Feeling full and being full : how gastric content relates to appetite, food properties and neural activation

Aim: This thesis aimed to further determine how gastric content relates to subjective experiences regarding appetite, how this relation is affected by food properties and whether this is visible in neural activation changes. Method: This was studied using questionnaires, MRI of the stomach and fMRI of the brain. Randomized, controlled crossover experiments with healthy men and for one experiment women were performed. Results: MRI measurements of the stomach as opposed to an indirect measurement by proxy, such as 13C breath testing are to be preferred. We show that gastric emptying is affected by energy load, and to a much smaller extent by viscosity. Additionally we show that a thick shake containing 100 kcal will yield higher fullness sensations than a thin shake containing 500 kcal. In the chapter we name this phenomenon ‘phantom fullness’, i.e., a sense of fullness and satiation caused by the taste and mouthfeel of a food which is irrespective of actual stomach fullness. A liquid meal followed by a drink of water empties about twice as fast in the first 35 minutes compared to the same amount of water incorporated within the liquid meal. Using MRI we were able to show layering within the stomach and increased emptying of this watery layer. With 300mL of increased gastric content inducing distention, appetite was lowered. Ingestion led to significant changes in activation in the right insula and parts of the left and right inferior frontal cortices over time. Women retain significantly more fluid after a carbonated drink in their stomach than men. When comparing correlations between subjective ratings and intragastric liquid and gas and total gastric volume, nausea and fullness correlated strongest with the liquid fraction within the stomach, bloating strongest with total gastric volume. Conclusion: There are marked differences betweengastric content and subjective experiences regarding appetite. Viscosity is a main driver of these differences. Combined gastric MRI and brain fMRI measurements need to be performed to understand this further.</p

Eat-Radar: Continuous Fine-Grained Eating Gesture Detection Using FMCW Radar and 3D Temporal Convolutional Network

Unhealthy dietary habits are considered as the primary cause of multiple chronic diseases such as obesity and diabetes. The automatic food intake monitoring system has the potential to improve the quality of life (QoF) of people with dietary related diseases through dietary assessment. In this work, we propose a novel contact-less radar-based food intake monitoring approach. Specifically, a Frequency Modulated Continuous Wave (FMCW) radar sensor is employed to recognize fine-grained eating and drinking gestures. The fine-grained eating/drinking gesture contains a series of movement from raising the hand to the mouth until putting away the hand from the mouth. A 3D temporal convolutional network (3D-TCN) is developed to detect and segment eating and drinking gestures in meal sessions by processing the Range-Doppler Cube (RD Cube). Unlike previous radar-based research, this work collects data in continuous meal sessions. We create a public dataset that contains 48 meal sessions (3121 eating gestures and 608 drinking gestures) from 48 participants with a total duration of 783 minutes. Four eating styles (fork & knife, chopsticks, spoon, hand) are included in this dataset. To validate the performance of the proposed approach, 8-fold cross validation method is applied. Experimental results show that our proposed 3D-TCN outperforms the model that combines a convolutional neural network and a long-short-term-memory network (CNN-LSTM), and also the CNN-Bidirectional LSTM model (CNN-BiLSTM) in eating and drinking gesture detection. The 3D-TCN model achieves a segmental F1-score of 0.887 and 0.844 for eating and drinking gestures, respectively. The results of the proposed approach indicate the feasibility of using radar for fine-grained eating and drinking gesture detection and segmentation in meal sessions

Eating Speed Measurement Using Wrist-Worn IMU Sensors in Free-Living Environments

Eating speed is an important indicator that has been widely scrutinized in nutritional studies. The relationship between eating speed and several intake-related problems such as obesity, diabetes, and oral health has received increased attention from researchers. However, existing studies mainly use self-reported questionnaires to obtain participants' eating speed, where they choose options from slow, medium, and fast. Such a non-quantitative method is highly subjective and coarse in individual level. In this study, we propose a novel approach to measure eating speed in free-living environments automatically and objectively using wrist-worn inertial measurement unit (IMU) sensors. Specifically, a temporal convolutional network combined with a multi-head attention module (TCN-MHA) is developed to detect bites (including eating and drinking gestures) from free-living IMU data. The predicted bite sequences are then clustered to eating episodes. Eating speed is calculated by using the time taken to finish the eating episode to divide the number of bites. To validate the proposed approach on eating speed measurement, a 7-fold cross validation is applied to the self-collected fine-annotated full-day-I (FD-I) dataset, and a hold-out experiment is conducted on the full-day-II (FD-II) dataset. The two datasets are collected from 61 participants in free-living environments with a total duration of 513 h, which are publicly available. Experimental results shows that the proposed approach achieves a mean absolute percentage error (MAPE) of 0.110 and 0.146 in the FD-I and FD-II datasets, respectively, showcasing the feasibility of automated eating speed measurement. To the best of our knowledge, this is the first study investigating automated eating speed measurement

Emergent vulnerability to climate-driven disturbances in European forests

Forest disturbance regimes are expected to intensify as Earth's climate changes. Quantifying forest vulnerability to disturbances and understanding the underlying mechanisms is crucial to develop mitigation and adaptation strategies. However, observational evidence is largely missing at regional to continental scales. Here, we quantify the vulnerability of European forests to fires, windthrows and insect outbreaks during the period 1979-2018 by integrating machine learning with disturbance data and satellite products. We show that about 33.4 billion tonnes of forest biomass could be seriously affected by these disturbances, with higher relative losses when exposed to windthrows (40%) and fires (34%) compared to insect outbreaks (26%). The spatial pattern in vulnerability is strongly controlled by the interplay between forest characteristics and background climate. Hotspot regions for vulnerability are located at the borders of the climate envelope, in both southern and northern Europe. There is a clear trend in overall forest vulnerability that is driven by a warming-induced reduction in plant defence mechanisms to insect outbreaks, especially at high latitudes. Natural disturbances imperil healthy and productive forests, but quantifying their effects at large scales is challenging. Here the authors apply machine learning to disturbance records and satellite data to quantify and map European forest vulnerability to fires, windthrows, and insect outbreaks through 1979-2018.Peer reviewe

Gastric Emptying and Intragastric Behavior of Breast Milk and Infant Formula in Lactating Mothers

Background: When sufficient breast milk is not available, infant formula is often used as an alternative. As for digestion, gastric behavior of infant formula and breast milk have not been studied in detail. Objective: This study aimed to compare gastric emptying and intragastric behavior between breast milk and infant formula in vivo using MRI. Methods: In this randomized crossover study, 16 lactating mothers (age: 31.7 ± 2.9 y; time since giving birth: 9.3 ± 2 mo), underwent gastric MRI scans before and after consumption of 200 mL of infant formula or their own breast milk. MRI scans were performed after an overnight fast (baseline) and every 10 min up until 60 min following ingestion. Primary outcomes were gastric emptying measures and the secondary outcome was gastric layer volume over time. Differences between infant formula and breast milk in total gastric volume and layering volume were tested using linear mixed models. Results: Gastric emptying half-time was 5.1 min faster for breast milk than for infant formula (95% CI: -19.0 to 29.2) (n = 14). Within a subgroup (n = 12) with similar initial gastric volume (<20 mL difference), gastric emptying half-time was 20 min faster for breast milk (95% CI: 1.23-43.1). Top layer volume (n = 16) was 6.4 mL greater for infant formula than for breast milk (95% CI: 1.9-10.8). This effect is driven by t = 10 and t = 20 min postingestion. Conclusions: When taking initial gastric volume into account, breast milk emptied faster than infant formula in women, which is in line with previous findings in infants. Infant formula showed a significantly larger top layer volume in the first 20 min after ingestion. MRI in adults may find application in studies assessing gastric behavior of infant formula

Earth system data cubes unravel global multivariate dynamics

Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model-data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model-data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model-data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved

Earth system data cubes unravel global multivariate dynamics

Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model-data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model-data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model-data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved

Intra‐ and interindividual variability in fasted gastric content volume

Background: Gastric fluid plays a key role in food digestion and drug dissolution, therefore, the amount of gastric fluid present in a fasted state may influence subsequent digestion and drug delivery. We aimed to describe intra-and interindividual variation in fasted gastric content volume (FGCV) and to determine the association with age, sex, and body size characteristics.Methods: Data from 24 MRI studies measuring FGCV in healthy, mostly young individuals after an overnight fast were pooled. The analysis included 366 participants who had up to 6 repeated measurements, with a total of 870 measurements. Linear mixed model analysis was performed to calculate intra-and interindividual variability and to assess the effects of age, sex, weight, height, weight*height as a proxy for body size, and body mass index (BMI).Results: FGCV ranged from 0 to 156 mL, with a mean (± SD) value of 33 ± 25 mL. The overall coefficient of variation within the study population was 75.6%, interindividual SD was 15 mL, and the intraindividual SD was 19 mL. Age, weight, height, weight*height, and BMI had no effect on FGCV. Women had lower volumes compared to men (MD: −6 mL), when corrected for the aforementioned factors. Conclusion: FGCV is highly variable, with higher intraindividual compared to interindividual variability, indicating that FGCV is subject to day-to-day and within-day variation and is not a stable personal characteristic. This highlights the importance of considering FGCV when studying digestion and drug dissolution. Exact implications remain to be studied

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries