Assessment and the regulation of adaptive phenotypic plasticity.

Organisms can react to environmental variation by altering their phenotype, and such phenotypic plasticity is often adaptive. This plasticity contributes to the diversity of phenotypes across the tree of life. Generally, the production of these phenotypes must be preceded by assessment, where the individual acquires information about its environment and phenotype relative to that environment, and then determines if and how to respond with an alternative phenotype. The role of assessment in adaptive plasticity is, therefore, crucial. In this Review, we (1) highlight the need for explicitly considering the role of assessment in plasticity; (2) present two different models for how assessment and the facultative production of phenotypes are related; and (3) describe an overarching framework for how assessment evolves. In doing so, we articulate avenues of future work and suggest that explicitly considering the role of assessment in the evolution of plasticity is key to explaining how and when plasticity occurs. Moreover, we emphasize the need to understand the role of assessment in adaptive versus maladaptive plasticity, which is an issue that will become increasingly important in a rapidly changing world.

Shifting decision thresholds can undermine the probative value and legal utility of forensic pattern-matching evidence.

Forensic pattern analysis requires examiners to compare the patterns of items such as fingerprints or tool marks to assess whether they have a common source. This article uses signal detection theory to model examiners' reported conclusions (e.g., identification, inconclusive, or exclusion), focusing on the connection between the examiner's decision threshold and the probative value of the forensic evidence. It uses a Bayesian network model to explore how shifts in decision thresholds may affect rates and ratios of true and false convictions in a hypothetical legal system. It demonstrates that small shifts in decision thresholds, which may arise from contextual bias, can dramatically affect the value of forensic pattern-matching evidence and its utility in the legal system.

How to make better forensic decisions.

Much of forensic practice today involves human decisions about the origins of patterned sensory evidence, such as tool marks and fingerprints discovered at a crime scene. These decisions are made by trained observers who compare the evidential pattern to an exemplar pattern produced by the suspected source of the evidence. The decision consists of a determination as to whether the two patterns are similar enough to have come from the same source. Although forensic pattern comparison disciplines have for decades played a valued role in criminal investigation and prosecution, the extremely high personal and societal costs of failure-the conviction of innocent people-has elicited calls for caution and for the development of better practices. These calls have been heard by the scientific community involved in the study of human information processing, which has begun to offer much-needed perspectives on sensory measurement, discrimination, and classification in a forensic context. Here I draw from a well-established theoretical and empirical approach in sensory science to illustrate the vulnerabilities of contemporary pattern comparison disciplines and to suggest specific strategies for improvement.

Perceptual Difficulty Regulates Attentional Gain Modulations in Human Visual Cortex.

Perceptual difficulty is sometimes used to manipulate selective attention. However, these two factors are logically distinct. Selective attention is defined by priority given to specific stimuli based on their behavioral relevance, whereas perceptual difficulty is often determined by perceptual demands required to discriminate relevant stimuli. That said, both perceptual difficulty and selective attention are thought to modulate the gain of neural responses in early sensory areas. Previous studies found that selectively attending to a stimulus or increasing perceptual difficulty enhanced the gain of neurons in visual cortex. However, some other studies suggest that perceptual difficulty can have either a null or even reversed effect on gain modulations in visual cortex. According to Yerkes-Dodson's Law, it is possible that this discrepancy arises because of an interaction between perceptual difficulty and attentional gain modulations yielding a nonlinear inverted-U function. Here, we used EEG to measure modulations in the visual cortex of male and female human participants performing an attention-cueing task where we systematically manipulated perceptual difficulty across blocks of trials. The behavioral and neural data implicate a nonlinear inverted-U relationship between selective attention and perceptual difficulty: a focused-attention cue led to larger response gain in both neural and behavioral data at intermediate difficulty levels compared with when the task was more or less difficult. Moreover, difficulty-related changes in attentional gain positively correlated with those predicted by quantitative modeling of the behavioral data. These findings suggest that perceptual difficulty mediates attention-related changes in perceptual performance via selective neural modulations in human visual cortex.SIGNIFICANCE STATEMENT Both perceptual difficulty and selective attention are thought to influence perceptual performance by modulating response gain in early sensory areas. That said, less is known about how selective attention interacts with perceptual difficulty. Here, we measured neural gain modulations in the visual cortex of human participants performing an attention-cueing task where perceptual difficulty was systematically manipulated. Consistent with Yerkes-Dodson's Law, our behavioral and neural data implicate a nonlinear inverted-U relationship between selective attention and perceptual difficulty. These results suggest that perceptual difficulty mediates attention-related changes in perceptual performance via selective neural modulations in visual cortex, extending our understanding of the attentional operation under different levels of perceptual demands.

Texture congruence modulates perceptual bias but not sensitivity to visuotactile stimulation during the rubber hand illusion.

The sense of body ownership is the feeling that one's body belongs to oneself. To study body ownership, researchers use bodily illusions, such as the rubber hand illusion (RHI), which involves experiencing a visible rubber hand as part of one's body when the rubber hand is stroked simultaneously with the hidden real hand. The RHI is based on a combination of vision, touch, and proprioceptive information following the principles of multisensory integration. It has been posited that texture incongruence between rubber hand and real hand weakens the RHI, but the underlying mechanisms remain poorly understood. To investigate this, we recently developed a novel psychophysical RHI paradigm. Based on fitting psychometric functions, we discovered the RHI resulted in shifts in the point of subjective equality when the rubber hand and the real hand were stroked with matching materials. We analysed these datasets further by using signal detection theory analysis, which distinguishes between the participants' sensitivity to visuotactile stimulation and the associated perceptual bias. We found that texture incongruence influences the RHI's perceptual bias but not its sensitivity to visuotactile stimulation. We observed that the texture congruence bias effect was the strongest in shorter visuotactile asynchronies (50-100 ms) and weaker in longer asynchronies (200 ms). These results suggest texture-related perceptual bias is most prominent when the illusion's sensitivity is at its lowest. Our findings shed light on the intricate interactions between top-down and bottom-up processes in body ownership, the links between body ownership and multisensory integration, and the impact of texture congruence on the RHI.

Self-Powered Hybrid Motion and Health Sensing System Based on Triboelectric Nanogenerators.

Triboelectric nanogenerator (TENG) represents an effective approach for the conversion of mechanical energy into electrical energy and has been explored to combine multiple technologies in past years. Self-powered sensors are not only free from the constraints of mechanical energy in the environment but also capable of efficiently harvesting ambient energy to sustain continuous operation. In this review, the remarkable development of TENG-based human body sensing achieved in recent years is presented, with a specific focus on human health sensing solutions, such as body motion and physiological signal detection. The movements originating from different parts of the body, such as body, touch, sound, and eyes, are systematically classified, and a thorough review of sensor structures and materials is conducted. Physiological signal sensors are categorized into non-implantable and implantable biomedical sensors for discussion. Suggestions for future applications of TENG-based biomedical sensors are also indicated, highlighting the associated challenges.

Environmental complexity and regularity shape the evolution of cognition.

The environmental complexity hypothesis suggests that cognition evolves to allow animals to negotiate a complex and changing environment. By contrast, signal detection theory suggests cognition exploits environmental regularities by containing biases (e.g. to avoid dangerous predators). Therefore, two significant bodies of theory on cognitive evolution may be in tension: one foregrounds environmental complexity, the other regularity. Difficulty in reconciling these theories stems from their focus on different aspects of cognition. The environmental complexity hypothesis focuses on the reliability of sensors in the origin of cognition, while signal detection theory focuses on decision making in cognitively sophisticated animals. Here, we extend the signal detection model to examine the joint evolution of mechanisms for detecting information (sensory systems) and those that process information to produce behaviour (decision-making systems). We find that the transition to cognition can only occur if processing compensates for unreliable sensors by trading-off errors. Further, we provide an explanation for why animals with sophisticated sensory systems nonetheless disregard the reliable information it provides, by having biases for particular behaviours. Our model suggests that there is greater nuance than has been previously appreciated, and that both complexity and regularity can promote cognition.

Physically stressed bees expect less reward in an active choice judgement bias test.

Emotion-like states in animals are commonly assessed using judgment bias tests that measure judgements of ambiguous cues. Some studies have used these tests to argue for emotion-like states in insects. However, most of these results could have other explanations, including changes in motivation and attention. To control for these explanations, we developed a novel judgment bias test, requiring bumblebees to make an active choice indicating their interpretation of ambiguous stimuli. Bumblebees were trained to associate high or low rewards, in two different reward chambers, with distinct colours. We subsequently presented bees with ambiguous colours between the two learnt colours. In response, physically stressed bees were less likely than control bees to enter the reward chamber associated with high reward. Signal detection and drift diffusion models showed that stressed bees were more likely to choose low reward locations in response to ambiguous cues. The signal detection model further showed that the behaviour of stressed bees was explained by a reduction in the estimated probability of high rewards. We thus provide strong evidence for judgement biases in bees and suggest that their stress-induced behaviour is explained by reduced expectation of higher rewards, as expected for a pessimistic judgement bias.

The eco-evolutionary dynamics of Batesian mimicry.

Batesian mimicry is a strategy in which palatable prey species (mimic-species) resemble unpalatable prey species with aposematism (model-species). Theoretical studies on Batesian mimicry have been conducted in terms of their evolutionary significance and ecological consequences. However, despite the importance of eco-evolutionary feedback, the evolution and population dynamics of mimicry complex have long been explored separately. Previous studies on the dynamics of mimicry complex have proposed the possibility of the extinction of unpalatable species due to high predation by predators confusing palatable and unpalatable species. If the abundance of palatable species was large in comparison with unpalatable species, predation pressure on both unpalatable and palatable species became severe, resulting in the extinction of the unpalatable species. We hypothesized that palatable species evolved not to be similar to unpalatable species when unpalatable species became rare, because this situation is no longer advantageous for palatable species to mimic unpalatable species. Here, we constructed the eco-evolutionary dynamics of unpalatable and palatable species, and demonstrated that the evolutionary process of palatable species, which has been overlooked in previous theoretical studies, could rescue the unpalatable species from extinction. We modeled predators' foraging decisions based on signal detection theory. We assumed that palatable species evolve in a trait space, in which there are separate adaptive peaks on either side of an adaptive valley for mimicry and cryptic phenotypes. Then, we derived the stability conditions of the equilibria. As a result, the evolution of a cryptic phenotype in palatable species was driven when unpalatable species was rare, which mitigated predation pressure on unpalatable species through the reduction in the probability to be attacked. This could work to rescue unpalatable species from extinction.

A Bayesian method to detect drug-drug interaction using external information for spontaneous reporting system.

Due to the insufficiency of safety assessments of clinical trials for drugs, further assessments are required for post-marketed drugs. In addition to adverse drug reactions (ADRs) induced by one drug, drug-drug interaction (DDI)-induced ADR should also be investigated. The spontaneous reporting system (SRS) is a powerful tool for evaluating the safety of drugs continually. In this study, we propose a novel Bayesian method for detecting potential DDIs in a database collected by the SRS. By applying a power prior, the proposed method can borrow information from similar drugs for a drug assessed DDI to increase sensitivity of detection. The proposed method can also adjust the amount of the information borrowed by tuning the parameters in power prior. In the simulation study, we demonstrate the aforementioned increase in sensitivity. Depending on the scenarios, approximately 20 points of sensitivity of the proposed method increase from an existing method to a maximum. We also indicate the possibility of early detection of potential DDIs by the proposed method through analysis of the database shared by the Food and Drug Administration. In conclusion, the proposed method has a higher sensitivity and a novel criterion to detect potential DDIs early, provided similar drugs have similar observed-expected ratios to the drug under assessment.

Safety signal detection with control of latent factors.

Postmarket drug safety database like vaccine adverse event reporting system (VAERS) collect thousands of spontaneous reports annually, with each report recording occurrences of any adverse events (AEs) and use of vaccines. We hope to identify signal vaccine-AE pairs, for which certain vaccines are statistically associated with certain adverse events (AE), using such data. Thus, the outcomes of interest are multiple AEs, which are binary outcomes and could be correlated because they might share certain latent factors; and the primary covariates are vaccines. Appropriately accounting for the complex correlation among AEs could improve the sensitivity and specificity of identifying signal vaccine-AE pairs. We propose a two-step approach in which we first estimate the shared latent factors among AEs using a working multivariate logistic regression model, and then use univariate logistic regression model to examine the vaccine-AE associations after controlling for the latent factors. Our simulation studies show that this approach outperforms current approaches in terms of sensitivity and specificity. We apply our approach in analyzing VAERS data and report our findings.

Investigating the safety profiles of exogenous melatonin and associated adverse events: A pharmacovigilance study using WHO-VigiBase.

Melatonin, a pineal hormone that modulates circadian rhythms, sleep, and neurotransmitters, is widely used to treat sleep disorders. However, there are limited studies on the safety of melatonin. Therefore, we aimed to present the overall patterns of adverse events (AEs) following melatonin administration and identify potential safety signals associated with melatonin. Using VigiBase, a global individual case safety report (ICSRs) database managed by the World Health Organization (WHO), we conducted a retrospective, observational, pharmacovigilance study of melatonin between January 1996 and September 2022. Disproportionality analysis was conducted using two comparator settings: all other drugs and other sleep medications. We used multivariable logistic regression to estimate reporting odds ratios (RORs) with 95% confidence intervals (CIs) to compare the frequencies of AEs reporting between melatonin and each comparator setting. Furthermore, we assessed adverse events of special interests (AESIs) that could potentially be associated with melatonin. Signals were identified when the following criteria were met: cases ≥3, x2 ≥ 4, IC025 ≥ 0, and the lower end of the 95% CI of ROR > 2. These signals were then compared with the AE information on the drug labels provided by regulatory bodies. A total of 35 479 AE reports associated with melatonin were identified, with a higher proportion of reports from females (57.1%) and individuals aged 45-64 years (20.8%). We identified 21 AEs that were commonly detected as safety signals in the disproportionality analyses, including tic, educational problems, disturbance in social behavior, body temperature fluctuation, and growth retardation. In AESI analyses, accidents and injuries (adjusted ROR 2.97; 95% CI, 2.80-3.16), fall (2.24; 2.12-2.37), nightmare (4.90; 4.37-5.49), and abnormal dreams (3.68; 3.19-4.25) were detected as a signal of melatonin when compared to all other drugs, whereas those signals were not detected when compared to other sleep medications. In this pharmacovigilance study, exogenous melatonin showed safety profiles comparable to other sleep medications. However, several unexpected potential safety signals were identified, underscoring the need for further investigation at the population level.

Prescription Sequence Symmetry Analysis (PSSA) to assess prescribing cascades: a step-by-step guide.

Prescribing cascades occur when patients are prescribed medication to treat the adverse drug reaction of previously prescribed medication. Prescription sequence symmetry analysis (PSSA) can be used to assess the association between two medications in prescription or dispensing databases and thus the potential occurrence of prescribing cascades. In this article, a step-by-step guide is presented for conducting PSSA to assess prescribing cascades. We describe considerations for medication data collection and setting time periods for relevant parameters, including washout window, exposure window, continued exposure interval and blackout period. With two examples, we illustrate the impact of changes in these parameters on the strengths of associations observed. Given the impact seen, we recommend that researchers clearly specify and explain all considerations regarding medication included and time windows set when studying prescribing cascades with PSSA, and conduct subgroup and sensitivity analyses.

Statistical methods leveraging the hierarchical structure of adverse events for signal detection in clinical trials: a scoping review of the methodological literature.

BACKGROUND: In randomised controlled trials with efficacy-related primary outcomes, adverse events are collected to monitor potential intervention harms. The analysis of adverse event data is challenging, due to the complex nature of the data and the large number of unprespecified outcomes. This is compounded by a lack of guidance on best analysis approaches, resulting in widespread inadequate practices and the use of overly simplistic methods; leading to sub-optimal exploitation of these rich datasets. To address the complexities of adverse events analysis, statistical methods are proposed that leverage existing structures within the data, for instance by considering groupings of adverse events based on biological or clinical relationships. METHODS: We conducted a methodological scoping review of the literature to identify all existing methods using structures within the data to detect signals for adverse reactions in a trial. Embase, MEDLINE, Scopus and Web of Science databases were systematically searched. We reviewed the analysis approaches of each method, extracted methodological characteristics and constructed a narrative summary of the findings. RESULTS: We identified 18 different methods from 14 sources. These were categorised as either Bayesian approaches (n=11), which flagged events based on posterior estimates of treatment effects, or error controlling procedures (n=7), which flagged events based on adjusted p-values while controlling for some type of error rate. We identified 5 defining methodological characteristics: the type of outcomes considered (e.g. binary outcomes), the nature of the data (e.g. summary data), the timing of the analysis (e.g. final analysis), the restrictions on the events considered (e.g. rare events) and the grouping systems used. CONCLUSIONS: We found a large number of analysis methods that use the group structures of adverse events. Continuous methodological developments in this area highlight the growing awareness that better practices are needed. The use of more adequate analysis methods could help trialists obtain a better picture of the safety-risk profile of an intervention. The results of this review can be used by statisticians to better understand the current methodological landscape and identify suitable methods for data analysis - although further research is needed to determine which methods are best suited and create adequate recommendations.

Perceptual decoupling or trigger happiness: the effect of response delays and shorter presentation times on a go-no-go task with a high go prevalence.

In the current investigation, we modified the high Go, low No-Go Sustained Attention to Response Task (SART). Some researchers argue a commission error, an inappropriate response to a No-Go stimulus, in the SART is due to the participant being inattentive, or perceptually decoupled, during stimulus onset. Response delays in the SART reduce commission errors. A response delay may therefore enable a participant who is initially inattentive to recouple their attention in time to appropriately perceive the stimulus and withhold a response to a No-Go stimulus. However, shortening stimulus display duration in the SART should limit the possibility of the participant identifying the stimulus later, if they are initially not attending the stimulus. A response delay should not reduce commission errors if stimulus duration is kept to the minimum duration enabling stimulus recognition. In two experiments, we shortened stimulus onset to offset duration and added response delays of varying lengths. In both experiments, even when stimulus duration was shortened, response delays notably reduced commission errors if the delay was greater than 250 ms. In addition, using the Signal Detection Theory perspective in which errors of commission in the SART are due to a lenient response bias-trigger happiness, we predicted that response delays would result in a shift to a more conservative response bias in both experiments. These predictions were verified. The errors of commission in the SART may not be a measures of conscious awareness per se, but instead indicative of the level of participant trigger happiness-a lenient response bias.

Perceptual decoupling in the sustained attention to response task is unlikely.

Researchers dispute the cause of errors in high Go, low No Go target detection tasks, like the Sustained Attention to Response Task (SART). Some researchers propose errors in the SART are due to perceptual decoupling, where a participant is unaware of stimulus identity. This lack of external awareness causes an erroneous response. Other researchers suggest the majority of the errors in the SART are instead due to response leniency, not perceptual decoupling. Response delays may enable a participant who is initially unaware of stimulus identity, perceptually decoupled, to become aware of stimulus identity, or perceptually recoupled. If, however, the stimulus presentation time is shortened to the minimum necessary for stimulus recognition and the stimulus is disrupted with a structured mask, then there should be no time to enable perception to recouple even with a response delay. From the perceptual decoupling perspective, there should be no impact of a response delay on performance in this case. Alternatively if response bias is critical, then even in this case a response delay may impact performance. In this study, we shortened stimulus presentation time and added a structured mask. We examined whether a response delay impacted performance in the SART and tasks where the SART's response format was reversed. We expected a response delay would only impact signal detection theory bias, c, in the SART, where response leniency is an issue. In the reverse formatted SART, since bias was not expected to be lenient, we expected no impact or minimal impact of a response delay on response bias. These predictions were verified. Response bias is more critical in understanding SART performance, than perceptual decoupling, which is rare if it occurs at all in the SART.

Neuronal toxicity of monoclonal antibodies (mAbs): an analysis of post-marketing reports from FDA Adverse Event Reporting System (FAERS) safety database.

BACKGROUND: Monoclonal antibodies (mAbs) are pivotal in treating various diseases, including cancers and autoimmune disorders. Despite their therapeutic benefits, mAb therapy has been associated with neurological toxicity. OBJECTIVES: This study aimed to assess the occurrence of neuronal toxicity associated with mAbs, utilizing data from the FDA Adverse Event Reporting System (FAERS) safety database. The study also sought to delineate the medical characteristics of the reported cases. METHODS: A comprehensive analysis of neurological adverse events reported in the FAERS database was conducted, employing computational methodologies such as proportional relative risk (PRR), information component (IC025), and chi-square (χ2). Individual case safety reports (ICSRs) pertaining to neurological disorders linked to mAbs from the date of first global marketing authorization until June 30, 2023, were meticulously examined. RESULTS: The FAERS safety database contains 79,022 ICSRs linking mAbs to nervous system disorders. Rituximab, bevacizumab, denosumab, nivolumab, and trastuzumab were frequently cited. Reported adverse events include headache, peripheral neuropathy, dizziness, and cerebrovascular accident. Most ICSRs (85.81%) were serious, mainly affecting females (57.04%) with a 14.09% fatality rate. Panitumumab, atezolizumab, bevacizumab, and trastuzumab showed strong drug-event associations. Signal disproportionate reporting (SDR) analysis flagged myasthenia gravis, peripheral neuropathy, and neurotoxicity across multiple mAbs, suggesting potential signals. CONCLUSIONS: Interdisciplinary collaboration between oncologists and neurologists is crucial for safe mAb use. Our study enhances understanding of mAb neurological safety. Disproportionality signal analysis provides valuable evidence for risk mitigation.

Identifying high-risk medications and error types in Danish patient safety database using disproportionality analysis.

BACKGROUND: Medication error (ME) surveillance in Danish healthcare relies on the mandatory national incident reporting system, the Danish Patient Safety Database (DPSD). Individual case reviews and descriptive statistics with frequency counts are the most often used approaches when analyzing MEs in incident reporting systems, including the DPSD. However, incident reporting systems often generate a large number of reports and may suffer from underreporting; consequently, additional approaches are needed to overcome these challenges. Disproportionality analysis (DPA) is a statistical tool used for signal detection of adverse drug reactions in pharmacovigilance reports, but the evidence for using DPA on ME analysis in safety reporting systems is limited. OBJECTIVES: We aimed to test the feasibility of DPA by analysing harmful MEs reported to DPSD 2014-2018. METHODS: We utilized proportional reporting ratios (PRR) to identify signals of diproportionality. RESULTS: We identified well-known high-risk medicines, including anticoagulants, opioids, insulins, antiepileptic, and antipsychotic drugs, and their association with several ME types and stages in a medication process. CONCLUSION: DPA might be suggested as an additional tool for screening MEs and identifying priority areas for further investigation in safety reporting systems.

Adverse drug reaction signal detection methods in spontaneous reporting system: A systematic review.

BACKGROUND: A series of signal detection methods have been developed to detect adverse drug reaction (ADR) signals in spontaneous reporting system. However, different signal detection methods yield quite different signal detection results, and we do not know which method has the best detection performance. How to choose the most suitable signal detection method is an urgent problem to be solved. In this study, we systematically reviewed the characteristics and application scopes of current signal detection methods, with the goal of providing references for the optimization selection of signal detection methods in spontaneous reporting system. METHODS: We searched six databases from inception to January 2023. The search strategy targeted literatures regarding signal detection methods in spontaneous reporting system. We used thematic analysis approach to summarize the advantages, disadvantages, and application scope of each signal detection method. RESULTS: A total of 93 literatures were included, including 27 reviews and 66 methodological studies. Moreover, 31 signal detection methods were identified in these literatures. Each signal detection method has its inherent advantages and disadvantages, resulting in different application scopes of these methods. CONCLUSION: Our systematic review finds that there are variabilities in the advantages, disadvantages, and application scopes of different signal detection methods. This finding indicates that the most suitable signal detection method varies across different drug safety scenarios. Moreover, when selecting signal detection method in a particular drug safety scenario, the following factors need to be considered: purpose of research, database size, drug characteristics, adverse event characteristics, and characteristics of the relations between drugs and adverse events.

The precision test of metacognitive sensitivity and confidence criteria.

Humans experience feelings of confidence in their decisions. In perception, these feelings are typically accurate - we tend to feel more confident about correct decisions. The degree of insight people have into the accuracy of their decisions is known as metacognitive sensitivity. Currently popular methods of estimating metacognitive sensitivity are subject to interpretive ambiguities because they assume people have normally shaped distributions of different experiences when they are repeatedly exposed to a single input. If this normality assumption is violated, calculations can erroneously underestimate metacognitive sensitivity. Here, we describe a means of estimating metacognitive sensitivity that is more robust to violations of the normality assumption. This improved method can easily be added to standard behavioral experiments, and the authors provide Matlab code to help researchers implement these analyses and experimental procedures.