Bayesian Theory Research Articles

Enhancing the synergy governance capability of urban communities based on Bayesian network: A case of Jinan city

The rapid pace of urbanization has brought about a plethora of challenges for urban communities, prompting the advocacy of collaborative governance as a means to tackle management issues effectively. However, fostering strong collaboration among multiple governance entities remains a significant hurdle in urban communities governance. This study addresses this challenge by exploring the complex structure of urban communities' collaborative governance systems, with a focus on enhancing collaborative governance capability. Drawing on Bayesian network (BN) theory and utilizing the case of Jinan, this article investigates the intrinsic mechanisms of achieving collaboration in urban communities. This study establishes a Bayesian network model for collaborative governance of urban communities, providing a visual representation of the intricate interactions among multiple governance entities. We employ a data-knowledge-driven approach for BN structure learning. This approach tackles issues such as the lack of cyclic dependency capability and the impact of small datasets on model accuracy. Furthermore, we analyze the collaborative mechanism through Bayesian reasoning. The findings underscore the pivotal entities and five key factors that significantly influence collaborative governance capability. Lastly, policy implications are drawn to enhance the collaborative governance capability of urban communities.

Bayesian credibility model with heavy tail random variables: calibration of the prior and application to natural disasters and cyber insurance

Bayesian credibility model with heavy tail random variables: calibration of the prior and application to natural disasters and cyber insurance

An integrated approach to Bayesian weight regulations and multitasking learning methods for generating emotion-based content in the metaverse

This paper introduces an integrated model designed to analyze topics and sentiments in textual data simultaneously, recognizing their interdependence. By tackling challenges such as data scarcity, missing information, and biased distributions, the proposed model effectively captures the dynamic interactions between topics and sentiments within complex datasets. The method combines the use of Convolutional Neural Networks (CNN) for detecting topic-related patterns with a revised Recurrent Neural Network (RNN) to trace the emotional flow within contexts, leveraging the strengths of sequential data processing. These components are integrated within a Bayesian probability framework, modeling the conditional probabilities of sentences expressing specific sentiments and documents being associated with particular topics. The combined feature and state vectors from the CNN and revised RNN within this Bayesian setup enable precise classification and prediction of topics and sentiments. Furthermore, this paper explores innovative research avenues, including sentiment analysis in 3D virtual reality environments and the development of new algorithms that reflect content creation techniques in the metaverse, offering dynamic system construction. This integrated approach not only enhances data-driven decision-making but also unlocks new possibilities for advanced multi-text analysis.

A Bayesian theory of market impact

Abstract The available liquidity at any time in financial markets falls largely short of the typical size of the orders that institutional investors would trade. In order to reduce the impact on prices due to the execution of large orders, traders in financial markets split large orders into a series of smaller ones, which are executed sequentially. The resulting sequence of trades is called a meta-order. Empirical studies have revealed a non-trivial set of statistical laws on how meta-orders affect prices, which include (i) the square-root behaviour of the expected price variation with the total volume traded, (ii) its crossover to a linear regime for small volumes and (iii) a reversion of average prices towards its initial value, after the sequence of trades is over. Here we recover this phenomenology within a minimal theoretical framework where the market sets prices by incorporating all information on the direction and speed of trade of the meta-order in a Bayesian manner. The simplicity of this derivation lends further support to the robustness and universality of market impact laws. In particular, it suggests that the square-root impact law originates from over-estimation of order flows originating from meta-orders.

Gambling habits and Probability Judgements in a Bayesian Task Environment.

Little is known about how gamblers form probability assessments. This paper reports on a preregistered study that administered an incentivized Bayesian choice task to n = 465 self-reported gamblers and non-gamblers. The task elicits subjective probability assessments and allows one to estimate the degree to which distinct information sources are weighted in forming probability assessments. Our data failed to support our main hypotheses that experienced online gamblers would be more accurate than non-gamblers in estimating probabilities, that gamblers experienced in games of skill (e.g., poker) would be more accurate than gamblers experienced only in non-skill games (e.g., slots), that accuracy would differ by sex, or that information sources would be weighted differently across different participant groups. Exploratory analysis, however, revealed that gambling frequency predicted lower Bayesian accuracy, while cognitive reflection predicted higher accuracy. The decline in accuracy linked to self-reported gambling frequency was stronger for female participants. Decision modeling estimated a decreased weight place on new evidence (over base rate odds) for those participant groups who showed decreased accuracy, which suggests that a proper incorporation of new information is important for probability assessments. Our results link online gambling frequency to worse performance in the critical probability assessment skills that should benefit gambling success (i.e., in skill-based games). Additional research is needed to better understand the mechanism linking reported gambling frequency to probability assessment accuracy.

A Novel Group Decision Making Model to Compare Online Shopping Platforms

Over the years, E-commerce industry has been witnessing a phenomenal growth, thanks to rapid technological advancement in Industry 4.0. There has been a standout surge in the use of various online shopping platforms (OSP) for daily use. The recent pandemic has accelerated the growth trajectory and made a transformational change in the digital commerce landscape. As a result, there has been a proliferation of OSPs in the competitive domain. It is therefore pertinent to address the questions: How do the customers select their favorite OSP? To what extent the OSPs differ based on consumers’ preferences? The present work addresses these questions by proposing a novel group decision making framework. The ongoing study provides several innovative extensions of multi criteria decision making models like Borda count, criteria importance assessment (CIMAS), modified preference selection index (MPSI), and root assessment method (RAM). In this paper, the researchers provide a novel use of the Borda count method, integrated with CIMAS for determining criteria weights utilizing ranking of the criteria. Further, a novel extension of MPSI and RAM has been made with multiple normalizations. In this paper, the authors demonstrate a rare combination of vector and non-linear normalization using the Heron mean. The present paper derives the final criteria weights by combining Borda count, CIMAS and multi-normalization based MPSI (MNMPSI) using Bayesian logic. The criteria are selected based on Uses and Gratification theory (UGT). The findings reveal that interactive app interface and features (C16), user-friendly interface and search (C13), convenience in shopping (C14), product availability and variety (C12) and discounts and offers (C8) exert significant influence in selecting the OSP. Further, it is observed that Flipkart (A2) and Amazon (A1) are the top performers in the eyes of the users. The stability and reliability of the proposed methodology are examined by conducting a sensitivity analysis and comparing with several other models. The robustness of the proposed methodology and practical relevance of the findings of the present work shall provide notable impetus to the analysts and strategic decision-makers.

Construction of a decision model for donor testing in cases of suspected antibody-mediated transfusion-related-acute-lung-injury.

To propose a rational basis for donor testing in cases of suspected antibody-mediated transfusion-related lung injury (AMT). Anti-leukocyte antibodies in donated blood are established causes of transfusion-related lung injury (TRALI). However, the question of whether to test donors for antibodies is not identical to whether the case meets definition criteria for TRALI. There is a balance between the potential benefits of testing and the costs of donor deferral and investigation. We propose that a decision-making process based on optimising the balance between risk and benefit requires a subjective choice of the relative value of different outcomes of testing. We have developed a formal decision model to illustrate how these choices affect testing decisions. Using a Bayesian probability model, we show that the diagnostic benefit and TRALI prevention benefit of testing donors have a complex interrelationship with the number of implicated donors and clinical suspicion of antibody-mediated TRALI (AMT) and that rational testing choices vary according to value assigned to outcomes. The challenges to the use of a formal decision model for clinical testing are discussed and conclude that a formal model is a useful consensus-building tool for improving consistency and openness in decision making.

Lithofacies identification of deep coalbed methane reservoir based on high-resolution seismic inversion

During the exploration and development of deep coalbed methane (CBM), delineating the thickness of coal seam and lithofacies of the roof and floor is one of the major challenging tasks. In past attempts, the prediction methods of these parameters have been limited to the conventional inversion. However, the effect of coal shielding on adjacent reflecting layers restricts the identification of underlying sand effectively by conventional inversion. Also, the depth at which the deep CBM zone is located (1,500–2000 m) produces a significant overlap of P-wave impedance and Vp/Vs of sands and shale which increases classification uncertainty between these two lithofacies. We proposed a new workflow for high-precision quantitative seismic interpretation of deep CBM reservoir. Not only P-wave impedance but also GR is selected as the optimized attributes for lithofacies classification. To reduce the effect of strong reflection of coal seam and identifying thin coal layers, the seismic waveform indication inversion method is used to obtain high-resolution results of P-wave impedance and GR. It uses horizontal changes in seismic waveforms to reflect lithological assemblage characteristics for facies-controlled constraints. Then, Bayesian classification theory is used to achieve three-dimensional lithofacies classification with multi-source data. To improve the continuity and accuracy of the interpreted results, a Markov chain is applied in the Bayesian rule as the spatial prior constraint. A well-associated synthetic test and field data application in Ordos Basin demonstrates the accuracy of the proposed workflow. Compared with conventional inversion, the results of proposed workflow showed higher resolution and accuracy. By providing a new solution for the identification of roof and floor lithofacies of deep CBM reservoir, this workflow aims to contribute to the better exploration and development of deep CBM.

Matching-Adjusted Indirect Comparison of the Efficacy and Safety of Erdafitinib vs Enfortumab Vedotin in Patients with Locally Advanced Metastatic Urothelial Carcinoma.

Background: For patients with locally advanced or metastatic urothelial carcinoma (la/mUC), prognosis is poor and effective treatment options are limited. Erdafitinib is an oral fibroblast growth factor receptor (FGFR) kinase inhibitor approved by the FDA for the treatment of adults with la/mUC harboring FGFR alterations whose disease progressed following at least 1 prior line of therapy, including a PD-1 or PD-L(1) inhibitor, based on the phase 3, randomized THOR trial (NCT03390504, Cohort 1). Objective: To compare the efficacy and safety of erdafitinib vs enfortumab vedotin-ejfv (EV) in the absence of head-to-head comparison via an anchored matching-adjusted indirect comparison (MAIC). Methods: An anchored MAIC was conducted according to the National Institute for Health and Care Excellence Decision Support Unit guidance, with physician's choice of chemotherapy (docetaxel/paclitaxel and vinflunine) as the common comparator. Individual patient data from THOR were adjusted to match published key eligibility criteria and average baseline characteristics of EV-301, such as Bellmunt risk score, liver or visceral metastases, primary site, among others. Erdafitinib was then indirectly compared with EV using the relative treatment effects for the reweighted THOR population and those published for EV-301. Results: After matching, the effective sample size for THOR was 126 patients. The MAIC-recalculated hazard ratio (95% credible interval) for erdafitinib vs EV was 0.92 (0.54, 1.57) for overall survival and 0.93 (0.55, 1.56) for progression-free survival, yielding Bayesian probabilities of erdafitinib being better than EV of 62.1% and 60.5%, respectively. For response outcomes, the MAIC-recalculated risk ratio was 1.49 (0.56, 3.90) for confirmed objective response rate and 2.89 (0.27, 30.33) for confirmed complete response with probabilities of 72.6% and 81.3% for erdafitinib being better than EV, respectively. For safety, MAIC-yielded risk ratios of 1.09 (0.99, 1.21) for any treatment-related adverse events, 0.86 (0.57, 1.28) for grade 3+ TRAEs, and 1.02 (0.98, 1.06) for any treatment-emergent adverse events. Conclusion: The MAIC indicates comparable efficacy of erdafitinib vs EV for overall survival and progression-free survival, with erdafitinib showing a higher probability of achieving deep responses. While erdafitinib is associated with slightly more adverse events compared with EV, these events seem to be less severe.

Matching-Adjusted Indirect Comparison of the Efficacy and Safety of Erdafitinib vs Enfortumab Vedotin in Patients with Locally Advanced Metastatic Urothelial Carcinoma

Background: For patients with locally advanced or metastatic urothelial carcinoma (la/mUC), prognosis is poor and effective treatment options are limited. Erdafitinib is an oral fibroblast growth factor receptor (FGFR) kinase inhibitor approved by the FDA for the treatment of adults with la/mUC harboring FGFR alterations whose disease progressed following at least 1 prior line of therapy, including a PD-1 or PD-L(1) inhibitor, based on the phase 3, randomized THOR trial (NCT03390504, Cohort 1). Objective: To compare the efficacy and safety of erdafitinib vs enfortumab vedotin-ejfv (EV) in the absence of head-to-head comparison via an anchored matching-adjusted indirect comparison (MAIC). Methods: An anchored MAIC was conducted according to the National Institute for Health and Care Excellence Decision Support Unit guidance, with physician’s choice of chemotherapy (docetaxel/paclitaxel and vinflunine) as the common comparator. Individual patient data from THOR were adjusted to match published key eligibility criteria and average baseline characteristics of EV-301, such as Bellmunt risk score, liver or visceral metastases, primary site, among others. Erdafitinib was then indirectly compared with EV using the relative treatment effects for the reweighted THOR population and those published for EV-301. Results: After matching, the effective sample size for THOR was 126 patients. The MAIC-recalculated hazard ratio (95% credible interval) for erdafitinib vs EV was 0.92 (0.54, 1.57) for overall survival and 0.93 (0.55, 1.56) for progression-free survival, yielding Bayesian probabilities of erdafitinib being better than EV of 62.1% and 60.5%, respectively. For response outcomes, the MAIC-recalculated risk ratio was 1.49 (0.56, 3.90) for confirmed objective response rate and 2.89 (0.27, 30.33) for confirmed complete response with probabilities of 72.6% and 81.3% for erdafitinib being better than EV, respectively. For safety, MAIC-yielded risk ratios of 1.09 (0.99, 1.21) for any treatment-related adverse events, 0.86 (0.57, 1.28) for grade 3+ TRAEs, and 1.02 (0.98, 1.06) for any treatment-emergent adverse events. Conclusion: The MAIC indicates comparable efficacy of erdafitinib vs EV for overall survival and progression-free survival, with erdafitinib showing a higher probability of achieving deep responses. While erdafitinib is associated with slightly more adverse events compared with EV, these events seem to be less severe.

Enhanced Agricultural Vehicle Positioning through Ultra-Wideband-Assisted Global Navigation Satellite Systems and Bayesian Integration Techniques

This paper introduces a cooperative positioning algorithm for agricultural vehicles, which uses the relative distance of the workshop to improve the performance of the Global Navigation Satellite Systems (GNSS), to improve the positioning accuracy and stability. Firstly, the extended Kalman filter (EKF) fuses the vehicle motion state data with GNSS observation data to improve the independent GNSS positioning accuracy. Subsequently, vehicle state and observation models are formulated using Bayesian theory, incorporating GNSS/UWB data with UWB tag network ranging and with GNSS positioning data among agricultural vehicles and Inter-Vehicular Ranges (IVRs). This integration addresses the significant drift issue in GNSS elevation positioning by employing a high-dimensional decoupling algorithm, standardizing the discrete elevation data, and improving the data’s continuity and predictability. A particle filter is used to refine the vehicle’s position estimation further. Finally, experiments are carried out to verify the robustness of the proposed algorithm under different working conditions.

Subjective Probability Increases Across Communication Chains: Introducing the Probability Escalation Effect.

A severity effect has previously been documented, whereby numerical translations of verbal probability expressions are higher for severe outcomes than for non-severe outcomes. Recent work has additionally shown the same effect in the opposite direction (translating numerical probabilities into words). Here, we aimed to test whether these effects lead to an escalation of subjective probabilities across a communication chain. In four ‘communication chain’ studies, participants at each communication stage either translated a verbal probability expression into a number, or a number into a verbal expression (where the probability to be translated was yoked to a previous participant). Across these four studies, we found a general Probability Escalation Effect, whereby subjective probabilities increased with subsequent communications for severe, non-severe and positive events. Having ruled out some alternative explanations, we propose that the most likely explanation is in terms of communications directing attention towards an event's occurrence. Probability estimates of focal outcomes increase across communication stages.

System-of-Systems Resilience Analysis and Design Using Bayesian and Dynamic Bayesian Networks

A System-of-Systems (SoS) is characterized both by independence and by inter-dependency. This inter-dependency, while allowing an SoS to achieve its objectives, also means that failures can cascade throughout the SoS. An SoS needs to be resilient to deal with the impact of complex internal and external environments. In this paper, we propose a resilience analysis method of an SoS based on a hierarchy structure. Firstly, we establish a hierarchy structure, which is ranked from high to low as capability level, activity level and system level. Then, Bayesian Networks (BNs) and Dynamic Bayesian Networks (DBNs) are used to analyze the resilience of the SoS. A resilience-based system importance metric is introduced, which is used in the budget allocation optimization problem during the development domain of an SoS. This paper proposes a mathematical programming model aimed at optimizing SoS resilience by optimally using budget to the subsystem. The application of the proposed approach is demonstrated using a case study: a Next Generation Air Transportation setting. The study results provide evidence that the proposed inter-dependency analysis based on Bayesian theory and the SoS resilience design approach can assist SoS system engineers in increasing expected SoS resilience during the development domain.

Investigating agricultural water sustainability in arid regions with Bayesian network and water footprint theories

Water scarcity is a significant constraint in agricultural ecosystems of arid regions, necessitating sustainable development of agricultural water resources. This study innovatively combines Bayesian theory and Water Footprint (WF) to construct a Bayesian Network (BN). Water quantity and quality data were evaluated comprehensively by WF in agricultural production. This evaluation integrates WF and local water resources to establish a sustainability assessment framework. Selected nodes are incorporated into a BN and continuously updated through structural and parameter learning, resulting in a robust model. Results reveal a nearly threefold increase of WF in the arid regions of Northwest China from 1989 to 2019, averaging 189.95 × 108 m3 annually. The region's agricultural scale is expanding, and economic development is rapid, but the unsustainability of agricultural water use is increasing. Blue WF predominates in this region, with cotton having the highest WF among crops. The BN indicates a 70.1 % probability of unsustainable water use. Sensitivity analysis identifies anthropogenic factors as primary drivers influencing water resource sustainability. Scenario analysis underscores the need to reduce WF production and increase agricultural water supply for sustainable development in arid regions. Proposed strategies include improving irrigation methods, implementing integrated water-fertilizer management, and selecting drought-resistant, economically viable crops to optimize crop planting structures and enhance water use efficiency in current agricultural practices in arid regions. This study not only offers insights into water management in arid regions but also provides practical guidance for similar agricultural contexts. The BN model serves as a flexible tool for informed decision-making in dynamic environments.

Bayesian Predictive Probability Based on a Bivariate Index Vector for Single-Arm Phase II Study With Binary Efficacy and Safety Endpoints.

In oncology, Phase II studies are crucial for clinical development plans as such studies identify potent agents with sufficient activity to continue development in the subsequent Phase III trials. Traditionally, Phase II studies are single-arm studies, with the primary endpoint being short-term treatment efficacy. However, drug safety is also an important consideration. In the context of such multiple-outcome designs, predictive probability-based Bayesian monitoring strategies have been developed to assess whether a clinical trial will provide enough evidence to continue with a Phase III study at the scheduled end of the trial. Therefore, we propose a new simple index vector to summarize the results that cannot be captured by existing strategies. Specifically, we define the worst and most promising situations for the potential effect of a treatment, then use the proposed index vector to measure the deviation between the two situations. Finally, simulation studies are performed to evaluate the operating characteristics of the design. The obtained results demonstrate that the proposed method makes appropriate interim go/no-go decisions.

Linking Opinions Shared on Social Media About COVID-19 Public Health Measures to Adherence: Repeated Cross-Sectional Surveys of Twitter Use in Canada.

The effectiveness of public health measures (PHMs) depends on population adherence. Social media were suggested as a tool to assess adherence, but representativeness and accuracy issues have been raised. The objectives of this repeated cross-sectional study were to compare self-reported PHM adherence and sociodemographic characteristics between people who used Twitter (subsequently rebranded X) and people who did not use Twitter. Repeated Canada-wide web-based surveys were conducted every 14 days from September 2020 to March 2022. Weighted proportions were calculated for descriptive variables. Using Bayesian logistic regression models, we investigated associations between Twitter use, as well as opinions in tweets, and self-reported adherence with mask wearing and vaccination. Data from 40,230 respondents were analyzed. As self-reported, Twitter was used by 20.6% (95% CI 20.1%-21.2%) of Canadians, of whom 29.9% (95% CI 28.6%-31.3%) tweeted about COVID-19. The sociodemographic characteristics differed across categories of Twitter use and opinions. Overall, 11% (95% CI 10.6%-11.3%) of Canadians reported poor adherence to mask-wearing, and 10.8% (95% CI 10.4%-11.2%) to vaccination. Twitter users who tweeted about COVID-19 reported poorer adherence to mask wearing than nonusers, which was modified by the age of the respondents and their geographical region (odds ratio [OR] 0.79, 95% Bayesian credibility interval [BCI] 0.18-1.69 to OR 4.83, 95% BCI 3.13-6.86). The odds of poor adherence to vaccination of Twitter users who tweeted about COVID-19 were greater than those of nonusers (OR 1.76, 95% BCI 1.48-2.07). English- and French-speaking Twitter users who tweeted critically of PHMs were more likely (OR 4.07, 95% BCI 3.38-4.80 and OR 7.31, 95% BCI 4.26-11.03, respectively) to report poor adherence to mask wearing than non-Twitter users, and those who tweeted in support were less likely (OR 0.47, 95% BCI 0.31-0.64 and OR 0.96, 95% BCI 0.18-2.33, respectively) to report poor adherence to mask wearing than non-Twitter users. The OR of poor adherence to vaccination for those tweeting critically about PHMs and for those tweeting in support of PHMs were 4.10 (95% BCI 3.40-4.85) and 0.20 (95% BCI 0.10-0.32), respectively, compared to non-Twitter users. Opinions shared on Twitter can be useful to public health authorities, as they are associated with adherence to PHMs. However, the sociodemographics of social media users do not represent the general population, calling for caution when using tweets to assess general population-level behaviors.

A Bayesian deep recommender system for uncertainty-aware online physician recommendation

Online physician recommender systems alleviate information overload by automatically recommending the best-fit physicians to patients. In contrast to general recommendations, physicians with greater uncertainty (i.e., greater variance in patients’ feedback) may not be preferred as this could affect patients’ treatment. However, most existing recommender systems don't consider uncertainty, reducing systems’ reliability and patients’ readiness to trust. To address this concern, this study leverages Bayesian theory and develops an uncertainty-aware online physician recommender system, including a Bayesian deep collaborative filtering (BDCF) model and a novel uncertainty-aware ranking algorithm. Experiments on real-world data demonstrate the superiority of BDCF and the ranking algorithm.

ICU patients receiving remifentanil do not experience reduced duration of mechanical ventilation: a systematic review of randomized controlled trials and network meta-analyses based on Bayesian theories.

The purpose of this network meta-analysis (NMA) was to evaluate the efficacy of intravenous opioid μ-receptor analgesics in shortening the duration of mechanical ventilation (MV) in ICU patients. Randomized controlled trials comparing the efficacy of remifentanil, sufentanil, morphine, and fentanyl on the duration of MV in ICU patients were searched in Embase, Cochrane, Pubmed, and Web of Science electronic databases. The primary outcome was MV duration. The Bayesian random-effects framework was used to evaluate relative efficacy. In total 20 studies were included in this NMA involving 3,442 patients. Remifentanil was not associated with a reduction in the duration of MV compared with fentanyl (mean difference (MD) -0.16; 95% credible interval (CrI): -4.75 ~ 5.63) and morphine (MD 3.84; 95% CrI: -0.29 ~ 10.68). The secondary outcomes showed that, compared with remifentanil, sufentanil can prolong the duration of extubation. No regimen significantly shortened the ICU length of stay and improved the ICU mortality, efficacy, safety, and drug-related adverse events. Among these analgesics, remifentanil did not appear to be associated with a reduction in MV duration. Clinicians should carefully titrate the analgesia of MV patients to prevent a potentially prolonged duration of MV due to excessive or inadequate analgesic therapy. https://www.crd.york.ac.uk/prospero/, CRD42021232604.

Right anterior insula ASL hypoperfusion as a diagnostic biomarker of prodromal and mild dementia with Lewy bodies: preliminary evidence using a Bayesian approach.

Identifying and validating a biomarker with high specificity in early-stage dementia with Lewy bodies (DLB) using a feasible method is crucial to enhance the current suboptimal diagnostic procedure. Previous research revealed abnormalities, including hypoperfusion in the right anterior insular cortex at group level, in prodromal DLB. Exploring hypoperfusion of the right anterior insula, at an individual-level and assessing its relevance as a potential imaging biomarker in early DLB, has, to our knowledge, not been investigated. Our preliminary study aims to assess the feasibility of the technique and to provide a methodological framework for further investigation. We assessed the feasibility and accuracy of the hypoperfusion of the right anterior insula per arterial spin labelling magnetic resonance imaging (ASL-MRI) as a diagnostic biomarker in early DLB and provided rough estimates of its sensitivity and specificity. Defining the region of interest based on previous research, we established the biomarker as the hypoperfusion of the right anterior insula. Discriminative and analytical performances were assessed in comparison to a control group of treatment-resistant depression patients. Bayesian diagnostic reasoning was employed to assess the biomarker diagnostic usability in early DLB in two scenarios: healthy elderly controls and mild cognitive impairment. Additionally, we updated probabilities by integrating data from the Mayo-clinic cognitive fluctuations scale and real-time quaking-induced conversion (RT-QuIC) α-synuclein data. Lastly, a whole-brain perfusion analysis of DLB patients was conducted to identify further brain regions with discriminative abilities. We successfully replicated the right anterior insular hypoperfusion (RAI-Hypo) in all DLB patients at the individual level. The overall sensitivity of the biomarker was 96%, and the specificity was 92%. Bayesian testing revealed the biomarker's highest performance in early-stage DLB with cognitive fluctuations, showcasing a diagnostic potential associated with a high precision and moderate accuracy. In a cognitively non-impaired population, the RAI-Hypo showed a limited usability and lacked in selectivity to qualify as a screening tool. The exploratory whole-brain analysis revealed perfect discriminative capacities in the bilateral anterior insulae and the left inferior parietal lobule. Further studies are needed to confirm our preliminary results. If performance is maintained in subsequent studies and is compared to a more suitable control population, the proposed biomarker may be eventually sufficient to discriminate early-stage DLB from non-DLB.

Preparing Children for Invasive MedicalCancer Treatment with "My Logbook": Preliminary Results of a Pilot Study.

Pediatric cancer is one of the most burdensome chronic diseases, necessitating a variety of severe medical interventions. As a result, the disease and its treatment cause numerous acute and long-term medical, psychological, and socioeconomic strains for young patients and their families. Therefore, psychosocial care using evidence-based interventions (EBIs) before, during, and after medical treatments is essential to ensure that patients receive adequate information and to minimize the adverse emotional and psychosocial impacts such as insecurity, fear, and shame. The present study reports the first promising results of applying cancer-specific psychosocial methods developed in the quality improvement project "My Logbook." The four assessed tools are specifically designed to adequately prepare pediatric cancer patients for surgery, chemotherapy, radiotherapy, and stem cell transplantation. Self and proxy ratings were used to assess the patients' subjective knowledge and emotional well-being before and after each intervention session. The results showed that patient-centered interventions using various creative and developmentally adapted methodologies (e.g., psychoeducation, crafting, games) have the potential to effectively enhance patient health literacy (V = 120.5, p < .001, r = 0.33) and well-being as manifested in more positive (slope = 0.121, p = .016) and less negative (slope = - 0.350, p < .001) or neutral emotions (slope = - 0.202, p = .002). These findings highlight the importance of developing and implementing psychosocial tools in pediatric oncology to prevent psychological overload and negative emotions and to increase subjective control beliefs, autonomy, and empowerment. Moreover, the effective application and systematic evaluation of evidence-based psychosocial tools can facilitate the establishment of standardized guidelines for psychosocial care in pediatric oncology. Thereby, the final goal is to ensure the quality of care and to use education to increase the quality of life for all pediatric cancer patients.Trial registration: ClinicalTrials.gov Identifier: NCT04474678 (July 17, 2020).