Number of consumers and days of display necessary for the assessment of meat colour acceptability

Visual assessment is regarded as the gold standard to evaluate meat colour shelf-life, but it is costly and time consuming. To address this issue, this paper aims to evaluate the number of consumers and days of display that are necessaries in order to assess the colour shelf-life of meat, presented with different methods, all using images. Photographs of thirty-six lamb steaks were taken just after cutting (day 0) and on each of the following days until the 14th day of display under standardized conditions. Images were presented in three different manners: 1) with days of display and animals in random order (Random); 2) days of display in sequential and animals in random order (Sequential); and, 3) days of display and animals in sequential order (Animal); they were presented to 211 consumers who evaluated visual acceptability on a 9-point scale. At day zero, visual acceptability scores were the highest in Animal, followed by Sequential, and then by the Random (P <.05) method. Scores decreased over time for all methods tested (P <.05). The Random method presented the highest standard deviation; however, an increase in standard deviation among consumers along days of display was observed for all methods tested (P <.05). Shelf-life determined by regression varied according to the method of presentation (7.83, 7.00 and 7.54 days for Random, Sequential and Animal, respectively). A minimum number of 4 day points before and 4 day points after neutral scores had been reached (scores = 5.0) were necessary in order to obtain a robust model. The minimum number of required consumers (a = 0.05; d = 0.1 and ß = 0.2 or 0.1) varied according to methodology: it was 81 to 109 consumers for Random, 69 to 92 for Sequential, and 55 to 74 for Animal. Our study indicates that an optimal number of days and evaluators can be calculated depending on the manner of sample presentation. These findings should be taken into account in further studies that aim to balance data reliability with the cost involved in meat colour analyses

Interacting models may be key to solve the cosmic coincidence problem

It is argued that cosmological models that feature a flow of energy from dark energy to dark matter may solve the coincidence problem of late acceleration (i.e., "why the energy densities of both components are of the same order precisely today?"). However, much refined and abundant observational data of the redshift evolution of the Hubble factor are needed to ascertain whether they can do the job.Comment: 25 pages, 11 figures; accepted for publication in JCA

Comparison of Recent SnIa datasets

We rank the six latest Type Ia supernova (SnIa) datasets (Constitution (C), Union (U), ESSENCE (Davis) (E), Gold06 (G), SNLS 1yr (S) and SDSS-II (D)) in the context of the Chevalier-Polarski-Linder (CPL) parametrization $w(a)=w_0+w_1 (1-a)$, according to their Figure of Merit (FoM), their consistency with the cosmological constant ($\Lambda$CDM), their consistency with standard rulers (Cosmic Microwave Background (CMB) and Baryon Acoustic Oscillations (BAO)) and their mutual consistency. We find a significant improvement of the FoM (defined as the inverse area of the 95.4% parameter contour) with the number of SnIa of these datasets ((C) highest FoM, (U), (G), (D), (E), (S) lowest FoM). Standard rulers (CMB+BAO) have a better FoM by about a factor of 3, compared to the highest FoM SnIa dataset (C). We also find that the ranking sequence based on consistency with $\Lambda$CDM is identical with the corresponding ranking based on consistency with standard rulers ((S) most consistent, (D), (C), (E), (U), (G) least consistent). The ranking sequence of the datasets however changes when we consider the consistency with an expansion history corresponding to evolving dark energy $(w_0,w_1)=(-1.4,2)$ crossing the phantom divide line $w=-1$ (it is practically reversed to (G), (U), (E), (S), (D), (C)). The SALT2 and MLCS2k2 fitters are also compared and some peculiar features of the SDSS-II dataset when standardized with the MLCS2k2 fitter are pointed out. Finally, we construct a statistic to estimate the internal consistency of a collection of SnIa datasets. We find that even though there is good consistency among most samples taken from the above datasets, this consistency decreases significantly when the Gold06 (G) dataset is included in the sample.Comment: 13 pages, 9 figures. Included recently released SDSS-II dataset. Improved presentation. Main results unchanged. The mathematica files and datasets used for the production of the figures may be downloaded from http://leandros.physics.uoi.gr/datacomp

Fractional Action Cosmology: Emergent, Logamediate, Intermediate, Power law Scenarios of the Universe and Generalized Second Law of Thermodynamics

In the framework of Fractional Action Cosmology (FAC), we study the generalized second law of thermodynamics for the Friedmann Universe enclosed by a boundary. We use the four well-known cosmic horizons as boundaries namely, apparent horizon, future event horizon, Hubble horizon and particle horizon. We construct the generalized second law (GSL) using and without using the first law of thermodynamics. To check the validity of GSL, we express the law in the form of four different scale factors namely emergent, logamediate, intermediate and power law. For Hubble, apparent and particle horizons, the GSL holds for emergent and logamediate expansions of the universe when we apply with and without using first law. For intermediate scenario, the GSL is valid for Hubble, apparent, particle horizons when we apply with and without first law. Also for intermediate scenario, the GSL is valid for event horizon when we apply first law but it breaks down without using first law. But for power law expansion, the GSL may be valid for some cases and breaks down otherwise.Comment: 24 pages, 32 figures, Accepted in Int. J. Theor. Phy

The selection of comparators for randomized controlled trials of health-related behavioral interventions: recommendations of an NIH expert panel

Objectives: To provide recommendations for the selection of comparators for randomized controlled trials of health-related behavioral interventions. Study Design and Setting: The National Institutes of Health Office of Behavioral and Social Science Research convened an expert panel to critically review the literature on control or comparison groups for behavioral trials and to develop strategies for improving comparator choices and for resolving controversies and disagreements about comparators. Results: The panel developed a Pragmatic Model for Comparator Selection in Health-Related Behavioral Trials. The model indicates that the optimal comparator is the one that best serves the primary purpose of the trial but that the optimal comparator's limitations and barriers to its use must also be taken into account. Conclusion: We developed best practice recommendations for the selection of comparators for health-related behavioral trials. Use of the Pragmatic Model for Comparator Selection in Health-Related Behavioral Trials can improve the comparator selection process and help resolve disagreements about comparator choices

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Implications of early respiratory support strategies on disease progression in critical COVID-19: a matched subanalysis of the prospective RISC-19-ICU cohort.

Uncertainty about the optimal respiratory support strategies in critically ill COVID-19 patients is widespread. While the risks and benefits of noninvasive techniques versus early invasive mechanical ventilation (IMV) are intensely debated, actual evidence is lacking. We sought to assess the risks and benefits of different respiratory support strategies, employed in intensive care units during the first months of the COVID-19 pandemic on intubation and intensive care unit (ICU) mortality rates. Subanalysis of a prospective, multinational registry of critically ill COVID-19 patients. Patients were subclassified into standard oxygen therapy ≥10 L/min (SOT), high-flow oxygen therapy (HFNC), noninvasive positive-pressure ventilation (NIV), and early IMV, according to the respiratory support strategy employed at the day of admission to ICU. Propensity score matching was performed to ensure comparability between groups. Initially, 1421 patients were assessed for possible study inclusion. Of these, 351 patients (85 SOT, 87 HFNC, 87 NIV, and 92 IMV) remained eligible for full analysis after propensity score matching. 55% of patients initially receiving noninvasive respiratory support required IMV. The intubation rate was lower in patients initially ventilated with HFNC and NIV compared to those who received SOT (SOT: 64%, HFNC: 52%, NIV: 49%, p = 0.025). Compared to the other respiratory support strategies, NIV was associated with a higher overall ICU mortality (SOT: 18%, HFNC: 20%, NIV: 37%, IMV: 25%, p = 0.016). In this cohort of critically ill patients with COVID-19, a trial of HFNC appeared to be the most balanced initial respiratory support strategy, given the reduced intubation rate and comparable ICU mortality rate. Nonetheless, considering the uncertainty and stress associated with the COVID-19 pandemic, SOT and early IMV represented safe initial respiratory support strategies. The presented findings, in agreement with classic ARDS literature, suggest that NIV should be avoided whenever possible due to the elevated ICU mortality risk