Analysis of Metabolic Capabilities Using Singular Value Decomposition of Extreme Pathway Matrices

It is now possible to construct genome-scale metabolic networks for particular microorganisms. Extreme pathway analysis is a useful method for analyzing the phenotypic capabilities of these networks. Many extreme pathways are needed to fully describe the functional capabilities of genome-scale metabolic networks, and therefore, a need exists to develop methods to study these large sets of extreme pathways. Singular value decomposition (SVD) of matrices of extreme pathways was used to develop a conceptual framework for the interpretation of large sets of extreme pathways and the steady-state flux solution space they define. The key results of this study were: 1), convex steady-state solution cones describing the potential functions of biochemical networks can be studied using the modes generated by SVD; 2), Helicobacter pylori has a more rigid metabolic network (i.e., a lower dimensional solution space and a more dominant first singular value) than Haemophilus influenzae for the production of amino acids; and 3), SVD allows for direct comparison of different solution cones resulting from the production of different amino acids. SVD was used to identify key network branch points that may identify key control points for regulation. Therefore, SVD of matrices of extreme pathways has proved to be a useful method for analyzing the steady-state solution space of genome-scale metabolic networks.     

Asymptotic Conditional Singular Value Decomposition for High‐Dimensional Genomic Data

Summary High‐dimensional data, such as those obtained from a gene expression microarray or second generation sequencing experiment, consist of a large number of dependent features measured on a small number of samples. One of the key problems in genomics is the identification and estimation of factors that associate with many features simultaneously. Identifying the number of factors is also important for unsupervised statistical analyses such as hierarchical clustering. A conditional factor model is the most common model for many types of genomic data, ranging from gene expression, to single nucleotide polymorphisms, to methylation. Here we show that under a conditional factor model for genomic data with a fixed sample size, the right singular vectors are asymptotically consistent for the unobserved latent factors as the number of features diverges. We also propose a consistent estimator of the dimension of the underlying conditional factor model for a finite fixed sample size and an infinite number of features based on a scaled eigen‐decomposition. We propose a practical approach for selection of the number of factors in real data sets, and we illustrate the utility of these results for capturing batch and other unmodeled effects in a microarray experiment using the dependence kernel approach of Leek and Storey (2008, Proceedings of the National Academy of Sciences of the United States of America 105 , 18718–18723) .     

Singular Value Decomposition of the Radial Distribution Function for Hard Sphere and Square Well Potentials

We compute the singular value decomposition of the radial distribution function for hard sphere, and square well solutions. We find that decomposes into a small set of basis vectors allowing for an extremely accurate representation at all interpolated densities and potential strengths. In addition, we find that the coefficient vectors describing the magnitude of each basis vector are well described by a low-order polynomial. We provide a program to calculate in this compact representation for the investigated parameter range.     

Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the Generalized Singular Value Decomposition

Background

One of the major goals in gene and protein expression profiling of cancer is to identify biomarkers and build classification models for prediction of disease prognosis or treatment response. Many traditional statistical methods, based on microarray gene expression data alone and individual genes' discriminatory power, often fail to identify biologically meaningful biomarkers thus resulting in poor prediction performance across data sets. Nonetheless, the variables in multivariable classifiers should synergistically interact to produce more effective classifiers than individual biomarkers.

Results

We developed an integrated approach, namely network-constrained support vector machine (netSVM), for cancer biomarker identification with an improved prediction performance. The netSVM approach is specifically designed for network biomarker identification by integrating gene expression data and protein-protein interaction data. We first evaluated the effectiveness of netSVM using simulation studies, demonstrating its improved performance over state-of-the-art network-based methods and gene-based methods for network biomarker identification. We then applied the netSVM approach to two breast cancer data sets to identify prognostic signatures for prediction of breast cancer metastasis. The experimental results show that: (1) network biomarkers identified by netSVM are highly enriched in biological pathways associated with cancer progression; (2) prediction performance is much improved when tested across different data sets. Specifically, many genes related to apoptosis, cell cycle, and cell proliferation, which are hallmark signatures of breast cancer metastasis, were identified by the netSVM approach. More importantly, several novel hub genes, biologically important with many interactions in PPI network but often showing little change in expression as compared with their downstream genes, were also identified as network biomarkers; the genes were enriched in signaling pathways such as TGF-beta signaling pathway, MAPK signaling pathway, and JAK-STAT signaling pathway. These signaling pathways may provide new insight to the underlying mechanism of breast cancer metastasis.

Conclusions

We have developed a network-based approach for cancer biomarker identification, netSVM, resulting in an improved prediction performance with network biomarkers. We have applied the netSVM approach to breast cancer gene expression data to predict metastasis in patients. Network biomarkers identified by netSVM reveal potential signaling pathways associated with breast cancer metastasis, and help improve the prediction performance across independent data sets.     

Amplifying the Interaction Between Two Identical Metallic Nanoparticles with a Large Interface Distance Based on the Strong Coupling-Like Phenomenon Involving Molecular J-aggregates

We introduce a new way to amplify the interaction between two identical metallic nanoparticles with a large interface distance (≥the radius of each nanoparticle). The proposed structure consists of two identical metallic nanoparticles embedded in molecular J-aggregates and the strong coupling-like phenomenon is described by the scattering spectra. Finite difference time domain (FDTD) method is employed to simulate this structure and the simulation results match the experiment well (Eizner et al., Nano Lett 15:6215–6221 2015; Lin et al., Nano Lett 15:4699–4703 2015; Zengin et al., Phys Rev Lett 114:157401 2015). Molecular J-aggregates take important roles in the strong coupling-like phenomenon and can be used to amplify the interaction between the particles. The scattering spectra of this proposed structure have two separated peaks, whose shifts are larger than those in the air with the interface distance decreasing. The coupling strength between the nanoparticles and the amplification of the interaction can be tuned by the incident polarization. This structure has potential applications in the field of quantum communications such as the quantum network, the quantum key distributions, and so on.

     

花椰菜主要经济性状的配合力及遗传效应研究

采用Griffing 6×6完全双列杂交设计,对长江流域内秋冬栽培的6个主要花椰菜（Brassica oleraceaLinn.var.botrytis Linn.）资源的自交系育种材料的球高、球径、单球重、成熟期、毛花率和内叶盖球度等6个花球主要经济性状进行一般配合力（GCA）和特殊配合力（SCA）分析,并对花椰菜的各个性状进行了遗传相关性分析。结果表明：花椰菜的6个经济性状同时受加性和非加性效应控制。其中成熟期、毛花率、球高和内叶盖球度性状的一般配合力大于特殊配合力,受加性基因效应影响大;而球径、单球重等性状间一般配合力小于特殊配合力,在相当程度上受非加性基因控制。花椰菜各性状的狭义遗传力由大到小依次为：成熟期、毛花率、内叶盖球度、球高、球径和单球重。花椰菜的球高与单球重、球径与单球重、球高与成熟期、单球重与内叶盖球度、成熟期与内叶盖球度等5个成对性状应该同时选择;而球高与毛花率、球高与内叶盖球度、球径与成熟期、球径与毛花率、单球重与毛花率、成熟期与毛花率、毛花率与内叶盖球度等7个成对性状的选择可以独立进行,彼此间不会相互影响,可实现同一组合或品种具备多种优质性状。在6个自交系中,P2和P5是理想亲本;P4是配制耐寒组合的亲本材料;亲本P6应用价值最低。组合中表现最好的为P2×P4,各性状的特殊配合力均较优。     

TRiFLe, a Program for In Silico Terminal Restriction Fragment Length Polymorphism Analysis with User-Defined Sequence Sets

We describe TRiFLe, a freely accessible computer program that generates theoretical terminal restriction fragments (T-RFs) from any user-supplied sequence set tailored to a particular group of organisms, sequences from clone libraries, or sequences from specific genes. The program allows a rapid identification of the most polymorphic enzymes, creates a collection of T-RFs for the data set, and can potentially identify specific T-RFs in T-RF length polymorphism (T-RFLP) patterns by comparing theoretical and experimental results. TRiFLE was used for analyzing T-RFLP data generated for the amoA and pmoA genes. The peaks identified in the T-RFLP patterns show an overlap of ammonia- and methane-oxidizing bacteria in the metalimnion of a subtropical lake.     

Mixture与响应面法结合开发BHK-21细胞无血清悬浮培养基

采用Mixture与响应面实验设计相结合的方法开发BHK-21细胞无血清悬浮培养基。在实验室已知配方的6种培养基A-F的基础上,通过Mixture实验筛选出BHK-21细胞无血清培养基的最优组合为A∶B∶C∶D∶E∶F=0∶0∶11∶0∶9∶0。利用响应面法针对培养基中的几种关键组分进行浓度优化,确定谷氨酰胺、酪氨酸、牛血清白蛋白和钙离子的最优浓度分别为3 mmol/L、2.5 g/L、0 g/L和0 mmol/L。该无血清悬浮培养基能很好地支持BHK细胞悬浮生长,培养时细胞最大活细胞密度可达140.21×105个/m L,比商业培养基提高了1.95倍。采用Mixture试验设计和响应面分析法能够在较短的时间内开发出适合BHK-21细胞生长的无血清悬浮培养基,为采用BHK-21细胞大规模工业化生产口蹄疫疫苗奠定了基础。     

Combining the Finite Element Method with Structural Connectome-based Analysis for Modeling Neurotrauma: Connectome Neurotrauma Mechanics

This article presents the integration of brain injury biomechanics and graph theoretical analysis of neuronal connections, or connectomics, to form a neurocomputational model that captures spatiotemporal characteristics of trauma. We relate localized mechanical brain damage predicted from biofidelic finite element simulations of the human head subjected to impact with degradation in the structural connectome for a single individual. The finite element model incorporates various length scales into the full head simulations by including anisotropic constitutive laws informed by diffusion tensor imaging. Coupling between the finite element analysis and network-based tools is established through experimentally-based cellular injury thresholds for white matter regions. Once edges are degraded, graph theoretical measures are computed on the "damaged" network. For a frontal impact, the simulations predict that the temporal and occipital regions undergo the most axonal strain and strain rate at short times (less than 24 hrs), which leads to cellular death initiation, which results in damage that shows dependence on angle of impact and underlying microstructure of brain tissue. The monotonic cellular death relationships predict a spatiotemporal change of structural damage. Interestingly, at 96 hrs post-impact, computations predict no network nodes were completely disconnected from the network, despite significant damage to network edges. At early times ([Formula: see text]) network measures of global and local efficiency were degraded little; however, as time increased to 96 hrs the network properties were significantly reduced. In the future, this computational framework could help inform functional networks from physics-based structural brain biomechanics to obtain not only a biomechanics-based understanding of injury, but also neurophysiological insight.     

基于流域复合生态系统阈值特性的动态赋权理论分析

本文基于流域开发复合生态环境系统累积环境效应的产生机理,以及系统的非线性动力学特征和累积环境效应反应过程特征曲线,提出了时序多指标决策理论模式,建立了复合生态环境效应的动态监测因子确定评判标度的量化模型.建立的动态决策理论是在决策空间和目标空间基础上,引入时间和空间参数,使决策过程与结果充分体现了时序特征,对生态系统规划决策和区域环境长期或不定期动态评估具有一定的指导.     

利用SSR标记分析水稻亲本间遗传距离与杂种优势的关系

利用5个光温敏核不育系与40个恢复系（品种）配制了200个组合,应用SSR标记估算了这5个不育系与40个恢复系之间的遗传距离,分析了遗传距离与杂种优势的关系。结果表明：（1）不同材料、不同遗传距离范围之间,遗传距离与单株产量以及有效穗数、穗长、每穗粒敷、着粒密度、结实率、千粒重、单株产量7个性状超亲优势的相关性有很大差别,表现出很复杂的关系。（2）田丰S与父本遗传距离在0．6286～2．5257之间时,F1单株产量及其超亲优势与遗传距离极显著相关;培矮64S与父本遗传距离在0．8247～1．5315之间时,F1单株产量与遗传距离显著相关。（3）所有两系组合亲本间遗传距离在0．5333～1．5之间时,F1单株产量超亲优势与遗传距离显著相关;遗传距离在0．5333～1．0之间时,F1单株产量与遗传距离显著相关,遗传距离分别在1．0～1．5、0．5333～1．5和0．5333～2．5257之间时极显著相关。（4）另外,F1单株产量与遗传距离的相关程度普遍高于其超亲优势与遗传距离的相关程度。     

Directional Variance Adjustment: Bias Reduction in Covariance Matrices Based on Factor Analysis with an Application to Portfolio Optimization

Robust and reliable covariance estimates play a decisive role in financial and many other applications. An important class of estimators is based on factor models. Here, we show by extensive Monte Carlo simulations that covariance matrices derived from the statistical Factor Analysis model exhibit a systematic error, which is similar to the well-known systematic error of the spectrum of the sample covariance matrix. Moreover, we introduce the Directional Variance Adjustment (DVA) algorithm, which diminishes the systematic error. In a thorough empirical study for the US, European, and Hong Kong stock market we show that our proposed method leads to improved portfolio allocation.     

古拉定联合三七脂肝丸治疗酒精性脂肪肝的疗效分析

目的探讨古拉定联合三七脂肝丸治疗酒精性脂肪肝的临床效果。方法选取我院2013年2月~2015年2月收治的酒精性脂肪肝患者90例为研究对象,按照抽签法随机分为观察组和对照组各45例。观察组患者采用古拉定联合三七脂肝丸治疗,对照组患者采用甘草酸二铵和硫普罗宁治疗,观察两组的治疗效果和并发症的发生情况。结果观察组患者的治疗总有效率为95.6%,明显优于对照组的80.0%,差异有统计学意义(χ2=5.0748,P0.05);两组并发症发生率比较,观察组为8.9%,对照组28.9%,差异具有统计学意义(P0.05)。结论采用古拉定联合三七脂肝丸治疗酒精性脂肪肝的临床效果显著,可有效缓解患者的病痛,降低并发症发生率,具有较高的临床应用价值。     

Combining Multi-Element Analysis with Statistical Modeling for Tracing the Origin of Green Coffee Beans from Amhara Region,Ethiopia

Biological Trace Element Research - Characterization of coffee terroirs is important to determine authenticity and provide confidence for consumers to select the right product. In this regard,...     

Relationship of Parental Genetic Distance with Heterosis and Specific Combining Ability in Sesame (<Emphasis Type="Italic">Sesamum indicum</Emphasis> L.) Based on Phenotypic and Molecular Marker Analysis

The genetic distance analysis for selection of suitable parents has been established and effectively used in many crops; however, there is dearth of conclusive report of relationship of genetic distance analysis with heterosis in sesame. In the present study, an attempt was made to estimate the associations of genetic distances using SSR (GDSSR), seed-storage protein profiling (GDSDS) and agro-morphological traits (GDMOR) with hybrid performance. Seven parents were selected from 60 exotic and Indian genotypes based on genetic distance from clustering pattern based on SSR, seed-storage protein, morphological traits and per se performance. For combining ability analysis, 7 parents and 21 crosses generated from 7 × 7 half diallel evaluated at two environments in a replicated field trial during pre-kharif season of 2013. Compared with the average parents yield (12.57 g plant^?1), eight hybrids had a significant (P < 0.01) yield advantage across environments, with averages of 26.94 and 29.99% for better-parent heterosis (BPH) and mid-parent heterosis (MPH), respectively, across environments. Highly significant positive correlation was observed between specific combining ability (SCA) and per se performance (0.97), while positive non-significant correlation of BPH with GDSSR (0.048), and non-significant negative correlations with GDMOR (? 0.01) and GDSDS (? 0.256) were observed. The linear regressions of SCA on MPH, BPH and per se performance of F₁s were significant with R² value of 0.88, 0.84 and 0.95 respectively. The present findings revealed a weak association of GDSSR with F₁’s performance; however, SCA has appeared as an important factor in the determination of heterosis and per se performance of the hybrids. The present findings also indicated that parental divergence in the intermediate group would likely produce high heterotic crosses in sesame.     

Screening of Potential Biomarkers for Gastric Cancer with Diagnostic Value Using Label-free Global Proteome Analysis

Gastric cancer (GC) is known as a top malignant type of tumors worldwide. Despite the recent decrease in mortality rates, the prognosis remains poor. Therefore, it is necessary to find novel biomarkers with early diagnostic value for GC. In this study, we present a large-scale proteomic analysis of 30 GC tissues and 30 matched healthy tissues using label-free global proteome profiling. Our results identified 537 differentially expressed proteins, including 280 upregulated and 257 downregulated proteins. The ingenuity pathway analysis (IPA) results indicated that the sirtuin signaling pathway was the most activated pathway in GC tissues whereas oxidative phosphorylation was the most inhibited. Moreover, the most activated molecular function was cellular movement, including tissue invasion by tumor cell lines. Based on IPA results, 15 hub proteins were screened. Using the receiver operating characteristic curve, most of hub proteins showed a high diagnostic power in distinguishing between tumors and healthy controls. A four-protein (ATP5B-ATP5O-NDUFB4-NDUFB8) diagnostic signature was built using a random forest model. The area under the curve (AUC) values of this model were 0.996 and 0.886 for the training and testing sets, respectively, suggesting that the four-protein signature has a high diagnostic power. This signature was further tested with independent datasets using plasma enzyme-linked immune sorbent assays, resulting in an AUC value of 0.778 for distinguishing GC tissues from healthy controls, and using immunohistochemical tissue microarray analysis, resulting in an AUC value of 0.805. In conclusion, this study identifies potential biomarkers and improves our understanding of the pathogenesis, providing novel therapeutic targets for GC.     

CT诊断用于胰腺癌侵犯胰周动静脉的临床价值分析

目的：探究CT诊断对于胰腺癌侵犯胰周动静脉的临床价值。方法：随机选取在我院就诊的64例胰腺癌患者,在他们进行手术前全在距离肿瘤边缘1cm内的血管进行分期和诊断进而进行螺旋CT检查。结果：经组织学术后病理切片染色发现胰周动脉29条,静脉48条。运用外科手术探查方法发现86条胰周动脉,89条胰周静脉。在这些血管中,有23条动脉、47条静脉经外科手术证实的确是肿瘤侵犯,并且经过CT诊断,我们最终断定为有25条动脉、46条静脉处于1～4级。结论：胰周动、静脉受到侵犯时,具有不同的CT表现特征,因此在利用CT方法判断胰周动、静脉遭受侵犯时应当根据不同情况不同对待。