Biased Results Research Articles

Assessment of performance and comparison of three commercial HDV RNA detection assays: implications for diagnosis and treatment monitoring.

Precise HDV-RNA detection and quantification are pivotal for diagnosis and monitoring of response to newly approved treatment. We evaluate the performance of three HDV RNA detection and quantification assays. Hepatitis Delta RT-PCR system kit, EurobioPlex HDV assay, and RoboGene HDV RNA Quantification kit 2.0 were used for testing 151 HBsAg-positive samples, 90 HDV-RNA negative and 61 HDV-RNA positive. We also evaluated serial dilutions of the WHO international standard for HDV, PEI 7657/12. All HDV-RNA positive samples were genotyped using a next-generation sequencing strategy. Qualitative results indicated a 100% concordance between tests. Quantitative results correlated well, r2 = 0.703 (Vircell-vs-Eurobio), r2 = 0.833 (Vircell-vs-RoboGene), r2 = 0.835 (Robogene-vs-Eurobio). Bias index was 2.083 (Vircell-vs-Eurobio), -1.283 (Vircell-vs-RoboGene), and -3.36 (Robogene-vs-Eurobio). Using the WHO IS, Vircell overestimated the viral load by 0.98 log IU/mL, Eurobio by 1.46 log IU/mL, and RoboGene underestimated it by 0.98 log IU/mL. Fifty-nine samples were successfully genotyped (Genotype 1, n=52; Genotype 5, n=7; Genotype 6, n=1), with similar results for correlation and bias. This study underscores the necessity of using reliable HDV-RNA detection and quantification assays, as evidenced by the high concordance rates in qualitative detection and the observed variability in quantitative results. These findings highlight the importance of consistent assay use in clinical practice to ensure accurate diagnosis and effective treatment monitoring of HDV infection.

Sensitive fluorescence detection of miRNA-124 in cardiomyocytes under oxidative stress using a nucleic acid probe

MicroRNAs (miRNAs) are small noncoding RNAs of 18–25 bases. miRNAs are also important new biomarkers that can be used for disease diagnosis in the future. Studies have shown that miR-124 levels are significantly elevated during acute myocardial infarction (AMI) and play a key role in the cardiovascular system. A variety of methods have been established to detect myocardial infarction-related miRNAs. However, most require complex miRNA extraction and isolation, and these methods are virtually undetectable when RNA levels are low in the sample. It may lead to biased results. Thus, it is necessary to develop a technique that can detect miRNA without extracting it, which means that intracellular detection is of great significance. Here, we improved the traditional silicon spheres and obtained a biosensor that could effectively capture and detect specific noncoding nucleic acids through the layer-by-layer assembly method. The sensor is protected by hyaluronic acid so it can successfully escape the lysosome into the cell and achieve detection. With the help of a full-featured microplate reader, we determined that the detection limit of the biosensor could reach 1 fM, meeting the needs of intracellular detection. At the same time, we prepared an oxidative stress cardiomyocyte infarction model and successfully captured the overexpressed miR-124 in the infarcted cells to achieve in situ detection. This study could provide a new potential tool to develop miRNAs for sensitive diagnosis in AMI, and the proposed strategy implies its potential for biomedical research.

Exploring the Social Role of Contemporary Women from the Perspective of the Lack of Historical Female Characters

Historically, women have been underrepresented in literature, the arts, and historical records, and their achievements and roles have often been marginalized or ignored. This phenomenon has led to an intriguing paradox: despite the fact that contemporary women have demonstrated strong abilities and potential in various fields, they are still usually considered to be in a disadvantaged position compared to men. This paper aims to explore the complex reasons behind the paradox of womens underrepresentation in history. To this end, it will review the lack of womens image in history, analyze the impact of social, cultural, and institutional factors on this lack of representation, and explore the gender issues that exist in contemporary society. The paper discusses in detail women's achievements in contemporary society, the embodiment of womens power, and the struggle for womens rights, demonstrating the strong capabilities and potential of contemporary women in many fields. The study concludes that the marginalization of women in historical narratives is a result of social structures, cultural norms, and institutional biases. Despite these challenges, contemporary women have made significant strides in various fields, suggesting that gender should not be a determining factor in assessing competence or potential. This paper argues for a greater awareness of gender equality and calls for a fairer and more inclusive reconstruction of historical narratives to ensure that the contributions of all genders are fairly recognized.

A fast neural emulator for interstellar chemistry

ABSTRACT Astrochemical models are important tools to interpret observations of molecular and atomic species in different environments. However, these models are time-consuming, precluding a thorough exploration of the parameter space, leading to uncertainties and biased results. Using neural networks to simulate the behaviour of astrochemical models is a way to circumvent this problem, providing fast calculations that are based on real astrochemical models. In this paper, we present a fast neural emulator of the astrochemical code Nautilus based on conditional neural fields. The resulting model produces the abundance of 192 species for arbitrary times between 1 and 107 yr. Uncertainties well below 0.2 dex are found for all species, while the computing time is of the order of 104 smaller than Nautilus. This will open up the possibility of performing much more complex forward models to better understand the physical properties of the interstellar medium. As an example of the power of these models, we ran a feature importance analysis on the electron abundance predicted by Nautilus. We found that the electron density is coupled to the initial sulphur abundance in a low-density gas. Increasing the initial sulphur abundance from a depleted scenario to the cosmic abundance leads to an enhancement of an order of magnitude of the electron density. This enhancement can potentially influence the dynamics of the gas in star formation sites.

How to mitigate the risks of deployment of artificial intelligence in medicine?

The aim of this study is to examine the risks associated with the use of artificial intelligence (AI) in medicine and to offer policy suggestions to reduce these risks and optimize the benefits of AI technology. AI is a multifaceted technology. If harnessed effectively, it has the capacity to significantly impact the future of humanity in the field of health, as well as in several other areas. However, the rapid spread of this technology also raises significant ethical, legal, and social issues. This study examines the potential dangers of AI integration in medicine by reviewing current scientific work and exploring strategies to mitigate these risks. Biases in data sets for AI systems can lead to inequities in health care. Educational data that is narrowly represented based on a demographic group can lead to biased results from AI systems for those who do not belong to that group. In addition, the concepts of explainability and accountability in AI systems could create challenges for healthcare professionals in understanding and evaluating AI-generated diagnoses or treatment recommendations. This could jeopardize patient safety and lead to the selection of inappropriate treatments. Ensuring the security of personal health information will be critical as AI systems become more widespread. Therefore, improving patient privacy and security protocols for AI systems is imperative. The report offers suggestions for reducing the risks associated with the increasing use of AI systems in the medical sector. These include increasing AI literacy, implementing a participatory society-in-the-loop management strategy, and creating ongoing education and auditing systems. Integrating ethical principles and cultural values into the design of AI systems can help reduce healthcare disparities and improve patient care. Implementing these recommendations will ensure the efficient and equitable use of AI systems in medicine, improve the quality of healthcare services, and ensure patient safety.

EDiffuRec: An Enhanced Diffusion Model for Sequential Recommendation

Sequential recommender models should capture evolving user preferences over time, but there is a risk of obtaining biased results such as false positives and false negatives due to noisy interactions. Generative models effectively learn the underlying distribution and uncertainty of the given data to generate new data, and they exhibit robustness against noise. In particular, utilizing the Diffusion model, which generates data through a multi-step process of adding and removing noise, enables stable and effective recommendations. The Diffusion model typically leverages a Gaussian distribution with a mean fixed at zero, but there is potential for performance improvement in generative models by employing distributions with higher degrees of freedom. Therefore, we propose a Diffusion model-based sequential recommender model that uses a new noise distribution. The proposed model improves performance through a Weibull distribution with two parameters determining shape and scale, a modified Transformer architecture based on Macaron Net, normalized loss, and a learning rate warmup strategy. Experimental results on four types of real-world e-commerce data show that the proposed model achieved performance gains ranging from a minimum of 2.53% to a maximum of 13.52% across HR@K and NDCG@K metrics compared to the existing Diffusion model-based sequential recommender model.

Bridging the gap between two-stage and joint models: The case of tumor growth inhibition and overall survival models.

Many clinical trials generate both longitudinal biomarker and time-to-event data. We might be interested in their relationship, as in the case of tumor size and overall survival in oncology drug development. Many well-established methods exist for analyzing such data either sequentially (two-stage models) or simultaneously (joint models). Two-stage modeling (2stgM) has been challenged (i) for not acknowledging that biomarkers are endogenous covariable to the survival submodel and (ii) for not propagating the uncertainty of the longitudinal biomarker submodel to the survival submodel. On the other hand, joint modeling (JM), which properly circumvents both problems, has been criticized for being time-consuming, and difficult to use in practice. In this paper, we explore a third approach, referred to as a novel two-stage modeling (N2stgM). This strategy reduces the model complexity without compromising the parameter estimate accuracy. The three approaches (2stgM, JM, and N2stgM) are formulated, and a Bayesian framework is considered for their implementation. Both real and simulated data were used to analyze the performance of such approaches. In all scenarios, our proposal estimated the parameters approximately as JM but without being computationally expensive, while 2stgM produced biased results.

Automated Bayesian variable selection methods for binary regression models with missing covariate data

Data collection and the availability of large data sets has increased over the last decades. In both statistical and machine learning frameworks, two methodological issues typically arise when performing regression analysis on large data sets. First, variable selection is crucial in regression modeling, as it helps to identify an appropriate model with respect to the considered set of conditioning variables. Second, especially in the context of survey data, handling of missing values is important for estimation, which occur even with state-of-the-art data collection and processing methods. Within this paper, we provide an Bayesian approach based on a spike-and-slab prior for the regression coefficients, which allows for simultaneous handling of variable selection and estimation in combination with handling of missing values in covariate data. The paper also discusses the implementation of the approach using Markov chain Monte Carlo techniques and provides results for simulated data sets and an empirical illustration based on data from the German National Educational Panel Study. The suggested Bayesian approach is compared to other statistical and machine learning frameworks such as Lasso, ridge regression, and Elastic net, and is shown to perform well in terms of estimation performance and variable selection accuracy. The simulation results demonstrate that ignoring the handling of missing values in data sets can lead to the generation of biased selection results. Overall, the proposed Bayesian method offers a holistic, flexible, and powerful framework for variable selection in the presence of missing covariate data.

Understanding discordance in systematic reviews for treating chemotherapy-induced peripheral neuropathy: A focused umbrella review.

e24111 Background: Despite the abundance of publications on chemotherapy-induced peripheral neuropathy (CIPN), we are still lacking effective management options. CIPN-focused systematic reviews (SRs) are increasingly encountered but have not aided much in clarifying treatment guidance. Additionally, there is a rising concern that SRs are now over-saturating the clinical literature, leading to redundant and misleading publications. Umbrella reviews (URs) are an attempt to appraise SRs but do not yet have firm methodologic frameworks. The aims of our UR were to appraise the discordance in recently published CIPN SRs and use content analysis as a method for identifying risks of discordance. Methods: This is a focused UR as part of a larger study to characterize the number of efficacy trials for established CIPN. Following PRISMA guidelines, we conducted a systematic literature search of databases MEDLINE, Embase, and Cochrane Library for publications in English from 1/1/2023 to 11/21/2023. Inclusion criteria were published systematic reviews attempting a comprehensive effort to synthesize any treatment outcomes for established CIPN. The AMSTAR 2 framework was used to appraise results for bias. Measures of randomized controlled trial (RCT) overlap are presented using a novel measure of time-adjusted CA, which accounts for RCT publication dates and SR search dates. Grounded theory coding was used for content analysis on efficacy. Results: We identified 18 SRs containing CIPN treatment RCTs published in less than 1 year, resulting in 32 unique CIPN RCTs. Seven SRs assessed exercise interventions, covering 6 exercise RCTs (41% time-adjusted CA). Five SRs assessed acupuncture, with 9 RCTs (50% time-adjusted CA). One SR searched for all CIPN treatments for pain but missed 4 RCTs. One SR was concerning for spurious data. Bias was variable and imperfect across SRs. Contextual analysis identified the themes of SRs combining preventative and treatment RCTs, confusing comparative vs. controlled trials, and frequent use of p-value only reporting to conclude treatment efficacy as major risks of SR discordance. Conclusions: UR was useful in identifying 32 CIPN RCTs that were not comprehensively identified by SRs. We presented a time-adjusted CA measure to indicate concordance percentage. A higher percentage indicates more re-citation of the same RCTs across SRs, with likely better chances of more concordant conclusions. This measure showed that SRs for CIPN treatment are discordant across the topics of exercise and acupuncture interventions. Content analysis revealed multiple potential causes of discordance. However, SR redundancy was helpful for identifying more unique RCTs. CIPN SR content standards should be developed to minimize discordancy and improve clinical applicability. Clinicians should be cautious when applying CIPN SR recommendations given the high discordance.

Modelling of surface brightness fluctuation measurements

Aims. The goal of this work is to scrutinise the surface brightness fluctuation (SBF) calculation methodology. We analysed the SBF derivation procedure, measured the accuracy of the fitted SBF under controlled conditions, retrieved the uncertainty associated with the variability of a system that is inherently stochastic, and studied the SBF reliability under a wide range of conditions. Additionally, we address the possibility of an SBF gradient detection. We also examine the problems related with biased measurements of the SBF and low luminosity sources. All of this information allows us to put forward guidelines to ensure a valid SBF retrieval. Methods. To perform all the experiments described above, we carried out Monte Carlo simulations of mock galaxies as an ideal laboratory. Knowing its underlying properties, we attempted to retrieve SBFs under different conditions. The uncertainty was evaluated through the accuracy, the precision, and the standard deviation of the fitting. Results. We demonstrate how the usual mathematical approximations taken in the SBF theoretical derivation have a negligible impact on the results and how modelling the instrumental noise reduces the uncertainty. We conducted various studies where we varied the size of the mask applied over the image, the surface and fluctuation brightness of the galaxy, its size and profile, its point spread function, and the sky background. It is worth highlighting that we find a strong correlation between having a high number of pixels within the studied mask and retrieving a low uncertainty result. We address how the standard deviation of the fitting underestimates the actual uncertainty of the measurement. Lastly, we find that, when studying SBF gradients, the result is a pixel-weighted average of all the SBFs present within the studied region. Retrieving an SBF gradient requires high-quality data and a sufficient difference in the fluctuation value through the different radii. We show how the SBF uncertainty can be obtained and we present a collection of qualitative recommendations for a safe SBF retrieval. Conclusions. Our main findings are as follows. It is important to model the instrumental noise, rather than fitting it. The target galaxies must be observed under appropriate observational conditions. In a traditional SBF derivation, one should avoid pixels with fluxes lower than ten times the SBF estimate to prevent biased results. The uncertainty associated with the intrinsic variability of the system can be obtained using sets of Monte Carlo mock galaxy simulations. We offer our computational implementation in the form of a simple code designed to estimate the uncertainty of the SBF measurement. This code can be used to predict the quality of future observations or to evaluate the reliability of those already conducted.

Time-varying causality relationships between trade openness, technological innovation, industrialization, financial development, and carbon emissions in Thailand.

Over the last twenty years, there has been swift growth in industrialization and technological advancements, driving economic progress. Nevertheless, it is inevitable that these sectors will bring about environmental shifts. Thus far, endeavors have been undertaken to assess the influence of industrialization and technological advancements on environmental deterioration. Additionally, the extensive discussion surrounding the impact of financial development, trade openness, and technological innovation on the environment has not yielded conclusive empirical findings. Studies often operate under the assumption of symmetric relationships, potentially leading to biased results. Adding to the discussion on the drivers of carbon neutrality, the time-dependent effects of critical aspects such as financial development and technological innovation should inform meaningful policies for environmental management. This article explores the time-varying causal association between trade openness, industrialization, financial development, technological innovation, and CO2 emissions in Thailand using novel time-varying Granger causality tests. The time-varying causality outcomes demonstrate that the associations change significantly over time, in contrast to the results of Toda-Yamamoto causality. Overall, there exists a bidirectional relationship between industrialization, financial development, trade openness, technological innovation, and CO2 emissions over different time sequences. These outcomes have implications for both policy and research.

Re-validating the Phillips Curve hypothesis in Africa and the role of oil prices: A mixed-frequency approach

This paper investigates the relationship between unemployment and inflation in 29 African economies by re-examining the Phillips Curve hypothesis and evaluating the role of global oil prices in predicting inflation. Existing studies often use uniform-frequency variables, which can lead to biased results by omitting information from high-frequency series. We employ a mixed data sampling regression model to analyze inflation in selected African countries by regressing monthly inflation rates on annual unemployment rates and daily oil price returns. Our findings show that the Phillips Curve hypothesis holds in a few countries across Central Africa (Cameroon, the Central African Republic, Chad, and Republic of the Congo), East Africa (Rwanda), North Africa (Algeria), Southern Africa (Botswana and Malawi), and West Africa (Burkina Faso and Mauritania). In the other countries, the hypothesis does not hold, potentially indicating a predominance of cost-push inflation. Our analysis demonstrates that global oil prices impact inflation differently across these countries. Additionally, daily oil price data significantly enhance inflation forecasting compared to monthly data. Incorporating additional economic variables, such as exchange rates and interest rates, further improves forecast accuracy. We recommend targeted monetary policies to mitigate the impacts of oil price fluctuations and unemployment on inflation.

TERRA ONTseq: a long-read-based sequencing pipeline to study the human telomeric transcriptome.

The long noncoding RNA TERRA is transcribed from telomeres in virtually all eukaryotes with linear chromosomes. In humans, TERRA transcription is driven in part by promoters comprising CpG dinucleotide-rich repeats of 29 bp repeats, believed to be present in half of the subtelomeres. Thus far, TERRA expression has been analyzed mainly using molecular biology-based approaches that only generate partial and somehow biased results. Here, we present a novel experimental pipeline to study human TERRA based on long-read sequencing (TERRA ONTseq). By applying TERRA ONTseq to different cell lines, we show that the vast majority of human telomeres produce TERRA and that the cellular levels of TERRA transcripts vary according to their chromosomes of origin. Using TERRA ONTseq, we also identified regions containing TERRA transcription start sites (TSSs) in more than half of human subtelomeres. TERRA TSS regions are generally found immediately downstream from 29 bp repeat-related sequences, which appear to be more widespread than previously estimated. Finally, we isolated a novel TERRA promoter from the highly expressed subtelomere of the long arm of Chromosome 7. With the development of TERRA ONTseq, we provide a refined picture of human TERRA biogenesis and expression and we equip the scientific community with an invaluable tool for future studies.

Big data, machine learning and uncertainty in foresight studies

PurposeCurrently, foresight studies have been adapted to incorporate new techniques based on big data and machine learning (BDML), which has led to new approaches and conceptual changes regarding uncertainty and how to prospect future. The purpose of this study is to explore the effects of BDML on foresight practice and on conceptual changes in uncertainty.Design/methodology/approachThe methodology is twofold: a bibliometric analysis of BDML-supported foresight studies collected from Scopus up to 2021 and a survey analysis with 479 foresight experts to gather opinions and expectations from academics and practitioners related to BDML in foresight studies. These approaches provide a comprehensive understanding of the current landscape and future paths of BDML-supported foresight research, using quantitative analysis of literature and qualitative input from experts in the field, and discuss potential theoretical changes related to uncertainty.FindingsIt is still incipient but increasing the number of prospective studies that use BDML techniques, which are often integrated into traditional foresight methodologies. Although it is expected that BDML will boost data analysis, there are concerns regarding possible biased results. Data literacy will be required from the foresight team to leverage the potential and mitigate risks. The article also discusses the extent to which BDML is expected to affect uncertainty, both theoretically and in foresight practice.Originality/valueThis study contributes to the conceptual debate on decision-making under uncertainty and raises public understanding on the opportunities and challenges of using BDML for foresight and decision-making.

What question are we trying to answer? Embracing causal inference.

This study summarizes a presentation at the symposium for the Calvin Schwabe Award for Lifetime Achievement in Veterinary Epidemiology and Preventive Medicine, which was awarded to the first author. As epidemiologists, we are taught that "correlation does not imply causation." While true, identifying causes is a key objective for much of the research that we conduct. There is empirical evidence that veterinary epidemiologists are conducting observational research with the intent to identify causes; many studies include control for confounding variables, and causal language is often used when interpreting study results. Frameworks for studying causes include the articulation of specific hypotheses to be tested, approaches for the selection of variables, methods for statistical estimation of the relationship between the exposure and the outcome, and interpretation of that relationship as causal. When comparing observational studies in veterinary populations to those conducted in human populations, the application of each of these steps differs substantially. The a priori identification of exposure-outcome pairs of interest are less common in observational studies in the veterinary literature compared to the human literature, and prior knowledge is used to select confounding variables in most observational studies in human populations, whereas data-driven approaches are the norm in veterinary populations. The consequences of not having a defined exposure-outcome hypotheses of interest and using data-driven analytical approaches include an increased probability of biased results and poor replicability of results. A discussion by the community of researchers on current approaches to studying causes in observational studies in veterinary populations is warranted.

HIV estimation using population-based surveys with non-response: A partial identification approach.

HIV estimation using data from the demographic and health surveys (DHS) is limited by the presence of non-response and test refusals. Conventional adjustments such as imputation require the data to be missing at random. Methods that use instrumental variables allow the possibility that prevalence is different between the respondents and non-respondents, but their performance depends critically on the validity of the instrument. Using Manski's partial identification approach, we form instrumental variable bounds for HIV prevalence from a pool of candidate instruments. Our method does not require all candidate instruments to be valid. We use a simulation study to evaluate and compare our method against its competitors. We illustrate the proposed method using DHS data from Zambia, Malawi and Kenya. Our simulations show that imputation leads to seriously biased results even under mild violations of non-random missingness. Using worst case identification bounds that do not make assumptions about the non-response mechanism is robust but not informative. By taking the union of instrumental variable bounds balances informativeness of the bounds and robustness to inclusion of some invalid instruments. Non-response and refusals are ubiquitous in population based HIV data such as those collected under the DHS. Partial identification bounds provide a robust solution to HIV prevalence estimation without strong assumptions. Union bounds are significantly more informative than the worst case bounds without sacrificing credibility.

Noise reduction by bias cooling in gated Si/Six Ge1−x quantum dots

Silicon–germanium heterostructures are a promising quantum circuit platform, but crucial aspects, such as the long-term charge dynamics and cooldown-to-cooldown variations, are still widely unexplored quantitatively. In this Letter, we present the results of an extensive bias cooling study performed on gated silicon–germanium quantum dots with an Al2O3 dielectric. Over 80 cooldowns were performed in the course of our investigations. The performance of the devices is assessed by low-frequency charge noise measurements in the band of 200 μHz to 10 mHz. We measure the total noise power as a function of the applied voltage during cooldown in four different devices and find a minimum in noise at 0.7 V bias cooling voltage for all observed samples. We manage to decrease the total noise power median by a factor of 6 and compute a reduced tunneling current density using Schrödinger–Poisson simulations. Furthermore, we show the variation in noise from the same device in the course of eleven different cooldowns performed under the nominally same conditions.

Negative variance components and intercept-slope correlations greater than one in magnitude: How do such "non-regular" random intercept and slope models arise, and what should be done when they do?

Statistical models with random intercepts and slopes (RIAS models) are commonly used to analyze longitudinal data. Fitting such models sometimes results in negative estimates of variance components or estimates on parameter space boundaries. This can be an unlucky chance occurrence, but can also occur because certain marginal distributions are mathematically identical to those from RIAS models with negative intercept and/or slope variance components and/or intercept-slope correlations greater than one in magnitude. We term such parameters "pseudo-variances" and "pseudo-correlations," and the models "non-regular." We use eigenvalue theory to explore how and when such non-regular RIAS models arise, showing: (i) A small number of measurements, short follow-up, and large residual variance increase the parameter space for which data (with a positive semidefinite marginal variance-covariance matrix) are compatible with non-regular RIAS models. (ii) Non-regular RIAS models can arise from model misspecification, when non-linearity in fixed effects is ignored or when random effects are omitted. (iii) A non-regular RIAS model can sometimes be interpreted as a regular linear mixed model with one or more additional random effects, which may not be identifiable from the data. (iv) Particular parameterizations of non-regular RIAS models have no generality for all possible numbers of measurements over time. Because of this lack of generality, we conclude that non-regular RIAS models can only be regarded as plausible data-generating mechanisms in some situations. Nevertheless, fitting a non-regular RIAS model can be acceptable, allowing unbiased inference on fixed effects where commonly recommended alternatives such as dropping the random slope result in bias.

Phenological mismatch between trees and wildflowers: Reconciling divergent findings in two recent analyses

Abstract Recent evidence suggests that community science and herbarium datasets yield similar estimates of species' phenological sensitivities to temperature. Despite this, two recent studies by Alecrim et al. (2023) and Miller et al. (2022) found very different results when using different data sources (community science and herbarium specimens, respectively) to investigate whether warming threatens wildflowers with phenological mismatch in relation to shading by deciduous trees. Here, we investigated whether differences between the two studies' results could be reconciled by testing four hypotheses related to model design, species, spatiotemporal data extent and phenophase. Hybrid model structures brought results from the two datasets closer together but did not fully reconcile the differences between the studies. Neither the species nor the phenophase selected for analysis seemed to be responsible for differences in results. Cropping the datasets to match spatial and temporal extents appeared to reconcile most differences but only at the cost of much higher uncertainty associated with reduced sample size. Synthesis: Our analysis suggests that although species‐level estimates of phenological sensitivity may be similar between community science and herbarium datasets, inherent differences in the types and extent of data may lead to contradictory inference about complex biotic interactions. We conclude that, until community science data repositories expand to match the range of climate conditions present in herbarium collections or until herbarium collections match the spatial extent and temporal frequency of community science repositories, ecological studies should ideally be evaluated using both datasets to test the possibility of biased results from either.

Perpetuation of Gender Bias in Visual Representation of Professions in the Generative AI Tools DALL·E and Bing Image Creator

Artificial intelligence (AI)-based generative imaging systems such as DALL·E, Midjourney, Stable Diffusion, and Adobe Firefly, which work by transforming natural language descriptions into images, are revolutionizing computer vision. In this exploratory and qualitative research, we have replicated requests for images of women in different professions by comparing these representations in previous studies with DALL·E, observing that this model continues to provide in its last version, DALL·E 3, inequitable results in terms of gender. In addition, Bing Image Creator, Microsoft’s free tool that is widely used among the population and runs under DALL·E, has been tested for the first time. It also presents a sexualization of women and stereotypical children’s representations. The results reveal the following: 1. A slight improvement in terms of the presence of women in professions previously shown only with men. 2. They continue to offer biased results in terms of the objectification of women by showing sexualized women. 3. The representation of children highlights another level of gender bias, reinforcing traditional stereotypes associated with gender roles from childhood, which can impact future decisions regarding studies and occupations.