Challenges in the Multivariate Analysis of Mass Cytometry Data: The Effect of Randomization

Cytometry by time-of-flight (CyTOF) has emerged as a high-throughput single cell technology able to provide large samples of protein readouts. Already, there exists a large pool of advanced high-dimensional analysis algorithms that explore the observed heterogeneous distributions making intriguing biological inferences. A fact largely overlooked by these methods, however, is the effect of the established data preprocessing pipeline to the distributions of the measured quantities. In this article, we focus on randomization, a transformation used for improving data visualization, which can negatively affect multivariate data analysis methods such as dimensionality reduction, clustering, and network reconstruction algorithms. Our results indicate that randomization should be used only for visualization purposes, but not in conjunction with high-dimensional analytical tools

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.This study was supported by COST Action CA18131 “Statistical and machine learning techniques in human microbiome studies”. Estonian Research Council grant PRG548 (JT). Spanish State Research Agency Juan de la Cierva Grant IJC2019-042188-I (LM-Z). EO was founded and OA was supported by Estonian Research Council grant PUT 1371 and EMBO Installation grant 3573. AG was supported by Statutory Research project of the Department of Computer Networks and Systems

Blue Tilapia (Oreochromis aureus) growth rate in relation to dissolved oxygen concentration under recirculated water conditions

The growth rate of Oreochromis aureus in relation to dissolved oxygen concentrations (2.63+/-0.12, 3.75+/-0.12, 6.51+/-0.13 ppm or 31.3, 44.6 and 77.5% saturation, respectively) was investigated. Three duplicated populations of 29 specimens (mean initial body weight similar to 27.3 g) were reared in 100-litre tanks for 200 days under recirculated water conditions. Fish were offered an artificial diet three times per day, 6 days per week. The obtained results showed statistically significant final body weight differences (P < 0.05) between oxygen groups and actual differences regarding their specific growth rate and food conversion ratios. However; although mean body weight increased according to the dissolved oxygen concentration the best food conversion ratio was shown by fish of the intermediate dissolved oxygen group. It is concluded that the lowest feeding cost involved in tilapia controlled mass production, could be achieved with relatively low dissolved oxygen concentration under simple recirculated water system conditions

A Cold Ironing Study on Modern Ports, Implementation and Benefits Thriving for Worldwide Ports

139 σ.This report presents an in-­‐depth analysis of specific air Emissions Control Options (ECO) that may be available now and in future to a wide variety of modern sea ports globally. The study focuses on providing shore-­‐based energy to vessels while at berth, for powering a range of on-­‐board activities. (AMP). This report also takes under consideration the various challenges that emerge from the operation of cold ironing techniques in modern ports, in grounds of financial technical social and regulatory issues. Cold Ironing lately is receiving much attention, being promoted as one of the prime strategies, bearing great significance, with major contribution in reducing air emissions generated from global maritime industry. This report focuses on the key-­‐role of Cold Ironing towards a "Greener Commercial Maritime Industry"Θεόδωρος Γ. Παπουτσόγλο

Effect of musical stimuli and white noise on rainbow trout (Oncorhynchus mykiss) growth and physiology in recirculating water conditions

Two musical stimuli transmissions (Mozart and Romanza) as compared with white noise treatment or control, both resulted in significantly higher growth performance in juvenile (6.7 +/- 0.12 g) rainbow trout (Oncorhynchus mykiss) reared for 14 weeks. Carcass chemical composition and fatty acid composition (% of total fatty acids and mg/g carcass wet weight) did not differ among experimental treatments. The same was observed with regard to liver composition. Brain serotonin (5-HT) and its metabolite (5-HIAA) levels were increased in Mozart fish groups compared to all other treatments. However, serotonergic activity (as defined by the 5-HIAA: 5-HT ratio) for the Mozart groups was similar to control groups and was increased in Romanza and white noise fish groups. Brain dopaminergic activity (as defined by the DOPAC: DA ratio, i.e. dopamine metabolite to dopamine levels) was lower in Mozart compared to control fish groups. Differences were also observed as regards total carbohydrase and protease activity in several parts of the digestive tract. In conclusion, the results of the present data indicate that the musical stimuli transmitted were beneficial for the growth performance of rainbow trout. The fact that white noise treatment presented no major differences from control fish groups suggests that this specific stimulus was neither beneficially nor negatively perceived by rainbow trout, while it further supports the hypothesis that it is the musical stimuli per se that make all the difference. (C) 2013 Elsevier B.V. All rights reserved

Gilthead seabream (Sparus aurata) response to three music stimuli (Mozart-"Eine Kleine Nachtmusik," Anonymous-"Romanza," Bach-"Violin Concerto No. 1") and white noise under recirculating water conditions

This study presents the results of the response of Sparus aurata to three different musical stimuli, derived from the transmission (4 h per day, 5 days per week) of particular music pieces by Mozart, Romanza and Bach (140 dB(rms) re 1 mu Pa), compared to the same transmission level of white noise, while the underwater ambient noise in all the experimental tanks was 121 dB(rms) re 1 mu Pa. Using recirculating sea water facilities, 10 groups, 2 for each treatment, of 20 specimens of 11.2 +/- A 0.02 g (S.E.), were reared for 94 days, under 150 +/- A 10 lx 12L-12D, and were fed an artificial diet three times per day. Fish body weight showed significant differences after 55 days, while its maximum level was observed after the 69th day until the end of the experiment, the highest value demonstrated in Mozart (M) groups, followed by those of Romanza (R), Bach (B), control (C) and white noise (WN). SGR (M = B), %WG (M = B) and FCR (all groups fed same % b.w.) were also improved for M group. Brain neurotransmitters results exhibited significant differences in DA-dopamine, (M > B), 5HIAA (C > B), 5HIAA:5HT (WN > R), DOPAC (M > B), DOPAC:DA and (DOPAC + HVA):DA, (C > M), while no significant differences were observed in 5HT, NA, HVA and HVA:DA. No differences were observed in biometric measurements, protease activity, % fatty acids of fillet, visceral fat and liver, while differences were observed regarding carbohydrase activity and the amount (mg/g w.w.) of some fatty acids in liver, fillet and visceral fat. In conclusion, present results confirm those reported for S. aurata, concerning the observed relaxing influence-due to its brain neurotransmitters action-of the transmission of Mozart music (compared to R and B), which resulted in the achievement of maximum growth rate, body weight and improved FCR. This conclusion definitely supports the musical "understanding" and sensitivity of S. aurata to music stimuli as well as suggesting a specific effect of white noise

Challenges in the Multivariate Analysis of Mass Cytometry Data: The Effect of Randomization

Cytometry by time-of-flight (CyTOF) has emerged as a high-throughput single cell technology able to provide large samples of protein readouts. Already, there exists a large pool of advanced high-dimensional analysis algorithms that explore the observed heterogeneous distributions making intriguing biological inferences. A fact largely overlooked by these methods, however, is the effect of the established data preprocessing pipeline to the distributions of the measured quantities. In this article, we focus on randomization, a transformation used for improving data visualization, which can negatively affect multivariate data analysis methods such as dimensionality reduction, clustering, and network reconstruction algorithms. Our results indicate that randomization should be used only for visualization purposes, but not in conjunction with high-dimensional analytical tools