Caffeine as an ingredient in sugar sweetened beverages

Caffeine is the most widely used psychoactive drug in the world, with more than 80% of the US population classed as regular consumers (Garrett and Griffiths 1998). An analysis of the Continuing Survey of Food Intakes by Individuals (CSFII) in the US indicates that 870/o of US population over 2 years of age consumed caffeine daily and the average intake in caffeine consumers was 193 mg per day or 1.2 mgkg-l per day (Frary er a/ 2005). SSB were the primary source of caffeine in children and adolescents under 18 years of age and provided between 50-64% of the daily caffeine intake. For adults 18-34 years, SSB provided 30% of total daily caffeine, dropping fo llo/o for adults 34 years and older (Frary et a|2005). The total daily intake of caffeine observed in the CSFII is slightly lower that than observed in the 1995 National Nutrition Survey of Australian adults who reported consuming on average 270 mg of caffeine per day. Caffeine intakes amongst children, aged2 to 14 years, were reported as 17 mg per day. It is suggested that cola flavored SSB provide around 62o/, of this intake (Desbrow et al 2004).

Is the popularity of caffeinated foods mere coincidence? Is the flavor coffee, chocolate, tea and cola soft drinks such that without caffeine they would still be widely consumed? Or is the popularity of caffeine containing foods due to the influence of caffeine in the body?

The acute effects of caffeine in a sugar-sweetened beverage on energy consumption

Background: It has been suggested that those who are habitually high caffeine consumers ingest greater quantities of snack foods both in and outside the laboratory. Sugar-sweetened beverages (SSBs) are a major contributor to caffeine consumption and evidence links SSB consumption with poor dietary intake.

Objective: To determine whether varying the concentration of caffeine in SSBs influences snack food consumption and energy intake.

Methods: Caffeine taste thresholds were assessed using the International Standards Organization method for assessing taste sensitivity. In a crossover study design, participants (n=23, 26±5 years old, 58% female) were provided with a standardized meal on 4 days and simultaneously consumed SSBs with varied levels of caffeine (0, 0.67, 1.16, and 1.65 mM). The intake of food and beverage was recorded following each meal session.

Results: A one way between groups analysis of variance revealed no significant main effect of caffeine concentration on consumption of SSBs [F (3, 92)=0.154, p=0.927] or food [F (3, 92)=0.305, p=0.822]. Pearson correlation analysis identified no significant correlations between the amount of food and SSB consumed (R=−0.031–0.415, p=0.062–0.893), or the amount of food and SSB consumed with body mass index and waist circumference (R=0.000 to −0.380, p=0.073–0.999). An individual's oral sensitivity to caffeine was not associated with SSB consumption (R=0.045 to −0.309, p=0.152–0.839) or the consumption of food (R=−0.052 to −0.327, p=0.128–0.812).

Conclusions: The concentration of caffeine in SSBs did not influence the amount of food or SSB consumed.

Dietary sodium intake is associated with total fluid and sugar-sweetened beverage consumption in US children and adolescents aged 2 - 18 y : NHANES 2005 - 2008

Background: Increasing dietary sodium drives the thirst response. Because sugar-sweetened beverages (SSBs) are frequently consumed by children, sodium intake may drive greater consumption of SSBs and contribute to obesity risk.

Objective: We examined the association between dietary sodium, total fluid, and SSB consumption in a nationally representative sample of US children and adolescents aged 2–18 y.

Design: We analyzed cross-sectional data from NHANES 2005–2008. Dietary sodium, fluid, and SSB intakes were assessed with a 24-h dietary recall. Multiple regression analysis was used to assess associations between sodium, fluid, and SSBs adjusted for age, sex, race-ethnic group, body mass index (BMI), socioeconomic status (SES), and energy intake.

Results: Of 6400 participants, 51.3% (n = 3230) were males, and the average (±SEM) age was 10.1 ± 0.1 y. The average sodium intake was 3056 ± 48 mg/d (equivalent to 7.8 ± 0.1 g salt/d). Dietary sodium intake was positively associated with fluid consumption (r = 0.42, P < 0.001). After adjustment for age, sex, race-ethnic group, SES, and BMI, each additional 390 mg Na/d (1 g salt/d) was associated with a 74-g/d greater intake of fluid (P < 0.001). In consumers of SSBs (n = 4443; 64%), each additional 390 mg Na/d (1 g salt/d) was associated with a 32-g/d higher intake of SSBs (P < 0.001) adjusted for age, sex, race-ethnic group, SES, and energy intake.

Conclusions: Dietary sodium is positively associated with fluid consumption and predicted SSB consumption in consumers of SSBs. The high dietary sodium intake of US children and adolescents may contribute to a greater consumption of SSBs identifying a possible link between dietary sodium intake and excess energy intake.

Fast growing self organizing map for text clustering

This paper presents an integration of a novel document vector representation technique and a novel Growing Self Organizing Process. In this new approach, documents are represented as a low dimensional vector, which is composed of the indices and weights derived from the keywords of the document.

An index based similarity calculation method is employed on this low dimensional feature space and the growing self organizing process is modified to comply with the new feature representation model.

The initial experiments show that this novel integration outperforms the state-of-the-art Self Organizing Map based techniques of text clustering in terms of its efficiency while preserving the same accuracy level.

Growing Self Organizing Map with an Imposed Binary Search Tree for Discovering Temporal Input Patterns

In this paper the Binary Search Tree Imposed Growing Self Organizing Map (BSTGSOM) is presented as an extended version of the Growing Self Organizing Map (GSOM), which has proven advantages in knowledge discovery applications. A Binary Search Tree imposed on the GSOM is mainly used to investigate the dynamic perspectives of the GSOM based on the inputs and these generated temporal patterns are stored to further analyze the behavior of the GSOM based on the input sequence. Also, the performance advantages are discussed and compared with that of the original GSOM.

Genome sequence of Ensifer arboris strain LMG 14919T, a microsymbiont of the legume Prosopis chilensis growing in Kosti, Sudan

Ensifer arboris LMG 14919T is an aerobic, motile, Gram-negative, non-spore-forming rod that can exist as a soil saprophyte or as a legume microsymbiont of several species of legume trees. LMG 14919T was isolated in 1987 from a nodule recovered from the roots of the tree Prosopis chilensis growing in Kosti, Sudan. LMG 14919T is highly effective at fixing nitrogen with P. chilensis (Chilean mesquite) and Acacia senegal (gum Arabic tree or gum acacia). LMG 14919T does not nodulate the tree Leucena leucocephala, nor the herbaceous species Macroptilium atropurpureum, Trifolium pratense, Medicago sativa, Lotus corniculatus and Galega orientalis. Here we describe the features of E. arboris LMG 14919T, together with genome sequence information and its annotation. The 6,850,303 bp high-quality-draft genome is arranged into 7 scaffolds of 12 contigs containing 6,461 protein-coding genes and 84 RNA-only encoding genes, and is one of 100 rhizobial genomes sequenced as part of the DOE Joint Genome Institute 2010 Genomic Encyclopedia for Bacteria and Archaea-Root Nodule Bacteria (GEBA-RNB) project.

Growing self-organizing map for online continuous clustering

The internet age has fuelled an enormous explosion in the amount of information generated by humanity. Much of this information is transient in nature, created to be immediately consumed and built upon (or discarded). The field of data mining is surprisingly scant with algorithms that are geared towards the unsupervised knowledge extraction of such dynamic data streams. This chapter describes a new neural network algorithm inspired by self-organising maps. The new algorithm is a hybrid algorithm from the growing self-organising map (GSOM) and the cellular probabilistic self-organising map (CPSOM). The result is an algorithm which generates a dynamically growing feature map for the purpose of clustering dynamic data streams and tracking clusters as they evolve in the data stream.