Seismic events miss important kinematically governed grain scale mechanisms during shear failure of porous rock.

Catastrophic failure in brittle, porous materials initiates when smaller-scale fractures localise along an emergent fault zone in a transition from stable crack growth to dynamic rupture. Due to the rapid nature of this critical transition, the precise micro-mechanisms involved are poorly understood and difficult to image directly. Here, we observe these micro-mechanisms directly by controlling the microcracking rate to slow down the transition in a unique rock deformation experiment that combines acoustic monitoring (sound) with contemporaneous in-situ x-ray imaging (vision) of the microstructure. We find seismic amplitude is not always correlated with local imaged strain; large local strain often occurs with small acoustic emissions, and vice versa. Local strain is predominantly aseismic, explained in part by grain/crack rotation along an emergent shear zone, and the shear fracture energy calculated from local dilation and shear strain on the fault is half of that inferred from the bulk deformation.

The multiscale backbone of the human phenotype network based on biological pathways.

BACKGROUND: Networks are commonly used to represent and analyze large and complex systems of interacting elements. In systems biology, human disease networks show interactions between disorders sharing common genetic background. We built pathway-based human phenotype network (PHPN) of over 800 physical attributes, diseases, and behavioral traits; based on about 2,300 genes and 1,200 biological pathways. Using GWAS phenotype-to-genes associations, and pathway data from Reactome, we connect human traits based on the common patterns of human biological pathways, detecting more pleiotropic effects, and expanding previous studies from a gene-centric approach to that of shared cell-processes. RESULTS: The resulting network has a heavily right-skewed degree distribution, placing it in the scale-free region of the network topologies spectrum. We extract the multi-scale information backbone of the PHPN based on the local densities of the network and discarding weak connection. Using a standard community detection algorithm, we construct phenotype modules of similar traits without applying expert biological knowledge. These modules can be assimilated to the disease classes. However, we are able to classify phenotypes according to shared biology, and not arbitrary disease classes. We present examples of expected clinical connections identified by PHPN as proof of principle. CONCLUSIONS: We unveil a previously uncharacterized connection between phenotype modules and discuss potential mechanistic connections that are obvious only in retrospect. The PHPN shows tremendous potential to become a useful tool both in the unveiling of the diseases' common biology, and in the elaboration of diagnosis and treatments.

A biofilm model for prediction of pollutant transformation in sewers.

This study developed a new sewer biofilm model to simulate the pollutant transformation and biofilm variation in sewers under aerobic, anoxic and anaerobic conditions. The biofilm model can describe the activities of heterotrophic, autotrophic, and sulfate-reducing bacteria (SRB) in the biofilm as well as the variations in biofilm thickness, the spatial profiles of SRB population and biofilm density. The model can describe dynamic biofilm growth, multiple biomass evolution and competitions among organic oxidation, denitrification, nitrification, sulfate reduction and sulfide oxidation in a heterogeneous biofilm growing in a sewer. The model has been extensively verified by three different approaches, including direct verification by measurement of the spatial concentration profiles of dissolved oxygen, nitrate, ammonia, and hydrogen sulfide in sewer biofilm. The spatial distribution profile of SRB in sewer biofilm was determined from the fluorescent in situ hybridization (FISH) images taken by a confocal laser scanning microscope (CLSM) and were predicted well by the model.

A mouse atlas of gene expression: large-scale digital gene-expression profiles from precisely defined developing C57BL/6J mouse tissues and cells.

We analyzed 8.55 million LongSAGE tags generated from 72 libraries. Each LongSAGE library was prepared from a different mouse tissue. Analysis of the data revealed extensive overlap with existing gene data sets and evidence for the existence of approximately 24,000 previously undescribed genomic loci. The visual cortex, pancreas, mammary gland, preimplantation embryo, and placenta contain the largest number of differentially expressed transcripts, 25% of which are previously undescribed loci.

Biofilm in the sediment phase of a sanitary gravity sewer.

Microbial activity of the sediment phase in a 1.5-km-long concrete sewer section with a cement pipe in a 540-mm diameter was investigated in this paper. SEM examinations and elementary composition analyses of the sediment samples have identified the presence of a biofilm layer at the sediment surface. Bacterial counting results with a DNA-staining technique have revealed that the amount of bacteria in this layer was 2.1 x 10(11)cellg(-1) dry wt, which is close to that of activated sludge. ATP content in the sewer biofilm was found relatively high, demonstrating that the sewer biofilm is active. Throughout the entire 1.5-km sewer section, the biofilm activity was maintained at almost the same level. Lab-scale sediment oxygen uptake flux (SOUF) tests showed that the shear flow velocity above the sediment phase linearly increases the SOUF, which of the potential value was determined to be 32gO(2)m(-2) day(-1) at an estimated shear flow velocity of 0.055ms(-1) at 25 degrees C in the sewer line, provided that the mean flow velocity was 1.5ms(-1), and the mean water depth was 220mm. Such a high SOUF value further endorsed the existence of the active sewer biofilm.