Transgenic expression of the dicotyledonous pattern recognition receptor EFR in rice leads to ligand-dependent activation of defense responses

Plant plasma membrane localized pattern recognition receptors (PRRs) detect extracellular pathogen-associated molecules. PRRs such as Arabidopsis EFR and rice XA21 are taxonomically restricted and are absent from most plant genomes. Here we show that rice plants expressing EFR or the chimeric receptor EFR::XA21, containing the EFR ectodomain and the XA21 intracellular domain, sense both Escherichia coli- and Xanthomonas oryzae pv. oryzae (Xoo)-derived elf18 peptides at sub-nanomolar concentrations. Treatment of EFR and EFR::XA21 rice leaf tissue with elf18 leads to MAP kinase activation, reactive oxygen production and defense gene expression. Although expression of EFR does not lead to robust enhanced resistance to fully virulent Xoo isolates, it does lead to quantitatively enhanced resistance to weakly virulent Xoo isolates. EFR interacts with OsSERK2 and the XA21 binding protein 24 (XB24), two key components of the rice XA21-mediated immune response. Rice-EFR plants silenced for OsSERK2, or overexpressing rice XB24 are compromised in elf18-induced reactive oxygen production and defense gene expression indicating that these proteins are also important for EFR-mediated signaling in transgenic rice. Taken together, our results demonstrate the potential feasibility of enhancing disease resistance in rice and possibly other monocotyledonous crop species by expression of dicotyledonous PRRs. Our results also suggest that Arabidopsis EFR utilizes at least a subset of the known endogenous rice XA21 signaling components

Impact of fungal drug transporters on fungicide sensitivity, multidrug resistance and virulence.

Made available in DSpace on 2018-06-05T00:35:58Z (GMT). No. of bitstreams: 1 ID274961.pdf: 414736 bytes, checksum: 2afa045f21d0e1c11ea3da5fbea74d88 (MD5) Previous issue date: 2006-11-0

IndustReal: A Dataset for Procedure Step Recognition Handling Execution Errors in Egocentric Videos in an Industrial-Like Setting

Although action recognition for procedural tasks has received notable attention, it has a fundamental flaw in that no measure of success for actions is provided. This limits the applicability of such systems especially within the industrial domain, since the outcome of procedural actions is often significantly more important than the mere execution. To address this limitation, we define the novel task of procedure step recognition (PSR), focusing on recognizing the correct completion and order of procedural steps. Alongside the new task, we also present the multi-modal IndustReal dataset. Unlike currently available datasets, IndustReal contains procedural errors (such as omissions) as well as execution errors. A significant part of these errors are exclusively present in the validation and test sets, making IndustReal suitable to evaluate robustness of algorithms to new, unseen mistakes. Additionally, to encourage reproducibility and allow for scalable approaches trained on synthetic data, the 3D models of all parts are publicly available. Annotations and benchmark performance are provided for action recognition and assembly state detection, as well as the new PSR task. IndustReal, along with the code and model weights, is available at: https://github.com/TimSchoonbeek/IndustReal .Comment: Accepted for WACV 2024. 15 pages, 9 figures, including supplementary material

Substituent interference on supramolecular assembly in urea gelators: synthesis, structure prediction and NMR

Eighteen N-aryl-N'-alkyl urea gelators were synthesised in order to understand the effect of head substituents on gelation performance. Minimum gelation concentration values obtained from gel formation studies were used to rank the compounds and revealed the remarkable performance of 4-methoxyphenyl urea gelator 15 in comparison to 4-nitrophenyl analogue 14, which could not be simply ascribed to substituent effects on the hydrogen bonding capabilities of the urea protons. Crystal structure prediction calculations indicated alternative low energy hydrogen bonding arrangements between the nitro group and urea protons in gelator 14, which were supported experimentally by NMR spectroscopy. As a consequence, it was possible to relate the observed differences to interference of the head substituents with the urea tape motif, disrupting the order of supramolecular packing. The combination of unbiased structure prediction calculations with NMR is proposed as a powerful approach to investigate the supramolecular arrangement in gel fibres and help understand the relationships between molecular structure and gel formation

Use of beneficial bacteria and their secondary metabolites to control grapevine pathogen diseases

Grapevine is one of the most important economic crops yielding berries, wine products as well as derivates. However, due to the large array of pathogens inducing diseases on this plant, considerable amounts of pesticides—with possible negative impact on the environment and health—have been used and are currently used in viticulture. To avoid negative impacts of such products and to ensure product quality, a substantial fraction of pesticides needs to be replaced in the near future. One solution can be related to the use of beneficial bacteria inhabiting the rhizo- and/or the endosphere of plants. These biocontrol bacteria and their secondary metabolites can reduce directly or indirectly pathogen diseases by affecting pathogen performance by antibiosis, competition for niches and nutrients, interference with pathogen signaling or by stimulation of host plant defenses. Due to the large demand for biocontrol of grapevine diseases, such biopesticides, their modes of actions and putative consequences of their uses need to be described. Moreover, the current knowledge on new strains from the rhizo- and endosphere and their metabolites that can be used on grapevine plants to counteract pathogen attack needs to be discussed. This is in particular with regard to the control of root rot, grey mould, trunk diseases, powdery and downy mildews, pierce’s disease, grapevine yellows as well as crown gall. Future prospects on specific beneficial microbes and their secondary metabolites that can be used as elicitors of plant defenses and/or as biocontrol agents with potential use in a more sustainable viticulture will be further discussed

Quality and efficiency of integrating customised large language model-generated summaries versus physician-written summaries:a validation study

OBJECTIVES: To compare the quality and time efficiency of physician-written summaries with customised large language model (LLM)-generated medical summaries integrated into the electronic health record (EHR) in a non-English clinical environment.DESIGN: Cross-sectional non-inferiority validation study.SETTING: Tertiary academic hospital.PARTICIPANTS: 52 physicians from 8 specialties at a large Dutch academic hospital participated, either in writing summaries (n=42) or evaluating them (n=10).INTERVENTIONS: Physician writers wrote summaries of 50 patient records. LLM-generated summaries were created for the same records using an EHR-integrated LLM. An independent, blinded panel of physician evaluators compared physician-written summaries to LLM-generated summaries.PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcome measures were completeness, correctness and conciseness (on a 5-point Likert scale). Secondary outcomes were preference and trust, and time to generate either the physician-written or LLM-generated summary.RESULTS: The completeness and correctness of LLM-generated summaries did not differ significantly from physician-written summaries. However, LLM summaries were less concise (3.0 vs 3.5, p=0.001). Overall evaluation scores were similar (3.4 vs 3.3, p=0.373), with 57% of evaluators preferring LLM-generated summaries. Trust in both summary types was comparable, and interobserver variability showed excellent reliability (intraclass correlation coefficient 0.975). Physicians took an average of 7 min per summary, while LLMs completed the same task in just 15.7 s.CONCLUSIONS: LLM-generated summaries are comparable to physician-written summaries in completeness and correctness, although slightly less concise. With a clear time-saving benefit, LLMs could help reduce clinicians' administrative burden without compromising summary quality.</p

AI-generated draft replies to patient messages:exploring effects of implementation

INTRODUCTION: The integration of Large Language Models (LLMs) in Electronic Health Records (EHRs) has the potential to reduce administrative burden. Validating these tools in real-world clinical settings is essential for responsible implementation. In this study, the effect of implementing LLM-generated draft responses to patient questions in our EHR is evaluated with regard to adoption, use and potential time savings.MATERIAL AND METHODS: Physicians across 14 medical specialties in a non-English large academic hospital were invited to use LLM-generated draft replies during this prospective observational clinical cohort study of 16 weeks, choosing either the drafted or a blank reply. The adoption rate, the level of adjustments to the initial drafted responses compared to the final sent messages (using ROUGE-1 and BLEU-1 natural language processing scores), and the time spent on these adjustments were analyzed.RESULTS: A total of 919 messages by 100 physicians were evaluated. Clinicians used the LLM draft in 58% of replies. Of these, 43% used a large part of the suggested text for the final answer (≥10% match drafted responses: ROUGE-1: 86% similarity, vs. blank replies: ROUGE-1: 16%). Total response time did not significantly different when using a blank reply compared to using a drafted reply with ≥10% match (157 vs. 153 s, p  = 0.69). DISCUSSION: General adoption of LLM-generated draft responses to patient messages was 58%, although the level of adjustments on the drafted message varied widely between medical specialties. This implicates safe use in a non-English, tertiary setting. The current implementation has not yet resulted in time savings, but a learning curve can be expected.REGISTRATION NUMBER: 19035.</p