The immunosuppressive tumor microenvironment limits the success of current immunotherapies. The host retains memory T cells specific for previous infections throughout the entire body that are ...capable of executing potent and immediate immunostimulatory functions. Here we show that virus-specific memory T cells extend their surveillance to mouse and human tumors. Reactivating these antiviral T cells can arrest growth of checkpoint blockade-resistant and poorly immunogenic tumors in mice after injecting adjuvant-free non-replicating viral peptides into tumors. Peptide mimics a viral reinfection event to memory CD8+ T cells, triggering antigen presentation and cytotoxic pathways within the tumor, activating dendritic cells and natural killer cells, and recruiting the adaptive immune system. Viral peptide treatment of ex vivo human tumors recapitulates immune activation gene expression profiles observed in mice. Lastly, peptide therapy renders resistant mouse tumors susceptible to PD-L1 blockade. Thus, re-stimulating known antiviral immunity may provide a unique therapeutic approach for cancer immunotherapy.
Spectral matching of MS
fragmentation spectra has become a popular method for characterizing natural products libraries but identification remains challenging due to differences in MS
fragmentation ...properties between instruments and the low coverage of current spectral reference libraries. To address this bottleneck we present Structural similarity Network Annotation Platform for Mass Spectrometry (SNAP-MS) which matches chemical similarity grouping in the Natural Products Atlas to grouping of mass spectrometry features from molecular networking. This approach assigns compound families to molecular networking subnetworks without the need for experimental or calculated reference spectra. We demonstrate SNAP-MS can accurately annotate subnetworks built from both reference spectra and an in-house microbial extract library, and correctly predict compound families from published molecular networks acquired on a range of MS instrumentation. Compound family annotations for the microbial extract library are validated by co-injection of standards or isolation and spectroscopic analysis. SNAP-MS is freely available at www.npatlas.org/discover/snapms .
Both language and genes evolve by transmission over generations with opportunity for differential replication of forms. The understanding that gene frequencies change at random by genetic drift, even ...in the absence of natural selection, was a seminal advance in evolutionary biology. Stochastic drift must also occur in language as a result of randomness in how linguistic forms are copied between speakers. Here we quantify the strength of selection relative to stochastic drift in language evolution. We use time series derived from large corpora of annotated texts dating from the 12th to 21st centuries to analyse three well-known grammatical changes in English: the regularization of past-tense verbs, the introduction of the periphrastic 'do', and variation in verbal negation. We reject stochastic drift in favour of selection in some cases but not in others. In particular, we infer selection towards the irregular forms of some past-tense verbs, which is likely driven by changing frequencies of rhyming patterns over time. We show that stochastic drift is stronger for rare words, which may explain why rare forms are more prone to replacement than common ones. This work provides a method for testing selective theories of language change against a null model and reveals an underappreciated role for stochasticity in language evolution.
AbstractObjectiveTo characterise the clinical features of patients admitted to hospital with coronavirus disease 2019 (covid-19) in the United Kingdom during the growth phase of the first wave of ...this outbreak who were enrolled in the International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) World Health Organization (WHO) Clinical Characterisation Protocol UK (CCP-UK) study, and to explore risk factors associated with mortality in hospital.DesignProspective observational cohort study with rapid data gathering and near real time analysis.Setting208 acute care hospitals in England, Wales, and Scotland between 6 February and 19 April 2020. A case report form developed by ISARIC and WHO was used to collect clinical data. A minimal follow-up time of two weeks (to 3 May 2020) allowed most patients to complete their hospital admission.Participants20 133 hospital inpatients with covid-19.Main outcome measuresAdmission to critical care (high dependency unit or intensive care unit) and mortality in hospital.ResultsThe median age of patients admitted to hospital with covid-19, or with a diagnosis of covid-19 made in hospital, was 73 years (interquartile range 58-82, range 0-104). More men were admitted than women (men 60%, n=12 068; women 40%, n=8065). The median duration of symptoms before admission was 4 days (interquartile range 1-8). The commonest comorbidities were chronic cardiac disease (31%, 5469/17 702), uncomplicated diabetes (21%, 3650/17 599), non-asthmatic chronic pulmonary disease (18%, 3128/17 634), and chronic kidney disease (16%, 2830/17 506); 23% (4161/18 525) had no reported major comorbidity. Overall, 41% (8199/20 133) of patients were discharged alive, 26% (5165/20 133) died, and 34% (6769/20 133) continued to receive care at the reporting date. 17% (3001/18 183) required admission to high dependency or intensive care units; of these, 28% (826/3001) were discharged alive, 32% (958/3001) died, and 41% (1217/3001) continued to receive care at the reporting date. Of those receiving mechanical ventilation, 17% (276/1658) were discharged alive, 37% (618/1658) died, and 46% (764/1658) remained in hospital. Increasing age, male sex, and comorbidities including chronic cardiac disease, non-asthmatic chronic pulmonary disease, chronic kidney disease, liver disease and obesity were associated with higher mortality in hospital.ConclusionsISARIC WHO CCP-UK is a large prospective cohort study of patients in hospital with covid-19. The study continues to enrol at the time of this report. In study participants, mortality was high, independent risk factors were increasing age, male sex, and chronic comorbidity, including obesity. This study has shown the importance of pandemic preparedness and the need to maintain readiness to launch research studies in response to outbreaks.Study registrationISRCTN66726260.
Ambiphilic molecules, which contain a Lewis base and Lewis acid, are of great interest based on their unique ability to activate small molecules. Phosphine boronates are one class of these substrates ...that have interesting catalytic activity. Direct access to these phosphine boronates is described through the iridium‐catalyzed C−H borylation of phosphines. An unconventional cationic iridium catalyst was identified as optimal for a range of phosphines, providing good yields and selectivity across a diverse class of phosphine boronates (isolated as the borane‐protected phosphine). A complimentary catalyst system (quinoline‐based silane ligand with (COD)IrOMe2) was optimal for biphenyl‐based phosphines. Selective polyborylation was also shown providing bis‐ and tris‐borylated phosphines. Deprotection of the phosphine boronate provided free ambiphilic phosphine boronates, which do not have detectable interactions between the phosphorus and boron atoms in solution or the solid state.
Phosphine‐directed C−H borylation: A series of commonly used phosphines undergo iridium‐catalyzed C−H borylation, providing a range of ambiphilic phosphine boronates. The unexpected cationic iridium catalyst overcomes limited reactivity, allowing a large range of phosphines to be selectively functionalized, including selective polyborylation of several phosphines.
ABSTRACT
For stars with unresolved companions, motions of the centre of light and that of mass decouple, causing a single-source astrometric model to perform poorly. We show that such stars can be ...easily detected with the reduced χ2 statistic, or renormalized unit weight error (RUWE), provided as part of Gaia DR2. We convert RUWE into the amplitude of the image centroid wobble, which, if scaled by the source distance, is proportional to the physical separation between companions (for periods up to several years). We test this idea on a sample of known spectroscopic binaries and demonstrate that the amplitude of the centroid perturbation scales with the binary period and the mass ratio as expected. We apply this technique to the Gaia DR2 data and show how the binary fraction evolves across the Hertzsprung–Russell diagram. The observed incidence of unresolved companions is high for massive young stars and drops steadily with stellar mass, reaching its lowest levels for white dwarfs. We highlight the elevated binary fraction for the nearby blue stragglers and blue horizontal branch stars. We also illustrate how unresolved hierarchical triples inflate the relative velocity signal in wide binaries. Finally, we point out a hint of evidence for the existence of additional companions to the hosts of extrasolar hot Jupiters.
Ambiphilic ligands have received considerable attention over the last two decades due to their unique reactivity as organocatalysts and ligands. The iridium‐catalyzed CH borylation of phosphines is ...described in which the phosphine is used as a directing group to provide selective formation of arylboronate esters with unique scaffolds of ambiphilic compounds. A variety of aryl and benzylic phosphines were subjected to the reaction conditions, selectively providing stable, isolable boronate esters upon protection of the phosphine as the borane complex. After purification, the phosphine‐substituted boronate esters could be deprotected and isolated in pure form.
Directed borylation: Ambiphilic phosphine boronate esters are accessed by a phosphine‐directed CH borylation reaction. The Lewis basic phosphine directs the borylation to the adjacent CH bond, providing access to intriguing ligand and catalyst scaffolds.
Purpose
Supervised machine learning (ML) provides a compelling alternative to traditional model fitting for parameter mapping in quantitative MRI. The aim of this work is to demonstrate and quantify ...the effect of different training data distributions on the accuracy and precision of parameter estimates when supervised ML is used for fitting.
Methods
We fit a two‐ and three‐compartment biophysical model to diffusion measurements from in‐vivo human brain, as well as simulated diffusion data, using both traditional model fitting and supervised ML. For supervised ML, we train several artificial neural networks, as well as random forest regressors, on different distributions of ground truth parameters. We compare the accuracy and precision of parameter estimates obtained from the different estimation approaches using synthetic test data.
Results
When the distribution of parameter combinations in the training set matches those observed in healthy human data sets, we observe high precision, but inaccurate estimates for atypical parameter combinations. In contrast, when training data is sampled uniformly from the entire plausible parameter space, estimates tend to be more accurate for atypical parameter combinations but may have lower precision for typical parameter combinations.
Conclusion
This work highlights that estimation of model parameters using supervised ML depends strongly on the training‐set distribution. We show that high precision obtained using ML may mask strong bias, and visual assessment of the parameter maps is not sufficient for evaluating the quality of the estimates.
•B-tensor encoding enables estimation of spherical cellular structures in the brain.•Spherical compartments may provide markers for apparent neural soma density.•Model parameters can be estimated in ...a fast and robust way using deep learning.•Practical acquisition times are achievable on widely available clinical scanners.
Diffusion MRI is a valuable tool for probing tissue microstructure in the brain noninvasively. Today, model-based techniques are widely available and used for white matter characterisation where their development is relatively mature. Conversely, tissue modelling in grey matter is more challenging, and no generally accepted models exist. With advances in measurement technology and modelling efforts, a clinically viable technique that reveals salient features of grey matter microstructure, such as the density of quasi-spherical cell bodies and quasi-cylindrical cell projections, is an exciting prospect. As a step towards capturing the microscopic architecture of grey matter in clinically feasible settings, this work uses a biophysical model that is designed to disentangle the diffusion signatures of spherical and cylindrical structures in the presence of orientation heterogeneity, and takes advantage of B-tensor encoding measurements, which provide additional sensitivity compared to standard single diffusion encoding sequences. For the fast and robust estimation of microstructural parameters, we leverage recent advances in machine learning and replace conventional fitting techniques with an artificial neural network that fits complex biophysical models within seconds. Our results demonstrate apparent markers of spherical and cylindrical geometries in healthy human subjects, and in particular an increased volume fraction of spherical compartments in grey matter compared to white matter. We evaluate the extent to which spherical and cylindrical geometries may be interpreted as correlates of neural soma and neural projections, respectively, and quantify parameter estimation errors in the presence of various departures from the modelling assumptions. While further work is necessary to translate the ideas presented in this work to the clinic, we suggest that biomarkers focussing on quasi-spherical cellular geometries may be valuable for the enhanced assessment of neurodevelopmental disorders and neurodegenerative diseases.
Gastrulation controls the emergence of cellular diversity and axis patterning in the early embryo. In mammals, this transformation is orchestrated by dynamic signalling centres at the interface of ...embryonic and extraembryonic tissues
. Elucidating the molecular framework of axis formation in vivo is fundamental for our understanding of human development
and to advance stem-cell-based regenerative approaches
. Here we illuminate early gastrulation of marmoset embryos in utero using spatial transcriptomics and stem-cell-based embryo models. Gaussian process regression-based 3D transcriptomes delineate the emergence of the anterior visceral endoderm, which is hallmarked by conserved (HHEX, LEFTY2, LHX1) and primate-specific (POSTN, SDC4, FZD5) factors. WNT signalling spatially coordinates the formation of the primitive streak in the embryonic disc and is counteracted by SFRP1 and SFRP2 to sustain pluripotency in the anterior domain. Amnion specification occurs at the boundaries of the embryonic disc through ID1, ID2 and ID3 in response to BMP signalling, providing a developmental rationale for amnion differentiation of primate pluripotent stem cells (PSCs). Spatial identity mapping demonstrates that primed marmoset PSCs exhibit the highest similarity to the anterior embryonic disc, whereas naive PSCs resemble the preimplantation epiblast. Our 3D transcriptome models reveal the molecular code of lineage specification in the primate embryo and provide an in vivo reference to decipher human development.
PDF
