Image-based assessment of plant disease progression identifies new genetic loci for resistance to Ralstonia solanacearum in tomato.

A major challenge in global crop production is mitigating yield loss due to plant diseases. One of the best strategies to control these losses is through breeding for disease resistance. One barrier to the identification of resistance genes is the quantification of disease severity, which is typically based on the determination of a subjective score by a human observer. We hypothesized that image-based, non-destructive measurements of plant morphology over an extended period after pathogen infection would capture subtle quantitative differences between genotypes, and thus enable identification of new disease resistance loci. To test this, we inoculated a genetically diverse biparental mapping population of tomato (Solanum lycopersicum) with Ralstonia solanacearum, a soilborne pathogen that causes bacterial wilt disease. We acquired over 40 000 time-series images of disease progression in this population, and developed an image analysis pipeline providing a suite of 10 traits to quantify bacterial wilt disease based on plant shape and size. Quantitative trait locus (QTL) analyses using image-based phenotyping for single and multi-traits identified QTLs that were both unique and shared compared with those identified by human assessment of wilting, and could detect QTLs earlier than human assessment. Expanding the phenotypic space of disease with image-based, non-destructive phenotyping both allowed earlier detection and identified new genetic components of resistance.

Temporal Dietary Pattern Cluster Membership Varies on Weekdays and Weekends but Both Link to Health.

BACKGROUND: Energy and dietary quality are known to differ between weekdays and weekends. Data-driven approaches that incorporate time, amount, and duration of dietary intake have previously been used to partition participants' daily weekday dietary intake time series into clusters representing weekday temporal dietary patterns (TDPs) linked to health indicators in United States adults. Yet, neither the relationship of weekend day TDPs to health indicators nor how the TDP membership may change from weekday to weekend is known. OBJECTIVES: This study was conducted to determine the association between TDPs on weekdays and weekend days and health indicators [diet quality, waist circumference (WC), body mass index (BMI), and obesity] and their overlap among participants. METHODS: A weekday and weekend day 24-hour dietary recall of 9494 nonpregnant United States adults aged 20-65 years from the cross-sectional National Health and Nutrition Examination Survey 2007-2018 was used to determine the timing and amount of energy intake. Modified dynamic time warping and kernel k-means algorithm clustered participants into 4 TDPs on weekdays and weekend days. Multivariate regression models determined the associations between TDPs and health indicators, controlling for potential confounders and adjusting for the survey design and multiple comparisons. The percentages of overlap in cluster membership between TDPs on weekdays and weekend days were also determined. RESULTS: United States adults with a TDP of evenly spaced, energy-balanced eating occasions, representing the TDP of more than one-third of all adults on weekdays and weekends, had significantly higher diet quality, lower BMI, WC, and odds of obesity when compared to those with other TDPs. Membership of most United States adults to TDPs varied from weekdays to weekends. CONCLUSIONS: Both weekday and weekend TDPs were significantly associated with health indicators. TDP membership of most United States adults was not consistent on weekdays and weekends.

Optimizing Corn Tar Spot Measurement: A Deep Learning Approach Using Red-Green-Blue Imaging and Stromata Contour Detection Algorithm for Leaf-Level Disease Severity Analysis.

Visual detection of stromata (brown-black, elevated fungal fruiting bodies) is a primary method for quantifying tar spot early in the season, as these structures are definitive signs of the disease and essential for effective disease monitoring and management. Here, we present Stromata Contour Detection Algorithm version 2 (SCDA v2), which addresses the limitations of the previously developed SCDA version 1 (SCDA v1) without the need for empirical search of the optimal Decision Making Input Parameters (DMIPs), while achieving higher and consistent accuracy in tar spot stromata detection. SCDA v2 operates in two components: (i) SCDA v1 producing tar-spot-like region proposals for a given input corn leaf Red-Green-Blue (RGB) image, and (ii) a pre-trained Convolutional Neural Network (CNN) classifier identifying true tar spot stromata from the region proposals. To demonstrate the enhanced performance of the SCDA v2, we utilized datasets of RGB images of corn leaves from field (low, middle, and upper canopies) and glasshouse conditions under variable environments, exhibiting different tar spot severities at various corn developmental stages. Various accuracy analyses (F1-score, linear regression, and Lin's concordance correlation), showed that SCDA v2 had a greater agreement with the reference data (human visual annotation) than SCDA v1. SCDA v2 achievd 73.7% mean Dice values (overall accuracy), compared to 30.8% for SCDA v1. The enhanced F1-score primarily resulted from eliminating overestimation cases using the CNN classifier. Our findings indicate the promising potential of SCDA v2 for glasshouse and field-scale applications, including tar spot phenotyping and surveillance projects.

Temporal Dietary Patterns Are Associated with Obesity in US Adults.

BACKGROUND: The integration of time with dietary patterns throughout a day, or temporal dietary patterns (TDPs), have been linked with dietary quality but relations to health are unknown. OBJECTIVE: The association between TDPs and selected health status indicators and obesity, type 2 diabetes (T2D), and metabolic syndrome (MetS) was determined. METHODS: The first-day 24-h dietary recall from 1627 nonpregnant US adult participants aged 20-65 y from the NHANES 2003-2006 was used to determine timing, amount of energy intake, and sequence of eating occasions (EOs). Modified dynamic time warping (MDTW) and kernel k-means algorithm clustered participants into 4 groups representing distinct TDPs. Multivariate regression models determined associations between TDPs and health status, controlling for potential confounders, and adjusting for the survey design and multiple comparisons (P <0.05/6). RESULTS: A cluster representing a TDP with evenly spaced, energy balanced EOs reaching ≤1200 kcal between 06:00 to 10:00, 12:00 to 15:00, and 18:00 to 22:00, had statistically significant and clinically meaningful lower mean BMI (P <0.0001), waist circumference (WC) (P <0.0001), and 75% lower odds of obesity compared with 3 other clusters representing patterns with much higher peaks of energy: 1000-2400 kcal between 15:00 and 18:00 (OR: 5.3; 95% CI: 2.8, 10.1), 800-2400 kcal between 11:00 and 15:00 (OR: 4.4; 95% CI: 2.5, 7.9), and 1000-2600 kcal between 18:00 and 23:00 (OR: 6.7; 95% CI: 3.9, 11.6). CONCLUSIONS: Individuals with a TDP characterized by evenly spaced, energy balanced EOs had significantly lower mean BMI, WC, and odds of obesity compared with the other patterns with higher energy intake peaks at different times throughout the day, providing evidence that incorporating time with other aspects of a dietary pattern may be important to health status.

Perception v. actual intakes of junk food and sugar-sweetened beverages in Australian young adults: assessed using the mobile food record.

OBJECTIVE: To determine perception v. actual intakes of energy-dense nutrient-poor 'junk food' (JF) and sugar-sweetened beverages (SSB) in young adults, using the mobile food record (mFR). DESIGN: Before-and-after eating images using a 4 d mFR were assessed for standardised 600 kJ (143 kcal) servings of JF and SSB (excluding diet drinks). Participants reported their concern about the health aspects of their diet, perceptions and intentions regarding JF and SSB. SETTING: Perth, Western Australia. SUBJECTS: Adults (n 246) aged 18-30 years. RESULTS: The mean (sd) intake of JF+SSB was 3·7 (2·0) servings/d. Women thinking about drinking less SSB consumed more SSB servings/d (1·5 (1·2)) than men (0·7 (0·5); P<0·05) who were thinking about drinking less. Men not thinking about cutting down JF consumed more servings/d (4·6 (2·4)) than women (2·5 (0·7); P<0·01) who were not thinking about cutting down. Those who paid a lot of attention to the health aspects of their diet consumed less JF+SSB than those who took only a bit of notice (P<0·001), were not really thinking much about it (P<0·001) or who didn't think at all about the health aspects of food (P<0·01). CONCLUSIONS: Perceptions and attitudes regarding JF and SSB were associated with level of consumption. Those not thinking about cutting down their intake of these foods represent an important target group as they consume more than their peers. Further research is needed to identify how amenable young adults are to changing their intake, particularly given the lack of attention paid to the health aspects of their diet.

The connecting health and technology study: a 6-month randomized controlled trial to improve nutrition behaviours using a mobile food record and text messaging support in young adults.

BACKGROUND: Early adulthood represents the transition to independent living which is a period when changes in diet and body weight are likely to occur. This presents an ideal time for health interventions to reduce the effect of health problems and risk factors for chronic disease in later life. As young adults are high users of mobile devices, interventions that use this technology may improve engagement. The Connecting Health and Technology study aimed to evaluate the effectiveness of tailored dietary feedback and weekly text messaging to improve dietary intake of fruit, vegetables and junk food over 6 months among a population-based sample of men and women (aged 18-30 years). METHODS: A three-arm, parallel, randomized control trial was conducted. After baseline assessments, participants were randomized to one of three groups: A) dietary feedback and weekly text messages, B) dietary feedback only or C) control group. Dietary intake was assessed using a mobile food record App (mFR) where participants captured images of foods and beverages consumed over 4-days at baseline and post-intervention. The primary outcomes were changes in serves of fruits, vegetables, energy-dense nutrient-poor (EDNP) foods and sugar-sweetened beverages (SSB). The intervention effects were assessed using linear mixed effect models for change in food group serves. RESULTS: Young adults (n = 247) were randomized to group A (n = 82), group B (n = 83), or group C (n = 82). Overall, no changes in food group serves for either intervention groups were observed. An unanticipated outcome was a mean weight reduction of 1.7 kg (P = .02) among the dietary feedback only. Men who received dietary feedback only, significantly reduced their serves of EDNP foods by a mean of 1.4 serves/day (P = .02). Women who received dietary feedback only significantly reduced their intake of SSB (P = .04) by an average of 0.2 serves/day compared with controls. CONCLUSIONS: Tailored dietary feedback only resulted in a decrease in EDNP foods in men and SSB in women, together with a reduction in body weight. Using a mobile food record for dietary assessment and tailored feedback has great potential for future health promotion interventions targeting diet and weight in young adults. TRIAL REGISTRATION: Australian Clinical Trials Registry Registration number: ACTRN12612000250831 .

Dietary Assessment of domoic acid Exposure: What can be learned from traditional methods and new applications for a technology assisted device.

Three Tribal Nations in the Pacific Northwest United States comprise the members of the CoASTAL cohort. These populations may be at risk for neurobehavioral impairment, i.e., amnesic shellfish poisoning, from shellfish consumption as a result of repeated, low-level domoic acid (DA) exposure present in local clams. Previous work with this cohort confirmed a high proportion of clam consumers with varying levels of potential exposure over time. Since clams are an episodically consumed food, traditional dietary records do not fully capture exposure. Frequency questionnaires can capture accumulated doses over time and this data can be used to examine dose-response relationships with periodic studies of memory and learning. However, frequency questionnaires cannot be used to assess consumption and memory response in real time. To address this shortcoming, a modified technology assisted dietary assessment (TADA) iPod application was developed to capture images of the clam meal, sourcing data, and associated memory functioning within 24 hours and seven days after consumption. This methodology was piloted with razor clam meals consumed by members from the CoASTAL cohort. Preliminary findings suggest that the TADA iPod application is potentially useful in collecting real-time data with respect to razor clam consumption, as well as one day and seven day memory outcome data. This technology holds promise for addressing the challenges of other HAB related dietary exposure outcome studies.

Super-Resolving and Denoising 4D flow MRI of Neurofluids Using Physics-Guided Neural Networks.

PURPOSE: To obtain high-resolution velocity fields of cerebrospinal fluid (CSF) and cerebral blood flow by applying a physics-guided neural network (div-mDCSRN-Flow) to 4D flow MRI. METHODS: The div-mDCSRN-Flow network was developed to improve spatial resolution and denoise 4D flow MRI. The network was trained with patches of paired high-resolution and low-resolution synthetic 4D flow MRI data derived from computational fluid dynamic simulations of CSF flow within the cerebral ventricles of five healthy cases and five Alzheimer's disease cases. The loss function combined mean squared error with a binary cross-entropy term for segmentation and a divergence-based regularization term for the conservation of mass. Performance was assessed using synthetic 4D flow MRI in one healthy and one Alzheimer' disease cases, an in vitro study of healthy cerebral ventricles, and in vivo 4D flow imaging of CSF as well as flow in arterial and venous blood vessels. Comparison was performed to trilinear interpolation, divergence-free radial basis functions, divergence-free wavelets, 4DFlowNet, and our network without divergence constraints. RESULTS: The proposed network div-mDCSRN-Flow outperformed other methods in reconstructing high-resolution velocity fields from synthetic 4D flow MRI in healthy and AD cases. The div-mDCSRN-Flow network reduced error by 22.5% relative to linear interpolation for in vitro core voxels and by 49.5% in edge voxels. CONCLUSION: The results demonstrate generalizability of our 4D flow MRI super-resolution and denoising approach due to network training using flow patches and physics-based constraints. The mDCSRN-Flow network can facilitate MRI studies involving CSF flow measurements in cerebral ventricles and association of MRI-based flow metrics with cerebrovascular health.

Integrated image and sensor-based food intake detection in free-living.

The first step in any dietary monitoring system is the automatic detection of eating episodes. To detect eating episodes, either sensor data or images can be used, and either method can result in false-positive detection. This study aims to reduce the number of false positives in the detection of eating episodes by a wearable sensor, Automatic Ingestion Monitor v2 (AIM-2). Thirty participants wore the AIM-2 for two days each (pseudo-free-living and free-living). The eating episodes were detected by three methods: (1) recognition of solid foods and beverages in images captured by AIM-2; (2) recognition of chewing from the AIM-2 accelerometer sensor; and (3) hierarchical classification to combine confidence scores from image and accelerometer classifiers. The integration of image- and sensor-based methods achieved 94.59% sensitivity, 70.47% precision, and 80.77% F1-score in the free-living environment, which is significantly better than either of the original methods (8% higher sensitivity). The proposed method successfully reduces the number of false positives in the detection of eating episodes.

Diet Quality and Eating Frequency Were Associated with Insulin-Taking Status among Adults.

OBJECTIVE: This pilot cross-sectional study explored differences in dietary intake and eating behaviors between healthy adults and a group of adults taking insulin to manage diabetes. METHODS: A characteristic questionnaire and up to four Automated Self-Administered 24-Hour dietary recalls were collected from 152 adults aged 18-65 years (96 healthy and 56 adults taking insulin) from Indiana and across the U.S. from 2022 to 2023. The macronutrient intake, diet quality via the Healthy Eating Index (HEI)-2015, eating frequency, and consistency of timing of eating were calculated and compared between the two groups using adjusted linear or logistic regression models. RESULTS: The total mean HEI scores were very low, at 56 out of 100 and 49 out of 100 for the healthy and insulin-taking groups, respectively. Insulin-taking adults had significantly lower HEI total (p = 0.003) and component scores compared to the healthy group for greens and beans (2.0 vs. 3.0, p = 0.02), whole fruit (2.1 vs. 2.9, p = 0.05), seafood and plant proteins (2.1 vs. 3.3, p = 0.004), and saturated fats (3.7 vs. 5.4, p = 0.05). Eating frequency was significantly lower in the insulin-taking group than in the healthy group (3.0 vs. 3.4 eating occasions/day, p = 0.05). CONCLUSION: Evidence of the low diet quality and eating frequency of insulin takers may help inform and justify nutrition education to control and manage diabetes.

Accuracy of energy and nutrient intake estimation versus observed intake using 4 technology-assisted dietary assessment methods: a randomized crossover feeding study.

BACKGROUND: Technology-assisted 24-h dietary recalls (24HRs) have been widely adopted in population nutrition surveillance. Evaluations of 24HRs inform improvements, but direct comparisons of 24HR methods for accuracy in reference to a measure of true intake are rarely undertaken in a single study population. OBJECTIVES: To compare the accuracy of energy and nutrient intake estimation of 4 technology-assisted dietary assessment methods relative to true intake across breakfast, lunch, and dinner. METHODS: In a controlled feeding study with a crossover design, 152 participants [55% women; mean age 32 y, standard deviation (SD) 11; mean body mass index 26 kg/m2, SD 5] were randomized to 1 of 3 separate feeding days to consume breakfast, lunch, and dinner, with unobtrusive weighing of foods and beverages consumed. Participants undertook a 24HR the following day [Automated Self-Administered Dietary Assessment Tool-Australia (ASA24); Intake24-Australia; mobile Food Record-Trained Analyst (mFR-TA); or Image-Assisted Interviewer-Administered 24-hour recall (IA-24HR)]. When assigned to IA-24HR, participants referred to images captured of their meals using the mobile Food Record (mFR) app. True and estimated energy and nutrient intakes were compared, and differences among methods were assessed using linear mixed models. RESULTS: The mean difference between true and estimated energy intake as a percentage of true intake was 5.4% (95% CI: 0.6, 10.2%) using ASA24, 1.7% (95% CI: -2.9, 6.3%) using Intake24, 1.3% (95% CI: -1.1, 3.8%) using mFR-TA, and 15.0% (95% CI: 11.6, 18.3%) using IA-24HR. The variances of estimated and true energy intakes were statistically significantly different for all methods (P < 0.01) except Intake24 (P = 0.1). Differential accuracy in nutrient estimation was present among the methods. CONCLUSIONS: Under controlled conditions, Intake24, ASA24, and mFR-TA estimated average energy and nutrient intakes with reasonable validity, but intake distributions were estimated accurately by Intake24 only (energy and protein). This study may inform considerations regarding instruments of choice in future population surveillance. This trial was registered at Australian New Zealand Clinical Trials Registry as ACTRN12621000209897.

Row selection in remote sensing from four-row plots of maize and sorghum based on repeatability and predictive modeling.

Remote sensing enables the rapid assessment of many traits that provide valuable information to plant breeders throughout the growing season to improve genetic gain. These traits are often extracted from remote sensing data on a row segment (rows within a plot) basis enabling the quantitative assessment of any row-wise subset of plants in a plot, rather than a few individual representative plants, as is commonly done in field-based phenotyping. Nevertheless, which rows to include in analysis is still a matter of debate. The objective of this experiment was to evaluate row selection and plot trimming in field trials conducted using four-row plots with remote sensing traits extracted from RGB (red-green-blue), LiDAR (light detection and ranging), and VNIR (visible near infrared) hyperspectral data. Uncrewed aerial vehicle flights were conducted throughout the growing seasons of 2018 to 2021 with data collected on three years of a sorghum experiment and two years of a maize experiment. Traits were extracted from each plot based on all four row segments (RS) (RS1234), inner rows (RS23), outer rows (RS14), and individual rows (RS1, RS2, RS3, and RS4). Plot end trimming of 40 cm was an additional factor tested. Repeatability and predictive modeling of end-season yield were used to evaluate performance of these methodologies. Plot trimming was never shown to result in significantly different outcomes from non-trimmed plots. Significant differences were often observed based on differences in row selection. Plots with more row segments were often favorable for increasing repeatability, and excluding outer rows improved predictive modeling. These results support long-standing principles of experimental design in agronomy and should be considered in breeding programs that incorporate remote sensing.

Integration of USDA Food Classification System and Food Composition Database for Image-Based Dietary Assessment among Individuals Using Insulin.

New imaging technologies to identify food can reduce the reporting burden of participants but heavily rely on the quality of the food image databases to which they are linked to accurately identify food images. The objective of this study was to develop methods to create a food image database based on the most commonly consumed U.S. foods and those contributing the most to energy. The objective included using a systematic classification structure for foods based on the standardized United States Department of Agriculture (USDA) What We Eat in America (WWEIA) food classification system that can ultimately be used to link food images to a nutrition composition database, the USDA Food and Nutrient Database for Dietary Studies (FNDDS). The food image database was built using images mined from the web that were fitted with bounding boxes, identified, annotated, and then organized according to classifications aligning with USDA WWEIA. The images were classified by food category and subcategory and then assigned a corresponding USDA food code within the USDA's FNDDS in order to systematically organize the food images and facilitate a linkage to nutrient composition. The resulting food image database can be used in food identification and dietary assessment.

NISNet3D: three-dimensional nuclear synthesis and instance segmentation for fluorescence microscopy images.

The primary step in tissue cytometry is the automated distinction of individual cells (segmentation). Since cell borders are seldom labeled, cells are generally segmented by their nuclei. While tools have been developed for segmenting nuclei in two dimensions, segmentation of nuclei in three-dimensional volumes remains a challenging task. The lack of effective methods for three-dimensional segmentation represents a bottleneck in the realization of the potential of tissue cytometry, particularly as methods of tissue clearing present the opportunity to characterize entire organs. Methods based on deep learning have shown enormous promise, but their implementation is hampered by the need for large amounts of manually annotated training data. In this paper, we describe 3D Nuclei Instance Segmentation Network (NISNet3D) that directly segments 3D volumes through the use of a modified 3D U-Net, 3D marker-controlled watershed transform, and a nuclei instance segmentation system for separating touching nuclei. NISNet3D is unique in that it provides accurate segmentation of even challenging image volumes using a network trained on large amounts of synthetic nuclei derived from relatively few annotated volumes, or on synthetic data obtained without annotated volumes. We present a quantitative comparison of results obtained from NISNet3D with results obtained from a variety of existing nuclei segmentation techniques. We also examine the performance of the methods when no ground truth is available and only synthetic volumes were used for training.

Temporal Patterns of Diet and Physical Activity and of Diet Alone Have More Numerous Relationships With Health and Disease Status Indicators Compared to Temporal Patterns of Physical Activity Alone.

BACKGROUND: Daily temporal patterns of energy intake (temporal dietary patterns [TDPs]) and physical activity (temporal physical activity patterns [TPAPs]) have been independently and jointly (temporal dietary and physical activity patterns [TDPAPs]) associated with health and disease status indicators. OBJECTIVE: The aim of this study was to compare the number and strength of association between clusters of daily TDPs, TPAPs, and TDPAPs and multiple health and disease status indicators. DESIGN: This cross-sectional study used 1 reliable weekday dietary recall and 1 random weekday of accelerometer data to partition to create clusters of participants representing the 3 temporal patterns. Four clusters were created via kernel-k means clustering algorithm of the same constrained dynamic time warping distance computed over the time series for each temporal pattern. PARTICIPANTS/SETTING: From the National Health and Nutrition Examination Survey (2003-2006), 1,836 US adults aged 20 through 65 years who were not pregnant and had valid diet, physical activity, sociodemographic, anthropometric, questionnaire, and health and disease status indicator data were included. MAIN OUTCOME MEASURES: Health status indicators used as outcome measures were body mass index, waist circumference, fasting plasma glucose, hemoglobin A1c, triglycerides, high-density lipoprotein cholesterol, total cholesterol, and systolic and diastolic blood pressure; disease status indicators included obesity, type 2 diabetes mellitus, and metabolic syndrome. STATISTICAL ANALYSES PERFORMED: Multivariate regression models determined associations between the clusters representing each pattern and health and disease status indicators, controlling for confounders and adjusting for multiple comparisons. The number of significant differences among clusters and adjusted R2 and Akaike information criterion compared the strength of associations between clusters of patterns and continuous and categorical health and disease status indicators. RESULTS: TDPAPs showed 21 significant associations with health and disease status indicators, including body mass index, waist circumference, obesity, and type 2 diabetes; TDPs showed 19 significant associations; and TPAPs showed 8 significant associations. CONCLUSIONS: TDPAPs and TDPs had stronger and more numerous associations with health and disease status indicators compared with TPAPs. Patterns representing the integration of daily dietary habits hold promise for early detection of obesity.

Cluster Analysis to Find Temporal Physical Activity Patterns Among US Adults.

Physical activity (PA) is known to be a risk factor for obesity and chronic diseases such as diabetes and metabolic syndrome. Few attempts have been made to pattern the time of physical activity while incorporating intensity and duration in order to determine the relationship of this multi-faceted behavior with health. In this paper, we explore a distance-based approach for clustering daily physical activity time series to estimate temporal physical activity patterns among U.S. adults (ages 20-65) from the National Health and Nutrition Examination Survey 2003-2006 (NHANES). A number of distance measures and distance-based clustering methods were investigated and compared using various metrics. These metrics include the Silhouette and the Dunn Index (internal criteria), and the associations of the clusters with health status indicators (external criteria). Our experiments indicate that using a distance-based cluster analysis approach to estimate temporal physical activity patterns through the day, has the potential to describe the complexity of behavior rather than characterizing physical activity patterns solely by sums or labels of maximum activity levels.

Joint Temporal Patterns By Integrating Diet and Physical Activity.

Both diet and physical activity are associated with obesity and chronic diseases such as diabetes and metabolic syndrome. Early efforts in connecting dietary and physical activity behaviors to generate patterns rarely considered the use of time. In this paper, we propose a distance-based cluster analysis approach to find joint temporal diet and physical activity patterns among U.S. adults ages 20-65. Dynamic Time Warping (DTW) generalized to multi-dimensions is combined with commonly used clustering methods to generate unbiased partitioning of the National Health and Nutrition Examination Survey 2003-2006 (NHANES) dataset. The clustering results are evaluated using visualization of the clusters, the Silhouette Index, and the associations between clusters and health status indicators based on multivariate regression models. Our experiments indicate that the integration of diet, physical activity, and time has the potential to discover joint temporal patterns with association to health.

Image-based plant wilting estimation.

BACKGROUND: Environmental stress due to climate or pathogens is a major threat to modern agriculture. Plant genetic resistance to these stresses is one way to develop more resilient crops, but accurately quantifying plant phenotypic responses can be challenging. Here we develop and test a set of metrics to quantify plant wilting, which can occur in response to abiotic stress such as heat or drought, or in response to biotic stress caused by pathogenic microbes. These metrics can be useful in genomic studies to identify genes and genomic regions underlying plant resistance to a given stress. RESULTS: We use two datasets: one of tomatoes inoculated with Ralstonia solanacearum, a soilborne pathogen that causes bacterial wilt disease, and another of soybeans exposed to water stress. For both tomato and soybean, the metrics predict the visual wilting score provided by human experts. Specific to the tomato dataset, we demonstrate that our metrics can capture the genetic difference of bacterium wilt resistance among resistant and susceptible tomato genotypes. In soybean, we show that our metrics can capture the effect of water stress. CONCLUSION: Our proposed RGB image-based wilting metrics can be useful for identifying plant wilting caused by diverse stresses in different plant species.

Two-photon imaging within the murine thorax without respiratory and cardiac motion artifact.

Intravital microscopy has been recognized for its ability to make physiological measurements at cellular and subcellular levels while maintaining the complex natural microenvironment. Two-photon microscopy (TPM), using longer wavelengths than single-photon excitation, has extended intravital imaging deeper into tissues, with minimal phototoxicity. However, due to a relatively slow acquisition rate, TPM is especially sensitive to motion artifact, which presents a challenge when imaging tissues subject to respiratory and cardiac movement. Thoracoabdominal organs that cannot be exteriorized or immobilized during TPM have generally required the use of isolated, pump-perfused preparations. However, this approach entails significant alteration of normal physiology, such as a lack of neural inputs, increased vascular resistance, and leukocyte activation. We adapted techniques of intravital microscopy that permitted TPM of organs maintained within the thoracoabdominal cavity of living, breathing rats or mice. We obtained extended intravital TPM imaging of the intact lung, arguably the organ most susceptible to both respiratory and cardiac motion. Intravital TPM detected the development of lung microvascular endothelial activation manifested as increased leukocyte adhesion and plasma extravasation in response to oxidative stress inducers PMA or soluble cigarette smoke extract. The pulmonary microvasculature and alveoli in the intact animal were imaged with comparable detail and fidelity to those in pump-perfused animals, opening the possibility for TPM of other thoracoabdominal organs under physiological and pathophysiological conditions.

Connecting Health and Technology (CHAT): protocol of a randomized controlled trial to improve nutrition behaviours using mobile devices and tailored text messaging in young adults.

BACKGROUND: Increasing intakes of fruits and vegetables intake, in tandem with reducing consumption of energy-dense and nutrient poor foods and beverages are dietary priorities to prevent chronic disease. Although most adults do not eat enough fruit and vegetables, teenagers and young adults tend to have the lowest intakes. Young adults typically consume a diet which is inconsistent with the dietary recommendations. Yet little is known about the best approaches to improve dietary intakes and behaviours among this group. This randomised controlled trial aims to evaluate the effectiveness of using a mobile device to assess dietary intake, provide tailored dietary feedback and text messages to motivate changes in fruit, vegetable and junk food consumption among young adults. METHODS/DESIGN: The CHAT project will involve the development of the mobile device food record (MDFR), and evaluation of dietary feedback and implementation of a 6-month intervention in young adults aged 18 to 30 years. The participants will be randomly assigned to one of three groups (1) Intervention Group 1: MDFR + Text Messages + Dietary Feedback; (2) Intervention Group 2: MDFR + Dietary Feedback; (3) Control Group 3: MDFR, no feedback. All groups will undertake a 3-day dietary record using the MDFR but only the Intervention Groups 1 and 2 will receive tailored dietary feedback at baseline and at 6-months which will consist of assessment of serves of fruits, vegetables and junk food in comparison to dietary recommendations. Tailored nutrition text messages will be sent to Intervention Group 1 over the 6 months. Data will be collected at baseline and again at the 6-month completion. DISCUSSION: This trial will test if applications running on mobile devices have potential to assess diet, provide tailored feedback and nutrition messages as an effective way of improving fruit and vegetable consumption and reducing energy-dense nutrient poor foods in young adults. The CHAT project will assess the impact of the intervention on behavioural intention to eat a more healthful diet. This innovative approach if successful may provide a means to deliver a low cost health promotion program that has the potential to reach large groups, particularly young adults. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry ACTRN12612000250831.