contributions and try to elucidate the key statistical principles underlying these methods and approaches. Our goals are to demonstrate major ways in which statisticians have contributed to bioinformatics, encourage statisticians to get involved early in methods development as new technologies emerge, and to stimulate future methodological work based on the statistical principles elucidated in this article and utilizing all availble information to uncover new biological insights.

Assessment of pharmacogenomic agreement

In 2013 we published an analysis demonstrating that drug response data and gene-drug associations reported in two independent large-scale pharmacogenomic screens, Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE), were inconsistent. The GDSC and CCLE investigators recently reported that their respective studies exhibit reasonable agreement and yield similar molecular predictors of drug response, seemingly contradicting our previous findings. Reanalyzing the authors' published methods and results, we found that their analysis failed to account for variability in the genomic data and more importantly compared different drug sensitivity measures from each study, which substantially deviate from our more stringent consistency assessment. Our comparison of the most updated genomic and pharmacological data from the GDSC and CCLE confirms our published findings that the measures of drug response reported by these two groups are not consistent. We believe that a principled approach to assess the reproducibility of drug sensitivity predictors is necessary before envisioning their translation into clinical settings.

The Method of Randomization for Cluster-Randomized Trials: Challenges of Including Patients with Multiple Chronic Conditions

Cluster-randomized clinical trials (CRT) are trials in which the unit of randomization is not a participant but a group (e.g. healthcare systems or community centers). They are suitable when the intervention applies naturally to the cluster (e.g. healthcare policy); when lack of independence among participants may occur (e.g. nursing home hygiene); or when it is most ethical to apply an intervention to all within a group (e.g. school-level immunization). Because participants in the same cluster receive the same intervention, CRT may approximate clinical practice, and may produce generalizable findings. However, when not properly designed or interpreted, CRT may induce biased results. CRT designs have features that add complexity to statistical estimation and inference. Chief among these is the cluster-level correlation in response measurements induced by the randomization. A critical consideration is the experimental unit of inference; often it is desirable to consider intervention effects at the level of the individual rather than the cluster. Finally, given that the number of clusters available may be limited, simple forms of randomization may not achieve balance between intervention and control arms at either the cluster- or participant-level. In non-clustered clinical trials, balance of key factors may be easier to achieve because the sample can be homogenous by exclusion of participants with multiple chronic conditions (MCC). CRTs, which are often pragmatic, may eschew such restrictions. Failure to account for imbalance may induce bias and reducing validity. This article focuses on the complexities of randomization in the design of CRTs, such as the inclusion of patients with MCC, and imbalances in covariate factors across clusters.

Robust Optimization of Biological Protocols

When conducting high-throughput biological experiments, it is often necessary to develop a protocol that is both inexpensive and robust. Standard approaches are either not cost-effective or arrive at an optimized protocol that is sensitive to experimental variations. We show here a novel approach that directly minimizes the cost of the protocol while ensuring the protocol is robust to experimental variation. Our approach uses a risk-averse conditional value-at-risk criterion in a robust parameter design framework. We demonstrate this approach on a polymerase chain reaction protocol and show that our improved protocol is less expensive than the standard protocol and more robust than a protocol optimized without consideration of experimental variation.

Optimization of Three Dimensional Culturing of the HepG2 Cell Line in Fibrin Scaffold

BACKGROUND

A potential treatment for healing hepatic tissue is delivering isolated hepatic cells to the site of injury to promote hepatic cells formation. In this technology, providing an appropriate injectable system for delivery of hepatic cells is an important issue. In this regard, fibrin scaffolds were designed with many advantages over other scaffolds like cell delivery vehicles for biodegradation, biocompatibility and hemostasis.

OBJECTIVES

The aim of this study was to determine suitable cell culture circumstances for HepG2 cell proliferation and differentiation in 3D fibrin scaffolds by evaluating Ca(2+) concentrations, cell numbers, various ratios of plasma/RPMI 1640 and thickness of fibrin scaffold.

MATERIALS AND METHODS

In a one-stage experimental design, Box-Behnken design strategy was performed by Minitab 15 software (version 15, Minitab. State College, PA) with three factors at three levels (low, medium and high) and 27 runs for identification of the effects of ratio of plasma/RPMI 1640, Ca(2+) concentration and thickness on the formation of fibrin gel scaffold and 3D HepG2 culture.

RESULTS

The optimal concentrations for fibrin scaffold fabrication were achieved by adding 0.15 mol CaCl2 (50 µL) and 1 × 10(5) cells to 1:4 of plasma/RPMI 1640 ratio (500 µL with 2.3 mm thickness per well).

CONCLUSIONS

Our approach provided easy handle method using inexpensive materials like human plasma instead of purified fibrinogen to fabricate fibrin scaffold.