Earthquakes can be stress-forecast

In 1997, Geller et al. wrote 'Earthquakes Cannot Be Predicted' because scale invariance is ubiquitous in self-organized critical systems, and the Earth is in a state of self-organized criticality where small earthquakes have some probability of cascading into a large event. Physically however, large earthquakes can only occur if there is sufficient stress-energy available for release by the specific earthquake magnitude. This stress dependence can be exploited for stress-forecasting by using shear wave splitting to monitor stress-accumulation in the rock mass surrounding impending earthquakes. The technique is arguably successful but, because of the assumed unpredictability, requires explicit justification before it can be generally accepted. Avalanches are also phenomena with self-organized criticality. Recent experimental observations of avalanches in 2-D piles of spherical beads show that natural physical phenomena with self-organized criticality, such as avalanches, and earthquakes, can be predicted. The key to predicting both earthquakes and avalanches is monitoring the matrix material, not monitoring impending source zones

The Turkish Dilatancy Project (TDP3): multidisciplinary studies of a potential earthquake source region

The section of the North Anatolian Fault lying near the city of Izmit, at the east of the Marmara Sea, has been identified as a seismic gap and the possible site of a future major earthquake. Previously published studies of records from an earthquake swarm within the gap (TDPl and TDP2) provided the first evidence that shear-wave splitting occurs in earthquake source regions, a conclusion since verified by many studies at other locations. A third field study (TDP3) was mounted in the Izmit region during the summer of 1984. Observations were made over an eight-month period and included geomagnetic and geoelectric measurements in addition to a series of observations utilising dense arrays of three-component seismometers. Earthquake activity in the principal study area was monitored over a period of eight months. Records showed features similar to those observed in the earlier studies. In particular: (1) almost all shear waves emerging within the shear-wave window displayed shear-wave splitting; (2)the polarizations of the first arriving (faster) split shear-waves showed sub-parallel alignments, characteristic of propagation through a distribution of parallel vertical cracks striking perpendicular to the minimum compressional stress. These and other observations support the conclusion of earlier studies - that the upper crust is pervaded by distributions of micro­ cracks aligned by stress, known as extensive-dilatancy anisotropy. A search for time dependence in shear-wave phenomena has revealed temporal variations in the delays between the split shear-waves throughout the course of the TDP3 study, but as yet this has not been correlated wi th specific earthquake activity

Predicting population extinction in lattice-based birth-death-movement models

The question of whether a population will persist or go extinct is of key interest throughout ecology and biology. Various mathematical techniques allow us to generate knowledge regarding individual behaviour, which can be analysed to obtain predictions about the ultimate survival or extinction of the population. A common model employed to describe population dynamics is the lattice-based random walk model with crowding (exclusion). This model can incorporate behaviour such as birth, death and movement, while including natural phenomena such as finite size effects. Performing sufficiently many realisations of the random walk model to extract representative population behaviour is computationally intensive. Therefore, continuum approximations of random walk models are routinely employed. However, standard continuum approximations are notoriously incapable of making accurate predictions about population extinction. Here, we develop a new continuum approximation, the state space diffusion approximation, which explicitly accounts for population extinction. Predictions from our approximation faithfully capture the behaviour in the random walk model, and provides additional information compared to standard approximations. We examine the influence of the number of lattice sites and initial number of individuals on the long-term population behaviour, and demonstrate the reduction in computation time between the random walk model and our approximation

Patterns of Mesenchymal Condensation in a Multiscale, Discrete Stochastic Model

Cells of the embryonic vertebrate limb in high-density culture undergo chondrogenic pattern formation, which results in the production of regularly spaced “islands” of cartilage similar to the cartilage primordia of the developing limb skeleton. The first step in this process, in vitro and in vivo, is the generation of “cell condensations,” in which the precartilage cells become more tightly packed at the sites at which cartilage will form. In this paper we describe a discrete, stochastic model for the behavior of limb bud precartilage mesenchymal cells in vitro. The model uses a biologically motivated reaction–diffusion process and cell-matrix adhesion (haptotaxis) as the bases of chondrogenic pattern formation, whereby the biochemically distinct condensing cells, as well as the size, number, and arrangement of the multicellular condensations, are generated in a self-organizing fashion. Improving on an earlier lattice-gas representation of the same process, it is multiscale (i.e., cell and molecular dynamics occur on distinct scales), and the cells are represented as spatially extended objects that can change their shape. The authors calibrate the model using experimental data and study sensitivity to changes in key parameters. The simulations have disclosed two distinct dynamic regimes for pattern self-organization involving transient or stationary inductive patterns of morphogens. The authors discuss these modes of pattern formation in relation to available experimental evidence for the in vitro system, as well as their implications for understanding limb skeletal patterning during embryonic development

Cell Cycle Gene Networks Are Associated with Melanoma Prognosis

BACKGROUND: Our understanding of the molecular pathways that underlie melanoma remains incomplete. Although several published microarray studies of clinical melanomas have provided valuable information, we found only limited concordance between these studies. Therefore, we took an in vitro functional genomics approach to understand melanoma molecular pathways. METHODOLOGY/PRINCIPAL FINDINGS: Affymetrix microarray data were generated from A375 melanoma cells treated in vitro with siRNAs against 45 transcription factors and signaling molecules. Analysis of this data using unsupervised hierarchical clustering and Bayesian gene networks identified proliferation-association RNA clusters, which were co-ordinately expressed across the A375 cells and also across melanomas from patients. The abundance in metastatic melanomas of these cellular proliferation clusters and their putative upstream regulators was significantly associated with patient prognosis. An 8-gene classifier derived from gene network hub genes correctly classified the prognosis of 23/26 metastatic melanoma patients in a cross-validation study. Unlike the RNA clusters associated with cellular proliferation described above, co-ordinately expressed RNA clusters associated with immune response were clearly identified across melanoma tumours from patients but not across the siRNA-treated A375 cells, in which immune responses are not active. Three uncharacterised genes, which the gene networks predicted to be upstream of apoptosis- or cellular proliferation-associated RNAs, were found to significantly alter apoptosis and cell number when over-expressed in vitro. CONCLUSIONS/SIGNIFICANCE: This analysis identified co-expression of RNAs that encode functionally-related proteins, in particular, proliferation-associated RNA clusters that are linked to melanoma patient prognosis. Our analysis suggests that A375 cells in vitro may be valid models in which to study the gene expression modules that underlie some melanoma biological processes (e.g., proliferation) but not others (e.g., immune response). The gene expression modules identified here, and the RNAs predicted by Bayesian network inference to be upstream of these modules, are potential prognostic biomarkers and drug targets