Effects of long-term fertilization on yield of siderates and organic matter content of soil in the process of recultivation

The aim of this research was to determine the possibility of increasing organic matter content in humusless deposol topsoil and forming of a more favourable adsorptive complex by introducing green manure. Green manure biomass came from these compound plant species: winter rye + common vetch, forage pea + rapeseed mustard and Sudan grass. Compound feed was sown on degraded soil (type deposol) of the Stanari coal mine. Applied cultivation practices included primary and secondary tillage and additional plant nutrition. Mineral fertilizers were applied: NPK 7:20:30 (400 kg ha(-1)) and CAN 27% (200 kg ha(-1)). One of the treatments included addition of bentonite clay as absorbent of nutrients. During intensive vegetation the growth of the green biomass was measured, the crops were harvested, cut and ploughed in deposol topsoil. Organic matter content in deposol was determined when soil samples were taken 6 months after green manure incorporation. The results show that the mineral fertilization of siderates significantly increased green biomass yield and Sudan grass gave two cuts, which positively affected the increase of organic matter content in soil

Literature on applied machine learning in metagenomic classification: A scoping review

Applied machine learning in bioinformatics is growing as computer science slowly invades all research spheres. With the arrival of modern next-generation DNA sequencing algorithms, metagenomics is becoming an increasingly interesting research field as it finds countless practical applications exploiting the vast amounts of generated data. This study aims to scope the scientific literature in the field of metagenomic classification in the time interval 2008–2019 and provide an evolutionary timeline of data processing and machine learning in this field. This study follows the scoping review methodology and PRISMA guidelines to identify and process the available literature. Natural Language Processing (NLP) is deployed to ensure efficient and exhaustive search of the literary corpus of three large digital libraries: IEEE, PubMed, and Springer. The search is based on keywords and properties looked up using the digital libraries’ search engines. The scoping review results reveal an increasing number of research papers related to metagenomic classification over the past decade. The research is mainly focused on metagenomic classifiers, identifying scope specific metrics for model evaluation, data set sanitization, and dimensionality reduction. Out of all of these subproblems, data preprocessing is the least researched with considerable potential for improvement

Luminescence Properties of a Fibonacci Photonic Quasicrystal

We report the realization of an active one-dimensional Fibonacci photonic quasi-crystal via spin coating. Manipulation of the luminescence properties of an organic dye embedded in the quasi-crystal is presented and compared to theoretical simulations. The luminescence occurs via the pseudo-bandedge mode and follows the dispersion properties of the Fibonacci crystal. Time resolved luminescence measurement of the active structure shows faster spontaneous emission rate, indicating the effect of the large photon densities available at the bandedge due to the presence of critically localized states. The experimental results are in excellent agreement with the theoretical calculations.Comment: PDF file, 14 pages 4 figure

The Application of Experimental Design Methodology for the Investigation of Liquid Radioactive Waste Treatment

The sorption properties of waste facade, brick, and asphalt sample towards Sr(II), Co(II), and Ni(II) ions from single and multicomponent solutions were investigated. The highest sorption capacity was found for Ni(II) ions, while the most effective sorbent was facade. Simplex Centroid Mixture Design was used in order to investigate the sorption processes of ions from solutions with different composition as well as the competition between the cations. Based on the statistical analysis results, the equations for data modeling were proposed. According to the observations, the investigated solid matrices can be effectively used for the liquid radioactive waste treatment. Furthermore, the applied methodology turned out to be an easy and operational way for the investigations of multicomponent sorption processes

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

Interdisciplinary project-based learning: technology for improving student cognition

The article studies a way of enhancing student cognition by using interdisciplinary project-based learning (IPBL) in a higher education institution. IPBL is a creative pedagogic approach allowing students of one area of specialisation to develop projects for students with different academic profiles. The application of this approach in the Ural State University of Economics resulted in a computer-assisted learning system (CALS) designed by IT students. The CALS was used in an analytical chemistry course with students majoring in Commodities Management and Expertise (‘expert’ students). To test how effective the technology was, the control and experimental groups were formed. In the control group, learning was done with traditional methods. In the experimental group, it was reinforced by IPBL. A statistical analysis of the results, with an application of Pearson χ 2 test, showed that the cognitive levels in both IT and ‘expert’ experimental groups improved as compared with the control groups. The findings demonstrated that IPBL can significantly enhance learning. It can be implemented in any institution of higher or secondary education that promotes learning, including the CALS development and its use for solving problems in different subject areas

The Circadian System Is a Target and Modulator of Prenatal Cocaine Effects

BACKGROUND. Prenatal exposure to cocaine can be deleterious to embryonic brain development, but the results in humans remain controversial, the mechanisms involved are not well understood and effective therapies are yet to be designed. We hypothesize that some of the prenatal effects of cocaine might be related to dysregulation of physiological rhythms due to alterations in the integrating circadian clock function. METHODOLOGY AND PRINCIPLE FINDINGS. Here we introduce a new high-throughput genetically well-characterized diurnal vertebrate model for studying the mechanisms of prenatal cocaine effects by demonstrating reduced viability and alterations in the pattern of neuronal development following repeated cocaine exposure in zebrafish embryos. This effect is associated with acute cocaine-induced changes in the expression of genes affecting growth (growth hormone, zGH) and neurotransmission (dopamine transporter, zDAT). Analysis of circadian gene expression, using quantitative real-time RT-PCR (QPCR), demonstrates that cocaine acutely and dose-dependently changes the expression of the circadian genes (zPer-3, zBmal-1) and genes encoding melatonin receptors (zMelR) that mediate the circadian message to the entire organism. Moreover, the effects of prenatal cocaine depend on the time of treatment, being more robust during the day, independent of whether the embryos are raised under the light-dark cycle or in constant light. The latter suggests involvement of the inherited circadian factors. The principal circadian hormone, melatonin, counteracts the effects of cocaine on neuronal development and gene expression, acting via specific melatonin receptors. CONCLUSIONS/SIGNIFICANCE. These findings demonstrate that, in a diurnal vertebrate, prenatal cocaine can acutely dysregulate the expression of circadian genes and those affecting melatonin signaling, growth and neurotransmission, while repeated cocaine exposure can alter neuronal development. Daily variation in these effects of cocaine and their attenuation by melatonin suggest a potential prophylactic or therapeutic role for circadian factors in prenatal cocaine exposure.National Institutes of Health (DA1541801, MH 065528); National Institute on Drug Abuse (DA-4-7733

Brown Adipose Tissue in Humans Is Activated by Elevated Plasma Catecholamines Levels and Is Inversely Related to Central Obesity

BACKGROUND: Recent studies have shown that adult human possess active brown adipose tissue (BAT), which might be important in controlling obesity. It is known that ß-adrenoceptor-UCP1 system regulates BAT in rodent, but its influence in adult humans remains to be shown. The present study is to determine whether BAT activity can be independently stimulated by elevated catecholamines levels in adult human, and whether it is associated with their adiposity. METHODOLOGY/PRINCIPAL FINDINGS: We studied 14 patients with pheochromocytoma and 14 normal subjects who had performed both ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) and plasma total metanephrine (TMN) measurements during 2007-2010. The BAT detection rate and the mean BAT activity were significantly higher in patients with elevated TMN levels (Group A: 6/8 and 6.7±2.1 SUVmean· g/ml) than patients with normal TMN concentrations (Group B: 0/6 and 0.4±0.04 SUVmean· g/ml) and normal subjects (Group C: 0/14 and 0.4±0.03 SUVmean·g/ml). BAT activities were positively correlated with TMN levels (R = 0.83, p<0.0001) and were inversely related to body mass index (R = -0.47, p = 0.010), visceral fat areas (R = -0.39, p = 0.044), visceral/total fat areas (R = -0.52, p = 0.0043) and waist circumferences (R = -0.43, p = 0.019). Robust regression revealed that TMN (R = 0.81, p<0.0001) and waist circumferences (R = -0.009, p = 0.009) were the two independent predictors of BAT activities. CONCLUSIONS/SIGNIFICANCE: Brown adipose tissue activity in adult human can be activated by elevated plasma TMN levels, such as in the case of patients with pheochromocytoma, and is negatively associated with central adiposity