Effect of <i>Spirulina</i> Dietary Supplementation in Modifying the Rumen Microbiota of Ewes

Supplementing ruminant diets with microalgae, may prove an effective nutritional strategy to manipulate rumen microbiota. Forty-eight ewes were divided into four homogenous groups (n = 12) according to their fat-corrected milk yield (6%), body weight, age, and days in milk, and were fed individually with concentrate, alfalfa hay, and wheat straw. The concentrate of the control group (CON) had no Spirulina supplementation, while in the treated groups 5 (SP5), 10 (SP10), and 15 g (SP15) of Spirulina were supplemented as an additive in the concentrate. An initial screening using metagenomic next-generation sequencing technology was followed by RT-qPCR analysis for the targeting of specific microbes, which unveiled the main alterations of the rumen microbiota under the Spirulina supplementation levels. The relative abundance of Eubacterium ruminantium and Fibrobacter succinogenes in rumen fluid, as well as Ruminococcus albus in rumen solid fraction, were significantly increased in the SP15 group. Furthermore, the relative abundance of Prevotella brevis was significantly increased in the rumen fluid of the SP5 and SP10 groups. In contrast, the relative abundance of Ruminobacter amylophilus was significantly decreased in the rumen fluid of the SP10 compared to the CON group, while in the solid fraction it was significantly decreased in the SP groups. Moreover, the relative abundance of Selenomonas ruminantium was significantly decreased in the SP5 and SP15 groups, while the relative abundance of Streptococcus bovis was significantly decreased in the SP groups. Consequently, supplementing 15 g Spirulina/ewe/day increased the relative abundance of key cellulolytic species in the rumen, while amylolytic species were reduced only in the solid fraction

Using neural networks to predict the outcome of refractive surgery for myopia

Introduction: Refractive Surgery (RS), has advanced immensely in the last decades, utilizing methods and techniques that fulfill stringent criteria for safety, efficacy, cost-effectiveness, and predictability of the refractive outcome. Still, a non-negligible percentage of RS require corrective retreatment. In addition, surgeons should be able to advise their patients, beforehand, as to the probability that corrective RS will be necessary. The present article addresses these issues with regard to myopia and explores the use of Neural Networks as a solution to the problem of the prediction of the RS outcome. Methods: We used a computerized query to select patients who underwent RS with any of the available surgical techniques (PRK, LASEK, Epi-LASIK, LASIK) between January 2010 and July 2017 and we investigated 13 factors which are related to RS. The data were normalized by forcing the weights used in the forward and backward propagations to be binary; each integer was represented by a 12-bit serial code, so that following this preprocessing stage, the vector of the data values of all 13 parameters was encoded in a binary vector of 1 × (13 × 12) = 1 × 156 size. Following the preprocessing stage, eight independent Learning Vector Quantization (LVQ) networks were created in random way using the function Ivqnet of Matlab, each one of them responding to one query with (0 retreat class) or (1 correct class). The results of the eight LVQs were then averaged to permit a best estimate of the network’s performance while a voting procedure by the neural nets was used to arrive at the outcome Results: Our algorithm was able to predict in a statistically significant way (as evidenced by Cohen’s Kappa test result of 0.7595) the need for retreatment after initial RS with good sensitivity (0.8756) and specificity (0.9286). Conclusion: The results permit us to be optimistic about the future of using neural networks for the prediction of the outcome and, eventually, the planning of RS