Genetically engineered pre-microRNA-34a prodrug suppresses orthotopic osteosarcoma xenograft tumor growth via the induction of apoptosis and cell cycle arrest.

Osteosarcoma (OS) is the most common primary malignant bone tumor in children, and microRNA-34a (miR-34a) replacement therapy represents a new treatment strategy. This study was to define the effectiveness and safety profiles of a novel bioengineered miR-34a prodrug in orthotopic OS xenograft tumor mouse model. Highly purified pre-miR-34a prodrug significantly inhibited the proliferation of human 143B and MG-63 cells in a dose dependent manner and to much greater degrees than controls, which was attributed to induction of apoptosis and G2 cell cycle arrest. Inhibition of OS cell growth and invasion were associated with release of high levels of mature miR-34a from pre-miR-34a prodrug and consequently reduction of protein levels of many miR-34a target genes including SIRT1, BCL2, c-MET, and CDK6. Furthermore, intravenous administration of in vivo-jetPEI formulated miR-34a prodrug significantly reduced OS tumor growth in orthotopic xenograft mouse models. In addition, mouse blood chemistry profiles indicated that therapeutic doses of bioengineered miR-34a prodrug were well tolerated in these animals. The results demonstrated that bioengineered miR-34a prodrug was effective to control OS tumor growth which involved the induction of apoptosis and cell cycle arrest, supporting the development of bioengineered RNAs as a novel class of large molecule therapeutic agents

Application of mean-shift clustering to Blood oxygen level dependent functional MRI activation detection

BACKGROUND: Functional magnetic resonance imaging (fMRI) analysis is commonly done with cross-correlation analysis (CCA) and the General Linear Model (GLM). Both CCA and GLM techniques, however, typically perform calculations on a per-voxel basis and do not consider relationships neighboring voxels may have. Clustered voxel analyses have then been developed to improve fMRI signal detections by taking advantages of relationships of neighboring voxels. Mean-shift clustering (MSC) is another technique which takes into account properties of neighboring voxels and can be considered for enhancing fMRI activation detection. METHODS: This study examines the adoption of MSC to fMRI analysis. MSC was applied to a Statistical Parameter Image generated with the CCA technique on both simulated and real fMRI data. The MSC technique was then compared with CCA and CCA plus cluster analysis. A range of kernel sizes were used to examine how the technique behaves. RESULTS: Receiver Operating Characteristic curves shows an improvement over CCA and Cluster analysis. False positive rates are lower with the proposed technique. MSC allows the use of a low intensity threshold and also does not require the use of a cluster size threshold, which improves detection of weak activations and highly focused activations. CONCLUSION: The proposed technique shows improved activation detection for both simulated and real Blood Oxygen Level Dependent fMRI data. More detailed studies are required to further develop the proposed technique

A Novel Three-stage Process for Continuous Production of Penicillin G Acylase by a Temperature - sensitive Expression System of Bacillus subtilis Phagephi 105

This study is pertaining to the production of penicillin G acylase (PGA) by Bacillus subtilis 8105MU331 in which PGA gene, under the control of thermal-induced promoter, was integrated. The key process parameters including induced-temperature, induced- time, and culture temperature were optimized in flask culture. A three-stage cultivation process was developed for PGA production with the expression system of B. subtilis 8105MU331. Furthermore, a bioreactor with a thermal-induced apparatus was designed for continuous production of PGA, where cell growth, induction, and PGA expression could be conducted separately. At a dilution rate of 0.20 h–1, PGA production was taken under continuous cultivation in three-stage process. After continuous feeding, the cell density, pH, and residual glucose in the first- and third-reactor were maintained steady for up to 40 h. These results suggested that the new three-stage process might be feasible and very efficient for production of heterologous proteins

Game theoretic approaches to cooperation in wireless networks

Ph.DDOCTOR OF PHILOSOPH

Stability Assessment of Homogeneous Slopes Loaded with Mobile Tracked Cranes – An Artificial Neural Network Approach

The assessment of stability of homogeneous slopes found as part of embankments, approach ramps, in bridge construction or flood protection levees could be a complex task. Either during construction or at a point in the operating life of the earth structure it can be subjected to loads from the equipment operating on it. Mobile tracked cranes used in heavy lifting or dredging operations can apply loads due to their substantial self-weight combined with the load carried by them. It is important to be able to determine the minimum factor of safety for such slopes. However due to the combination of soil parameters, slope geometry and the variable nature of loading imposed, a substantial number (measured perhaps in hundreds of combinations) slope stability analyses would be required to find the minimum factor of safety. One approach to reduce the number of analyses needed is to develop an Artificial Neural Network, train it using a representative dataset of stability analyses, and rely on its predicting capabilities to determine the minimum factor of safety for the slope for any combination of model parameters. Artificial Neural Networks can simulate the central nervous system of a human brain, by training them using the input data and target data one can build a neural network and use them for the factor of safety prediction. Since this thesis considers the case of homogeneous constructed slopes, thus the slope stability analysis was performed using Bishop Simplified Method, and the load distribution due to mobile tracked cranes was represented by an equivalent triangular distribution acting on the slope surface. The slope stability analysis was performed using Slide (from rocscience Inc.) to obtain the training dataset and MATLAB was used to develop and train the artificial neural network. A detailed investigation to assess and improve the network accuracy was carried out, and it was established that by increasing the neuron numbers and hidden layers, the ultimate average error in predicting the factor of safety for an independent test data set was 0.677%. This error, considering the inherent uncertainty of soil properties, instils confidence in using the Artificial Neural Network for predicting the factor of safety of homogeneous slopes loaded by mobile cranes

Explicit Estimation of Magnitude and Phase Spectra in Parallel for High-Quality Speech Enhancement

Phase information has a significant impact on speech perceptual quality and intelligibility. However, existing speech enhancement methods encounter limitations in explicit phase estimation due to the non-structural nature and wrapping characteristics of the phase, leading to a bottleneck in enhanced speech quality. To overcome the above issue, in this paper, we proposed MP-SENet, a novel Speech Enhancement Network which explicitly enhances Magnitude and Phase spectra in parallel. The proposed MP-SENet adopts a codec architecture in which the encoder and decoder are bridged by time-frequency Transformers along both time and frequency dimensions. The encoder aims to encode time-frequency representations derived from the input distorted magnitude and phase spectra. The decoder comprises dual-stream magnitude and phase decoders, directly enhancing magnitude and wrapped phase spectra by incorporating a magnitude estimation architecture and a phase parallel estimation architecture, respectively. To train the MP-SENet model effectively, we define multi-level loss functions, including mean square error and perceptual metric loss of magnitude spectra, anti-wrapping loss of phase spectra, as well as mean square error and consistency loss of short-time complex spectra. Experimental results demonstrate that our proposed MP-SENet excels in high-quality speech enhancement across multiple tasks, including speech denoising, dereverberation, and bandwidth extension. Compared to existing phase-aware speech enhancement methods, it successfully avoids the bidirectional compensation effect between the magnitude and phase, leading to a better harmonic restoration. Notably, for the speech denoising task, the MP-SENet yields a state-of-the-art performance with a PESQ of 3.60 on the public VoiceBank+DEMAND dataset.Comment: Submmited to IEEE Transactions on Audio, Speech and Language Processin