HBV PreS1-EGFP融合表达质粒高效转化毕赤氏酵母的研究

目的:实现HBV PreS1-EGFP融合表达质粒高效转化毕赤氏酵母.方法:分别将乙肝病毒前S1蛋白和绿色荧光蛋白克隆到表达载体pPIC9K,获得重组质粒pPIC9K-EGFP-preS1,Sac1位点酶切线性化,采用醋酸锂(LiAc)和二硫苏糖醇(DDT)对巴斯德毕赤氏酵母Pichia Pastoris GS115预处理,在电压1.5 kv、电容25 F条件下进行电击转化.电击后立即迅速稀释在冰预冷的1 M的山梨醇中,混匀后取100 l的菌液均匀涂布YNBG平板,利用HIS4标记筛选转化子.结果:发现改进后的转化方法pPIC9K-EGFP-preS1的转化率提高到164×10 4个转化子/1μgpPIC9K-EGFP-preS1 DNA,是传统方法的6.6倍.结论:采用改进后的方法能有效的提高质粒pPIC9K-EGFP-preS1的转化率,为筛选高表达HBV PreS-EGFP融合蛋白的转化子打下基础.     

Tucker Tensor Regression and Neuroimaging Analysis

Neuroimaging data often take the form of high-dimensional arrays, also known as tensors. Addressing scientific questions arising from such data demands new regression models that take multidimensional arrays as covariates. Simply turning an image array into a vector would both cause extremely high dimensionality and destroy the inherent spatial structure of the array. In a recent work, Zhou et al. (J Am Stat Assoc, 108(502):540–552, 2013) proposed a family of generalized linear tensor regression models based upon the CP (CANDECOMP/PARAFAC) decomposition of regression coefficient array. Low-rank approximation brings the ultrahigh dimensionality to a manageable level and leads to efficient estimation. In this article, we propose a tensor regression model based on the more flexible Tucker decomposition. Compared to the CP model, Tucker regression model allows different number of factors along each mode. Such flexibility leads to several advantages that are particularly suited to neuroimaging analysis, including further reduction of the number of free parameters, accommodation of images with skewed dimensions, explicit modeling of interactions, and a principled way of image downsizing. We also compare the Tucker model with CP numerically on both simulated data and real magnetic resonance imaging data, and demonstrate its effectiveness in finite sample performance.     

Subspace Identification and Classification of Healthy Human Gait

Purpose

The classification between different gait patterns is a frequent task in gait assessment. The base vectors were usually found using principal component analysis (PCA) is replaced by an iterative application of the support vector machine (SVM). The aim was to use classifyability instead of variability to build a subspace (SVM space) that contains the information about classifiable aspects of a movement. The first discriminant of the SVM space will be compared to a discriminant found by an independent component analysis (ICA) in the SVM space.

Methods

Eleven runners ran using shoes with different midsoles. Kinematic data, representing the movements during stance phase when wearing the two shoes, was used as input to a PCA and SVM. The data space was decomposed by an iterative application of the SVM into orthogonal discriminants that were able to classify the two movements. The orthogonal discriminants spanned a subspace, the SVM space. It represents the part of the movement that allowed classifying the two conditions. The data in the SVM space was reconstructed for a visual assessment of the movement difference. An ICA was applied to the data in the SVM space to obtain a single discriminant. Cohen''s d effect size was used to rank the PCA vectors that could be used to classify the data, the first SVM discriminant or the ICA discriminant.

Results

The SVM base contains all the information that discriminates the movement of the two shod conditions. It was shown that the SVM base contains some redundancy and a single ICA discriminant was found by applying an ICA in the SVM space.

Conclusions

A combination of PCA, SVM and ICA is best suited to extract all parts of the gait pattern that discriminates between the two movements and to find a discriminant for the classification of dichotomous kinematic data.     

水稻叶绿体转化载体pBS的构建及融合基因功能的初步鉴定

利用PCR方法分别从载体pchN-CAT-Bar和Psk51中分离得到编码膦丝菌素乙酰转移酶基因bar和绿色荧光蛋白基因sgfp,并在bar基因前段连接T7 leader sequence/5′-UTR和T7 10 N-terminus的8个氨基酸序列.运用融合PCR技术,得到bar和sgfp的融合基因片段BS.以水稻叶绿体基因组中的trnI和trnA为同源重组片段,来自烟草叶绿体16S rRNA的Prrn为启动子,psbA基因的TpsbA为终止子,融合片段作为双效筛选标记,构建水稻叶绿体转化载体pBS.大肠杆菌原核表达检测到明显荧光,基因枪轰击TP309愈伤,筛选抗性愈伤.     

Face Recognition Using Sparse Representation-Based Classification on K-Nearest Subspace

The sparse representation-based classification (SRC) has been proven to be a robust face recognition method. However, its computational complexity is very high due to solving a complex -minimization problem. To improve the calculation efficiency, we propose a novel face recognition method, called sparse representation-based classification on k-nearest subspace (SRC-KNS). Our method first exploits the distance between the test image and the subspace of each individual class to determine the nearest subspaces and then performs SRC on the selected classes. Actually, SRC-KNS is able to reduce the scale of the sparse representation problem greatly and the computation to determine the nearest subspaces is quite simple. Therefore, SRC-KNS has a much lower computational complexity than the original SRC. In order to well recognize the occluded face images, we propose the modular SRC-KNS. For this modular method, face images are partitioned into a number of blocks first and then we propose an indicator to remove the contaminated blocks and choose the nearest subspaces. Finally, SRC is used to classify the occluded test sample in the new feature space. Compared to the approach used in the original SRC work, our modular SRC-KNS can greatly reduce the computational load. A number of face recognition experiments show that our methods have five times speed-up at least compared to the original SRC, while achieving comparable or even better recognition rates.     

Membrane Fusion

The fusion of biological membranes results in two bilayer-based membranes merging into a single membrane. In this process the lipids have to undergo considerable rearrangement. The nature of the intermediates that are formed during this rearrangement has been investigated. Certain fusion proteins facilitate this process. In many cases short segments of these fusion proteins have a particularly important role in accelerating the fusion process. Studies of the interaction of model peptides with membranes have allowed for increased understanding at the molecular level of the mechanism of the promotion of membrane fusion by fusion proteins. There is an increased appreciation of the roles of several independent segments of fusion proteins in promoting the fusion process.Many of the studies of the fusion of biological membranes have been done with the fusion of enveloped viruses with other membranes. One reason for this is that the number of proteins involved in viral fusion is relatively simple, often requiring only a single protein. For many enveloped viruses, the structure of their fusion proteins has certain common elements, suggesting that they all promote fusion by an analogous mechanism. Some aspects of this mechanism also appears to be common to intracellular fusion, although several proteins are involved in that process which is more complex and regulated than is fusion.     

Diffusion Tensor Metrics as Biomarkers in Alzheimer's Disease

Background

Although diffusion tensor imaging has been a major research focus for Alzheimer’s disease in recent years, it remains unclear whether it has sufficient stability to have biomarker potential. To date, frequently inconsistent results have been reported, though lack of standardisation in acquisition and analysis make such discrepancies difficult to interpret. There is also, at present, little knowledge of how the biometric properties of diffusion tensor imaging might evolve in the course of Alzheimer’s disease.

Methods

The biomarker question was addressed in this study by adopting a standardised protocol both for the whole brain (tract-based spatial statistics), and for a region of interest: the midline corpus callosum. In order to study the evolution of tensor changes, cross-sectional data from very mild (N = 21) and mild (N = 22) Alzheimer’s disease patients were examined as well as a longitudinal cohort (N = 16) that had been rescanned at 12 months.

Findings and Significance

The results revealed that increased axial and mean diffusivity are the first abnormalities to occur and that the first region to develop such significant differences was mesial parietal/splenial white matter; these metrics, however, remained relatively static with advancing disease indicating they are suitable as ‘state-specific’ markers. In contrast, increased radial diffusivity, and therefore decreased fractional anisotropy–though less detectable early–became increasingly abnormal with disease progression, and, in the splenium of the corpus callosum, correlated significantly with dementia severity; these metrics therefore appear ‘stage-specific’ and would be ideal for monitoring disease progression. In addition, the cross-sectional and longitudinal analyses showed that the progressive abnormalities in radial diffusivity and fractional anisotropy always occurred in areas that had first shown an increase in axial and mean diffusivity. Given that the former two metrics correlate with dementia severity, but the latter two did not, it would appear that increased axial diffusivity represents an upstream event that precedes neuronal loss.     

Diffusion Tensor Magnetic Resonance Imaging of the Pancreas

Purpose

To develop a diffusion-tensor-imaging (DTI) protocol that is sensitive to the complex diffusion and perfusion properties of the healthy and malignant pancreas tissues.

Materials and Methods

Twenty-eight healthy volunteers and nine patients with pancreatic-ductal-adenocacinoma (PDAC), were scanned at 3T with T2-weighted and DTI sequences. Healthy volunteers were also scanned with multi-b diffusion-weighted-imaging (DWI), whereas a standard clinical protocol complemented the PDAC patients’ scans. Image processing at pixel resolution yielded parametric maps of three directional diffusion coefficients λ1, λ2, λ3, apparent diffusion coefficient (ADC), and fractional anisotropy (FA), as well as a λ1-vector map, and a main diffusion-direction map.

Results

DTI measurements of healthy pancreatic tissue at b-values 0,500 s/mm²yielded: λ1 = (2.65±0.35)×10⁻³, λ2 = (1.87±0.22)×10⁻³, λ3 = (1.20±0.18)×10⁻³, ADC = (1.91±0.22)×10⁻³ (all in mm²/s units) and FA = 0.38±0.06. Using b-values of 100,500 s/mm² led to a significant reduction in λ1, λ2, λ3 and ADC (p<.0001) and a significant increase (p<0.0001) in FA. The reduction in the diffusion coefficients suggested a contribution of a fast intra-voxel-incoherent-motion (IVIM) component at b≤100 s/mm², which was confirmed by the multi-b DWI results. In PDACs, λ1, λ2, λ3 and ADC in both 0,500 s/mm² and 100,500 s/mm² b-values sets, as well as the reduction in these diffusion coefficients between the two sets, were significantly lower in comparison to the distal normal pancreatic tissue, suggesting higher cellularity and diminution of the fast-IVIM component in the cancer tissue.

Conclusion

DTI using two reference b-values 0 and 100 s/mm² enabled characterization of the water diffusion and anisotropy of the healthy pancreas, taking into account a contribution of IVIM. The reduction in the diffusion coefficients of PDAC, as compared to normal pancreatic tissue, and the smaller change in these coefficients in PDAC when the reference b-value was modified from 0 to 100 s/mm², helped identifying the presence of malignancy.     

Distinct Requirements for HIV-Cell Fusion and HIV-mediated Cell-Cell Fusion

Whether HIV-1 enters cells by fusing with the plasma membrane or with endosomes is a subject of active debate. The ability of HIV-1 to mediate fusion between adjacent cells, a process referred to as “fusion-from-without” (FFWO), shows that this virus can fuse with the plasma membrane. To compare FFWO occurring at the cell surface with HIV-cell fusion through a conventional entry route, we designed an experimental approach that enabled the measurements of both processes in the same sample. The following key differences were observed. First, a very small fraction of viruses fusing with target cells participated in FFWO. Second, whereas HIV-1 fusion with adherent cells was insensitive to actin inhibitors, post-CD4/coreceptor binding steps during FFWO were abrogated. A partial dependence of HIV-cell fusion on actin remodeling was observed in CD4⁺ T cells, but this effect appeared to be due to the actin dependence of virus uptake. Third, deletion of the cytoplasmic tail of HIV-1 gp41 dramatically enhanced the ability of the virus to promote FFWO, while having a modest effect on virus-cell fusion. Distinct efficiencies and actin dependences of FFWO versus HIV-cell fusion are consistent with the notion that, except for a minor fraction of particles that mediate fusion between the plasma membranes of adjacent cells, HIV-1 enters through an endocytic pathway. We surmise, however, that cell-cell contacts enabling HIV-1 fusion with the plasma membrane could be favored at the sites of high density of target cells, such as lymph nodes.     

Fusion Step-Specific Influence of Cholesterol on SNARE-Mediated Membrane Fusion

Cholesterol is a major component of biological membranes and is known to affect vesicle fusion. However, the mechanism by which cholesterol modulates SNARE-dependent intracellular fusion is not well understood. Using the fluorescence assay and dye-labeled SNAREs and the fluorescent lipids, we dissected cholesterol effects on individual fusion steps including SNARE complex formation, hemifusion, pore formation, and pore dilation. At physiological high concentrations, cholesterol stimulated hemifusion as much as 30-fold, but its stimulatory effect diminished to 10-fold and three-fold for subsequent pore formation and pore expansion at 40 mol %, respectively. The results show that cholesterol serves as a strong stimulator for hemifusion but acts as mild stimulators for pore opening and expansion. Strong stimulation of hemifusion and mild stimulation of pore formation are consistent with the fusion model based on the intrinsic negative curvature of cholesterol. However, even a milder effect of cholesterol on pore expansion is contradictory to such a simple curvature-based prediction. Thus, we speculate that cholesterol also affects the conformation of the transmembrane domains of SNAREs, which modulates the fusion kinetics.     

A Multifaceted Independent Performance Analysis of Facial Subspace Recognition Algorithms

Face recognition has emerged as the fastest growing biometric technology and has expanded a lot in the last few years. Many new algorithms and commercial systems have been proposed and developed. Most of them use Principal Component Analysis (PCA) as a base for their techniques. Different and even conflicting results have been reported by researchers comparing these algorithms. The purpose of this study is to have an independent comparative analysis considering both performance and computational complexity of six appearance based face recognition algorithms namely PCA, 2DPCA, A2DPCA, (2D)²PCA, LPP and 2DLPP under equal working conditions. This study was motivated due to the lack of unbiased comprehensive comparative analysis of some recent subspace methods with diverse distance metric combinations. For comparison with other studies, FERET, ORL and YALE databases have been used with evaluation criteria as of FERET evaluations which closely simulate real life scenarios. A comparison of results with previous studies is performed and anomalies are reported. An important contribution of this study is that it presents the suitable performance conditions for each of the algorithms under consideration.     

Identifying Subspace Gene Clusters from Microarray Data Using Low-Rank Representation

Identifying subspace gene clusters from the gene expression data is useful for discovering novel functional gene interactions. In this paper, we propose to use low-rank representation (LRR) to identify the subspace gene clusters from microarray data. LRR seeks the lowest-rank representation among all the candidates that can represent the genes as linear combinations of the bases in the dataset. The clusters can be extracted based on the block diagonal representation matrix obtained using LRR, and they can well capture the intrinsic patterns of genes with similar functions. Meanwhile, the parameter of LRR can balance the effect of noise so that the method is capable of extracting useful information from the data with high level of background noise. Compared with traditional methods, our approach can identify genes with similar functions yet without similar expression profiles. Also, it could assign one gene into different clusters. Moreover, our method is robust to the noise and can identify more biologically relevant gene clusters. When applied to three public datasets, the results show that the LRR based method is superior to existing methods for identifying subspace gene clusters.     

Fusion of mitochondria

In this communication we reported the fusion of mitochondria of hepatocytes extracted from a rat liver. It was found that fusion occurred at an electric field of 1.56-1.8 kV/cm at room temperature. Further increase in the field strength (greater than 1.8 kV/cm) was accompanied by the breakdown of mitochondria.