Assessing the credibility of a drug's effects: identification and judgment of uncertainty by the Dutch Medicines Evaluation Board.

Medicine regulators need to judge whether a drug's favorable effects outweigh its unfavorable effects based on a dossier submitted by an applicant, such as a pharmaceutical company. Because scientific knowledge is inherently uncertain, regulators also need to judge the credibility of these effects by identifying and evaluating uncertainties. We performed an ethnographic study of assessment procedures at the Dutch Medicines Evaluation Board (MEB) and describe how regulators evaluate the credibility of an applicant's claims about the benefits and risks of a drug in practice. Our analysis shows that regulators use an investigative approach, which illustrates the effort required to identify uncertainties. Moreover, we show that regulators' expectations about the presentation, the design, and the results of studies can shape how they perceive a medicine's dossier. We highlight the importance of regulatory experience and expertise in the identification and evaluation of uncertainties. In light of our observations, we provide two recommendations to reduce avoidable uncertainty: less reliance on evidence generated by the applicant; and better communication about, and enforcement of, regulatory frameworks toward drug developers.

Subjective logic as a complementary tool to meta-analysis to explicitly address second-order uncertainty in research findings: A case from infant studies.

Any experiment brings about results and conclusions that necessarily have a component of uncertainty. Many factors influence the degree of this uncertainty, yet they can be overlooked when drawing conclusions from a body of research. Here, we showcase how subjective logic could be employed as a complementary tool to meta-analysis to incorporate the chosen sources of uncertainty into the answer that researchers seek to provide to their research question. We illustrate this approach by focusing on a body of research already meta-analyzed, whose overall aim was to assess if human infants prefer prosocial agents over antisocial agents. We show how each finding can be encoded as a subjective opinion, and how findings can be aggregated to produce an answer that explicitly incorporates uncertainty. We argue that a core feature and strength of this approach is its transparency in the process of factoring in uncertainty and reasoning about research findings. Subjective logic promises to be a powerful complementary tool to incorporate uncertainty explicitly and transparently in the evaluation of research.

Modality redundancy for MRI-based glioblastoma segmentation.

PURPOSE: Automated glioblastoma segmentation from magnetic resonance imaging is generally performed on a four-modality input, including T1, contrast T1, T2 and FLAIR. We hypothesize that information redundancy is present within these image combinations, which can possibly reduce a model's performance. Moreover, for clinical applications, the risk of encountering missing data rises as the number of required input modalities increases. Therefore, this study aimed to explore the relevance and influence of the different modalities used for MRI-based glioblastoma segmentation. METHODS: After the training of multiple segmentation models based on nnU-Net and SwinUNETR architectures, differing only in their amount and combinations of input modalities, each model was evaluated with regard to segmentation accuracy and epistemic uncertainty. RESULTS: Results show that T1CE-based segmentation (for enhanced tumor and tumor core) and T1CE-FLAIR-based segmentation (for whole tumor and overall segmentation) can reach segmentation accuracies comparable to the full-input version. Notably, the highest segmentation accuracy for nnU-Net was found for a three-input configuration of T1CE-FLAIR-T1, suggesting the confounding effect of redundant input modalities. The SwinUNETR architecture appears to suffer less from this, where said three-input and the full-input model yielded statistically equal results. CONCLUSION: The T1CE-FLAIR-based model can therefore be considered as a minimal-input alternative to the full-input configuration. Addition of modalities beyond this does not statistically improve and can even deteriorate accuracy, but does lower the segmentation uncertainty.

Seasonal solar irradiance forecasting using artificial intelligence techniques with uncertainty analysis.

Renewable integration in utility grid is crucial in the current energy scenario. Optimized utilization of renewable energy can minimize the energy consumption from the grid. This demands accurate forecasting of renewable contribution and planning. Most of the researches aim to find a suitable forecasting model in terms of accuracy and error metrics. However, the uncertainty and variability in these forecasts are also significant. This work combines point forecast with interval forecast to provide comprehensive information about the forecast uncertainty. In this work, solar irradiance forecasting is carried out using artificial intelligence (AI) techniques. Forecasting is done using seasonal auto-regressive moving average with exogenous factors (SARIMAX), support vector regression (SVR), long short term memory (LSTM) techniques and performance is evaluated. SVR model exhibited the best performance with R 2 values of 0.97 and 0.96 for winter and summer respectively and 0.85 for monsoon and post-monsoon seasons. This is followed by forecast error distribution studies and uncertainty analysis. For this, SVR forecast error data is fitted using laplace distribution. Uncertainty study is carried out using confidence intervals and coverage rates. Excellent coverage rates are obtained for various confidence levels for all seasons, indicating the appropriate fitting of error distribution. For the narrow 85% confidence band, coverage rates of 89%, 95%, 90%, and 88% are obtained for winter, summer, monsoon and post-monsoon respectively. The work emphasizes the need for error-distribution studies, modeling of forecast errors and their application in providing reliable forecast intervals with the perspective of enhancing system reliability.

Accelerating denoising diffusion probabilistic model via truncated inverse processes for medical image segmentation.

Medical image segmentation demands precise accuracy and the capability to assess segmentation uncertainty for informed clinical decision-making. Denoising Diffusion Probability Models (DDPMs), with their advancements in image generation, can treat segmentation as a conditional generation task, providing accurate segmentation and uncertainty estimation. However, current DDPMs used in medical image segmentation suffer from low inference efficiency and prediction errors caused by excessive noise at the end of the forward process. To address this issue, we propose an accelerated denoising diffusion probabilistic model via truncated inverse processes (ADDPM) that is specifically designed for medical image segmentation. The inverse process of ADDPM starts from a non-Gaussian distribution and terminates early once a prediction with relatively low noise is obtained after multiple iterations of denoising. We employ a separate powerful segmentation network to obtain pre-segmentation and construct the non-Gaussian distribution of the segmentation based on the forward diffusion rule. By further adopting a separate denoising network, the final segmentation can be obtained with just one denoising step from the predictions with low noise. ADDPM greatly reduces the number of denoising steps to approximately one-tenth of that in vanilla DDPMs. Our experiments on four segmentation tasks demonstrate that ADDPM outperforms both vanilla DDPMs and existing representative accelerating DDPMs methods. Moreover, ADDPM can be easily integrated with existing advanced segmentation models to improve segmentation performance and provide uncertainty estimation. Implementation code: https://github.com/Guoxt/ADDPM.

Challenges with segmenting intraoperative ultrasound for brain tumours.

Objective - Addressing the challenges that come with identifying and delineating brain tumours in intraoperative ultrasound. Our goal is to both qualitatively and quantitatively assess the interobserver variation, amongst experienced neuro-oncological intraoperative ultrasound users (neurosurgeons and neuroradiologists), in detecting and segmenting brain tumours on ultrasound. We then propose that, due to the inherent challenges of this task, annotation by localisation of the entire tumour mass with a bounding box could serve as an ancillary solution to segmentation for clinical training, encompassing margin uncertainty and the curation of large datasets. Methods - 30 ultrasound images of brain lesions in 30 patients were annotated by 4 annotators - 1 neuroradiologist and 3 neurosurgeons. The annotation variation of the 3 neurosurgeons was first measured, and then the annotations of each neurosurgeon were individually compared to the neuroradiologist's, which served as a reference standard as their segmentations were further refined by cross-reference to the preoperative magnetic resonance imaging (MRI). The following statistical metrics were used: Intersection Over Union (IoU), Sørensen-Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD). These annotations were then converted into bounding boxes for the same evaluation. Results - There was a moderate level of interobserver variance between the neurosurgeons [ I o U : 0.789 , D S C : 0.876 , H D : 103.227 ] and a larger level of variance when compared against the MRI-informed reference standard annotations by the neuroradiologist, mean across annotators [ I o U : 0.723 , D S C : 0.813 , H D : 115.675 ] . After converting the segments to bounding boxes, all metrics improve, most significantly, the interquartile range drops by [ I o U : 37 % , D S C : 41 % , H D : 54 % ] . Conclusion - This study highlights the current challenges with detecting and defining tumour boundaries in neuro-oncological intraoperative brain ultrasound. We then show that bounding box annotation could serve as a useful complementary approach for both clinical and technical reasons.

Decision-making styles, magical thinking, and intolerance of uncertainty in opioid use disorder.

Background: The global opioid use problem presents a complex public health challenge characterized by increasing overdoses, addiction rates, and fatalities. Personal factors such as cognitive traits, distress tolerance, and decision-making styles play a crucial role in influencing opioid use trajectories. Aim: This study aimed to investigate decision-making styles, magical ideation, and intolerance of uncertainty among current and past opioid users and healthy controls to contribute to the literature on opioid use disorder. Methods: Three groups were involved: current opioid users (n = 94), past opioid users (n = 93), and healthy controls (n = 100). Participants completed self-report scales assessing magical ideation, intolerance of uncertainty, and decision-making styles. Data were analyzed using descriptive statistics, analysis of variance, correlation analysis, and multiple linear regression. Results: Current opioid users exhibited lower vigilance decision-making styles and higher magical ideation scores than past users and controls. Decisional self-esteem correlated positively with vigilant decision-making in current and past opioid users. Magical ideation scores correlated positively with maladaptive decision-making styles across all groups. Intolerance of uncertainty did not differ significantly between groups. Conclusions: This study emphasizes that decision-making styles and magical thinking might have significant roles in opioid use disorder. These results contribute valuable insights to tailor interventions and support systems for individuals struggling with opioid use disorder.

Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer.

With the ever-increasing number of artificial intelligence (AI) systems, mitigating risks associated with their use has become one of the most urgent scientific and societal issues. To this end, the European Union passed the EU AI Act, proposing solution strategies that can be summarized under the umbrella term trustworthiness. In anti-cancer drug sensitivity prediction, machine learning (ML) methods are developed for application in medical decision support systems, which require an extraordinary level of trustworthiness. This review offers an overview of the ML landscape of methods for anti-cancer drug sensitivity prediction, including a brief introduction to the four major ML realms (supervised, unsupervised, semi-supervised, and reinforcement learning). In particular, we address the question to what extent trustworthiness-related properties, more specifically, interpretability and reliability, have been incorporated into anti-cancer drug sensitivity prediction methods over the previous decade. In total, we analyzed 36 papers with approaches for anti-cancer drug sensitivity prediction. Our results indicate that the need for reliability has hardly been addressed so far. Interpretability, on the other hand, has often been considered for model development. However, the concept is rather used intuitively, lacking clear definitions. Thus, we propose an easily extensible taxonomy for interpretability, unifying all prevalent connotations explicitly or implicitly used within the field.

Illness uncertainty, resilience, and perceived social support among patients with moyamoya disease: a cross-sectional study.

Objective: The present study aims to investigate the levels of illness uncertainty in patients with moyamoya disease and to determine the association of socio-demographic characteristics, perceived social support and resilience with illness uncertainty in patients with moyamoya disease. Method: A cross-sectional survey using convenience sampling was conducted in two hospitals in China from August to December 2023. A socio-demographic characteristics questionnaire, the Chinese versions of Mishel's Unsurety in Disease Scale (MUIS), the Chinese version of Connor-Davidson Resilience Scale (CD-RISC), and the Chinese version of Multidimensional Scale of Perceived Social Support (MSPSS) were used to perform this research. The collected data were analyzed using SPSS 24.0 statistical software. The t-test, one-way analysis of variance (ANOVA), pearson correlation analysis and hierarchical regression analysis were used to identify associated factors. Result: A total of 263 patients with moyamoya disease were recruited in this survey. The score of illness uncertainty was at a moderate level of (100.03 ± 18.59). The present study identified a negative correlation between illness uncertainty with resilience perceived social support. Hierarchical regression analysis showed that gender, occupation, education level, resilience and perceived social support were the related factors of illness uncertainty. Conclusion: Patients with moyamoya disease experienced moderate disease uncertainty on average, which was related to gender, occupation, education level, resilience and perceived social support. Future research is needed to better explore the complex relationships between illness uncertainty, resilience, and perceived social support with different types of moyamoya disease using longitudinal research.

Improve carbon emission efficiency: What role does the ESG initiatives play?

Driven by the "dual carbon" goal, it is essential to investigate whether companies can enhance carbon emission efficiency by improving Environmental, Social, and Governance (ESG) performance. This study investigates the relationship between ESG ratings and carbon emission efficiency among Chinese A-share listed companies. The study reveals that a higher ESG rating significantly improves carbon efficiency. Mechanism studies indicate that the effect of ESG mainly comes from easing financing constraints, promoting green innovation, and strengthening supervision. Additionally, the study finds that the impact of ESG on carbon emission efficiency is more pronounced in non-heavy polluting and non-state-owned enterprises. Economic policy uncertainty diminishes the positive effects of ESG initiatives on carbon efficiency, while enhanced governmental concerns to environmental significantly bolsters these impacts. This paper offers empirical insights that can inform adjustment of policies concerning ESG performance and carbon emission.

A flexible consistent framework for modelling multiple interacting environmental responses to management in space and time.

Management of resources is often a large-scale task addressed using many small-scale interventions. The range of scales at which organisms respond to those interventions, along with the many outcomes which management aims to achieve can make determining the success of management complex. Environmental flow is an example of management where there is a recognized need for managers to demonstrate the impact of their actions by integrating different types of environmental responses. Here, we aim to support decision making in environmental management via the development of a new modelling framework (eFlowEval). It has the capacity to capture best-available knowledge, to scale it in space and time, explore interactions among species, compare scenarios, and account for uncertainty. Thus, it provides a basis for including multiple target groups in a common system. The framework is readily updatable as new information becomes available and can identify where data are insufficient to be scientifically robust. We demonstrate the eFlowEval framework using three very different environmental responses: 1) metabolism, which is a measure of the energy produced and then used in an ecosystem, 2) favorability for a bird species of interest (royal spoonbill Platalea regia), and 3) competing wetland plants (Centipeda cunninghamii and lippia Phyla canescens). These demonstrations illustrate the capability of the eFlowEval framework but the specific outputs shown here should not be used to assess environmental responses to management. Using these demonstrations, we illustrate the capacity of the eFlowEval framework to provide assessments across a range of scales (local to landscape) and from short time frames (weeks to months) to multi-year assessments. Further, we illustrate the ability to: i) scale responses from local to basin scales, ii) vary driver-response model types, iii) represent uncertainty, iv) compare scenarios, v) accommodate variable parameter values at different locations, and vi) incorporate spatial and temporal dependencies and dependencies among species. We also illustrate the framework's ability to capture inter- and intraspecific interactions and their impact in space and time. The eFlowEval framework extends the capacity of the component response models to provide novel modeling capabilities for management at scale. It allows for interactions among species or processes to be incorporated, as well as in space and time. A large degree of flexibility is offered by the framework, in terms of driver-response model types, input data, and aggregation methods. Thus, the eFlowEval framework provides a mechanism to enhance the transparency of environmental watering decision making, capture institutional knowledge, enhance adaptive management and undertake evaluation of the impact of environmental watering at a range of spatial and temporal scales.

Nature-friendly hands: The relationship between nursing students' climate change anxiety, intolerance of uncertainty, and anxiety about the future.

AIM/OBJECTIVE: This study examined the relationship between climate change anxiety, intolerance of uncertainty, and future anxiety levels of nursing students. BACKGROUND: The effects of climate change, an important global problem, on people's emotional and intellectual states are becoming increasingly important. It is important to understand to what extent prospective health professionals, such as nursing students, are affected by such environmental concerns and the possible impact of this level on their professional behaviors to develop an environmentally focused approach to health services. DESIGN: This study was conducted using a descriptive and correlational design. METHODS: Students enrolled in the Nursing Undergraduate Program of a university in Turkey in the 2023-2024 academic year participated in the study. The participants were administered a personal information form, climate change anxiety scale, intolerance of uncertainty scale, and future anxiety scale in university students. The data were evaluated using advanced statistical analyses, and relationships were examined. RESULTS: As a result of these analyses, it was determined that there was a significant relationship between future anxiety and climate change anxiety in university students (R = 0.234, p = .000). In addition, there was a substantial relationship between climate change anxiety and intolerance of uncertainty (R = 0.562, p = .000). CONCLUSIONS: These findings indicate significant and linear relationships between nursing students' emotional and cognitive states associated with environmental factors such as climate change, uncertainty, and future anxiety. Developing support and interventions in nursing education is crucial to help students cope with these issues and function more effectively in their future professional lives.

Improving monitoring network design to detect leaks at hazardous facilities: Lessons from a CO2 storage site.

Exploring the challenges posed by uncertainties in numerical modeling for hazardous material storage, this study introduces methodologies to improve monitoring networks for detecting subsurface leakages. The proposed approaches were applied to the Korea CO2 Storage Environmental Management (K-COSEM) test site, undergoing calibration, validation and uncertainty analysis through hydraulic and controlled-CO2 release tests. The calibration phase involved inter-well tracer and multi-well pumping tests, leveraging the Parameter ESTimation (PEST) model to determine the aquifer flow and solute transport properties of the K-COSEM site. To tackle uncertainties with limited observation data, we adopted Latin Hypercube simulation. Our uncertainty analysis confirmed model accuracy in simulating observed CO2 breakthrough curves. We also explored a probabilistic method to identify the environmental change point (EnCP) through correlation analysis with the distance from the CO2 injection well, revealing a linear trend and pinpointed potential preferential flow pathways by assessing detection probabilities. Evaluating CO2 detection capabilities was crucial for optimizing monitoring well placement, highlighting strategic well selection based on detection probabilities. This study advances managing uncertainties in hydrogeological modeling, underscoring the importance of sophisticated models in designing monitoring networks for hazardous leak detection in complex subsurface conditions.

A protocol for assessing bias and robustness of social network metrics using GPS based radio-telemetry data.

BACKGROUND: Social network analysis of animal societies allows scientists to test hypotheses about social evolution, behaviour, and dynamic processes. However, the accuracy of estimated metrics depends on data characteristics like sample proportion, sample size, and frequency. A protocol is needed to assess for bias and robustness of social network metrics estimated for the animal populations especially when a limited number of individuals are monitored. METHODS: We used GPS telemetry datasets of five ungulate species to combine known social network approaches with novel ones into a comprehensive five-step protocol. To quantify the bias and uncertainty in the network metrics obtained from a partial population, we presented novel statistical methods which are particularly suited for autocorrelated data, such as telemetry relocations. The protocol was validated using a sixth species, the fallow deer, with a known population size where ∼ 85 % of the individuals have been directly monitored. RESULTS: Through the protocol, we demonstrated how pre-network data permutations allow researchers to assess non-random aspects of interactions within a population. The protocol assesses bias in global network metrics, obtains confidence intervals, and quantifies uncertainty of global and node-level network metrics based on the number of nodes in the network. We found that global network metrics like density remained robust even with a lowered sample size, while local network metrics like eigenvector centrality were unreliable for four of the species. The fallow deer network showed low uncertainty and bias even at lower sampling proportions, indicating the importance of a thoroughly sampled population while demonstrating the accuracy of our evaluation methods for smaller samples. CONCLUSIONS: The protocol allows researchers to analyse GPS-based radio-telemetry or other data to determine the reliability of social network metrics. The estimates enable the statistical comparison of networks under different conditions, such as analysing daily and seasonal changes in the density of a network. The methods can also guide methodological decisions in animal social network research, such as sampling design and allow more accurate ecological inferences from the available data. The R package aniSNA enables researchers to implement this workflow on their dataset, generating reliable inferences and guiding methodological decisions.

Level of decision confidence shapes motor memory.

Decision making is often necessary before performing an action. Traditionally, it has been assumed that decision making and motor control are independent, sequential processes. Ogasa et al. challenge this view, and demonstrate that the decision-making process significantly impacts on the formation and retrieval of motor memory by tagging it with the level of confidence.

Mitigate cross-market competition caused by the risk of uncertainty and improve firm performance through business intelligence.

In the face of various risks and uncertainties including political instability, technological advancements, and natural disasters, businesses involved in cross-market activities are encountering a more competitive environment. This study investigates the relationship between competitive intensity, business intelligence, internal process efficiency, and the performance of small and medium-sized manufacturing enterprises in China. By incorporating the Technology-Organization-Environment (TOE) framework and Dynamic Capabilities Theory, a research model is developed to demonstrate that competitive intensity improves firms' performance through the utilization of business intelligence capabilities and internal process efficiency. Through the use of a structural equation model, data collected from 15 industrial parks in Henan province, China, involving 429 participants, was analyzed. The findings show a positive correlation between competitive intensity and business intelligence sensing capability (both internal and external). The impact of business intelligence sensing capability on the performance of small and medium-sized manufacturing enterprises is shown to be mediated through internal process efficiency. Our study reveals the mediating roles of business intelligence capability and internal process efficiency in improving organizational performance among Chinese small and medium-sized manufacturing enterprises. This research not only fills gaps in business intelligence research from a management perspective but also contributes to the literature on the interactions among competitive intensity, business intelligence, internal processes, and organizational performance. It also sheds light on the relationship between competitive intensity and organizational performance, offering insights for companies involved in cross-market activities. By highlighting the importance of business intelligence capabilities in adapting to environmental influences, this study offers practical guidance for enterprise digital transformation efforts.

Vision Measurement System for Gender-Based Counting of Acheta domesticus.

The exploitation of insects as protein sources in the food industry has had a strong impact in recent decades for many reasons. The emphasis for this phenomenon has its primary basis on sustainability and also to the nutritional value provided. The gender of the insects, specifically Acheta domesticus, is strictly related to their nutritional value and therefore the availability of an automatic system capable of counting the number of Acheta in an insect farm based on their gender will have a strong impact on the sustainability of the farm itself. This paper presents a non-contact measurement system designed for gender counting and recognition in Acheta domesticus farms. A specific test bench was designed and realized to force the crickets to travel inside a transparent duct, across which they were framed by means of a high-resolution camera able to capture the ovipositor, the distinction element between male and female. All possible sources of uncertainty affecting the identification and counting of individuals were considered, and methods to mitigate their effect were described. The proposed method, which achieves 2.6 percent error in counting and 8.6 percent error in gender estimation, can be of significant impact in the sustainable food industry.

Time-frequency co-movements between commodities and global economic policy uncertainty across different crises.

Commodity futures constitute an attractive asset class for portfolio managers. Propelled by their low correlation with other assets, commodities begin gaining popularity among investors, as they allow to capture diversification benefits. This comprehensive study examines the time and frequency spillovers between the Economic Policy Uncertainty [1] and a broad set of commodities encompassing ferrous, non-ferrous, and precious metals, food, and energy commodities over a period from December 1997 to April 2022, which includes various political, economic and health crises. The novelty of this research lies in its extensive temporal and categorical coverage, providing an understanding of how different types of commodities respond to various crises. Furthermore, our study breaks new ground by employing wavelet analysis to gain detailed insights in both time and frequency domains in the financial time series of interest, providing a deeper understanding of the co-movements and lead-lag relationships. Specifically, we introduce the Cross Wavelet Transform (XWT) and Wavelet Coherence (WTC) analysis. Our findings demonstrate that not all crises uniformly impact commodities. Notably, during the global financial crisis and the COVID-19 pandemic, co-movements between commodities became significantly stronger. These results highlight the heterogeneity within the commodity asset class, where individual commodities exhibit diverse underlying dynamics. Importantly, the proposed methodology facilitates the extraction of robust results even when dealing with nonlinearities and nonstationary time series data. Consequently, our work offers valuable insights for policymakers (including regulatory bodies), investors, and fund managers.

Assessing the information-content of messy data to reconstruct population recovery dynamics for the world's rarest primate.

Understanding the dynamics of population recovery in threatened species requires robust longitudinal monitoring datasets. However, evidence-based decision-making is often impeded by variable data collection approaches, necessitating critical evaluation of restricted available baselines. The Hainan gibbon, the world's rarest primate, had possibly declined to only seven or eight individuals in 1978 at Bawangling National Nature Reserve but has experienced subsequent population growth. Past population estimates lack detailed reporting of survey effort, and multiple conflicting estimates are available, hindering assessment of gibbon recovery. We investigated all reported estimates of Bawangling gibbon population size from 1978 to 2022, to evaluate the biological signal of population trends and the extent to which noise associated with varying survey effort, reporting and estimation may mask or misrepresent any underlying signal. This longitudinal dataset demonstrates that the Bawangling population experienced a series of bottlenecks and recoveries, with three successive periods of growth interspersed by population crashes (1978-1989, 1989-2000 and 2000-2022). The rate of gibbon population recovery was progressively slower over time in each successive period of growth, and this potential decline in recovery rate following serial bottlenecks suggests that additional management strategies may be required alongside "nature-based solutions" for this species. However, population viability analysis suggests the 1978 founder population is unlikely to have been as low as seven individuals, raising concerns for interpreting reported historical population counts and understanding the dynamics of the species' recovery. We caution against overinterpreting potential signals within "messy" conservation datasets, and we emphasise the crucial importance of standardised replicable survey methods and transparent reporting of data and effort in all future surveys of Hainan gibbons and other highly threatened species.

The association between intolerance of uncertainty and academic burnout among university students: the role of self-regulatory fatigue and self-compassion.

Introduction: Increased uncertainty is a major feature of the current society that poses significant challenges to university students' mental health and academics. However, current research has not paid sufficient attention to this issue, and no study has explored the underlying mechanisms between intolerance of uncertainty and academic burnout among university students. Methods: This study examined the association between uncertainty intolerance and academic burnout among university students and the role of self-regulatory fatigue and self-compassion in light of the theory of limited resources. Convenience sampling was used to survey 1,022 Chinese university students. Results: The findings demonstrated that intolerance of uncertainty significantly influenced university students' academic burnout with self-regulatory fatigue serving as a key mediator. Additionally, self-compassion can effectively moderate the effects of intolerance of uncertainty on self-regulatory fatigue and academic burnout. Discussion: These results indicated that the depletion of cognitive resources brought about by uncertainty in the current highly uncertain social environment may be one of the key pathways to academic burnout among university students. Furthermore, current research provides insights into how to mitigate the negative effects of uncertainty on university students.