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Despite the known effects of diet on gut microbiota composition, not many studies have evaluated the relationship between distinct dietary patterns and gut microbiota. The aim of our study was to determine whether gut microbiota composition could be a useful indicator of a long-term dietary pattern. We collected data from 89 subjects adhering to omnivorous, vegetarian, vegan, and low-carbohydrate, high-fat diet that were equally distributed between groups and homogenous by age, gender, and BMI. Gut microbiota composition was analyzed with a metabarcoding approach using V4 hypervariable region of the 16S rRNA gene. K-means clustering of gut microbiota at the genus level was performed and the nearest neighbor classifier was applied to predict microbiota clustering classes. Our results suggest that gut microbiota composition at the genus level is not a useful indicator of a subject’s dietary pattern, with the exception of a vegan diet that is represented by a high abundance of Prevotella 9. Based on our model, a combination of 26 variables (anthropometric measurements, serum biomarkers, lifestyle factors, gastrointestinal symptoms, psychological factors, specific nutrients intake) is more important to predict an individual’s microbiota composition cluster, with 91% accuracy, than the dietary intake alone. Our findings could serve to develop strategies to educate individuals about changes of some modifiable lifestyle factors, aiming to classify them into clusters with favorable health markers, independent of their dietary pattern.

Helichrysum italicum (HI) is a Mediterranean plant with well-reported use in traditional medicine for a wide range of applications, including digestive and liver disorders, intestinal parasitic infections, wound healing, stomach ache and asthma. However, little is known about the global mechanism behind its pleiotropic activity.The aim of this study was to explain the mechanism behind the previously demonstrated effects of HI and to justify its use in traditional medicine.A microarray-based transcriptome analysis was used to discover the global transcriptional alterations in primary colon fibroblasts after exposure to HI infusion for 6 h and 24 h. In addition, quantitative real-time PCR was used to verify the microarray results.Altogether we identified 217 differentially expressed genes compared to non-treated cells, and only 8 were common to both treatments. Gene ontology analysis revealed that 24 h treatment with HI infusion altered the expression of genes involved in cytoskeletal rearrangement and cell growth, whereas pathway analysis further showed the importance of interleukin signaling and transcriptional regulation by TP53. For the 6 h treatment only the process of hemostasis appeared in the results of both enrichment analyses. In functional assays, HI infusion increased cell migration and decreased blood clotting and prothrombin time.With the careful evaluation of the role of individual genes, especially SERPING1, ARHGAP1, IL33 and CDKN1A, represented in the enriched pathways and processes, we propose the main mode of HI action, which is wound healing. In addition to its indirect prevention of diseases resulting from the impaired barrier integrity, HI also effects inflammation and metabolic processes directly, as it regulates genes such as LRPPRC, LIPA, ABCA12, PRKAR1A and ANXA6.

Protein model analysis For details see the paper: https://www.nature.com/articles/s41598-019-38658-9 The R script (Filename: ProtModAna.r) was used to analyze protein models. The script reads the PDB file, converts the 3D protein model into a graph and calculates mean node degree, Zscore, poor and long subgraphs. Smooth mean and standard deviation was calculated using data source (raw_data.csv), with a window length [0.8N, 1.2N], where N is the length of protein (Filename: SmoothData.csv). USAGE Test run Save "ProtModAna.r" and "SmoothData.csv" in to the same directory and run R script "ProtModAna.r". Custom analyze The user can change the path to the data file "SmoothData.csv". Note that this file is required to calculate the Zscore. To validate the protein model, change the line where the R script reads the PDB file (see lines 19 and 20 in "ProtModAna.r")

For details see paper https://doi.org/10.1142/S021972002050050X File: DataSet.csv Column description PDBid ... Protein Data Bank identification CAatoms ... number of CA atoms (residues) Rg-rgyr ... bio3d (R) Radius of gyration MND ... Mean Node Degree Radius ... min eccentricity - Radius MEC ... mean eccentricity Diameter ... max eccentricity - Diameter central ... number of central nodes in graph peripheral ... number of peripheral nodes in graph tot.solv.A. ... total solvent-accessible area of protein File: DataSetHistogram.csv column ... histogram breaks row ... PDBid Abstract The application of graph theory in structural biology offers an alternative means of studying 3D models of large macromolecules, such as proteins. However, basic structural parameters still play an important role in the description of macromolecules. For example, the radius of gyration, which scales with exponent ~0.4, provides quantitative information about the compactness of the protein structure. In this study, we combine two proven methods, the graph-theoretical and the fundamental scaling laws, to study 3D protein models. This study shows that the mean node degree of the protein graphs, which scales with exponent 0.038, is scale-invariant. In addition, proteins that differ in size have a highly similar node degree distribution, which peaks at node degree 7, and additionally conforms to the same statistical properties at any scale. Linear regression analysis showed that the graph parameters (radius, diameter and mean eccentricity) can explain up to 90% of the total radius of gyration variance. Thus, the graph parameters of radius, diameter and mean eccentricity scale with the same exponent as the radius of gyration. The main advantage of graph eccentricity compared to the radius of gyration is that it can be used to analyse the distribution of the central and peripheral amino acids/nodes of the macromolecular structure. The central nodes are hydrophobic amino acids (Val, Leu, Ile, Phe), which tend to be buried, while the peripheral nodes are more hydrophilic residues (Asp, Glu, Lys). Furthermore, it has been shown that the number of central and peripheral nodes is more related to the fold of the protein than to the protein length.

Four years of time-series data on the particulate matter (PM) concentrations from 801 monitoring stations located in Europe and 234 stations in North America were analyzed. Using k-means clustering with distance correlation as a measure for similarity, 5 distinct PM clusters in Europe and 9 clusters across the United States of America (USA) were found. This study shows that meteorology has an important role in controlling PM concentrations, as comparison between Koppen-Geiger climate zones and identified PM clusters revealed very good spatial overlapping. Moreover, the Koppen-Geiger boundaries in Europe show a high similarity to the boundaries as defined by PM clusters. The western USA is much more diverse regarding climate zones; this characteristic was confirmed by cluster analysis, as 6 clusters were identified in the west, and only 3 were identified on the eastern side of the USA. The lowest similarity between PM time-series in Europe was observed between the Iberian Peninsula and the north Europe clusters. These two regions also show considerable differences, as the cold semi-arid climate has a long and hot summer period, while the cool continental climate has a short summertime and long and cold winters. Additionally, intra-continental examination of European clusters showed meteorologically driven phenomena in autumn 2011 encompassing a large European region from Bulgaria in the south, Germany in central Europe and Finland in the north with high PM concentrations in November and a decline in December 2011. Inter-continental comparison between Europe and the USA clusters revealed a remarkable difference between the PM time-series located in humid continental zone. It seems that because of higher shortwave downwelling radiation (≈210 W m−2) over the USA's continental zone, and consequently more intense production of secondary aerosols, a summer peak in PM concentration was observed. On the other hand, Europe's humid continental climate region experiences lower solar radiation (≈180 W m−2); consequently, the elevated summer-time PM concentrations were not detected.

This article belongs to the Section Nutrition and Metabolism., The dynamics and diversity of human gut microbiota that can remarkably influence the wellbeing and health of the host are constantly changing through the host’s lifetime in response to various factors. The aim of the present study was to determine a set of parameters that could have a major impact on classifying subjects into a single cluster regarding gut bacteria composition. Therefore, a set of demographical, environmental, and clinical data of healthy adults aged 25–50 years (117 female and 83 men) was collected. Fecal microbiota composition was characterized using Illumina MiSeq 16S rRNA gene amplicon sequencing. Hierarchical clustering was performed to analyze the microbiota data set, and a supervised machine learning model (SVM; Support Vector Machines) was applied for classification. Seventy variables from collected data were included in machine learning analysis. The agglomerative clustering algorithm suggested the presence of four distinct community types of most abundant bacterial phyla. Each cluster harbored a statistically significant different proportion of bacterial phyla. Regarding prediction, the most important features classifying subjects into clusters were measures of obesity (waist to hip ratio, BMI, and visceral fat index), total body water, blood pressure, energy intake, total fat, olive oil intake, total fiber intake, and water intake. In conclusion, the SVM model was shown as a valuable tool to classify healthy individuals based on their gut microbiota composition., The study was supported by the European foundation for alcohol research through a Spanish Slovenian bilateral project (ERAB EA. 14.44). This work was also supported by the Slovenian Research Agency (Programme P1-0386).

Validation of three-dimensional structures is at the core of structural determination methods. The local validation criteria, such as deviations from ideal bond length and bonding angles, Ramachandran plot outliers and clashing contacts, are a standard part of structure analysis before structure deposition, whereas the global and regional packing may not yet have been addressed. In the last two decades, three-dimensional models of macromolecules such as proteins have been successfully described by a network of nodes and edges. Amino acid residues as nodes and close contact between the residues as edges have been used to explore basic network properties, to study protein folding and stability and to predict catalytic sites. Using complex network analysis, we introduced common network parameters to distinguish between correct and incorrect three-dimensional protein structures. The analysis showed that correct structures have a higher average node degree, higher graph energy, and lower shortest path length than their incorrect counterparts. Thus, correct protein models are more densely intra-connected, and in turn, the transfer of information between nodes/amino acids is more efficient. Moreover, protein graph spectra were used to investigate model bias in protein structure.

In last decade space-density of monitoring stations increased, in to addition also air pollution modeling made big progress. Using diversity of big data can lead to better knowledge about air pollution at continental scale. The focus of presented study is the data-driven approach using non-negative matrix factorization to provide new insights and to study the characteristic space-time particulate-matter patterns across Europe. We analyzed the PM10 concentrations obtained from 1097 monitoring stations (AirBase data) and the Monitoring Atmospheric Composition and Climate (MACC) modeled fields for a period of 3 years. We distinguished five characteristic patterns obtained from the AirBase data and five patterns from the MACC data. A comparison between the AirBase and MACC data shows a good spatial overlap for the east Europe, central Europe and the Mediterranean patterns. However, it should be noted that an analysis of the MACC data revealed two additional marine patterns: the Celtic and the North Seas. The Po Valley and Balkan patterns were very clearly identified when analyzing the AirBase data. In order to better understand the influence of the synoptic situation on the particulate-matter concentrations the synoptic meteorological situations were additionally analyzed. The cold season, low wind and very stable conditions, which can last for several days, is the most common situation linked to high concentrations of anthropogenic air pollution with particulate matter. In contrast, for the Mediterranean pattern the most common situation (high factor loadings) is observed during the summer period. This pattern also exhibits a clearer annual cycle. A closer look at the sea-salt patterns (Celtic and North Seas) shows low time-series correlations between these two factors. Nevertheless, the physical mechanism is the same: a steep gradient between the cyclone and the anti-cyclone that causes high winds and, consequently, higher sea-salt production.

In this work, we characterise the performance of a Sharp optical aerosol sensor model GP2Y1010AU0F. The sensor was exposed to different environments: to a clean room, to a controlled atmosphere with known aerosol size distribution and to the ambient atmosphere on a busy city street. During the exposure, the output waveforms of the sensor pulses were digitised, saved and a following offline analysis enabled us to study the behaviour of the sensor pulse-by-pulse. A linear response of the sensor on number concentration of the monosized dispersed PSL particles was shown together with an almost linear dependence on particle diameters in the 0.4 to 4 micrometer range. The gathered data about the sensor were used to predict its response to an ambient atmosphere, which was observed simultaneously with a calibrated optical particle counter.

This paper investigates the dust lifting phenomenon at the coal and iron ore stockpile at the Port of Koper, Slovenia. Dust lifting presents a serious environment and health hazard, thus the main objective of our study was to propose efficient measures for wind erosion reduction. A numerical model of the stockpile was created using the computational fluid dynamics (CFD) software. Wind velocity fields above the piles were calculated for the current stockpile layout and for several modified cases with rearranged piles and added porous barriers. Results from numerical modelling were used in the USEPA model to determine dust emission factors. Comparison of selected cases shows a positive, although limited effect of porous fences and barriers on reduction of local velocities and consequently, dust erosion rate. On the other hand, pile rearrangement has little effect and may not be practical for implementation. Realistic stockpile geometry with adjacent structures has shown to cause wind velocity distributions, which are not consistent with the most studied cases of regular pile shapes and layouts. The angle of incoming wind is a key factor influencing effectiveness of both solid and porous windbarriers. The proposed placement of porous barriers between the piles has shown to be effective in reducing wind exposure and dust emission. To maintain the dust emissions at an acceptably low level, other measures such as spraying with water and crust-forming liquids may still be necessary at higher wind velocities. Glavna okoljska in varnostna problematika, ki se pojavi pri transportu in shranjevanju razsutega tovora, je povezana s pojavom prašenja. Pri shranjevanju tovora na velikih odprtih deponijah, kot jih običajno srečamo v tovornih pristaniščih, je prašenje še posebej problematično zaradi izpostavljenosti deponije vetru. Prihaja do vetrne erozije in odnašanja finih delcev, kar pomeni onesnaženje okolice ter resno tveganje za zdravje ljudi zaradi delcev v zraku. V prispevku so obravnavane vetrovne razmere in pojav prašenja na Evropskem Energetskem Terminalu (EET) v Luki Koper, ki je namenjen shranjevanju in pretovoru premoga in železove rude. EET je tekom celega leta občasno izpostavljen močnim vetrovom severovzhodne in vzhodne smeri (burja) ter zahodne smeri (tramontana), ki zlahka povzročijo dviganje in odnašanje prašnih delcev z deponije.

In the presented study a comprehensive statistical analysis of the chemical composition of atmospheric particulate matter was carried out. The data were collected from April 2003 to August 2008 with a 7-day time resolution in the Northern Adriatic Port of Koper and analyzed by the Proton Induced X-ray method (PIXE). The Positive Matrix Factorization (PMF) analysis of fifteen chemical elements identified six source factors, three natural-regional sources and three local-anthropogenic sources. Heavy machinery, industry and iron ore factor were marked as anthropogenic sources. Heavy machinery source was represented by the elements V, Ni and Cu. The elements Fe and Mn are attributed to the Iron ore source and were explained by the proximity of the bulk-cargo warehouse and the intense handling of iron ore in Port of Koper. The heavy industry source represented by Pb and Zn was the only anthropogenic factor, which shows clear seasonal pattern. In contrast to the local-anthropogenic source factors, natural and regional source factors show significant negative trend. The reduction of the crustal elements Ca, Ti and Sr, joined in a soil source, and sulfur-biomass source, represented by elements K and S, have been attributed to more intense precipitation and to the negative trend of the North Atlantic Oscillation (NAO) index. The negative trend of the Cl and Br elements was in line with the negative trend of the wind speed above the sea surface and the significant sea-wave height.

The monitoring of air-pollution constituents like particulate matter (PM10) and black carbon (BC) can provide information about air quality and the dynamics of emissions. Air quality depends on natural and anthropogenic sources of emissions as well as the weather conditions. For a one-year period the diurnal concentrations of PM10 and BC in the Port of Koper were analysed by clustering days into similar groups according to the similarity of the BC and PM10 hourly derived day-profiles without any prior assumptions about working and non-working days, weather conditions or hot and cold seasons. The analysis was performed by using k-means clustering with the squared Euclidean distance as the similarity measure. The analysis showed that 10 clusters in the BC case produced 3 clusters with just one member day and 7 clusters that encompasses more than one day with similar BC profiles. Similar results were found in the PM10 case, where one cluster has a single-member day, while 7 clusters contain several member days. The clustering analysis revealed that the clusters with less pronounced bimodal patterns and low hourly and average daily concentrations for both types of measurements include the most days in the one-year analysis. A typical day profile of the BC measurements includes a bimodal pattern with morning and evening peaks, while the PM10 measurements reveal a less pronounced bimodality. There are also clusters with single-peak day-profiles. The BC data in such cases exhibit morning peaks, while the PM10 data consist of noon or afternoon single peaks. Single pronounced peaks can be explained by appropriate cluster wind speed profiles. The analysis also revealed some special day-profiles. The BC cluster with a high midnight peak at 30/04/2010 and the PM10 cluster with the highest observed concentration of PM10 at 01/05/2010 (208.0 μg m − 3 ) coincide with 1 May, which is a national holiday in Slovenia and has very strong tradition of bonfire parties. The clustering of the diurnal concentration showed that various different day-profiles are presented in a cold period, while this is not the case for the hot season. Additional analysis of ship traffic and rain fall data showed that there is no statistically significant difference between the ship gross (bruto) registered tonnage (BRT) values in the case of BC and PM10 clusters, but that there is statistically significant differences between the rain fall in the BC and PM10 clusters. The wind-rose for clusters which included most days in the sampling period indicating that emitted PM10 and BC from Port of Koper were manly transported in the west direction over the sea and in the east direction, where there is in no populated area. Presented analysis showed that both BC and PM10 concentrations were driven by rain intensity and wind speed.

The effects of particulate matter air pollution on respiratory health and on the cardiovascular system Particulate matter (PM) is a major component of urban air pollution and has a significant effect on human health. Natural PM sources are volcanic eruptions, dust storms, forest and grassland fires, living vegetation and sea spray. Traffic, domestic heating, power plants and various industrial processes generate significant amounts of anthropogenic PM. PM consists of a complex mixture of solid and liquid particles of organic and inorganic substances suspended in the air. The chemical composition of particles is very complex and depends on emission sources, meteorological conditions and their aerodynamic diameter. Several epidemiological studies have demonstrated that exposure to PM of varying size fractions is associated with an increased risk of respiratory and cardiovascular diseases. Adverse health effects have been documented from studies of both acute and chronic exposure. The most severe effects in terms of overall health burden include a significant reduction in life expectancy by a several months for the average population, which is linked to long-term exposure to moderate concentrations of PM. Nevertheless, numerous deaths and serious cardiovascular and respiratory problems have also been attributed to short-term exposure to peak levels of PM. Although many studies attribute greater toxicity to smaller size fractions, which are able to penetrate deeper into the lung, the molecular mechanisms and the size fractions of the PM that are responsible for the observed diseases are not completely understood.

Averaged kick maps are the sum of a series of individual kick maps, where each map is calculated from atomic coordinates modified by random shifts. These maps offer the possibility of an improved and less model-biased map interpretation., Use of reliable density maps is crucial for rapid and successful crystal structure determination. Here, the averaged kick (AK) map approach is investigated, its application is generalized and it is compared with other map-calculation methods. AK maps are the sum of a series of kick maps, where each kick map is calculated from atomic coordinates modified by random shifts. As such, they are a numerical analogue of maximum-likelihood maps. AK maps can be unweighted or maximum-likelihood (σA) weighted. Analysis shows that they are comparable and correspond better to the final model than σA and simulated-annealing maps. The AK maps were challenged by a difficult structure-validation case, in which they were able to clarify the problematic region in the density without the need for model rebuilding. The conclusion is that AK maps can be useful throughout the entire progress of crystal structure determination, offering the possibility of improved map interpretation.

The number and variety of macromolecular structures in complex with `hetero' ligands is growing. The need for rapid delivery of correct geometric parameters for their refinement, which is often crucial for understanding the biological relevance of the structure, is growing correspondingly. The current standard for describing protein structures is the Engh–Huber parameter set. It is an expert data set resulting from selection and analysis of the crystal structures gathered in the Cambridge Structural Database (CSD). Clearly, such a manual approach cannot be applied to the vast and ever‐growing number of chemical compounds. Therefore, a database, named PURY, of geometric parameters of chemical compounds has been developed, together with a server that accesses it. PURY is a compilation of the whole CSD. It contains lists of atom classes and bonds connecting them, as well as angle, chirality, planarity and conformation parameters. The current compilation is based on CSD 5.28 and contains 1978 atom classes and 32 702 bonding, 237 068 angle, 201 860 dihedral and 64 193 improper geometric restraints. Analysis has confirmed that the restraints from the PURY database are suitable for use in macromolecular crystal structure refinement and should be of value to the crystallographic community. The database can be accessed through the web server http://pury.ijs.si/, which creates topology and parameter files from deposited coordinates in suitable forms for the refinement programs MAIN, CNS and REFMAC. In the near future, the server will move to the CSD website http://pury.ccdc.cam.ac.uk/.

The maximum-likelihood free-kick target, which calculates model error estimates from the work set and a randomly displaced model, proved superior in the accuracy and consistency of refinement of crystal structures compared with the maximum-likelihood cross-validation target, which calculates error estimates from the test set and the unperturbed model., The refinement of a molecular model is a computational procedure by which the atomic model is fitted to the diffraction data. The commonly used target in the refinement of macromolecular structures is the maximum-likelihood (ML) function, which relies on the assessment of model errors. The current ML functions rely on cross-validation. They utilize phase-error estimates that are calculated from a small fraction of diffraction data, called the test set, that are not used to fit the model. An approach has been developed that uses the work set to calculate the phase-error estimates in the ML refinement from simulating the model errors via the random displacement of atomic coordinates. It is called ML free-kick refinement as it uses the ML formulation of the target function and is based on the idea of freeing the model from the model bias imposed by the chemical energy restraints used in refinement. This approach for the calculation of error estimates is superior to the cross-validation approach: it reduces the phase error and increases the accuracy of molecular models, is more robust, provides clearer maps and may use a smaller portion of data for the test set for the calculation of R free or may leave it out completely.