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Circulating n-3 PUFA, which integrate endogenous and exogenous n-3 PUFA, can be better used to investigate the relationship between n-3 PUFA and disease. However, studies examining the associations between circulating n-3 PUFA and colorectal cancer (CRC) risk were limited, and the results remained inconclusive. This case–control study aimed to examine the association between serum n-3 PUFA and CRC risk in Chinese population. A total of 680 CRC cases and 680 sex- and age-matched (5-year interval) controls were included. Fatty acids were assayed by GC. OR and 95 % CI were calculated using multivariable logistic regression after adjustment for potential confounders. Higher level of serum α-linolenic acid (ALA), docosapentaenoic acid (DPA), DHA, long-chain n-3 PUFA and total n-3 PUFA were associated with lower odds of CRC. The adjusted OR and 95 % CI were 0·34 (0·24, 0·49, Pfor trend < 0·001) for ALA, 0·57 (0·40, 0·80, Pfor trend < 0·001) for DPA, 0·48 (0·34, 0·68, Pfor trend < 0·001) for DHA, 0·39 (0·27, 0·56, Pfor trend < 0·001) for long-chain n-3 PUFA and 0·31 (0·22, 0·45, Pfor trend < 0·001) for total n-3 PUFA comparing the highest with the lowest quartile. However, there was no statistically significant association between EPA and odds of CRC. Analysis stratified by sex showed that ALA, DHA, long-chain n-3 PUFA and total n-3 PUFA were inversely associated with odds of CRC in both sexes. This study indicated that serum ALA, DPA, DHA, long-chain n-3 PUFA and total n-3 PUFA were inversely associated with odds of having CRC in Chinese population.
We present experimental results of irregular long-crested waves propagating over a submerged trapezoidal bar with the presence of a background current in a wave flume. We investigate the non-equilibrium phenomenon (NEP) induced by significant changes of water depth and mean horizontal flow velocity as wave trains pass over the bar. Using skewness and kurtosis as proxies, we show evidence that an accelerating following current could increase the sea-state non-Gaussianity and enhance both the magnitude and spatial extent of the NEP. We also find that below a ‘saturation relative water depth’ $k_p h_2 \approx 0.5$ ($k_p$ being the peak wavenumber in the shallow area of depth $h_2$), although the NEP manifests, the decrease of the relative water depth does not further enhance the maximum skewness and kurtosis over the bar crest. This work highlights the nonlinear physics according to which a following current could provoke higher freak wave risk in coastal areas where modulation instability plays an insignificant role.
High-molecular-weight glutenin subunits (HMW-GS) contribute to dough elasticity and bread baking quality in wheat. In this study, wheat varieties were classified based on their HMW-GS composition into three groups: 1Dx5 (5 + 10, Gaoyou 8901, Xinmai 28, Xinmai 19, Xinmai 26 and Jinbaoyin), 1Dx2 (2 + 12, Zhoumai 24, Xinmai 9 and Yumai) and 1Dx4 (4 + 12, Aikang 58). Sequence analysis showed that 1Dx-GY8901, 1Dx-XM28, 1Dx-XM19 and 1Dx-XM26 were similar to the 1Dx5 gene and clustered on the same branch, while 1Dx-AK58, 1Dx-ZM24, 1Dx-JBY, 1Dx-YM, 1Dx-XM9 and 1Dx-JBY were more similar to the 1Dx2 gene and clustered on the same branch with 1Dx.2.2. There was a mutation of Ser to Cys at position S2, for an extra Cys in the repeat regions of 1Dx-XM19, 1Dx-XM26, 1Dx-XM28 and 1Dx-GY8901. The wheat HMW-GS genes exhibited similar percentages of α-helix, extended strand, β-turn and random coil structure, with ranges of 13.33–13.59, 4.77–5.78, 7.08–9.18 and 72.3–73.94%, respectively. Sequence conservation and the composition of HMW-GS subunits were also analysed for a series of strong gluten wheat varieties, Xinmai 9 (1, 7 + 8, 2 + 12), Xinmai 19 (1, 7 + 9, 5 + 10), Xinmai 26 (1, 7 + 8, 5 + 10) and Xinmai 28 (1, 7 + 9, 5 + 10). The results of this work should facilitate future breeding efforts and provide the theoretical basis for wheat quality improvement.
This chapter covers other canonical applications of network tomography that have been studied in the literature but fallen out of the scope of the previous chapters. This includes the inference of network routing topology (network topology tomography) and the inference of traffic demands (traffic matrix or origin-destination tomography). It also covers miscellaneous techniques used in network tomography that are not covered in the previous chapters (e.g., network coding). The chapter then concludes the book with discussions on practical issues in the deployment of tomography-based monitoring systems and future directions in addressing these issues.
Additive network tomography, which addresses the inference of link/node performance metrics (e.g., delays) that are additive from the sum metrics on measurement paths, represents the most well-studied branch in the realm of network tomography, upon which a rich body of seminal works have been conducted. This chapter focuses on the case in which the metrics of interest are additive and constant, which allows the network tomography problem to be cast as a linear system inversion problem. After introducing the abstract definitions of link identifiability and network identifiability using linear algebraic conditions, the chapter presents a series of graph-theoretic conditions that establish the necessary and sufficient requirements to achieve identifiability in terms of the number of monitors, the locations of monitors, the connectivity of the network topology, and the routing mechanism. It also contains extended conditions that allow the evaluation of robust link identifiability under failures and partial link identifiability when the network-wide identifiability condition is not satisfied.
This chapter completes the topic of measurement design for additive network tomography, started in Chapter 3, by discussing how to construct suitable measurement paths to identify additive link metrics using a given set of monitors. As in Chapter 3, the focus is on the design of efficient path construction algorithms that make novel use of certain graph algorithms (specifically, algorithms for constructing independent spanning trees) to find a set of paths that form a basis of the link space without enumerating all possible paths. The chapter also discusses a variation of the path construction problem when the number of measurement paths is constrained and each measurement path may fail with certain probability.
Chapters 7 and 8 are designated for network tomography for stochastic link metrics, which is a more fine-grained model than the models of deterministic additive/Boolean metrics, capturing the inherent randomness in link performances at a small time scale. Referred to as stochastic network tomography, these problems are typically cast as parameter estimation problems, which model each link metric as a random variable with a (partially) unknown distribution and aim at inferring the parameters of these distributions from end-to-end measurements. Chapter 7 focuses on one branch of stochastic network tomography that is based on unicast measurements. It introduces a framework based on concepts from estimation theory (e.g., maximum likelihood estimation, Fisher information matrix, Cramér–Rao bound), within which probing experiments and parameter estimators are designed to estimate link parameters from unicast measurements with minimum errors. Closed-form solutions are given for inferring parameters of packet losses (i.e., loss tomography) and packet delay variations (i.e., packet delay variation tomography).
Boolean network tomography is another well-studied branch of network tomography, which addresses the inference of binary performance indicators (e.g., normal vs. failed, or uncongested vs. congested) of internal network elements from the corresponding binary performance indicators on measurement paths. Boolean network tomography fundamentally differs from additive network tomography in that it is a Boolean linear system inversion problem in which each measurement path only provides one bit of information and hence deserves a separate discussion. This chapter introduces a series of identifiability measures (e.g., k-identifiability, maximum identifiability index) to quantify the capability of Boolean network tomography in uniquely detecting and localizing failed/congested network elements. As the definitions of these identifiability measures are combinatorial in nature and hard to verify for large networks, the discussion focuses on polynomial-time verifiable conditions and computable bounds, as well as the associated algorithms.
Based on the conditions for identifying additive link metrics presented in Chapter 2, this chapter addresses two network design questions: (1) Given an unbounded number of monitors, where should they be placed in the network to identify the metrics of all the links using a minimum number of monitors? (2) Given a bounded number of monitors, where should they be placed in the network to identify the metrics of the largest subset of links? The focus here is on the design of intelligent algorithms that can efficiently compute the optimal monitor locations without enumerating all possible monitor placements, achieved through strategic decomposition of the network topology based on the required identifiability conditions. Variations of these algorithms are also given to address cases with predictable or unpredictable topology changes and limited links of interest. In addition to theoretical analysis, empirical results are given to demonstrate the capability of selected algorithms for which such results are available.
In contrast to unicast measurements considered in Chapter 7, this chapter focuses on stochastic network tomography based on multicast measurements, where each probe is sent along a multicast tree from one source to multiple destinations, duplicated at each intermediate node with at least two outgoing links. Using loss tomography as an example, the chapter details how the correlations between the measurements at different destinations sharing links in the multicast tree can be utilized to infer link loss rates, while briefly discussing how this approach applies to other performance metrics. Moreover, this chapter further illustrates how correlated loss observations obtained from multicast probes can be used to reliably infer the topology of the multicast tree, which belongs to another branch of network tomography (network topology tomography) that will be formally introduced in Chapter 9, and complements the high-level discussions there.
This chapter covers preliminary materials required to understand the presentation in the following chapters, including selected definitions from graph theory, linear algebra, and parameter estimation. We also introduce a classification of routing mechanisms based on the controllability of the routing of probes by monitors generating the probes, which will facilitate the discussion in the following chapters.
Based on the identifiability measures for Boolean network tomography presented in Chapter 5, this chapter addresses the follow-up question of how to design the measurement system to optimize the identifiability measure of interest, with a focus on the placement of monitoring nodes. Depending on the mechanism to collect measurements, the problem is divided into (1) monitor placement, (2) beacon placement, and (3) monitoring-aware service placement, where the first approach requires monitoring nodes at both endpoints of each measurement path, the second approach requires a monitoring node only at one of the endpoints of each measurement path, and the third approach requires each measurement path to be the default routing path between a client and a server. As many of such problems are NP-hard, the focus is put on establishing the hardness of the optimal solution and developing polynomial-time suboptimal algorithms with performance guarantees. The chapter also covers a suite of path construction problems addressing how to construct or select measurement paths to optimize the tradeoff between identifiability and probing cost.
Providing the first truly comprehensive overview of Network Tomography - a novel network monitoring approach that makes use of inference techniques to reconstruct the internal network state from external vantage points - this rigorous yet accessible treatment of the fundamental theory and algorithms of network tomography covers the most prominent results demonstrated on real-world data, including identifiability conditions, measurement design algorithms, and network state inference algorithms, alongside practical tools for applying these techniques to real-world network management. It describes the main types of mathematical problems, along with their solutions and properties, and emphasizes the actions that can be taken to improve the accuracy of network tomography. With proofs and derivations introduced in an accessible language for easy understanding, this is an essential resource for professional engineers, academic researchers, and graduate students in network management and network science.
The present study investigated the association between fibre degradation and the concentration of dissolved molecular hydrogen (H2) in the rumen. Napier grass (NG) silage and corn stover (CS) silage were compared as forages with contrasting structures and degradation patterns. In the first experiment, CS silage had greater 48-h DM, neutral-detergent fibre (NDF) and acid-detergent fibre degradation, and total gas and methane (CH4) volumes, and lower 48-h H2 volume than NG silage in 48-h in vitro incubations. In the second experiment, twenty-four growing beef bulls were fed diets including 55 % (DM basis) NG or CS silages. Bulls fed the CS diet had greater DM intake (DMI), average daily gain, total-tract digestibility of OM and NDF, ruminal dissolved methane (dCH4) concentration and gene copies of protozoa, methanogens, Ruminococcus albus and R. flavefaciens, and had lower ruminal dH2 concentration, and molar proportions of valerate and isovalerate, in comparison with those fed the NG diet. There was a negative correlation between dH2 concentration and NDF digestibility in bulls fed the CS diet, and a lack of relationship between dH2 concentration and NDF digestibility with the NG diet. In summary, the fibre of CS silage was more easily degraded by rumen microorganisms than that of NG silage. Increased dCH4 concentration with the CS diet presumably led to the decreased ruminal dH2 concentration, which may be helpful for fibre degradation and growth of fibrolytic micro-organisms in the rumen.