Editorial for the Special Issue "Sensing-Based Biomedical Communication and Intelligent Identification for Healthcare".

The integration of sensor technology in healthcare has become crucial for disease diagnosis and treatment [...].

Egocentric Image Captioning for Privacy-Preserved Passive Dietary Intake Monitoring.

Camera-based passive dietary intake monitoring is able to continuously capture the eating episodes of a subject, recording rich visual information, such as the type and volume of food being consumed, as well as the eating behaviors of the subject. However, there currently is no method that is able to incorporate these visual clues and provide a comprehensive context of dietary intake from passive recording (e.g., is the subject sharing food with others, what food the subject is eating, and how much food is left in the bowl). On the other hand, privacy is a major concern while egocentric wearable cameras are used for capturing. In this article, we propose a privacy-preserved secure solution (i.e., egocentric image captioning) for dietary assessment with passive monitoring, which unifies food recognition, volume estimation, and scene understanding. By converting images into rich text descriptions, nutritionists can assess individual dietary intake based on the captions instead of the original images, reducing the risk of privacy leakage from images. To this end, an egocentric dietary image captioning dataset has been built, which consists of in-the-wild images captured by head-worn and chest-worn cameras in field studies in Ghana. A novel transformer-based architecture is designed to caption egocentric dietary images. Comprehensive experiments have been conducted to evaluate the effectiveness and to justify the design of the proposed architecture for egocentric dietary image captioning. To the best of our knowledge, this is the first work that applies image captioning for dietary intake assessment in real-life settings.

The effect of green channel for stroke patients on treatment of severe aneurysmal subarachnoid hemorrhage.

PURPOSE: To explore the effect of green channel for stroke patients on the treatment of severe aneurysmal subarachnoid hemorrhage. METHODS: This is a retrospective case-control study. The clinical data of patients with severe aneurysmal subarachnoid hemorrhage admitted to the emergency department of our hospital from January 2015 to June 2022 were retrospectively analyzed. Patients diagnosed with subarachnoid hemorrhage, confirmed intracranial aneurysm by preoperative CT angiography or digital subtraction, graded Hunt-Hess grade III, IV, and V, < 72 h from the onset to the time of consultation received surgical treatment in our hospital were included in this study. Patients with serious underlying diseases, such as heart, liver, kidney diseases, or malignant tumors, traumatic subarachnoid hemorrhage, previous history of cerebral hemorrhage, and incomplete data were excluded. The control group included patients with severe aneurysmal subarachnoid hemorrhage admitted from January 2015 to December 2018 before the establishment of the green channel for stroke patients, and the observation group included patients with severe aneurysmal subarachnoid hemorrhage admitted from January 2019 to June 2022 after the establishment of the green channel. The control group received routine treatment in the emergency department; the observation group received improved treatment of green channel for stroke patients. Gender, age, Hunt-Hess grade on admission, modified Rankin scale (mRS) on admission, aneurysm location, aneurysm size and whether accompanied by intracerebral hemorrhage, the time from onset to emergency department, the time from emergency department to vascular diagnostic examination, the time from onset to surgery, the time from emergency department to surgery, the time from hospital admission to surgery, length of hospital stay, complications, treatment effect were analyzed and compared between the 2 groups. SPSS 23.0 software was utilized to conduct comparisons between the 2 groups. The t-test, Chi-square test, or Mann-Whitney U test was chosen based on the data type. Statistical significance was established when p < 0.05. RESULTS: A total of 71 patients were included in this study, of whom 37 were in the control group and 34 were in the observation group. There were no statistical differences in age, gender, Hunt-Hess grade, mRS scores, aneurysm location, aneurysm size, intracerebral hemorrhage, the time from onset to emergency department, length of hospital stay, complications between the observation group and the control group (all p > 0.05). The time (min) from visit to vascular diagnostic test (60.50 vs. 120.00, p = 0.027), the time (min) from onset to surgery (1792.00 vs. 2868.00, p = 0.023), the time (min) from emergency department to surgery (1568.50 vs. 2778.00, p = 0.016), the time (min) from hospital admission to surgery (1188.50 vs. 2708.00, p = 0.043), all of them were shorter in the observation group than those in the control group. The relative values of admission and 7-day postoperative mRS scores and the relative values of admission and discharge mRS scores ≥ 2 were used as the criteria for determining better efficacy, and the treatment effect was better than that in the control group, and the differences were statistically significant (admission to 7 days postoperative mRS score ≥ 2, 17 (50.0 %) vs. 8 (21.6 %), p = 0.012; admission to discharge mRS score ≥ 2, 19 (55.9 %) vs. 11 (29.7 %), p = 0.026). CONCLUSION: The green channel for stroke patients with severe aneurysmal subarachnoid hemorrhage can effectively shorten the time from arrival at the emergency department to vascular diagnostic examination and the time from the emergency department to surgery, and achieve a better therapeutic effect, which is worth popularizing and applying.

Evaluation of Acceptability, Functionality, and Validity of a Passive Image-Based Dietary Intake Assessment Method in Adults and Children of Ghanaian and Kenyan Origin Living in London, UK.

BACKGROUND: Accurate estimation of dietary intake is challenging. However, whilst some progress has been made in high-income countries, low- and middle-income countries (LMICs) remain behind, contributing to critical nutritional data gaps. This study aimed to validate an objective, passive image-based dietary intake assessment method against weighed food records in London, UK, for onward deployment to LMICs. METHODS: Wearable camera devices were used to capture food intake on eating occasions in 18 adults and 17 children of Ghanaian and Kenyan origin living in London. Participants were provided pre-weighed meals of Ghanaian and Kenyan cuisine and camera devices to automatically capture images of the eating occasions. Food images were assessed for portion size, energy, nutrient intake, and the relative validity of the method compared to the weighed food records. RESULTS: The Pearson and Intraclass correlation coefficients of estimates of intakes of food, energy, and 19 nutrients ranged from 0.60 to 0.95 and 0.67 to 0.90, respectively. Bland-Altman analysis showed good agreement between the image-based method and the weighed food record. Under-estimation of dietary intake by the image-based method ranged from 4 to 23%. CONCLUSIONS: Passive food image capture and analysis provides an objective assessment of dietary intake comparable to weighed food records.

I2N: image to nutrients, a sensor guided semi-automated tool for annotation of images for nutrition analysis of eating episodes.

Introduction: Dietary assessment is important for understanding nutritional status. Traditional methods of monitoring food intake through self-report such as diet diaries, 24-hour dietary recall, and food frequency questionnaires may be subject to errors and can be time-consuming for the user. Methods: This paper presents a semi-automatic dietary assessment tool we developed - a desktop application called Image to Nutrients (I2N) - to process sensor-detected eating events and images captured during these eating events by a wearable sensor. I2N has the capacity to offer multiple food and nutrient databases (e.g., USDA-SR, FNDDS, USDA Global Branded Food Products Database) for annotating eating episodes and food items. I2N estimates energy intake, nutritional content, and the amount consumed. The components of I2N are three-fold: 1) sensor-guided image review, 2) annotation of food images for nutritional analysis, and 3) access to multiple food databases. Two studies were used to evaluate the feasibility and usefulness of I2N: 1) a US-based study with 30 participants and a total of 60 days of data and 2) a Ghana-based study with 41 participants and a total of 41 days of data). Results: In both studies, a total of 314 eating episodes were annotated using at least three food databases. Using I2N's sensor-guided image review, the number of images that needed to be reviewed was reduced by 93% and 85% for the two studies, respectively, compared to reviewing all the images. Discussion: I2N is a unique tool that allows for simultaneous viewing of food images, sensor-guided image review, and access to multiple databases in one tool, making nutritional analysis of food images efficient. The tool is flexible, allowing for nutritional analysis of images if sensor signals aren't available.

Food volume estimation by multi-layer superpixel.

Estimating the volume of food plays an important role in diet monitoring. However, it is difficult to perform this estimation automatically and accurately. A new method based on the multi-layer superpixel technique is proposed in this paper to avoid tedious human-computer interaction and improve estimation accuracy. Our method includes the following steps: 1) obtain a pair of food images along with the depth information using a stereo camera; 2) reconstruct the plate plane from the disparity map; 3) warp the input image and the disparity map to form a new direction of view parallel to the plate plane; 4) cut the warped image into a series of slices according to the depth information and estimate the occluded part of the food; and 5) rescale superpixels for each slice and estimate the food volume by accumulating all available slices in the segmented food region. Through a combination of image data and disparity map, the influences of noise and visual error in existing interactive food volume estimation methods are reduced, and the estimation accuracy is improved. Our experiments show that our method is effective, accurate and convenient, providing a new tool for promoting a balanced diet and maintaining health.

Dietary Self-Management Using Mobile Health Technology for Adults With Type 2 Diabetes: A Scoping Review.

OBJECTIVE: Dietary self-management is one key component to achieve optimal glycemic control. Advances in mobile health (mHealth) technology have reduced the burden of diabetes self-management; however, limited evidence has been known regarding the status of the current body of research using mHealth technology for dietary management for adults with type 2 diabetes. METHODS: Literature searches were conducted electronically using PubMed, CINAHL (EBSCO), Web of Science Core Collection, PsycINFO (Ovid), EMBASE (Ovid), and Scopus. Keywords and subject headings covered dietary management, type 2 diabetes, and mHealth. Inclusion criteria included studies that applied mHealth for dietary self-management for adults with type 2 diabetes and were published in English as full articles. RESULTS: This review (N = 15 studies) revealed heterogeneity of the mHealth-based dietary self-management or interventions and reported results related to physiological, dietary behavioral, and psychosocial outcomes. Twelve studies applied smartphone apps with varied functions for dietary management or intervention, while three studies applied continuous glucose monitoring (CGM) to guide dietary changes. Among 15 reviewed studies, only three of them were two-arm randomized clinical trial (RCT) with larger sample and 12-month study duration and 12 of them were pilot testing. Nine of 12 pilot studies showed improved HbA1c; most of them resulted in varied dietary changes; and few of them showed improved diabetes distress and depression. CONCLUSION: Our review provided evidence that the application of mHealth technology for dietary intervention for adults with type 2 diabetes is still in pilot testing. The preliminary effects are inconclusive on physiological, dietary behavioral, and psychosocial outcomes.

Independent factors associated with birth defects during the whole of pregnancy in Shenyang City, China: a case-control study.

Background: Birth defects, as a kind of diseases that seriously affect human life, have always attracted much attention. In the past, perinatal data have been studied for birth defects. This study analyzed the surveillance data of birth defects during the perinatal period and the whole of pregnancy, as well as the independent influencing factors, to help to minimize their risk of birth defects. Methods: A total of 23,649 fetuses delivered in the hospital from January 2017 to December 2020, were enrolled in this study. There were 485 cases of birth defects, including live births and stillbirths by detailed inclusion and exclusion criteria. Maternal and neonatal clinical data were collated to analyze the influencing factors associated with birth defects. Pregnancy complications and comorbidities were diagnosed according to the criteria of the Chinese Medical Association. Univariate and multivariate logistic regression models were used to investigate the association between independent variables and birth defect events. Results: The incidence of birth defects during the whole of pregnancy was 175.46/10,000, while the incidence of perinatal birth defects was 96.22/10,000. The birth defect group had significantly higher maternal age, gravidity, parity, rate of preterm birth, cesarean section (CS) rate, scarred uterus, stillborn, and male newborns compared to the control group. Multivariate logistic regression model analysis showed that preterm birth [odds ratio (OR): 1.69, 95% confidence interval (CI): 1.01 to 2.86], CS (OR: 1.46, 95% CI: 1.08 to 1.98), scarred uterus (OR: 1.70, 95% CI: 1.01 to 2.85), and low birth weight (OR >4 compared to the other two classes) were significantly associated with birth defects during the whole of pregnancy (all P<0.05). The independent influencing factors associated with perinatal birth defects included CS (OR: 1.43, 95% CI: 1.05 to 1.93), gestational hypertension (OR: 1.70, 95%: 1.04 to 2.78), and low birth weight (OR >3.70 compared to the other two classes). Conclusions: The discovery and monitoring of known influencing factors associated with birth defects, such as, preterm birth, gestational hypertension, low birth weight, should be enhanced. For the controllable influencing factors, obstetrics providers should work with patients to minimize their risk of birth defects.

Estimating Amount of Food in a Circular Dining Bowl from a Single Image.

Unhealthy diet is a top risk factor causing obesity and numerous chronic diseases. To help the public adopt healthy diet, nutrition scientists need user-friendly tools to conduct Dietary Assessment (DA). In recent years, new DA tools have been developed using a smartphone or a wearable device which acquires images during a meal. These images are then processed to estimate calories and nutrients of the consumed food. Although considerable progress has been made, 2D food images lack scale reference and 3D volumetric information. In addition, food must be sufficiently observable from the image. This basic condition can be met when the food is stand-alone (no food container is used) or it is contained in a shallow plate. However, the condition cannot be met easily when a bowl is used. The food is often occluded by the bowl edge, and the shape of the bowl may not be fully determined from the image. However, bowls are the most utilized food containers by billions of people in many parts of the world, especially in Asia and Africa. In this work, we propose to premeasure plates and bowls using a marked adhesive strip before a dietary study starts. This simple procedure eliminates the use of a scale reference throughout the DA study. In addition, we use mathematical models and image processing to reconstruct the bowl in 3D. Our key idea is to estimate how full the bowl is rather than how much food is (in either volume or weight) in the bowl. This idea reduces the effect of occlusion. The experimental data have shown satisfactory results of our methods which enable accurate DA studies using both plates and bowls with reduced burden on research participants.

Improved Wearable Devices for Dietary Assessment Using a New Camera System.

An unhealthy diet is strongly linked to obesity and numerous chronic diseases. Currently, over two-thirds of American adults are overweight or obese. Although dietary assessment helps people improve nutrition and lifestyle, traditional methods for dietary assessment depend on self-report, which is inaccurate and often biased. In recent years, as electronics, information, and artificial intelligence (AI) technologies advanced rapidly, image-based objective dietary assessment using wearable electronic devices has become a powerful approach. However, research in this field has been focused on the developments of advanced algorithms to process image data. Few reports exist on the study of device hardware for the particular purpose of dietary assessment. In this work, we demonstrate that, with the current hardware design, there is a considerable risk of missing important dietary data owing to the common use of rectangular image screen and fixed camera orientation. We then present two designs of a new camera system to reduce data loss by generating circular images using rectangular image sensor chips. We also present a mechanical design that allows the camera orientation to be adjusted, adapting to differences among device wearers, such as gender, body height, and so on. Finally, we discuss the pros and cons of rectangular versus circular images with respect to information preservation and data processing using AI algorithms.

Feasibility of the automatic ingestion monitor (AIM-2) for infant feeding assessment: a pilot study among breast-feeding mothers from Ghana.

OBJECTIVE: Passive, wearable sensors can be used to obtain objective information in infant feeding, but their use has not been tested. Our objective was to compare assessment of infant feeding (frequency, duration and cues) by self-report and that of the Automatic Ingestion Monitor-2 (AIM-2). DESIGN: A cross-sectional pilot study was conducted in Ghana. Mothers wore the AIM-2 on eyeglasses for 1 d during waking hours to assess infant feeding using images automatically captured by the device every 15 s. Feasibility was assessed using compliance with wearing the device. Infant feeding practices collected by the AIM-2 images were annotated by a trained evaluator and compared with maternal self-report via interviewer-administered questionnaire. SETTING: Rural and urban communities in Ghana. PARTICIPANTS: Participants were thirty eight (eighteen rural and twenty urban) breast-feeding mothers of infants (child age ≤7 months). RESULTS: Twenty-five mothers reported exclusive breast-feeding, which was common among those < 30 years of age (n 15, 60 %) and those residing in urban communities (n 14, 70 %). Compliance with wearing the AIM-2 was high (83 % of wake-time), suggesting low user burden. Maternal report differed from the AIM-2 data, such that mothers reported higher mean breast-feeding frequency (eleven v. eight times, P = 0·041) and duration (18·5 v. 10 min, P = 0·007) during waking hours. CONCLUSION: The AIM-2 was a feasible tool for the assessment of infant feeding among mothers in Ghana as a passive, objective method and identified overestimation of self-reported breast-feeding frequency and duration. Future studies using the AIM-2 are warranted to determine validity on a larger scale.

[Preparation and application of porous organic cage capillary electrochromatographic chiral column].

Capillary electrochromatography for enantioseparation has received considerable research attention in the past decades, because it integrates the advantages of classical electrophoresis and modern micro-column separation. Chirality is a fundamental feature of compounds found in nature and is also a major concern in the modern pharmaceutical industry. Porous organic cages (POCs) are defined as a class of porous materials with permanent ordered three-dimensional cavity structures that are different from those of porous materials, such as zeolite, metal-organic frameworks, covalent organic frameworks, and mesoporous silica. POCs have good solubility in general organic solvents and can be used as a chromatographic stationary phase conveniently coated inside a standard capillary column. Homochiral POCs with hydroxyl groups on the cage molecules were synthesized by imine-linked condensation of 2-hydroxy-1,3,5-triformylbenzene with (1R,2R)-1,2-diphenylethylenediamine. The thus-synthesized POCs were characterized by nuclear magnetic resonance (NMR) spectroscopy, Fourier-transform infrared (FT-IR) spectroscopy, X-ray powder diffraction (XRD) analysis, etc. In the FT-IR spectra, the absorption peaks at 1602, 1489, and 1458 cm-1 were attributed to the C=C-H and C=C tensile vibrations in the benzene ring. The strong characteristic absorption peak at 1636 cm-1 was attributed to the imine bond (C=N) stretching, the two peaks at about 2900 cm-1 were attributed to C-H bond vibration, and the absorption peak at 3420 cm-1 was attributed to the O-H pulling vibration. In the XRD patterns, the powder diffraction peaks of the POCs were consistent with the simulated data. These results indicated that POCs were successfully synthesized. Thermogravimetric analysis was performed in the temperature range of 25-800 ℃ (10 ℃/min), and the POCs were found to be stable up to 380 ℃. Dichloromethane was used as solvent to uniformly coat POCs on the capillary wall to prepare an electrochromatography column. Joule heat generated in electrophoresis was negligible under the experiment condition used for the open-tubular column. Four chiral compounds, viz. dihydroflavone, praziquantel, naproxen, and 3,5-dinitro-N-(1-phenylethyl)benzamide, were used as test compounds, and the electrochromatography separation conditions were optimized such that the best separations were obtained. The voltage was applied to separate the selected enantiomers in the range of 10-20 kV. Considering the good separation and appropriate migration time simultaneously, applied voltages of 13 kV and 12 kV were recommended for dihydroflavones and 3,5-dinitro-N-(1-phenylethyl)benzamide, respectively, as well as 14 kV for praziquantel and naproxen. The concentration of the buffer solution for dihydroflavonoids was 0.075 mol/L, and those for praziquantel, naproxen, and 3,5-dinitro-N-(1-phenylethyl)benzamide were 0.100 mol/L. The pH was 3.51 for all four substances. Resolutions of 2.99, 2.10, 2.58, and 3.59 were achieved on a POC chiral column for dihydroflavonoids, praziquantel, naproxen, and 3,5-dinitro-N-(1-phenylethyl)benzamide, respectively. Two positional isomers, viz. o,m,p-nitrophenol and o,m,p-nitrophenilamine, were also successfully separated with 0.100 mol/L Tris-H3PO4 at pH 3.51. Therefore, the chiral electrochromatography column showed good chiral recognition ability and the POC is an excellent separation material with excellent application prospect in chiral electrochromatography.

A Novel Approach to Dining Bowl Reconstruction for Image-Based Food Volume Estimation.

Knowing the amounts of energy and nutrients in an individual's diet is important for maintaining health and preventing chronic diseases. As electronic and AI technologies advance rapidly, dietary assessment can now be performed using food images obtained from a smartphone or a wearable device. One of the challenges in this approach is to computationally measure the volume of food in a bowl from an image. This problem has not been studied systematically despite the bowl being the most utilized food container in many parts of the world, especially in Asia and Africa. In this paper, we present a new method to measure the size and shape of a bowl by adhering a paper ruler centrally across the bottom and sides of the bowl and then taking an image. When observed from the image, the distortions in the width of the paper ruler and the spacings between ruler markers completely encode the size and shape of the bowl. A computational algorithm is developed to reconstruct the three-dimensional bowl interior using the observed distortions. Our experiments using nine bowls, colored liquids, and amorphous foods demonstrate high accuracy of our method for food volume estimation involving round bowls as containers. A total of 228 images of amorphous foods were also used in a comparative experiment between our algorithm and an independent human estimator. The results showed that our algorithm overperformed the human estimator who utilized different types of reference information and two estimation methods, including direct volume estimation and indirect estimation through the fullness of the bowl.

Are Highly Stable Covalent Organic Frameworks the Key to Universal Chiral Stationary Phases for Liquid and Gas Chromatographic Separations?

High-performance liquid chromatography (HPLC) and gas chromatography (GC) over chiral stationary phases (CSPs) represent the most popular and highly applicable technology in the field of chiral separation, but there are currently no CSPs that can be used for both liquid and gas chromatography simultaneously. We demonstrate here that two olefin-linked covalent organic frameworks (COFs) featuring chiral crown ether groups can be general CSPs for extensive separation not only in GC but also in normal-phase and reversed-phase HPLC. Both COFs have the same 2D layered porous structure but channels of different sizes and display high stability under different chemical environments including water, organic solvents, acids, and bases. Chiral crown ethers are periodically aligned within the COF channels, allowing for enantioselective recognition of guest molecules through intermolecular interactions. The COF-packed HPLC and GC columns show excellent complementarity and each affords high resolution, selectivity, and durability for the separation of a wide range of racemic compounds, including amino acids, esters, lactones, amides, alcohols, aldehydes, ketones, and drugs. The resolution performances are comparable to and the versatility is superior to those of the most widely used commercial chiral columns, showing promises for practical applications. This work thus advances COFs with high stability as potential universal CSPs for chromatography that are otherwise hard or impossible to produce.

Recombinant angiopoietin-like protein 4 attenuates intestinal barrier structure and function injury after ischemia/reperfusion.

BACKGROUND: Intestinal barrier breakdown, a frequent complication of intestinal ischemia-reperfusion (I/R) including dysfunction and the structure changes of the intestine, is characterized by a loss of tight junction and enhanced permeability of the intestinal barrier and increased mortality. To develop effective and novel therapeutics is important for the improvement of outcome of patients with intestinal barrier deterioration. Recombinant human angiopoietin-like protein 4 (rhANGPTL4) is reported to protect the blood-brain barrier when administered exogenously, and endogenous ANGPTL4 deficiency deteriorates radiation-induced intestinal injury. AIM: To identify whether rhANGPTL4 may protect intestinal barrier breakdown induced by I/R. METHODS: Intestinal I/R injury was elicited through clamping the superior mesenteric artery for 60 min followed by 240 min reperfusion. Intestinal epithelial (Caco-2) cells and human umbilical vein endothelial cells were challenged by hypoxia/ reoxygenation to mimic I/R in vitro. RESULTS: Indicators including fluorescein isothiocyanate-conjugated dextran (4 kilodaltons; FD-4) clearance, ratio of phosphorylated myosin light chain/total myosin light chain, myosin light chain kinase and loss of zonula occludens-1, claudin-2 and VE-cadherin were significantly increased after intestinal I/R or cell hypoxia/reoxygenation. rhANGPTL4 treatment significantly reversed these indicators, which were associated with inhibiting the inflammatory and oxidative cascade, excessive activation of cellular autophagy and apoptosis and improvement of survival rate. Similar results were observed in vitro when cells were challenged by hypoxia/reoxygenation, whereas rhANGPTL4 reversed the indicators close to normal level in Caco-2 cells and human umbilical vein endothelial cells significantly. CONCLUSION: rhANGPTL4 can function as a protective agent against intestinal injury induced by intestinal I/R and improve survival via maintenance of intestinal barrier structure and functions.

Human-Mimetic Estimation of Food Volume from a Single-View RGB Image Using an AI System.

It is well known that many chronic diseases are associated with unhealthy diet. Although improving diet is critical, adopting a healthy diet is difficult despite its benefits being well understood. Technology is needed to allow an assessment of dietary intake accurately and easily in real-world settings so that effective intervention to manage being overweight, obesity, and related chronic diseases can be developed. In recent years, new wearable imaging and computational technologies have emerged. These technologies are capable of performing objective and passive dietary assessments with a much simplified procedure than traditional questionnaires. However, a critical task is required to estimate the portion size (in this case, the food volume) from a digital image. Currently, this task is very challenging because the volumetric information in the two-dimensional images is incomplete, and the estimation involves a great deal of imagination, beyond the capacity of the traditional image processing algorithms. In this work, we present a novel Artificial Intelligent (AI) system to mimic the thinking of dietitians who use a set of common objects as gauges (e.g., a teaspoon, a golf ball, a cup, and so on) to estimate the portion size. Specifically, our human-mimetic system "mentally" gauges the volume of food using a set of internal reference volumes that have been learned previously. At the output, our system produces a vector of probabilities of the food with respect to the internal reference volumes. The estimation is then completed by an "intelligent guess", implemented by an inner product between the probability vector and the reference volume vector. Our experiments using both virtual and real food datasets have shown accurate volume estimation results.

Food/Non-Food Classification of Real-Life Egocentric Images in Low- and Middle-Income Countries Based on Image Tagging Features.

Malnutrition, including both undernutrition and obesity, is a significant problem in low- and middle-income countries (LMICs). In order to study malnutrition and develop effective intervention strategies, it is crucial to evaluate nutritional status in LMICs at the individual, household, and community levels. In a multinational research project supported by the Bill & Melinda Gates Foundation, we have been using a wearable technology to conduct objective dietary assessment in sub-Saharan Africa. Our assessment includes multiple diet-related activities in urban and rural families, including food sources (e.g., shopping, harvesting, and gathering), preservation/storage, preparation, cooking, and consumption (e.g., portion size and nutrition analysis). Our wearable device ("eButton" worn on the chest) acquires real-life images automatically during wake hours at preset time intervals. The recorded images, in amounts of tens of thousands per day, are post-processed to obtain the information of interest. Although we expect future Artificial Intelligence (AI) technology to extract the information automatically, at present we utilize AI to separate the acquired images into two binary classes: images with (Class 1) and without (Class 0) edible items. As a result, researchers need only to study Class-1 images, reducing their workload significantly. In this paper, we present a composite machine learning method to perform this classification, meeting the specific challenges of high complexity and diversity in the real-world LMIC data. Our method consists of a deep neural network (DNN) and a shallow learning network (SLN) connected by a novel probabilistic network interface layer. After presenting the details of our method, an image dataset acquired from Ghana is utilized to train and evaluate the machine learning system. Our comparative experiment indicates that the new composite method performs better than the conventional deep learning method assessed by integrated measures of sensitivity, specificity, and burden index, as indicated by the Receiver Operating Characteristic (ROC) curve.

An automatic electronic instrument for accurate measurements of food volume and density.

OBJECTIVE: Accurate measurements of food volume and density are often required as 'gold standards' for calibration of image-based dietary assessment and food database development. Currently, there is no specialised laboratory instrument for these measurements. We present the design of a new volume of density (VD) meter to bridge this technological gap. DESIGN: Our design consists of a turntable, a load sensor, a set of cameras and lights installed on an arc-shaped stationary support, and a microcomputer. It acquires an array of food images, reconstructs a 3D volumetric model, weighs the food and calculates both food volume and density, all in an automatic process controlled by the microcomputer. To adapt to the complex shapes of foods, a new food surface model, derived from the electric field of charged particles, is developed for 3D point cloud reconstruction of either convex or concave food surfaces. RESULTS: We conducted two experiments to evaluate the VD meter. The first experiment utilised computer-synthesised 3D objects with prescribed convex and concave surfaces of known volumes to investigate different food surface types. The second experiment was based on actual foods with different shapes, colours and textures. Our results indicated that, for synthesised objects, the measurement error of the electric field-based method was <1 %, significantly lower compared with traditional methods. For real-world foods, the measurement error depended on the types of food volumes (detailed discussion included). The largest error was approximately 5 %. CONCLUSION: The VD meter provides a new electronic instrument to support advanced research in nutrition science.

Hyperandrogenism and insulin resistance modulate gravid uterine and placental ferroptosis in PCOS-like rats.

Women with polycystic ovary syndrome (PCOS) have hyperandrogenism and insulin resistance and a high risk of miscarriage during pregnancy. Similarly, in rats, maternal exposure to 5α-dihydrotestosterone (DHT) and insulin from gestational day 7.5 to 13.5 leads to hyperandrogenism and insulin resistance and subsequently increased fetal loss. A variety of hormonal and metabolic stimuli are able to trigger different types of regulated cell death under physiological and pathological conditions. These include ferroptosis, apoptosis and necroptosis. We hypothesized that, in rats, maternal hyperandrogenism and insulin-resistance-induced fetal loss is mediated, at least in part, by changes in the ferroptosis, apoptosis and necroptosis pathways in the gravid uterus and placenta. Compared with controls, we found that co-exposure to DHT and insulin led to decreased levels of glutathione peroxidase 4 (GPX4) and glutathione, increased glutathione + glutathione disulfide and malondialdehyde, aberrant expression of ferroptosis-associated genes (Acsl4, Tfrc, Slc7a11, and Gclc), increased iron deposition and activated ERK/p38/JNK phosphorylation in the gravid uterus. In addition, we observed shrunken mitochondria with electron-dense cristae, which are key features of ferroptosis-related mitochondrial morphology, as well as increased expression of Dpp4, a mitochondria-encoded gene responsible for ferroptosis induction in the uteri of rats co-exposed to DHT and insulin. However, in the placenta, DHT and insulin exposure only partially altered the expression of ferroptosis-related markers (e.g. region-dependent GPX4, glutathione + glutathione disulfide, malondialdehyde, Gls2 and Slc7a11 mRNAs, and phosphorylated p38 levels). Moreover, we found decreased expression of Dpp4 mRNA and increased expression of Cisd1 mRNA in placentas of rats co-exposed to DHT and insulin. Further, DHT + insulin-exposed pregnant rats exhibited decreased apoptosis in the uterus and increased necroptosis in the placenta. Our findings suggest that maternal hyperandrogenism and insulin resistance causes the activation of ferroptosis in the gravid uterus and placenta, although this is mediated via different mechanisms operating at the molecular and cellular levels. Our data also suggest that apoptosis and necroptosis may play a role in coordinating or compensating for hyperandrogenism and insulin-resistance-induced ferroptosis when the gravid uterus and placenta are dysfunctional.

The Healthy Cooking Index: Nutrition Optimizing Home Food Preparation Practices across Multiple Data Collection Methods.

BACKGROUND: Food preparation interventions are an increasingly popular target for hands-on nutrition education for adults, children, and families, but assessment tools are lacking. Objective data on home cooking practices, and how they are interpreted through different data collection methods, are needed. OBJECTIVE: The goal of this study was to explore the utility of the Healthy Cooking Index in coding multiple types of home food preparation data and elucidating healthy cooking behavior patterns. DESIGN: Parent-child dyads were recruited between October 2017 and June 2018 in Houston and Austin, Texas for this observational study. Food preparation events were observed and video recorded. Participants also wore a body camera (eButton) and completed a questionnaire during the same event. PARTICIPANTS/SETTING: Parents with a school-aged child were recruited as dyads (n=40). Data collection procedures took place in participant homes during evening meal preparation events. MAIN OUTCOME MEASURES: Food preparation data were collected from parents through direct observation during preparation as well as eButton and paper questionnaires completed immediately after the event. STATISTICAL ANALYSES PERFORMED: All data sets were analyzed using the Healthy Cooking Index coding system and compared for concordance. A paired sample t test was used to examine significant differences between the scores. Cronbach's α and principal components analysis were conducted on the observed Healthy Cooking Index items to examine patterns of cooking practices. RESULTS: Two main components of cooking practices emerged from the principal components analysis: one focused on meat products and another on health and taste enhancing practices. The eButton was more accurate in collecting Healthy Cooking Index practices than the self-report questionnaire. Significant differences were found between participant reported and observed summative Healthy Cooking Index scores (P<0.001), with no significant differences between scores computed from eButton images and observations (P=0.187). CONCLUSIONS: This is the first study to examine nutrition optimizing home cooking practices by observational, wearable camera and self-report data collection methods. By strengthening cooking behavior assessment tools, future research will be able to elucidate the transmission of cooking education through interventions and the relationships between cooking practices, disease prevention, and health.