How does trade policy uncertainty affect green innovation in the USA and China? A nonlinear perspective

Green innovations are the most critical factor in promoting environmental sustainability worldwide. Trade can speed up the adoption of green innovations by facilitating the transfer of information, skills, and technology. However, trade policy uncertainty can create significant challenges for businesses investing in eco-innovations, leading to increased risk, reduced investment, and slower progress toward sustainable technologies. Recently, a growing number of researchers have shown their interest in finding the factors that can impact green innovations, but none have investigated the influence of trade policy uncertainty on green innovations in the USA and China. In addition, none of the past studies has relied on the nonlinear assumption. This analysis fills these gaps by examining the nonlinear impacts of trade policy uncertainty on eco-innovations in China and the USA over 2000Q1-2021Q4 by employing a nonlinear ARDL model. The finding reveals that a positive shock in trade policy uncertainty results in a decrease in green innovation in the USA and China, while a negative shock in trade policy uncertainty leads to an increase in green innovation in the USA over the long run. The nonlinear models also indicate that a positive shock in trade policy uncertainty harms green innovation in both the USA and China in the short run. The robustness of these results is confirmed by the NQARDL model, which confirms that an upsurge in trade policy uncertainty lowers green innovation in most quantiles in the USA and China in the short and long run. Conversely, negative shocks in trade policy uncertainty stimulate green innovation at most quantiles in both China and the USA, in the short and long run. Thus, policymakers need to consider the potential impact of trade policies on eco-innovations and work to create stable and predictable trade environments that support the growth of renewable technologies and other sustainable solutions.

NPB-REC: A non-parametric Bayesian deep-learning approach for undersampled MRI reconstruction with uncertainty estimation

The ability to reconstruct high-quality images from undersampled MRI data is vital in improving MRI temporal resolution and reducing acquisition times. Deep learning methods have been proposed for this task, but the lack of verified methods to quantify the uncertainty in the reconstructed images hampered clinical applicability. We introduce "NPB-REC", a non-parametric fully Bayesian framework, for MRI reconstruction from undersampled data with uncertainty estimation. We use Stochastic Gradient Langevin Dynamics during training to characterize the posterior distribution of the network parameters. This enables us to both improve the quality of the reconstructed images and quantify the uncertainty in the reconstructed images. We demonstrate the efficacy of our approach on a multi-coil MRI dataset from the fastMRI challenge and compare it to the baseline End-to-End Variational Network (E2E-VarNet). Our approach outperforms the baseline in terms of reconstruction accuracy by means of PSNR and SSIM (34.55, 0.908 vs. 33.08, 0.897, p<0.01, acceleration rate R=8) and provides uncertainty measures that correlate better with the reconstruction error (Pearson correlation, R=0.94 vs. R=0.91). Additionally, our approach exhibits better generalization capabilities against anatomical distribution shifts (PSNR and SSIM of 32.38, 0.849 vs. 31.63, 0.836, p<0.01, training on brain data, inference on knee data, acceleration rate R=8). NPB-REC has the potential to facilitate the safe utilization of deep learning-based methods for MRI reconstruction from undersampled data. Code and trained models are available at https://github.com/samahkh/NPB-REC.

Meta-analysis prediction intervals are under reported in sport and exercise medicine

AIM

Prediction intervals are a useful measure of uncertainty for meta-analyses that capture the likely effect size of a new (similar) study based on the included studies. In comparison, confidence intervals reflect the uncertainty around the point estimate but provide an incomplete summary of the underlying heterogeneity in the meta-analysis. This study aimed to estimate (i) the proportion of meta-analysis studies that report a prediction interval in sports medicine; and (ii) the proportion of studies with a discrepancy between the reported confidence interval and a calculated prediction interval.

METHODS

We screened, at random, 1500 meta-analysis studies published between 2012 and 2022 in highly ranked sports medicine and medical journals. Articles that used a random effect meta-analysis model were included in the study. We randomly selected one meta-analysis from each article to extract data from, which included the number of estimates, the pooled effect, and the confidence and prediction interval.

RESULTS

Of the 1500 articles screened, 866 (514 from sports medicine) used a random effect model. The probability of a prediction interval being reported in sports medicine was 1.7% (95% CI = 0.9%, 3.3%). In medicine the probability was 3.9% (95% CI = 2.4%, 6.6%). A prediction interval was able to be calculated for 220 sports medicine studies. For 60% of these studies, there was a discrepancy in study findings between the reported confidence interval and the calculated prediction interval. Prediction intervals were 3.4 times wider than confidence intervals.

CONCLUSION

Very few meta-analyses report prediction intervals and hence are prone to missing the impact of between-study heterogeneity on the overall conclusions. The widespread misinterpretation of random effect meta-analyses could mean that potentially harmful treatments, or those lacking a sufficient evidence base, are being used in practice. Authors, reviewers, and editors should be aware of the importance of prediction intervals.

Digging into uncertainty: a case report on Spitz lesions

Spitz lesions represent a spectrum of melanocytic proliferations, and they include Spitz nevi, atypical Spitz tumors, and Spitz melanomas. Atypical Spitz tumors are intermediate melanocytic lesions with features between benign Spitz nevi and malignant Spitz melanomas. They often present a diagnostic challenge to pathologists and dermatologists alike because they can mimic melanoma, especially high-grade atypical Spitz tumors. Importantly, they present a relevant clinical management challenge because definite recommendations for their management and treatment have not yet been established. Here we present the case of a young patient with a high-grade atypical Spitz tumor along with the diagnostic procedure and further management. We also review potential pitfalls in the literature that should alert clinicians to the more aggressive potential of the lesion, such as some BRAF fusions.

Effects of aging on externally cued and internally driven uncertainty representations

OBJECTIVE

The Hick-Hyman law states that response time (RT) increases linearly with increasing information uncertainty. The effects of aging on uncertainty representations in choice RT paradigms remain unclear, including whether aging differentially affects processes mediating externally cued versus internally driven uncertainty. This study sought to characterize age-related differences in uncertainty representations using a card-sorting task.

METHOD

The task separately manipulated internally driven uncertainty (i.e., probability of each stimulus type with fixed number of response piles) and externally cued uncertainty (i.e., number of response piles with fixed probability of each stimulus type).

RESULTS

Older adults (OA) showed greater RT slowing than younger adults in response to uncertainty load, an effect that was stronger in the externally cued than internally driven condition. While both age groups showed lower accuracy and greater RTs in response to unexpected (surprising) stimuli in the internally driven condition at low uncertainty loads, OA were unable to distinguish between expected and nonexpected stimuli at higher uncertainty loads when the probability of each stimulus type was close to equal. Among OA, better performance on the internally driven, but not externally cued, condition was associated with better global cognitive performance and verbal fluency.

CONCLUSIONS

Collectively, these findings provide behavioral evidence of age-related disruptions to bottom-up (externally cued) and top-down (supporting internally driven mental representations) resources to process uncertainty and coordinate task-relevant action. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Potentiality switches and epistemic uncertainty: the Argument from Potential in times of human embryo-like structures

Recent advancements in developmental biology enable the creation of embryo-like structures from human stem cells, which we refer to as human embryo-like structures (hELS). These structures provide promising tools to complement-and perhaps ultimately replace-the use of human embryos in clinical and fundamental research. But what if these hELS-when further improved-also have a claim to moral status? What would that imply for their research use? In this paper, we explore these questions in relation to the traditional answer as to why human embryos should be given greater protection than other (non-)human cells: the so-called Argument from Potential (AfP). According to the AfP, human embryos deserve special moral status because they have the unique potential to develop into persons. While some take the development of hELS to challenge the very foundations of the AfP, the ongoing debate suggests that its dismissal would be premature. Since the AfP is a spectrum of views with different moral implications, it does not need to imply that research with human embryos or hELS that (may) have 'active' potential should be completely off-limits. However, the problem with determining active potential in hELS is that this depends on development passing through 'potentiality switches' about the precise coordinates of which we are still in the dark. As long as this epistemic uncertainty persists, extending embryo research regulations to research with specific types of hELS would amount to a form of regulative precaution that as such would require further justification.

Designing explainable AI to improve human-AI team performance: A medical stakeholder-driven scoping review

The rise of complex AI systems in healthcare and other sectors has led to a growing area of research called Explainable AI (XAI) designed to increase transparency. In this area, quantitative and qualitative studies focus on improving user trust and task performance by providing system- and prediction-level XAI features. We analyze stakeholder engagement events (interviews and workshops) on the use of AI for kidney transplantation. From this we identify themes which we use to frame a scoping literature review on current XAI features. The stakeholder engagement process lasted over nine months covering three stakeholder group's workflows, determining where AI could intervene and assessing a mock XAI decision support system. Based on the stakeholder engagement, we identify four major themes relevant to designing XAI systems - 1) use of AI predictions, 2) information included in AI predictions, 3) personalization of AI predictions for individual differences, and 4) customizing AI predictions for specific cases. Using these themes, our scoping literature review finds that providing AI predictions before, during, or after decision-making could be beneficial depending on the complexity of the stakeholder's task. Additionally, expert stakeholders like surgeons prefer minimal to no XAI features, AI prediction, and uncertainty estimates for easy use cases. However, almost all stakeholders prefer to have optional XAI features to review when needed, especially in hard-to-predict cases. The literature also suggests that providing both system- and prediction-level information is necessary to build the user's mental model of the system appropriately. Although XAI features improve users' trust in the system, human-AI team performance is not always enhanced. Overall, stakeholders prefer to have agency over the XAI interface to control the level of information based on their needs and task complexity. We conclude with suggestions for future research, especially on customizing XAI features based on preferences and tasks.

Livelihood and fertility variance increases in response to rapid change

OBJECTIVES

The extreme condition that we address in this special issue is how people adapt to rapid change, which in this case study is instigated by globalization and the process of market integration. Although market integration has been underway for centuries in some parts of the world, it often occurs precipitously in small-scale societies, initiating an abrupt break with traditional ways of life and fostering a keen sense of uncertainty.

METHODS

Using cross sections from 30-years of data collected in a Yucatec Maya subsistence farming community, we test the expectation that when payoffs to pursue new livelihood and reproductive options are uncertain, variance in social, economic, and reproductive traits will increase in the population. Our data span the transition from subsistence farming to a mixed economy, and bridge the transition from natural to contracepting fertility. Exposure to globalizing and market forces occurred when a paved road was built in the early 2000s.

RESULTS

We find that livelihood traits (a household's primary economic strategy, amount of land under cultivation, amount of maize and honey sold), become more variable as new, but uncertain options become available. Variance in levels of education and family size likewise immediately increase following the road, but show signs of settling back down a decade later. Rather than replacing one way of life with another, Maya farmers conservatively adopt some new elements (family planning, wage labor), until the tradeoffs to commit to smaller families and the labor market become clearer.

CONCLUSION

Our findings highlight that in rapidly changing environments when the payoffs to assimilate new options are uncertain, some households and individuals intensify what they know best, while others adopt new opportunities, driving variance up in the population.

Intolerance of Uncertainty and Worry Prospectively Predict COVID-19 Anxiety and Distress: A 4-Year Longitudinal Study

The COVID-19 pandemic precipitated an uptick in poor mental health outcomes, including coronavirus-related anxiety and distress. Preliminary research has shown that intolerance of uncertainty (IU) and worry proneness, two transdiagnostic risk factors for anxiety and related disorders, are associated cross-sectionally with pandemic-related fear and distress. However, the extent to which IU and worry proneness prospectively predict coronavirus-related anxiety and distress is unclear. Whether IU and worry may also interact in prospectively predicting coronavirus-related anxiety and distress is also unknown. To address this knowledge gap, the present study examined IU and trait worry as prospective predictors of the level and trajectory of coronavirus anxiety and COVID stress syndrome over time, as well as the extent to which worry moderated the relation between IU and pandemic-related outcomes. Participants (n = 310) who completed self-report measures of IU and trait worry in 2016 were contacted following the onset of COVID-19 in 2020 and completed biweekly measures of coronavirus anxiety and COVID stress syndrome for 30 weeks. Multilevel models revealed that IU assessed in 2016 significantly predicted the severity of both coronavirus anxiety and COVID stress syndrome throughout the study period in 2020. Worry also moderated the link between IU and coronavirus anxiety, such that individuals with high levels of trait worry and high IU in 2016 experienced the most coronavirus anxiety in 2020. Results suggest that IU and worry functioned as independent and interactive vulnerability factors for subsequent adverse psychological reactions to COVID-19. Clinical implications and future directions are discussed.

Uncertainty-based saltwater intrusion prediction using integrated Bayesian machine learning modeling (IBMLM) in a deep aquifer

Data-driven machine learning approaches are promising to substitute physically based groundwater numerical models and capture input-output relationships for reducing computational burden. But the performance and reliability are strongly influenced by different sources of uncertainty. Conventional researches generally rely on a stand-alone machine learning surrogate approach and fail to account for errors in model outputs resulting from structural deficiencies. To overcome this issue, this study proposes a flexible integrated Bayesian machine learning modeling (IBMLM) method to explicitly quantify uncertainties originating from structures and parameters of machine learning surrogate models. An Expectation-Maximization (EM) algorithm is combined with Bayesian model averaging (BMA) to find out maximum likelihood and construct posterior predictive distribution. Three machine learning approaches representing different model complexity are incorporated in the framework, including artificial neural network (ANN), support vector machine (SVM) and random forest (RF). The proposed IBMLM method is demonstrated in a field-scale real-world "1500-foot" sand aquifer, Baton Rouge, USA, where overexploitation caused serious saltwater intrusion (SWI) issues. This study adds to the understanding of how chloride concentration transport responds to multi-dimensional extraction-injection remediation strategies in a sophisticated saltwater intrusion model. Results show that most IBMLM exhibit r values above 0.98 and NSE values above 0.93, both slightly higher than individual machine learning, confirming that the IBMLM is well established to provide better model predictions than individual machine learning models, while maintaining the advantage of high computing efficiency. The IBMLM is found useful to predict saltwater intrusion without running the physically based numerical simulation model. We conclude that an explicit consideration of machine learning model structure uncertainty along with parameters improves accuracy and reliability of predictions, and also corrects uncertainty bounds. The applicability of the IBMLM framework can be extended in regions where a physical hydrogeologic model is difficult to build due to lack of subsurface information.

Patient Welfare Implications of Indication-Specific Value-Based Pricing of Multi-Indication Drugs

OBJECTIVES

Indication-specific value-based pricing (ISVBP) is a mechanism that allows the prices of multi-indication drugs to vary across indications by aligning the drug prices with value. However, the overall impact of ISVBP on patients across indications is uncertain. This study examines the theoretical welfare effects of ISVBP for multi-indication drugs and compares consumer surplus under ISVBP and single pricing, the latter of which is based on the weighted average value.

METHODS

We considered a healthcare system with government-negotiated drug prices based on the value of drugs. We assumed a drug with 2 indications and 1 relevant comparator for each indication. The value of the drug was uniformly distributed among the patients of each indication in the base case. We also considered alternative scenarios with exponentially and Pareto distributed drug values. Numerical simulations were conducted to explore potential settings where ISVBP was welfare-improving for patients compared with single pricing.

RESULTS

The theoretical analysis showed that the consumer surplus change was strictly non-positive from single pricing to ISVBP. Therefore, it was not welfare-improving for patients in the settings of interest. Numerical simulations confirmed this result across various scenarios of value distributions.

CONCLUSIONS

This study provides insights into the patient welfare implications of ISVBP for multi-indication drugs. We did not identify conditions under which ISVBP can enhance overall patient well-being, suggesting that it should be implemented cautiously. Future research should examine dynamic welfare implications related to innovation incentives because they may significantly affect population health in the future.

Examining anxious temperament in anorexia nervosa: Behavioural inhibition and intolerance of uncertainty and their contribution to trait anxiety in adolescents with anorexia nervosa

BACKGROUND

Anorexia nervosa (AN) is a serious and complex psychiatric disorder yet treatment results are suboptimal. Insight into the etiology of this illness is much needed. Research highlights the implication of anxiety-related traits in the development and maintenance of AN. This study investigates firstly, behavioural inhibition and intolerance for uncertainty (IU) in adolescents with and without AN, and secondly relations between these traits.

METHODS

In a cross-sectional study, 165 adolescent girls (AN = 94, HC = 71) completed questionnaires measuring behavioural inhibition, IU and trait anxiety. ANOVAs tested differences between AN and HC groups, and mediation models with IU as a mediator between behavioural inhibition and trait anxiety were run.

RESULTS

AN adolescents reported significantly higher levels of behavioural inhibition, IU and trait anxiety compared to their peers. In both AN and HC, a direct and a total effect of behavioural inhibition on trait anxiety was found. However, only in the AN group IU partially mediated the relation between behavioural inhibition and trait anxiety.

LIMITATIONS

Data is cross-sectional and longitudinal studies are required. A mean illness duration of nearly 2 years may mean early effects of malnourishment and habituation and future studies should include patients with shorter illness duration.

CONCLUSIONS

Results highlight that behavioural inhibition and IU may contribute to anxiety in AN whilst their peers may have developed better executive and social-emotional skills to manage uncertainty. Adolescents with AN may benefit from interventions targeting behavioural inhibition and IU.

[Experiences of uncertainty about the future, poverty risks, and precarious living situations in nursing homes]

The living situation of people in nursing homes, with a specific vulnerability to care-related impoverishment, is underresearched. This article deals with experiences of uncertainty, poverty and a precarious life situation of care home residents in later life. The data basis is supplied by an ethnographic study of ageing and living with chronic health conditions. A range of precarious living situations are outlined on the basis of real cases. Insights gleaned include: people with diverse sociobiographical backgrounds can be affected by insecurity, poverty and precarious life situations as a result of becoming dependent on help and the transition to care homes. Not knowing whether available funds will suffice to cover rising costs until the end of life is a common form of uncertainty about the future. The threat of poverty or becoming dependent on state benefits can exacerbate experiences of loss associated with institutional care, losing autonomy, opportunities for participation, and options for shaping one's life and can further destabilize the overall condition. This anguish remains largely invisible; structural problems are individualized.

U-PASS: An uncertainty-guided deep learning pipeline for automated sleep staging

With the increasing prevalence of machine learning in critical fields like healthcare, ensuring the safety and reliability of these systems is crucial. Estimating uncertainty plays a vital role in enhancing reliability by identifying areas of high and low confidence and reducing the risk of errors. This study introduces U-PASS, a specialized human-centered machine learning pipeline tailored for clinical applications, which effectively communicates uncertainty to clinical experts and collaborates with them to improve predictions. U-PASS incorporates uncertainty estimation at every stage of the process, including data acquisition, training, and model deployment. Training is divided into a supervised pre-training step and a semi-supervised recording-wise finetuning step. We apply U-PASS to the challenging task of sleep staging and demonstrate that it systematically improves performance at every stage. By optimizing the training dataset, actively seeking feedback from domain experts for informative samples, and deferring the most uncertain samples to experts, U-PASS achieves an impressive expert-level accuracy of 85% on a challenging clinical dataset of elderly sleep apnea patients. This represents a significant improvement over the starting point at 75% accuracy. The largest improvement gain is due to the deferral of uncertain epochs to a sleep expert. U-PASS presents a promising AI approach to incorporating uncertainty estimation in machine learning pipelines, improving their reliability and unlocking their potential in clinical settings.

Characteristics of Anticancer Drugs Approved Under the Accelerated Approval Program in the US: Success or Failure in Converting to Regular Approval

BACKGROUND

Accelerated approval (AA) program expedites access to promising drugs for life-threatening conditions, particularly in oncology. However, challenges arise from the trade-off between faster access and the certainty of clinical benefits. We examined the differences between the indications for successful conversion of AA to regular approval (RA) and those withdrawn from the perspective of whether the confirmatory trial was appropriately designed and conducted to verify the efficacy estimated in the pivotal trial for AA (AA trial).

METHODS

All the anticancer drugs approved by the United States (US) Food and Drug Administration (FDA) between January 2016 and December 2019 were identified on the FDA website. From these, we selected drugs granted AA for solid tumors based on single-arm trials. We compared the characteristics of the AA and confirmatory trials between products that were successfully converted to RA and those that were withdrawn.

RESULTS

Twenty-four AA indications were identified, of which 11 were converted to RA and 6 were withdrawn. The magnitude of the objective response rate (ORR) in both the AA and confirmatory trials was not a factor that clearly determined the conversion or withdrawal of AA. However, if the experimental arm did not achieve a certain level of ORR over the control arm in the confirmatory trial, it was thought to increase the uncertainty of successful conversion to RA.

CONCLUSION

A relatively high ORR compared with that of the control arm in the confirmatory trial, after AA, is important for successfully obtaining RA.

Commutability and Uncertainty of Blood Hemoglobin A1c Testing Materials

BACKGROUND

Hemoglobin A1C (HbA1C) is used to evaluate glycemic control over a three-month period. Blood matrix-based HbA1C materials are needed for quality control and evaluation of HbA1C measurements. This study investigated the commutability of blood materials (BMs) and aimed to upgrade BMs for HbA1C testing for use as proficiency test (PT) material.

METHODS

We measured BMs from a DM blood donor (n = 1), an in vitro glycation procedure (n = 3), and from commercial sources (n = 2) for HbA1C in parallel with fresh unprocessed BMs (n = 3) and clinical blood samples (n = 25). Two NGSP-certified methods, including a turbidimetric and an enzymatic immunoassay, were used for HbA1C determinations. Commutability as investigated according to CLSI EP14-Ed4 guidelines.

RESULTS

The commutable BMs exhibited a mean paired difference of 0% to 9% when compared to reference methods, whereas the non-commutable BMs represented a mean paired difference of 3% to 11%. Fresh, unprocessed BMs with a low HbA1C of 6.0% were more commutable than BMs with a high HbA1C. The values of HbA1C in BMs (mean and uncertainty following ISO Guide 35 for RM production) were applied to upgrade the PT material to be used as a reference material. The relative uncertainty of BM-Ndm-1 and BM-Gcb-3 were 1 and 0.4%, respectively.

CONCLUSIONS

The commutability of hemoglobin in BMs is dependent on the preparation process. Blood materials with a high HbA1C content are usually less commutable versus materials with low HbA1C content when prepared by the same process. Our study showed BMs can be successfully used as quality control or PT materials.

TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses

Whole-transcriptome spatial profiling of genes at single-cell resolution remains a challenge. To address this limitation, spatial gene expression prediction methods have been developed to infer the spatial expression of unmeasured transcripts, but the quality of these predictions can vary greatly. Here we present Transcript Imputation with Spatial Single-cell Uncertainty Estimation (TISSUE) as a general framework for estimating uncertainty for spatial gene expression predictions and providing uncertainty-aware methods for downstream inference. Leveraging conformal inference, TISSUE provides well-calibrated prediction intervals for predicted expression values across 11 benchmark datasets. Moreover, it consistently reduces the false discovery rate for differential gene expression analysis, improves clustering and visualization of predicted spatial transcriptomics and improves the performance of supervised learning models trained on predicted gene expression profiles. Applying TISSUE to a MERFISH spatial transcriptomics dataset of the adult mouse subventricular zone, we identified subtypes within the neural stem cell lineage and developed subtype-specific regional classifiers.

A data-driven newsvendor model for elective-emergency admission control under uncertain inpatient bed capacity

OBJECTIVE

Elective-emergency admission control referred to allocating available inpatient bed capacity between elective and emergency hospitalization demand. Existing approaches for admission control often excluded several complex factors when making decisions, such as uncertain bed capacity and unknown true probability distributions of patient arrivals and departures. We aimed to create a data-driven newsvendor framework to study the elective-emergency admission control problem to achieve bed operational efficiency and effectiveness.

METHODS

We developed a data-driven approach that utilized the newsvendor framework to formulate the admission control problem. We also created approximation algorithms to generate a pool of candidate admission control solutions. Past observations and relevant emergency demand and bed capacity features were modeled in a newsvendor framework. Using approximation algorithmic approaches (sample average approximation, separated estimation and optimization, linear programing-LP, and distribution-free model) allowed us to derive computationally efficient data-driven solutions with tight bounds on the expected in-sample and out-of-sample cost guaranteed.

RESULTS

Tight generalization bounds on the expected out-of-sample cost of the feature-based model were derived with respect to the LP and quadratic programing (QP) algorithms, respectively. Results showed that the optimal feature-based model outperformed the optimal observation-based model with respect to the expected cost. In a setting where the unit overscheduled cost was higher than the unit under-scheduled cost, scheduling fewer elective patients would replace the benefit of incorporating related features in the model. The tighter the available bed capacity for elective patients, the bigger the difference of the schedule cost between the feature-based model and the observation-based model.

CONCLUSIONS

The study provides a reference for the theoretical study on bed capacity allocation between elective and emergency patients under the condition of the unknown true probability distribution of bed capacity and emergency demand, and it also proves that the approximate optimal policy has good performance.

Baby cry recognition based on WOA-VMD and an improved Dempster-Shafer evidence theory

BACKGROUND AND OBJECTIVE

Conflict may happen when more than one classifier is used to perform prediction or classification. The recognition model error leads to conflicting evidence. These conflicts can cause decision errors in a baby cry recognition and further decrease its recognition accuracy. Thus, the objective of this study is to propose a method that can effectively minimize the conflict among deep learning models and improve the accuracy of baby cry recognition.

METHODS

An improved Dempster-Shafer evidence theory (DST) based on Wasserstein distance and Deng entropy was proposed to reduce the conflicts among the results by combining the credibility degree between evidence and the uncertainty degree of evidence. To validate the effectiveness of the proposed method, examples were analyzed, and applied in a baby cry recognition. The Whale optimization algorithm-Variational mode decomposition (WOA-VMD) was used to optimally decompose the baby cry signals. The deep features of decomposed components were extracted using the VGG16 model. Long Short-Term Memory (LSTM) models were used to classify baby cry signals. An improved DST decision method was used to obtain the decision fusion.

RESULTS

The proposed fusion method achieves an accuracy of 90.15% in classifying three types of baby cry. Improvement between 2.90% and 4.98% was obtained over the existing DST fusion methods. Recognition accuracy was improved by between 5.79% and 11.53% when compared to the latest methods used in baby cry recognition.

CONCLUSION

The proposed method optimally decomposes baby cry signal, effectively reduces the conflict among the results of deep learning models and improves the accuracy of baby cry recognition.

Uncertainties and opportunities in delivering environmentally sustainable surgery: the surgeons' view

Surgery is a carbon-heavy activity and creates a high volume of waste. Surgical teams around the world want to deliver more environmentally sustainable surgery but are unsure what to do and how to create change. There are many interventions available, but resources and time are limited. Capital investment into healthcare and engagement of senior management are challenging. However, frontline teams can change behaviours and drive wider change. Patients have a voice here too, as they would like to ensure their surgery does not harm their local community but are concerned about the effects on them when changes are made. Environmentally sustainable surgery is at the start of its journey. Surgeons need to rapidly upskill their generic knowledge base, identify which measures they can implement locally and take part in national research programmes. Surgical teams in the NHS have the chance to create a world-leading programme that can bring change to hospitals around the world. This article provides an overview of how surgeons see the surgical team being involved in environmentally sustainable surgery.

Risk preferences over health: Empirical estimates and implications for medical decision-making

Mainstream health economic theory implies that an expected gain in health-related quality of life (HRQoL) produces the same value for consumers, regardless of baseline health. Several strands of recent research call this implication into question. Generalized Risk-Adjusted Cost-Effectiveness (GRACE) demonstrates theoretically that baseline health status influences value, so long as consumers are not risk-neutral over health. Prior empirical literature casts doubt on risk-neutral expected utility-maximization in the health domain. We estimate utility over HRQoL in a nationally representative U.S. population and use our estimates to measure risk preferences over health. We find that individuals are risk-seeking at low levels of health, become risk-averse at health equal to 0.485 (measured on a 0-1 scale), and are most risk-averse at perfect health (coefficient of relative risk aversion = 4.51). We develop the resulting implications for medical decision making, cost-effectiveness analyses, and the proper theory of health-related decision making under uncertainty.

The long tail of COVID and the tale of long COVID: Diagnostic construction and the management of ignorance

We bring together insights from the sociology of diagnosis and the sociology of ignorance to examine the early diagnostic unfolding of 'Long COVID' (LC). Originally described by patient activists, researchers set out to ponder its unwieldy clinical boundaries. Using a scoping review method in tandem with qualitative content analytic techniques, we analyse medicine's initial struggles to construct a LC diagnosis. Paying attention to the dynamics of ignorance, we highlight three consequential conceptual manoeuvres in the early classifications of LC: causal agnosticism concerning the relationship between COVID-19 and LC, evasion of lumping LC with similar conditions; and the predictable splitting off of medically explainable cases from the LC designation. These manoeuvres are not maleficent, inept or unreasonable. They are practical but impactful responses to the classificatory dilemmas present in the construction of diagnoses amidst ignorance. Although there are unique aspects to LC, we suggest that its early fate is nevertheless emblematic of medicine's diagnostic standardisation processes more generally. To varying degrees, diagnoses are ignorance management strategies; they create a pathway through the uncertainty at the core of disease realities. However, while diagnoses circumscribe some types of ignorance, they produce others through the creation of blind spots and paths not taken.

The Ethical Obligation for Research During Public Health Emergencies: Insights From the COVID-19 Pandemic

In times of crises, public health leaders may claim that trials of public health interventions are unethical. One reason for this claim can be that equipoise-i.e. a situation of uncertainty and/or disagreement among experts about the evidence regarding an intervention-has been disturbed by a change of collective expert views. Some might claim that equipoise is disturbed if the majority of experts believe that emergency public health interventions are likely to be more beneficial than harmful. However, such beliefs are not always justified: where high quality research has not been conducted, there is often considerable residual uncertainty about whether interventions offer net benefits. In this essay we argue that high-quality research, namely by means of well-designed randomized trials, is ethically obligatory before, during, and after implementing policies in public health emergencies (PHEs). We contend that this standard applies to both pharmaceutical and non-pharmaceutical interventions, and we elaborate an account of equipoise that captures key features of debates in the recent pandemic. We build our case by analyzing research strategies employed during the COVID-19 pandemic regarding drugs, vaccines, and non-pharmaceutical interventions; and by providing responses to possible objections. Finally, we propose a public health policy reform: whenever a policy implemented during a PHE is not grounded in high-quality evidence that expected benefits outweigh harms, there should be a planned approach to generate high-quality evidence, with review of emerging data at preset time points. These preset timepoints guarantee that policymakers pause to review emerging evidence and consider ceasing ineffective or even harmful policies, thereby improving transparency and accountability, as well as permitting the redirection of resources to more effective or beneficial interventions.

Disrupting optimal decision making in visual foraging: The impact of search experience

This study introduces the diet-choice problem in foraging as a framework to investigate search and decision making in an uncertain environment. Using a mathematical model based on signal detection-based optimal foraging theory and conducting behavioral experiments, we examined whether the choice of uncertain options in a visual foraging task followed the optimal strategy. In addition, we explored whether search history affects behavior by changing the environment in a stepwise manner. We used a visual foraging task in which participants searched for visual stimuli and selected them using mouse clicks. To introduce uncertainty, the stimuli were designed in a way that they could not be completely discriminated by visual inspection. The study consisted of four sessions, during which the ratio of the number of gains to loss stimuli in Experiment 1 and the magnitude of loss in Experiment 2 were varied in a stepwise manner. Although search strategies can adapt to environmental changes, this adjustment is not always optimal. Specifically, although both the rising and falling groups experienced the same environment, their performance differed depending on the order in which participants experienced changing environments. Search strategy can be adjusted in the presence of environmental uncertainty, but it deviates from the optimal strategy due to the influence of the search history in the experienced environment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Need for Specialized Oncology Training for Clinical Ethicists

Numerous ethical issues are raised in cancer treatment and research. Informed consent is challenging due to complex treatment modalities and prognostic uncertainty. Busy oncology clinics limit the ability of oncologists to spend time reinforcing patient understanding and facilitating end-of-life planning. Despite these issues and the ethics consultations they generate, clinical ethicists receive little if any focused education about cancer and its treatment. As the field of clinical ethics develops standards for training, we argue that a basic knowledge of cancer should be included and offer an example of what cancer ethics training components might look like. We further suggest some specific steps to increase collaboration between clinical ethicists and oncology providers in the outpatient setting to facilitate informed consent and proactively identify ethical issues.

The Relationship Between Uncertainty and Psychological Distress Among Family Caregivers of Patients With Delirium in Intensive Care Units: A Cross-Sectional Survey

BACKGROUND

Although family caregivers experienced negative psychological symptoms associated with witnessing intensive care unit delirium in their loved ones, there is a lack of clear understanding of how delirium is associated with family caregiver psychological distress. Uncertainty could be a factor contributed to this association.

OBJECTIVES

The aim of this study was to examine the relationship between uncertainty and psychological distress among family caregivers of patients with delirium in intensive care units.

METHODS

A cross-sectional correlational design was used for this observational study of adult family caregivers of patients admitted to the intensive care unit and who reported witnessing delirium symptoms in their loved ones. Family caregivers completed an electronic survey in January 2022 that consisted of a family caregiver and patient demographic form, the Mishel Uncertainty in Illness Scale-Family Member, and the Kessler Psychological Distress Scale. Descriptive, correlational, and regression statistical analyses were applied.

RESULTS

One hundred twenty-one adult family caregivers were enrolled. Family caregivers reported substantial uncertainty (mean, 106.15, on a scale of 31-155) and moderate to severe psychological distress (mean, 31.37, on a scale of 10-50) regarding their witnessing of delirium episodes in their loved ones. Uncertainty was significantly correlated with psychological distress among family caregivers (rs = 0.52, P < .001). Uncertainty significantly predicted psychological distress among family caregivers (regression coefficient, 0.27; P < .001).

DISCUSSION

Family caregiver uncertainty was positively associated with psychological distress. This distress can interfere with family caregiver involvement in patient delirium care. These findings are essential to increase critical care nurse awareness and inform the development of nursing interventions to alleviate possible uncertainty and distress.

Sentence-final completion norms for 2925 Mexican Spanish sentence contexts

Sentence-final completion tasks serve as valuable tools in studying language processing and the associated predictive mechanisms. There are several established sentence-completion norms for languages like English, Portuguese, French, and Spanish, each tailored to the language it was designed for and evaluated in. Yet, cultural variations among native speakers of the same language complicate the claim of a universal application of these norms. In this study, we developed a corpus of 2925 sentence-completion norms specifically for Mexican Spanish. This corpus is distinctive for several reasons: Firstly, it is the most comprehensive set of sentence-completion norms for Mexican Spanish to date. Secondly, it offers a substantial range of experimental stimuli with considerable variability in terms of the predictability of word sentence completion (cloze probability/surprisal) and the level of uncertainty inherent in the sentence context (entropy). Thirdly, the syntactic complexity of the sentences in the corpus is varied, as are the characteristics of the final word nouns (including aspects of concreteness/abstractness, length, and frequency). This paper details the generation of the sentence contexts, explains the methodology employed for data collection from a total of 1470 participants, and outlines the approach to data analysis for the establishment of sentence-completion norms. These norms provide a significant contribution to fields such as linguistics, cognitive science, and machine learning, among others, by enhancing our understanding of language, predictive mechanisms, knowledge representation, and context representation. The collected data is accessible through the Open Science Framework (OSF) at the following link: https://osf.io/js359/?view_only=bb1b328d37d643df903ed69bb2405ac0 .

Curiosity: primate neural circuits for novelty and information seeking

For many years, neuroscientists have investigated the behavioural, computational and neurobiological mechanisms that support value-based decisions, revealing how humans and animals make choices to obtain rewards. However, many decisions are influenced by factors other than the value of physical rewards or second-order reinforcers (such as money). For instance, animals (including humans) frequently explore novel objects that have no intrinsic value solely because they are novel and they exhibit the desire to gain information to reduce their uncertainties about the future, even if this information cannot lead to reward or assist them in accomplishing upcoming tasks. In this Review, I discuss how circuits in the primate brain responsible for detecting, predicting and assessing novelty and uncertainty regulate behaviour and give rise to these behavioural components of curiosity. I also briefly discuss how curiosity-related behaviours arise during postnatal development and point out some important reasons for the persistence of curiosity across generations.

Dual consistency regularization with subjective logic for semi-supervised medical image segmentation

Semi-supervised learning plays a vital role in computer vision tasks, particularly in medical image analysis. It significantly reduces the time and cost involved in labeling data. Current methods primarily focus on consistency regularization and the generation of pseudo labels. However, due to the model's poor awareness of unlabeled data, aforementioned methods may misguide the model. To alleviate this problem, we propose a dual consistency regularization with subjective logic for semi-supervised medical image segmentation. Specifically, we introduce subjective logic into our semi-supervised medical image segmentation task to estimate uncertainty, and based on the consistency hypothesis, we construct dual consistency regularization under weak and strong perturbations to guide the model's learning from unlabeled data. To evaluate the performance of the proposed method, we performed experiments on three widely used datasets: ACDC, LA, and Pancreas. Experiments show that the proposed method achieved improvement compared with other state-of-the-art (SOTA) methods.

Resident training in psychopathology and uncertainty in a clinical situation

The central theme of this article is the way in which psychiatry physicians-in-training deal with uncertainty in the discussion of clinical cases in Mexico. Methodologically, it is approached from the field of clinical ethnography and the narrative interpretation of plots in performative actions where there are sequences of communicative exchanges. In this way, it focuses on a detailed description of situations where clinical cases are reviewed to decipher, explain, and understand intersubjective meanings in the face of the emergence of uncertainty, its management, and the implications on decisions and actions. The study finds that limitations within the field of psychiatry lie in the nosographic construction of disease and its translation into the diagnostic hypotheses made by clinicians, where there are wide margins of ambiguity. The strategies implemented in the face of uncertainty are use of drugs, the collegiate review of the case, and utilization of intuition as a spontaneous, preconscious daily practice. The specific case described here provides a microscopic observation of the complex scenarios in which uncertainty occurs in educational and teaching processes, clearly revealing how patient care is articulated. The narratives and their interpretation are materials for training/curriculum and psychiatric clinical practice.

Sleep quality impacts the link between reactivity to uncertain threat and anxiety and alcohol use in youth

Individual differences in reactivity to unpredictable threat (U-threat) have repeatedly been linked to symptoms of anxiety and drinking behavior. An emerging theory is that individuals who are hyper-reactive to U-threat experience chronic anticipatory anxiety, hyperarousal, and are vulnerable to excessive alcohol use via negative reinforcement processes. Notably, anxiety and alcohol use commonly relate to disruptions in sleep behavior and recent findings suggest that sleep quality may impact the link between reactivity to U-threat and psychiatric symptoms and behaviors. The aim of the current study was to examine the unique and interactive effects of reactivity to U-threat and sleep quality on anxiety symptoms and drinking behavior in a cohort of youth, ages 16-19 years. Participants (N = 112) completed a well-validated threat-of-shock task designed to probe individual differences in reactivity to U-threat and predictable threat (P-threat). Startle eyeblink potentiation was recorded during the task as an index of aversive reactivity. Participants also completed well-validated self-report measures of anxiety and depression symptoms, lifetime alcohol use, and current sleep quality. Results revealed significant startle reactivity to U-threat by sleep quality interactions on anxiety symptoms and lifetime drinking behavior. At high levels of sleep disturbance (only), greater reactivity to U-threat was associated with greater anxiety symptoms and total number of lifetime alcoholic beverages. These results suggest that sensitivity to uncertainty and chronic hyperarousal increases anxiety symptoms and alcohol use behavior, particularly in the context of poor sleep quality.

Human biology and the study of precarity: How the intersection of uncertainty and inequality is taking us to new extremes

Inequality represents an extreme environment to which humans must respond. One phenomenon that contributes to this growing extreme is precarity or the intersection of uncertainty and some form of inequality. While precarity has an important intellectual history in the fields of sociology and sociocultural anthropology, it has not been well studied in the field of human biology. Rather human biologists have engaged with the study of closely related concepts such as uncertainty and resource insecurity. In this article, we propose that human biology take on the study of precarity as a novel way of investigating inequality. We first provide a brief intellectual history of precarity which is followed by a review of research on uncertainty and resource security in human biology which, while not exhaustive, illustrates some key gaps that precarity may aid us in addressing. We then review some of the pathways through which precarity comes to affect human biology and health and some of the evidence for why the unpredictable nature of precarity may make it a unique physiological stress. A case study based on research in Nuñoa, Peru provides an important example of how precarity can elucidate the influences of health in an extreme setting, albeit with insights that apply more broadly. We conclude that precarity holds important potential for the study of human biology, including helping us more effectively operationalize and study uncertainty, encouraging us to explore the predictability of resources and stressors, and reminding us to think about the intersectional nature of stressors.

The assessment of the clinical impact of using a single set of radiotherapy planning data for two kilovoltage therapy units

Kilovoltage therapy units are used for superficial radiotherapy treatment delivery. Peer reviewed studies for MV linear accelerators describe tolerances to dosimetrically match multiple linear accelerators enabling patient treatment on any matched machine. There is an absence of literature on using a single planning data set for multiple kilovoltage units which have limited ability for beam adjustment. This study reviewed kilovoltage dosimetry and treatment planning scenarios to evaluate the feasibility of using ACPSEM annual QA tolerances to determine whether two units (of the same make and model) were dosimetrically matched. The dosimetric characteristics, such as measured half value layer (HVL), percentage depth dose (PDD), applicator factor and output variation with stand-off distance for each kV unit were compared to assess the agreement. Independent planning data based on the measured HVL for each beam energy from each kV unit was prepared. Monitor unit (MU) calculations were performed using both sets of planning data for approximately 200 clinical scenarios and compared with an overall agreement between units of < 2%. Additionally, a dosimetry measurement comparison was completed at each site for a subset of nine scenarios. All machine characterisation measurements were within the ACPSEM Annual QA tolerances, and dosimetric testing was within 2.5%. This work demonstrates that using a single set of planning data for two kilovoltage units is feasible, resulting in a clinical impact within published uncertainty.

A probabilistic successor representation for context-dependent learning

Two of the main impediments to learning complex tasks are that relationships between different stimuli, including rewards, can be uncertain and context-dependent. Reinforcement learning (RL) provides a framework for learning, by predicting total future reward directly (model-free RL), or via predictions of future states (model-based RL). Within this framework, "successor representation" (SR) predicts total future occupancy of all states. A recent theoretical proposal suggests that the hippocampus encodes the SR in order to facilitate prediction of future reward. However, this proposal does not take into account how learning should adapt under uncertainty and switches of context. Here, we introduce a theory of learning SRs using prediction errors which includes optimally balancing uncertainty in new observations versus existing knowledge. We then generalize that approach to a multicontext setting, allowing the model to learn and maintain multiple task-specific SRs and infer which one to use at any moment based on the accuracy of its predictions. Thus, the context used for predictions can be determined by both the contents of the states themselves and the distribution of transitions between them. This probabilistic SR model captures animal behavior in tasks which require contextual memory and generalization, and unifies previous SR theory with hippocampal-dependent contextual decision-making. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

What's in a sample? Epistemic uncertainty and metacognitive awareness in risk taking

In a fundamentally uncertain world, sound information processing is a prerequisite for effective behavior. Given that information processing is subject to inevitable cognitive imprecision, decision makers should adapt to this imprecision and to the resulting epistemic uncertainty when taking risks. We tested this metacognitive ability in two experiments in which participants estimated the expected value of different number distributions from sequential samples and then bet on their own estimation accuracy. Results show that estimates were imprecise, and this imprecision increased with higher distributional standard deviations. Importantly, participants adapted their risk-taking behavior to this imprecision and hence deviated from the predictions of Bayesian models of uncertainty that assume perfect integration of information. To explain these results, we developed a computational model that combines Bayesian updating with a metacognitive awareness of cognitive imprecision in the integration of information. Modeling results were robust to the inclusion of an empirical measure of participants' perceived variability. In sum, we show that cognitive imprecision is crucial to understanding risk taking in decisions from experience. The results further demonstrate the importance of metacognitive awareness as a cognitive building block for adaptive behavior under (partial) uncertainty.

Impacts of improved horizontal resolutions in the simulations of mean and extreme precipitation using CMIP6 HighResMIP models over West Africa

We conducted an analysis of 16 historical simulations from the High-Resolution Model Intercomparison Project (HighResMIP) as part of the Coupled Model Intercomparison Project (CMIP) phase 6 (CMIP6). These simulations encompass both high- and low-resolution models and aim to investigate the impact of improved horizontal resolution on mean and extreme precipitation in West Africa between 1985 and 2014. Six Expert Team on Climate Change Detection and Indices (ETCCDI) were used to charactererize extreme indices. Bias adjustment was used to detect and adjust the biases in the models. Our observations indicate that the southeastern and southwestern regions of West Africa experience the most significant precipitation, which aligns with the simulations from HighResMIP. The enhanced horizontal resolution notably influences the simulation of orographically induced rainfall in elevated areas and intensifies precipitation in various aspects. When examining the highest 1-day precipitation, our observations reveal that most of the Guinea Coast region had 1-day rainfall exceeding 100 mm. However, this was overestimated and in some simulations underestimated by HighResMIP simulations. Furthermore, an increase in horizontal resolution appears to enhance the ability of high-resolution models to replicate the observed patterns of heavy precipitation (R10mm) and very heavy rainfall (R20mm) days. Spatial and temporal analysis suggests that uncertainty exists in the simulation of extreme precipitation in both high- and low-resolution simulations over West Africa. Also, bias adjustment shows a significant bias in the simulations. To address this issue, we employed a bias adjustment approach.

Training With Uncertain Annotations for Semantic Segmentation of Basal Cell Carcinoma From Full-Field OCT Images

Semantic segmentation of basal cell carcinoma (BCC) from full-field optical coherence tomography (FF-OCT) images of human skin has received considerable attention in medical imaging. However, it is challenging for dermatopathologists to annotate the training data due to OCT's lack of color specificity. Very often, they are uncertain about the correctness of the annotations they made. In practice, annotations fraught with uncertainty profoundly impact the effectiveness of model training and hence the performance of BCC segmentation. To address this issue, we propose an approach to model training with uncertain annotations. The proposed approach includes a data selection strategy to mitigate the uncertainty of training data, a class expansion to consider sebaceous gland and hair follicle as additional classes to enhance the performance of BCC segmentation, and a self-supervised pre-training procedure to improve the initial weights of the segmentation model parameters. Furthermore, we develop three post-processing techniques to reduce the impact of speckle noise and image discontinuities on BCC segmentation. The mean Dice score of BCC of our model reaches 0.503±0.003, which, to the best of our knowledge, is the best performance to date for semantic segmentation of BCC from FF-OCT images.

Unraveling the impact of digital transformation on green innovation through microdata and machine learning

How to use digitalization to support the green transformation of organizations has drawn much attention based on the rapid development of digitalization. However, digital transformation (DT) may be hindered by the "IT productivity paradox." Exploring the influence of DT on green innovation, we analyze panel data encompassing A-share listed companies in Shanghai and Shenzhen spanning the period from 2010 to 2018. It tests the DT's non-linear impact, employing a random-forest and mediation effect models. The results reveal that (i) DT can promote green innovation; (ii) regarding heterogeneity, the promotion effect is mainly manifested in enterprises in non-state-owned and highly competitive industries; (iii) based on mechanism testing, DT relies on two routes to encourage green innovation: improving environmental information disclosure and reducing environmental uncertainty; and (iv) random-forest analysis shows that DT exhibits an inverted U-shaped non-linear effect on green innovation, including the "IT productivity paradox." This study enhances the existing discourse on DT and green innovation by furnishing empirical substantiation for the non-linear influence exerted by DT on green innovation. Furthermore, it imparts insights into the mechanisms and contextual limitations governing this association.

Self-help interventions for persistent physical symptoms: a systematic review of behaviour change components and their potential effects

Persistent physical symptoms (PPS) remain a challenge in the healthcare system due to time-constrained consultations, uncertainty and limited specialised care capacity. Self-help interventions may be a cost-effective way to widen the access to treatment. As a foundation for future interventions, we aimed to describe intervention components and their potential effects in self-help interventions for PPS. A systematic literature search was made in PubMed, EMBASE, PsycINFO and CENTRAL. Fifty-one randomised controlled trials were included. Interventions were coded for effect on outcomes (standardised mean difference ≥0.2) related to symptom burden, anxiety, depression, quality of life, healthcare utilisation and sickness absence. The Behaviour Change Technique (BCT) Taxonomy v1 was used to code intervention components. An index of potential was calculated for each BCT within an outcome category. Each BCT was assessed as 'potentially effective' or 'not effective' based on a two-sided test for binomial random variables. Sixteen BCTs showed potential effect as treatment components. These BCTs represented the themes: goals and planning, feedback and monitoring, shaping knowledge, natural consequences, comparison of behaviour, associations, repetition and substitution, regulation, antecedents and identity. The results suggest that specific BCTs should be included in new PPS self-help interventions aiming to improve the patients' physical and mental health.

Life Is Uncertain: Inherent Variability Exhibited by Organisms, and at Higher Levels of Biological Organization

Organisms act stochastically. A not uncommon view in the ecological literature is that this is mainly due to the observer having insufficient information or a stochastic environment-and not partly because organisms themselves respond with inherent unpredictability. In this study, I compile the evidence that contradicts that view. Organisms generate uncertainty internally, which results in irreducible stochastic responses. I consider why: for instance, stochastic responses are associated with greater adaptability to changing environments and resource availability. Over longer timescales, biologically generated uncertainty influences behavior, evolution, and macroecological processes. Indeed, it could be stated that organisms are systems defined by the internal generation, magnification, and record-keeping of uncertainty as inputs to responses. Important practical implications arise if organisms can indeed be defined by an association with specific classes of inherent uncertainty: not least that isolating those signatures then provides a potential means for detecting life, for considering the forms that life could theoretically take, and for exploring the wider limits to how life might become distributed. These are all fundamental goals in astrobiology.

Examining Perceptions of Uncertain Language in Potential E-Cigarette Warning Labels: Results from 16 Focus Groups with Adult Tobacco Users and Youth

E-cigarette use among youth presents a public health risk. Yet, cigarette smokers who substantially reduce their smoking or switch completely from traditional combustible cigarettes could benefit. As science about e-cigarettes is continually emerging, any potential warnings are likely to contain uncertain language. Hedged verbiage may impact decision making. To assess reactions, we conducted 16 online focus groups; 8 with youth (n = 32, grouped by gender and by vaping experience) and 8 with adult tobacco users (n = 37, grouped by smokers, dual users of e-cigarettes and cigarettes, and former smokers who switched to e-cigarettes). Each focus group viewed and discussed 8 potential warnings messages. We conducted an inductive thematic analysis of the reactions to warning messages that contain uncertain language. Respondents' reactions were often negative, but varied based on specific usages of uncertainty, existing beliefs about uncertainty in law and science, and smoking/vaping use patterns that supported the use of uncertainty related to e-cigarettes. Many youth (and some adults) believed that uncertain language enabled audiences to minimize the likelihood of harm or interpreted it as meaning there are both healthy and unhealthy e-cigarettes. This qualitative study provides evidence that the use of types of uncertain language, the frequency of that use, and/or the selection of particular words in warnings, might not achieve the intended public health aims of increasing understanding of risk, deterring youth uptake, and/or facilitating a substantial switch from cigarettes. The use of certain types of uncertain language appears to have significant potential to bring unintended consequences. Suggestions for research and policy are included.

Climate uncertainty and vulnerability of urban flooding associated with regional risk using multi-criteria analysis in Mumbai, India

The study made a comprehensive effort to examine climatic uncertainties at both yearly and monthly scales, along with mapping flood risks based on different land use categories. Recent studies have progressively been engrossed in demonstrating regional climate variations and associated flood probability to maintain the geo-ecological balance at micro to macro-regions. To carry out this investigation, various historical remote sensing record, reanalyzed and in-situ data sets were acquired with a high level of spatial precision using the Google Earth Engine (GEE) web-based remote sensing platform. Non-parametric techniques and multi-layer integration methods were then employed to illustrate the fluctuations in climate factors alongside creating maps indicating the susceptibility to floods. The study reveals an increased pattern in LST (Land Surface Temperature) (0.03 °C/year), albeit marginal declined in southern coastal regions (-0.15 °C/year) along with uneven rainfall patterns (1.42 mm/year). Moreover, long-term LULC change estimation divulges increased trends of urbanization (16.4 km2/year) together with vegetation growth (8.7 km2/year) from 2002 to 2022. Furthermore, this inquiry involves numerous environmental factors that influence the situation (elevation data, topographic wetness index, drainage density, proximity to water bodies, slope, and soil properties) as well as socio-economic attributes (population) to assess flood risk areas through the utilization of Analytical Hierarchy Process and overlay methods with assigned weights. The outcomes reveal nearly 55 percent of urban land is susceptible to flood in 2022, which were 45 and 37 percent in 2012 and 2002 separately. Additionally, 106 km2 of urban area is highly susceptible to inundation, whereas vegetation also occupies a significant proportion (52 km2). This thorough exploration offers a significant chance to formulate flood management and mitigation strategies tailored to specific regions during the era of climate change.

US FDA's Dose Optimization Postmarketing Requirements and Commitments of Oncology Approvals and the Impact on Product Labels from 2010 to 2022: An Emerging Landscape from Traditional to Novel Therapies

BACKGROUND

Dose optimization is a focal point of many US Food and Drug Administration (FDA) drug approvals. We sought to understand the impact of the FDA's Postmarketing Commitments/Postmarketing Requirements (PMCs/PMRs) on dose optimization and prescriber labeling for oncology drugs.

METHODS

Publicly available information was aggregated for all FDA oncology drug approvals between January 1, 2010, and December 31, 2022. Study completion dates were compared to product labeling before and after PMC/PMR fulfillment dates to evaluate labeling changes associated with dose-related PMCs/PMRs. Data were analyzed individually (2010-2015 and 2016-2022) due to differences in available information.

RESULTS

From 2010 to 2015, 14 of 42 (33.3%) new molecular entities (NMEs) had dose-related PMCs/PMRs, with 6 of 14 (42.9%) resulting in a relevant label change. From 2016 to 2022, of the 314 new or supplemental applications approved, 21 had dose-related PMCs/PMRs (6.7%), which trended upward over time; 71.4% of dose-related PMCs/PMRs were NMEs. Kinase inhibitors (KIs) and antibody/peptide drug conjugates (ADCs/PDCs) were the most affected drug classes. Ten of the 21 approvals with dose-related PMCs/PMRs fulfilled their dosing PMCs/PMRs, and 3 of the 10 (30%) had relevant label changes.

CONCLUSION

Most dose-related PMRs/PMCs were issued for NMEs. Of these, KIs and ADCs/PDCs were highly represented, reflecting their novelty and greater uncertainty around their safety profile. PMC/PMR issuance broadly increased over time. With the implementation of the FDA's Project Optimus in 2021, it remains to be seen whether fewer dose-related PMCs/PMRs emerge in future due to enhanced dose optimization in the premarketing setting.

Are you sure it's an artifact? Artifact detection and uncertainty quantification in histological images

Modern cancer diagnostics involves extracting tissue specimens from suspicious areas and conducting histotechnical procedures to prepare a digitized glass slide, called Whole Slide Image (WSI), for further examination. These procedures frequently introduce different types of artifacts in the obtained WSI, and histological artifacts might influence Computational Pathology (CPATH) systems further down to a diagnostic pipeline if not excluded or handled. Deep Convolutional Neural Networks (DCNNs) have achieved promising results for the detection of some WSI artifacts, however, they do not incorporate uncertainty in their predictions. This paper proposes an uncertainty-aware Deep Kernel Learning (DKL) model to detect blurry areas and folded tissues, two types of artifacts that can appear in WSIs. The proposed probabilistic model combines a CNN feature extractor and a sparse Gaussian Processes (GPs) classifier, which improves the performance of current state-of-the-art artifact detection DCNNs and provides uncertainty estimates. We achieved 0.996 and 0.938 F1 scores for blur and folded tissue detection on unseen data, respectively. In extensive experiments, we validated the DKL model on unseen data from external independent cohorts with different staining and tissue types, where it outperformed DCNNs. Interestingly, the DKL model is more confident in the correct predictions and less in the wrong ones. The proposed DKL model can be integrated into the preprocessing pipeline of CPATH systems to provide reliable predictions and possibly serve as a quality control tool.