How to Change the Weight of Rare Events in Decisions from Experience

When making risky choices, two kinds of information are crucial: outcome values and outcome probabilities. Here, we demonstrate that the juncture at which value and probability information is provided has a fundamental effect on choice. Across four experiments involving 489 participants, we compare two decision making scenarios: one where value information is revealed during sampling (Standard), and one where value information is revealed after sampling (Value-Ignorance). On average, participants made riskier choices when value information was provided after sampling. Moreover, parameter estimates from a hierarchical Bayesian implementation of cumulative prospect theory suggested that participants overweighted rare events when value information was absent during sampling, but showed no overweighting in the Standard condition. This suggests that the impact of rare events on choice relies crucially on the timing of probability and value integration. We provide paths towards mechanistic explanations of our results based on frameworks which assume different underlying cognitive architectures

Biomaterial Strategies for Immunomodulation

Strategies to enhance, suppress, or qualitatively shape the immune response are of importance for diverse biomedical applications, such as the development of new vaccines, treatments for autoimmune diseases and allergies, strategies for regenerative medicine, and immunotherapies for cancer. However, the intricate cellular and molecular signals regulating the immune system are major hurdles to predictably manipulating the immune response and developing safe and effective therapies. To meet this challenge, biomaterials are being developed that control how, where, and when immune cells are stimulated in vivo, and that can finely control their differentiation in vitro. We review recent advances in the field of biomaterials for immunomodulation, focusing particularly on designing biomaterials to provide controlled immunostimulation, targeting drugs and vaccines to lymphoid organs, and serving as scaffolds to organize immune cells and emulate lymphoid tissues. These ongoing efforts highlight the many ways in which biomaterials can be brought to bear to engineer the immune system.Bill & Melinda Gates FoundationUnited States. Army Research Office. Institute for Soldier Nanotechnologies (Contract W911NF-13-D-0001)Ragon Institute of MGH, MIT and HarvardCancer Research Institute (New York, N.Y.) (Irvington Postdoctoral Fellowship)National Institutes of Health (U.S.) (Awards AI104715, CA172164, CA174795, and AI095109

A call for standardized outcomes in microTESE

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136713/1/andr12356.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136713/2/andr12356_am.pd

MEM-EX: An exemplar memory model of decisions from experience

Many real-world decisions must be made on basis of experienced outcomes. However, there is little consensus about the mechanisms by which people make these decisions from experience (DfE). Across five experiments, we identified several factors influencing DfE. We also introduce a novel computational modeling framework, the memory for exemplars model (MEM-EX), which posits that decision makers rely on memory for previously experienced outcomes to make choices. Using MEM-EX, we demonstrate how several cognitive mechanisms provide intuitive and parsimonious explanations for the effects of value-ignorance, salience, outcome order, and sample size. We also conduct a cross-validation analysis of several models within the MEM-EX framework, as well as a baseline model built on principles of reinforcement-learning. We find that MEM-EX consistently outperforms this baseline, demonstrating its value as a tool for making quantitative predictions without overfitting. We discuss the implications of these findings on our understanding of the interplay between attention, memory, and experience-based choice

New insights into the manual activities of individuals from the Phaleron cemetery (Archaic Athens, Greece)

Until the early 5th century BC, Phaleron Bay was the main port of ancient Athens (Greece). On its shore, archaeologists have discovered one of the largest known cemeteries in ancient Greece, including a range of burial forms, simple pits, cremations, larnaces (clay tubs), and series of burials of male individuals who appear to have died violent deaths, referred to here as “atypical burials”. Reconstructing the osteobiographies of these individuals will help create a deeper understanding of the socio-political conditions preceding the rise of Classical Athens. Here, we assess the habitual manual behavior of the people of Archaic Phaleron (ca. 7th – 6th cent. BC), relying on a new and precise three-dimensional method for reconstructing physical activity based on hand muscle attachment sites. This approach has been recently validated on laboratory animal samples as well as on recent human skeletons with a detailed level of long-term occupational documentation (i.e., the mid-19th century Basel Spitalfriedhof sample). Our Phaleron sample consists of 48 adequately preserved hand skeletons, of which 14 correspond to atypical burials. Our results identified consistent differences in habitual manual behaviors between atypical burials and the rest. The former present a distinctive power-grasping tendency in most skeletons, which was significantly less represented in the latter (p-values of <0.01 and 0.03). Based on a comparison with the uniquely documented Basel sample (45 individuals), this entheseal pattern of the atypical burials was exclusively found in long-term heavy manual laborers. These findings reveal an important activity difference between burials typical for the Phaleron cemetery and atypical burials, suggesting that the latter were likely involved in distinctive, strenuous manual activities. The results of this pilot study comprise an important first step towards reconstructing the identity of these human skeletal remains. Future research can further elucidate the occupational profiles of these individuals through the discovery of additional well-preserved hand skeletons and by extending our analyses to other anatomical regions

Deep learning predicts function of live retinal pigment epithelium from quantitative microscopy.

Increases in the number of cell therapies in the preclinical and clinical phases have prompted the need for reliable and non-invasive assays to validate transplant function in clinical biomanufacturing. We developed a robust characterization methodology composed of quantitative bright-field absorbance microscopy (QBAM) and deep neural networks (DNNs) to non-invasively predict tissue function and cellular donor identity. The methodology was validated using clinical-grade induced pluripotent stem cell derived retinal pigment epithelial cells (iPSC-RPE). QBAM images of iPSC-RPE were used to train DNNs that predicted iPSC-RPE monolayer transepithelial resistance, predicted polarized vascular endothelial growth factor (VEGF) secretion, and matched iPSC-RPE monolayers to the stem cell donors. DNN predictions were supplemented with traditional machine learning algorithms that identified shape and texture features of single cells that were used to predict tissue function and iPSC donor identity. These results demonstrate non-invasive cell therapy characterization can be achieved with QBAM and machine learning

Glycaemic control and risk of incident urinary incontinence in women with Type 1 diabetes: results from the Diabetes Control and Complications Trial and Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study

AimsTo study the impact of glycaemic control on urinary incontinence in women who participated in the Diabetes Control and Complications Trial (DCCT; 1983–1993) and its observational follow‐up study, the Epidemiology of Diabetes Interventions and Complications (EDIC; 1994–present).MethodsStudy participants were women who completed, at both years 10 (2003) and 17 (2010) of the EDIC follow‐up, the urological assessment questionnaire (UroEDIC). Urinary incontinence was defined as self‐reported involuntary leakage of urine that occurred at least weekly. Incident urinary incontinence was defined as weekly urinary incontinence present at EDIC year 17 but not at EDIC year 10. Multivariable regression models were used to examine the association of incident urinary incontinence with comorbid prevalent conditions and glycaemic control (mean HbA1c over the first 10 years of EDIC).ResultsA total of 64 (15.3%) women with Type 1 diabetes (mean age 43.6 ± 6.3 years at EDIC year 10) reported incident urinary incontinence at EDIC year 17. When adjusted for clinical covariates (including age, DCCT cohort assignment, DCCT treatment arm, BMI, insulin dosage, parity, hysterectomy, autonomic neuropathy and urinary tract infection in the last year), the mean EDIC HbA1c was associated with increased odds of incident urinary incontinence (odds ratio 1.03, 95% CI 1.01–1.06 per mmol/mol increase; odds ratio 1.41, 95% CI 1.07–1.89 per % HbA1c increase).ConclusionsIncident urinary incontinence was associated with higher HbA1c levels in women with Type 1 diabetes, independent of other recognized risk factors. These results suggest the potential for women to modify their risk of urinary incontinence with improved glycaemic control. (Clinical Trials Registry no: NCT00360815 and NCT00360893).What’s new?Research to date has failed to show an association between glycaemic control and urinary incontinence (UI) in women with diabetes.We examined the relationship between HbA1c and UI using longitudinal data from the Diabetes Control and Complications Trial (DCCT) and its observational follow‐up, the Epidemiology of Diabetes Interventions and Complications (EDIC) study.Our findings show that the odds of UI increase with poor glycaemic control in women with Type 1 diabetes, independently of other well‐described predictors of UI.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134490/1/dme13126.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134490/2/dme13126_am.pd