Metabolic fingerprinting of Angelica sinensis during growth using UPLC-TOFMS and chemometrics data analysis

BACKGROUND: The radix of Angelica sinensis is widely used as a medicinal herbal and metabolomics research of this plant during growth is necessary. RESULTS: Principal component analysis of the UPLC-QTOFMS data showed that these 27 samples could be separated into 4 different groups. The chemical markers accounting for these separations were identified from the PCA loadings plot. These markers were further verified by accurate mass tandem mass and retention times of available reference standards. The study has shown that accumulation of secondary metabolites of Angelica sinensis is closely related to the growth periods. CONCLUSIONS: The UPLC-QTOFMS based metabolomics approach has great potential for analysis of the alterations of secondary metabolites of Angelica sinensis during growth

Computational Complexity of Atomic Chemical Reaction Networks

Informally, a chemical reaction network is "atomic" if each reaction may be interpreted as the rearrangement of indivisible units of matter. There are several reasonable definitions formalizing this idea. We investigate the computational complexity of deciding whether a given network is atomic according to each of these definitions. Our first definition, primitive atomic, which requires each reaction to preserve the total number of atoms, is to shown to be equivalent to mass conservation. Since it is known that it can be decided in polynomial time whether a given chemical reaction network is mass-conserving, the equivalence gives an efficient algorithm to decide primitive atomicity. Another definition, subset atomic, further requires that all atoms are species. We show that deciding whether a given network is subset atomic is in $\textsf{NP}$, and the problem "is a network subset atomic with respect to a given atom set" is strongly $\textsf{NP}$-$\textsf{Complete}$. A third definition, reachably atomic, studied by Adleman, Gopalkrishnan et al., further requires that each species has a sequence of reactions splitting it into its constituent atoms. We show that there is a $\textbf{polynomial-time algorithm}$ to decide whether a given network is reachably atomic, improving upon the result of Adleman et al. that the problem is $\textbf{decidable}$. We show that the reachability problem for reachably atomic networks is $\textsf{Pspace}$-$\textsf{Complete}$. Finally, we demonstrate equivalence relationships between our definitions and some special cases of another existing definition of atomicity due to Gnacadja

Liver resection or combined chemoembolization and radiofrequency ablation improve survival in patients with hepatocellular carcinoma

Background/ Aims: To evaluate the long-term outcome of surgical and non-surgical local treatments of patients with hepatocellular carcinoma (HCC). Methods: We stratified a cohort of 278 HCC patients using six independent predictors of survival according to the Vienna survival model for HCC (VISUM- HCC). Results: Prior to therapy, 224 HCC patients presented with VISUM stage 1 (median survival 18 months) while 29 patients were classified as VISUM stage 2 (median survival 4 months) and 25 patients as VISUM stage 3 (median survival 3 months). A highly significant (p < 0.001) improved survival time was observed in VISUM stage 1 patients treated with liver resection ( n = 52; median survival 37 months) or chemoembolization (TACE) and subsequent radiofrequency ablation ( RFA) ( n = 44; median survival 45 months) as compared to patients receiving chemoembolization alone (n = 107; median survival 13 months) or patients treated by tamoxifen only (n = 21; median survival 6 months). Chemoembolization alone significantly (p <= 0.004) improved survival time in VISUM stage 1 - 2 patients but not (p = 0.341) in VISUM stage 3 patients in comparison to those treated by tamoxifen. Conclusion: Both liver resection or combined chemoembolization and RFA improve markedly the survival of patients with HCC

Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI

Magnetic resonance imaging (MRI) using hyperpolarized noble gases provides a way to visualize the structure and function of human lung, but the long imaging time limits its broad research and clinical applications. Deep learning has demonstrated great potential for accelerating MRI by reconstructing images from undersampled data. However, most existing deep conventional neural networks (CNN) directly apply square convolution to k-space data without considering the inherent properties of k-space sampling, limiting k-space learning efficiency and image reconstruction quality. In this work, we propose an encoding enhanced (EN2) complex CNN for highly undersampled pulmonary MRI reconstruction. EN2 employs convolution along either the frequency or phase-encoding direction, resembling the mechanisms of k-space sampling, to maximize the utilization of the encoding correlation and integrity within a row or column of k-space. We also employ complex convolution to learn rich representations from the complex k-space data. In addition, we develop a feature-strengthened modularized unit to further boost the reconstruction performance. Experiments demonstrate that our approach can accurately reconstruct hyperpolarized 129Xe and 1H lung MRI from 6-fold undersampled k-space data and provide lung function measurements with minimal biases compared with fully-sampled image. These results demonstrate the effectiveness of the proposed algorithmic components and indicate that the proposed approach could be used for accelerated pulmonary MRI in research and clinical lung disease patient care

Topological Surface States Protected From Backscattering by Chiral Spin Texture

Topological insulators are a new class of insulators in which a bulk gap for electronic excitations is generated by strong spin orbit coupling. These novel materials are distinguished from ordinary insulators by the presence of gapless metallic boundary states, akin to the chiral edge modes in quantum Hall systems, but with unconventional spin textures. Recently, experiments and theoretical efforts have provided strong evidence for both two- and three-dimensional topological insulators and their novel edge and surface states in semiconductor quantum well structures and several Bi-based compounds. A key characteristic of these spin-textured boundary states is their insensitivity to spin-independent scattering, which protects them from backscattering and localization. These chiral states are potentially useful for spin-based electronics, in which long spin coherence is critical, and also for quantum computing applications, where topological protection can enable fault-tolerant information processing. Here we use a scanning tunneling microscope (STM) to visualize the gapless surface states of the three-dimensional topological insulator BiSb and to examine their scattering behavior from disorder caused by random alloying in this compound. Combining STM and angle-resolved photoemission spectroscopy, we show that despite strong atomic scale disorder, backscattering between states of opposite momentum and opposite spin is absent. Our observation of spin-selective scattering demonstrates that the chiral nature of these states protects the spin of the carriers; they therefore have the potential to be used for coherent spin transport in spintronic devices.Comment: to be appear in Nature on August 9, 200

A self-organized model for cell-differentiation based on variations of molecular decay rates

Systemic properties of living cells are the result of molecular dynamics governed by so-called genetic regulatory networks (GRN). These networks capture all possible features of cells and are responsible for the immense levels of adaptation characteristic to living systems. At any point in time only small subsets of these networks are active. Any active subset of the GRN leads to the expression of particular sets of molecules (expression modes). The subsets of active networks change over time, leading to the observed complex dynamics of expression patterns. Understanding of this dynamics becomes increasingly important in systems biology and medicine. While the importance of transcription rates and catalytic interactions has been widely recognized in modeling genetic regulatory systems, the understanding of the role of degradation of biochemical agents (mRNA, protein) in regulatory dynamics remains limited. Recent experimental data suggests that there exists a functional relation between mRNA and protein decay rates and expression modes. In this paper we propose a model for the dynamics of successions of sequences of active subnetworks of the GRN. The model is able to reproduce key characteristics of molecular dynamics, including homeostasis, multi-stability, periodic dynamics, alternating activity, differentiability, and self-organized critical dynamics. Moreover the model allows to naturally understand the mechanism behind the relation between decay rates and expression modes. The model explains recent experimental observations that decay-rates (or turnovers) vary between differentiated tissue-classes at a general systemic level and highlights the role of intracellular decay rate control mechanisms in cell differentiation.Comment: 16 pages, 5 figure

Intervention in gene regulatory networks via greedy control policies based on long-run behavior

<p>Abstract</p> <p>Background</p> <p>A salient purpose for studying gene regulatory networks is to derive intervention strategies, the goals being to identify potential drug targets and design gene-based therapeutic intervention. Optimal stochastic control based on the transition probability matrix of the underlying Markov chain has been studied extensively for probabilistic Boolean networks. Optimization is based on minimization of a cost function and a key goal of control is to reduce the steady-state probability mass of undesirable network states. Owing to computational complexity, it is difficult to apply optimal control for large networks.</p> <p>Results</p> <p>In this paper, we propose three new greedy stationary control policies by directly investigating the effects on the network long-run behavior. Similar to the recently proposed mean-first-passage-time (MFPT) control policy, these policies do not depend on minimization of a cost function and avoid the computational burden of dynamic programming. They can be used to design stationary control policies that avoid the need for a user-defined cost function because they are based directly on long-run network behavior; they can be used as an alternative to dynamic programming algorithms when the latter are computationally prohibitive; and they can be used to predict the best control gene with reduced computational complexity, even when one is employing dynamic programming to derive the final control policy. We compare the performance of these three greedy control policies and the MFPT policy using randomly generated probabilistic Boolean networks and give a preliminary example for intervening in a mammalian cell cycle network.</p> <p>Conclusion</p> <p>The newly proposed control policies have better performance in general than the MFPT policy and, as indicated by the results on the mammalian cell cycle network, they can potentially serve as future gene therapeutic intervention strategies.</p

Disentangling the effects of post-entry speed of internationalization on INVs’ export performance

This paper aims to explore the under-researched topic of post-entry speed of internationalization (PSI) in the context of international new ventures (INVs). We unbundle PSI and examine its relationship with both financial and non-financial export performance, considering three related, but conceptually distinct, dimensions of PSI: internationalization intensity, spread, and geographical diversity. Building on organizational learning theory, we highlight different mechanisms that contribute to post-entry performance outcomes among INVs. Our findings from a sample of 112 INVs in New Zealand provide evidence that the three dimensions of PSI are distinct and that they have different impacts on financial and non-financial export performance. This paper contributes to the limited, yet growing body of literature on PSI by providing a deeper understanding of PSI and its constituent dimensions. In addition, this study offers new theoretical insights into how and why different dimensions of post-entry speed of internationalization can contribute to stronger export performance

Fungal iron availability during deep seated candidiasis is defined by a complex interplay involving systemic and local events

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