Intra-field Nitrogen Estimation for Wheat and Corn using Unmanned Aerial Vehicle-based and Satellite Multispectral Imagery, Plant Biophysical Variables, Field Properties, and Machine Learning Methods

Management of nitrogen (N) fertilizers is an important agricultural practice and field of research to increase productivity, minimize environmental impacts and the cost of production. To apply N fertilizer at the right rate, time, and place depends on the crop type, desired yield, and field conditions. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral imagery, PlanetScope satellite imagery, vegetation indices (VI), crop height, leaf area index (LAI), field topographic metrics, and soil properties to predict canopy nitrogen weight (g/m2) of corn and wheat fields in southwestern Ontario, Canada. Random Forests (RF) and Support Vector Regression (SVR) machine learning models were tested with combinations of variable datasets and evaluated for accuracy of canopy nitrogen weight prediction. The results demonstrate that UAV and satellite-based prediction models including spectral variables, crop biophysical parameters, and field conditions can provide accurate and useful information for fertilizer management

Combined Computational and Intracellular Peptide Library Screening: Towards a Potent and Selective Fra1 Inhibitor

To date, most research into the inhibition of oncogenic transcriptional regulator, Activator Protein 1 (AP-1), has focused on heterodimers of cJun and cFos. However, the Fra1 homologue remains an important cancer target. Here we describe library design coupled with computational and intracellular screening as an effective methodology to derive an antagonist that is selective for Fra1 relative to Jun counterparts. To do so the isCAN computational tool was used to rapidly screen >75 million peptide library members, narrowing the library size by >99.8% to one accessible to intracellular PCA selection. The resulting 131,072-member library was predicted to contain high quality binders with both a high likelihood of target engagement, while simultaneously avoiding homodimerization and off-target interaction with Jun homologues. PCA screening was next performed to enrich those members that meet these criteria. In particular, optimization was achieved via inclusion of options designed to generate the potential for compromised intermolecular contacts in both desired and non-desired species. This is an often-overlooked prerequisite in the conflicting design requirement of libraries that must be selective for their target in the context of a range of alternative potential interactions. Here we demonstrate that specificity is achieved via a combination of both hydrophobic and electrostatic contacts as exhibited by the selected peptide (Fra1W). In vitro analysis of the desired Fra1-Fra1W interaction further validates high Fra1 affinity (917 nM) yet selective binding relative to Fra1W homodimers or affinity for cJun. The isCANPCA based multidisciplinary approach provides a robust screening pipeline in generating target-specific hits, as well as new insight into rational peptide design in the search for novel bZIP family inhibitors

Patients Prescribed Direct-acting Oral Anticoagulants Have Low Risk of Post-Polypectomy Complications

Background & Aims Use of direct-acting oral anticoagulants (DOACs) is increasing, but little is known about the associated risks in patients undergoing colonoscopy with polypectomy. We aimed to determine the risk of post-polypectomy complications in patients prescribed DOACs. Methods We performed a retrospective analysis using the Clinformatics Data Mart Database (a de-identified administrative database from a large national insurance provider) to identify adults who underwent colonoscopy with polypectomy or endoscopic mucosal resection (EMR) from January 1, 2011, through December 31, 2015. We collected data from 11,504 patients prescribed antithrombotic agents (1590 DOAC, 3471 warfarin, and 6443 clopidogrel) and 599,983 patients not prescribed antithrombotics of interest (controls). We compared 30-day post-polypectomy complications, including gastrointestinal bleeding (GIB), cerebrovascular accident (CVA), myocardial infarction (MI), and hospital admissions, of patients prescribed DOACs, warfarin, or clopidogrel vs controls. Results Post-polypectomy complications were uncommon but occurred in a significantly higher proportion of patients receiving any antithrombotic vs controls (P<0.001). The percentage of patients in the DOAC group with GIB was 0.63% (95% CI, 0.3%–1.2%) vs 0.2% (95% CI, 0.2%–0.3%) in controls. The percentage of patients with CVA in the DOAC group was 0.06% (95% CI, 0.01%–0.35%) vs 0.04% (95% CI, 0.04%–0.05%) in controls. After we adjusted for bridge anticoagulation, EMR, Charlson comorbidity index (CCI), and CHADS2 (congestive heart failure, hypertension, age over 75, diabetes, stroke [double weight]) score, patients prescribed DOACs no longer had a statistically significant increase in the odds of GIB (odds ratio [OR], 0.90; 95% CI, 0.44–1.85), CVA (OR, 0.45; 95% CI, 0.06–3.28), MI (OR, 1.07; 95% CI, 0.14–7.72), or hospital admission (OR, 0.86; 95% CI, 0.64–1.16). Clopidogrel, warfarin, bridge anticoagulation, higher CHADS2, CCI, and EMR were associated with increased odds of complications. Conclusion In our retrospective analysis of a large national dataset, we found that patients prescribed DOACs did not have significantly increased adjusted odds of post-polypectomy GIB, MI, CVA, or hospital admission. Bridge anticoagulation, higher CHADS2 score, CCI, and EMR were risk factors for GIB, MI, CVA, and hospital admissions. Studies are needed to determine the optimal peri-procedural dose for high-risk patients

Feature Weighted Models (FWM) to address lineage dependency in drug-resistance prediction from Mycobacterium tuberculosis genome sequences.

MOTIVATION: Tuberculosis (TB) is caused by members of the Mycobacterium tuberculosis complex (MTBC), which has a strain- or lineage-based clonal population structure. The evolution of drug-resistance in the MTBC poses a threat to successful treatment and eradication of TB. Machine learning approaches are being increasingly adopted to predict drug-resistance and characterise underlying mutations from whole genome sequences. However, such approaches may not generalise well in clinical practice due to confounding from the population structure of the MTBC. RESULTS: To investigate how population structure affects machine learning prediction, we compared three different approaches to reduce lineage dependency in random forest (RF) models, including stratification, feature selection and feature weighted models. All RF models achieved moderate-high performance (AUC-ROC range: 0.60-0.98). First-line drugs had higher performance than second-line drugs, but it varied depending on the lineages in the training dataset. Lineage-specific models generally had higher sensitivity than global models which may be underpinned by strain-specific drug-resistance mutations or sampling effects. The application of feature weights and feature selection approaches reduced lineage dependency in the model and had comparable performance to unweighted RF models. AVAILABILITY AND IMPLEMENTATION: https://github.com/NinaMercedes/RF_lineages. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Transcriptional repressor ZEB2 promotes terminal differentiation of CD8⁺ effector and memory T cell populations during infection

ZEB2 is a multi-zinc-finger transcription factor known to play a significant role in early neurogenesis and in epithelial-mesenchymal transition-dependent tumor metastasis. Although the function of ZEB2 in T lymphocytes is unknown, activity of the closely related family member ZEB1 has been implicated in lymphocyte development. Here, we find that ZEB2 expression is up-regulated by activated T cells, specifically in the KLRG1(hi) effector CD8(+) T cell subset. Loss of ZEB2 expression results in a significant loss of antigen-specific CD8(+) T cells after primary and secondary infection with a severe impairment in the generation of the KLRG1(hi) effector memory cell population. We show that ZEB2, which can bind DNA at tandem, consensus E-box sites, regulates gene expression of several E-protein targets and may directly repress Il7r and Il2 in CD8(+) T cells responding to infection. Furthermore, we find that T-bet binds to highly conserved T-box sites in the Zeb2 gene and that T-bet and ZEB2 regulate similar gene expression programs in effector T cells, suggesting that T-bet acts upstream and through regulation of ZEB2. Collectively, we place ZEB2 in a larger transcriptional network that is responsible for the balance between terminal differentiation and formation of memory CD8(+) T cells

Recruitment of multi-segment genomic RNAs by Bluetongue virus requires a preformed RNA network.

How do segmented RNA viruses correctly recruit their genome has yet to be clarified. Bluetongue virus is a double-stranded RNA virus with 10 segments of different sizes, but it assembles its genome in single-stranded form through a series of specific RNA-RNA interactions prior to packaging. In this study, we determined the structure of each BTV transcript, individually and in different combinations, using 2'-hydroxyl acylation analysed by primer extension and mutational profiling (SHAPE-MaP). SHAPE-MaP identified RNA structural changes during complex formation and putative RNA-RNA interaction sites. Our data also revealed a core RNA-complex of smaller segments which serves as the foundation ('anchor') for the assembly of a complete network composed of ten ssRNA segments. The same order of core RNA complex formation was identified in cells transfected with viral RNAs. No viral protein was required for these assembly reactions. Further, substitution mutations in the interacting bases within the core assemblies, altered subsequent segment addition and affected virus replication. These data identify a wholly RNA driven reaction that may offer novel opportunities for designed attenuation or antiviral therapeutics