Graphical methods in RNA structure matching

Eukaryotic genomes are pervasively transcribed; almost every base can be found in an RNA transcript. This is a surprising observation since most of the genome does not encode proteins. This RNA must serve an important regulatory function – important because producing non-coding RNA is an energy intensive process, and in the absence of strong selection one would expect it to disappear. RNA families with common functions have specifically conserved structural motifs, which are directly related to the functional roles of RNA in catalysis and regulation. Because the conserved structures depend on base-pairing, similar RNA structures may have little or no detectable sequence similarity, making the identification of conserved RNAs difficult. This is a particularly serious problem when studying regulatory structures in RNA. In many cases, such as that of cellular internal ribosome entry sites, although we can identify RNAs that have similar regulatory responses, it is difficult to tell whether the RNAs have common structural features using current methods. Available tools for identifying common structures based on RNA sequence suffer from one or more of the following problems: they do not consider pseudoknots, which are important in many catalytic and regulatory structures; they do not consider near minimum free energy structures, which is important as many RNAs exist as an ensemble of structures of nearly equal energy; they require many examples of known structures in order to train a computational model; they require impractical amounts of computational time, precluding their use on long sequences or genomic scale; or they use a similarity function that cannot identify RNAs as having similar structure, even when they are from one of the well characterized known classes. The approach presented here has the potential to address all of these issues, allowing novel RNA structures that are shared between RNAs with little or no sequence similarity to be discovered. This provides a powerful tool to investigate and explain the pervasive transcription observed in eukaryotic genomes

Dynamic properties of boron carbide

Boron carbide (BC) is potentially useful as a lightweight armor material, but its penetration resistance drops under dynamic impact, the reasons for which are not entirely understood. To examine the mechanisms of failure under dynamic loading, we subjected samples of boron carbide to uniaxial compression at strain rates of up to 104 s-1 while visualizing the failure in real time using x-ray phase contrast imaging (XPCI) at the Advanced Photon Source. However, we can only record 2D images during dynamic loading, as the crack density increases it becomes difficult to track individual defects and understand the failure processes. Since the intensity variations in XPCI encode information about the 3D crack size distribution (CSD), a physics-based model is used to try to extract this information, which is potentially useful to show how it correlates with dynamic crack propagations and distributions from XPCI

On the Finite Time Blowup of the De Gregorio Model for the 3D Euler Equation

We present a novel method of analysis and prove finite time self-similar blowup of the original De Gregorio model [DG90,DG96] for smooth initial data on the real line with compact support. We also prove self-similar blowup results for the generalized De Gregorio model [OSW08] for the entire range of parameter on R or S¹ for Hölder continuous initial data with compact support. Our strategy is to reformulate the problem of proving finite time self-similar singularity into the problem of establishing the nonlinear stability of an approximate self-similar profile using the dynamic rescaling equation. We use the energy method with appropriate singular weight functions and take into account cancellation among various nonlinear terms to extract the inviscid damping effect from the linearized operator around the approximate self-similar profile. We remark that our analysis does not rule out the possibility that the original De Gregorio model is well posed for smooth initial data on a circle. The method of analysis presented in this paper provides a promising new framework to analyze finite time singularity of nonlinear nonlocal systems of partial differential equations

Efficacy of some selected neo-adjuvant chemotherapy regimens in the treatment of advanced oral squamous cell carcinoma, and their effects on immune function

Purpose: To investigate the clinical efficacy of different neo-adjuvant chemotherapy (NACT) regimens in the treatment of advanced oral squamous cell carcinoma (OSCC), and their influence on immune function of the patients.Methods: Advanced OSCC patients (n = 94) who received NACT served as subjects in this study. They were assigned to 2 different treatment groups. Forty patients received docetaxel and fluorouracil regimen (DF group), while 54 patients received taxotere, cisplatin and fluorouracil regimen (TPF group). Surgery was performed after NACT. Changes in clinical efficacy and immune function were monitored in both groups. The clinical baseline data of patients were assessed prior to the treatments. Independent indicators of prognosis were determined using Cox regression analysis (CRA).Results: Clinical treatment efficacy was higher in TPF group than in DF group (p < 0.05). Objective remission rate (ORR) in DF group was lower than that in TPF group (p < 0.05). After chemotherapy, both groups had increased levels of CD4+ and CD4+/CD8+, and reduced level of CD8+, when compared with pre-chemotherapy values, with higher levels of CD4+ and CD4+/CD8+ ratio, and lower level of CD8+ in TPF group than in DF group (p < 0.05). Multivariate CRA revealed that the independent factors for prognosis of oral carcinoma (OC) were tumor node metastasis (TNM) stage and lymph node metastasis.Conclusion: These results indicate that TFP regimen improves clinical efficacy and immune function in patients with advanced OSCC