Mycoprotein reduces energy intake and postprandial insulin release without altering glucagon-like peptide-1 and peptide tyrosine-tyrosine concentrations in healthy overweight and obese adults: a randomised-controlled trial

Dietary mycoprotein decreases energy intake in lean individuals. The effects in overweight individuals are unclear, and the mechanisms remain to be elucidated. This study aimed to investigate the effect of mycoprotein on energy intake, appetite regulation, and the metabolic phenotype in overweight and obese volunteers. In two randomised-controlled trials, fifty-five volunteers (age: 31 (95 % CI 27, 35) years), BMI: 28·0 (95 % CI 27·3, 28·7) kg/m2) consumed a test meal containing low (44 g), medium (88 g) or high (132 g) mycoprotein or isoenergetic chicken meals. Visual analogue scales and blood samples were collected to measure appetite, glucose, insulin, peptide tyrosine-tyrosine (PYY) and glucagon-like peptide-1 (GLP-1). Ad libitum energy intake was assessed after 3 h in part A (n 36). Gastric emptying by the paracetamol method, resting energy expenditure and substrate oxidation were recorded in part B (n 14). Metabonomics was used to compare plasma and urine samples in response to the test meals. Mycoprotein reduced energy intake by 10 % (280 kJ (67 kcal)) compared with chicken at the high content (P=0·009). All mycoprotein meals reduced insulin concentrations compared with chicken (incremental AUClow (IAUClow): -8 %, IAUCmedium: -12 %, IAUChigh: -21 %, P=0·004). There was no significant difference in glucose, PYY, GLP-1, gastric emptying rate and energy expenditure. Following chicken intake, paracetamol-glucuronide was positively associated with fullness. After mycoprotein, creatinine and the deamination product of isoleucine, α-keto-β-methyl-N-valerate, were inversely related to fullness, whereas the ketone body, β-hydroxybutyrate, was positively associated. In conclusion, mycoprotein reduces energy intake and insulin release in overweight volunteers. The mechanism does not involve changes in PYY and GLP-1. The metabonomics analysis may bring new understanding to the appetite regulatory properties of food.</p

Domoic acid poisoning as a possible cause of seasonal cetacean mass stranding events in Tasmania, Australia

The periodic trend to cetacean mass stranding events in the Australian island state of Tasmania remains unexplained. This article introduces the hypothesis that domoic acid poisoning may be a causative agent in these events. The hypothesis arises from the previously evidenced role of aeolian dust as a vector of iron input to the Southern Ocean; the role of iron enrichment in Pseudo-nitzschia bloom proliferation and domoic acid production; and importantly, the characteristic toxicosis of domoic acid poisoning in mammalian subjects leading to spatial navigation deficits. As a pre-requisite for quantitative evaluation, the plausibility of this hypothesis was considered through correlation analyses between historical monthly stranding event numbers, mean monthly chlorophyll concentration and average monthly atmospheric dust loading. Correlation of these variables, which under the domoic acid stranding scenario would be linked, revealed strong agreement (r=0.80-0.87). We therefore advocate implementation of strategic quantitative investigation of the role of domoic acid in Tasmanian cetacean mass stranding events

West Nile virus outbreak among horses in New York State, 1999 and 2000.

West Nile (WN) virus was identified in the Western Hemisphere in 1999. Along with human encephalitis cases, 20 equine cases of WN virus were detected in 1999 and 23 equine cases in 2000 in New York. During both years, the equine cases occurred after human cases in New York had been identified

Candidate target genes for loss of heterozygosity on human chromosome 17q21

Loss of heterozygosity (LOH) on chromosome 17q21 has been detected in 30% of primary human breast tumours. The smallest common region deleted occurred in an interval between the D17S746 and D17S846 polymorphic sequences tagged sites that are located on two recombinant PI-bacteriophage clones of chromosome 17q21: 122F4 and 50H1, respectively. To identify the target gene for LOH, we defined a map of this chromosomal region. We found the following genes: JUP, FK506BP10, SC65, Gastrin (GAS) and HAP1. Of the genes that have been identified in this study, only JUP is located between D17S746 and D17S846. This was of interest since earlier studies have shown that JUP expression is altered in breast, lung and thyroid tumours as well as cell lines having LOH in chromosome 17q21. However, no mutations were detected in JUP using single-strand conformation polymorphism analysis of primary breast tumour DNAs having LOH at 17q21. We could find no evidence that the transcription promoter for JUP is methylated in tumour DNAs having LOH at 17q21. We suspect that the target gene for LOH in primary human breast tumours on chromosome 17q21 is either JUP and results in a haploinsufficiency for expression or may be an unidentified gene located in the interval between D17S846 and JUP. © 2004 Cancer Research UK

Derivative based global sensitivity measures

The method of derivative based global sensitivity measures (DGSM) has recently become popular among practitioners. It has a strong link with the Morris screening method and Sobol' sensitivity indices and has several advantages over them. DGSM are very easy to implement and evaluate numerically. The computational time required for numerical evaluation of DGSM is generally much lower than that for estimation of Sobol' sensitivity indices. This paper presents a survey of recent advances in DGSM concerning lower and upper bounds on the values of Sobol' total sensitivity indices $S\_{i}^{tot}$. Using these bounds it is possible in most cases to get a good practical estimation of the values of $S\_{i}^{tot}$. Several examples are used to illustrate an application of DGSM

Performance of random forests and logic regression methods using mini-exome sequence data

Machine learning approaches are an attractive option for analyzing large-scale data to detect genetic variants that contribute to variation of a quantitative trait, without requiring specific distributional assumptions. We evaluate two machine learning methods, random forests and logic regression, and compare them to standard simple univariate linear regression, using the Genetic Analysis Workshop 17 mini-exome data. We also apply these methods after collapsing multiple rare variants within genes and within gene pathways. Linear regression and the random forest method performed better when rare variants were collapsed based on genes or gene pathways than when each variant was analyzed separately. Logic regression performed better when rare variants were collapsed based on genes rather than on pathways