Modeling and analysis of the transmission dynamics of cystic echinococcosis: Effects of increasing the number of sheep.

A transmission dynamics model with the logistic growth of cystic echinococcus in sheep was formulated and analyzed. The basic reproduction number was derived and the results showed that the global dynamical behaviors were determined by its value. The disease-free equilibrium is globally asymptotically stable when the value of the basic reproduction number is less than one; otherwise, there exists a unique endemic equilibrium and it is globally asymptotically stable. Sensitivity analysis and uncertainty analysis of the basic reproduction number were also performed to screen the important factors that influence the spread of cystic echinococcosis. Contour plots of the basic reproduction number versus these important factors are presented, too. The results showed that the higher the deworming rate of dogs, the lower the prevalence of echinococcosis in sheep and dogs. Similarly, the higher the slaughter rate of sheep, the lower the prevalence of echinococcosis in sheep and dogs. It also showed that the spread of echinococcosis has a close relationship with the maximum environmental capacity of sheep, and that they have a remarkable negative correlation. This reminds us that the risk of cystic echinococcosis may be underestimated if we ignore the increasing number of sheep in reality.

Multifaceted information-seeking motives in children.

From an early age, children need to gather information to learn about their environment. Deciding which knowledge to pursue can be difficult because information can serve several, sometimes competing, purposes. Here, we examine the developmental trajectories of such diverse information-seeking motives. Over five experiments involving 521 children (aged 4-12), we find that school-age children integrate three key factors into their information-seeking choices: whether information reduces uncertainty, is useful in directing action, and is likely to be positive. Choices that likely reveal positive information and are useful for action emerge as early as age 4, followed by choices that reduce uncertainty (at ~age 5). Our results suggest that motives related to usefulness and uncertainty reduction become stronger with age, while the tendency to seek positive news does not show a statistically significant change throughout development. This study reveals how the relative importance of diverging, sometimes conflicting, information-seeking motives emerges throughout development.

Mathematical modelling with Bayesian inference to quantitatively characterize therapeutic cell behaviour in nerve tissue engineering.

Cellular engineered neural tissues have significant potential to improve peripheral nerve repair strategies. Traditional approaches depend on quantifying tissue behaviours using experiments in isolation, presenting a challenge for an overarching framework for tissue design. By comparison, mathematical cell-solute models benchmarked against experimental data enable computational experiments to be performed to test the role of biological/biophysical mechanisms, as well as to explore the impact of different design scenarios and thus accelerate the development of new treatment strategies. Such models generally consist of a set of continuous, coupled, partial differential equations relying on a number of parameters and functional forms. They necessitate dedicated in vitro experiments to be informed, which are seldom available and often involve small datasets with limited spatio-temporal resolution, generating uncertainties. We address this issue and propose a pipeline based on Bayesian inference enabling the derivation of experimentally informed cell-solute models describing therapeutic cell behaviour in nerve tissue engineering. We apply our pipeline to three relevant cell types and obtain models that can readily be used to simulate nerve repair scenarios and quantitatively compare therapeutic cells. Beyond parameter estimation, the proposed pipeline enables model selection as well as experiment utility quantification, aimed at improving both model formulation and experimental design.

Technical evaluation of a simulator for accurate reproduction of oscillometric blood pressure pulses, providing traceability for automated oscillometric sphygmomanometers.

Oscillometric blood pressure measurement devices are not directly traceable to primary standards. Currently, device accuracy is measured by comparison between a sample device and reference measurements in a clinical trial. We researched in this study the potential for an alternative evaluation with a simulator. Our research simulator was studied for repeatability and accuracy in delivering simulated blood pressure pulses. Clinical cuff pressure measurements were obtained, along with simultaneous recordings of oscillometric pulse waveforms, spanning the clinical range of cuff pressures, pulse intervals and pulse shapes. Oscillometric pulse peak amplitudes ranged from 1.1 to 3.6 mmHg. Simulated repeatability results showed an average Standard Deviation (SD) for pulse peaks of 0.018 mmHg; 1.0% of peak amplitudes. Comparing simulated pulse shapes, the average repeat SD was 0.015 mmHg; 0.8% of the normalised pulse shapes. The simulated accuracy results had a mean error of - 0.014 ± 0.042 mmHg with a mean accuracy of 97.8%. For pulse shape the corresponding values were - 0.104 ± 0.071 mmHg with a mean accuracy of 95.4%. The correlation between the reference and simulated pulse shapes ranged from 0.991 to 0.996 (all p < 0.00003), with a mean 0.994. We conclude that oscillometric pulses can be reproduced with high repeatability and high accuracy with our research simulator. The extended uncertaintyU(psim) = 0.3 mmHg for the simulated pulses is dominated by the uncertainty (64%) of the clinical reference data. These results underpin the potential of the simulator to become a secondary standard for millions of oscillometric sphygmomanometers.

A novel hybrid framework based on temporal convolution network and transformer for network traffic prediction.

BACKGROUND: Accurately predicting mobile network traffic can help mobile network operators allocate resources more rationally and can facilitate stable and fast network services to users. However, due to burstiness and uncertainty, it is difficult to accurately predict network traffic. METHODOLOGY: Considering the spatio-temporal correlation of network traffic, we proposed a deep-learning model, Convolutional Block Attention Module (CBAM) Spatio-Temporal Convolution Network-Transformer, for time-series prediction based on a CBAM attention mechanism, a Temporal Convolutional Network (TCN), and Transformer with a sparse self-attention mechanism. The model can be used to extract the spatio-temporal features of network traffic for prediction. First, we used the improved TCN for spatial information and added the CBAM attention mechanism, which we named CSTCN. This model dealt with important temporal and spatial features in network traffic. Second, Transformer was used to extract spatio-temporal features based on the sparse self-attention mechanism. The experiments in comparison with the baseline showed that the above work helped significantly to improve the prediction accuracy. We conducted experiments on a real network traffic dataset in the city of Milan. RESULTS: The results showed that CSTCN-Transformer reduced the mean square error and the mean average error of prediction results by 65.16%, 64.97%, and 60.26%, and by 51.36%, 53.10%, and 38.24%, respectively, compared to CSTCN, a Long Short-Term Memory network, and Transformer on test sets, which justified the model design in this paper.

The shadow of dementia: Listening to undecidability in ethnographic interviews with persons suspecting possible dementia.

Even before a diagnosis of dementia, people may negotiate in their everyday lives the fears and suspicions about the possibility of a future with dementia. My field of research involves JewishIsraeli older adult individuals who suspect that they are beginning to lose their memory, but before seeking out a formal diagnosis-and when not seeking a diagnosis at all is an equal possibility. By distinguishing their experience of suspecting possible dementia from this of living with dementia, I attempt to illuminate the social, bio-diagnostic and cultural shadows of dementia hovering in the background of their everyday experience. I begin by shedding light on the ethical and methodological context of my specific field in Israel. I next reflect upon the concept of "shadow," that is constituted within and reflecting the assemblages of lurking presences accompanying my interlocutors' daily negotiations of the possibility of dementia. I then situate their lived experiences, as well as my ethnographic engagement with them, in the context of the prevailing cultural and social moralities surrounding this possibility. Finally, I show how a negotiation of the place that this shadow occupies in their lives arises in the encounter with the ethnographer. This first account of people before diagnosis and not through the diagnostic event, while keeping the space for deciding about a possible future of diagnosis open, can contribute to the understanding of undecidability as an ethical stance in ethnography, incorporating the suspension of the need to order realities through the imperatives of a diagnosis of dementia. Further, understanding these mundane negotiations with these shadows can help us allow more space for uncertainty and unpredictability as legitimate forms of living with dementia.

Influence of Intolerance of Uncertainty on Preoccupation With Coronavirus Disease 2019 Among Frontline Nursing Professionals: Mediating Role of Reassurance-Seeking Behavior and Adherence to Physical Distancing.

BACKGROUND: This study investigated the relationship between preoccupation with coronavirus disease 2019 (COVID-19), reassurance-seeking behavior, viral anxiety, intolerance of uncertainty, and adherence to physical distancing among frontline nursing professionals working in COVID-19 inpatient wards. Additionally, the study aimed to determine whether the commitment to physical distancing mediates the influence of intolerance of uncertainty on viral anxiety. METHODS: Frontline healthcare professionals working in the COVID-19 inpatient wards at three tertiary-level affiliated hospitals in Korea were surveyed between April 7 and 26, 2022. The survey included scales-such as the Obsession with COVID-19 Scale, Coronavirus Reassurance-Seeking Behaviors Scale, Fear of COVID-19 Scale, and Intolerance of Uncertainty Scale-12 and a questionnaire on adherence to physical distancing. A total of 256 responses were analyzed after excluding inappropriate or incomplete responses. RESULTS: Pearson's correlation analysis found that age was significantly associated with the Obsession with COVID-19 Scale (r = -0.12, P < 0.05) and adherence to physical distancing (r = 0.27, P < 0.01). Linear regression analysis ascertained that age (ß = -0.07, P = 0.002), Coronavirus Reassurance-Seeking Behaviors Scale (ß = 0.35, P < 0.001), and Fear of COVID-19 Scale (ß = 0.24, P < 0.001) were predictors of obsession with COVID-19 (Adjusted R² = 0.60, F = 78.1, P < 0.001). The indirect pathway by mediation analysis showed that reassurance-seeking and viral anxiety mediated the effect of intolerance of uncertainty on the preoccupation with COVID-19. CONCLUSION: During the pandemic, there may be a strong association between reassurance-seeking behavior, viral anxiety, and a heightened preoccupation with COVID-19 among frontline healthcare workers. Thus, from the early stages of infectious disease, a psychological support team for medical staff responding to the disease should be established, and periodic evaluations should be conducted to identify high-risk groups.

How Does Temporal Blurring Alter Movement Timing?

Subjective uncertainty arises because the estimation of the timing of an event into the future is error prone. This impact of stimulus-bound uncertainty on movement preparation has often been investigated using reaction time tasks where a warning stimulus (WS) predicts the occurrence of a "go" signal. The timing of the "go" signal can be chosen from a particular probability distribution with a given variance or uncertainty. It has been repeatedly shown that reaction times covary with the shape of the used "go" signal distribution. This is interpreted as evidence for temporal preparation. Moreover, the variance of the response time should always increase with the duration of the delay between the WS and the "go" signal. This increasing variance has been interpreted as a consequence of the temporal "blurring" of future events (scalar expectancy). The present paper tested the validity of the temporal "blurring" hypothesis in humans with a simple oculomotor reaction time task where subjective and stimulus-bound uncertainties were increased. Subjective uncertainty about the timing of a "go" signal was increased by lengthening the delay between the WS and the "go" signal. Objective uncertainty was altered by increasing the variance of "go" signal timing. Contrary to temporal blurring hypotheses, the study has shown that increasing the delay between events did not significantly increase movement timing variability. These results suggest that temporal blurring could not be a property of movement timing in an implicit timing context.

Majority rule can help solve difficult tasks even when confident members opt out to serve individual interests.

When sharing a common goal, confident and competent members are often motivated to contribute to the group, boosting its decision performance. However, it is unclear whether this process remains effective when members can opt in or out of group decisions and prioritize individual interests. Our laboratory experiment (n = 63) and cognitive modeling showed that at the individual level, confidence, competence, and a preference for risk motivated participants' opt-out decisions. We then analyzed the group-level accuracy of majority decisions by creating many virtual groups of 25 members resampled from the 63 participants in the experiment. Whereas the majority decisions by voters who preferred to participate in group decision making were inferior to individual decisions by loners who opted out in an easy task, this was reversed in a difficult task. Bootstrap-simulation analyses decomposed these outcomes into the effects of a decrease in group size and a decrease in voters' accuracy accruing from the opt-in/out mechanism, demonstrating how these effects interacted with task difficulty. Our results suggest that the majority rule still works to tackle challenging problems even when individual interests are emphasized over collective performance, playing a functional as well as a democratic role in consensus decision making under uncertainty.

Assessing comparability and uncertainty of analytical methods for methylated mercury species in seawater.

BACKGROUND: The relative distribution and importance of monomethylmercury (MMHg) and dimethylmercury (DMHg) in seawater is still under debate. A lack of comparability between measurements at sub-picomolar levels hampered the further understanding of the biogeochemical Hg cycle. To overcome this, we assessed the relative standard measurement uncertainties (Uex,r) for direct measurements of MMHg and DMHg by species-specific isotope dilution ICP-MS and cryo-focusing GC-ICP-MS at femtomolar concentrations. Furthermore, Uex,r was determined for the indirect determination of DMHg (DMHgcalc = MeHg - MMHg) and MeHg (MeHgcalc = MMHg + DMHg) to compare the two methodologies. RESULTS: Expanded Uex,r (confidence interval of 95%) for cryo-focusing GC-ICP-MS was 14.4 (<50 fM) and 14.2% (>50 fM) and for SS-ID GC-ICP-MS 5.6 (<50 fM) and 3.7% (>50 fM). For concentrations above 50 fM, Uex,r for DMHgcalc was always lower than for direct measurements (14.2%). For MeHgcalc, on the other hand, Uex,r was always higher for concentrations above 115 fM (range: 3.7-13.9%) than for direct measurements (3.7%). We evaluated the comparability of directly measured and calculated DMHg and MeHg concentrations based on Hg speciation measurements for two vertical profiles in the Mediterranean Sea. We show that directly measured and indirectly determined DMHg and MeHg concentrations yield comparable results. SIGNIFICANCE: Our results validate the application of the indirect method for the determination of DMHg if a direct measurement method with a low Uex,r such as isotope dilution is used for MMHg and MeHg measurements. The validation of the indirect measurement approach opens new possibilities to generate more precise and accurate DMHg data in the global ocean.

Topology switching during window thresholding fMRI-based functional networks of patients with major depressive disorder: Consensus network approach.

We present a novel method for analyzing brain functional networks using functional magnetic resonance imaging data, which involves utilizing consensus networks. In this study, we compare our approach to a standard group-based method for patients diagnosed with major depressive disorder (MDD) and a healthy control group, taking into account different levels of connectivity. Our findings demonstrate that the consensus network approach uncovers distinct characteristics in network measures and degree distributions when considering connection strengths. In the healthy control group, as connection strengths increase, we observe a transition in the network topology from a combination of scale-free and random topologies to a small-world topology. Conversely, the MDD group exhibits uncertainty in weak connections, while strong connections display small-world properties. In contrast, the group-based approach does not exhibit significant differences in behavior between the two groups. However, it does indicate a transition in topology from a scale-free-like structure to a combination of small-world and scale-free topologies. The use of the consensus network approach also holds immense potential for the classification of MDD patients, as it unveils substantial distinctions between the two groups.

Contributions of the Basal Ganglia to Visual Perceptual Decisions.

The basal ganglia (BG) make up a prominent nexus between visual and motor-related brain regions. In contrast to the BG's well-established roles in movement control and value-based decision making, their contributions to the transformation of visual input into an action remain unclear, especially in the context of perceptual decisions based on uncertain visual evidence. This article reviews recent progress in our understanding of the BG's contributions to the formation, evaluation, and adjustment of such decisions. From theoretical and experimental perspectives, the review focuses on four key stations in the BG network, namely, the striatum, pallidum, subthalamic nucleus, and midbrain dopamine neurons, which can have different roles and together support the decision process.

Addressing clinician moral distress: Implications from a mixed methods evaluation during Covid-19.

Clinician moral distress has been documented over the past several decades as occurring within numerous healthcare disciplines, often in relation to clinicians' involvement in patients' end-of-life decision-making. The resulting harms impact clinician well-being, patient well-being, and healthcare system functioning. Given Covid-19's catastrophic death toll and associated demands on end-of-life decision-making processes, the pandemic represents a particularly important context within which to understand clinician moral distress. Thus, we conducted a convergent mixed methods study to examine its prevalence, associations with clinicians' demographic and professional characteristics, and contributing circumstances among Veterans Health Administration (VA) clinicians. The study, conducted in April 2021, consisted of a cross-sectional on-line survey of VA clinicians at 20 VA Medical Centers with professional jurisdiction to place life-sustaining treatment orders working who were from a number of select specialties. The survey collected quantitative data on respondents' demographics, clinical practice characteristics, attitudes and behaviors related to goals of care conversations, intensity of moral distress during "peak-Covid," and qualitative data via an open-ended item asking for respondents to describe contributing circumstances if they had indicated any moral distress. To understand factors associated with heightened moral distress, we analyzed quantitative data using bivariate and multivariable regression analyses and qualitative data using a hybrid deductive/inductive thematic approach. Mixed methods analysis followed, whereby we compared the quantitative and qualitative datasets and integrated findings at the analytic level. Out of 3,396 eligible VA clinicians, 323 responded to the survey (9.5% adjusted response rate). Most respondents (81%) reported at least some moral distress during peak-Covid. In a multivariable logistic regression, female gender (OR 3.35; 95% CI 1.53-7.37) was associated with greater odds of moral distress, and practicing in geriatrics/palliative care (OR 0.40; 95% CI 0.18-0.87) and internal medicine/family medicine/primary care (OR 0.46; 95% CI 0.22-0.98) were associated with reduced odds of moral distress compared to medical subspecialties. From the 191 respondents who completed the open-ended item, five qualitative themes emerged as moral distress contributors: 1) patient visitation restrictions, 2) anticipatory actions, 3) clinical uncertainty related to Covid, 4) resource shortages, and 5) personal risk of contracting Covid. Mixed methods analysis found that quantitative results were consistent with these last two qualitative themes. In sum, clinician moral distress was prevalent early in the pandemic. This moral distress was associated with individual-, system-, and situation-level contributors. These identified contributors represent leverage points for future intervention to mitigate clinician moral distress and its negative outcomes during future healthcare crises and even during everyday clinical care.

Diagnostic accuracy of 18F-FP-CIT PET for clinically uncertain Parkinsonian syndrome.

18F-FP-CIT is a high-resolution imaging marker of nigrostriatal neuronal integrity, differentiating Parkinsonism with loss of dopaminergic terminals (presynaptic Parkinsonian syndrome [PS]) from Parkinsonism without nigrostriatal degeneration (non-PS). We assessed the diagnostic accuracy of 18F-FP-CIT PET in patients with clinically uncertain PS (CUPS) at the first visit. Among the 272 patients who underwent 18F-FP-CIT PET imaging at the first visit between September 2008 and July 2012, 111 had CUPS (age, 62.6 ± 10.5 y; male:female, 45:66; symptom duration, 13.1 ± 8.8 months). Uncertainty criteria included only one of the three cardinal signs of Parkinsonism, two signs without bradykinesia, or atypical signs. The baseline clinical and 18F-FP-CIT PET imaging diagnostic accuracy was compared with the accuracy of clinical diagnosis after > 2-year follow-up. Nuclear medicine physicians assessed the 18F-FP-CIT PET images visually. Focal dopamine transporter binding deficit in the posterior putamen was considered PS. Bilateral symmetric striatum without focal deficit, suggesting normal 18F-FP-CIT PET, and focal deficits elsewhere in the striatum suggesting vascular Parkinsonism were considered non-PS. Seventy-nine patients had PS, and 32 did not. Baseline clinical diagnosis included PS in 45 patients, non-PS in 24, and inconclusive in 42. Among patients in whom initial clinical diagnosis (PS or non-PS) was possible, the sensitivity, specificity, and accuracy of the baseline clinical and 18F-FP-CIT PET imaging diagnoses were 54.4, 50.0, and 53.2%, and 98.7, 100, and 99.1%, respectively. The respective positive and negative predictive values were 95.6 and 66.7%, and 100 and 97.0%. Among those with initially inconclusive diagnosis, 64.2% were eventually diagnosed with PS while 35.7% were diagnosed with non-PS. The final clinical diagnosis of these patients all matched those made by 18F-FP-CIT PET imaging, except in one patient with scan without evidence of dopaminergic deficit (SWEDD). 18F-FP-CIT PET diagnosis was more accurate than clinical diagnosis, reducing the false-negative and inconclusive clinical diagnosis rates at baseline in patients with CUPS.

DUA: A Domain-Unified Approach for Cross-Dataset 3D Human Pose Estimation.

Human pose estimation is an important Computer Vision problem, whose goal is to estimate the human body through joints. Currently, methods that employ deep learning techniques excel in the task of 2D human pose estimation. However, the use of 3D poses can bring more accurate and robust results. Since 3D pose labels can only be acquired in restricted scenarios, fully convolutional methods tend to perform poorly on the task. One strategy to solve this problem is to use 2D pose estimators, to estimate 3D poses in two steps using 2D pose inputs. Due to database acquisition constraints, the performance improvement of this strategy can only be observed in controlled environments, therefore domain adaptation techniques can be used to increase the generalization capability of the system by inserting information from synthetic domains. In this work, we propose a novel method called Domain Unified approach, aimed at solving pose misalignment problems on a cross-dataset scenario, through a combination of three modules on top of the pose estimator: pose converter, uncertainty estimator, and domain classifier. Our method led to a 44.1mm (29.24%) error reduction, when training with the SURREAL synthetic dataset and evaluating with Human3.6M over a no-adaption scenario, achieving state-of-the-art performance.

Does It Matter Whether a Psychiatric Intervention Is "Palliative"?

Palliative interventions are intended to alleviate suffering and improve quality, not quantity, of life and are not intended to cure illness. In psychiatry, uncertainty about which interventions count as palliative stems from the fact that psychiatry generally prioritizes symptom management irrespective of diagnosis or specific pathophysiology of illness. This commentary on a case considers how distinctions between palliative and other psychiatric interventions might not be all that helpful in resolving clinical and ethical questions about which interventions are-and when they are-appropriate.

The uncertainty related to the inexactitude of prioritization based on consistent pairwise comparisons.

When the in/consistency in Pairwise Comparisons (PCs) is taken into consideration as the subarea of the Multi Attribute Decision Making (MADM) scientific field, it has many repercussions in various types of research areas including different modelling scenarios e.g. reduction of inconsistency during PCs, deriving appropriate consistency thresholds for inconsistent Pairwise Comparison Matrices (PCMs), completing of incomplete PCMs, aggregating of individual PCMs in relation to Group Decision Making (GDM) aspects, and PCMs in/consistency relation to credibility of Priority Vectors (PV) derived from PCMs with the application of various Priorities Deriving Methods (PDMs). The examination objective in the latter area of research is the uncertainty related to the inexactitude of prioritization based on derived PVs. However, only few research studies examine this problem from the perspective of PCM applicability for credible designation of decision maker's (DM) priorities in the way that leads to minimization of the prioritization uncertainty related to possible, and sometimes very probable, ranking fluctuations. This problem constitutes the primary area of interest for this research paper as no research study was thus far identified that examines this problem from the perspective of consistent PCMs. Hence, a research gap was identified. Thus, the objective of this research paper is to fill in this scientific gap. The research findings have serious repercussions in relation to prioritization quality with the application of PCs methodology, mostly in relation to the interpretation and reliability evaluation of prioritization results. Firstly, the research study outcome changes the perspective of the rank reversal phenomenon, which shed new light on many research studies that have been presented in the subject's literature for many decades. Secondly, the research study results throw new light on the discussion concerning the fuzziness of AHP's results. Last but not least, the effect of the research opens the unique opportunity to evaluate the prioritization outcome obtained within the process of consistent PCs from the well-known perspective of statistical hypothesis testing i.e. the probability designation of the chance that accepted ranking results which were considered as correct due to low probability of change may be incorrect, hence they should be rejected, and the probability designation of the chance that rejected ranking results which were considered as incorrect due to high probability of change may be correct and should be accepted. The paramount finding of the research is the fact that consistent PCMs provide PVs, which elements cannot be considered as established, but only approximated within certain confidence intervals estimated with a certain level of probability. As problems related to heuristics can be analyzed only via a computer simulation process, because they cannot be mathematically determined, the problem examined in this research paper is examined via Monte Carlo simulations, appropriately coded and executed with the application of Wolfram's Mathematica Software. It is believed that this research findings should be very important and useful for all decision makers and researchers during their problems' examinations that relate to prioritization processes with the application of PCs methodology.

Investigating the aftermath of the Türkiye 2023 earthquake: exploring post-disaster uncertainty among Syrian migrants using social network analysis with public health approach.

Objectives: On February 6th, 2023, a doublet earthquake struck Türkiye, impacting more than 15 million people including migrants, and resulting in over 50,000 deaths. The Syrian migrants experience multiple uncertainties in their daily lives which are further compounded by multifaceted challenges of the post-disaster environment. Social media was used intensively and with impunity in this environment and thereby provides a window into the explicit and implicit dynamics of daily life after a disaster. We aimed to explore how a post-disaster environment potentially generates new uncertainties or exacerbating pre-existing ones for migrants through social media analysis with an indirect perspective, in the context of 2023-Earthquake in Türkiye and Syrian migrants. Methods: Social network analysis was used to analyze Twitter-data with the hashtags 'Syrian' and 'earthquake' during a 10-day period beginning on March 22nd, 2023. We calculated network metrics, including degree-values and betweenness-centrality and clustered the network to understand groups. We analyzed a combination of 27 tweets with summative content analysis using a text analysis tool, to identify the most frequently used words. We identified the main points of each tweet and assessed these as possible contributors to post-disaster uncertainty among migrants by using inductive reasoning. Results: There were 1918 Twitter users, 274 tweets, 124 replies and 1726 mentions. Discussions about Syrian migrants and earthquakes were established across various groups (ngroups(edges > 15) = 16). Certain users had a greater influence on the overall network. The nine most frequently used words were included under uncertainty-related category (nmost_frequently_used_words = 20); 'aid, vote, house, citizen, Afghan, illegal, children, border, and leave'. Nine main points were identified as possible post-disaster uncertainties among migrants. Conclusion: The post-disaster environment has the potential to exacerbate existing uncertainties, such as being an undocumented migrant, concerns about deportation and housing, being or having a child, inequality of rights between being a citizen and non-citizen, being in minority within minority, political climate of the host nation and access to education or to generate new ones such equitable distribution of aid, which can lead to poor health outcomes. Recognizing the possible post-disaster uncertainties among migrants and addressing probable underlying factors might help to build more resilient and healthy communities.

Adaptive Bayesian learning for making risk-aware decisions: A case of trauma survival prediction.

Decision tree (DT) models provide a transparent approach to prediction of patient's outcomes within a probabilistic framework. Averaging over DT models under certain conditions can deliver reliable estimates of predictive posterior probability distributions, which is of critical importance in the case of predicting an individual patient's outcome. Reliable estimations of the distribution can be achieved within the Bayesian framework using Markov chain Monte Carlo (MCMC) and its Reversible Jump extension enabling DT models to grow to a reasonable size. Existing MCMC strategies however have limited ability to control DT structures and tend to sample overgrown DT models, making unreasonably small partitions, thus deteriorating the uncertainty calibration. This happens because the MCMC explores a DT model parameter space within a limited knowledge of the distribution of data partitions. We propose a new adaptive strategy which overcomes this limitation, and show that in the case of predicting trauma outcomes the number of data partitions can be significantly reduced, so that the unnecessary uncertainty of estimating the predictive posterior density is avoided. The proposed and existing strategies are compared in terms of entropy which, being calculated for predicted posterior distributions, represents the uncertainty in decisions. In this framework, the proposed method has outperformed the existing sampling strategies, so that the unnecessary uncertainty in decisions is efficiently avoided.

MLNAN: Multi-level noise-aware network for low-dose CT imaging implemented with constrained cycle Wasserstein generative adversarial networks.

Low-dose CT techniques attempt to minimize the radiation exposure of patients by estimating the high-resolution normal-dose CT images to reduce the risk of radiation-induced cancer. In recent years, many deep learning methods have been proposed to solve this problem by building a mapping function between low-dose CT images and their high-dose counterparts. However, most of these methods ignore the effect of different radiation doses on the final CT images, which results in large differences in the intensity of the noise observable in CT images. What'more, the noise intensity of low-dose CT images exists significantly differences under different medical devices manufacturers. In this paper, we propose a multi-level noise-aware network (MLNAN) implemented with constrained cycle Wasserstein generative adversarial networks to recovery the low-dose CT images under uncertain noise levels. Particularly, the noise-level classification is predicted and reused as a prior pattern in generator networks. Moreover, the discriminator network introduces noise-level determination. Under two dose-reduction strategies, experiments to evaluate the performance of proposed method are conducted on two datasets, including the simulated clinical AAPM challenge datasets and commercial CT datasets from United Imaging Healthcare (UIH). The experimental results illustrate the effectiveness of our proposed method in terms of noise suppression and structural detail preservation compared with several other deep-learning based methods. Ablation studies validate the effectiveness of the individual components regarding the afforded performance improvement. Further research for practical clinical applications and other medical modalities is required in future works.