R2R3-MYB Gene Family in Coptis teeta Wall.: Genome-Wide Identification, Phylogeny, Evolutionary Expansion, and Expression Analyses during Floral Development.

The R2R3-MYB gene family represents a widely distributed class of plant transcription factors. This gene family plays an important role in many aspects of plant growth and development. However, the characterization of R2R3-MYB genes present in the genome of Coptis teeta has not been reported. Here, we describe the bioinformatic identification and characterization of 88 R2R3-MYB genes in this species, and the identification of members of the R2R3-MYB gene family in species within the order Ranales most closely related to Coptis teeta. The CteR2R3-MYB genes were shown to exhibit a higher degree of conservation compared to those of A. thaliana, as evidenced by phylogeny, conserved motifs, gene structure, and replication event analyses. Cis-acting element analysis confirmed the involvement of CteR2R3-MYB genes in a variety of developmental processes, including growth, cell differentiation, and reproduction mediated by hormone synthesis. In addition, through homology comparisons with the equivalent gene family in A. thaliana, protein regulatory network prediction and transcriptome data analysis of floral organs across three time periods of flower development, 17 candidate genes were shown to exhibit biased expression in two floral phenotypes of C. teeta. This suggests their potential involvement in floral development (anther development) in this species.

Disease progression modelling of Alzheimer's disease using probabilistic principal components analysis.

The recent biological redefinition of Alzheimer's Disease (AD) has spurred the development of statistical models that relate changes in biomarkers with neurodegeneration and worsening condition linked to AD. The ability to measure such changes may facilitate earlier diagnoses for affected individuals and help in monitoring the evolution of their condition. Amongst such statistical tools, disease progression models (DPMs) are quantitative, data-driven methods that specifically attempt to describe the temporal dynamics of biomarkers relevant to AD. Due to the heterogeneous nature of this disease, with patients of similar age experiencing different AD-related changes, a challenge facing longitudinal mixed-effects-based DPMs is the estimation of patient-realigning time-shifts. These time-shifts are indispensable for meaningful biomarker modelling, but may impact fitting time or vary with missing data in jointly estimated models. In this work, we estimate an individual's progression through Alzheimer's disease by combining multiple biomarkers into a single value using a probabilistic formulation of principal components analysis. Our results show that this variable, which summarises AD through observable biomarkers, is remarkably similar to jointly estimated time-shifts when we compute our scores for the baseline visit, on cross-sectional data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Reproducing the expected properties of clinical datasets, we confirm that estimated scores are robust to missing data or unavailable biomarkers. In addition to cross-sectional insights, we can model the latent variable as an individual progression score by repeating estimations at follow-up examinations and refining long-term estimates as more data is gathered, which would be ideal in a clinical setting. Finally, we verify that our score can be used as a pseudo-temporal scale instead of age to ignore some patient heterogeneity in cohort data and highlight the general trend in expected biomarker evolution in affected individuals.

Automating Quality Assessment of Medical Evidence in Systematic Reviews: Model Development and Validation Study.

BACKGROUND: Assessment of the quality of medical evidence available on the web is a critical step in the preparation of systematic reviews. Existing tools that automate parts of this task validate the quality of individual studies but not of entire bodies of evidence and focus on a restricted set of quality criteria. OBJECTIVE: We proposed a quality assessment task that provides an overall quality rating for each body of evidence (BoE), as well as finer-grained justification for different quality criteria according to the Grading of Recommendation, Assessment, Development, and Evaluation formalization framework. For this purpose, we constructed a new data set and developed a machine learning baseline system (EvidenceGRADEr). METHODS: We algorithmically extracted quality-related data from all summaries of findings found in the Cochrane Database of Systematic Reviews. Each BoE was defined by a set of population, intervention, comparison, and outcome criteria and assigned a quality grade (high, moderate, low, or very low) together with quality criteria (justification) that influenced that decision. Different statistical data, metadata about the review, and parts of the review text were extracted as support for grading each BoE. After pruning the resulting data set with various quality checks, we used it to train several neural-model variants. The predictions were compared against the labels originally assigned by the authors of the systematic reviews. RESULTS: Our quality assessment data set, Cochrane Database of Systematic Reviews Quality of Evidence, contains 13,440 instances, or BoEs labeled for quality, originating from 2252 systematic reviews published on the internet from 2002 to 2020. On the basis of a 10-fold cross-validation, the best neural binary classifiers for quality criteria detected risk of bias at 0.78 F1 (P=.68; R=0.92) and imprecision at 0.75 F1 (P=.66; R=0.86), while the performance on inconsistency, indirectness, and publication bias criteria was lower (F1 in the range of 0.3-0.4). The prediction of the overall quality grade into 1 of the 4 levels resulted in 0.5 F1. When casting the task as a binary problem by merging the Grading of Recommendation, Assessment, Development, and Evaluation classes (high+moderate vs low+very low-quality evidence), we attained 0.74 F1. We also found that the results varied depending on the supporting information that is provided as an input to the models. CONCLUSIONS: Different factors affect the quality of evidence in the context of systematic reviews of medical evidence. Some of these (risk of bias and imprecision) can be automated with reasonable accuracy. Other quality dimensions such as indirectness, inconsistency, and publication bias prove more challenging for machine learning, largely because they are much rarer. This technology could substantially reduce reviewer workload in the future and expedite quality assessment as part of evidence synthesis.

Evaluation of the Effect of Leaf Development in Plectranthus amboinicus L. on Antimicrobial Activity and Virulence Factors of Pseudomonas aeruginosa PAO1 and Staphylococcus aureus NCTC8325.

Plectranthus amboinicus is widely recognized as a potential source of antimicrobial compounds due to the presence of bioactive components (essential oils) secreted by the glandular trichomes borne on the leaves. As such, an understanding of the effect of leaf development on the production of these essential oils (EOs) is of crucial importance to its medicinal applications. The current study represents the first comparative investigation of the effect of different stages of leaf development (lag, log, and stationary phase) upon the yield and bioactivity of phytochemicals produced. The effects of leaf extracts on the antimicrobial activity, cell surface hydrophobicity, biofilm formation, and motility of P. aeruginosa and Staphylococcus aureus were evaluated. Cryo-scanning electron microscopy was used to record the abundance and distribution of both glandular and non-glandular trichomes during leaf development. Gas chromatography-mass spectrometry analysis revealed that the potent phytochemical thymol is present primarily in log (30.28%) and stationary phase (20.89%) extracts. Log phase extracts showed the lowest minimum inhibitory concentration (25 mg/ml) when compared to other phases of development. Stationary phase extracts were shown to exhibit the highest biofilm dispersal activity against P. aeruginosa (80%), and log phase extracts against biofilms of S. aureus (59%). Log phase extracts showed the highest biofilm inhibitory activity against P. aeruginosa (66%) and S. aureus (63%). In conclusion, log phase leaf extracts of P. amboinicus exhibited a multimodal mechanism of action by displaying antimicrobial, antibiofilm activities and reducing the motility and hydrophobicity, which are important virulence factors in P. aeruginosa and S. aureus pathogenesis.

Predicting Publication of Clinical Trials Using Structured and Unstructured Data: Model Development and Validation Study.

BACKGROUND: Publication of registered clinical trials is a critical step in the timely dissemination of trial findings. However, a significant proportion of completed clinical trials are never published, motivating the need to analyze the factors behind success or failure to publish. This could inform study design, help regulatory decision-making, and improve resource allocation. It could also enhance our understanding of bias in the publication of trials and publication trends based on the research direction or strength of the findings. Although the publication of clinical trials has been addressed in several descriptive studies at an aggregate level, there is a lack of research on the predictive analysis of a trial's publishability given an individual (planned) clinical trial description. OBJECTIVE: We aimed to conduct a study that combined structured and unstructured features relevant to publication status in a single predictive approach. Established natural language processing techniques as well as recent pretrained language models enabled us to incorporate information from the textual descriptions of clinical trials into a machine learning approach. We were particularly interested in whether and which textual features could improve the classification accuracy for publication outcomes. METHODS: In this study, we used metadata from ClinicalTrials.gov (a registry of clinical trials) and MEDLINE (a database of academic journal articles) to build a data set of clinical trials (N=76,950) that contained the description of a registered trial and its publication outcome (27,702/76,950, 36% published and 49,248/76,950, 64% unpublished). This is the largest data set of its kind, which we released as part of this work. The publication outcome in the data set was identified from MEDLINE based on clinical trial identifiers. We carried out a descriptive analysis and predicted the publication outcome using 2 approaches: a neural network with a large domain-specific language model and a random forest classifier using a weighted bag-of-words representation of text. RESULTS: First, our analysis of the newly created data set corroborates several findings from the existing literature regarding attributes associated with a higher publication rate. Second, a crucial observation from our predictive modeling was that the addition of textual features (eg, eligibility criteria) offers consistent improvements over using only structured data (F1-score=0.62-0.64 vs F1-score=0.61 without textual features). Both pretrained language models and more basic word-based representations provide high-utility text representations, with no significant empirical difference between the two. CONCLUSIONS: Different factors affect the publication of a registered clinical trial. Our approach to predictive modeling combines heterogeneous features, both structured and unstructured. We show that methods from natural language processing can provide effective textual features to enable more accurate prediction of publication success, which has not been explored for this task previously.

Biodegradable PBAT/PLA Blend with Bioactive MCPA-PHBV Conjugate Suppresses Weed Growth.

The herbicide 2-methyl-4-chlorophenoxyacetic acid (MCPA) conjugated with poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) was prepared via a melt transesterification route. The resultant bioactive oligomer was then mixed with a blend of polylactide (PLA) and poly(butylene adipate-co-terephthalate) (PBAT) with different loadings to manufacture films to be used as a bioactive, biodegradable mulch to deliver the herbicide to target broadleaf weed species. The biological targeting of the MCPA-PHBV conjugate in the mulch film was investigated under glasshouse conditions using faba bean (Vicia faba) as a selective (nontarget) model crop species having broadleaf morphology. The presence of the MCPA-PHBV conjugate in the biodegradable PBTA/PLA blend was shown to completely suppress the growth of broadleaf weed species while displaying only a mild effect on the growth of the model crop. The degradation of the mulch film under glasshouse conditions was quite slow. The release of the MCPA-PHBV during this process was detected using NMR, GPC, EDS, and DSC analyses, indicating that the majority of the MCPA diffused out after MCPA-PHBV conjugate bond scission. These data provide a strong "proof of concept" and show that this biodegradable, bioactive film is a good candidate for future field applications and may be of wide agricultural applicability.

Abnormal glycosphingolipid mannosylation triggers salicylic acid-mediated responses in Arabidopsis.

The Arabidopsis thaliana protein GOLGI-LOCALIZED NUCLEOTIDE SUGAR TRANSPORTER (GONST1) has been previously identified as a GDP-d-mannose transporter. It has been hypothesized that GONST1 provides precursors for the synthesis of cell wall polysaccharides, such as glucomannan. Here, we show that in vitro GONST1 can transport all four plant GDP-sugars. However, gonst1 mutants have no reduction in glucomannan quantity and show no detectable alterations in other cell wall polysaccharides. By contrast, we show that a class of glycosylated sphingolipids (glycosylinositol phosphoceramides [GIPCs]) contains Man and that this mannosylation is affected in gonst1. GONST1 therefore is a Golgi GDP-sugar transporter that specifically supplies GDP-Man to the Golgi lumen for GIPC synthesis. gonst1 plants have a dwarfed phenotype and a constitutive hypersensitive response with elevated salicylic acid levels. This suggests an unexpected role for GIPC sugar decorations in sphingolipid function and plant defense signaling. Additionally, we discuss these data in the context of substrate channeling within the Golgi.

An investigation of self-incompatibility within the genus Restrepia.

UNLABELLED: • PREMISE OF THE STUDY: The genus Restrepia (Orchidaceae) is indigenous to montane rain forests of Central and South America. Recently, as habitat has fragmented and wild populations dwindled, the chances for successful cross-pollination within the genus have been reduced. Since cultivated species of Restrepia have been vegetatively propagated, they remain genetically close to those in the wild, making ex situ collections of the genus useful model populations for investigating breeding systems. Restrepia are found in clade B of the Pleurothallidinae, the only clade in which self-incompatibility (SI) has not yet been confirmed. In the current study, private collections of Restrepia were used to study the operation of SI within the genus to assist future ex situ conservation of this and related genera.• METHODS: A variety of self-pollination, intraspecific, and interspecific crosses were performed across the genus, and pollen tube growth was studied.• KEY RESULTS: Individual species exhibited varying degrees of SI. Self-pollinations performed across 26 species in the genus produced few viable seeds, with the exception of R. aberrans. Viable "filled" seeds with embryos were shown to require an intraspecific cross. Primary hybrids between species produced >90% seeds with embryos that germinated well.• CONCLUSIONS: The type of SI operating within the genus was considered to be best explained by gametophytic self-incompatibility (GSI) with interspecific variation in its phenotypic expression. The implications of these findings are discussed in relation to SI in the Pleurothallidinae and conservation strategies for Restrepia and related genera.

Zero- and few-shot prompting of generative large language models provides weak assessment of risk of bias in clinical trials.

Existing systems for automating the assessment of risk-of-bias (RoB) in medical studies are supervised approaches that require substantial training data to work well. However, recent revisions to RoB guidelines have resulted in a scarcity of available training data. In this study, we investigate the effectiveness of generative large language models (LLMs) for assessing RoB. Their application requires little or no training data and, if successful, could serve as a valuable tool to assist human experts during the construction of systematic reviews. Following Cochrane's latest guidelines (RoB2) designed for human reviewers, we prepare instructions that are fed as input to LLMs, which then infer the risk associated with a trial publication. We distinguish between two modelling tasks: directly predicting RoB2 from text; and employing decomposition, in which a RoB2 decision is made after the LLM responds to a series of signalling questions. We curate new testing data sets and evaluate the performance of four general- and medical-domain LLMs. The results fall short of expectations, with LLMs seldom surpassing trivial baselines. On the direct RoB2 prediction test set (n = 5993), LLMs perform akin to the baselines (F1: 0.1-0.2). In the decomposition task setup (n = 28,150), similar F1 scores are observed. Our additional comparative evaluation on RoB1 data also reveals results substantially below those of a supervised system. This testifies to the difficulty of solving this task based on (complex) instructions alone. Using LLMs as an assisting technology for assessing RoB2 thus currently seems beyond their reach.

Differential responses of Hollyhock (Alcea rosea L.) varieties to salt stress in relation to physiological and biochemical parameters.

The response of 14 Hollyhock (Alcea rosea L.) varieties to salinity were evaluated in a field experiment over two growing seasons. Carotenoid, Chl a, Chl b, total Chl, proline and MDA content, CAT, APX and GPX activity and petal and seeds yields were determined in order to investigate the mechanism of salt tolerance exhibited by Hollyhock, and too identify salt tolerant varieties. Overall, the photosynthetic pigment content,petal and seed yields were reduced by salt stress. Whereas the proline and MDA content, and the CAT, APX and GPX activities increased as salt levels increased. However, the values of the measured traits were dependent upon the on the level of salt stress, the Varietie and the interaction between the two variables. Based upon the smallest reduction in petal yield, the Masouleh variety was shown to be the most salt tolerant, when grown under severe salt stress. However, based upon the smallest reduction in seed yield, Khorrmabad was the most tolerant variety to severe salt stress. These data suggest that the selection of more salt tolerant Hollyhock genotypes may be possible based upon the wide variation in tolerance to salinity exhibited by the varieties tested.

Using natural language processing and patient journey clustering for temporal phenotyping of antimicrobial therapies for cat bite abscesses.

BACKGROUND: Temporal phenotyping of patient journeys, which capture the common sequence patterns of interventions in the treatment of a specific condition, is useful to support understanding of antimicrobial usage in veterinary patients. Identifying and describing these phenotypes can inform antimicrobial stewardship programs designed to fight antimicrobial resistance, a major health crisis affecting both humans and animals, in which veterinarians have an important role to play. OBJECTIVE: This research proposes a framework for extracting temporal phenotypes of patient journeys from clinical practice data through the application of natural language processing (NLP) and unsupervised machine learning (ML) techniques, using cat bite abscesses as a model condition. By constructing temporal phenotypes from key events, the relationship between antimicrobial administration and surgical interventions can be described, and similar treatment patterns can be grouped together to describe outcomes associated with specific antimicrobial selection. METHODS: Cases identified as having a cat bite abscess as a diagnosis were extracted from VetCompass Australia, a database of veterinary clinical records. A classifier was trained and used to label the most clinically relevant event features in each record as chosen by a group of veterinarians. The labeled records were processed into coded character strings, where each letter represents a summary of specific types of treatments performed at a given visit. The sequences of letters representing the cases were clustered based on weighted Levenshtein edit distances with KMeans+ + to identify the main variations of the patient treatment journeys, including the antimicrobials used and their duration of administration. RESULTS: A total of 13,744 records that met the selection criteria was extracted and grouped into 8436 cases. There were 9 clinically distinct event sequence patterns (temporal phenotypes) of patient journeys identified, representing the main sequences in which surgery and antimicrobial interventions are performed. Patients receiving amoxicillin and surgery had the shortest duration of antimicrobial administration (median of 3.4 days) and patients receiving cefovecin with no surgical intervention had the longest antimicrobial treatment duration (median of 27 days). CONCLUSION: Our study demonstrates methods to extract and provide an overview of temporal phenotypes of patient journeys, which can be applied to text-based clinical records for multiple species or clinical conditions. We demonstrate the effectiveness of this approach to derive real-world evidence of treatment impacts using cat bite abscesses as a model condition to describe patterns of antimicrobial therapy prescriptions and their outcomes.

Genome-wide identification and expression analysis of the C2H2-zinc finger transcription factor gene family and screening of candidate genes involved in floral development in Coptis teeta Wall. (Ranunculaceae).

Background: C2H2-zinc finger transcription factors comprise one of the largest and most diverse gene superfamilies and are involved in the transcriptional regulation of flowering. Although a large number of C2H2 zinc-finger proteins (C2H2-ZFPs) have been well characterized in a number of model plant species, little is known about their expression and function in Coptis teeta. C. teeta displays two floral phenotypes (herkogamy phenotypes). It has been proposed that the C2H2-zinc finger transcription factor family may play a crucial role in the formation of floral development and herkogamy observed in C. teeta. As such, we performed a genome-wide analysis of the C2H2-ZFP gene family in C. teeta. Results: The complexity and diversity of C. teeta C2H2 zinc finger proteins were established by evaluation of their physicochemical properties, phylogenetic relationships, exon-intron structure, and conserved motifs. Chromosome localization showed that 95 members of the C2H2 zinc-finger genes were unevenly distributed across the nine chromosomes of C. teeta, and that these genes were replicated in tandem and segmentally and had undergone purifying selection. Analysis of cis-acting regulatory elements revealed a possible involvement of C2H2 zinc-finger proteins in the regulation of phytohormones. Transcriptome data was then used to compare the expression levels of these genes during the growth and development of the two floral phenotypes (F-type and M-type). These data demonstrate that in groups A and B, the expression levels of 23 genes were higher in F-type flowers, while 15 genes showed higher expressions in M-type flowers. qRT-PCR analysis further revealed that the relative expression was highly consistent with the transcriptome data. Conclusion: These data provide a solid basis for further in-depth studies of the C2H2 zinc finger transcription factor gene family in this species and provide preliminary information on which to base further research into the role of the C2H2 ZFPs gene family in floral development in C. teeta.

Temporal and spatial regulation of glucomannan deposition and mobilization in corms of Amorphophallus konjac (Araceae).

PREMISE OF THE STUDY: Konjac glucomannan (KGM), the main biologically active constituent of konjac flour extracted from corms of Amorphophallus konjac (konjac), has potential to be used as a nutraceutical (satiety agent) to combat obesity. Here we present the results of an immunocytochemical investigation of the developmental regulation of the deposition and mobilization of glucomannan in corm tissues of konjac, using an antiheteromannan (mannan/glucomannan) antiserum. METHODS: The intensity of antibody binding to glucomannan idioblasts at six developmental stages (i.e., dormancy, leaf bud emergence, leaf bud elongation, leaflet emergence, leaf expansion, and shoot senescence) was compared. KEY RESULTS: A temporally regulated pattern of glucomannan deposition and mobilization within the glucomannan idioblasts was observed. A source-sink transition in the corm was shown to occur after leaflet emergence, prior to complete expansion of the leaves. Our data also suggest that the mobilization of KGM initiates at the periphery of the corm and proceeds inward toward the center of the corm. CONCLUSIONS: This study represents a significant milestone in our understanding of the mechanisms involved in the physiological and biochemical control of KGM biosynthesis, partitioning, storage, and remobilization. Moreover, this information and the methodology presented provide valuable data for future improvement of the yield and productivity of this important crop.

Analysis of predictive performance and reliability of classifiers for quality assessment of medical evidence revealed important variation by medical area.

OBJECTIVES: A major obstacle in deployment of models for automated quality assessment is their reliability. To analyze their calibration and selective classification performance. STUDY DESIGN AND SETTING: We examine two systems for assessing the quality of medical evidence, EvidenceGRADEr and RobotReviewer, both developed from Cochrane Database of Systematic Reviews (CDSR) to measure strength of bodies of evidence and risk of bias (RoB) of individual studies, respectively. We report their calibration error and Brier scores, present their reliability diagrams, and analyze the risk-coverage trade-off in selective classification. RESULTS: The models are reasonably well calibrated on most quality criteria (expected calibration error [ECE] 0.04-0.09 for EvidenceGRADEr, 0.03-0.10 for RobotReviewer). However, we discover that both calibration and predictive performance vary significantly by medical area. This has ramifications for the application of such models in practice, as average performance is a poor indicator of group-level performance (e.g., health and safety at work, allergy and intolerance, and public health see much worse performance than cancer, pain, and anesthesia, and Neurology). We explore the reasons behind this disparity. CONCLUSION: Practitioners adopting automated quality assessment should expect large fluctuations in system reliability and predictive performance depending on the medical area. Prospective indicators of such behavior should be further researched.

Evaluation of the Effect of Plectranthus amboinicus L. Leaf Extracts on the Bacterial Antioxidant System and Cell Membrane Integrity of Pseudomonas aeruginosa PA01 and Staphylococcus aureus NCTC8325.

Plectranthus amboinicus (Indian borage) has been extensively studied for its medicinal properties, which can be exploited to develop new antimicrobial therapeutics. The current study investigated the effect of Plectranthus amboinicus leaf extracts on the catalase activity, reactive oxygen species, lipid peroxidation, cytoplasmic membrane permeability, and efflux pump activity in S. aureus NCTC8325 and P. aeruginosa PA01. As the enzyme catalase protects bacteria against oxidative stress, disruption of its activity creates an imbalance in reactive oxygen species (ROS) levels, which subsequently oxidizes lipid chains, leading to lipid peroxidation. In addition, bacterial cell membranes are a potential target for new antibacterial agents, as efflux pump systems play a crucial role in antimicrobial resistance. Upon exposure of the microorganisms to Indian borage leaf extracts, the observed catalase activity decreased by 60% and 20% in P. aeruginosa and S. aureus, respectively. The generation of ROS can cause oxidation reactions to occur within the polyunsaturated fatty acids of the lipid membranes and induce lipid peroxidation. To investigate these phenomena, the increase in ROS activity in P. aeruginosa and S. aureus was studied using H2DCFDA, which is oxidized to 2',7'-dichlorofluorescein (DCF) by ROS. Furthermore, the concentration of lipid peroxidation product (malondialdehyde) was assessed using the Thiobarbituric acid assay and was shown to increase by 42.4% and 42.5% in P. aeruginosa and S. aureus, respectively. The effect of the extracts on the cell membrane permeability was monitored using diSC3-5 dye and it was observed that the cell membrane permeability of P. aeruginosa increased by 58% and of S. aureus by 83%. The effect on efflux pump activity was investigated using Rhodamine-6-uptake assay, which displayed a decrease in efflux activity of 25.5% in P. aeruginosa and 24.2% in S. aureus after treatment with the extracts. This combination of different methods to study various bacterial virulence factors provides a more robust, mechanistic understanding of the effect of P. amboinicus extracts on P. aeruginosa and S. aureus. This study thus represents the first report of the assessment of the effect of Indian borage leaf extracts on bacterial antioxidant systems and bacterial cell membranes, and can facilitate the future development of bacterial resistance modifying agents derived from P. amboinicus.

Distribution of lipid transfer protein 1 (LTP1) epitopes associated with morphogenic events during somatic embryogenesis of Arabidopsis thaliana.

Using immunocytochemical methods, at both the light and electron microscopic level, we have investigated the spatial and temporal distribution of lipid transfer protein 1 (LTP1) epitopes during the induction of somatic embryogenesis in explants of Arabidopsis thaliana. Immunofluorescence labelling demonstrated the presence of high levels of LTP1 epitopes within the proximal regions of the cotyledons (embryogenic regions) associated with particular morphogenetic events, including intense cell division activity, cotyledon swelling, cell loosening and callus formation. Precise analysis of the signal localization in protodermal and subprotodermal cells indicated that cells exhibiting features typical of embryogenic cells were strongly labelled, both in walls and the cytoplasm, while in the majority of meristematic-like cells no signal was observed. Staining with lipophilic dyes revealed a correlation between the distribution of LTP1 epitopes and lipid substances within the cell wall. Differences in label abundance and distribution between embryogenic and non-embryogenic regions of explants were studied in detail with the use of immunogold electron microscopy. The labelling was strongest in both the outer periclinal and anticlinal walls of the adaxial, protodermal cells of the proximal region of the cotyledon. The putative role(s) of lipid transfer proteins in the formation of lipid lamellae and in cell differentiation are discussed. Key message Occurrence of lipid transfer protein 1 epitopes in Arabidopsis explant cells accompanies changes in cell fate and may be correlated with the deposition of lipid substances in the cell walls.

Detecting modification of biomedical events using a deep parsing approach.

BACKGROUND: This work describes a system for identifying event mentions in bio-molecular research abstracts that are either speculative (e.g. analysis of IkappaBalpha phosphorylation, where it is not specified whether phosphorylation did or did not occur) or negated (e.g. inhibition of IkappaBalpha phosphorylation, where phosphorylation did not occur). The data comes from a standard dataset created for the BioNLP 2009 Shared Task. The system uses a machine-learning approach, where the features used for classification are a combination of shallow features derived from the words of the sentences and more complex features based on the semantic outputs produced by a deep parser. METHOD: To detect event modification, we use a Maximum Entropy learner with features extracted from the data relative to the trigger words of the events. The shallow features are bag-of-words features based on a small sliding context window of 3-4 tokens on either side of the trigger word. The deep parser features are derived from parses produced by the English Resource Grammar and the RASP parser. The outputs of these parsers are converted into the Minimal Recursion Semantics formalism, and from this, we extract features motivated by linguistics and the data itself. All of these features are combined to create training or test data for the machine learning algorithm. RESULTS: Over the test data, our methods produce approximately a 4% absolute increase in F-score for detection of event modification compared to a baseline based only on the shallow bag-of-words features. CONCLUSIONS: Our results indicate that grammar-based techniques can enhance the accuracy of methods for detecting event modification.

Evaluating the dose, indication and agreement with guidelines of antimicrobial use in companion animal practice with natural language processing.

BACKGROUND: As antimicrobial prescribers, veterinarians contribute to the emergence of MDR pathogens. Antimicrobial stewardship programmes are an effective means of reducing the rate of development of antimicrobial resistance. A key component of antimicrobial stewardship programmes is selecting an appropriate antimicrobial agent for the presenting complaint and using an appropriate dose rate for an appropriate duration. OBJECTIVES: To describe antimicrobial usage, including dose, for common indications for antimicrobial use in companion animal practice. METHODS: Natural language processing (NLP) techniques were applied to extract and analyse clinical records. RESULTS: A total of 343 668 records for dogs and 109 719 records for cats administered systemic antimicrobials from 1 January 2013 to 31 December 2017 were extracted from the database. The NLP algorithms extracted dose, duration of therapy and diagnosis completely for 133 046 (39%) of the records for dogs and 40 841 records for cats (37%). The remaining records were missing one or more of these elements in the clinical data. The most common reason for antimicrobial administration was skin disorders (n = 66 198, 25%) and traumatic injuries (n = 15 932, 19%) in dogs and cats, respectively. Dose was consistent with guideline recommendations in 73% of cases where complete clinical data were available. CONCLUSIONS: Automated extraction using NLP methods is a powerful tool to evaluate large datasets and to enable veterinarians to describe the reasons that antimicrobials are administered. However, this can only be determined when the data presented in the clinical record are complete, which was not the case in most instances in this dataset. Most importantly, the dose administered varied and was often not consistent with guideline recommendations.

RNA-seq analysis reveals key genes associated with seed germination of Fritillaria taipaiensis P.Y.Li by cold stratification.

Seed dormancy is an adaptive strategy for environmental evolution. However, the molecular mechanism of the breaking of seed dormancy at cold temperatures is still unclear, and the genetic regulation of germination initiated by exposure to cold temperature requires further investigation. In the initial phase of the current study, the seed coat characteristics and embryo development of Fritillaria taipaiensis P.Y.Li at different temperatures (0°C, 4°C, 10°C & 25°C) was recorded. The results obtained demonstrated that embryo elongation and the dormancy-breaking was most significantly affected at 4°C. Subsequently, transcriptome analyses of seeds in different states of dormancy, at two stratification temperatures (4°C and 25°C) was performed, combined with weighted gene coexpression network analysis (WGCNA) and metabolomics, to explore the transcriptional regulation of seed germination in F. taipaiensis at the two selected stratification temperatures. The results showed that stratification at the colder temperature (4°C) induced an up-regulation of gene expression involved in gibberellic acid (GA) and auxin biosynthesis and the down-regulation of genes related to the abscisic acid (ABA) biosynthetic pathway. Thereby promoting embryo development and the stimulation of seed germination. Collectively, these data constitute a significant advance in our understanding of the role of cold temperatures in the regulation of seed germination in F. taipaiensis and also provide valuable transcriptomic data for seed dormancy for other non-model plant species.

Word sense disambiguation for event trigger word detection in biomedicine.

This paper describes a method for detecting event trigger words in biomedical text based on a word sense disambiguation (WSD) approach. We first investigate the applicability of existing WSD techniques to trigger word disambiguation in the BioNLP 2009 shared task data, and find that we are able to outperform a traditional CRF-based approach for certain word types. On the basis of this finding, we combine the WSD approach with the CRF, and obtain significant improvements over the standalone CRF, gaining particularly in recall.