The interaction of macroeconomic variables may change as nominal short-term interest rates approach zero. In this paper, we propose to capture these changing dynamics with a state-switching parameter model which explicitly takes into account that the interest rate might be constrained near the zero lower bound by using a Tobit model. The probability of state transitions is affected by the lagged level of the interest rate. The endogenous specification of the state indicator permits dynamic conditional forecasts of the state and the system variables. We use Bayesian methods to estimate the model and to derive the forecast densities. In an application to Swiss data, we evaluate state-dependent impulse-responses to a risk premium shock identified with sign-restrictions. We provide an estimate of the latent rate, i.e. the rate lower than the constraint on the interest rate level which would be state- and model-consistent. Additionally, we discuss scenario-based forecasts and evaluate the probability of exiting the ZLB region. In terms of log predictive scores and the Bayesian information criterion, the model outperforms a model substituting switching with stochastic volatility and another including intercept switching only combined with stochastic volatility.
The histone variant 2AX (H2AX) is phosphorylated at Serine 139 by the PI3K-like kinase family members ATM, ATR and DNA-PK. Genotoxic stress, such as tumor radio- and chemotherapy, is considered to be the main inducer of phosphorylated H2AX (γH2AX), which forms distinct foci at sites of DNA damage where DNA repair factors accumulate. γH2AX accumulation under severe hypoxic/anoxic (0.02% oxygen) conditions has recently been reported to follow replication fork stalling in the absence of detectable DNA damage. In this study, we found HIF-dependent accumulation of γH2AX in several cancer cell lines and mouse embryonic fibroblasts exposed to physiologically relevant chronic hypoxia (0.2% oxygen), which did not induce detectable levels of DNA strand breaks. The hypoxic accumulation of γH2AX was delayed by the RNAi-mediated knockdown of HIF-1α or HIF-2α and further decreased when both HIF-αs were absent. Conversely, basal phosphorylation of H2AX was increased in cells with constitutively stabilized HIF-2α. These results suggest that both HIF-1 and HIF-2 are involved in γH2AX accumulation by tumor hypoxia, which might increase a cancer cell’s capacity to repair DNA damage, contributing to tumor therapy resistance.
Background: Severe traumatic brain injury (TBI) is associated with a 30%–70% mortality rate. S100B has been proposed as a biomarker for indicating outcome after TBI. Nevertheless, controversy has arisen concerning the predictive value of S100B for severe TBI in the context of multitrauma. Therefore, our aim was to determine whether S100B serum levels correlate with primary outcome following isolated severe TBI or multitrauma in males.
Methods: Twenty-three consecutive male patients (age 18–65years), victims of severe TBI [Glasgow Coma Scale (GCS) 3–8] (10 isolated TBI and 13 multitrauma with TBI) and a control group consisting of eight healthy volunteers were enrolled in this prospective study. Clinical outcome variables of severe TBI comprised: survival, time to intensive care unit (ICU) discharge, and neurological assessment [Glasgow Outcome Scale (GOS) at ICU discharge]. Venous blood samples were taken at admission in the ICU (study entry), 24h later, and 7days later. Serum S100B concentration was measured by an immunoluminometric assay.
Results: At study entry (mean time 10.9h after injury), mean S100B concentrations were significantly increased in the patient with TBI (1.448μg/L) compared with the control group (0.037μg/L) and patients with fatal outcome had higher mean S100B (2.10μg/L) concentrations when compared with survivors (0.85μg/L). In fact, there was a significant correlation between higher initial S100B concentrations and fatal outcome (Spearman's =0.485, p=0.019). However, there was no correlation between higher S100B concentrations and the presence of multitrauma. The specificity of S100B in predicting mortality according to the cut-off of 0.79μg/L was 73% at study entry.
Conclusions: Increased serum S100B levels constitute a valid predictor of unfavourable outcome in severe TBI, regardless of the presence of associated multitrauma.
Over the last decade, advances in high-throughput technologies
have resulted in a flood of new biological data. Here, individual
samples can extend up into terabyte size. While potential applications
are broad, ranging from biotechnology to medical applications, the
analysis of these datasets poses massive challenges.
In order to make use of the produced terabytes of data, these datasets need to
be integrated, need to be mapped onto existing biological knowledge,
and need to be explored by experts.
We present UniPAX and BiNA, a scalable system for the integration and analysis
of high-throughput data (genomics, transcriptomics, proteomics, and
metabolomics) in a network context. A central data warehouse holds the core
dataset. A flexible middleware can execute custom queries on this dataset and
communicate with our visual analytics tool BiNA, the Biological Network
Analyzer. We demonstrate how the combination of these tools permits an efficient
analysis of large-scale datasets for medical applications.