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Exploring the metabolic differences directly on tissues is essential for the comprehensive understanding of how multicellular organisms function. Mass spectrometry imaging (MSI) is an attractive technique toward this goal; however, MSI in metabolomics scale has been hindered by multiple limitations. This is most notable for single cell level high-spatial resolution imaging because of the limited number of molecules in small sampling size and the low ionization yields of many metabolites. Several on-tissue chemical derivatization approaches have been reported to increase MSI signals of targeted compounds, especially in matrix-assisted laser desorption/ionization (MALDI)-MSI. Herein, we adopt a combination of chemical derivatization reactions, to selectively enhance metabolite signals of a specific functional group for each consecutive tissue section. Three well-known on-tissue derivatization methods were used as a proof of concept experiment: coniferyl aldehyde for primary amines, Girard's reagent T for carbonyl groups, and 2-picolylamine for carboxylic acids. This strategy was applied to the cross-sections of leaves and roots from two different maize genotypes (B73 and Mo17), and enabled the detection of over six hundred new unique metabolite features compared to without modification. Statistical analysis indicated quantitative variation between metabolites in the tissue sections, while MS images revealed differences in localization of these metabolites. Combined, this untargeted approach facilitated the visualization of various classes of compounds, demonstrating the potential for untargeted MSI in the metabolomics scale.

[This corrects the article DOI: 10.3389/fpls.2019.00860.].[This corrects the article DOI: 10.3389/fpls.2019.00860.].

Dopant-assisted atmospheric pressure chemical ionization (dAPCI) is a soft ionization method rarely used for gas chromatography-mass spectrometry (GC-MS). The current study combines GC-dAPCI with tandem mass spectrometry (MS/MS) for analysis of a complex mixture such as lignin pyrolysis analysis. To identify the structures of volatile lignin pyrolysis products, collision-induced dissociation (CID) MS/MS using a quadrupole time-of-flight mass spectrometer (QTOFMS) and pseudo MS/MS through in-source collision-induced dissociation (ISCID) using a single stage TOFMS are utilized. To overcome the lack of MS/MS database, Compound Structure Identification (CSI):FingerID is used to interpret CID spectra and predict best matched structures from PubChem library. With this approach, a total of 59 compounds were positively identified in comparison to only 22 in NIST database search of GC-EI-MS dataset. This study demonstrates the effectiveness of GC-dAPCI-MS/MS to overcome the limitations of traditional GC-EI-MS analysis when EI-MS database is not sufficient. Graphical Abstract ᅟ

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Salicylate (SA) is a key plant immunity hormone, which induces immunity against biotrophic pathogens. Two SA biosynthetic pathways are known: the isochorismate synthase (ICS) and the phenylalanine ammonia-lyase (PAL) pathways. However, the primary pathways used are known for only a few plant species. For example, the ICS pathway evolved within the Brassicales order, and Arabidopsis, a member of this order, primarily uses it. It is challenging to determine the proportions of these pathway contributions using genetic perturbations because the SA biosynthetic pathways are typically regulated by feedback from SA. We developed a simple method to directly measure the relative contributions of these two pathways to SA synthesis using [13C6]glucose feeding to leaves and LC-MS/MS analysis of the leaf extracts. A survey of 16 diverse eudicot species and a monocot species, rice, showed that all these species, except for the Brassicales species with the ICS pathway, mainly use the PAL pathway. The Brassicales species mostly used the ICS pathway but did not use it exclusively: for example, the contribution of the ICS pathway in Arabidopsis was 84% on average. We found that the main pathway in two basal angiosperm species was neither the ICS nor the PAL pathway. Instead, the [13C]-labeling patterns of SA in these species were consistent with possible involvement of a type III polyketide synthase. Thus, during evolution of flowering plants, the main SA biosynthetic pathways changed drastically at least twice. Pathogen effectors targeting SA biosynthetic pathway components likely exerted selection pressure driving these drastic changes.

Though chickens are the most numerous and ubiquitous domestic bird, their origins, the circumstances of their initial association with people, and the routes along which they dispersed across the world remain controversial. In order to establish a robust spatial and temporal framework for their origins and dispersal, we assessed archaeological occurrences and the domestic status of chickens from ∼600 sites in 89 countries by combining zoogeographic, morphological, osteometric, stratigraphic, contextual, iconographic, and textual data. Our results suggest that the first unambiguous domestic chicken bones are found at Neolithic Ban Non Wat in central Thailand dated to ∼1650 to 1250 BCE, and that chickens were not domesticated in the Indian Subcontinent. Chickens did not arrive in Central China, South Asia, or Mesopotamia until the late second millennium BCE, and in Ethiopia and Mediterranean Europe by ∼800 BCE. To investigate the circumstances of their initial domestication, we correlated the temporal spread of rice and millet cultivation with the first appearance of chickens within the range of red junglefowl species. Our results suggest that agricultural practices focused on the production and storage of cereal staples served to draw arboreal red junglefowl into the human niche. Thus, the arrival of rice agriculture may have first facilitated the initiation of the chicken domestication process, and then, following their integration within human communities, allowed for their dispersal across the globe.

Although recent research has shown that the frontal cortex has a critical role in perceptual decision making, an overarching theory of frontal functional organization for perception has yet to emerge. Perceptual decision making is temporally organized such that it requires the processes of selection, criterion setting, and evaluation. We hypothesized that exploring this temporal structure would reveal a large-scale frontal organization for perception. A causal intervention with transcranial magnetic stimulation revealed clear specialization along the rostrocaudal axis such that the control of successive stages of perceptual decision making was selectively affected by perturbation of successively rostral areas. Simulations with a dynamic model of decision making suggested distinct computational contributions of each region. Finally, the emergent frontal gradient was further corroborated by functional MRI. These causal results provide an organizational principle for the role of frontal cortex in the control of perceptual decision making and suggest specific mechanistic contributions for its different subregions.

MDV3100 is an androgen-receptor antagonist that blocks androgens from binding to the androgen receptor and prevents nuclear translocation and co-activator recruitment of the ligand-receptor complex. It also induces tumour cell apoptosis, and has no agonist activity. Because growth of castration-resistant prostate cancer is dependent on continued androgen-receptor signalling, we assessed the antitumour activity and safety of MDV3100 in men with this disease. This phase 1–2 study was undertaken in five US centres in 140 patients. Patients with progressive, metastatic, castration-resistant prostate cancer were enrolled in dose-escalation cohorts of three to six patients and given an oral daily starting dose of MDV3100 30 mg. The final daily doses studied were 30 mg (n=3), 60 mg (27), 150 mg (28), 240 mg (29), 360 mg (28), 480 mg (22), and 600 mg (3). The primary objective was to identify the safety and tolerability profile of MDV3100 and to establish the maximum tolerated dose. The trial is registered with ClinicalTrials.gov, number NCT00510718. We noted antitumour effects at all doses, including decreases in serum prostate-specific antigen of 50% or more in 78 (56%) patients, responses in soft tissue in 13 (22%) of 59 patients, stabilised bone disease in 61 (56%) of 109 patients, and conversion from unfavourable to favourable circulating tumour cell counts in 25 (49%) of the 51 patients. PET imaging of 22 patients to assess androgen-receptor blockade showed decreased 18F-fluoro-5α-dihydrotestosterone binding at doses from 60 mg to 480 mg per day (range 20–100%). The median time to progression was 47 weeks (95% CI 34–not reached) for radiological progression. The maximum tolerated dose for sustained treatment (>28 days) was 240 mg. The most common grade 3–4 adverse event was dose-dependent fatigue (16 [11%] patients), which generally resolved after dose reduction. We recorded encouraging antitumour activity with MDV3100 in patients with castration-resistant prostate cancer. The results of this phase 1–2 trial validate in man preclinical studies implicating sustained androgen-receptor signalling as a driver in this disease. Medivation, the Prostate Cancer Foundation, National Cancer Institute, the Howard Hughes Medical Institute, Doris Duke Charitable Foundation, and Department of Defense Prostate Cancer Clinical Trials Consortium.

Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries. However, interpretation of knee MRI is time-intensive and subject to diagnostic error and variability. An automated system for interpreting knee MRI could prioritize high-risk patients and assist clinicians in making diagnoses. Deep learning methods, in being able to automatically learn layers of features, are well suited for modeling the complex relationships between medical images and their interpretations. In this study we developed a deep learning model for detecting general abnormalities and specific diagnoses (anterior cruciate ligament [ACL] tears and meniscal tears) on knee MRI exams. We then measured the effect of providing the model's predictions to clinical experts during interpretation. Our dataset consisted of 1,370 knee MRI exams performed at Stanford University Medical Center between January 1, 2001, and December 31, 2012 (mean age 38.0 years; 569 [41.5%] female patients). The majority vote of 3 musculoskeletal radiologists established reference standard labels on an internal validation set of 120 exams. We developed MRNet, a convolutional neural network for classifying MRI series and combined predictions from 3 series per exam using logistic regression. In detecting abnormalities, ACL tears, and meniscal tears, this model achieved area under the receiver operating characteristic curve (AUC) values of 0.937 (95% CI 0.895, 0.980), 0.965 (95% CI 0.938, 0.993), and 0.847 (95% CI 0.780, 0.914), respectively, on the internal validation set. We also obtained a public dataset of 917 exams with sagittal T1-weighted series and labels for ACL injury from Clinical Hospital Centre Rijeka, Croatia. On the external validation set of 183 exams, the MRNet trained on Stanford sagittal T2-weighted series achieved an AUC of 0.824 (95% CI 0.757, 0.892) in the detection of ACL injuries with no additional training, while an MRNet trained on the rest of the external data achieved an AUC of 0.911 (95% CI 0.864, 0.958). We additionally measured the specificity, sensitivity, and accuracy of 9 clinical experts (7 board-certified general radiologists and 2 orthopedic surgeons) on the internal validation set both with and without model assistance. Using a 2-sided Pearson's chi-squared test with adjustment for multiple comparisons, we found no significant differences between the performance of the model and that of unassisted general radiologists in detecting abnormalities. General radiologists achieved significantly higher sensitivity in detecting ACL tears (p-value = 0.002; q-value = 0.019) and significantly higher specificity in detecting meniscal tears (p-value = 0.003; q-value = 0.019). Using a 1-tailed t test on the change in performance metrics, we found that providing model predictions significantly increased clinical experts' specificity in identifying ACL tears (p-value < 0.001; q-value = 0.006). The primary limitations of our study include lack of surgical ground truth and the small size of the panel of clinical experts. Our deep learning model can rapidly generate accurate clinical pathology classifications of knee MRI exams from both internal and external datasets. Moreover, our results support the assertion that deep learning models can improve the performance of clinical experts during medical imaging interpretation. Further research is needed to validate the model prospectively and to determine its utility in the clinical setting.

Identification of specific species in metagenomic samples is critical for several key applications, yet many tools available require large computational power and are often prone to false positive identifications. Here we describe High-AccuracY and Scalable Taxonomic Assignment of MetagenomiC data (HAYSTAC), which can estimate the probability that a specific taxon is present in a metagenome. HAYSTAC provides a user-friendly tool to construct databases, based on publicly available genomes, that are used for competitive read mapping. It then uses a novel Bayesian framework to infer the abundance and statistical support for each species identification and provide per-read species classification. Unlike other methods, HAYSTAC is specifically designed to efficiently handle both ancient and modern DNA data, as well as incomplete reference databases, making it possible to run highly accurate hypothesis-driven analyses ( i . e ., assessing the presence of a specific species) on variably sized reference databases while dramatically improving processing speeds. We tested the performance and accuracy of HAYSTAC using simulated Illumina libraries, both with and without ancient DNA damage, and compared the results to other currently available methods ( i . e ., Kraken2/Bracken, KrakenUniq, MALT/HOPS, and Sigma). HAYSTAC identified fewer false positives than both Kraken2/Bracken, KrakenUniq and MALT in all simulations, and fewer than Sigma in simulations of ancient data. It uses less memory than Kraken2/Bracken, KrakenUniq as well as MALT both during database construction and sample analysis. Lastly, we used HAYSTAC to search for specific pathogens in two published ancient metagenomic datasets, demonstrating how it can be applied to empirical datasets. HAYSTAC is available from https://github.com/antonisdim/HAYSTAC .