Multi-fidelity reduced-order surrogate modeling

High-fidelity numerical simulations of partial differential equations (PDEs) given a restricted computational budget can significantly limit the number of parameter configurations considered and/or time window evaluated for modeling a given system. Multi-fidelity surrogate modeling aims to leverage less accurate, lower-fidelity models that are computationally inexpensive in order to enhance predictive accuracy when high-fidelity data are limited or scarce. However, low-fidelity models, while often displaying important qualitative spatio-temporal features, fail to accurately capture the onset of instability and critical transients observed in the high-fidelity models, making them impractical as surrogate models. To address this shortcoming, we present a new data-driven strategy that combines dimensionality reduction with multi-fidelity neural network surrogates. The key idea is to generate a spatial basis by applying the classical proper orthogonal decomposition (POD) to high-fidelity solution snapshots, and approximate the dynamics of the reduced states - time-parameter-dependent expansion coefficients of the POD basis - using a multi-fidelity long-short term memory (LSTM) network. By mapping low-fidelity reduced states to their high-fidelity counterpart, the proposed reduced-order surrogate model enables the efficient recovery of full solution fields over time and parameter variations in a non-intrusive manner. The generality and robustness of this method is demonstrated by a collection of parametrized, time-dependent PDE problems where the low-fidelity model can be defined by coarser meshes and/or time stepping, as well as by misspecified physical features. Importantly, the onset of instabilities and transients are well captured by this surrogate modeling technique

AIDAN: An Attention-Guided Dual-Path Network for Pediatric Echocardiography Segmentation

Accurate segmentation of pediatric echocardiography images is essential for a wide range of diagnostic and pre-interventional planning, but remains challenging (e.g., low signal to noise ratio and internal variability in heart appearance). To address these problems, in this paper, we propose a novel Cardiac Attention-guided Dual-path Network (i.e., AIDAN). AIDAN comprises a convolutional block attention module (CBAM) attached to a spatial (i.e., SPA) and context paths (i.e., CPA), which can guide the network and learn the most discriminative features. The spatial path captures low-level spatial features, and the context path is designed to exploit high-level context. Finally, features learned from the two paths are fused efficiently using a specially designed feature fusion module (FFM), and these are used to predict the final segmentation map. We experiment on a self-collected dataset of 127 pediatric echocardiography cases which are videos containing at least a complete cardiac cycle, and obtain a Dice coefficient of 0.951 and 0.914, in the left ventricle and atrium segments, respectively. AIDAN outperforms other state-of-the-art methods and has great potential for pediatric echocardiography images analysis

Litter decomposition and net foliar nutrient release of Austrocedrus chilensis (D. Don) Pic. Serm. et Bizzarri forests in El Bolsón, Río Negro

Se determinó la descomposición de hojas, ramas y troncos en tres rodales maduros de Austrocedrus chilensis. La tasa de descomposición (k) foliar no mostró diferencias significativas entre rodales, siendo el promedio de 0.27 año-1. La tasa de descomposición de ramas finas, gruesas y troncos fue de 0.095, 0.06 y 0.013 año-1, respectivamente. La vida media fue 2.6, 7.4, 12 y 53 años, respectivamente. La concentración mineral foliar aumentó (N, Ca, Al, Fe, cenizas totales), no cambió (S, Mn) o descendió (P, K, Mg) durante el tiempo del estudio. El N no mostró liberación neta. La tasa de liberación de los otros elementos y el orden fueron: K (0.60) ³ P (0.59) > Mg (0.40) > S (0.21) > Ca (0.10). La liberación de Ca se realizó a menor tasa que la descomposición; el Mg, el P y el K lo hicieron más rápidamente. El Fe, el Al y el Mn, inicialmente liberados, mostraron una consistente pero no significativa inmovilización. Concluimos que (1) las tasas de descomposición de detritos decrecen con su tamaño, (2) las diferencias entre rodales no influyen significativamente en la k foliar, (3) la liberación foliar neta difiere para cada elemento químico y algunos se apartan de modelos previos, (4) el contenido de N foliar se mantiene constante durante la descomposición, (5) la liberación es más rápida para los nutrientes más móviles, (6) el aumento de la concentración de Ca y su menor tasa de liberación respecto de la descomposición llevaría a un aumento relativo del mismo en la capa F, (7) el leve aumento de Fe y Al en el mantillo sugiere que su disponibilidad no causa una elevada traslocación e inmovilización microbiana, ni alcanza niveles tóxicos, (8) las tasas de descomposición foliar están dentro del rango de las coníferas del hemisferio norte y son similares o menores a las de Nothofagus spp. perennes y deciduos, respectivamente, (9) las tasas de descomposición de detritos gruesos son similares o levemente mayores a las de coníferas del hemisferio norte e inferiores a las de Nothofagus spp. de Tierra del Fuego.Decomposition constant of fallen leaves, thin ( 5 cm) were determined in mature stands of Austrocedrus chilensis. Leaves and branches were field-incubated (522 days) using litter bags while a chronosequence of stems was employed. Foliar decay rate k did not show significant differences among stands and the mean for the pool was 0.27 year-1. The k constant was 0.095, 0.06 and 0.013 year-1 for thin, coarse branches and stems, respectively. Half-life was 2.6, 7.4, 12 and 53 years for leaves, thin and coarse branches and stems, respectively. Elemental concentration (N, Ca, Al, Fe) and total ash in decaying leaves increased, did not change (S, Mn) or decreased (P, K, Mg) during leaves decomposition assay. There was no net N-release from leaves decomposition. For other nutrients, release rate and ranking was K (0.60) ³ P (0.59) > Mg (0.40) > S (0.21) > Ca (0.10). Calcium release rate was slower than decay, while Mg, P and K released more quickly. After initial release, Fe, Al and Mn showed a non-significant but consistent slight immobilization. We conclude that (1) detritus decay rates decrease with size increase, (2) differences among stands do not influence significantly foliar decay rates, (3) foliar nutrient release is different for each chemical element and some of them do not fit described models, (4) N-content seems to be constant during decomposition period, (5) release is faster for the more mobile nutrients, especially during phase I, (6) the increase in Ca and its lower release rate with respect to dry mass decay rate may cause a relative Ca-increment in the F-layer, and a somewhat similar but more marked sink could occur with microelements, (7) the slight increase of Fe and Al in leaf-litter suggests that their availability do not allow high microbial traslocation and immobilization, and do not attain toxic levels, (8) foliar decay rates were in the range of Northern Hemisphere conifers, and similar or lower than those of broad-leaved evergreen and deciduous Nothofagus spp., respectively, from South America, (9) coarse woody debris decay rates were similar or slightly higher than for Northern Hemisphere conifers and lower than for Nothofagus spp. from Tierra del Fueg

MicroRNA profile changes in human immunodeficiency virus type 1 (HIV-1) seropositive individuals

MicroRNAs (miRNAs) play diverse roles in regulating cellular and developmental functions. We have profiled the miRNA expression in peripheral blood mononuclear cells from 36 HIV-1 seropositive individuals and 12 normal controls. The HIV-1-positive individuals were categorized operationally into four classes based on their CD4+ T-cell counts and their viral loads. We report that specific miRNA signatures can be observed for each of the four classes

Deep Placental Vessel Segmentation for Fetoscopic Mosaicking

During fetoscopic laser photocoagulation, a treatment for twin-to-twin transfusion syndrome (TTTS), the clinician first identifies abnormal placental vascular connections and laser ablates them to regulate blood flow in both fetuses. The procedure is challenging due to the mobility of the environment, poor visibility in amniotic fluid, occasional bleeding, and limitations in the fetoscopic field-of-view and image quality. Ideally, anastomotic placental vessels would be automatically identified, segmented and registered to create expanded vessel maps to guide laser ablation, however, such methods have yet to be clinically adopted. We propose a solution utilising the U-Net architecture for performing placental vessel segmentation in fetoscopic videos. The obtained vessel probability maps provide sufficient cues for mosaicking alignment by registering consecutive vessel maps using the direct intensity-based technique. Experiments on 6 different in vivo fetoscopic videos demonstrate that the vessel intensity-based registration outperformed image intensity-based registration approaches showing better robustness in qualitative and quantitative comparison. We additionally reduce drift accumulation to negligible even for sequences with up to 400 frames and we incorporate a scheme for quantifying drift error in the absence of the ground-truth. Our paper provides a benchmark for fetoscopy placental vessel segmentation and registration by contributing the first in vivo vessel segmentation and fetoscopic videos dataset.Comment: Accepted at MICCAI 202

Use of the Resection Map system as guidance during hepatectomy

Industrial DesignIndustrial Design Engineerin

Self-calibrated brain network estimation and joint non-convex multi-task learning for identification of early Alzheimer's disease

Detection of early stages of Alzheimer's disease (AD) (i.e., mild cognitive impairment (MCI)) is important to maximize the chances to delay or prevent progression to AD. Brain connectivity networks inferred from medical imaging data have been commonly used to distinguish MCI patients from normal controls (NC). However, existing methods still suffer from limited performance, and classification remains mainly based on single modality data. This paper proposes a new model to automatically diagnosing MCI (early MCI (EMCI) and late MCI (LMCI)) and its earlier stages (i.e., significant memory concern (SMC)) by combining low-rank self-calibrated functional brain networks and structural brain networks for joint multi-task learning. Specifically, we first develop a new functional brain network estimation method. We introduce data quality indicators for self-calibration, which can improve data quality while completing brain network estimation, and perform correlation analysis combined with low-rank structure. Second, functional and structural connected neuroimaging patterns are integrated into our multi-task learning model to select discriminative and informative features for fine MCI analysis. Different modalities are best suited to undertake distinct classification tasks, and similarities and differences among multiple tasks are best determined through joint learning to determine most discriminative features. The learning process is completed by non-convex regularizer, which effectively reduces the penalty bias of trace norm and approximates the original rank minimization problem. Finally, the most relevant disease features classified using a support vector machine (SVM) for MCI identification. Experimental results show that our method achieves promising performance with high classification accuracy and can effectively discriminate between different sub-stages of MCI

Emergent global patterns of ecosystem structure and function from a mechanistic general ecosystem model

Anthropogenic activities are causing widespread degradation of ecosystems worldwide, threatening the ecosystem services upon which all human life depends. Improved understanding of this degradation is urgently needed to improve avoidance and mitigation measures. One tool to assist these efforts is predictive models of ecosystem structure and function that are mechanistic: based on fundamental ecological principles. Here we present the first mechanistic General Ecosystem Model (GEM) of ecosystem structure and function that is both global and applies in all terrestrial and marine environments. Functional forms and parameter values were derived from the theoretical and empirical literature where possible. Simulations of the fate of all organisms with body masses between 10 µg and 150,000 kg (a range of 14 orders of magnitude) across the globe led to emergent properties at individual (e.g., growth rate), community (e.g., biomass turnover rates), ecosystem (e.g., trophic pyramids), and macroecological scales (e.g., global patterns of trophic structure) that are in general agreement with current data and theory. These properties emerged from our encoding of the biology of, and interactions among, individual organisms without any direct constraints on the properties themselves. Our results indicate that ecologists have gathered sufficient information to begin to build realistic, global, and mechanistic models of ecosystems, capable of predicting a diverse range of ecosystem properties and their response to human pressures

Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection

By 2040, ~100 million people will have glaucoma. To date, there are a lack of high-efficiency glaucoma diagnostic tools based on visual fields (VFs). Herein, we develop and evaluate the performance of ‘iGlaucoma’, a smartphone application-based deep learning system (DLS) in detecting glaucomatous VF changes. A total of 1,614,808 data points of 10,784 VFs (5542 patients) from seven centers in China were included in this study, divided over two phases. In Phase I, 1,581,060 data points from 10,135 VFs of 5105 patients were included to train (8424 VFs), validate (598 VFs) and test (3 independent test sets—200, 406, 507 samples) the diagnostic performance of the DLS. In Phase II, using the same DLS, iGlaucoma cloud-based application further tested on 33,748 data points from 649 VFs of 437 patients from three glaucoma clinics. With reference to three experienced expert glaucomatologists, the diagnostic performance (area under curve [AUC], sensitivity and specificity) of the DLS and six ophthalmologists were evaluated in detecting glaucoma. In Phase I, the DLS outperformed all six ophthalmologists in the three test sets (AUC of 0.834–0.877, with a sensitivity of 0.831–0.922 and a specificity of 0.676–0.709). In Phase II, iGlaucoma had 0.99 accuracy in recognizing different patterns in pattern deviation probability plots region, with corresponding AUC, sensitivity and specificity of 0.966 (0.953–0.979), 0.954 (0.930–0.977), and 0.873 (0.838–0.908), respectively. The ‘iGlaucoma’ is a clinically effective glaucoma diagnostic tool to detect glaucoma from humphrey VFs, although the target population will need to be carefully identified with glaucoma expertise input