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Cameras cannot capture the same colors as those seen by the human eye because the eye and the cameras' sensors differ in their spectral sensitivity. To obtain a plausible approximation of perceived colors, the camera's Image Signal Processor (ISP) employs a color correction step. However, even advanced color correction methods cannot solve this underdetermined problem, and visible color inaccuracies are always present. Here, we explore an approach in which we can capture accurate colors with a regular camera by optimizing the spectral composition of the illuminant and capturing one or more exposures. We jointly optimize for the signal‐to‐noise ratio and for the color accuracy irrespective of the spectral composition of the scene. One or more images captured under controlled multispectral illuminants are then converted into a color‐accurate image as seen under the standard illuminant of D65. Our optimization allows us to reduce the color error by 20–60% (in terms of CIEDE 2000), depending on the number of exposures and camera type. The method can be used in applications in which illumination can be controlled, and high colour accuracy is required, such as product photography or with a multispectral camera flash. The code is available at https://github.com/gfxdisp/multispectral_color_correction. [ABSTRACT FROM AUTHOR]

In Australia, most threatened species translocations conducted into areas where feral predators are present fail to establish viable, self‐sustaining populations despite intensive predator control. These translocations are occurring amidst a lack of understanding regarding the conditions required for native species to survive, including predator densities. This study investigated whether population trends of in situ common species could be used as bioindicators to determine the effectiveness of predator management for threatened species protection. We compared changes in capture rates of four small mammals and four reptile species inside and outside a predator‐proof reserve for 11 years after cat and fox removal to identify which species responded to cat and fox eradication at various time scales. We only used sites inside the reserve where threatened species had not yet been reintroduced to isolate the effects of cat and fox removal. The effectiveness of these bioindicators was then tested at an unfenced reserve where predators were controlled using baiting, trapping, and shooting. There was a significant increase in the abundance of native rodents (spinifex hopping mouse and Bolam's mouse) inside the fenced reserve compared to outside, however, these differences were not detected in the unfenced reserve possibly due to inadequate predator control or insufficient time for in situ species to recover with sustained predator control. Captures of the introduced house mouse were higher at sites inside the fenced reserve in some years, but the difference was not consistent. Native dunnarts and all four reptile species did not respond consistently over the 11‐year time frame at either reserve. Native rodent abundance was the best indicator of effective feral predator control and may provide a useful bioindicator for threatened species management, such as reintroductions. [ABSTRACT FROM AUTHOR]

BACKGROUND/OBJECTIVES: The Control of Eating Questionnaire (CoEQ) comprises 21-items that are designed to assess the severity and type of food cravings an individual experiences over the previous 7 days. The CoEQ has been used in clinical trials as a multi-dimensional measure of appetite, craving and mood regulation however its underlying component structure has yet to be determined. The current paper has two aims;(1) to examine the psychometric properties, and internal consistency of the CoEQ; and (2) to provide a preliminary examination of the underlying components by exploring their construct and predictive validity. SUBJECTS/METHODS: Data were pooled from four studies in which a total 215 adults (80% women; Age = 29.7 ± 10.3; BMI = 26.5 ± 5.2) had completed the CoEQ alongside measures of psychometric eating behaviour traits, ad libitum food intake, and body composition. A principal components analysis (PCA) and parallel analysis was conducted to examine the underlying structure of the questionnaire. The resulting subscales were tested for internal consistency (Cronbach's a = 0.66-0.88). RESULTS: PCA revealed four components that explained 54.5% of the variance. The components were identified as: Craving Control, Positive Mood, Craving for Sweet, and Craving for Savoury. Associations between the underlying CoEQ subscales and measures of body composition and eating behaviour traits confirmed construct validity of the subscales. The associations between the subscales and snack food selection and intake of palatable snack foods supported the CoEQ's predictive validity. CONCLUSIONS: The CoEQ has good psychometric properties with a clear component structure and acceptable internal consistency. This preliminary validation supports the CoEQ as a measure of the experience of food cravings. European Journal of Clinical Nutrition (2015) 69, 1313-1317; doi: 10.1038/ejcn.2015.57; published online 8 April 2015, INTRODUCTION Food craving is defined as an intense desire to eat a specific food and has been associated with a loss of control over eating and poor weight management. For [...]

BACKGROUND/OBJECTIVES: A decline in resting energy expenditure (REE) beyond that predicted from changes in body composition has been noted following dietary-induced weight loss. However, it is unknown whether a compensatory downregulation in REE also accompanies exercise (EX)-induced weight loss, or whether this adaptive metabolic response influences energy intake (EI). SUBJECTS/METHODS: Thirty overweight and obese women (body mass index (BMI) = 30.6 ± 3.6 kg/[m.sup.2]) completed 12 weeks of supervised aerobic EX. Body composition, metabolism, EI and metabolic-related hormones were measured at baseline, week 6 and post intervention. The metabolic adaptation (MA), that is, difference between predicted and measured REE was also calculated post intervention ([MA.sub.post]), with REE predicted using a regression equation generated in an independent sample of 66 overweight and obese women (BMI = 31.0 ± 3.9 kg/[m.sup.2]). RESULTS: Although mean predicted and measured REE did not differ post intervention, 43% of participants experienced a greater-than-expected decline in REE (-102.9 ± 77.5 kcal per day). MApost was associated with the change in leptin (r = 0.47; P = 0.04), and the change in resting fat (r = 0.52;P = 0.01) and carbohydrate oxidation (r = -0.44; P = 0.02). Furthermore, MApost was also associated with the change in EI following EX (r = -0.44; P = 0.01). CONCLUSIONS: Marked variability existed in the adaptive metabolic response to EX. Importantly, those who experienced a downregulation in REE also experienced an upregulation in EI, indicating that the adaptive metabolic response to EX influences both physiological and behavioural components of energy balance. European Journal of Clinical Nutrition (2014) 68, 581-586; doi: 10.1038/ejcn.2013.277; published online 8 January 2014 Keywords: exercise-induced weight loss;energy intake; resting energy expenditure; leptin, INTRODUCTION Although a reduction in resting energy expenditure (REE) following dietary energy restriction is well documented, (1) it has been suggested that some individuals experience a greater-than-expected decline in REE [...]

Increasing scientific interest in diurnal patterns of appetite and food reward means it is important to employ valid methodologies that are time-of-day appropriate. Moreover, it is unknown whether people with type 2 diabetes (T2D) experience similar diurnal patterns to healthy individuals. The Steno Biometric Food Preference Task (SBFPT)(1) utilises an array of food images to assess food reward and biometric responses to food cues varying in fat content and sweet taste. We aimed to adapt and validate the SBFPT to assess diurnal appetite and food reward in Danish adults with a range of BMI with/without T2D. An anonymous online survey was used to validate 28 food images (7 high-fat sweet (HFSW), 7 lowfat sweet (LFSW), 7 high-fat savoury (HFSA) and 7 low-fat savoury (LFSA)) in Danish residents in order to select 16 images for the diurnal-SBFPT (4 from each category). For each food image, participants had to name the food, report the frequency of consumption, and rate its tastiness, sweetness, fat content, appropriateness to consume in the morning (between 8:00-12:00), afternoon (between 12:00-16:00), and evening (between 16:00-20:00) on a 100-mm visual analogue scale(2). We obtained 207 complete responses (with >80% completion). Participants included 61 men, 144 women and 2 not specified. Of these, 124 did not have T2D and 83 had T2D with a median [Q1, Q3] diagnosis duration of 12 [8, 18] years. Median [Q1, Q3] age was 61 [50, 67] years and BMI was 26.4 [23.4, 31.8] kg/m2. After data were cleaned, the mean of the individual ratings for each food was calculated and adequacy was judged according to pre-specified criteria(2). As there were overall differences in ratings of appropriate consumption time between those with/without T2D regardless of time of day (linear mixed model β = -5.3mm; p=.01), a difference score between morning (AM) and afternoon/evening (PM) ratings was computed (AM-PM difference). Adequacy for this criterion was based on foods with the smallest absolute mean across those with/without T2D and smallest absolute difference between groups. Overall, the majority of the final 4 images from each food category met the criteria for recognition, frequency of consumption, liking, taste and perceived fat content. LFSW foods had the smallest absolute AM-PM differences (i.e., more similar appropriateness scores in AM vs PM; range 110mm), followed by LFSA (5-21mm), HFSA (8-21mm) and HFSW (8-38mm). This image validation study will allow us to examine appetite and food reward across the day using a validated tool among Danish adults ranging in BMI with/without T2D. Future studies should consider the perceived appropriateness of the foods selected for appetite-related assessments in T2D, especially high-fat sweet foods. [ABSTRACT FROM AUTHOR]