Semi-supervised learning guided by the modularity measure in complex networks

Semi-supervised learning techniques have gained increasing attention in the machine learning community, as a result of two main factors: (1) the available data is exponentially increasing; (2) the task of data labeling is cumbersome and expensive, involving human experts in the process. In this paper, we propose a network-based semi-supervised learning method inspired by the modularity greedy algorithm, which was originally applied for unsupervised learning. Changes have been made in the process of modularity maximization in a way to adapt the model to propagate labels throughout the network. Furthermore, a network reduction technique is introduced, as well as an extensive analysis of its impact on the network. Computer simulations are performed for artificial and real-world databases, providing a numerical quantitative basis for the performance of the proposed method.

Effective number of accessed nodes in complex networks

The measurement called accessibility has been proposed as a means to quantify the efficiency of the communication between nodes in complex networks. This article reports results regarding the properties of accessibility, including its relationship with the average minimal time to visit all nodes reachable after h steps along a random walk starting from a source, as well as the number of nodes that are visited after a finite period of time. We characterize the relationship between accessibility and the average number of walks required in order to visit all reachable nodes (the exploration time), conjecture that the maximum accessibility implies the minimal exploration time, and confirm the relationship between the accessibility values and the number of nodes visited after a basic time unit. The latter relationship is investigated with respect to three types of dynamics: traditional random walks, self-avoiding random walks, and preferential random walks.

Functional diversity of bacterial genes associated with aromatic hydrocarbon degradation in anthropogenic dark earth of Amazonia

The objective of this work was to evaluate the catabolic gene diversity for the bacterial degradation of aromatic hydrocarbons in anthropogenic dark earth of Amazonia (ADE) and their biochar (BC). Functional diversity analyses in ADE soils can provide information on how adaptive microorganisms may influence the fertility of soils and what is their involvement in biogeochemical cycles. For this, clone libraries containing the gene encoding for the alpha subunit of aromatic ring-hydroxylating dioxygenases (alpha-A RH D bacterial gene) were constructed, totaling 800 clones. These libraries were prepared from samples of an ADE soil under two different land uses, located at the Caldeirao Experimental Station secondary forest (SF) and agriculture (AG)-, and the biochar (SF_BC and AG_BC, respectively). Heterogeneity estimates indicated greater diversity in BC libraries; and Venn diagrams showed more unique operational protein clusters (OPC) in the SF_BC library than the ADE soil, which indicates that specific metabolic processes may occur in biochar. Phylogenetic analysis showed unidentified dioxygenases in ADE soils. Libraries containing functional gene encoding for the alpha subunit of the aromatic ring-hydroxylating dioxygenases (ARHD) gene from biochar show higher diversity indices than those of ADE under secondary forest and agriculture.

NATURAL REGENERATION IN ABANDONED FIELDS FOLLOWING INTENSIVE AGRICULTURAL LAND USE IN AN ATLANTIC FOREST ISLAND, BRAZIL

The time required to regrowth a forest in degraded areas depends on how the forest is removed and on the type of land use following removal. Natural regeneration was studied in abandoned old fields after intensive agricultural land use in areas originally covered by Brazilian Atlantic Forests of the Anchieta Island, Brazil in order to understand how plant communities reassemble following human disturbances as well as to determine suitable strategies of forest restoration. The fields were classified into three vegetation types according to the dominant plant species in: 1) Miconia albicans (Sw.) Triana (Melastomataceae) fields, 2) Dicranopteris flexuosa (Schrader) Underw. (Gleicheniaceae) thickets, and 3) Gleichenella pectinata (Willd.) Ching. (Gleicheniaceae) thickets. Both composition and structure of natural regeneration were compared among the three dominant vegetation types by establishing randomly three plots of 1 x 3 m in five sites of the island. A gradient in composition and abundance of species in natural regeneration could be observed along vegetation types from Dicranopteris fern thickets to Miconia fields. The gradient did not accurately follow the pattern of spatial distribution of the three dominant vegetation types in the island regarding their proximity of the remnant forests. A complex association of biotic and abiotic factors seems to be affecting the seedling recruitment and establishment in the study plots. The lowest plant regeneration found in Dicranopteris and Gleichenella thickets suggests that the ferns inhibit the recruitment of woody and herbaceous species. Otherwise, we could not distinguish different patterns of tree regeneration among the three vegetation types. Our results showed that forest recovery following severe anthropogenic disturbances is not direct, predictable or even achievable on its own. Appropriated actions and methods such as fern removal, planting ground covers, and enrichment planting with tree species were suggested in order to restore the natural forest regeneration process in the abandoned old fields.

Stochastic competitive learning in complex networks

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning.

Phylodynamics and movement of Phycodnaviruses among aquatic environments

Phycodnaviruses have a significant role in modulating the dynamics of phytoplankton, thereby influencing community structure and succession, nutrient cycles and potentially atmospheric composition because phytoplankton fix about half the carbon dioxide (CO2) on the planet, and some algae release dimethylsulphoniopropionate when lysed by viruses. Despite their ecological importance and widespread distribution, relatively little is known about the evolutionary history, phylogenetic relationships and phylodynamics of the Phycodnaviruses from freshwater environments. Herein we provide novel data on Phycodnaviruses from the largest river system on earth-the Amazon Basin-that were compared with samples from different aquatic systems from several places around the world. Based on phylogenetic inference using DNA polymerase (pol) sequences we show the presence of distinct populations of Phycodnaviridae. Preliminary coarse-grained phylodynamics and phylogeographic inferences revealed a complex dynamics characterized by long-term fluctuations in viral population sizes, with a remarkable worldwide reduction of the effective population around 400 thousand years before the present (KYBP), followed by a recovery near to the present time. Moreover, we present evidence for significant viral gene flow between freshwater environments, but crucially almost none between freshwater and marine environments. The ISME Journal (2012) 6, 237-247; doi: 10.1038/ismej.2011.93; published online 28 July 2011

Cross-taxon congruence of alpha and beta diversity among five leaf litter arthropod groups in Colombia

In this study alpha and beta diversity patterns of five leaf litter arthropod groups (ants, predatory ants, oribatid mites, spiders and other arachnids) were described and compared in 39 sampling patches of a transformed landscape in southwestern Colombia, that represented five vegetation types: secondary forest, riparian forest, giant bamboo forest, pasture and sugarcane crop. It was also assessed whether some taxa could be used as diversity surrogates. A total of 6,765 individuals grouped in 290 morphospecies were collected. Species richness in all groups was lower in highly transformed vegetation types (pasture, sugarcane crop) than in native ones (forests). In contrast, there were no clear tendencies of beta diversity among vegetation types. Considering sampling patches, 0.1-42% of the variation in alpha diversity of one taxonomic group could be explained from the alpha diversity of another, and 0.2-33% of the variation of beta diversity of a given taxon was explained by that in other groups. Contrary to recent findings, we concluded that patterns of alpha diversity are more congruent than patterns of beta diversity. This fact could be attributed to a sampling effect that promotes congruence in alpha diversity and to a lack of a clear regional ecological gradient that could promote congruent patterns of beta diversity. We did not find evidence for an ideal diversity surrogate although diversity patterns of predatory ants had the greatest congruencies. These results support earlier multi-taxon evaluations in that conservation planning should not be based on only one leaf litter arthropod group.

Lactobacillus acidophilus CRL 1014 improved “gut health” in the SHIME® reactor

Abstract Background How to maintain “gut health” is a goal for scientists throughout the world. Therefore, microbiota management models for testing probiotics, prebiotics, and synbiotics have been developed. Methods The SHIME® model was used to study the effect of Lactobacillus acidophilus 1014 on the fermentation pattern of the colon microbiota. Initially, an inoculum prepared from human feces was introduced into the reactor vessels and stabilized over 2-wk using a culture medium. This stabilization period was followed by a 2-wk control period during which the microbiota was monitored. The microbiota was then subjected to a 4-wk treatment period by adding 5 mL of sterile peptone water with L. acidophilus CRL1014 at the concentration of 108 CFU/mL to vessel one (the stomach compartment). Plate counts, Denaturing Gradient Gel Electrophoresis (DGGE), short-chain fatty acid (SCFA) and ammonium analyses were carried out for monitoring of the microbial community from the colon compartments. Results A significant increase (p < 0.01) in the Lactobacillus spp. and Bifidobacterium spp. populations was observed during the treatment period. The DGGE obtained showed changes in the lactobacilli community from the colon compartments of the SHIME® reactor. The (SCFA) concentration increased (p < 0.01) during the treatment period, due mainly to significant increased levels of acetic, butyric, and propionic acids. However, ammonium concentrations decreased during the same period (p < 0.01). Conclusions This study showed the beneficial influence of L. acidophilus CRL 1014 on microbial metabolism and lactobacilli community composition for improving human health.

Evaluation of impacts of climate change and local stressors on the biotechnological potential of marine macroalgae: a brief theoretical discussion of likely scenarios

Climate change can be associated with variations in the frequency and intensity of extreme temperatures and precipitation events on the local and regional scales. Along coastal areas, flooding associated with increased occupation has seriously impacted products and services generated by marine life, in particular the biotechnological potential that macroalgae hold. Therefore, this paper analyzes the available information on the taxonomy, ecology and physiology of macroalgae and discusses the impacts of climate change and local stress on the biotechnological potential of Brazilian macroalgae. Based on data compiled from a series of floristic and ecological works, we note the disappearance in some Brazilian regions of major groups of biotechnological interest. In some cases, the introduction of exotic species has been documented, as well as expansion of the distribution range of economically important species. We also verify an increase in the similarities between the Brazilian phycogeographic provinces, although they still remain different. It is possible that these changes have resulted from the warming of South Atlantic water, as observed for its surface in southeastern Brazilian, mainly during the winter. However, unplanned urbanization of coastal areas can also produce similar biodiversity losses, which requires efforts to generate long-term temporal data on the composition, community structure and physiology of macroalgae.

Changes in phytoplankton composition in response to tides, wind-induced mixing conditions, and freshwater outflows in an urbanised estuarine complex

Recent reports have shown an increase in potentially harmful phytoplankton in Santos bay (Southeastern Brazilian Coast), located in a highly urbanised estuarine complex. Prediction of blooms is, thus, essential but the phytoplankton community structure in very dynamic regions is difficult to determine. In the present work, we discriminate bloom forming microphytoplankton dominance and their relationship to physical and meteorological variables to look for patterns observed in different tides and seasons. Comparing 8 distinct situations, we found five scenarios of dominance that could be related to winds, tides and rainfall: i) Surfers, diatoms occurring during high surf zone energies; ii) Sinkers, represented by larger celled diatoms during spring tide, after periods of high precipitation rates; iii) Opportunistic mixers, composed of chain forming diatoms with small or elongate cells occurring during neap tides; iv) Local mixers, microplanktonic diatoms and dinoflagellates which occurred throughout the 298 sampling stations; and v) Mixotrophic dinoflagellates, after intense estuarine discharges. Results suggest alterations in the temporal patterns for some bloom-forming species, while others appeared in abundances above safe limits for public health. This approach can also illustrate possible impacts of changes in freshwater discharge in highly urbanised estuaries.