The endoplasmic reticulum in plant immunity and cell death

The endoplasmic reticulum (ER) is a highly dynamic organelle in eukaryotic cells and a major production site of proteins destined for vacuoles, the plasma membrane, or apoplast in plants. At the ER, these secreted proteins undergo multiple processing steps, which are supervised and conducted by the ER quality control system. Notably, processing of secreted proteins can considerably elevate under stress conditions and exceed ER folding capacities. The resulting accumulation of unfolded proteins is defined as ER stress. The efficiency of cells to re-establish proper ER function is crucial for stress adaptation. Besides delivering proteins directly antagonizing and resolving stress conditions, the ER monitors synthesis of immune receptors. This indicates the significance of the ER for the establishment and function of the plant immune system. Recent studies point out the fragility of the entire system and highlight the ER as initiator of programed cell death (PCD) in plants as was reported for vertebrates. This review summarizes current knowledge on the impact of the ER on immune and PCD signaling. Understanding the integration of stress signals by the ER bears a considerable potential to optimize development and to enhance stress resistance of plants

Multidimensional Range Queries on Modern Hardware

Range queries over multidimensional data are an important part of database workloads in many applications. Their execution may be accelerated by using multidimensional index structures (MDIS), such as kd-trees or R-trees. As for most index structures, the usefulness of this approach depends on the selectivity of the queries, and common wisdom told that a simple scan beats MDIS for queries accessing more than 15%-20% of a dataset. However, this wisdom is largely based on evaluations that are almost two decades old, performed on data being held on disks, applying IO-optimized data structures, and using single-core systems. The question is whether this rule of thumb still holds when multidimensional range queries (MDRQ) are performed on modern architectures with large main memories holding all data, multi-core CPUs and data-parallel instruction sets. In this paper, we study the question whether and how much modern hardware influences the performance ratio between index structures and scans for MDRQ. To this end, we conservatively adapted three popular MDIS, namely the R*-tree, the kd-tree, and the VA-file, to exploit features of modern servers and compared their performance to different flavors of parallel scans using multiple (synthetic and real-world) analytical workloads over multiple (synthetic and real-world) datasets of varying size, dimensionality, and skew. We find that all approaches benefit considerably from using main memory and parallelization, yet to varying degrees. Our evaluation indicates that, on current machines, scanning should be favored over parallel versions of classical MDIS even for very selective queries

Growth versus immunity : a redirection of the cell cycle?

Diseases caused by plant pathogens significantly reduce growth and yield in agricultural crop production. Raising immunity in crops is therefore a major aim in breeding programs. However, efforts to enhance immunity are challenged by the occurrence of growth inhibition triggered by immunity that can be as detrimental as diseases. In this review, we will propose molecular models to explain the inhibitory growth-immunity crosstalk. We will briefly discuss why the resource reallocation model might not represent the driving force for the observed growth-immunity trade-offs. We suggest a model in which immunity redirects and initiates hormone signalling activities that can impair plant growth by antagonising cell cycle regulation and meristem activities

The mutualistic fungus Piriformospora indica colonizes Arabidopsis roots by inducing an endoplasmic reticulum stress-triggered caspase-dependent cell death

In Arabidopsis thaliana roots, the mutualistic fungus Piriformospora indica initially colonizes living cells, which die as the colonization proceeds. We aimed to clarify the molecular basis of this colonization-associated cell death. Our cytological analyses revealed endoplasmic reticulum (ER) swelling and vacuolar collapse in invaded cells, indicative of ER stress and cell death during root colonization. Consistent with this, P. indica–colonized plants were hypersensitive to the ER stress inducer tunicamycin. By clear contrast, ER stress sensors bZIP60 and bZIP28 as well as canonical markers for the ER stress response pathway, termed the unfolded protein response (UPR), were suppressed at the same time. Arabidopsis mutants compromised in caspase 1–like activity, mediated by cell death–regulating vacuolar processing enzymes (VPEs), showed reduced colonization and decreased cell death incidence. We propose a previously unreported microbial invasion strategy during which P. indica induces ER stress but inhibits the adaptive UPR. This disturbance results in a VPE/caspase 1–like-mediated cell death, which is required for the establishment of the symbiosis. Our results suggest the presence of an at least partially conserved ER stress–induced caspase-dependent cell death pathway in plants as has been reported for metazoans

Plant root-microbe communication in shaping root microbiomes

A growing body of research is highlighting the impacts root-associated microbial communities can have on plant health and development. These impacts can include changes in yield quantity and quality, timing of key developmental stages and tolerance of biotic and abiotic stresses. With such a range of effects it is clear that understanding the factors that contribute to a plant-beneficial root microbiome may prove advantageous. Increasing demands for food by a growing human population increases the importance and urgency of understanding how microbiomes may be exploited to increase crop yields and reduce losses caused by disease. In addition, climate change effects may require novel approaches to overcoming abiotic stresses such as drought and salinity as well as new emerging diseases. This review discusses current knowledge on the formation and maintenance of root-associated microbial communities and plant–microbe interactions with a particular emphasis on the effect of microbe–microbe interactions on the shape of microbial communities at the root surface. Further, we discuss the potential for root microbiome modification to benefit agriculture and food production

Single-cell transcriptomics : a high-resolution avenue for plant functional genomics

Plant function is the result of the concerted action of single cells in different tissues. Advances in RNA-seq technologies and tissue processing allow us now to capture transcriptional changes at single-cell resolution. The incredible potential of single-cell RNA-seq lies in the novel ability to study and exploit regulatory processes in complex tissues based on the behaviour of single cells. Importantly, the independence from reporter lines allows the analysis of any given tissue in any plant. While there are challenges associated with the handling and analysis of complex datasets, the opportunities are unique to generate knowledge of tissue functions in unprecedented detail and to facilitate the application of such information by mapping cellular functions and interactions in a plant cell atlas. [Abstract copyright: Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.

ClaSP -- Parameter-free Time Series Segmentation

The study of natural and human-made processes often results in long sequences of temporally-ordered values, aka time series (TS). Such processes often consist of multiple states, e.g. operating modes of a machine, such that state changes in the observed processes result in changes in the distribution of shape of the measured values. Time series segmentation (TSS) tries to find such changes in TS post-hoc to deduce changes in the data-generating process. TSS is typically approached as an unsupervised learning problem aiming at the identification of segments distinguishable by some statistical property. Current algorithms for TSS require domain-dependent hyper-parameters to be set by the user, make assumptions about the TS value distribution or the types of detectable changes which limits their applicability. Common hyperparameters are the measure of segment homogeneity and the number of change points, which are particularly hard to tune for each data set. We present ClaSP, a novel, highly accurate, hyper-parameter-free and domain-agnostic method for TSS. ClaSP hierarchically splits a TS into two parts. A change point is determined by training a binary TS classifier for each possible split point and selecting the one split that is best at identifying subsequences to be from either of the partitions. ClaSP learns its main two model-parameters from the data using two novel bespoke algorithms. In our experimental evaluation using a benchmark of 107 data sets, we show that ClaSP outperforms the state of the art in terms of accuracy and is fast and scalable. Furthermore, we highlight properties of ClaSP using several real-world case studies

Raising the ClaSS of Streaming Time Series Segmentation

Ubiquitous sensors today emit high frequency streams of numerical measurements that reflect properties of human, animal, industrial, commercial, and natural processes. Shifts in such processes, e.g. caused by external events or internal state changes, manifest as changes in the recorded signals. The task of streaming time series segmentation (STSS) is to partition the stream into consecutive variable-sized segments that correspond to states of the observed processes or entities. The partition operation itself must in performance be able to cope with the input frequency of the signals. We introduce ClaSS, a novel, efficient, and highly accurate algorithm for STSS. ClaSS assesses the homogeneity of potential partitions using self-supervised time series classification and applies statistical tests to detect significant change points (CPs). In our experimental evaluation using two large benchmarks and six real-world data archives, we found ClaSS to be significantly more precise than eight state-of-the-art competitors. Its space and time complexity is independent of segment sizes and linear only in the sliding window size. We also provide ClaSS as a window operator with an average throughput of 538 data points per second for the Apache Flink streaming engine

High-order methods for the numerical simulation of vortical and turbulent flows: Foreword

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