Solutes determine the temperature windows for microbial survival and growth

Microbial cells, and ultimately the Earth's biosphere, function within a narrow range of physicochemical conditions. For the majority of ecosystems, productivity is cold-limited, and it is microbes that represent the failure point. This study was carried out to determine if naturally occurring solutes can extend the temperature windows for activity of microorganisms. We found that substances known to disorder cellular macromolecules (chaotropes) did expand microbial growth windows, fungi preferentially accumulated chaotropic metabolites at low temperature, and chemical activities of solutes determined microbial survival at extremes of temperature as well as pressure. This information can enhance the precision of models used to predict if extraterrestrial and other hostile environments are able to support life; furthermore, chaotropes may be used to extend the growth windows for key microbes, such as saprotrophs, in cold ecosystems and manmade biomes.

Simulated predator extinctions: Predator identity affects survival and recruitment of oysters

The rate of species loss is increasing at a global scale, and human-induced extinctions are biased toward predator species. We examined the effects of predator extinctions on a foundation species, the eastern oyster (Crassostrea virginica). We performed a factorial experiment manipulating the presence and abundance of three of the most common predatory crabs, the blue crab (Callinectes sapidus), stone crab (Menippe mercenaria), and mud crab (Panopeus herbstii) in estuaries in the eastern United States. We tested the effects of species richness and identity of predators on juvenile oyster survival, oyster recruitment, and organic matter content of sediment. We also manipulated the density of each of the predators and controlled for the loss of biomass of species by maintaining a constant mass of predators in one set of treatments and simultaneously using an additive design. This design allowed us to test the density dependence of our results and test for functional compensation by other species. The identity of predator species, but not richness, affected oyster populations. The loss of blue crabs, alone or in combination with either of the other species, affected the survival rate of juvenile oysters. Blue crabs and stone crabs both affected oyster recruitment and sediment organic matter negatively. Mud crabs at higher than ambient densities, however, could fulfill some of the functions of blue and stone crabs, suggesting a level of ecological redundancy. Importantly, the strong effects of blue crabs in all processes measured no longer occurred when individuals were present at higher-than-ambient densities. Their role as dominant predator is, therefore, dependent on their density within the system and the density of other species within their guild (e.g., mud crabs). Our findings support the hypothesis that the effects of species loss at higher trophic levels are determined by predator identity and are subject to complex intraguild interactions that are largely density dependent. Understanding the role of biodiversity in ecosystem functioning or addressing practical concerns, such as loss of predators owing to overharvesting, remains complicated because accurate predictions require detailed knowledge of the system and should be drawn from sound experimental evidence, not based on observations or generalized models.

A Bayesian Network Hybrid Model for Representing Accident and Emergency Waiting Times

The paper introduces a new modeling approach that represents the waiting times in an Accident and Emergency (A&E) Department in a UK based National Health Service (NHS) hospital. The technique uses Bayesian networks to capture the heterogeneity of arriving patients by representing how patient covariates interact to influence their waiting times in the department. Such waiting times have been reviewed by the NHS as a means of investigating the efficiency of A&E departments (Emergency Rooms) and how they operate. As a result activity targets are now established based on the patient total waiting times with much emphasis on trolley waits.

A Bayesian network hybrid model for representing accident and emergency waiting times

The paper introduces a new modeling approach that represents the waiting times in an accident and emergency (A&E) department in a UK based national health service (NHS) hospital. The technique uses Bayesian networks to capture the heterogeneity of arriving patients by representing how patient covariates interact to influence their waiting times in the department. Such waiting times have been reviewed by the NHS as a means of investigating the efficiency of A&E departments (emergency rooms) and how they operate. As a result activity targets are now established based on the patient total waiting times with much emphasis on trolley waits.

Intrinsic subtypes of gastric cancer, based on gene expression pattern, predict survival and respond differently to chemotherapy

BACKGROUND & AIMS:
Gastric cancer (GC) is a heterogeneous disease comprising multiple subtypes that have distinct biological properties and effects in patients. We sought to identify new, intrinsic subtypes of GC by gene expression analysis of a large panel of GC cell lines. We tested if these subtypes might be associated with differences in patient survival times and responses to various standard-of-care cytotoxic drugs.
METHODS:
We analyzed gene expression profiles for 37 GC cell lines to identify intrinsic GC subtypes. These subtypes were validated in primary tumors from 521 patients in 4 independent cohorts, where the subtypes were determined by either expression profiling or subtype-specific immunohistochemical markers (LGALS4, CDH17). In vitro sensitivity to 3 chemotherapy drugs (5-fluorouracil, cisplatin, oxaliplatin) was also assessed.
RESULTS:
Unsupervised cell line analysis identified 2 major intrinsic genomic subtypes (G-INT and G-DIF) that had distinct patterns of gene expression. The intrinsic subtypes, but not subtypes based on Lauren's histopathologic classification, were prognostic of survival, based on univariate and multivariate analysis in multiple patient cohorts. The G-INT cell lines were significantly more sensitive to 5-fluorouracil and oxaliplatin, but more resistant to cisplatin, than the G-DIF cell lines. In patients, intrinsic subtypes were associated with survival time following adjuvant, 5-fluorouracil-based therapy.
CONCLUSIONS:
Intrinsic subtypes of GC, based on distinct patterns of expression, are associated with patient survival and response to chemotherapy. Classification of GC based on intrinsic subtypes might be used to determine prognosis and customize therapy.