User Bias Research Articles

Accelerated Discovery of Cell Migration Regulators Using Label-Free Deep Learning-Based Automated Tracking.

Cell migration plays a key role in normal developmental programs and in disease, including immune responses, tissue repair, and metastasis. Unlike other cell functions, such as proliferation which can be studied using high-throughput assays, cell migration requires more sophisticated instruments and analysis, which decreases throughput and has led to more limited mechanistic advances in our understanding of cell migration. Current assays either preclude single-cell level analysis, require tedious manual tracking, or use fluorescently labeled cells, which greatly limit the number of extracellular conditions and molecular manipulations that can be studied in a reasonable amount of time. Using the migration of cancer cells as a testbed, we established a workflow that images large numbers of cells in real time, using a 96-well plate format. We developed and validated a machine-vision and deep-learning analysis method, DeepBIT, to automatically detect and track the migration of individual cells from time-lapsed videos without cell labeling and user bias. We demonstrate that our assay can examine cancer cell motility behavior in many conditions, using different small-molecule inhibitors of known and potential regulators of migration, different extracellular conditions such as different contents in extracellular matrix and growth factors, and different CRISPR-mediated knockouts. About 1500 cells per well were tracked in 840 different conditions, for a total of ~1.3M tracked cells, in 70h (5 min per condition). Manual tracking of these cells by a trained user would take ~5.5 years. This demonstration reveals previously unidentified molecular regulators of cancer cell migration and suggests that collagen content can change the sign of how cytoskeletal molecules can regulate cell migration.

Associations between oral hormonal contraceptives and internalising problems in adolescent girls.

Oral contraceptive pills (OCP) have received increased critical attention recently owing to their perceived link with mental health, especially among adolescent girls. The empirical literature, however, includes mixed findings on whether OCP use is associated with poorer mental health. To examine the association between the use of OCP and internalising problems in adolescent girls. This study was embedded in the iBerry study, a population-based cohort of adolescents oversampled for behavioural and emotional problems from the greater Rotterdam area, The Netherlands. In 372 girls, internalising problems were measured using the Youth Self Report, and use of OCP was determined by parental interview and self-report questionnaire across two subsequent waves (mean ages 14.9 and 17.9 years, respectively). Multiple regression analyses were performed to determine the association. Analyses were adjusted for various sociodemographic factors and adjusted for previous internalising problems assessed at a mean age of 14.9 years. In total, 204 girls (54.8%) used OCP. OCP use was associated with fewer internalising problems in adolescent girls compared with non-use (adjusted β = -2.22, 95% CI [-4.24, -0.20]; P = 0.031). In this research, we found that adolescent girls using OCP reported fewer internalising problems compared with non-users. This association was most prominent for girls with pre-existing internalising problems. Although healthy user bias may have a role, our observations suggest a potential therapeutic benefit for those with greater baseline challenges.

Deactivated Cas9-Engineered Magnetic Micromotors toward a Point-of-Care Digital Viral RNA Assay.

Digital nucleic acid assays, known for their high sensitivity and specificity, typically rely on fluorescent readouts and expensive and complex nanowell manufacturing, which constrain their broader use in point-of-care (POC) application. Here, we introduce an alternative digital molecular diagnostics, termed dCRISTOR, by seamlessly integrating deactivated Cas9 (dCas9)-engineered micromotors, extraction-free loop-mediated isothermal amplification (LAMP), low-cost bright field microscopy, and deep learning-enabled image processing. The micromotor, composed of a polystyrene sphere attached to a magnetic bead, incorporates a dCas9 ribonucleoprotein complex. The presence of human immunodeficiency virus-1 (HIV-1) RNA in a sample results in the formation of large-sized amplicons that can be specifically captured by the micromotors, reducing their velocity induced by an external magnetic field. The micromotor is propelled by an external magnetic field, which eliminates the need for chemical fuels, reducing system complexity, and allowing for precise control over micromotor movement, enhancing accuracy and reliability. A convolutional neural network classification-based multiobject tracking algorithm, CNN-MOT, accurately measures the change in micromotor motion, facilitating the binary digital assay format ("1" or "0") for simplified result interpretation without user bias. Incorporating an extraction-free LAMP assay streamlines the dCRISTOR workflow, enabling qualitative HIV-1 detection in spiked plasma (n = 21) that demonstrates 100% sensitivity and specificity and achieves a limit of detection (LOD) of 0.96 copies/μL. The assay also achieved 100% correlation with reverse transcription-quantitative polymerase chain reaction (RT-qPCR) in clinical patient samples (n = 9). The dCRISTOR assay, a label-free digital nucleic acid testing system that eliminates the need for fluorescence readouts, absorbance measurements, or expensive manufacturing processes, represents a substantial advancement in digital viral RNA diagnostics.

Target Trial Emulation: A Concept Simply Explained.

Target trial emulation (TTE) is an observational, quasi-experimental research design that emulates a randomized clinical trial (RCT) structure within a large set of observational data; the "target trial" is a hypothetical RCT that would have ideally answered the research question. TTEs can address study objectives that, for ethical or logistic reasons, cannot easily be examined in RCTs. Advantages of TTEs over conventional approaches to observational data are that TTEs can reduce bias, improve the understanding of findings, and facilitate causal inference. This article explains what TTEs are, how TTEs are performed, and how TTEs differ from observational studies, quasi controlled studies, and RCTs. Prevalent user bias and immortal time bias are explained, as is how TTEs are designed to avoid these biases. Strengths and limitations of TTEs are discussed. This article also presents 2 recent studies: one, comprising 3 TTEs that examined scholastic outcomes in children gestationally exposed to benzodiazepines and z-drugs in different periods during pregnancy; and the other, a TTE that examined manic switch as an outcome in bipolar depression patients who received antidepressant treatment. The TTEs found that early, mid, or late pregnancy exposure to benzodiazepines or z-drugs was not associated with impairment in fifth-grade numeracy and literacy performance; and that, in patients with bipolar depression, antidepressant drugs (with or without concurrent mood stabilizers) did not increase the 1-year risk of hypomania, mania, or mixed episodes, nor did they reduce the risk of recurrence of bipolar depression. The TTEs that yielded these results had limitations, and so these findings are suggestive, not definitive. As a general conclusion, TTEs may be viewed as pragmatic, naturalistic, real-world emulations of RCTs, with some advantages over conventional observational studies, but they cannot drive causal inference.

Digital fiber length determination for injection molded glass fiber reinforced composite materials

Fibrous materials are essential in industries such as lightweight automotive materials [1] [2], filtration [3], and hygiene products [4]. The properties of these materials depend on microstructural geometric statistics, making the analysis of individual fibers a powerful tool in material engineering. Fiber length, for example, is a fundamental parameter influencing material properties in composites and nonwovens. Traditional methods for determining fiber length are labor-intensive, prone to user bias, and time-consuming. In contrast, CT-scan-based approaches offer a novel, non-destructive, and reproducible method to assess fiber length distribution. These digital techniques enable the easy analysis of freshly manufactured samples and their re-examination after use to investigate material aging, including changes in fiber length, the matrix, or other components. In this work, we present our method to measure fiber length in micro-CT-scans and compare it to an established experimental method.

The Influence of Information Cocoon Effect on the Spread of Feminism: Taking Douyin as an Example

The rapid expansion of social media platforms has provided new avenues for feminist discourse, enabling broader public engagement and visibility. However, algorithm-driven environments like Douyin (Chinese TikTok) introduce the information cocoon effect, which reinforces user biases by tailoring content to their preferences. This study investigates how these algorithmic constraints impact the dissemination of feminist content, shaping public perception and influencing the strategies of feminist creators. Using literature review and case study methods, the paper examines the effects of Douyin's recommendation algorithms on feminist narratives. It explores how feminist creators navigate the platform's algorithmic and audience-driven challenges, addressing the tension between generating visibility and delivering substantive, intersectional feminist discourse. The findings highlight the limitations imposed by the information cocoon effect, which prioritizes superficial, sensational content, thereby narrowing feminist perspectives and perpetuating misconceptions. The study underscores the importance of quality-driven, inclusive content strategies in breaking through these constraints to foster meaningful discussions and promote gender equality in digital spaces.

High-parameter immunophenotyping reveals distinct immune cell profiles in pruritic dogs and cats.

Immunophenotyping is a powerful tool for grading disease severity, aiding in diagnosis, predicting clinical response, and guiding the development of novel therapeutics. This pilot study employs high parameter immunophenotyping panels (15 markers for dog, 12 for cat) and leverages unsupervised clustering to identify immune cell populations. Our analysis uses machine learning and statistical algorithms to perform unsupervised clustering, multiple visualizations, and statistical analysis of high parameter flow cytometry data. This method reduces user bias and precisely identifies cell populations, demonstrating its potential to detect variations and differentiate populations effectively. To enhance our understanding of cat and dog biology and test the unsupervised clustering approach on real-world samples, we performed in-depth profiling of immune cell populations in blood collected from client-owned and laboratory animals [dogs (n = 55) and cats (n = 68)]. These animals were categorized based on pruritic behavior or routine check-ups (non-pruritic controls). Unsupervised clustering revealed various immune cell populations, including T-cell subsets distinguished by CD62L expression and distinct monocyte subsets. Notably, there were significant differences in monocyte subsets between pruritic and non-pruritic animals. Pruritic dogs and cats showed significant shifts in CD62LHi T-cell subsets compared to non-pruritic controls, with opposite trends observed between pruritic cats and dogs. These findings underscore the importance of advancing veterinary immunophenotyping, expanding our knowledge about marker expression on circulating immune cells and driving progress in understanding veterinary-specific biology and uncovering new insights into various conditions and diseases.

FIRM image analysis: A machine learning workflow for quantifying extracellular matrix components from electron microscopy images.

The extracellular matrix (ECM) is a complex network of biomolecules that plays an integral role in the structure, processes, and signaling mechanisms of cells and tissues. Identifying and quantifying changes in these matrix components provides insight into the mechanisms behind specific tissue remodeling processes; however, quantifying these changes is challenging due to difficult imaging conditions, complexity of the ECM, and the subtlety of these changes. Current imaging techniques allow us to visualize these critical remodeling events and developments in image analysis have employed a combination of analysis software and machine learning techniques to improve the efficiency and accuracy with which features are measured. Although image analysis has seen much improvement in recent years, there has been no technique developed to address ambiguity in feature edges in electron microscopy images. Presented here is a new machine learning-based workflow for the analysis of microscopy images named FIRM (Feature Identification from Raw Microscopy) that uses a random forest classifier to identify ECM features of interest and generate binary segmentation masks for quantification with ImageJ-FIJI. FIRM performed with an F1 score of 0.794 and greater than 80% accuracy for number and size of features detected. FIRM had similar deviation from the ground truth in the number of identified fibrils, fibril size, and size distributions when compared to human analyses. The results suggest that FIRM performs as well as manual analysis and requires a fraction of the time. This analysis technique is more efficient, eliminates user bias, and can be easily optimized to identify a variety of features, making it useful for any discipline requiring image analysis.

Social interaction models for trust systems design

Fueled by the social dynamics inherent in traditional networking, the Internet has come to shape a new paradigm of social interaction and collaboration, formed by the active engagement of its users. The emergence of social networks has not only expanded the avenues for blending the social essence of trust, but also harnessed the technical capabilities of the Internet. While many computational trust models employ models from social theories of interaction to accurately identify trustworthy individuals, there is a notable gap in addressing how these models are perceived and acted upon by the users, who are the core of trust systems. Recognizing and understanding the relationship between trust models and user perceptions is key to simplifying their complexity and enhancing model explainability and system performance.This paper delves into the nuanced roles of structural balance, subjectivity, and user bias within trust systems, investigating their implications on the design of computational trust models. Through qualitative and quantitative analyses, we pinpoint instances where the nature of social interactions emerges as a significant concern that needs preemptive consideration during the design phase of trust systems. Building upon the findings, we suggest practical solutions to enhance the effectiveness of trust models through alignment between the contextual system traits and the design assumptions.

NETQUANT2: automated web-based quantification of neutrophil extracellular traps from fluorescence microscopy.

Neutrophil extracellular traps (NETs) are structures that neutrophils form in response to various stimuli, including invading pathogens. NETs are increasingly studied, and their importance has been demonstrated in autoimmunity and infection. However, no consensus has emerged on their quantification, with many studies resorting to manually counting NETs in microscopy images. NETQUANT is a free software for the automated quantification of neutrophil extracellular traps in fluorescence microscopy images. By employing automated image analysis based on biologically relevant criteria for defining NETs, NETQUANT eliminates user bias and reduces analysis time. Despite these advantages, NETQUANT has not reached widespread adoption, partly due to its dependence on proprietary software and challenges associated with local program setup, which has hindered its appeal. Here, we present NETQUANT2, an improved version based on the principles of NETQUANT, released as a web-based software for fast, simple, and unbiased NET quantification from microscopy images. The software guides researchers by displaying relevant morphological data from their sample and allows researchers to interactively configure the analysis, immediately seeing the impact on the result. NETQUANT2 further improves NETQUANT by enabling easy sharing and reusing of configurations and results and enhanced configuration options to handle complex samples better. We believe that the improved accessibility of NETQUANT2 will lead to better reproducibility in NET research and open the field to more researchers while keeping the quality of analysis high.

Using Double Negative Controls to Adjust for Healthy User Bias in a Recombinant Zoster Vaccine Safety Study.

Unmeasured confounding is a major concern in many epidemiologic studies that are not randomized. Negative control methods can detect and reduce confounding by leveraging the proxies of the unmeasured confounders, including negative control outcomes (NCO) and exposures (NCE). An NCO is presumably unaffected by the exposure of interest but would be associated with unmeasured confounders; an NCE presumably does not affect the outcome of interest but would be associated with unmeasured confounders. A recently proposed double negative control method leverages both NCO and NCE for unmeasured confounding bias. To demonstrate this relatively new methodology in pharmacoepidemiologic studies, we re-analyzed data from a prior safety study of Recombinant Zoster Vaccine (RZV). The prior study compared risk of safety outcomes of RZV versus unvaccinated comparators, using logistic regression with propensity score adjustment. We identified NCOs and NCEs that could be used to adjust for unmeasured confounding bias that could arise if RZV recipients are incomparable to the comparators due to unmeasured factors. The double negative control analysis yielded relative risk estimates slightly closer to 1.0 than those reported previously, providing additional evidence of RZV safety that is less vulnerable to unmeasured confounding.

Biases in COVID-19 vaccine effectiveness studies using cohort design.

Observational studies on COVID-19 vaccine effectiveness (VE) have provided critical real-world data, informing public health policy globally. These studies, primarily using pre-existing data sources, have been indispensable in assessing VE across diverse populations and developing sustainable vaccination strategies. Cohort design is frequently employed in VE research. The rapid implementation of vaccination campaigns during the COVID-19 pandemic introduced differential vaccination influenced by sociodemographic disparities, public policies, perceived risks, health-promoting behaviors, and health status, potentially resulting in biases such as healthy user bias, healthy vaccinee effect, frailty bias, differential depletion of susceptibility bias, and confounding by indication. The overwhelming burden on healthcare systems has escalated the risk of data inaccuracies, leading to outcome misclassifications. Additionally, the extensive array of diagnostic tests used during the pandemic has also contributed to misclassification biases. The urgency to publish quickly may have further influenced these biases or led to their oversight, affecting the validity of the findings. These biases in studies vary considerably depending on the setting, data sources, and analytical methods and are likely more pronounced in low- and middle-income country (LMIC) settings due to inadequate data infrastructure. Addressing and mitigating these biases is essential for accurate VE estimates, guiding public health strategies, and sustaining public trust in vaccination programs. Transparent communication about these biases and rigorous improvement in the design of future observational studies are essential.

Deep-Learning-Based Segmentation of Cells and Analysis (DL-SCAN).

With the recent surge in the development of highly selective probes, fluorescence microscopy has become one of the most widely used approaches to studying cellular properties and signaling in living cells and tissues. Traditionally, microscopy image analysis heavily relies on manufacturer-supplied software, which often demands extensive training and lacks automation capabilities for handling diverse datasets. A critical challenge arises if the fluorophores employed exhibit low brightness and a low signal-to-noise ratio (SNR). Consequently, manual intervention may become a necessity, introducing variability in the analysis outcomes even for identical samples when analyzed by different users. This leads to the incorporation of blinded analysis, which ensures that the outcome is free from user bias to a certain extent but is extremely time-consuming. To overcome these issues, we developed a tool called DL-SCAN that automatically segments and analyzes fluorophore-stained regions of interest such as cell bodies in fluorescence microscopy images using deep learning. We demonstrate the program's ability to automate cell identification and study cellular ion dynamics using synthetic image stacks with varying SNR. This is followed by its application to experimental Na+ and Ca2+ imaging data from neurons and astrocytes in mouse brain tissue slices exposed to transient chemical ischemia. The results from DL-SCAN are consistent, reproducible, and free from user bias, allowing efficient and rapid analysis of experimental data in an objective manner. The open-source nature of the tool also provides room for modification and extension to analyze other forms of microscopy images specific to the dynamics of different ions in other cell types.

A 5-Year Mortality Prediction Model for Prostate Cancer Patients Based on the Korean Nationwide Health Insurance Claims Database.

Prostate cancer is the fourth most common cancer and eighth leading cause of cancer-related mortality worldwide. Its incidence is increasing in South Korea. This study aimed to investigate a predictive model for the 5-year survival probability of prostate cancer patients in a Korean primary care setting. This retrospective study used data from the nationwide insurance claims database. The main outcome was survival probability 5 years after the initial diagnosis of prostate cancer. Potential confounding factors such as age, body mass index (BMI), blood pressure, laboratory results, lifestyle behaviors, household income, and comorbidity index were considered. These variables were available in the national health check-up information. A Cox proportional hazards regression model was used to develop the predictive model. The predictive performance was calculated based on the mean area under the receiver operating characteristic curve (AUC) after 10-fold cross-validation. The mean 5-year survival probability was 82.0%. Age, fasting glucose and gamma-glutamyl transferase levels, current smoking, and multiple comorbidities were positively associated with mortality, whereas BMI, alkaline phosphatase levels, total cholesterol levels, alcohol intake, physical activity, and household income were inversely associated with mortality. The mean AUC after 10-fold cross-validation was 0.71. The 5-year survival probability model showed a moderately good predictive performance. This may be useful in predicting the survival probability of prostate cancer patients in primary care settings. When interpreting these results, potential limitations, such as selection or healthy user biases, should be considered.

Super-resolution enhancement and segmentation for digital rock based on multi-task joint deep neural network

Super-resolution enhancement and segmentation for digital rock based on multi-task joint deep neural network

ARCHITECTURAL AND CIRCUIT MODEL OF NAVIGATION DEVICE FOR ULTRA-LIGHT LAUNCH VEHICLE

The paper considered the architectural and circuitry model of a navigation device for an ultra-lightweight launch vehicle designed to improve the accuracy and reliability of positioning and orientation in flight conditions. The study utilized modern approaches to the integration of global navigation satellite system (GNSS) and inertial navigation system (INS) to achieve high accuracy and stability of the system. The key components of the device including UM980 module, LSM6DSV32X inertial measurement module, STM32 F7 series microcontroller, E220-400T30S radio module, LMR50410XDBVR converter based power supply and RS422 interface were described in detail. The UM980 module, including SAW and LNA filter blocks, demonstrated the ability to efficiently process incoming RF signals. The NebulasIV main function block combined GNSS BB, GNSS RF, interfaces and power management unit (PMU) modules, supporting multiple communication interfaces such as UART, SPI and I²C, as well as RTK and PPS signals. The study analyzed the integration and accuracy aspects of the module in navigation systems. The signal processing scheme of the inertial navigation system module was also presented, including digital filters, free-fall and activity/inactivity detection functions, and interfaces for data communication. User bias functions and filter modes are considered to ensure the accuracy and stability of the navigation device. The work lays the groundwork for further improvements in navigation technology, especially in the context of increasing availability and accuracy for small scale space launches.

Mining delivery customer claims in social media in Colombia. An exploratory analysis applying machine learning algorithms.

The amount of available data, computational developments, and growing social demands from customers pose enormous challenges for delivery companies. Recognizing this need, we sought to investigate how delivery customer claims through Twitter. For this purpose, we used a sequential mixed methodology, starting with a literature review by applying a bibliometric analysis and its subsequent interpretation through content analysis. Subsequently, we scraped the mentions made by different users to deliver brands on Twitter to consolidate a significant corpus of data that would allow us its subsequent review through an exploratory analysis. Finally, applying natural language processing and machine learning algorithms, we discovered how users of these delivery companies usually compare brands with their competitors when complaining. We specifically highlighted the psychological bias of users, who polarize between love and hate for brands, respond to other users' posts, and achieve meaningful interactions with their likes as the main predictors of this type of claim.

Investigating the Correlation Between Presence and Reaction Time in Mixed Reality.

Measuring presence is critical to improving user involvement and performance in Mixed Reality (MR). Presence, a crucial aspect of MR, is traditionally gauged using subjective questionnaires, leading to a lack of time-varying responses and susceptibility to user bias. Inspired by the existing literature on the relationship between presence and human performance, the proposed methodology systematically measures a user's reaction time to a visual stimulus as they interact within a manipulated MR environment. We explore the user reaction time as a quantity that can be easily measured using the systemic tools available in modern MR devices. We conducted an exploratory study (N = 40) with two experiments designed to alter the users' sense of presence by manipulating place illusion and plausibility illusion. We found a significant correlation between presence scores and reaction times with a correlation coefficient -0.65, suggesting that users with a higher sense of presence responded more swiftly to stimuli. We develop a model that estimates a user's presence level using the reaction time values with high accuracy of up to 80%. While our study suggests that reaction time can be used as a measure of presence, further investigation is needed to improve the accuracy of the model.

The effect of blockability affordance on confrontation against cyberbullying on social networking sites: theoretical and methodological implications

PurposeSocial networking sites (SNS) have become popular mediums for individuals to interact with others. However, despite the positive impact of SNS on people’s lives, cyberbullying has become prevalent. Due to this prevalence, substantial research has examined cyberbullying from the perspectives of perpetrators, bystanders, and victims, but little is known about SNS users’ confrontations with cyberbullying. The objectives of this study are to examine confrontation as a victim’s coping response, the effect of blockability affordance on victims’ protection motivation, the impact of a victim’s experiences with cyberbullying perpetration, and social desirability (SD) bias in the context of cyberbullying victimization.Design/methodology/approachThis study examines the effect of blockability affordance on SNS users’ protection motivation. It also investigates the relationships among perceived threat, perceived coping efficacy, and use of confrontation. Furthermore, this investigation analyzes the effect of SNS users’ experiences as perpetrators on their decision to confront cyberbullies. Finally, this study assesses and controls SD bias in SNS users’ confrontation behavior. To test the research model, we used an online vignette study to collect 314 data points.FindingsBlockability affordance, perceived threat, perceived coping efficacy, and cyberbullying perpetration experiences are essential factors in explaining use of confrontation. This study also finds SD bias in the context of cyberbullying victimization.Originality/valueThis is one of the first studies in information systems research to empirically examine the effect of blockability affordance in the context of cyberbullying.

The impact of smartphone deprivation on attentional bias in problematic smartphone users: Evidence from behavioral and physiological perspectives

The impact of smartphone deprivation on attentional bias in problematic smartphone users: Evidence from behavioral and physiological perspectives