Caffeine as a flavor additive in soft-drinks

Over 60% of soft-drinks sold in the United States contain caffeine, a mildly addictive psycho-active chemical, as a flavor additive. Using sweeteners as controls, we assessed whether caffeine has flavor activity in a cola soft-drink. A forced-choice triangle discrimination methodology was used to determine detection thresholds of caffeine in sweeteners and a cola beverage. The subjects (n=30, 28 female, 23±4 years old) were trained tasters and completed over 1600 discrimination tests during the study. The mean detection thresholds for caffeine in the sweet solutions were: 0.333±0.1 mM sucrose; 0.467±0.29 mM aspartame; 0.462±0.3 mM sucralose, well below the concentration in common cola beverages (0.55–0.67 mM). A fixed concentration of caffeine, corresponding to the concentration of caffeine in a common cola beverage (0.67 mM) was added to the sweeteners and a non-caffeinated cola beverage. Subjects could distinguish between caffeinated and non-caffeinated sweeteners (p<0.001), but all subjects failed to distinguish between caffeinated and non-caffeinated cola beverage (p=1.0). Caffeine has no flavor activity in soft-drinks yet will induce a physiologic and psychologic desire to consume the drink.

The effect of coordinating and non-coordinating additives on the transport properties in ionic liquid electrolytes for lithium batteries

In the present study we expand our analysis of using two contrasting organic solvent additives (toluene and THF) in an ionic liquid (IL)/Li NTf 2 electrolyte. Multinuclear Pulsed-Field Gradient (PFG) NMR, spin-lattice (T1) relaxation times and conductivity measurements over a wide temperature range are discussed in terms of transport properties and structuring of the liquid. The conductivity of both additive samples is enhanced the most at low temperatures, with THF slightly more effective than toluene. Both the anion and lithium self-diffusivity are enhanced in the same order by the additives (THF > toluene) while that of the pyrrolidinium cation is marginally enhanced. 1H spin-lattice relaxation times indicate a reasonable degree of structuring and anisotropic motion within all of the samples and both 19F and 7Li highlight the effectiveness of THF at influencing the lithium coordination within these systems.

Modelling grouped survival times in toxicological studies using generalized additive models

A method for combining a proportional-hazards survival time model with a bioassay model where the log-hazard function is modelled as a linear or smoothing spline function of log-concentration combined with a smoothing spline function of time is described. The combined model is fitted to mortality numbers, resulting from survival times that are grouped due to a common set of observation times, using Generalized Additive Models (GAMs). The GAM fits mortalities as conditional binomials using an approximation to the log of the integral of the hazard function and is implemented using freely-available, general software for fitting GAMs. Extensions of the GAM are described to allow random effects to be fitted and to allow for time-varying concentrations by replacing time with a calibrated cumulative exposure variable with calibration parameter estimated using profile likelihood. The models are demonstrated using data from a studies of a marine and a, previously published, freshwater taxa. The marine study involved two replicate bioassays of the effect of zinc exposure on survival of an Antarctic amphipod, Orchomenella pinguides. The other example modelled survival of the daphnid, Daphnia magna, exposed to potassium dichromate and was fitted by both the GAM and the process-based DEBtox model. The GAM fitted with a cubic regression spline in time gave a 61 % improvement in fit to the daphnid data compared to DEBtox due to a non-monotonic hazard function. A simulation study using each of these hazard functions as operating models demonstrated that the GAM is overall more accurate in recovering lethal concentration values across the range of forms of the underlying hazard function compared to DEBtox and standard multiple endpoint probit analyses.

Distilled technical cashew nut shell liquid (DT-CNSL) as an effective biofuel and additive to stabilize triglyceride biofuels in diesel

We report distilled technical cashew nut shell liquid (DT-CNSL) as a non-transesterified biofuel and also as an additive to convert triglycerides to biofuel, without the need for the formation of methyl esters. DT-CNSL blends of diesel obey physico-chemical parameters of diesel. DT-CNSL offers stability to blends of straight vegetable oil (SVO) and tallow oil in diesel. Fluorescence studies using charge transfer probes show that the blend of DT-CNSL, triglycerides and diesel is a uniform solution, and fluorescence behavior is similar to that of diesel. The economics for the cultivation of cashew (Anacardium occidentale), its industrial use and rich carbon sink properties indicate that DT-CNSL could complement or replace traditional biodiesel crops like Jatropha and improve income for farmers. © 2014 Elsevier Ltd.

Stream quantiles via maximal entropy histograms

We address the problem of estimating the running quantile of a data stream when the memory for storing observations is limited.We (i) highlight the limitations of approaches previously described in the literature which make them unsuitable for non-stationary streams, (ii) describe a novel principle for the utilization of the available storage space, and (iii) introduce two novel algorithms which exploit the proposed principle. Experiments on three large realworld data sets demonstrate that the proposed methods vastly outperform the existing alternatives.

Expected shannon entropy and shannon differentiation between subpopulations for neutral genes under the finite island model

Shannon entropy H and related measures are increasingly used in molecular ecology and population genetics because (1) unlike measures based on heterozygosity or allele number, these measures weigh alleles in proportion to their population fraction, thus capturing a previously-ignored aspect of allele frequency distributions that may be important in many applications; (2) these measures connect directly to the rich predictive mathematics of information theory; (3) Shannon entropy is completely additive and has an explicitly hierarchical nature; and (4) Shannon entropy-based differentiation measures obey strong monotonicity properties that heterozygosity-based measures lack. We derive simple new expressions for the expected values of the Shannon entropy of the equilibrium allele distribution at a neutral locus in a single isolated population under two models of mutation: the infinite allele model and the stepwise mutation model. Surprisingly, this complex stochastic system for each model has an entropy expressable as a simple combination of well-known mathematical functions. Moreover, entropy- and heterozygosity-based measures for each model are linked by simple relationships that are shown by simulations to be approximately valid even far from equilibrium. We also identify a bridge between the two models of mutation. We apply our approach to subdivided populations which follow the finite island model, obtaining the Shannon entropy of the equilibrium allele distributions of the subpopulations and of the total population. We also derive the expected mutual information and normalized mutual information ("Shannon differentiation") between subpopulations at equilibrium, and identify the model parameters that determine them. We apply our measures to data from the common starling (Sturnus vulgaris) in Australia. Our measures provide a test for neutrality that is robust to violations of equilibrium assumptions, as verified on real world data from starlings.

Simplifying medical additive manufacturing: making the surgeon the designer

Additive Manufacturing, a technology which has been in existence since three decades, is now successfully being transitioned from a research setting to finding technologically and financially viable end-user applications. A key sector in which Additive Manufacturing is being used is the medical devices and healthcare sector. Drivers in this sector include the ability to create customized, patient specific devices and implants with quick turnaround time in a cost-effective manner. Doctors and surgeons are important change agents and innovators in the creation of new healthcare devices as well as surgical methods. Often times, they may find it necessary at first to build devices and plan surgeries which are not even being thought of or acted upon by the major healthcare companies. In this sense, they perform the roles of designers, creating new ideas and improving on them until they can be implemented and adopted by others. However, the scope for performing this creative activity is often limited in their workplaces, with resource, time and financial impediments often being present. Additive Manufacturing can be helpful to speed up the iterative process of designing such medical devices or planning surgeries as well as help convince people outside of the surgery room of the feasibility and business case for such innovations. This paper proposes to introduce a framework of design, processes and tools which will enable non-engineers (specifically surgeons) to create custom-built products. It is hoped that this paper will motivate more surgeons and non-engineers to get involved in the process of designing for additive manufacturing.

Distribution Entropy (DistEn) : a complexity measure to detect arrhythmia from short length RR interval time series

Heart rate complexity analysis is a powerful non-invasive means to diagnose several cardiac ailments. Non-linear tools of complexity measurement are indispensable in order to bring out the complete non-linear behavior of Physiological signals. The most popularly used non-linear tools to measure signal complexity are the entropy measures like Approximate entropy (ApEn) and Sample entropy (SampEn). But, these methods become unreliable and inaccurate at times, in particular, for short length data. Recently, a novel method of complexity measurement called Distribution Entropy (DistEn) was introduced, which showed reliable performance to capture complexity of both short term synthetic and short term physiologic data. This study aims to i) examine the competence of DistEn in discriminating Arrhythmia from Normal sinus rhythm (NSR) subjects, using RR interval time series data; ii) explore the level of consistency of DistEn with data length N; and iii) compare the performance of DistEn with ApEn and SampEn. Sixty six RR interval time series data belonging to two groups of cardiac conditions namely `Arrhythmia' and `NSR' have been used for the analysis. The data length N was varied from 50 to 1000 beats with embedding dimension m = 2 for all entropy measurements. Maximum ROC area obtained using ApEn, SampEn and DistEn were 0.83, 0.86 and 0.94 for data length 1000, 1000 and 500 beats respectively. The results show that DistEn undoubtedly exhibits a consistently high performance as a classification feature in comparison with ApEn and SampEn. Therefore, DistEn shows a promising behavior as bio marker for detecting Arrhythmia from short length RR interval data.

Distribution entropy analysis of epileptic EEG signals

It is an open-ended challenge to accurately detect the epileptic seizures through electroencephalogram (EEG) signals. Recently published studies have made elaborate attempts to distinguish between the normal and epileptic EEG signals by advanced nonlinear entropy methods, such as the approximate entropy, sample entropy, fuzzy entropy, and permutation entropy, etc. Most recently, a novel distribution entropy (DistEn) has been reported to have superior performance compared with the conventional entropy methods for especially short length data. We thus aimed, in the present study, to show the potential of DistEn in the analysis of epileptic EEG signals. The publicly-accessible Bonn database which consisted of normal, interictal, and ictal EEG signals was used in this study. Three different measurement protocols were set for better understanding the performance of DistEn, which are: i) calculate the DistEn of a specific EEG signal using the full recording; ii) calculate the DistEn by averaging the results for all its possible non-overlapped 5 second segments; and iii) calculate it by averaging the DistEn values for all the possible non-overlapped segments of 1 second length, respectively. Results for all three protocols indicated a statistically significantly increased DistEn for the ictal class compared with both the normal and interictal classes. Besides, the results obtained under the third protocol, which only used very short segments (1 s) of EEG recordings showed a significantly (p <; 0.05) increased DistEn for the interictal class in compassion with the normal class, whereas both analyses using relatively long EEG signals failed in tracking this difference between them, which may be due to a nonstationarity effect on entropy algorithm. The capability of discriminating between the normal and interictal EEG signals is of great clinical relevance since it may provide helpful tools for the detection of a seizure onset. Therefore, our study suggests that the DistEn analysis of EEG signals is very promising for clinical and even portable EEG monitoring.

Classification of 5-S epileptic EEG recordings using distribution entropy and sample entropy

Epilepsy is an electrophysiological disorder of the brain, the hallmark of which is recurrent and unprovoked seizures. Electroencephalogram (EEG) measures electrical activity of the brain that is commonly applied as a non-invasive technique for seizure detection. Although a vast number of publications have been published on intelligent algorithms to classify interictal and ictal EEG, it remains an open question whether they can be detected using short-length EEG recordings. In this study, we proposed three protocols to select 5 s EEG segment for classifying interictal and ictal EEG from normal. We used the publicly-accessible Bonn database, which consists of normal, interical, and ictal EEG signals with a length of 4097 sampling points (23.6 s) per record. In this study, we selected three segments of 868 points (5 s) length from each recordings and evaluated results for each of them separately. The well-studied irregularity measure-sample entropy (SampEn)-and a more recently proposed complexity measure-distribution entropy (DistEn)-were used as classification features. A total of 20 combinations of input parameters m and τ for the calculation of SampEn and DistEn were selected for compatibility. Results showed that SampEn was undefined for half of the used combinations of input parameters and indicated a large intra-class variance. Moreover, DistEn performed robustly for short-length EEG data indicating relative independence from input parameters and small intra-class fluctuations. In addition, it showed acceptable performance for all three classification problems (interictal EEG from normal, ictal EEG from normal, and ictal EEG from interictal) compared to SampEn, which showed better results only for distinguishing normal EEG from interictal and ictal. Both SampEn and DistEn showed good reproducibility and consistency, as evidenced by the independence of results on analysing protocol.