Mining tissue microarray data to uncover combinations of biomarker expression patterns that improve intermediate staging and grading of clear cell renal cell cancer

PURPOSE: Tumor stage and nuclear grade are the most important prognostic parameters of clear cell renal cell carcinoma (ccRCC). The progression risk of ccRCC remains difficult to predict particularly for tumors with organ-confined stage and intermediate differentiation grade. Elucidating molecular pathways deregulated in ccRCC may point to novel prognostic parameters that facilitate planning of therapeutic approaches. EXPERIMENTAL DESIGN: Using tissue microarrays, expression patterns of 15 different proteins were evaluated in over 800 ccRCC patients to analyze pathways reported to be physiologically controlled by the tumor suppressors von Hippel-Lindau protein and phosphatase and tensin homologue (PTEN). Tumor staging and grading were improved by performing variable selection using Cox regression and a recursive bootstrap elimination scheme. RESULTS: Patients with pT2 and pT3 tumors that were p27 and CAIX positive had a better outcome than those with all remaining marker combinations. A prolonged survival among patients with intermediate grade (grade 2) correlated with both nuclear p27 and cytoplasmic PTEN expression, as well as with inactive, nonphosphorylated ribosomal protein S6. By applying graphical log-linear modeling for over 700 ccRCC for which the molecular parameters were available, only a weak conditional dependence existed between the expression of p27, PTEN, CAIX, and p-S6, suggesting that the dysregulation of several independent pathways are crucial for tumor progression. CONCLUSIONS: The use of recursive bootstrap elimination, as well as graphical log-linear modeling for comprehensive tissue microarray (TMA) data analysis allows the unraveling of complex molecular contexts and may improve predictive evaluations for patients with advanced renal cancer.

A triplet spike-timing–dependent plasticity model generalizes the Bienenstock–Cooper–Munro rule to higher-order spatiotemporal correlations

Synaptic strength depresses for low and potentiates for high activation of the postsynaptic neuron. This feature is a key property of the Bienenstock–Cooper–Munro (BCM) synaptic learning rule, which has been shown to maximize the selectivity of the postsynaptic neuron, and thereby offers a possible explanation for experience-dependent cortical plasticity such as orientation selectivity. However, the BCM framework is rate-based and a significant amount of recent work has shown that synaptic plasticity also depends on the precise timing of presynaptic and postsynaptic spikes. Here we consider a triplet model of spike-timing–dependent plasticity (STDP) that depends on the interactions of three precisely timed spikes. Triplet STDP has been shown to describe plasticity experiments that the classical STDP rule, based on pairs of spikes, has failed to capture. In the case of rate-based patterns, we show a tight correspondence between the triplet STDP rule and the BCM rule. We analytically demonstrate the selectivity property of the triplet STDP rule for orthogonal inputs and perform numerical simulations for nonorthogonal inputs. Moreover, in contrast to BCM, we show that triplet STDP can also induce selectivity for input patterns consisting of higher-order spatiotemporal correlations, which exist in natural stimuli and have been measured in the brain. We show that this sensitivity to higher-order correlations can be used to develop direction and speed selectivity.

Grazing response patterns indicate isolation of semi-natural European grasslands

Identifying drivers of species diversity is a major challenge in understanding and predicting the dynamics of species-rich semi-natural grasslands. In particular in temperate grasslands changes in land use and its consequences, i.e. increasing fragmentation, the on-going loss of habitat and the declining importance of regional processes such as seed dispersal by livestock, are considered key drivers of the diversity loss witnessed within the last decades. It is a largely unresolved question to what degree current temperate grassland communities already reflect a decline of regional processes such as longer distance seed dispersal. Answering this question is challenging since it requires both a mechanistic approach to community dynamics and a sufficient data basis that allows identifying general patterns. Here, we present results of a local individual- and trait-based community model that was initialized with plant functional types (PFTs) derived from an extensive empirical data set of species-rich grasslands within the `Biodiversity Exploratories' in Germany. Driving model processes included above- and belowground competition, dynamic resource allocation to shoots and roots, clonal growth, grazing, and local seed dispersal. To test for the impact of regional processes we also simulated seed input from a regional species pool. Model output, with and without regional seed input, was compared with empirical community response patterns along a grazing gradient. Simulated response patterns of changes in PFT richness, Shannon diversity, and biomass production matched observed grazing response patterns surprisingly well if only local processes were considered. Already low levels of additional regional seed input led to stronger deviations from empirical community pattern. While these findings cannot rule out that regional processes other than those considered in the modeling study potentially play a role in shaping the local grassland communities, our comparison indicates that European grasslands are largely isolated, i.e. local mechanisms explain observed community patterns to a large extent.

Genetic Control of Plant Development by Overriding a Geometric Division Rule

Formative cell divisions are critical for multicellular patterning. In the early plant embryo, such divisions follow from orienting the division plane. A major unanswered question is how division plane orientation is genetically controlled, and in particular whether this relates to cell geometry. We have generated a complete 4D map of early Arabidopsis embryogenesis and used computational analysis to demonstrate that several divisions follow a rule that uses the smallest wall area going through the center of the cell. In other cases, however, cell division clearly deviates from this rule, which invariably leads to asymmetric cell division. By analyzing mutant embryos and through targeted genetic perturbation, we show that response to the hormone auxin triggers a deviation from the ``shortest wall'' rule. Our work demonstrates that a simple default rule couples division orientation to cell geometry in the embryo and that genetic regulation can create patterns by overriding the default rule.

Mining frequent bug-fix code changes

Detecting bugs as early as possible plays an important role in ensuring software quality before shipping. We argue that mining previous bug fixes can produce good knowledge about why bugs happen and how they are fixed. In this paper, we mine the change history of 717 open source projects to extract bug-fix patterns. We also manually inspect many of the bugs we found to get insights into the contexts and reasons behind those bugs. For instance, we found out that missing null checks and missing initializations are very recurrent and we believe that they can be automatically detected and fixed.

Lateral impact in closed head injury: A substantially increased risk for diffuse axonal injury—A preliminary study

OBJECTIVE: Assessment, whether location of impact causing different facial fracture patterns was associated with diffuse axonal injury in patients with severe closed head injury. METHODS: Retrospectively all patients referred to the Trauma Unit of the University Hospital of Zurich, Switzerland between 1996 and 2002 presenting with severe closed head injuries (Abbreviated Injury Scale (AIS) (face) of 2-4 and an AIS (head and neck) of 3-5) were assessed according to the Glasgow Coma Scale (GCS) and the Injury Severity Score (ISS). Facial fracture patterns were classified as resulting from frontal, oblique or lateral impact. All patients had undergone computed tomography. The association between impact location and diffuse axonal injury when correcting for the level of consciousness (using the Glasgow scale) and severity of injury (using the ISS) was calculated with a multivariate regression analysis. RESULTS: Of 200 screened patients, 61 fulfilled the inclusion criteria for severe closed head injury. The medians (interquartile ranges 25;75) for GCS, AIS(face) AIS(head and neck) and ISS were 3 (3;13), 2 (2;4), 4 (4;5) and 30 (24;41), respectively. A total of 51% patients had frontal, 26% had an oblique and 23% had lateral trauma. A total of 21% patients developed diffuse axonal injury (DAI) when compared with frontal impact, the likelihood of diffuse axonal injury increased 11.0 fold (1.7-73.0) in patients with a lateral impact. CONCLUSIONS: Clinicians should be aware of the substantial increase of diffuse axonal injury related to lateral impact in patients with severe closed head injuries.