Formalization of Transform Methods using HOL Light

Transform methods, like Laplace and Fourier, are frequently used for analyzing the dynamical behaviour of engineering and physical systems, based on their transfer function, and frequency response or the solutions of their corresponding differential equations. In this paper, we present an ongoing project, which focuses on the higher-order logic formalization of transform methods using HOL Light theorem prover. In particular, we present the motivation of the formalization, which is followed by the related work. Next, we present the task completed so far while highlighting some of the challenges faced during the formalization. Finally, we present a roadmap to achieve our objectives, the current status and the future goals for this project.Comment: 15 Pages, CICM 201

A common biological basis of obesity and nicotine addiction

Smoking influences body weight such that smokers weigh less than non-smokers and smoking cessation often leads to weight increase. The relationship between body weight and smoking is partly explained by the effect of nicotine on appetite and metabolism. However, the brain reward system is involved in the control of the intake of both food and tobacco. We evaluated the effect of single-nucleotide polymorphisms (SNPs) affecting body mass index (BMI) on smoking behavior, and tested the 32 SNPs identified in a meta-analysis for association with two smoking phenotypes, smoking initiation (SI) and the number of cigarettes smoked per day (CPD) in an Icelandic sample (N=34 216 smokers). Combined according to their effect on BMI, the SNPs correlate with both SI (r=0.019, P=0.00054) and CPD (r=0.032, P=8.0 × 10<sup>−7</sup>). These findings replicate in a second large data set (N=127 274, thereof 76 242 smokers) for both SI (P=1.2 × 10<sup>−5</sup>) and CPD (P=9.3 × 10<sup>−5</sup>). Notably, the variant most strongly associated with BMI (rs1558902-A in FTO) did not associate with smoking behavior. The association with smoking behavior is not due to the effect of the SNPs on BMI. Our results strongly point to a common biological basis of the regulation of our appetite for tobacco and food, and thus the vulnerability to nicotine addiction and obesity

Effects of total fat intake on body fatness in adults

Background: The ideal proportion of energy from fat in our food and its relation to body weight is not clear. In order to prevent overweight and obesity in the general population, we need to understand the relationship between the proportion of energy from fat and resulting weight and body fatness in the general population. Objectives: To assess the effects of proportion of energy intake from fat on measures of body fatness (including body weight, waist circumference, percentage body fat and body mass index) in people not aiming to lose weight, using all appropriate randomised controlled trials (RCTs) of at least six months duration. Search methods: We searched CENTRAL, MEDLINE, Embase, Clinicaltrials.gov and the WHO International Clinical Trials Registry Platform (ICTRP) to October 2019. We did not limit the search by language. Selection criteria: Trials fulfilled the following criteria: 1) randomised intervention trial, 2) included adults aged at least 18 years, 3) randomised to a lower fat versus higher fat diet, without the intention to reduce weight in any participants, 4) not multifactorial and 5) assessed a measure of weight or body fatness after at least six months. We duplicated inclusion decisions and resolved disagreement by discussion or referral to a third party. Data collection and analysis: We extracted data on the population, intervention, control and outcome measures in duplicate. We extracted measures of body fatness (body weight, BMI, percentage body fat and waist circumference) independently in duplicate at all available time points. We performed random-effects meta-analyses, meta-regression, subgrouping, sensitivity, funnel plot analyses and GRADE assessment. Main results: We included 37 RCTs (57,079 participants). There is consistent high-quality evidence from RCTs that reducing total fat intake results in small reductions in body fatness; this was seen in almost all included studies and was highly resistant to sensitivity analyses (GRADE high-consistency evidence, not downgraded). The effect of eating less fat (compared with higher fat intake) is a mean body weight reduction of 1.4 kg (95% confidence interval (CI) -1.7 to -1.1 kg, in 53,875 participants from 26 RCTs, I2 = 75%). The heterogeneity was explained in subgrouping and meta-regression. These suggested that greater weight loss results from greater fat reductions in people with lower fat intake at baseline, and people with higher body mass index (BMI) at baseline. The size of the effect on weight does not alter over time and is mirrored by reductions in BMI (MD -0.5 kg/m2, 95% CI -0.6 to -0.3, 46,539 participants in 14 trials, I2 = 21%), waist circumference (MD -0.5 cm, 95% CI -0.7 to -0.2, 16,620 participants in 3 trials; I2 = 21%), and percentage body fat (MD -0.3% body fat, 95% CI -0.6 to 0.00, P = 0.05, in 2350 participants in 2 trials; I2 = 0%). There was no suggestion of harms associated with low fat diets that might mitigate any benefits on body fatness. The reduction in body weight was reflected in small reductions in LDL (-0.13 mmol/L, 95% CI -0.21 to -0.05), and total cholesterol (-0.23 mmol/L, 95% CI -0.32 to -0.14), with little or no effect on HDL cholesterol (-0.02 mmol/L, 95% CI -0.03 to 0.00), triglycerides (0.01 mmol/L, 95% CI -0.05 to 0.07), systolic (-0.75 mmHg, 95% CI -1.42 to -0.07) or diastolic blood pressure(-0.52 mmHg, 95% CI -0.95 to -0.09), all GRADE high-consistency evidence or quality of life (0.04, 95% CI 0.01 to 0.07, on a scale of 0 to 10, GRADE low-consistency evidence). Authors' conclusions: Trials where participants were randomised to a lower fat intake versus a higher fat intake, but with no intention to reduce weight, showed a consistent, stable but small effect of low fat intake on body fatness: slightly lower weight, BMI, waist circumference and percentage body fat compared with higher fat arms. Greater fat reduction, lower baseline fat intake and higher baseline BMI were all associated with greater reductions in weight. There was no evidence of harm to serum lipids, blood pressure or quality of life, but rather of small benefits or no effect

Reduction in saturated fat intake for cardiovascular disease

BACKGROUND: Reducing saturated fat reduces serum cholesterol, but effects on other intermediate outcomes may be less clear. Additionally, it is unclear whether the energy from saturated fats eliminated from the diet are more helpfully replaced by polyunsaturated fats, monounsaturated fats, carbohydrate or protein. OBJECTIVES: To assess the effect of reducing saturated fat intake and replacing it with carbohydrate (CHO), polyunsaturated (PUFA), monounsaturated fat (MUFA) and/or protein on mortality and cardiovascular morbidity, using all available randomised clinical trials. SEARCH METHODS: We updated our searches of the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE (Ovid) and Embase (Ovid) on 15 October 2019, and searched Clinicaltrials.gov and WHO International Clinical Trials Registry Platform (ICTRP) on 17 October 2019. SELECTION CRITERIA: Included trials fulfilled the following criteria: 1) randomised; 2) intention to reduce saturated fat intake OR intention to alter dietary fats and achieving a reduction in saturated fat; 3) compared with higher saturated fat intake or usual diet; 4) not multifactorial; 5) in adult humans with or without cardiovascular disease (but not acutely ill, pregnant or breastfeeding); 6) intervention duration at least 24 months; 7) mortality or cardiovascular morbidity data available. DATA COLLECTION AND ANALYSIS: Two review authors independently assessed inclusion, extracted study data and assessed risk of bias. We performed random-effects meta-analyses, meta-regression, subgrouping, sensitivity analyses, funnel plots and GRADE assessment. MAIN RESULTS: We included 15 randomised controlled trials (RCTs) (16 comparisons, ~59,000 participants), that used a variety of interventions from providing all food to advice on reducing saturated fat. The included long-term trials suggested that reducing dietary saturated fat reduced the risk of combined cardiovascular events by 21% (risk ratio (RR) 0.79; 95% confidence interval (CI) 0.66 to 0.93, 11 trials, 53,300 participants of whom 8% had a cardiovascular event, I² = 65%, GRADE moderate-quality evidence). Meta-regression suggested that greater reductions in saturated fat (reflected in greater reductions in serum cholesterol) resulted in greater reductions in risk of CVD events, explaining most heterogeneity between trials. The number needed to treat for an additional beneficial outcome (NNTB) was 56 in primary prevention trials, so 56 people need to reduce their saturated fat intake for ~four years for one person to avoid experiencing a CVD event. In secondary prevention trials, the NNTB was 32. Subgrouping did not suggest significant differences between replacement of saturated fat calories with polyunsaturated fat or carbohydrate, and data on replacement with monounsaturated fat and protein was very limited. We found little or no effect of reducing saturated fat on all-cause mortality (RR 0.96; 95% CI 0.90 to 1.03; 11 trials, 55,858 participants) or cardiovascular mortality (RR 0.95; 95% CI 0.80 to 1.12, 10 trials, 53,421 participants), both with GRADE moderate-quality evidence. There was little or no effect of reducing saturated fats on non-fatal myocardial infarction (RR 0.97, 95% CI 0.87 to 1.07) or CHD mortality (RR 0.97, 95% CI 0.82 to 1.16, both low-quality evidence), but effects on total (fatal or non-fatal) myocardial infarction, stroke and CHD events (fatal or non-fatal) were all unclear as the evidence was of very low quality. There was little or no effect on cancer mortality, cancer diagnoses, diabetes diagnosis, HDL cholesterol, serum triglycerides or blood pressure, and small reductions in weight, serum total cholesterol, LDL cholesterol and BMI. There was no evidence of harmful effects of reducing saturated fat intakes. AUTHORS' CONCLUSIONS: The findings of this updated review suggest that reducing saturated fat intake for at least two years causes a potentially important reduction in combined cardiovascular events. Replacing the energy from saturated fat with polyunsaturated fat or carbohydrate appear to be useful strategies, while effects of replacement with monounsaturated fat are unclear. The reduction in combined cardiovascular events resulting from reducing saturated fat did not alter by study duration, sex or baseline level of cardiovascular risk, but greater reduction in saturated fat caused greater reductions in cardiovascular events

Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19.2 million participants

Background Underweight and severe and morbid obesity are associated with highly elevated risks of adverse health outcomes. We estimated trends in mean body-mass index (BMI), which characterises its population distribution, and in the prevalences of a complete set of BMI categories for adults in all countries. Methods We analysed, with use of a consistent protocol, population-based studies that had measured height and weight in adults aged 18 years and older. We applied a Bayesian hierarchical model to these data to estimate trends from 1975 to 2014 in mean BMI and in the prevalences of BMI categories (<18·5 kg/m2 [underweight], 18·5 kg/m2 to <20 kg/m2, 20 kg/m2 to <25 kg/m2, 25 kg/m2 to <30 kg/m2, 30 kg/m2 to <35 kg/m2, 35 kg/m2 to <40 kg/m2, ≥40 kg/m2 [morbid obesity]), by sex in 200 countries and territories, organised in 21 regions. We calculated the posterior probability of meeting the target of halting by 2025 the rise in obesity at its 2010 levels, if post-2000 trends continue. Findings We used 1698 population-based data sources, with more than 19·2 million adult participants (9·9 million men and 9·3 million women) in 186 of 200 countries for which estimates were made. Global age-standardised mean BMI increased from 21·7 kg/m2 (95% credible interval 21·3–22·1) in 1975 to 24·2 kg/m2 (24·0–24·4) in 2014 in men, and from 22·1 kg/m2 (21·7–22·5) in 1975 to 24·4 kg/m2 (24·2–24·6) in 2014 in women. Regional mean BMIs in 2014 for men ranged from 21·4 kg/m2 in central Africa and south Asia to 29·2 kg/m2 (28·6–29·8) in Polynesia and Micronesia; for women the range was from 21·8 kg/m2 (21·4–22·3) in south Asia to 32·2 kg/m2 (31·5–32·8) in Polynesia and Micronesia. Over these four decades, age-standardised global prevalence of underweight decreased from 13·8% (10·5–17·4) to 8·8% (7·4–10·3) in men and from 14·6% (11·6–17·9) to 9·7% (8·3–11·1) in women. South Asia had the highest prevalence of underweight in 2014, 23·4% (17·8–29·2) in men and 24·0% (18·9–29·3) in women. Age-standardised prevalence of obesity increased from 3·2% (2·4–4·1) in 1975 to 10·8% (9·7–12·0) in 2014 in men, and from 6·4% (5·1–7·8) to 14·9% (13·6–16·1) in women. 2·3% (2·0–2·7) of the world's men and 5·0% (4·4–5·6) of women were severely obese (ie, have BMI ≥35 kg/m2). Globally, prevalence of morbid obesity was 0·64% (0·46–0·86) in men and 1·6% (1·3–1·9) in women. Interpretation If post-2000 trends continue, the probability of meeting the global obesity target is virtually zero. Rather, if these trends continue, by 2025, global obesity prevalence will reach 18% in men and surpass 21% in women; severe obesity will surpass 6% in men and 9% in women. Nonetheless, underweight remains prevalent in the world's poorest regions, especially in south Asia

Evaluation of Probabilistic Transformations for Evidential Data Association

International audienceData association is one of the main tasks to achieve in perception applications. Its aim is to match the sensor detections to the known objects. To treat such issue, recent research focus on the evidential approach using belief functions, which are interpreted as an extension of the probabilistic model for reasoning about uncertainty. The data fusion process begins by quantifying sensor data by belief masses. Thereafter, these masses are combined in order to provide more accurate information. Finally, a probabilistic approximation of these combined masses is done to make-decision on associations. Several probabilistic transformations have been proposed in the literature. However, to the best of our knowledge, these transformations have been evaluated only on simulated examples. For this reason, the objective of this paper is to benchmark most of interesting probabilistic transformations on real-data in order to evaluate their performances for the autonomous vehicle perception problematic