Refining Mishel's Reconceptualized Uncertainty in Illness Theory: Explaining the Development of Uncertainty in Chronic Illness.

Background and Purpose: Mishel's Reconceptualized Uncertainty in Illness Theory describes the changed, more positive appraisal of uncertainty over time in a chronic disease. Therefore, Mishel referred to "probabilistic thinking" and "self-organization." The description of these concepts remained highly abstract, limiting the understanding of how change of uncertainty comes about. We aimed to elaborate on this gap and at refining the theory. Methods: We conducted a study consisting of three parts: (a) concept analyses of "probabilistic thinking" and "self-organization," (b) longitudinal qualitative study to investigate uncertainty experience over time, and (c) triangulation of (a) and (b) to develop theoretical propositions. Results: We developed five theoretical propositions in syllogistic form: (a) if persons experience uncertainty, they think probabilistically to assess the existentiality of potential consequences, (b) if they expect existential consequences, they experience uncertainty as a threat, (c) if the existentiality of uncertainty diminishes, then individuals accept uncertainty as an inherent part of illness, (d) if they accept uncertainty, they cognitively reframe it in a positive way in order to promote recovery, and (e) if persons reexperience uncertainty, they reassess the existentiality of potential consequences. Implications for Practice: We propose "health belief" as a mechanism driving "cognitive reframing" to explain the interrelation between uncertainty and a more positive experience. "Existential uncertainty" offers a new perspective on preventing a change in uncertainty experience. The new concepts can provide guidance to take measures to reduce existential uncertainty and promote health beliefs to change the experience of uncertainty from a negative to a more positive one.

The relationship between COVID-19 stress and test anxiety in art students: the chain mediating roles of intolerance of uncertainty and sleep quality.

BACKGROUND: The global spread of COVID-19 has brought immense physiological and psychological distress to students, such as test anxiety and poor sleep quality. This study aims to explore the relationship between COVID-19 stress and test anxiety and the mediating roles of intolerance of uncertainty and sleep quality between them. METHODS: A study was conducted in China during the late stage of the pandemic. A total of 936 Chinese art students (age M = 18.51, SD = 2.11, 46.6% female) completed the Coronavirus Stress Measure (CSM), the 12-item Intolerance of Uncertainty (IUS-12), the Brief Version of the Pittsburgh Sleep Quality Index (B-PSQI), and the Test Anxiety Inventory (TAI). A chain mediation model analysis was conducted to examine the mediating effects of intolerance of uncertainty and sleep quality on the association with COVID-19 stress and test anxiety. RESULTS: COVID-19 stress was positively associated with test anxiety (ß = 0.50, p < 0.001). The intolerance of uncertainty and sleep quality partially and serially mediated the relationship between COVID-19 stress and test anxiety (ß = 0.01, 95% CI = 0.01 to 0.02). CONCLUSION: These findings suggest that art students' intolerance of uncertainty and sleep quality partially and serially mediate the relation between COVID-19 stress and test anxiety. The results have significant implications for the intervention and prevention of test anxiety, providing additional evidence for the relationship between COVID-19 stress and test anxiety.

Do incidental positive emotions induce more optimistic expectations of decision outcomes? An empirical study from the perspective of event-related potential.

INTRODUCTION: Previous research has found that incidental emotions of different valences (positive/negative/neutral) influence risky decision-making. However, the mechanism of their influence on psychological expectations of decision outcomes remains unclear. METHODS: We explored the effects of different incidental emotions on the behavioral, psychological, and electrophysiological responses of individuals in risky decision-making through a money gambling task using a one-way (emotion type: positive, negative, neutral emotions) between-subjects experimental design. RESULTS: Individuals with positive emotions had significantly greater risk-seeking rates than those with negative emotions during the decision selection phase (p < .01). In the feedback stage of decision outcomes, individuals showed stronger perceptions of uncertainty in the decision environment under gain and loss feedback compared with neutral feedback, as evidenced by a more positive P2 component (i.e., the second positive component of an event-related potential). Positive emotions produced greater than expected outcome bias than neutral emotions, as evidenced by a more negative FRN component (i.e., the feedback-related negativity component). CONCLUSION: Our results suggest that positive emotions increase individuals' psychological expectations of decision outcomes. This study provides new empirical insights to understand the influence of incidental emotions on risky decision outcome expectations.

Palliative sedation - revised recommendations.

Palliative sedation is defined as the monitored use of medications intended to induce a state of decreased or absent awareness (unconsciousness) to relieve the burden of otherwise intractable suffering in a manner ethically acceptable to the patient, their family, and healthcare providers. In Switzerland, the prevalence of continuous deep sedation until death increased from 4.7% in 2001 to 17.5% of all deceased in 2013, depending on the research method used and on regional variations. Yet, these numbers may be overestimated due to a lack of understanding of the term "continuous deep sedation" by for example respondents of the questionnaire-based study. Inadequately trained and inexperienced healthcare professionals may incorrectly or inappropriately perform palliative sedation due to uncertainties regarding its definitions and practice. Therefore, the expert members of the Bigorio group and the authors of this manuscript believe that national recommendations should be published and made available to healthcare professionals to provide practical, terminological, and ethical guidance. The Bigorio group is the working group of the Swiss Palliative Care Society whose task is to publish clinical recommendations at a national level in Switzerland. These recommendations aim to provide guidance on the most critical questions and issues related to palliative sedation. The Swiss Society of Palliative Care (palliative.ch) mandated a writing board comprising four clinical experts (three physicians and one ethicist) and two national academic experts to revise the 2005 Bigorio guidelines. A first draft was created based on a narrative literature review, which was internally reviewed by five academic institutions (Lausanne, Geneva, Bern, Zürich, and Basel) and the heads of all working groups of the Swiss Society of Palliative Care before finalising the guidelines. The following themes are discussed regarding palliative sedation: (a) definitions and clinical aspects, (b) the decision-making process, (c) communication with patients and families, (d) patient monitoring, (e) pharmacological approaches, and (f) ethical and controversial issues. Palliative sedation must be practised with clinical and ethical accuracy and competence to avoid harm and ethically questionable use. Specialist palliative care teams should be consulted before initiating palliative sedation to avoid overlooking other potential treatment options for the patient's symptoms and suffering.

Value of Information for Clinical Trial Design: The Importance of Considering All Relevant Comparators.

Value of Information (VOI) analyses calculate the economic value that could be generated by obtaining further information to reduce uncertainty in a health economic decision model. VOI has been suggested as a tool for research prioritisation and trial design as it can highlight economically valuable avenues for future research. Recent methodological advances have made it increasingly feasible to use VOI in practice for research; however, there are critical differences between the VOI approach and the standard methods used to design research studies such as clinical trials. We aimed to highlight key differences between the research design approach based on VOI and standard clinical trial design methods, in particular the importance of considering the full decision context. We present two hypothetical examples to demonstrate that VOI methods are only accurate when (1) all feasible comparators are included in the decision model when designing research, and (2) all comparators are retained in the decision model once the data have been collected and a final treatment recommendation is made. Omitting comparators from either the design or analysis phase of research when using VOI methods can lead to incorrect trial designs and/or treatment recommendations. Overall, we conclude that incorrectly specifying the health economic model by ignoring potential comparators can lead to misleading VOI results and potentially waste scarce research resources.

A robust optimization model for multi-objective blood supply chain network considering scenario analysis under uncertainty: a multi-objective approach.

Annually, different regions of the world are affected by natural disasters such as floods and earthquakes, resulting in significant loss of lives and financial resources. These events necessitate rescue operations, including the provision and distribution of relief items like food and clothing. One of the most critical challenges in such crises is meeting the blood requirement, as an efficient and reliable blood supply chain is indispensable. The perishable nature of blood precludes the establishment of a reserve stock, making it essential to minimize shortages through effective approaches and designs. In this study, we develop a mathematical programming model to optimize supply chains in post-crisis scenarios using multiple objectives. Presented model allocates blood to various demand facilities based on their quantity and location, considering potential situations. We employ real data from a case study in Iran and a robust optimization approach to address the issue. The study identifies blood donation centers and medical facilities, as well as the number and locations of new facilities needed. We also conduct scenario analysis to enhance the realism of presented approach. Presented research demonstrates that with proper management, crises of this nature can be handled with minimal expense and deficiency.

Measurement uncertainty of ethyl alcohol concentration in the exhaled breath of drivers and determination of sobriety at the time of incident. / Niepewnosc pomiaru stezenia alkoholu etylowego w powietrzu wydychanym kierowców oraz ustalenia stanu trzezwosci w chwili zdarzenia.

The aim of the study was to determine the components of measurement uncertainty in the concentration of alcohol in exhaled breath and to determine the state of sobriety at the time of incident. Based on the literature review and the authors' experience in providing opinions for law enforcement and the judiciary, the influence of various factors on the final interpretation of sobriety state is described on the basis of measurement uncertainty of breath analyzers, uncertainty of retrospective and prospective calculations, and uncertainty related to the conversion of alcohol concentrations detected during breath and blood tests. The paper pays particular attention to interpreting the concentrations of ethanol in exhaled breath close to the legal limits of the state of sobriety and the state after alcohol use, or the state after alcohol use and the state of insobriety. Analyzing the results of an exhaled breath test concerning concentrations close to the values of 0.1 mg/dm3 and 0.25 mg/dm3, it is necessary to take into account the factors affecting the measurements obtained, including the measurement uncertainty of the determination of alcohol in exhaled breath, the processes of absorption, distribution and metabolism of ethyl alcohol, and the possibility of the presence of alcohol lingering in the oral cavity. The incorrect execution of measurements of the tested person's alcohol concentration is also a problematic issue. When determining sobriety state by means of retrospective and prospective calculations, it is important to remember that the uncertainty of the result is affected by a number of factors and depends, among other things, on the information provided by the suspect. Hence, the expert should draw conclusions particularly cautiously and any overestimation or underestimation of the components of uncertainty can lead to erroneous conclusions. Awareness of the uncertainties inherent in the results of a sobriety test or alcohol calculation allows for meaningful interpretation of test results and determination of the sobriety state of the person tested.

Influence of age and cognitive demand on motor decision making under uncertainty: a study on goal directed reaching movements.

In everyday life, we constantly make decisions about actions to be performed subsequently. Research on motor decision making has provided empirical evidence for an influence of decision uncertainty on movement execution in young adults. Further, decision uncertainty was suggested to be increased in older adults due to limited cognitive resources for the integration of information and the prediction of the decision outcomes. However, the influence of cognitive aging on decision uncertainty during motor decision making and movement execution has not been investigated, yet. Thus, in the current study, we presented young and older adults with a motor decision making task, in which participants had to decide on pointing towards one out of five potential targets under varying cognitive demands. Statistical analyses revealed stronger decreases in correctly deciding upon the pointing target, i.e. task performance, from low to higher cognitive demand in older as compared to young adults. Decision confidence also decreased more strongly in older adults with increasing cognitive demand, however, only when collapsing across correct and incorrect decision trials, but not when considering correct decision trials, only. Further, older adults executed reaching movements with longer reaction times and increased path length, though the latter, again, not when considering correct decision trials, only. Last, reaction time and variability in movement execution were both affected by cognitive demand. The outcomes of this study provide a differentiated picture of the distinct and joint effects of aging and cognitive demand during motor decision making.

Uncertainty in the mating strategy of honeybees causes bias and unreliability in the estimates of genetic parameters.

BACKGROUND: Breeding queens may be mated with drones that are produced by a single drone-producing queen (DPQ), or a group of sister-DPQs, but often only the dam of the DPQ(s) is reported in the pedigree. Furthermore, datasets may include colony phenotypes from DPQs that were open-mated at different locations, and thus to a heterogeneous drone population. METHODS: Simulation was used to investigate the impact of the mating strategy and its modelling on the estimates of genetic parameters and genetic trends when the DPQs are treated in different ways in the statistical evaluation model. We quantified the bias and standard error of the estimates when breeding queens were mated to one DPQ or a group of DPQs, assuming that this information was known or not. We also investigated four alternative strategies to accommodate the phenotypes of open-mated DPQs in the genetic evaluation: excluding their phenotypes, adding a dummy pseudo-sire in the pedigree, or adding a non-genetic (fixed or random) effect to the statistical evaluation model to account for the origin of the mates. RESULTS: The most precise estimates of genetic parameters and genetic trends were obtained when breeding queens were mated with drones of single DPQs that are correctly assigned in the pedigree. However, when they were mated with drones from one or a group of DPQs, and this information was not known, erroneous assumptions led to considerable bias in these estimates. Furthermore, genetic variances were considerably overestimated when phenotypes of colonies from open-mated DPQs were adjusted for their mates by adding a dummy pseudo-sire in the pedigree for each subpopulation of open-mating drones. On the contrary, correcting for the heterogeneous drone population by adding a non-genetic effect in the evaluation model produced unbiased estimates. CONCLUSIONS: Knowing only the dam of the DPQ(s) used in each mating may lead to erroneous assumptions on how DPQs were used and severely bias the estimates of genetic parameters and trends. Thus, we recommend keeping track of DPQs in the pedigree, and not only of the dams of DPQ(s). Records from DPQ colonies with queens open-mated to a heterogeneous drone population can be integrated by adding non-genetic effects to the statistical evaluation model.

Dispositional factors in the explanation of symptoms of depression, anxiety, health anxiety and COVID-19 Phobia.

Maladaptive personality, the motivational systems, and intolerance of uncertainty play important roles in the statistical explanation of depression and anxiety. Here, we notably examined for the first time whether symptoms of depression, anxiety, health anxiety, and fear of COVID-19 share similar associations (e.g., variance explained) with these important dispositional dimensions. For this cross-sectional study, data from 1001 participants recruited in Germany (50% women; mean age = 47.26) were collected. In separate models, we examined the cross-sectional associations of the symptoms of depression, anxiety, health anxiety, and fear of COVID-19 with the Personality Inventory for DSM Short Form Plus scales, the Behavioral Inhibition System / Flight-Fight-Freeze System / Behavioral Activation System scales, and Intolerance of Uncertainty scales. Relative weight analyses were used to determine the within-model importance of the different scales in the prediction of the symptoms. All in all, our study showed that maladaptive personality and intolerance of uncertainty dimensions are more important sets of predictors of the studied outcomes (with which depressive and anxious symptomatology feature very similar associations) than are the motivational system dimensions. Within predictor sets, the scales with the most important predictors were: Negative Affectivity, the Behavioral Inhibition System, and Burden due to Intolerance of Uncertainty. Our findings highlight the relevance of focusing behavioral targets of psychotherapy on these within-set traits and identify potential research priorities (maladaptive personality and intolerance of uncertainty) in relation to the symptoms of interest.

Moderating role of political stability and economic policy uncertainty between country governance practice and stock market performance. A comparative analysis of Pakistan and Kurdistan Region of Iraq.

The objective of the study is to explore the relationship between country governance practices along with political stability and Economic policy uncertainty, and stock market performance of two different economies, Pakistan and Kurdistan region of Iraq. To meet our objectives, we used the 25 years past data from 1996 to 2021. Data is collected from the DataStream database. The regression analysis is used as the method of estimation for linear and moderation effect. Our results show that regulatory quality, rules of law and political stability has significant positive relationship with stock market performance of Pakistan, but all the governance indicators have significant positive relationship with stock market performance of the Kurdistan Region of Iraq. Moreover, political stability has significant moderating impact between the governance practices and the performance of the stock markets of both economies indicating that the governance practices perform well with the political stability that leads to rise in the stock market indices of selected countries. Economic policy uncertainty has significant negative moderation impact due to creating the risk in both economies that decrease the performance of the stock markets of the selected economies. Finally, our study advocated some implications for the investors to increase their confidence on the stock of high political stability and low economic policy uncertainty economies. Government can take significant measures to control the uncertainty of the policy and portfolio managers can adjust their risk on the ground of the political stability and efficient governance practices countries.

Uncertainty among patients with advanced-stage lung cancer.

BACKGROUND: Uncertainty is the inability to define the meaning of illness-related events, which may result in anxiety, depression, poor coping, the self-perception of being a burden and low quality of life. Uncertainty among Thai patients with advanced-stage lung cancer (ASLC) has not been well documented. AIMS: To assess uncertainty in patients with ASLC. METHODS: A cross-sectional survey design was adopted. Data were collected from 60 patients with ASLC at a university hospital. A demographic data form and the Mishel Uncertainty in Illness Scale (MUIS) were used to collect data. The data were analysed using descriptive statistics. RESULTS: The patients had moderate levels of uncertainty in illness (83.73±15.25). Ambiguity about the illness and unpredictability of the prognosis scored at a moderate level for patients, while complexity of treatment and the system of care and inconsistency or lack of information, about the diagnosis or severity of the illness were at a low level. CONCLUSION: The results of this study may help healthcare professionals better understand and manage uncertainty in patients with ASLC.

Is exercise therapy the right treatment for rotator cuff-related shoulder pain? Uncertainties, theory, and practice.

BACKGROUND: Exercise therapy is a popular non-surgical treatment to help manage individuals with rotator cuff-related shoulder pain (RCRSP) and is recommended in all clinical practice guidelines. Due to modest effect sizes, low quality evidence, uncertainty relating to efficacy, and mechanism(s) of benefit, exercise as a therapeutic intervention has been the subject of increasing scrutiny. AIMS: The aim of this critical review is to lay out where the purported uncertainties of exercise for RCRSP exist by exploring the relevant quantitative and qualitative literature. We conclude by offering theoretical and practical considerations to help reduce the uncertainty of delivering exercise therapy in a clinical environment. RESULTS AND DISCUSSION: Uncertainty underpins much of the theory and practice of delivering exercise therapy for individuals with RCRSP. Nonetheless, exercise is an often-valued treatment by individuals with RCRSP, when provided within an appropriate clinical context. We encourage clinicians to use a shared decision-making paradigm and embrace a pluralistic model when prescribing therapeutic exercise. This may take the form of using exercise experiments to trial different exercise approaches, adjusting, and adapting the exercise type, load, and context based on the individual's symptom irritability, preferences, and goals. CONCLUSION: We contend that providing exercise therapy should remain a principal treatment option for helping individuals with RCRSP. Limitations notwithstanding, exercise therapy is relatively low cost, accessible, and often valued by individuals with RCRSP. The uncertainty surrounding exercise therapy requires ongoing research and emphasis could be directed towards investigating causal mechanisms to better understand how exercise may benefit an individual with RCRSP.

Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson's disease behavioral analysis.

Extracting knowledge from hybrid data, comprising both categorical and numerical data, poses significant challenges due to the inherent difficulty in preserving information and practical meanings during the conversion process. To address this challenge, hybrid data processing methods, combining complementary rough sets, have emerged as a promising approach for handling uncertainty. However, selecting an appropriate model and effectively utilizing it in data mining requires a thorough qualitative and quantitative comparison of existing hybrid data processing models. This research aims to contribute to the analysis of hybrid data processing models based on neighborhood rough sets by investigating the inherent relationships among these models. We propose a generic neighborhood rough set-based hybrid model specifically designed for processing hybrid data, thereby enhancing the efficacy of the data mining process without resorting to discretization and avoiding information loss or practical meaning degradation in datasets. The proposed scheme dynamically adapts the threshold value for the neighborhood approximation space according to the characteristics of the given datasets, ensuring optimal performance without sacrificing accuracy. To evaluate the effectiveness of the proposed scheme, we develop a testbed tailored for Parkinson's patients, a domain where hybrid data processing is particularly relevant. The experimental results demonstrate that the proposed scheme consistently outperforms existing schemes in adaptively handling both numerical and categorical data, achieving an impressive accuracy of 95% on the Parkinson's dataset. Overall, this research contributes to advancing hybrid data processing techniques by providing a robust and adaptive solution that addresses the challenges associated with handling hybrid data, particularly in the context of Parkinson's disease analysis.

Nurses' perception of uncertainty regarding suspected pain in people with dementia: A qualitative descriptive study.

OBJECTIVES: This study aims to qualitatively examine nurses' perception of uncertainty regarding suspected pain in people with dementia (PWD). DESIGN: The study utilized a qualitative descriptive design. PARTICIPANTS: The participants in this study were nurses with a minimum of six months of experience caring for PWD and currently working in a university hospital in Irbid, Jordan. Twenty-five participants were selected using convenience sampling from the selected hospital. RESULTS: Four major themes and 12 subthemes relating to nurses' perceptions of uncertainty regarding suspected pain in PWD emerged. The main themes were (a) the culture-bound nature of uncertainty regarding suspected pain in PWD, (b) dimensions of uncertainty regarding suspected pain in PWD, (c) indicators of uncertainty regarding suspected pain in PWD, and (d) assessment methods of uncertainty to suspected pain in PWD. Moreover, for each major theme, different subthemes were developed. CONCLUSIONS: It is crucial to address factors influencing -nurses' uncertainty regarding suspected pain in PWD to improve pain assessment and management in PWD. Additionally, the study identified five indicators of uncertainty: complicated decision-making, knowledge deficit, bias, intuition, and misconceptions. Effective assessment methods, such as semi-structured interviews and simulated assessments, should be employed to evaluate uncertainty accurately. By addressing these issues and utilizing appropriate assessment approaches, healthcare professionals can enhance pain management for individuals with dementia.

Bayes Optimal Integration of Social and Endogenous Uncertainty in Numerosity Estimation.

One of the most prominent social influences on human decision making is conformity, which is even more prominent when the perceptual information is ambiguous. The Bayes optimal solution to this problem entails weighting the relative reliability of cognitive information and perceptual signals in constructing the percept from self-sourced/endogenous and social sources, respectively. The current study investigated whether humans integrate the statistics (i.e., mean and variance) of endogenous perceptual and social information in a Bayes optimal way while estimating numerosities. Our results demonstrated adjustment of initial estimations toward group means only when group estimations were more reliable (or "certain"), compared to participants' endogenous metric uncertainty. Our results support Bayes optimal social conformity while also pointing to an implicit form of metacognition.

Personalized, Precision Medicine to Cure Alzheimer's Dementia: Approach #1.

The goal of the treatment for Alzheimer's dementia (AD) is the cure of dementia. A literature review revealed 18 major elements causing AD and 29 separate medications that address them. For any individual with AD, one is unlikely to discern which major causal elements produced dementia. Thus, for personalized, precision medicine, all causal elements must be treated so that each individual patient will have her or his causal elements addressed. Twenty-nine drugs cannot concomitantly be administered, so triple combinations of drugs taken from that list are suggested, and each triple combination can be administered sequentially, in any order. Ten combinations given over 13 weeks require 2.5 years, or if given over 26 weeks, they require 5.0 years. Such sequential treatment addresses all 18 elements and should cure dementia. In addition, any comorbid risk factors for AD whose first presence or worsening was within ±1 year of when AD first appeared should receive appropriate, standard treatment together with the sequential combinations. The article outlines a randomized clinical trial that is necessary to assess the safety and efficacy of the proposed treatments; it includes a triple-drug Rx for equipoise. Clinical trials should have durations of both 2.5 and 5.0 years unless the data safety monitoring board (DSMB) determines earlier success or futility since it is uncertain whether three or six months of treatment will be curative in humans, although studies in animals suggest that the briefer duration of treatment might be effective and restore defective neural tracts.

The mediating role of hope in the relation between uncertainty and social support with self-management among patients with ESKD undergoing hemodialysis.

BACKGROUND: Self-management behaviours are critical for patients requiring regular hemodialysis (HD) therapy. This study aimed to test the relationship between social support, uncertainty and self-management among HD patients and to explore whether hope plays a mediating role. METHODS: In a cross-sectional study, a convenience sample of 212 HD patients from two hospitals completed the Perceived Social Support Scale (PSSS), Herth Hope Index (HHI), Short form Mishel Uncertainty in Illness Scale (SF-MUIS), and hemodialysis Self-Management Instrument (HD-SMI). Data were analysed using structural equation modelling. RESULTS: The main finding indicated that social support positively affected self-management (ß = 0.50, t = 4.97, p < 0.001), and uncertainty negatively affected self-management (ß =-0.37, t=-4.12, p = < 0.001). In mediational model analysis, the effect of social support on self-management was fully mediated [(ß = 0.12; 95% BC CI (0.047, 0.228)] by hope. Also, the effect of uncertainty on self-management was fully mediated [(ß=- 0.014; 95% BC CI (-0.114, -0.003)] by hope. CONCLUSIONS: "Considering factors influencing self-management in HD patients is crucial for improving quality of life. Receiving support and informational resources can not only foster hope but also reduce their uncertainty, thus aiding in enhancing clinical outcomes, quality of life, and reducing complications. "Health care providers, especially nurses were advised to accept the existence of uncertainty, help patients make optimal use of support resources, and give more importance to disambiguation to reassure them. Therefore, well-designed interventions that enhance social support and hope and reduce uncertainty may help improve self-management behaviour in HD patients.

Partial label learning for automated classification of single-cell transcriptomic profiles.

Single-cell RNA sequencing (scRNASeq) data plays a major role in advancing our understanding of developmental biology. An important current question is how to classify transcriptomic profiles obtained from scRNASeq experiments into the various cell types and identify the lineage relationship for individual cells. Because of the fast accumulation of datasets and the high dimensionality of the data, it has become challenging to explore and annotate single-cell transcriptomic profiles by hand. To overcome this challenge, automated classification methods are needed. Classical approaches rely on supervised training datasets. However, due to the difficulty of obtaining data annotated at single-cell resolution, we propose instead to take advantage of partial annotations. The partial label learning framework assumes that we can obtain a set of candidate labels containing the correct one for each data point, a simpler setting than requiring a fully supervised training dataset. We study and extend when needed state-of-the-art multi-class classification methods, such as SVM, kNN, prototype-based, logistic regression and ensemble methods, to the partial label learning framework. Moreover, we study the effect of incorporating the structure of the label set into the methods. We focus particularly on the hierarchical structure of the labels, as commonly observed in developmental processes. We show, on simulated and real datasets, that these extensions enable to learn from partially labeled data, and perform predictions with high accuracy, particularly with a nonlinear prototype-based method. We demonstrate that the performances of our methods trained with partially annotated data reach the same performance as fully supervised data. Finally, we study the level of uncertainty present in the partially annotated data, and derive some prescriptive results on the effect of this uncertainty on the accuracy of the partial label learning methods. Overall our findings show how hierarchical and non-hierarchical partial label learning strategies can help solve the problem of automated classification of single-cell transcriptomic profiles, interestingly these methods rely on a much less stringent type of annotated datasets compared to fully supervised learning methods.

Uncertainty quantification of the impact of peripheral arterial disease on abdominal aortic aneurysms in blood flow simulations.

Peripheral arterial disease (PAD) and abdominal aortic aneurysms (AAAs) often coexist and pose significant risks of mortality, yet their mutual interactions remain largely unexplored. Here, we introduce a fluid mechanics model designed to simulate the haemodynamic impact of PAD on AAA-associated risk factors. Our focus lies on quantifying the uncertainty inherent in controlling the flow rates within PAD-affected vessels and predicting AAA risk factors derived from wall shear stress. We perform a sensitivity analysis on nine critical model parameters through simulations of three-dimensional blood flow within a comprehensive arterial geometry. Our results show effective control of the flow rates using two-element Windkessel models, although specific outlets need attention. Quantities of interest like endothelial cell activation potential (ECAP) and relative residence time are instructive for identifying high-risk regions, with ECAP showing greater reliability and adaptability. Our analysis reveals that the uncertainty in the quantities of interest is 187% of that of the input parameters. Notably, parameters governing the amplitude and frequency of the inlet velocity exert the strongest influence on the risk factors' variability and warrant precise determination. This study forms the foundation for patient-specific simulations involving PAD and AAAs which should ultimately improve patient outcomes and reduce associated mortality rates.