Engineering personalized neural tissue using functionalized transcription factors

Diseases and disorders of the central nervous system often require significant interventions to restore lost function due to their com- plexity. Examples of such disorders include Parkinson＇s disease, Alzheimer＇s disease, multiple sclerosis, traumatic brain injury, and spinal cord in）ury. These diseases and disorders result trom healthy cells being destroyed, which in turn causes dysfunction in the cen- tral nervous system, The death of these cells can trigger a cascade of events that affect the rest of the body, causing symptoms that become progressively worse over time. Developing strategies for repairing the damage to the central nervous system remains chal- lenging, in part due to its inability to regenerate.

Mineralogy,microstructures and geomechanics of rock salt for underground gas storage

Rock salt has excellent properties for its use as underground leak‐proof containers for the storage of renewable energy.Salt solution mining has long been used for salt mining,and can now be employed in the construction of underground salt caverns for the storage of hydrogen gas.This paper presents a wide range of methods to study the mineralogy,geochemistry,microstructure and geomechanical characteristics of rock salt,which are important in the engineering of safe underground storage rock salt caverns.The mineralogical composition of rock salt varies and is linked to its depositional environment and diagenetic alterations.The microstructure in rock salt is related to cataclastic deformation,diffusive mass transfer and intracrystalline plastic deformation,which can then be associated with the macrostructural geomechanical behavior.Compared to other types of rock,rock salt exhibits creep at lower temperatures.This behavior can be divided into three phases based on the changes in strain with time.However,at very low effective confining pressure and high deviatoric stress,rock salt can exhibit dilatant behavior,where brittle deformation could compromise the safety of underground gas storage in rock salt caverns.The proposed review presents the impact of purity,geochemistry and water content of rock salt on its geomechanical behavior,and thus,on the safety of the caverns.

Traffic Flow Merging and Bifurcating at Junction on Two-Lane Highway

In this paper we study the traffic states and jams in vehicular traffic merging and bifurcating at a junction on a two-lane highway. The two-lane traffic model for the vehicular motion at the junction is presented where a jam occurs frequently due to merging, lane changing, and bifurcating. The traffic flow is called the weaving. At the weaving section, vehicles slow down and then move aside on the other lane for changing their direction. We derive the fundamental diagrams (flow-density diagrams) for the weaving traffic flow. The traffic states vary with the density, slowdown speed, and the fraction of vehicles changing the lane. The dynamical phase transitions occur. It is shown that the fundamental diagrams depend highly on the traffic states.

Heat Transfer Optimization for Air-Mist Cooling between a Stack of Parallel Plates

A theoretical model is developed to predict the upper limit heat transfer between a stack of parallel plates subject to multiphase cooling by air-mist flow.The model predicts the optimal separation distance between the plates based on the development of the boundary layers for small and large separation distances,and for dilute mist conditions.Simulation results show the optimal separation distance to be strongly dependent on the liquid-to-air mass flow rate loading ratio,and reach a limit for a critical loading.For these dilute spray conditions,complete evaporation of the droplets takes place.Simulation results also show the optimal separation distance decreases with the increase in the mist flow rate.The proposed theoretical model shall lead to a better understanding of the design of fins spacing in heat exchangers where multiphase spray cooling is used.

Review of micro/nano technologies and theories for electroporation of biological cells

Electroporation (EP) is one of the important techniques for the introduction of genes and drugs into cells with intense pulsed electric field to induce nanometer-sized electropores on cell membranes.Recently,micro/nano technology has been applied to many novel micro EP devices which can not only significantly increase uptake of biomolecules,DNA transfection and cell viability,but also enable large-scale single-cell EP.However,most EP theories developed in the past three decades can not precisely predict the experimental results of EP of biological cells.With the advanced micro EP chips for large-scale single-cell EP experiments,more precise EP theoretical models can be developed to describe the complicated multiscale dynamic behavior of EP.

Shock Wave Induced Separation Control By Streamwise Vortices

Control of shock wave and boundary layer interaction finds still a lot of attention. Methods of this interaction control have been especially investigated in recent decade. This research was mostly concerned with flows without separation. However, in many applications shock waves induce separation often leads to strong unsteady effects. In this context it is proposed to use streamwise vortices for the interaction control. The results of experimental investigations are presented here. The very promising results were obtained, meaning that the incipient separation was postponed and the separation size was reduced for the higher Mach numbers. The decrease of the RMS of average shock wave oscillation was also achieved.

A Novel Method of Gene Regulatory Network Structure Inference from Gene Knock-Out Expression Data

Inferring Gene Regulatory Networks(GRNs) structure from gene expression data has been a challenging problem in systems biology. It is critical to identify complicated regulatory relationships among genes for understanding regulatory mechanisms in cells. Various methods based on information theory have been developed to infer GRNs. However, these methods introduce many redundant regulatory relationships in the network inference process due to external noise in the original data, topology sparseness in the network structure, and non-linear dependency among genes. Especially as the network size increases, the performance of these methods decreases dramatically. In this paper, a novel network structure inference method named Loc-PCA-CMI is proposed that first identifies local overlapped gene clusters, and then infers the local network structure for each cluster by a Path Consistency Algorithm based on Conditional Mutual Information(PCA-CMI). The final structure of the GRN is denoted as dependence among genes by an ensemble of the obtained local network structures. Loc-PCA-CMI was evaluated on DREAM3 knock-out datasets, and its performance was compared to other information theorybased network inference methods including ARACNE, MRNET, PCA-CMI, and PCA-PMI. Experimental results demonstrate our novel method Loc-PCA-CMI outperforms the other four methods in DREAM3 datasets especially in size 50 and 100 networks.

Transonic Instability in Entrance Part of Mixing Chamber of High-Speed Ejector

The paper deals with experimental and numerical study of flow structure in two-dimensional model of supersonic ejector. Schlieren pictures of flow structure were taken, static pressure distributions on side wall were measured and Fluent software to calculate the flow structure in supersonic ejector was used. For certain region of back pressure ratio pb/p02 and stagnation pressure ratio of both streams p01/p02 the terminal shock wave is close to the trailing edge of the primary flow nozzle and a transonic instability occurs. The instability causes the movement of position of boundary layer separation, the structure of shock waves changes and all flow structure oscillates. The movie of these regimes during schlieren experiments was taken and relevant numerical modelling was performed.

A Reliable Neighbor-Based Method for Identifying Essential Proteins by Integrating Gene Expressions, Orthology,and Subcellular Localization Information

Essential proteins are those necessary for the survival or reproduction of species and discovering such essential proteins is fundamental for understanding the minimal requirements for cellular life, which is also meaningful to the disease study and drug design. With the development of high-throughput techniques, a large number of Protein-Protein Interactions（PPIs） can be used to identify essential proteins at the network level. Up to now, though a series of network-based computational methods have been proposed, it is still a challenge to improve the prediction precision as the high false positives in PPI networks. In this paper, we propose a new method GOS to identify essential proteins by integrating the Gene expressions, Orthology, and Subcellular localization information.The gene expressions and subcellular localization information are used to determine whether a neighbor in the PPI network is reliable. Only reliable neighbors are considered when we analyze the topological characteristics of a protein in a PPI network. We also analyze the orthologous attributes of each protein to reflect its conservative features, and use a random walk model to integrate a protein＇s topological characteristics and its orthology. The experimental results on the yeast PPI network show that the proposed method GOS outperforms the ten existing methods DC, BC, CC, SC, EC, IC, NC, Pe C, ION, and CSC.

Computational Approaches for Prioritizing Candidate Disease Genes Based on PPI Networks

With the continuing development and improvement of genome-wide techniques, a great number of candidate genes are discovered. How to identify the most likely disease genes among a large number of candidates becomes a fundamental challenge in human health. A common view is that genes related to a specific or similar disease tend to reside in the same neighbourhood of biomolecular networks. Recently, based on such observations,many methods have been developed to tackle this challenge. In this review, we firstly introduce the concept of disease genes, their properties, and available data for identifying them. Then we review the recent computational approaches for prioritizing candidate disease genes based on Protein-Protein Interaction（PPI） networks and investigate their advantages and disadvantages. Furthermore, some pieces of existing software and network resources are summarized. Finally, we discuss key issues in prioritizing candidate disease genes and point out some future research directions.

A Survey of MRI-Based Brain Tumor Segmentation Methods

Brain tumor segmentation aims to separate the different tumor tissues such as active cells, necrotic core,and edema from normal brain tissues of White Matter（WM）, Gray Matter（GM）, and Cerebrospinal Fluid（CSF）. MRIbased brain tumor segmentation studies are attracting more and more attention in recent years due to non-invasive imaging and good soft tissue contrast of Magnetic Resonance Imaging（MRI） images. With the development of almost two decades, the innovative approaches applying computer-aided techniques for segmenting brain tumor are becoming more and more mature and coming closer to routine clinical applications. The purpose of this paper is to provide a comprehensive overview for MRI-based brain tumor segmentation methods. Firstly, a brief introduction to brain tumors and imaging modalities of brain tumors is given. Then, the preprocessing operations and the state of the art methods of MRI-based brain tumor segmentation are introduced. Moreover, the evaluation and validation of the results of MRI-based brain tumor segmentation are discussed. Finally, an objective assessment is presented and future developments and trends are addressed for MRI-based brain tumor segmentation methods.

De Novo Assembly Methods for Next Generation Sequencing Data

The recent breakthroughs in next-generation sequencing technologies, such as those of Roche 454,Illumina/Solexa, and ABI SOLID, have dramatically reduced the cost of producing short reads of the genome of new species. The huge volume of reads, along with short read length, high coverage, and sequencing errors, poses a great challenge to de novo genome assembly. However, the paired-end information provides a new solution to these problems. In this paper, we review and compare some current assembly tools, including Newbler, CAP3, Velvet,SOAPdenovo, AllPaths, Abyss, IDBA, PE-Assembly, and Telescoper. In general, we compare the seed extension and graph-based methods that use the overlap/lapout/consensus approach and the de Bruijn graph approach for assembly. At the end of the paper, we summarize these methods and discuss the future directions of genome assembly.