On the origin of interface states at oxide/III-nitride heterojunction interfaces

The energy spectrum of interface state density, D-it(E), was determined at oxide/III-N heterojunction interfaces in the entire band gap, using two complementary photo-electric methods: (i) photo-assisted capacitance-voltage technique for the states distributed near the midgap and the conduction band (CB) and (ii) light intensity dependent photo-capacitance method for the states close to the valence band (VB). In addition, the Auger electron spectroscopy profiling was applied for the characterization of chemical composition of the interface region with the emphasis on carbon impurities, which can be responsible for the interface state creation. The studies were performed for the AlGaN/GaN metal-insulator-semiconductor heterostructures (MISH) with Al2O3 and SiO2 dielectric films and AlxGa1-x layers with x varying from 0.15 to 0.4 as well as for an Al2O3/InAlN/GaN MISH structure. For all structures, it was found that: (i) D-it(E) is an U-shaped continuum increasing from the midgap towards the CB and VB edges and (ii) interface states near the VB exhibit donor-like character. Furthermore, D-it(E) for SiO2/AlxGa1-x/GaN structures increased with rising x. It was also revealed that carbon impurities are not present in the oxide/III-N interface region, which indicates that probably the interface states are not related to carbon, as previously reported. Finally, it was proven that the obtained D-it(E) spectrum can be well fitted using a formula predicted by the disorder induced gap state model. This is an indication that the interface states at oxide/III-N interfaces can originate from the structural disorder of the interfacial region. Furthermore, at the oxide/barrier interface we revealed the presence of the positive fixed charge (Q(F)) which is not related to D-it(E) and which almost compensates the negative polarization charge (Q(pol)(-))

ChIP-on-chip significance analysis reveals large-scale binding and regulation by human transcription factor oncogenes

ChIP-on-chip has emerged as a powerful tool to dissect the complex network of regulatory interactions between transcription factors and their targets. However, most ChIP-on-chip analysis methods use conservative approaches aimed to minimize false-positive transcription factor targets. We present a model with improved sensitivity in detecting binding events from ChIP-on-chip data. Biochemically validated analysis in human T-cells reveals that three transcription factor oncogenes, NOTCH1, MYC, and HES1, bind one order of magnitude more promoters than previously thought. Gene expression profiling upon NOTCH1 inhibition shows broad-scale functional regulation across the entire range of predicted target genes, establishing a closer link between occupancy and regulation. Finally, the resolution of a more complete map of transcriptional targets reveals that MYC binds nearly all promoters bound by NOTCH1. Overall, these results suggest an unappreciated complexity of transcriptional regulatory networks and highlight the fundamental importance of genome-scale analysis to represent transcriptional programs

YPA: an integrated repository of promoter features in Saccharomyces cerevisiae

This study presents the Yeast Promoter Atlas (YPA, http://ypa.ee.ncku.edu.tw/ or http://ypa.csbb.ntu.edu.tw/) database, which aims to collect comprehensive promoter features in Saccharomyces cerevisiae. YPA integrates nine kinds of promoter features including promoter sequences, genes’ transcription boundaries—transcription start sites (TSSs), five prime untranslated regions (5′-UTRs) and three prime untranslated regions (3′UTRs), TATA boxes, transcription factor binding sites (TFBSs), nucleosome occupancy, DNA bendability, transcription factor (TF) binding, TF knockout expression and TF–TF physical interaction. YPA is designed to present data in a unified manner as many important observations are revealed only when these promoter features are considered altogether. For example, DNA rigidity can prevent nucleosome packaging, thereby making TFBSs in the rigid DNA regions more accessible to TFs. Integrating nucleosome occupancy, DNA bendability, TF binding, TF knockout expression and TFBS data helps to identify which TFBS is actually functional. In YPA, various promoter features can be accessed in a centralized and organized platform. Researchers can easily view if the TFBSs in an interested promoter are occupied by nucleosomes or located in a rigid DNA segment and know if the expression of the downstream gene responds to the knockout of the corresponding TFs. Compared to other established yeast promoter databases, YPA collects not only TFBSs but also many other promoter features to help biologists study transcriptional regulation

Variability in precipitation, temperature and river runoff in W Central Asia during the past ~2000yrs

The tributary rivers Amu Darya and Syr Darya contribute major amounts of water to the hydrological budget of the endorheic Aral Sea. Processes controlling the flow of water into rivers in the headwater systems in Tien Shan (Kyrgyzstan) and Pamir (Tajikistan) are therefore most relevant. Lake water mineralization is strongly dependent on river discharge and has been inferred from spectrometrically determined gypsum and other salt contents. Comparison of high-resolution mineralization data with tree ring data, other proxies for tracing temperature and snow cover in NW China, and accumulation rates in the Guliya Ice Core indicate that mineralization over the past ~2000. yrs in the Aral Sea reflects snow cover variability and glacier extent in Tien Shan and Pamir (at the NW and W edges of the Tibetan Plateau). Snow cover in W Central Asia is preferentially a winter expression controlled by temperature patterns that impact the moisture-loading capacity over N Europe and NW Asia (Clark et al., 1999). We observed that the runoff, resulting from warmer winter temperatures in W Central Asia and resulting in a reduction of snow cover, decreased between AD 100-300, AD 1150-1250, AD 1380-1450, AD 1580-1680 and during several low frequency events after AD 1800. Furthermore, we observed a negative relationship between the amount of mineralization in the Aral Sea and SW summer monsoon intensity starting with the Little Ice Age. Based on these observations, we conclude that the lake level changes during the past ~. 2000. yrs were mostly climatically controlled. Around AD 200, AD 1400 and during the late 20th century AD, human activities (namely irrigation) may also have synergistically influenced discharge dynamics in the lower river courses. © 2011 Elsevier B.V

Multiparametric MR Imaging of Diffusion and Perfusion in Contrast-enhancing and Nonenhancing Components in Patients with Glioblastoma

Purpose To determine whether regions of low apparent diffusion coefficient (ADC) with high relative cerebral blood volume (rCBV) represented elevated choline (Cho)-to-N-acetylaspartate (NAA) ratio (hereafter, Cho/NAA ratio) and whether their volumes correlated with progression-free survival (PFS) and overall survival (OS) in patients with glioblastoma (GBM). Materials and Methods This retrospective analysis was approved by the local research ethics committee. Volumetric analysis of imaging data from 43 patients with histologically confirmed GBM was performed. Patients underwent preoperative 3-T magnetic resonance imaging with conventional, diffusion-weighted, perfusion-weighted, and spectroscopic sequences. Patients underwent subsequent surgery with adjuvant chemotherapy and radiation therapy. Overlapping low-ADC and high-rCBV regions of interest (ROIs) (hereafter, ADC-rCBV ROIs) were generated in contrast-enhancing and nonenhancing regions. Cho/NAA ratio in ADC-rCBV ROIs was compared with that in control regions by using analysis of variance. All resulting ROI volumes were correlated with patient survival by using multivariate Cox regression. Results ADC-rCBV ROIs within contrast-enhancing and nonenhancing regions showed elevated Cho/NAA ratios, which were significantly higher than those in other abnormal tumor regions (P < .001 and P = .008 for contrast-enhancing and nonenhancing regions, respectively) and in normal-appearing white matter (P < .001 for both contrast-enhancing and nonenhancing regions). After Cox regression analysis controlling for age, tumor size, resection extent, O-6-methylguanine-DNA methyltransferase-methylation, and isocitrate dehydrogenase mutation status, the proportional volume of ADC-rCBV ROIs in nonenhancing regions significantly contributed to multivariate models of OS (hazard ratio, 1.132; P = .026) and PFS (hazard ratio, 1.454; P = .017). Conclusion Volumetric analysis of ADC-rCBV ROIs in nonenhancing regions of GBM can be used to identify patients with poor survival trends after accounting for known confounders of GBM patient outcome.Supported by a Clinician Scientist Award from the National Institute for Health Research (NIHR/CS/009/011) and by the NIHR Cambridge Biomedical Research Center and Commonwealth Scholarship Commission

Bioinformatics

Motivation: Theoretical efforts to understand the regulation of gene expression are traditionally centered around the identification of transcription factor binding sites at specific DNA positions. More recently these efforts have been supplemented by experimental data for relative binding affinities of proteins to longer intergenic sequences. The question arises to what extent these two approaches converge. In this paper, we adopt a physical binding model to predict the relative binding affinity of a transcription factor for a given sequence. Results: We find that a significant fraction of genome-wide binding data in yeast can be accounted for by simple count matrices and a physical model with only two parameters. We demonstrate that our approach is both conceptually and practically more powerful than traditional methods, which require selection of a cutoff. Our analysis yields biologically meaningful parameters, suitable for predicting relative binding affinities in the absence of experimental binding data. Availability: The C source code for our TRAP program is freely available for non-commercial use at http://www.molgen.mpg.de/~manke/papers/TFaffinities

Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma

OBJECTIVES: Integrating multiple imaging modalities is crucial for MRI data interpretation. The purpose of this study is to determine whether a previously proposed multi-view approach can effectively integrate the histogram features from multi-parametric MRI and whether the selected features can offer incremental prognostic values over clinical variables. METHODS: Eighty newly-diagnosed glioblastoma patients underwent surgery and chemoradiotherapy. Histogram features of diffusion and perfusion imaging were extracted from contrast-enhancing (CE) and non-enhancing (NE) regions independently. An unsupervised patient clustering was performed by the multi-view approach. Kaplan-Meier and Cox proportional hazards regression analyses were performed to evaluate the relevance of patient clustering to survival. The metabolic signatures of patient clusters were compared using multi-voxel spectroscopy analysis. The prognostic values of histogram features were evaluated by survival and ROC curve analyses. RESULTS: Two patient clusters were generated, consisting of 53 and 27 patients respectively. Cluster 2 demonstrated better overall survival (OS) (p = 0.007) and progression-free survival (PFS) (p < 0.001) than Cluster 1. Cluster 2 displayed lower N-acetylaspartate/creatine ratio in NE region (p = 0.040). A higher mean value of anisotropic diffusion in NE region was associated with worse OS (hazard ratio [HR] = 1.40, p = 0.020) and PFS (HR = 1.36, p = 0.031). The seven features selected by this approach showed significantly incremental value in predicting 12-month OS (p = 0.020) and PFS (p = 0.022). CONCLUSIONS: The multi-view clustering method can provide an effective integration of multi-parametric MRI. The histogram features selected may be used as potential prognostic markers

A Bayesian Search for Transcriptional Motifs

Identifying transcription factor (TF) binding sites (TFBSs) is an important step towards understanding transcriptional regulation. A common approach is to use gaplessly aligned, experimentally supported TFBSs for a particular TF, and algorithmically search for more occurrences of the same TFBSs. The largest publicly available databases of TF binding specificities contain models which are represented as position weight matrices (PWM). There are other methods using more sophisticated representations, but these have more limited databases, or aren't publicly available. Therefore, this paper focuses on methods that search using one PWM per TF. An algorithm, MATCHTM, for identifying TFBSs corresponding to a particular PWM is available, but is not based on a rigorous statistical model of TF binding, making it difficult to interpret or adjust the parameters and output of the algorithm. Furthermore, there is no public description of the algorithm sufficient to exactly reproduce it. Another algorithm, MAST, computes a p-value for the presence of a TFBS using true probabilities of finding each base at each offset from that position. We developed a statistical model, BaSeTraM, for the binding of TFs to TFBSs, taking into account random variation in the base present at each position within a TFBS. Treating the counts in the matrices and the sequences of sites as random variables, we combine this TFBS composition model with a background model to obtain a Bayesian classifier. We implemented our classifier in a package (SBaSeTraM). We tested SBaSeTraM against a MATCHTM implementation by searching all probes used in an experimental Saccharomyces cerevisiae TF binding dataset, and comparing our predictions to the data. We found no statistically significant differences in sensitivity between the algorithms (at fixed selectivity), indicating that SBaSeTraM's performance is at least comparable to the leading currently available algorithm. Our software is freely available at: http://wiki.github.com/A1kmm/sbasetram/building-the-tools

Multimodal MRI characteristics of the glioblastoma infiltration beyond contrast enhancement

Our inability to identify the invasive margin of glioblastomas hampers attempts to achieve local control. Diffusion tensor imaging (DTI) has been implemented clinically to delineate the margin of the tumor infiltration, its derived anisotropic (q) values can extend beyond the contrast-enhanced area and correlates closely with the tumor. However, its correlation with tumor infiltration shown on multivoxel proton magnetic resonance spectroscopy1 (MRS) and perfusion magnetic resonance imaging (MRI) should be investigated. In this study, we aimed to show tissue characteristics of the q-defined peritumoral invasion on MRS and perfusion MRI. Patients with a primary glioblastoma were included (n = 51). Four regions of interest were analyzed; the contrast-enhanced lesion, peritumoral abnormal q region, peritumoral normal q region, and contralateral normal-appearing white matter. MRS, including choline (Cho)/creatinine (Cr), Cho/N-acetyl-aspartate (NAA) and NAA/Cr ratios, and the relative cerebral blood volume (rCBV) were analyzed. Our results showed an increase in the Cho/NAA (p = 0.0346) and Cho/Cr (p = 0.0219) ratios in the peritumoral abnormal q region, suggestive of tumor invasion. The rCBV was marginally elevated (p = 0.0798). Furthermore, the size of the abnormal q regions was correlated with survival; patients with larger abnormal q regions showed better progression-free survival (median 287 versus 53 days, p = 0.001) and overall survival (median 464 versus 274 days, p = 0.006) than those with smaller peritumoral abnormal q regions of interest. These results support how the DTI q abnormal area identifies tumor activity beyond the contrast-enhanced area, especially correlating with MRS