Learning to predict target location with turbulent odor plumes

: Animal behavior and neural recordings show that the brain is able to measure both the intensity and the timing of odor encounters. However, whether intensity or timing of odor detections is more informative for olfactory-driven behavior is not understood. To tackle this question, we consider the problem of locating a target using the odor it releases. We ask whether the position of a target is best predicted by measures of timing vs intensity of its odor, sampled for a short period of time. To answer this question, we feed data from accurate numerical simulations of odor transport to machine learning algorithms that learn how to connect odor to target location. We find that both intensity and timing can separately predict target location even from a distance of several meters; however, their efficacy varies with the dilution of the odor in space. Thus, organisms that use olfaction from different ranges may have to switch among different modalities. This has implications on how the brain should represent odors as the target is approached. We demonstrate simple strategies to improve accuracy and robustness of the prediction by modifying odor sampling and appropriately combining distinct measures together. To test the predictions, animal behavior and odor representation should be monitored as the animal moves relative to the target, or in virtual conditions that mimic concentrated vs dilute environments

Extracellular Myocardial Volume in Patients With Aortic Stenosis

BACKGROUND: Myocardial fibrosis is a key mechanism of left ventricular decompensation in aortic stenosis and can be quantified using cardiovascular magnetic resonance (CMR) measures such as extracellular volume fraction (ECV%). Outcomes following aortic valve intervention may be linked to the presence and extent of myocardial fibrosis. OBJECTIVES: This study sought to determine associations between ECV% and markers of left ventricular decompensation and post-intervention clinical outcomes. METHODS: Patients with severe aortic stenosis underwent CMR, including ECV% quantification using modified Look-Locker inversion recovery-based T1 mapping and late gadolinium enhancement before aortic valve intervention. A central core laboratory quantified CMR parameters. RESULTS: Four-hundred forty patients (age 70 ± 10 years, 59% male) from 10 international centers underwent CMR a median of 15 days (IQR: 4 to 58 days) before aortic valve intervention. ECV% did not vary by scanner manufacturer, magnetic field strength, or T1 mapping sequence (all p > 0.20). ECV% correlated with markers of left ventricular decompensation including left ventricular mass, left atrial volume, New York Heart Association functional class III/IV, late gadolinium enhancement, and lower left ventricular ejection fraction (p < 0.05 for all), the latter 2 associations being independent of all other clinical variables (p = 0.035 and p < 0.001). After a median of 3.8 years (IQR: 2.8 to 4.6 years) of follow-up, 52 patients had died, 14 from adjudicated cardiovascular causes. A progressive increase in all-cause mortality was seen across tertiles of ECV% (17.3, 31.6, and 52.7 deaths per 1,000 patient-years; log-rank test; p = 0.009). Not only was ECV% associated with cardiovascular mortality (p = 0.003), but it was also independently associated with all-cause mortality following adjustment for age, sex, ejection fraction, and late gadolinium enhancement (hazard ratio per percent increase in ECV%: 1.10; 95% confidence interval [1.02 to 1.19]; p = 0.013). CONCLUSIONS: In patients with severe aortic stenosis scheduled for aortic valve intervention, an increased ECV% is a measure of left ventricular decompensation and a powerful independent predictor of mortality

Markers of Myocardial Damage Predict Mortality in Patients With Aortic Stenosis

Background: Cardiovascular magnetic resonance (CMR) is increasingly used for risk stratification in aortic stenosis (AS). However, the relative prognostic power of CMR markers and their respective thresholds remains undefined. Objectives: Using machine learning, the study aimed to identify prognostically important CMR markers in AS and their thresholds of mortality. Methods: Patients with severe AS undergoing AVR (n = 440, derivation; n = 359, validation cohort) were prospectively enrolled across 13 international sites (median 3.8 years’ follow-up). CMR was performed shortly before surgical or transcatheter AVR. A random survival forest model was built using 29 variables (13 CMR) with post-AVR death as the outcome. Results: There were 52 deaths in the derivation cohort and 51 deaths in the validation cohort. The 4 most predictive CMR markers were extracellular volume fraction, late gadolinium enhancement, indexed left ventricular end-diastolic volume (LVEDVi), and right ventricular ejection fraction. Across the whole cohort and in asymptomatic patients, risk-adjusted predicted mortality increased strongly once extracellular volume fraction exceeded 27%, while late gadolinium enhancement >2% showed persistent high risk. Increased mortality was also observed with both large (LVEDVi >80 mL/m2) and small (LVEDVi ≤55 mL/m2) ventricles, and with high (>80%) and low (≤50%) right ventricular ejection fraction. The predictability was improved when these 4 markers were added to clinical factors (3-year C-index: 0.778 vs 0.739). The prognostic thresholds and risk stratification by CMR variables were reproduced in the validation cohort. Conclusions: Machine learning identified myocardial fibrosis and biventricular remodeling markers as the top predictors of survival in AS and highlighted their nonlinear association with mortality. These markers may have potential in optimizing the decision of AVR

Equivalence of two diagram representations of links in lens spaces and essential invariants

We study the relation between two diagrammatic representations of links in lens spaces: the disk diagram introduced in [8] and the grid diagram introduced in [2, 9] and we find how to shift from one to the other. We also investigate whether the HOMFLY-PT invariant and the Link Floer Homology are essential invariants, that is, we try to understand if these invariants are able to distinguish links in L(p, q) covered by the same link in S3. In order to do so, we generalize the combinatorial definition of Knot Floer Homology in lens spaces developed in [2, 19] to the case of links and we analyze how both the invariants change when we switch the orientation of the link

Impact ionization and photon emission in MOS capacitors and FETs

This paper addresses the problem of the origin of majority and minority carriers' substrate currents in MOS devices. In particular, we present a critical analysis of published and original tunneling experiments by means of a novel, physically based model of impact ionization and hot carrier photon emission and re-absorption in the substrate. The model explains some relevant features of substrate minority carrier currents in saturated nMOSFETs, and provides a better understanding of the origin of substrate currents in tunneling MOS capacitors