Useful misrepresentation: perception as embodied proactive inference

According to the predictive processing framework, perception is geared to represent the environment in terms of embodied action opportunities as opposed to objective truth. Here, we argue that such an optimisation is reflected by biases in expectations (i.e., prior predictive information) that facilitate ‘useful’ inferences of external sensory causes. To support this, we highlight a body of literature suggesting that perception is systematically biased away from accurate estimates under conditions where utility and accuracy conflict with one another. We interpret this to reflect the brain’s attempt to adjudicate between conflicting sources of prediction error, as external accuracy is sacrificed to facilitate actions that proactively avoid physiologically surprising outcomes. This carries important theoretical implications and offers new insights into psychopathology.

Validation of Pre-Transplantation Plasma ST2 Levels As a Prognostic Marker of 1-Year Non-Relapse Mortality after Allogeneic Hematopoietic Cell Transplantation

Introduction: Accurate assessment of the risk of non-relapse mortality (NRM) is important for making the shared decision about treatment with allogeneic hematopoietic cell transplantation (allo-HCT). We have shown that the pre-transplantation plasma level of suppression of tumorigenicity 2 (ST2)-a protein that is released to the bloodstream upon inflammation, cellular stress and endothelial damage-was associated with NRM after myeloablative allo-HCT [Gjærde et al., ASH Annual Meeting 2020, abstract #1524]. In an expanded cohort of both myeloablative- and non-myeloablative conditioned patients, we aimed to validate the value of pre-transplant ST2 in predicting 1-year NRM after allo-HCT. Methods: Pre-transplantation plasma ST2 levels were measured by enzyme-linked immunosorbent assays in 374 adult patients who underwent allo-HCT at Rigshospitalet between July 2015 and December 2019 (Table 1), using stored plasma samples collected at a median (Q1, Q3) of 23 (21, 24) days before allo-HCT. All patients were followed-up for at least 1 year after transplant. NRM was defined as all deaths in relapse-free patients. Given our sample size and outcome proportion, we could include four parameters in a logistic regression model of 1-year NRM to avoid severe overfitting [Riley et al., BMJ, 2020]. Based on our current clinical risk assessment practice, we included age (linear), comorbidity index (HCT-CI [Sorror et al., Blood, 2005], linear) and conditioning intensity (myeloablative vs. non-myeloablative) in a base model, to which we added the pre-transplantation ST2 level (linear) and assessed its incremental prognostic value [Steyerberg et al., Epidemiology, 2019]. The internal validity of the full model was estimated by bootstrapping [Steyerberg et al., J Clin Epidemiol, 2001]. Results: The median (Q1, Q3) pre-transplantation plasma ST2 level was 20.4 (15.2, 27.2) ng/mL. NRM at 1-year was 9% (N = 33). The main causes of NRM were organ failure (39%), infection (23%) and acute graft-versus-host disease (21%). Relapse risk at 1-year was 18%. The patients who constituted the 33 cases of 1-year NRM had a 2.7 ng/mL higher median pre-transplantation ST2 level than the remaining 341 patients (95% bootstrap confidence interval [CI] of the difference: -1.9, 6.2 ng/mL, Figure Panel A). In the full logistic regression model-including age, HCT-CI, conditioning intensity and ST2-ST2 was associated with 1-year NRM with an odds ratio of 1.32 (CI: 1.05, 1.65) per 10 ng/mL increase. Adding ST2 to the base model increased the model likelihood ratio χ 2 from 12.1 to 17.3 (p = 0.02), i.e. ST2 added a fraction of 30% (12.1/17.3) of new predictive information to age, HCT-CI and conditioning intensity. However, the ability of the full model to discriminate cases of NRM at 1-year remained poor with minimal improvement after adding ST2 (AUC up to 0.675 from 0.674 in the base model). The bootstrap-corrected AUC (the expected AUC of the full model used in a new population) was 0.63. Moreover, bootstrap-corrected estimates of predicted vs. observed risk revealed slight model miscalibration: lower predicted risks were generally underestimated, while higher predicted risks were overestimated (Figure Panel B). Conclusion: Pre-transplantation plasma levels of ST2 was a prognostic biomarker of 1-year NRM after allo-HCT, adding new predictive information to age, HCT-CI and conditioning intensity. However, internal validation of the full ST2-based prediction model revealed poor overall performance, precluding further validation and use of the model in clinical practice. When identifying prognostic biomarkers, investigation of overall predictive performance (in addition to already known prognostic factors) is needed before clinical usefulness can be evaluated. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

Predicting Stock Return with Economic Constraint: Can Interquartile Range Truncate the Outliers?

We find that imposing economic constraint on stock return forecasts based on the Interquartile Range of equity premium can significantly strengthen predictive performance. Specifically, we construct a judgment mechanism that truncates the outliers in forecasts of stock return. We prove that our constraint approach can realize more accurate predictive information relative to the unconstraint approach from the perspective of statistics and economics. In addition, the new constraint approach can effectively defeat CT constraint and CDA strategy. The three mixed models we proposed can further enhance the accuracy of prediction, especially the mixed model combined with our constraint approach. Finally, utilizing our new constraint approach can help investors obtain considerable economic gains. With the application of extension and robustness analysis, our results are robust.

Insulin-mediated muscle microvascular perfusion and its phenotypic predictors in humans

Insulin increases muscle microvascular perfusion and enhances tissue insulin and nutrient delivery. Our aim was to determine phenotypic traits that foretell human muscle microvascular insulin responses. Hyperinsulinemic euglycemic clamps were performed in 97 adult humans who were lean and healthy, had class 1 obesity without comorbidities, or controlled type 1 diabetes without complications. Insulin-mediated whole-body glucose disposal rates (M-value) and insulin-induced changes in muscle microvascular blood volume (ΔMBV) were determined. Univariate and multivariate analyses were conducted to examine bivariate and multivariate relationships between outcomes, ΔMBV and M-value, and predictor variables, body mass index (BMI), total body weight (WT), percent body fat (BF), lean body mass, blood pressure, maximum consumption of oxygen (VO2max), plasma LDL (LDL-C) and HDL cholesterol, triglycerides (TG), and fasting insulin (INS) levels. Among all factors, only M-value (r = 0.23, p = 0.02) and VO2max (r = 0.20, p = 0.047) correlated with ΔMBV. Conversely, INS (r = − 0.48, p ≤ 0.0001), BF (r = − 0.54, p ≤ 0.001), VO2max (r = 0.5, p ≤ 0.001), BMI (r = − 0.40, p < 0.001), WT (r = − 0.33, p = 0.001), LDL-C (r = − 0.26, p = 0.009), TG (r = − 0.25, p = 0.012) correlated with M-value. While both ΔMBV (p = 0.045) and TG (p = 0.03) provided significant predictive information about M-value in the multivariate regression model, only M-value was uniquely predictive of ΔMBV (p = 0.045). Thus, both M-value and VO2max correlated with ΔMBV but only M-value provided unique predictive information about ΔMBV. This suggests that metabolic and microvascular insulin responses are important predictors of one another, but most metabolic insulin resistance predictors do not predict microvascular insulin responses.

Assessment of Cardiorespiratory Interactions during Apneic Events in Sleep via Fuzzy Kernel Measures of Information Dynamics

Apnea and other breathing-related disorders have been linked to the development of hypertension or impairments of the cardiovascular, cognitive or metabolic systems. The combined assessment of multiple physiological signals acquired during sleep is of fundamental importance for providing additional insights about breathing disorder events and the associated impairments. In this work, we apply information-theoretic measures to describe the joint dynamics of cardiorespiratory physiological processes in a large group of patients reporting repeated episodes of hypopneas, apneas (central, obstructive, mixed) and respiratory effort related arousals (RERAs). We analyze the heart period as the target process and the airflow amplitude as the driver, computing the predictive information, the information storage, the information transfer, the internal information and the cross information, using a fuzzy kernel entropy estimator. The analyses were performed comparing the information measures among segments during, immediately before and after the respiratory event and with control segments. Results highlight a general tendency to decrease of predictive information and information storage of heart period, as well as of cross information and information transfer from respiration to heart period, during the breathing disordered events. The information-theoretic measures also vary according to the breathing disorder, and significant changes of information transfer can be detected during RERAs, suggesting that the latter could represent a risk factor for developing cardiovascular diseases. These findings reflect the impact of different sleep breathing disorders on respiratory sinus arrhythmia, suggesting overall higher complexity of the cardiac dynamics and weaker cardiorespiratory interactions which may have physiological and clinical relevance.

Advances in Genomic Discovery and Implications for Personalized Prevention and Medicine: Estonia as Example

The current paradigm of personalized medicine envisages the use of genomic data to provide predictive information on the health course of an individual with the aim of prevention and individualized care. However, substantial efforts are required to realize the concept: enhanced genetic discoveries, translation into intervention strategies, and a systematic implementation in healthcare. Here we review how further genetic discoveries are improving personalized prediction and advance functional insights into the link between genetics and disease. In the second part we give our perspective on the way these advances in genomic research will transform the future of personalized prevention and medicine using Estonia as a primer.

Are individual stock returns predictable?

We show that the previously documented predictability of macroeconomic and technical variables for market returns is also evident in individual stock returns. Technical variables generate better predictability on firms with high limits to arbitrage (small, illiquid, volatile firms), while macroeconomic variables better predict firms with low limits to arbitrage. Technical predictors show a stronger predictive power for high limits to arbitrage firms across the business cycle, whereas macroeconomic variables capture more predictive information for firms with low limits to arbitrage during recessions. JEL Classification: C58, E32, G11, G12, G17