Competition and moral behavior: A meta-analysis of forty-five crowd-sourced experimental designs.

Does competition affect moral behavior? This fundamental question has been debated among leading scholars for centuries, and more recently, it has been tested in experimental studies yielding a body of rather inconclusive empirical evidence. A potential source of ambivalent empirical results on the same hypothesis is design heterogeneity-variation in true effect sizes across various reasonable experimental research protocols. To provide further evidence on whether competition affects moral behavior and to examine whether the generalizability of a single experimental study is jeopardized by design heterogeneity, we invited independent research teams to contribute experimental designs to a crowd-sourced project. In a large-scale online data collection, 18,123 experimental participants were randomly allocated to 45 randomly selected experimental designs out of 95 submitted designs. We find a small adverse effect of competition on moral behavior in a meta-analysis of the pooled data. The crowd-sourced design of our study allows for a clean identification and estimation of the variation in effect sizes above and beyond what could be expected due to sampling variance. We find substantial design heterogeneity-estimated to be about 1.6 times as large as the average standard error of effect size estimates of the 45 research designs-indicating that the informativeness and generalizability of results based on a single experimental design are limited. Drawing strong conclusions about the underlying hypotheses in the presence of substantive design heterogeneity requires moving toward much larger data collections on various experimental designs testing the same hypothesis.

Protein engineering via Bayesian optimization-guided evolutionary algorithm and robotic experiments.

Directed protein evolution applies repeated rounds of genetic mutagenesis and phenotypic screening and is often limited by experimental throughput. Through in silico prioritization of mutant sequences, machine learning has been applied to reduce wet lab burden to a level practical for human researchers. On the other hand, robotics permits large batches and rapid iterations for protein engineering cycles, but such capacities have not been well exploited in existing machine learning-assisted directed evolution approaches. Here, we report a scalable and batched method, Bayesian Optimization-guided EVOlutionary (BO-EVO) algorithm, to guide multiple rounds of robotic experiments to explore protein fitness landscapes of combinatorial mutagenesis libraries. We first examined various design specifications based on an empirical landscape of protein G domain B1. Then, BO-EVO was successfully generalized to another empirical landscape of an Escherichia coli kinase PhoQ, as well as simulated NK landscapes with up to moderate epistasis. This approach was then applied to guide robotic library creation and screening to engineer enzyme specificity of RhlA, a key biosynthetic enzyme for rhamnolipid biosurfactants. A 4.8-fold improvement in producing a target rhamnolipid congener was achieved after examining less than 1% of all possible mutants after four iterations. Overall, BO-EVO proves to be an efficient and general approach to guide combinatorial protein engineering without prior knowledge.

Planning and Analyzing a Low-Biomass Microbiome Study: A Data Analysis Perspective.

As investigations of low-biomass microbial communities have become more common, so too has the recognition of major challenges affecting these analyses. These challenges have been shown to compromise biological conclusions and have contributed to several controversies. Here, we review some of the most common and influential challenges in low-biomass microbiome research. We highlight key approaches to alleviate these potential pitfalls, combining experimental planning strategies and data analysis methods.

Are We There Yet? Assessing the Readiness of Single-Cell Proteomics to Answer Biological Hypotheses.

Single-cell analysis is an active area of research in many fields of biology. Measurements at single-cell resolution allow researchers to study diverse populations without losing biologically meaningful information to sample averages. Many technologies have been used to study single cells, including mass spectrometry-based single-cell proteomics (SCP). SCP has seen a lot of growth over the past couple of years through improvements in data acquisition and analysis, leading to greater proteomic depth. Because method development has been the main focus in SCP, biological applications have been sprinkled in only as proof-of-concept. However, SCP methods now provide significant coverage of the proteome and have been implemented in many laboratories. Thus, a primary question to address in our community is whether the current state of technology is ready for widespread adoption for biological inquiry. In this Perspective, we examine the potential for SCP in three thematic areas of biological investigation: cell annotation, developmental trajectories, and spatial mapping. We identify that the primary limitation of SCP is sample throughput. As proteome depth has been the primary target for method development to date, we advocate for a change in focus to facilitate measuring tens of thousands of single-cell proteomes to enable biological applications beyond proof-of-concept.

Principles of experimental design for ecology and evolution.

Good experimental design is critical for sound empirical ecology and evolution. However, many contemporary studies fail to replicate at the appropriate biological or organizational level, so causal inference might have less vigorous support than often assumed. Here, I provide a guide for how to identify the appropriate scale of replication for a range of common experimental designs in ecological and evolutionary studies. I discuss the merits of replicating multiple scales of biological organization. I suggest that experimental design be discussed in terms of the scale of replication relative to the scale at which inferences are sought when designing, discussing and reviewing experiments in ecology and evolution. I also suggest that more conversations about experimental design are needed, and I hope this piece stimulates such conversation.

Automated splitting into batches for observational biomedical studies with sequential processing.

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

Data Processing for Predicting DNA Damaging Properties of Complex UV Sources.

A growing number of experimental evidence emphasizes that photobiological phenomena are not always the sum of the effect of individual wavelengths present in the emission spectrum of light sources. Unfortunately, tools are missing to identify such non-additive effects and predict effects of various exposure conditions. In the present work, we addressed these points for the formation of pyrimidine dimers in DNA upon co-exposure to UVC, UVB and UVA radiation. We first applied a combination index approach to determine whether mixtures of theses UV ranges exhibited additive, inhibitory or synergistic effects on the formation of cyclobutane pyrimidine dimers, (6-4) photoproducts and Dewar valence isomers. A predictive approach based on an experimental design strategy was then used to quantify the contribution of each wavelength range to the formation of DNA photoproducts. The obtained models allowed us to accurately predict the level of pyrimidine dimers in DNA irradiated under different conditions. The data were found to be more accurate than those obtained with the simple additive approach underlying the use of action spectra. Experimental design thus appears as an attractive concept that could be widely applied in photobiology even for cellular experiments.

Considering ultraviolet radiation in experimental biology: a neglected pervasive stressor.

Ultraviolet radiation (UVR) is a pervasive factor that has shaped the evolution of life on Earth. Ambient levels of UVR mediate key biological functions but can also cause severe lethal and sublethal effects in a wide range of organisms. Furthermore, UVR is a powerful modulator of the effects of other environmental factors on organismal physiology, such as temperature, disease, toxicology and pH, among others. This is critically important in the context of global change, where understanding the effects of multiple stressors is a key challenge for experimental biologists. Ecological physiologists rarely afford UVR discussion or include UVR in experimental design, even when it is directly relevant to their study system. In this Commentary, we provide a guide for experimental biologists to better understand if, when, and how UVR can be integrated into experimental designs to improve the ecological realism of their experiments.

Isolation and characterization of Candida tropicalis B: a promising yeast strain for biodegradation of petroleum oil in marine environments.

The increasing interest in environmental protection laws has compelled companies to regulate the disposal of waste organic materials. Despite efforts to explore alternative energy sources, the world remains heavily dependent on crude petroleum oil and its derivatives. The expansion of the petroleum industry has significant implications for human and environmental well-being. Bioremediation, employing living microorganisms, presents a promising approach to mitigate the harmful effects of organic hydrocarbons derived from petroleum. This study aimed to isolate and purify local yeast strains from oil-contaminated marine water samples capable of aerobically degrading crude petroleum oils and utilizing them as sole carbon and energy sources. One yeast strain (isolate B) identified as Candida tropicalis demonstrated high potential for biodegrading petroleum oil in seawater. Physiological characterization revealed the strain's ability to thrive across a wide pH range (4-11) with optimal growth at pH 4, as well as tolerate salt concentrations ranging from 1 to 12%. The presence of glucose and yeast extract in the growth medium significantly enhanced the strain's biomass formation and biodegradation capacity. Scanning electron microscopy indicated that the yeast cell diameter varied based on the medium composition, further emphasizing the importance of organic nitrogenous sources for initial growth. Furthermore, the yeast strain exhibited remarkable capabilities in degrading various aliphatic and aromatic hydrocarbons, with a notable preference for naphthalene and phenol at 500 and 1000 mg/l, naphthalene removal reached 97.4% and 98.6%, and phenol removal reached 79.48% and 52.79%, respectively. Optimization experiments using multi-factorial sequential designs highlighted the influential role of oil concentration on the bioremediation efficiency of Candida tropicalis strain B. Moreover, immobilized yeast cells on thin wood chips demonstrated enhanced crude oil degradation compared to thick wood chips, likely due to increased surface area for cell attachment. These findings contribute to our understanding of the potential of Candida tropicalis for petroleum oil bioremediation in marine environments, paving the way for sustainable approaches to address oil pollution.

Do later school start times improve adolescents' sleep and substance use? A quasi-experimental study.

OBJECTIVE: A later school start time policy has been recommended as a solution to adolescents' sleep deprivation. We estimated the impacts of later school start times on adolescents' sleep and substance use by leveraging a quasi-experiment in which school start time was delayed in some regions in South Korea. METHODS: A later school start time policy was implemented in 2014 and 2015, which delayed school start times by approximately 30-90 minutes. We applied difference-in-differences and event-study designs to longitudinal data on a nationally representative cohort of adolescents from 2010 to 2015, which annually tracked sleep and substance use of 1133 adolescents from grade 7 through grade 12. RESULTS: The adoption of a later school start time policy was initially associated with a 19-minute increase in sleep duration (95% CI, 5.52 to 32.04), driven by a delayed wake time and consistent bedtime. The policy was also associated with statistically significant reductions in monthly smoking and drinking frequencies. However, approximately a year after implementation, the observed increase in sleep duration shrank to 7  minutes (95% CI, -12.60 to 25.86) and became statistically nonsignificant. Similarly, the observed reduction in smoking and drinking was attenuated a year after. CONCLUSIONS: Our findings suggest that policies that increase sleep in adolescents may have positive effects on health behaviors, but additional efforts may be required to sustain positive impacts over time. Physicians and education and health policymakers should consider the long-term effects of later school start times on adolescent health and well-being.

Design and statistical analysis reporting among interrupted time series studies in drug utilization research: a cross-sectional survey.

INTRODUCTION: Interrupted time series (ITS) design is a commonly used method for evaluating large-scale interventions in clinical practice or public health. However, improperly using this method can lead to biased results. OBJECTIVE: To investigate design and statistical analysis characteristics of drug utilization studies using ITS design, and give recommendations for improvements. METHODS: A literature search was conducted based on PubMed from January 2021 to December 2021. We included original articles that used ITS design to investigate drug utilization without restriction on study population or outcome types. A structured, pilot-tested questionnaire was developed to extract information regarding study characteristics and details about design and statistical analysis. RESULTS: We included 153 eligible studies. Among those, 28.1% (43/153) clearly explained the rationale for using the ITS design and 13.7% (21/153) clarified the rationale of using the specified ITS model structure. One hundred and forty-nine studies used aggregated data to do ITS analysis, and 20.8% (31/149) clarified the rationale for the number of time points. The consideration of autocorrelation, non-stationary and seasonality was often lacking among those studies, and only 14 studies mentioned all of three methodological issues. Missing data was mentioned in 31 studies. Only 39.22% (60/153) reported the regression models, while 15 studies gave the incorrect interpretation of level change due to time parameterization. Time-varying participant characteristics were considered in 24 studies. In 97 studies containing hierarchical data, 23 studies clarified the heterogeneity among clusters and used statistical methods to address this issue. CONCLUSION: The quality of design and statistical analyses in ITS studies for drug utilization remains unsatisfactory. Three emerging methodological issues warranted particular attention, including incorrect interpretation of level change due to time parameterization, time-varying participant characteristics and hierarchical data analysis. We offered specific recommendations about the design, analysis and reporting of the ITS study.

Neuromuscular electrical stimulation to combat cognitive aging in people with spinal cord injury: protocol for a single case experimental design study.

INTRODUCTION: Individuals with spinal cord injury (SCI) can experience accelerated cognitive aging. Myokines (factors released from muscle cells during contractions), such as brain-derived neurotrophic factor (BDNF), are thought to have beneficial effects on cognition. Neuromuscular electrical stimulation (NMES) was shown to elicit a large release of myokines. However, the effects of NMES on cognitive function have not been studied. OBJECTIVE: To present the study protocol for a clinical trial evaluating the effects of NMES aimed at improving cognition and BDNF. METHODS: A replicated randomized three-phases single-case experimental design (SCED) with sequential multiple baseline time series and a single-armed prospective trial will be conducted with 15 adults with chronic SCI (> 12 months after injury) above L1 neurological level undergoing 30-min quadriceps NMES, 3 days per week for 12 weeks. MAIN STUDY ENDPOINTS: Primary endpoint is cognitive performance (assessed by a smartphone test) conducted three times per week during the baseline phase with random duration of 3 to 8 weeks, the intervention phase of 12 weeks, and the follow-up phase of 3 weeks after a no measurement rest period of 12 weeks. Secondary endpoints are changes in BDNF levels and cognitive performance measured before the baseline period, before and after intervention and after a 12 weeks follow-up. CONCLUSION: This will be the first study investigating the effects of 12 weeks NMES on both cognition and BDNF levels in individuals with SCI. The SCED results provide information on individual treatment effect courses which may direct future research. TRIAL REGISTRATION: ClinicalTrials.gov (NCT05822297, 12/01/2023).

Informing the Exposure Landscape: The Fate of Microplastics in a Large Pelagic In-Lake Mesocosm Experiment.

Understanding microplastic exposure and effects is critical to understanding risk. Here, we used large, in-lake closed-bottom mesocosms to investigate exposure and effects on pelagic freshwater ecosystems. This article provides details about the experimental design and results on the transport of microplastics and exposure to pelagic organisms. Our experiment included three polymers of microplastics (PE, PS, and PET) ranging in density and size. Nominal concentrations ranged from 0 to 29,240 microplastics per liter on a log scale. Mesocosms enclosed natural microbial, phytoplankton, and zooplankton communities and yellow perch (Perca flavescens). We quantified and characterized microplastics in the water column and in components of the food web (biofilm on the walls, zooplankton, and fish). The microplastics in the water stratified vertically according to size and density. After 10 weeks, about 1% of the microplastics added were in the water column, 0.4% attached to biofilm on the walls, 0.01% within zooplankton, and 0.0001% in fish. Visual observations suggest the remaining >98% were in a surface slick and on the bottom. Our study suggests organisms that feed at the surface and in the benthos are likely most at risk, and demonstrates the value of measuring exposure and transport to inform experimental designs and achieve target concentrations in different matrices within toxicity tests.

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

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

Influence of multi-stressor combinations of pCO2, temperature, and salinity on the toxicity of Heterosigma akashiwo (Raphidophyceae), a fish-killing flagellate.

Climate change and global warming have led to more frequent harmful algal blooms in the last decade. Among these blooms, Heterosigma akashiwo, a golden-brown phytoflagellate, is one of the 40 species with a high potential to form harmful blooms, leading to significant fish mortality. Climate change leads to rising atmospheric and ocean temperatures. These changes, along with altered rainfall patterns and meltwater input, can cause fluctuations in ocean salinity. Elevated atmospheric carbon dioxide (CO2) levels increase water acidity as oceans absorb CO2. This study investigated the effects of temperature, salinity, and CO2 levels on lipid production, hemolytic activity, and toxicity of H. akashiwo using the design of experiment approach, which can be used to investigate the effect of two or more factors on the same response simultaneously in a precise manner with fewer experiments and materials but in a larger region of the factor space. The lipid content was measured using a high-throughput Nile Red method, and the highest level of lipid content was detected at 25°C, a salinity of 30, and a CO2 concentration of 400 ppm. Hemolytic activity was assessed using rabbit blood erythrocytes in a 96-well plate, and the optimal conditions for achieving the highest hemolytic activity were determined at 15°C, a salinity of 10, and a CO2 concentration of 400 ppm. As the chemical structure of the toxin is not known, we used the toxicity against the cell line RTgill-W1 as the cell toxicity proxy. The maximum toxicity was identified at 15°C, a salinity of 10, and a CO2 level of 700 ppm.

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

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

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

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

Response surface methodology use in construction of polianiline-coated carbon paste electrode-based biosensor: Modification and characterization.

In this study, the effect of amperometric glucose biosensor construction and using conditions on the current response was investigated in detail applying experimental design. Polyaniline (PANI) was synthesized on the carbon paste electrode (CPE) surface using the cyclic voltammetry technique in sodium oxalate (NaOx ) electrolyte medium, and an amperometric biosensor was constructed by immobilizing glucose oxidase (GOD). Biosensor preparation (aniline, GOD and NaOx concentrations, and scan rate) and operating conditions (pH and applied potential) were optimized by Box-Behnken and optimal designs, respectively, via State Ease Design Expert 7.0.1.1 software. ANOVA analyses showed that among the biosensor preparation parameters, the NaOx concentration has the highest effect on the current measured in the presence of glucose, whereas in the optimization of pH and potential parameters applied in current measurement studies, it has been revealed that pH has a very high effect on the measured current. Several compounds, such as MWCNT, two different ionic liquids and two different organic molecules were added to carbon paste, and, among them, 2-cyanoethylpyrrole (CPy) enhanced the efficacy highly, most probably due to its polymerization in the paste and increasing the electron transfer rate of the CPE. Sucrose- and lactose-sensitive biosensors were also constructed by co-immobilizing GOD with invertase (INV) or ß-galactosidase, respectively, onto modified CPE, and sensitivities to their substrates were shown by cyclic voltammetry and impedance analysis. CPy modification caused an increase in the current values, and also Imax /KM values increased approximately 11.8, 7.83, and 2.56 times for glucose-, sucrose-, and lactose-sensitive CPEs, respectively.

Artificial intelligence for neuro MRI acquisition: a review.

OBJECT: To review recent advances of artificial intelligence (AI) in enhancing the efficiency and throughput of the MRI acquisition workflow in neuroimaging, including planning, sequence design, and correction of acquisition artifacts. MATERIALS AND METHODS: A comprehensive analysis was conducted on recent AI-based methods in neuro MRI acquisition. The study focused on key technological advances, their impact on clinical practice, and potential risks associated with these methods. RESULTS: The findings indicate that AI-based algorithms have a substantial positive impact on the MRI acquisition process, improving both efficiency and throughput. Specific algorithms were identified as particularly effective in optimizing acquisition steps, with reported improvements in workflow efficiency. DISCUSSION: The review highlights the transformative potential of AI in neuro MRI acquisition, emphasizing the technological advances and clinical benefits. However, it also discusses potential risks and challenges, suggesting areas for future research to mitigate these concerns and further enhance AI integration in MRI acquisition.

Birthing a new maternal cognition literature: 10 recommendations for future research.

Whilst the field of maternal cognition is gaining interest, with a recent increase in publications, there are still only a handful of existing studies. This presents a unique opportunity for reflection and growth, advancing scientific rigor to ensure that future interpretations of maternal cognitive functioning are based on robust, generalizable data. With this in mind, we offer ten recommendations for future cognitive research in motherhood, with a focus on intentional study design. A study's design dictates the questions that can be asked, and the answers that can be gleaned from collected data, making study design a cornerstone of robust and reproducible science. These recommendations are intended as a resource for study conceptualization and design, participant recruitment, result interpretation, and peer review.