Synthesis and import of GDP-L‐fucose into the Golgi affect plant–water relations

Land plants evolved multiple adaptations to restrict transpiration. However, the underlying molecular mechanisms are not sufficiently understood. We used an ozone-sensitivity forward genetics approach to identify Arabidopsis thaliana mutants impaired in gas exchange regulation. High water loss from detached leaves and impaired decrease of leaf conductance in response to multiple stomata-closing stimuli were identified in a mutant of MURUS1 (MUR1), an enzyme required for GDP-l-fucose biosynthesis. High water loss observed in mur1 was independent from stomatal movements and instead could be linked to metabolic defects. Plants defective in import of GDP-l-Fuc into the Golgi apparatus phenocopied the high water loss of mur1 mutants, linking this phenotype to Golgi-localized fucosylation events. However, impaired fucosylation of xyloglucan, N-linked glycans, and arabinogalactan proteins did not explain the aberrant water loss of mur1 mutants. Partial reversion of mur1 water loss phenotype by borate supplementation and high water loss observed in boron uptake mutants link mur1 gas exchange phenotypes to pleiotropic consequences of l-fucose and boron deficiency, which in turn affect mechanical and morphological properties of stomatal complexes and whole-plant physiology. Our work emphasizes the impact of fucose metabolism and boron uptake on plant–water relations

LIMPRINT study - the Turkish experience

Background: Lymphedema and chronic oedema is a major healthcare problem in both developed and non-developed countries The LIMPRINT study is an international health service based study to determine the prevalence and functional impact in adult populations of member countries of the International Lymphoedema Framework (ILF). Methods: 1051 patients from 8 centers in Turkey were recruited using the LIMPRINT study protocol. Data were collected using the core and module tools which assess the demographic and clinical properties as well as disability and QoL. Results: Most of the Turkish patients were recruited from specialist lymphedema services and were found to be female, housewives and having secondary lymphedema due to cancer treatment. The duration of lymphedema was commonly less than 5 years and most of them had ISL Grade 2 lymphedema. Cellulitis, infection and wounds were uncommon. The majority of patients did not get any treatment or advice before. Most of the patients had impaired QoL and decreased functionality, but psychological support was neglected. Although most had social health security access to Lymphedema centres nevertheless access seemed difficult due to distance and cost. Conclusion: The study has shown the current status and characteristics of lymphedema patients, treatment conditions, the unmet need for the diagnosis and treatment as well as burden of the disease in both patients and families in Turkey. National health policies are needed for the prevention, diagnosis and treatment in Turkey that utilise this informative data

Genome sequencing and population genomic analyses provide insights into the adaptive landscape of silver birch

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Investigations into a putative role for the novel BRASSIKIN pseudokinases in compatible pollen-stigma interactions in Arabidopsis thaliana.

BACKGROUND: In the Brassicaceae, the early stages of compatible pollen-stigma interactions are tightly controlled with early checkpoints regulating pollen adhesion, hydration and germination, and pollen tube entry into the stigmatic surface. However, the early signalling events in the stigma which trigger these compatible interactions remain unknown. RESULTS: A set of stigma-expressed pseudokinase genes, termed BRASSIKINs (BKNs), were identified and found to be present in only core Brassicaceae genomes. In Arabidopsis thaliana Col-0, BKN1 displayed stigma-specific expression while the BKN2 gene was expressed in other tissues as well. CRISPR deletion mutations were generated for the two tandemly linked BKNs, and very mild hydration defects were observed for wild-type Col-0 pollen when placed on the bkn1/2 mutant stigmas. In further analyses, the predominant transcript for the stigma-specific BKN1 was found to have a premature stop codon in the Col-0 ecotype, but a survey of the 1001 Arabidopsis genomes uncovered three ecotypes that encoded a full-length BKN1 protein. Furthermore, phylogenetic analyses identified intact BKN1 orthologues in the closely related outcrossing Arabidopsis species, A. lyrata and A. halleri. Finally, the BKN pseudokinases were found to be plasma-membrane localized through the dual lipid modification of myristoylation and palmitoylation, and this localization would be consistent with a role in signaling complexes. CONCLUSION: In this study, we have characterized the novel Brassicaceae-specific family of BKN pseudokinase genes, and examined the function of BKN1 and BKN2 in the context of pollen-stigma interactions in A. thaliana Col-0. Additionally, premature stop codons were identified in the predicted stigma specific BKN1 gene in a number of the 1001 A. thaliana ecotype genomes, and this was in contrast to the out-crossing Arabidopsis species which carried intact copies of BKN1. Thus, understanding the function of BKN1 in other Brassicaceae species will be a key direction for future studies

Genome sequencing and population genomic analyses provide insights into the adaptive landscape of silver birch

Silver birch (Betula pendula) is a pioneer boreal tree that can be induced to flower within 1 year. Its rapid life cycle, small (440-Mb) genome, and advanced germplasm resources make birch an attractive model for forest biotechnology. We assembled and chromosomally anchored the nuclear genome of an inbred B. pendula individual. Gene duplicates from the paleohexaploid event were enriched for transcriptional regulation, whereas tandem duplicates were overrepresented by environmental responses. Population resequencing of 80 individuals showed effective population size crashes at major points of climatic upheaval. Selective sweeps were enriched among polyploid duplicates encoding key developmental and physiological triggering functions, suggesting that local adaptation has tuned the timing of and cross-talk between fundamental plant processes. Variation around the tightly-linked light response genes PHYC and FRS10 correlated with latitude and longitude and temperature, and with precipitation for PHYC. Similar associations characterized the growth-promoting cytokinin response regulator ARR1, and the wood development genes KAK and MED5A.Peer reviewe

Genome sequencing and population genomic analyses provide insights into the adaptive landscape of silver birch.

Silver birch (Betula pendula) is a pioneer boreal tree that can be induced to flower within 1 year. Its rapid life cycle, small (440-Mb) genome, and advanced germplasm resources make birch an attractive model for forest biotechnology. We assembled and chromosomally anchored the nuclear genome of an inbred B. pendula individual. Gene duplicates from the paleohexaploid event were enriched for transcriptional regulation, whereas tandem duplicates were overrepresented by environmental responses. Population resequencing of 80 individuals showed effective population size crashes at major points of climatic upheaval. Selective sweeps were enriched among polyploid duplicates encoding key developmental and physiological triggering functions, suggesting that local adaptation has tuned the timing of and cross-talk between fundamental plant processes. Variation around the tightly-linked light response genes PHYC and FRS10 correlated with latitude and longitude and temperature, and with precipitation for PHYC. Similar associations characterized the growth-promoting cytokinin response regulator ARR1, and the wood development genes KAK and MED5A

An Integrated Implementation Methodology of a Lifecycle-wide Tracking Simulation Architecture

A tracking simulator is a decision support application in which dynamic estimation is used to continuously align the results of an online first principle simulation model with the measurements of the targeted plant. They are a holistic application where current and future plant information is available for operation support of process plants. Existing tracking simulators have focused on the application of online and offline methods for estimation of their underlying first principle models (FPMs). However, these systems have been less attractive than similar alternatives based on empirical modelling, due to the lack of systematic approaches that address challenges across the tracking simulation lifecycle, such as laborious development of FPMs as well as high integration costs with the process or with other systems and simulation methods. In contrast, the approach presented in this work integrates a tracking simulation architecture and various simulation methods to address the described challenges as follows. In order to tackle time-consuming development of FPMs, a method for generating tracking simulation models from models created during design phase is proposed. The process of connecting the tracking simulator to the physical plant and initializing the tracking simulator is automated. An optimization method for tracking simulation applications is developed to overcome drawbacks of available methods. The simulation architecture developed applies the proposed methodology during the various phases of tracking simulation. Furthermore, it exploits industrial communication standards to avoid the need for point-to-point integration of various simulators and other systems used over the course of the tracking simulator lifecycle. The work is demonstrated with laboratory process equipment.Peer reviewe

Whitening CNN-based rotor system fault diagnosis model features

Abstract Intelligent fault diagnosis (IFD) models have the potential to increase the level of automation and the diagnosis accuracy of machine condition monitoring systems. Many of the latest IFD models rely on convolutional layers for feature extraction from vibration data. The majority of these models employ batch normalisation (BN) for centring and scaling the input for each neuron. This study includes a novel examination of a competitive approach for layer input normalisation in the scope of fault diagnosis. Network deconvolution (ND) is a technique that further decorrelates the layer inputs reducing redundancy among the learned features. Both normalisation techniques are implemented on three common 1D-CNN-based fault diagnosis models. The models with ND mostly outperform the baseline models with BN in three experiments concerning fault datasets from two different rotor systems. Furthermore, the models with ND significantly outperform the baseline models with BN in the common CWRU bearing fault tests with load domain shifts, if the data from drive-end and fan-end sensors are employed. The results show that whitened features can improve the performance of CNN-based fault diagnosis models