A Neural network based observation operator for coupled ocean acoustic variational data assimilation

Variational data assimilation requires implementing the tangent-linear and adjoint (TA/AD) version of any operator. This intrinsically hampers the use of complicated observations.Here, we assess a new data-driven approach to assimilate acoustic underwater propagation measurements [transmission loss (TL)] into a regional ocean forecasting system. TL measurements depend on the underlying sound speed fields, mostly temperature, and their inversion would require heavy coding of the TA/AD of an acoustic underwater propagation model. In this study, the nonlinear version of the acoustic model is applied to an ensemble of perturbed oceanic conditions. TL outputs are used to formulate both a statistical linear operator based on canonical correlation analysis (CCA), and a neural network based (NN) operator. For the latter, two linearization strategies are compared, the best-performing one relying on reverse-mode automatic differentiation. The new observation operator is applied in data assimilation experiments over the Ligurian Sea (Mediterranean Sea), using the observing system simulation experiments (OSSE) methodology to assess the impact of TL observations onto oceanic fields. TL observations are extracted from a nature run with perturbed surface boundary conditions and stochastic ocean physics. Sensitivity analyses indicate that theNNreconstruction of TL is significantly better than CCA. BothCCAandNNare able to improve the upper-ocean skill scores in forecast experiments, with NN outperforming CCA on the average. The use of the NN observation operator is computationally affordable, and its general formulation appears promising for the adjoint-free assimilation of any remote sensing observing network. SIGNIFICANCE STATEMENT: Deep learning algorithms are now widely spread in a diverse range of fields to help with solving automatic classification and regression problems. Here, we present and assess a strategy aimed at introducing an observation operator based on neural networks in data assimilation. Linearization of such an operator, required by variational schemes, is also discussed and implemented. The methodology is applied to the coupled oceanic acoustic data assimilation problem, and provides promising results. Our approach may be extended in the future to assimilate any remotely sensed type of observations

Early Lance-Adams syndrome after cardiac arrest: Prevalence, time to return to awareness, and outcome in a large cohort.

Early myoclonus after cardiac arrest (CA) is traditionally viewed as a poor prognostic sign (status myoclonus). However, some patients may present early Lance-Adams syndrome (LAS): under appropriate treatment, they can reach a satisfactory functional outcome. Our aim was to describe their profile, focusing on pharmacologic management in the ICU, time to return of awareness, and long-term prognosis. Adults with early LAS (defined as generalized myoclonus within 96h, with epileptiform EEG within 48h after CA) were retrospectively identified in our CA registry between 2006 and 2016. Functional outcome was assessed through cerebral performance categories (CPC) at 3 months, CPC 1-2 defined good outcome. Among 458 consecutive patients, 7 (1.5%) developed early LAS (4 women, median age 59 years). Within 72h after CA, in normothemia and off sedation, all showed preserved brainstem reflexes and localized pain. All patients were initially treated with valproate, levetiracetam and clonazepam; additional agents, including propofol and midazolam, were prescribed in the majority. First signs of awareness occurred after 3-23 days (median 11.8); 3/7 reached a good outcome at 3 months. Early after CA, myoclonus together with a reactive, epileptiform EEG, preserved evoked potentials and brainstem reflexes suggests LAS. This condition was managed with a combination of highly dosed, large spectrum antiepileptic agents including propofol and midazolam. Even if awakening was at times delayed, good outcome occurred in a substantial proportion of patients

A Nested Atlantic-Mediterranean Sea General Circulation Model for Operational Forecasting.

Abstract. A new numerical general circulation ocean model for the Mediterranean Sea has been implemented nested within an Atlantic general circulation model within the framework of the Marine Environment and Security for the European Area project (MERSEA, Desaubies, 2006). A 4-year twin experiment was carried out from January 2004 to December 2007 with two different models to evaluate the impact on the Mediterranean Sea circulation of open lateral boundary conditions in the Atlantic Ocean. One model considers a closed lateral boundary in a large Atlantic box and the other is nested in the same box in a global ocean circulation model. Impact was observed comparing the two simulations with independent observations: ARGO for temperature and salinity profiles and tide gauges and along-track satellite observations for the sea surface height. The improvement in the nested Atlantic-Mediterranean model with respect to the closed one is particularly evident in the salinity characteristics of the Modified Atlantic Water and in the Mediterranean sea level seasonal variability

A nested Atlantic-Mediterranean Sea general circulation model for operational forecasting

A new numerical general circulation ocean model for the Mediterranean Sea has been implemented nested within an Atlantic general circulation model within the framework of the Marine Environment and Security for the European Area project (MERSEA, Desaubies, 2006). A 4- year twin experiment was carried out from January 2004 to December 2007 with two different models to evaluate the impact on the Mediterranean Sea circulation of open lateral boundary conditions in the Atlantic Ocean. One model considers a closed lateral boundary in a large Atlantic box and the other is nested in the same box in a global ocean circulation model. Impact was observed comparing the two simulations with independent observations: ARGO for temperature and salinity profiles and tide gauges and along-track satellite observations for the sea surface height. The improvement in the nested Atlantic-Mediterranean model with respect to the closed one is particularly evident in the salinity characteristics of the Modified Atlantic Water and in the Mediterranean sea level seasonal variability

Deep Convolutional LSTM for improved flash flood prediction

Flooding remains one of the most devastating and costly natural disasters. As flooding events grow in frequency and intensity, it has become increasingly important to improve flood monitoring, prediction, and early warning systems. Recent efforts to improve flash flood forecasts using deep learning have shown promise, yet commonly-used techniques such as long short term memory (LSTM) models are unable to extract potentially significant spatial relationships among input datasets. Here we propose a hybrid approach using a Convolutional LSTM (ConvLSTM) network to predict stream stage heights using multi-modal hydrometeorological remote sensing and in-situ inputs. Results suggest the hybrid network can more effectively capture the specific spatiotemporal landscape dynamics of a flash flood-prone catchment relative to the current state-of-the-art, leading to a roughly 26% improvement in model error when predicting elevated stream conditions. Furthermore, the methodology shows promise for improving prediction accuracy and warning times for supporting local decision making

Thrust belts of the southern Central Andes: Along-strike variations in shortening, topography, crustal geometry, and denudation

The Andean fold-and-thrust belts of westcentral Argentina (33 S and 36 S), above the normal subduction segment, present important along-strike variations in mean topographic uplift, structural elevation, amount and rate of shortening, and crustal root geometry. To analyze the controlling factors of these latitudinal changes, we compare these parameters and the chronology of deformation along 11 balanced crustal cross sections across the thrust belts between 70 W and 69 W, where the majority of the uppercrustal deformation is concentrated, and reconstruct the Moho geometry along the transects. We propose two models of crustal deformation: a 33 40 S model, where the locus of upper-crustal shortening is aligned with respect to the maximum crustal thickness, and a 35 40 S model, where the uppercrustal shortening is uncoupled from the lower-crustal deformation and thickening. This degree of coupling between brittle upper crust and ductile lower crust deformation has strong influence on mean topographic ele vation. In the northern sector of the study area, an initial thick and felsic crust favors the coupling model, while in the southern sector, a thin and mafic lower crust allows the uncoupling model. Our results indicate that interplate dynamics may control the overall pattern of tectonic shortening; however, local variations in mean topographic elevation, deformation styles, and crustal root geometry are not fully explained and are more likely to be due to upper-plate lithospheric strength variations.Fil: Giambiagi, Laura Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; ArgentinaFil: Mescua, Jose Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; ArgentinaFil: Bechis, Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Diversidad Cultural y Procesos de Cambio. Universidad Nacional de Río Negro. Instituto de Investigaciones en Diversidad Cultural y Procesos de Cambio; ArgentinaFil: Tassara Oddo, Andres Humberto. Universidad de Concepción; ChileFil: Hoke, Gregory D.. Syracuse University; Estados Unido

Non-Ischemic Cerebral Energy Dysfunction at the Early Brain Injury Phase following Aneurysmal Subarachnoid Hemorrhage.

The pathophysiology of early brain injury following aneurysmal subarachnoid hemorrhage (SAH) is still not completely understood. Using brain perfusion CT (PCT) and cerebral microdialysis (CMD), we examined whether non-ischemic cerebral energy dysfunction may be a pathogenic determinant of EBI. A total of 21 PCTs were performed (a median of 41 h from ictus onset) among a cohort of 18 comatose mechanically ventilated SAH patients (mean age 58 years, median admission WFNS score 4) who underwent CMD and brain tissue PO2 (PbtO2) monitoring. Cerebral energy dysfunction was defined as CMD episodes with lactate/pyruvate ratio (LPR) >40 and/or lactate >4 mmol/L. PCT-derived global CBF was categorized as oligemic (CBF < 28 mL/100 g/min), normal (CBF 28-65 mL/100 g/min), or hyperemic (CBF 69-85 mL/100 g/min), and was matched to CMD/PbtO2 data. Global CBF (57 ± 14 mL/100 g/min) and PbtO2 (25 ± 9 mm Hg) were within normal ranges. Episodes with cerebral energy dysfunction (n = 103 h of CMD samples, average duration 7.4 h) were frequent (66% of CMD samples) and were associated with normal or hyperemic CBF. CMD abnormalities were more pronounced in conditions of hyperemic vs. normal CBF (LPR 54 ± 12 vs. 42 ± 7, glycerol 157 ± 76 vs. 95 ± 41 µmol/L; both p < 0.01). Elevated brain LPR correlated with higher CBF (r = 0.47, p < 0.0001). Cerebral energy dysfunction is frequent at the early phase following poor-grade SAH and is associated with normal or hyperemic brain perfusion. Our data support the notion that mechanisms alternative to ischemia/hypoxia are implicated in the pathogenesis of early brain injury after SAH

Prediction of awakening from hypothermic post anoxic coma based on auditory discrimination.

OBJECTIVE: Most of the available clinical tests for prognosis of post-anoxic coma are informative of poor outcome. Previous work has shown that an improvement in auditory discrimination over the first days of coma is predictive of awakening. Here, we aimed at evaluating this test on a large cohort of patients undergoing therapeutic hypothermia and at investigating its added value on existing clinical measures. METHODS: We recorded electroencephalography responses to auditory stimuli in 94 comatose patients, under hypothermia and after re-warming to normal temperature. Auditory discrimination was semi-automatically quantified by decoding electroencephalography responses to frequently repeated vs. rare sounds. Outcome prediction was based on the change of decoding performance from hypothermia to normothermia. RESULTS: An increase in auditory discrimination from hypothermia to normothermia was observed for 33 out of 94 patients. Among them, 27 awoke from coma, resulting in a positive predictive value of awakening of 82% (95% confidence interval: 0.65-0.93). Most non-survivors showing an improvement in auditory discrimination had incident status epilepticus. By excluding them, 27 out of 29 patients with improvement in auditory discrimination survived, resulting in a considerable improvement of the predictive value for awakening (93%, with 95% confidence interval: 0.77-0.99). Importantly, this test predicted the awakening of 13 out of 51 patients for which the outcome was uncertain based on current tests. INTERPRETATION: The progression of auditory discrimination from hypothermia to normothermia has a high predictive value for awakening. This quantitative measure provides an added value to existing clinical tests and encourages the maintenance of life support. This article is protected by copyright. All rights reserved

Evidence of trace conditioning in comatose patients revealed by the reactivation of EEG responses to alerting sounds.

Trace conditioning refers to a learning process occurring after repeated presentation of a neutral conditioned stimulus (CS+) and a salient unconditioned stimulus (UCS) separated by a temporal gap. Recent studies have reported that trace conditioning can occur in humans in reduced levels of consciousness by showing a transfer of the unconditioned autonomic response to the CS+ in healthy sleeping individuals and in vegetative state patients. However, no previous studies have investigated the neural underpinning of trace conditioning in the absence of consciousness in humans. In the present study, we recorded the EEG activity of 29 post-anoxic comatose patients while presenting a trace conditioning paradigm using neutral tones as CS+ and alerting sounds as UCS. Most patients received therapeutic hypothermia and all were deeply unconscious according to standardized clinical scales. After repeated presentation of the CS+ and UCS couple, learning was assessed by measuring the EEG activity during the period where the UCS is omitted after CS+ presentation. Specifically we assessed the 'reactivation' of the neural response to UCS omission by applying a decoding algorithm derived from the statistical model of the EEG activity in response to the UCS presentation. The same procedure was used in a group of 12 awake healthy controls. We found a reactivation of the UCS response in absence of stimulation in eight patients (five under therapeutic hypothermia) and four healthy controls. Additionally, the reactivation effect was temporally specific within trials since it manifested primarily at the specific latency of UCS presentation and significantly less before or after this period. Our results show for the first time that trace conditioning may manifest as a reactivation of the EEG activity related to the UCS and even in the absence of consciousness