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.

Estimation of Plate and Bowl Dimensions for Food Portion Size Assessment in a Wearable Sensor System.

Automatic food portion size estimation (FPSE) with minimal user burden is a challenging task. Most of the existing FPSE methods use fiducial markers and/or virtual models as dimensional references. An alternative approach is to estimate the dimensions of the eating containers prior to estimating the portion size. In this article, we propose a wearable sensor system (the automatic ingestion monitor integrated with a ranging sensor) and a related method for the estimation of dimensions of plates and bowls. The contributions of this study are: 1) the model eliminates the need for fiducial markers; 2) the camera system [automatic ingestion monitor version 2 (AIM-2)] is not restricted in terms of positioning relative to the food item; 3) our model accounts for radial lens distortion caused due to lens aberrations; 4) a ranging sensor directly gives the distance between the sensor and the eating surface; 5) the model is not restricted to circular plates; and 6) the proposed system implements a passive method that can be used for assessment of container dimensions with minimum user interaction. The error rates (mean ± std. dev) for dimension estimation were 2.01% ± 4.10% for plate widths/diameters, 2.75% ± 38.11% for bowl heights, and 4.58% ± 6.78% for bowl diameters.

Gender and Awareness of Laws on Intimate Partner Violence: A Study Among Bengali, Garo, and Santal Ethnic Communities in Rural Bangladesh.

Previous studies on intimate partner violence (IPV) against women in Bangladesh rarely focused on the effectiveness of primary prevention strategies like legal remedies. There is also a lack of studies on the issues among the ethnic minority communities in the country. This study examines the awareness of laws on IPV (such as recognizing the abusive acts and knowing the sanctions) among the ethnic Garo and Santal and mainstream Bengali communities in rural Bangladesh. The study randomly included 1929 married women and men from 24 villages. It appeared that the respondents were not adequately aware of the relevant legal provisions. There were also gender and ethnic differences in the issues. On average, the respondents maintained a low score on recognizing abusive acts. The awareness was further lower among the women compared to the men. In addition, multivariate analysis indicated that the Bengali women had relatively a better understanding of the issues than the Garo and Santal women. However, the Garo men showed poorer awareness of recognizing the abusive acts than the Bengali and Santal men. On the other hand, the respondents also maintained a very insufficient knowledge of the sanctions against such abusive acts, whereas women also showed a lower awareness compared to their male counterparts. Data further revealed that the Santal women had a more inadequate understanding of the issues than the Bengali and Garo women. However, the Garo men had more awareness of the sanctions than the Bengali and Santal men. The study reveals that people are unfamiliar with the laws governing IPV. It shows that understanding legal issues is another field of gender and ethnic inequality in the country. We suggest that there should be intervention to make aware the citizen, mainly women of all ethnicities, to ensure the efficacy of the laws.

Energy intake estimation using a novel wearable sensor and food images in a laboratory (pseudo-free-living) meal setting: quantification and contribution of sources of error.

OBJECTIVES: Dietary assessment methods not relying on self-report are needed. The Automatic Ingestion Monitor 2 (AIM-2) combines a wearable camera that captures food images with sensors that detect food intake. We compared energy intake (EI) estimates of meals derived from AIM-2 chewing sensor signals, AIM-2 images, and an internet-based diet diary, with researcher conducted weighed food records (WFR) as the gold standard. SUBJECTS/METHODS: Thirty adults wore the AIM-2 for meals self-selected from a university food court on one day in mixed laboratory and free-living conditions. Daily EI was determined from a sensor regression model, manual image analysis, and a diet diary and compared with that from WFR. A posteriori analysis identified sources of error for image analysis and WFR differences. RESULTS: Sensor-derived EI from regression modeling (R2 = 0.331) showed the closest agreement with EI from WFR, followed by diet diary estimates. EI from image analysis differed significantly from that by WFR. Bland-Altman analysis showed wide limits of agreement for all three test methods with WFR, with the sensor method overestimating at lower and underestimating at higher EI. Nutritionist error in portion size estimation and irreconcilable differences in portion size between food and nutrient databases used for WFR and image analyses were the greatest contributors to image analysis and WFR differences (44.4% and 44.8% of WFR EI, respectively). CONCLUSIONS: Estimation of daily EI from meals using sensor-derived features offers a promising alternative to overcome limitations of self-report. Image analysis may benefit from computerized analytical procedures to reduce identified sources of error.

Body mass index and variability in meal duration and association with rate of eating.

Background: A fast rate of eating is associated with a higher risk for obesity but existing studies are limited by reliance on self-report and the consistency of eating rate has not been examined across all meals in a day. The goal of the current analysis was to examine associations between meal duration, rate of eating, and body mass index (BMI) and to assess the variance of meal duration and eating rate across different meals during the day. Methods: Using an observational cross-sectional study design, non-smoking participants aged 18-45 years (N = 29) consumed all meals (breakfast, lunch, and dinner) on a single day in a pseudo free-living environment. Participants were allowed to choose any food and beverages from a University food court and consume their desired amount with no time restrictions. Weighed food records and a log of meal start and end times, to calculate duration, were obtained by a trained research assistant. Spearman's correlations and multiple linear regressions examined associations between BMI and meal duration and rate of eating. Results: Participants were 65% male and 48% white. A shorter meal duration was associated with a higher BMI at breakfast but not lunch or dinner, after adjusting for age and sex (p = 0.03). Faster rate of eating was associated with higher BMI across all meals (p = 0.04) and higher energy intake for all meals (p < 0.001). Intra-individual rates of eating were not significantly different across breakfast, lunch, and dinner (p = 0.96). Conclusion: Shorter beakfast and a faster rate of eating across all meals were associated with higher BMI in a pseudo free-living environment. An individual's rate of eating is constant over all meals in a day. These data support weight reduction interventions focusing on the rate of eating at all meals throughout the day and provide evidence for specifically directing attention to breakfast eating behaviors.

Improvement of Methodology for Manual Energy Intake Estimation From Passive Capture Devices.

Objective: To describe best practices for manual nutritional analyses of data from passive capture wearable devices in free-living conditions. Method: 18 participants (10 female) with a mean age of 45 ± 10 years and mean BMI of 34.2 ± 4.6 kg/m2 consumed usual diet for 3 days in a free-living environment while wearing an automated passive capture device. This wearable device facilitates capture of images without manual input from the user. Data from the first nine participants were used by two trained nutritionists to identify sources contributing to inter-nutritionist variance in nutritional analyses. The nutritionists implemented best practices to mitigate these sources of variance in the next nine participants. The three best practices to reduce variance in analysis of energy intake (EI) estimation were: (1) a priori standardized food selection, (2) standardized nutrient database selection, and (3) increased number of images captured around eating episodes. Results: Inter-rater repeatability for EI, using intraclass correlation coefficient (ICC), improved by 0.39 from pre-best practices to post-best practices (0.14 vs 0.85, 95% CI, respectively), Bland-Altman analysis indicated strongly improved agreement between nutritionists for limits of agreement (LOA) post-best practices. Conclusion: Significant improvement of ICC and LOA for estimation of EI following implementation of best practices demonstrates that these practices improve the reproducibility of dietary analysis from passive capture device images in free-living environments.

Hydrogeochemical processes and groundwater quality of over-exploited Dupi Tila aquifer in Dhaka city, Bangladesh.

Dhaka is one of the highly populated cities in the world. Increased urbanization and population growth in Dhaka are mostly dependent on groundwater, with 78% of the supply water coming from the Plio-Pleistocene Dupi Tila aquifer. This research was conducted with the objectives of identifying ion chemistry, hydrochemical processes and their relationship to groundwater quality and finding out the impact of over-exploitation on Dupi Tila aquifer. Three consecutive semi-confined aquifers have been delineated up to the explored depth: the Upper Dupi Tila aquifer (UDA), Middle Dupi Tila aquifer (MDA) and Lower Dupi Tila aquifer (LDA). Hydrogeochemical processes and water quality have been inferred from bivariate plots, correlation of major ions, piper plot and stable isotopes analysis. The total dissolved solids show an increasing downwards trend from an average of 267 mg/l in UDA to an average of 284 mg/l in LDA. Waters in all three aquifers as well as surface water (SW) are mainly of Ca/Mg-HCO3 type. The weathering of aluminosilicates controls the concentration of the major ions. Even though there is a significant decline in piezometric level both in UDA (> -85 m Public Works Department reference datum (PWD)) and MDA (> -65 m PWD), there is no evidence of groundwater recharge through direct infiltration from the river. The stable isotopes indicate most of the UDA, MDA and LDA waters are mainly from local precipitation. Increasingly enriched mean values for stable isotopes for the subsequent aquifers UDA, MDA and LDA, as well as increasing PCO2 for UDA < MDA < LDA, indicate increasingly warmer recharge conditions. The overall groundwater quality in the aquifers is good. At present, we have no indication that groundwater exploitation has altered groundwater quality of the Dupi Tila aquifer. Therefore, it is of vital importance to regularly monitor water quality for the purposes in order to timely detect any potential water quality alteration that could be aggravated by the vast decline in piezometric level, in view of the long-term sustainable development of the groundwater resources of this city.

Groundwater salinization and freshening processes in coastal aquifers from southwest Bangladesh.

The hydrogeochemical processes in the multilayer aquifers of southwest (SW) coastal area of Bangladesh provide important information for quantifying hydrochemical differences between different aquifers. Hydrogeochemical processes affecting groundwater chemistry in this coastal area have been evaluated by interpreting conventional plots, ionic delta, HFE-diagram, stable isotopes and geochemical modelling. The median TDS distribution of the aquifers has an increasing trend from below 1000 mg/l in the deep aquifer (DA) to 2622 mg/l in lower shallow aquifer (LSA) and 7012 mg/l in upper shallow aquifer (USA). Na+ is the dominant cation in all the aquifers. HCO3- is the dominant anion in DA with high median concentration (495 mg/l), which is more than double than that in LSA (214 mg/l) and USA (159 mg/l). The groundwater in the DA is freshened NaHCO3+ type due to cation exchange process that enhances second stage of calcite dissolution. Few NaCl+ type DA waters found in the SW corner of the study area indicate the presence of connate water confined in the inter-basin during the Holocene transgression. The stable isotopes indicate that the DA waters have been deposited during warmer periods. In contrast, both the LSA and USA are dominated by NaCl- type water; some CaCl- type waters have also been found in these aquifers, indicating salinization. The salinization processes consist of infiltration of redissolved evaporite salts, reverse cation exchange, and mixing with marine influenced flood water. Thus, the knowledge of different processes controlling freshening and salinization will help properly manage and preserve the environmental characteristics in the aquifer systems of SW Bangladesh as well as similar complex coastal geological settings.

Detection of Food Intake Sensor's Wear Compliance in Free-Living.

Objective detection of periods of wear and non-wear is critical for human studies that rely on information from wearable sensors, such as food intake sensors. In this paper, we present a novel method of compliance detection on the example of the Automatic Ingestion Monitor v2 (AIM-2) sensor, containing a tri-axial accelerometer, a still camera, and a chewing sensor. The method was developed and validated using data from a study of 30 participants aged 18-39, each wearing the AIM-2 for two days (a day in pseudo-free-living and a day in free-living). Four types of wear compliance were analyzed: 'normal-wear', 'non-compliant-wear', 'non-wear-carried', and 'non-wear-stationary'. The ground truth of those four types of compliance was obtained by reviewing the images of the egocentric camera. The features for compliance detection were the standard deviation of acceleration, average pitch, and roll angles, and mean square error of two consecutive images. These were used to train three random forest classifiers 1) accelerometer-based, 2) image-based, and 3) combined accelerometer and image-based. Satisfactory wear compliance measurement accuracy was obtained using the combined classifier (89.24%) on leave one subject out cross-validation. The average duration of compliant wear in the study was 9h with a standard deviation of 2h or 70.96% of total on-time. This method can be used to calculate the wear and non-wear time of AIM-2, and potentially be extended to other devices. The study also included assessments of sensor burden and privacy concerns. The survey results suggest recommendations that may be used to increase wear compliance.

"Automatic Ingestion Monitor Version 2" - A Novel Wearable Device for Automatic Food Intake Detection and Passive Capture of Food Images.

Use of food image capture and/or wearable sensors for dietary assessment has grown in popularity. "Active" methods rely on the user to take an image of each eating episode. "Passive" methods use wearable cameras that continuously capture images. Most of "passively" captured images are not related to food consumption and may present privacy concerns. In this paper, we propose a novel wearable sensor (Automatic Ingestion Monitor, AIM-2) designed to capture images only during automatically detected eating episodes. The capture method was validated on a dataset collected from 30 volunteers in the community wearing the AIM-2 for 24h in pseudo-free-living and 24h in a free-living environment. The AIM-2 was able to detect food intake over 10-second epochs with a (mean and standard deviation) F1-score of 81.8 ± 10.1%. The accuracy of eating episode detection was 82.7%. Out of a total of 180,570 images captured, 8,929 (4.9%) images belonged to detected eating episodes. Privacy concerns were assessed by a questionnaire on a scale 1-7. Continuous capture had concern value of 5.0 ± 1.6 (concerned) while image capture only during food intake had concern value of 1.9 ±1.7 (not concerned). Results suggest that AIM-2 can provide accurate detection of food intake, reduce the number of images for analysis and alleviate the privacy concerns of the users.

Slow radiological improvement and persistent low-grade inflammation after chemotherapy in tuberculosis patients with type 2 diabetes.

BACKGROUND: Diabetes mellitus type 2 (DM) may impede immune responses in tuberculosis (TB) and thus contribute to enhanced disease severity. In this study, we aimed to evaluate DM-mediated alterations in clinical, radiological and immunological outcomes in TB disease. METHODS: Newly diagnosed pulmonary TB patients with or without DM (TB n = 40; TB-DM n = 40) were recruited in Dhaka, Bangladesh. Clinical symptoms, sputum smear and culture conversion as well as chest radiography were assessed. Peripheral blood and sputum samples were collected at the time of diagnosis (baseline) and after 1, 2 and 6 months of standard anti-TB treatment. Blood samples were also obtained from healthy controls (n = 20). mRNA expression of inflammatory markers in blood and sputum samples were quantified using real-time PCR. RESULTS: The majority of TB-DM patients had poor glycemic control (HbA1c > 8%) and displayed elevated pulmonary pathology (P = 0.039) particularly in the middle (P < 0.004) and lower lung zones (P < 0.02) throughout the treatment period. However, reduction of clinical symptoms and time to sputum smear and culture conversion did not differ between the groups. Transcripts levels of the pro-inflammatory cytokines IL-1ß (P = 0.003 at month-1 and P = 0.045 at month-2) and TNF-α (P = 0.005 at month-1) and the anti-inflammatory cytokine IL-10 (P = 0.005 at month-2) were higher in peripheral blood after anti-TB treatment in TB-DM compared to TB patients. Conversely in sputum, TB-DM patients had reduced CD4 (P < 0.009 at month-1) and IL-10 (P = 0.005 at month-1 and P = 0.006 at month-2) transcripts, whereas CD8 was elevated (P = 0.016 at month-2). At 1- and 2-month post-treatment, sputum IL-10 transcripts were inversely correlated with fasting blood glucose and HbA1c levels in all patients. CONCLUSION: Insufficient up-regulation of IL-10 in the lung may fuel persistent local inflammation thereby promoting lung pathology in TB-DM patients with poorly controlled DM.

Real-Time Food Intake Monitoring Using Wearable Egocnetric Camera.

With technological advancement, wearable egocentric camera systems have extensively been studied to develop food intake monitoring devices for the assessment of eating behavior. This paper provides a detailed description of the implementation of CNN based image classifier in the Cortex-M7 microcontroller. The proposed network classifies the captured images by the wearable egocentric camera as food and no food images in real-time. This real-time food image detection can potentially lead the monitoring devices to consume less power, less storage, and more user-friendly in terms of privacy by saving only images that are detected as food images. A derivative of pre-trained MobileNet is trained to detect food images from camera captured images. The proposed network needs 761.99KB of flash and 501.76KB of RAM to implement which is built for an optimal trade-off between accuracy, computational cost, and memory footprint considering implementation on a Cortex-M7 microcontroller. The image classifier achieved an average precision of 82%±3% and an average F-score of 74%±2% while testing on 15343 (2127 food images and 13216 no food images) images of five full days collected from five participants.

Comparison of wearable sensors for estimation of chewing strength.

This paper presents wearable sensors for detecting differences in chewing strength while eating foods with different hardness (carrot as a hard, apple as moderate and banana as soft food). Four wearable sensor systems were evaluated. They were: (1) a gas pressure sensor measuring changes in ear pressure proportional to ear canal deformation during chewing, (2) a flexible, curved bend sensor attached to right temple of eyeglass measuring the contraction of the temporalis muscle, (3) a piezoelectric strain sensor placed on the temporalis muscle, and (4) an electromyography sensor with electrodes placed on the temporalis muscle. Data from 15 participants, wearing all four sensors at once were collected. Each participant took and consumed 10 bites of carrot, apple, and banana. The hardness of foods were measured by a food penetrometer. Single-factor ANOVA found a significant effect of food hardness on the standard deviation of signals for all four sensors (P-value < .001). Tukey's multiple comparison test with 5% significance level confirmed that the mean of the standard deviations were significantly different for the provided test foods for all four sensors. Results of this study indicate that the wearable sensors may potentially be used for measuring chewing strength and assessing the food hardness.

Automatic Count of Bites and Chews From Videos of Eating Episodes.

Methods for measuring of eating behavior (known as meal microstructure) often rely on manual annotation of bites, chews, and swallows on meal videos or wearable sensor signals. The manual annotation may be time consuming and erroneous, while wearable sensors may not capture every aspect of eating (e.g. chews only). The aim of this study is to develop a method to detect and count bites and chews automatically from meal videos. The method was developed on a dataset of 28 volunteers consuming unrestricted meals in the laboratory under video observation. First, the faces in the video (regions of interest, ROI) were detected using Faster R-CNN. Second, a pre-trained AlexNet was trained on the detected faces to classify images as a bite/no bite image. Third, the affine optical flow was applied in consecutively detected faces to find the rotational movement of the pixels in the ROIs. The number of chews in a meal video was counted by converting the 2-D images to a 1-D optical flow parameter and finding peaks. The developed bite and chew count algorithm was applied to 84 meal videos collected from 28 volunteers. A mean accuracy (±STD) of 85.4% (±6.3%) with respect to manual annotation was obtained for the number of bites and 88.9% (±7.4%) for the number of chews. The proposed method for an automatic bite and chew counting shows promising results that can be used as an alternative solution to manual annotation.

Wearable Egocentric Camera as a Monitoring Tool of Free-Living Cigarette Smoking: A Feasibility Study.

INTRODUCTION: Wearable sensors may be used for the assessment of behavioral manifestations of cigarette smoking under natural conditions. This paper introduces a new camera-based sensor system to monitor smoking behavior. The goals of this study were (1) identification of the best position of sensor placement on the body and (2) feasibility evaluation of the sensor as a free-living smoking-monitoring tool. METHODS: A sensor system was developed with a 5MP camera that captured images every second for continuously up to 26 hours. Five on-body locations were tested for the selection of sensor placement. A feasibility study was then performed on 10 smokers to monitor full-day smoking under free-living conditions. Captured images were manually annotated to obtain behavioral metrics of smoking including smoking frequency, smoking environment, and puffs per cigarette. The smoking environment and puff counts captured by the camera were compared with self-reported smoking. RESULTS: A camera located on the eyeglass temple produced the maximum number of images of smoking and the minimal number of blurry or overexposed images (53.9%, 4.19%, and 0.93% of total captured, respectively). During free-living conditions, 286,245 images were captured with a mean (±standard deviation) duration of sensor wear of 647(±74) minutes/participant. Image annotation identified consumption of 5(±2.3) cigarettes/participant, 3.1(±1.1) cigarettes/participant indoors, 1.9(±0.9) cigarettes/participant outdoors, and 9.02(±2.5) puffs/cigarette. Statistical tests found significant differences between manual annotations and self-reported smoking environment or puff counts. CONCLUSIONS: A wearable camera-based sensor may facilitate objective monitoring of cigarette smoking, categorization of smoking environments, and identification of behavioral metrics of smoking in free-living conditions. IMPLICATIONS: The proposed camera-based sensor system can be employed to examine cigarette smoking under free-living conditions. Smokers may accept this unobtrusive sensor for extended wear, as the sensor would not restrict the natural pattern of smoking or daily activities, nor would it require any active participation from a person except wearing it. Critical metrics of smoking behavior, such as the smoking environment and puff counts obtained from this sensor, may generate important information for smoking interventions.

Management of chronic neuritis with a combination regimen of lower doses prednisolone and methotrexate: a brief report.

INTRODUCTION: Recommended fixed duration prednisolone regimen was not found effective in the treatment of chronic neuritis. Alternate effective treatment was being sought to reduce the deformity in the field of leprosy. OBJECTIVE: We wished to see whether a prolonged course of prednisolone and methotrexate could be of any help for them. METHODOLOGY: In 2012-2014, an open pilot clinical study was undertaken where three chronic neuritic patients were treated with lower doses prednisolone and methotrexate for 12 months and a follow up period was delivered for 12 months. The study was undertaken in one of the outdoor clinics of the university. RESULTS: Complete and permanent remission of neuritis was achieved with appreciable functional recovery. Few mild self-limiting side-effects from prednisolone were observed and there was no side-effects from methotrexate. CONCLUSION: Prolonged course of prednisolone and methotrexate was found safe and effective in treating chronic neuritis.

Prevalence of leprosy-related disability in Bangladesh.

Disability due to leprosy often lasts lifelong, so estimates of the burden of leprosy in a community based on 'registered prevalence of leprosy cases' or on 'proportion with Grade 2 disability amongst new cases in past year' will seriously underestimate the number of disabled people in the community needing support or services. In a previously highly endemic are of Bangladesh, the accumulated prevalence of disability due to leprosy amongst adults was 45·35/100,000 population.

Production of high yield short duration Brassica napus by interspecific hybridization between B. oleracea and B. rapa.

Brassica napus is a leading oilseed crop throughout many parts of the world. It is well adapted to long day photoperiods, however, it does not adapt well to short day subtropical regions. Short duration B. napus plants were resynthesized through ovary culture from interspecific crosses in which B. rapa cultivars were reciprocally crossed with B. oleracea. From five different combinations, 17 hybrid plants were obtained in both directions. By self-pollinating the F1 hybrids or introgressing them with cultivated B. napus, resynthesized (RS) F3 and semi-resynthesized (SRS) F2 generations were produced, respectively. In field trial in Bangladesh, the RS B. napus plants demonstrated variation in days to first flowering ranging from 29 to 73 days; some of which were similar to cultivated short duration B. napus, but not cultivated short duration B. rapa. The RS and SRS B. napus lines produced 2-4.6 and 1.6-3.7 times higher yields, respectively, as compared to cultivated short duration B. napus. Our developed RS lines may be useful for rapeseed breeding not only for subtropical regions, but also for areas such as Canada and Europe where spring rapeseed production can suffer from late spring frosts. Yield and earliness in RS lines are discussed.

Structures and Stabilities of the Metal Doped Gold Nano-Clusters: M@Au10 (M = W, Mo, Ru, Co).

The structures and stabilities of a series of endohedral gold clusters containing ten gold atoms M@Au10 (M = W, Mo, Ru, Co) have been determined using density functional theory. The gradient-corrected functional BP86, the Tao-Perdew-Staroverov-Scuseria TPSS meta-GGA functional, and the hybrid density functionals B3LYP and PBE1PBE were employed to calculate the structures, binding energies, adiabatic ionization potentials, and adiabatic electron affinities for these clusters. The LanL2DZ effective core potentials and the corresponding valence basis sets were employed. The M@Au10 (M = W, Mo, Ru, Co) clusters have higher binding energies than an empty Au10 cluster. In addition, the large HOMO-LUMO gaps suggest that the M@Au10 (M = W, Mo, Ru, Co) clusters are all likely to be stable chemically. The ionization potentials and electron affinities for these clusters are very high, and the W@Au10 and Mo@Au10 clusters have electron affinities similar to the super-halogen Al13.