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New technologies reveal the deposition and remodeling of plant cell wall polysaccharides and their impact on plant development.

The extracellular matrix of plants, algae, bacteria, fungi, and some archaea consist of a semipermeable composite containing polysaccharides. Many of these polysaccharides are -acetylated imparting important physiochemical properties to the polymers. The position and degree of -acetylation is genetically determined and varies between organisms, cell types, and developmental stages. Despite the importance of wall polysaccharide -acetylation, only recently progress has been made to elucidate the molecular mechanism of -acetylation. In plants, three protein families are involved in the transfer of the acetyl substituents to the various polysaccharides. In other organisms, this mechanism seems to be conserved, although the number of required components varies. In this review, we provide an update on the latest advances on plant polysaccharide -acetylation and related information from other wall polysaccharide -acetylating organisms such as bacteria and fungi. The biotechnological impact of understanding wall polysaccharide -acetylation ranges from the design of novel drugs against human pathogenic bacteria to the development of improved lignocellulosic feedstocks for biofuel production.

Plant β-glucanases are enzymes involved in the synthesis, remodelling and turnover of cell wall components during multiple physiological processes. Based on the type of the glycoside bond they cleave, plant β-glucanases have been grouped into three categories: (i) β-1,4-glucanases degrade cellulose and other polysaccharides containing 1,4-glycosidic bonds to remodel and disassemble the wall during cell growth. (ii) β-1,3-glucanases are responsible for the mobilization of callose, governing the symplastic trafficking through plasmodesmata. (iii) β-1,3-1,4-glucanases degrade mixed linkage glucan, a transient wall polysaccharide found in cereals, which is broken down to obtain energy during rapid seedling growth. In addition to their roles in the turnover of self-glucan structures, plant β-glucanases are crucial in regulating the outcome in symbiotic and hostile plant–microbe interactions by degrading non-self glucan structures. Plants use these enzymes to hydrolyse β-glucans found in the walls of microbes, not only by contributing to a local antimicrobial defence barrier, but also by generating signalling glucans triggering the activation of global responses. As a counterpart, microbes developed strategies to hijack plant β-glucanases to their advantage to successfully colonize plant tissues. This review outlines our current understanding on plant β-glucanases, with a particular focus on the latest advances on their roles in adaptative responses.

Existing tests for factorial designs in the non-parametric case are based on hypotheses formulated in terms of distribution functions. Typical null hypotheses, however, are formulated in terms of some parameters or effect measures, particularly in heteroscedastic settings. Here this idea is extended to non-parametric models by introducing a novel non-parametric analysis-of-variance type of statistic based on ranks or pseudoranks which is suitable for testing hypotheses formulated in meaningful non-parametric treatment effects in general factorial designs. This is achieved by a careful detailed study of the common distribution of rank-based estimators for the treatment effects. Since the statistic is asymptotically not a pivotal quantity we propose three different approximation techniques, discuss their theoretic properties and compare them in extensive simulations together with two additional Wald-type tests. An extension of the presented idea to general repeated measures designs is briefly outlined. The rankand pseudorank-based procedures proposed maintain the preassigned type I error rate quite accurately, also in unbalanced and heteroscedastic models.

Background The exchange of knowledge between statisticians developing new methodology and clinicians, reviewers or authors applying them is fundamental. This is specifically true for clinical trials with time-to-event endpoints. Thereby, one of the most commonly arising questions is that of equal survival distributions in two-armed trial. The log-rank test is still the gold-standard to infer this question. However, in case of non-proportional hazards, its power can become poor and multiple extensions have been developed to overcome this issue. We aim to facilitate the choice of a test for the detection of survival differences in the case of crossing hazards. Methods We restricted the review to the most recent two-armed clinical oncology trials with crossing survival curves. Each data set was reconstructed using a state-of-the-art reconstruction algorithm. To ensure reproduction quality, only publications with published number at risk at multiple time points, sufficient printing quality and a non-informative censoring pattern were included. This article depicts the p -values of the log-rank and Peto-Peto test as references and compares them with nine different tests developed for detection of survival differences in the presence of non-proportional or crossing hazards. Results We reviewed 1400 recent phase III clinical oncology trials and selected fifteen studies that met our eligibility criteria for data reconstruction. After including further three individual patient data sets, for nine out of eighteen studies significant differences in survival were found using the investigated tests. An important point that reviewers should pay attention to is that 28% of the studies with published survival curves did not report the number at risk. This makes reconstruction and plausibility checks almost impossible. Conclusions The evaluation shows that inference methods constructed to detect differences in survival in presence of non-proportional hazards are beneficial and help to provide guidance in choosing a sensible alternative to the standard log-rank test.

Xyloglucan (XyG) is an abundant component of the primary cell walls of most plants. While the structure of XyG has been well studied, much remains to be learned about its biosynthesis. Here we employed reverse genetics to investigate the role of Arabidopsis cellulose synthase like-C (CSLC) proteins in XyG biosynthesis. We found that single mutants containing a T-DNA in each of the five Arabidopsis CSLC genes had normal levels of XyG. However, higher-order cslc mutants had significantly reduced XyG levels, and a mutant with disruptions in all five CSLC genes had no detectable XyG. The higher-order mutants grew with mild tissue-specific phenotypes. Despite the apparent lack of XyG, the cslc quintuple mutant did not display significant alteration of gene expression at the whole-genome level, excluding transcriptional compensation. The quintuple mutant could be complemented by each of the five CSLC genes, supporting the conclusion that each of them encodes a XyG glucan synthase. Phylogenetic analyses indicated that the CSLC genes are widespread in the plant kingdom and evolved from an ancient family. These results establish the role of the CSLC genes in XyG biosynthesis, and the mutants described here provide valuable tools with which to study both the molecular details of XyG biosynthesis and the role of XyG in plant cell wall structure and function.

Many planning and decision activities in logistics and supply chain management are based on forecasts of multiple time dependent factors. Therefore, the quality of planning depends on the quality of the forecasts. We compare different state-of-the-art forecasting methods in terms of forecasting performance. Differently from most existing research in logistics, we do not perform this in a case-dependent way but consider a broad set of simulated time series to give more general recommendations. We therefore simulate various linear and nonlinear time series that reflect different situations. Our simulation results showed that the machine learning methods, especially Random Forests, performed particularly well in complex scenarios, with the differentiated time series training significantly improving the robustness of the model. In addition, the time series approaches proved to be competitive in low noise scenarios.

Heteromannan (HM) is one of the most ancient cell wall polymers in the plant kingdom, consisting of β-(1–4)-linked backbones of glucose (Glc) and mannose (Man) units. Despite the widespread distribution of HM polysaccharides, their biosynthesis remains mechanistically unclear. HM is elongated by glycosyltransferases (GTs) from the cellulose synthase-like A (CSLA) family. MANNAN-SYNTHESIS RELATED (MSR) putative GTs have also been implicated in (gluco)mannan synthesis, but their roles have been difficult to decipher in planta and in vitro. To further characterize the products of the HM synthases and accessory proteins, we chose a synthetic biology approach to synthesize plant HM in yeast. The expression of a CSLA protein in Pichia pastoris led to the abundant production of plant HM: up to 30% of glycans in the yeast cell wall. Based on sequential chemical and enzymatic extractions, followed by detailed structural analyses, the newly produced HM polymers were unbranched and could be larger than 270 kDa. Using CSLAs from different species, we programmed yeast cells to produce an HM backbone composed exclusively of Man or also incorporating Glc. We demonstrate that specific MSR cofactors were indispensable for mannan synthase activity of a coffee CSLA or modulated a functional CSLA enzyme to produce glucomannan instead of mannan. Therefore, this powerful platform yields functional insight into the molecular machinery required for HM biosynthesis in plants.

The Mann–Whitney effect is an intuitive measure for discriminating two survival distributions. Here we analyse various inference techniques for this parameter in a two-sample survival setting with independent right-censoring, where the survival times are even allowed to be discretely distributed. This allows for ties in the data and requires the introduction of normalized versions of Kaplan–Meier estimators from which adequate point estimates are deduced. Asymptotically exact inference procedures based on standard normal, bootstrap, and permutation quantiles are developed and compared in simulations. Here, the asymptotically robust and—under exchangeable data—even finitely exact permutation procedure turned out to be the best. Finally, all procedures are illustrated using a real data set.

In this work, we present a rigorous application of the Expectation Maximization algorithm to determine the marginal distributions and the dependence structure in a Gaussian copula model with missing data. We further show how to circumvent a priori assumptions on the marginals with semiparametric modeling. Further, we outline how expert knowledge on the marginals and the dependency structure can be included. A simulation study shows that the distribution learned through this algorithm is closer to the true distribution than that obtained with existing methods and that the incorporation of domain knowledge provides benefits.