Time-dependent Effect of Graphene on the Microbial Activity of the Soil Under Single and Repeated Exposures

Graphene (GR) has huge industrial and biomedical potential, and its adverse effect on soil microorganisms has been evaluated in ecotoxicological studies. These studies focus on a single exposure to GR, but repeated exposures are more likely to occur. In this study, we compared the impact of single and repeated exposures of GR on structure, abundance and function of soil bacterial community based on soil enzyme activity and high-throughput sequencing. The activities of urease and fluorescein diacetate esterase and alpha diversity demonstrate that repeated exposure to GR increase the diversity of soil bacteria. The PCoA and sample level clustering tree showed single exposure to GR after 4 days alter the soil bacterial community to some extent. During the entire incubation process, no matter what kind of exposure scenarios to GR, the majority of bacterial phylotypes remained unchanged except for Proteobacteria and Actinobacteria according to the relative abundance of phylotypes.

VirClust, a tool for hierarchical clustering, core gene detection and annotation of (prokaryotic) viruses

Recent years have seen major changes in the classification criteria and taxonomy of viruses. The current classification scheme, also called megataxonomy of viruses, recognizes five different viral realms, defined based on the presence of viral hallmark genes. Within the realms, viruses are classified into hierarchical taxons, ideally defined by their shared genes. Therefore, there is currently a need for virus classification tools based on such shared genes / proteins. Here, VirClust is presented: a novel tool capable of performing i) hierarchical clustering of viruses based on intergenomic distances calculated from their protein cluster content, ii) identification of core proteins and iii) annotation of viral proteins. VirClust groups proteins into clusters both based on BLASTP sequence similarity, which identifies more related proteins, and also based on hidden markow models (HMM), which identifies more distantly related proteins. Furthermore, VirClust provides an integrated visualization of the hierarchical clustering tree and of the distribution of the protein content, which allows the identification of the genomic features responsible for the respective clustering. By using different intergenomic distances, the hierarchical trees produced by VirClust can be split into viral genome clusters of different taxonomic ranks. VirClust is freely available, as web-service (virclust.icbm.de) and stand-alone tool.

PSYCHOLOGICAL PECULIARITIES OF WOMEN WITH DIFFERENT LEVELS OF PROCRASTINATION

The paper examines personal characteristics that may affect the propensity for procrastination of women, in particular, self-esteem, self-regulation, the ability to make and implement decisions. Cluster analysis was used for the division of the participants into groups according to the level of propensity for procrastination. The clustering took into account the indicators on the scales of the methods "Scale of general procrastination" by C. H. Lay and "Procrastination scale" by B. W. Tuckman. Based on the clustering tree and the k-means method, two clusters with high and low levels of procrastination were determined. Further differences between these groups were analyzed on the basis of comparative, correlation and factor analysis. Women prone to procrastination show lower levels of self-esteem, self-regulation and subjective control. The external locus of control is combined with a tendency to procrastinate and delay the decision-making. The internal dissatisfaction with themselves, negativism in assessing their abilities and readiness for self-blame were observed. The inability to plan one's behavior, to build realistic, detailed, hierarchical, effective plans affects the tendency to procrastinate both in everyday affairs and in decision-making. As shown by factor analysis, these participants can overcome their procrastination tendencies by increasing a positive attitude towards themselves and their abilities, as well as developing the ability to manage their arbitrary behavior during the process of setting and achieving goals. Women who are not prone to procrastination are internally motivated, tend to build adequate and constructive plans for the future, demonstrate a high level of self-regulation determined by intrinsic motivators. Programming life goals, taking into account external and internal conditions, evaluation of the results and correction of their activity play an important role in various areas of activity of this category of women. Based on comparative and factor analysis, it can be argued that self-regulation plays a key role in overcoming the desire to procrastinate and put things off.

Color-complexity enabled exhaustive color-dots identification and spatial patterns testing in images

Our computational developments and analyses on experimental images are designed to evaluate the effectiveness of chemical spraying via unmanned aerial vehicle (UAV). Our evaluations are in accord with the two perspectives of color-complexity: color variety within a color system and color distributional geometry on an image. First, by working within RGB and HSV color systems, we develop a new color-identification algorithm relying on highly associative relations among three color-coordinates to lead us to exhaustively identify all targeted color-pixels. A color-dot is then identified as one isolated network of connected color-pixel. All identified color-dots vary in shapes and sizes within each image. Such a pixel-based computing algorithm is shown robustly and efficiently accommodating heterogeneity due to shaded regions and lighting conditions. Secondly, all color-dots with varying sizes are categorized into three categories. Since the number of small color-dot is rather large, we spatially divide the entire image into a 2D lattice of rectangular. As such, each rectangle becomes a collective of color-dots of various sizes and is classified with respect to its color-dots intensity. We progressively construct a series of minimum spanning trees (MST) as multiscale 2D distributional spatial geometries in a decreasing-intensity fashion. We extract the distributions of distances among connected rectangle-nodes in the observed MST and simulated MSTs generated under the spatial uniformness assumption. We devise a new algorithm for testing 2D spatial uniformness based on a Hierarchical clustering tree upon all involving MSTs. This new tree-based p-value evaluation has the capacity to become exact.

Brassica rapa domestication: untangling wild and feral forms and convergence of crop morphotypes

The study of domestication contributes to our knowledge of evolution and crop genetic resources. Human selection has shaped wild Brassica rapa into diverse turnip, leafy, and oilseed crops. Despite its worldwide economic importance and potential as a model for understanding diversification under domestication, insights into the number of domestication events and initial crop(s) domesticated in B. rapa have been limited due to a lack of clarity about the wild or feral status of conspecific non-crop relatives. To address this gap and reconstruct the domestication history of B. rapa, we analyzed 68,468 genotyping-by-sequencing-derived SNPs for 416 samples in the largest diversity panel of domesticated and weedy B. rapa to date. To further understand the center of origin, we modeled the potential range of wild B. rapa during the mid-Holocene. Our analyses of genetic diversity across B. rapa morphotypes suggest that non-crop samples from the Caucasus, Siberia, and Italy may be truly wild, while those occurring in the Americas and much of Europe are feral. Clustering, tree-based analyses, and parameterized demographic inference further indicate that turnips were likely the first crop type domesticated, from which leafy types in East Asia and Europe were selected from distinct lineages. These findings clarify the domestication history and nature of wild crop genetic resources for B. rapa, which provides the first step toward investigating cases of possible parallel selection, the domestication and feralization syndrome, and novel germplasm for Brassica crop improvement.

Graph-Based Analysis of RNA Secondary Structure Similarity Comparison

In organisms, ribonucleic acid (RNA) plays an essential role. Its function is being discovered more and more. Due to the conserved nature of RNA sequences, its function mainly depends on the RNA secondary structure. The discovery of an approximate relationship between two RNA secondary structures helps to understand their functional relationship better. It is an important and urgent task to explore structural similarities from the graphical representation of RNA secondary structures. In this paper, a novel graphical analysis method based on the triple vector curve representation of RNA secondary structures is proposed. A combinational method involving a discrete wavelet transform (DWT) and fractal dimension with sliding window is introduced to analyze and compare the graphs derived from feature extraction; after that, the distance matrix is generated. Then, the distance matrix is analyzed by clustering and visualized as a clustering tree. RNA virus and noncoding RNA datasets are applied to perform experiments and analyze the clustering tree. The results show that the proposed method yields more accurate results in the comparison of RNA secondary structures.

Clustering Simultaneous Occurrences of the Extreme Floods in the Neckar Catchment

Flood protection is crucial for making socioeconomic policies due to the high losses of extreme floods. So far, the synchronous occurrences of flood events have not been deeply investigated. In this paper, multivariate analysis was implemented to reveal the interconnection between these floods in spatiotemporal resolution. The discharge measurements of 46 gauges with a continuous daily time series for 55 years were taken over the Neckar catchment. Initially, the simultaneous floods were identified. The Kendall correlation between the pair sets of peaks was determined to scrutinize the similarities between the simultaneous events. Agglomerative hierarchical clustering tree (AHCT) and multidimensional scaling (MDS) were employed, and obtained clusters were compared and evaluated with the Silhouette verification method. AHCT shows that the Average and Ward algorithms are appropriate to detect reasonable clusters. The Neckar catchment has been divided into three major clusters: the first cluster mainly covers the western part and is bounded by the Black Forest and Swabian Alps. The second cluster is mostly located in the eastern part of the upper Neckar. The third cluster contains the remaining lowland areas of the Neckar basin. The results illustrate that the clusters act relatively as a function of topography, geology, and anthropogenic alterations of the catchment.

Weighted Gene Co-Expression Network Analysis Identifies Key Modules and Hub Genes Associated with Mycobacterial Infection of Human Macrophages

Tuberculosis (TB) is still a leading cause of death worldwide. Treatments remain unsatisfactory due to an incomplete understanding of the underlying host–pathogen interactions during infection. In the present study, weighted gene co-expression network analysis (WGCNA) was conducted to identify key macrophage modules and hub genes associated with mycobacterial infection. WGCNA was performed combining our own transcriptomic results using Mycobacterium aurum-infected human monocytic macrophages (THP1) with publicly accessible datasets obtained from three types of macrophages infected with seven different mycobacterial strains in various one-to-one combinations. A hierarchical clustering tree of 11,533 genes was built from 198 samples, and 47 distinct modules were revealed. We identified a module, consisting of 226 genes, which represented the common response of host macrophages to different mycobacterial infections that showed significant enrichment in innate immune stimulation, bacterial pattern recognition, and leukocyte chemotaxis. Moreover, by network analysis applied to the 74 genes with the best correlation with mycobacteria infection, we identified the top 10 hub-connecting genes: NAMPT, IRAK2, SOCS3, PTGS2, CCL20, IL1B, ZC3H12A, ABTB2, GFPT2, and ELOVL7. Interestingly, apart from the well-known Toll-like receptor and inflammation-associated genes, other genes may serve as novel TB diagnosis markers and potential therapeutic targets.