When is star formation episodic? A delay differential equation negative feedback model

We introduce a differential equation for star formation in galaxies that incorporates negative feedback with a delay. When the feedback is instantaneous, solutions approach a self-limiting equilibrium state. When there is a delay, even though the feedback is negative, the solutions can exhibit cyclic and episodic solutions. We find that periodic or episodic star formation only occurs when two conditions are satisfied. Firstly the delay timescale must exceed a cloud consumption timescale. Secondly the feedback must be strong. This statement is quantitatively equivalent to requiring that the timescale to approach equilibrium be greater than approximately twice the cloud consumption timescale. The period of oscillations predicted is approximately 4 times the delay timescale. The amplitude of the oscillations increases with both feedback strength and delay time. We discuss applications of the delay differential equation (DDE) model to star formation in galaxies using the cloud density as a variable. The DDE model is most applicable to systems that recycle gas and only slowly remove gas from the system. We propose likely delay mechanisms based on the requirement that the delay time is related to the observationally estimated time between episodic events. The proposed delay timescale accounting for episodic star formation in galaxy centers on periods similar to P 10 Myrs, irregular galaxies with P 100 Myrs, and the Milky Way disk with P~ 2Gyr, could be that for exciting turbulence following creation of massive stars, that for gas pushed into the halo to return and interact with the disk and that for spiral density wave evolution, respectively.Comment: submitted to MNRA

Phosphoinositide 3-kinase δ inhibition promotes antitumor responses but antagonizes checkpoint inhibitors.

Multiple modes of immunosuppression restrain immune function within tumors. We previously reported that phosphoinositide 3-kinase δ (PI3Kδ) inactivation in mice confers resistance to a range of tumor models by disrupting immunosuppression mediated by regulatory T cells (Tregs). The PI3Kδ inhibitor idelalisib has proven highly effective in the clinical treatment of chronic lymphocytic leukemia and the potential to extend the use of PI3Kδ inhibitors to nonhematological cancers is being evaluated. In this work, we demonstrate that the antitumor effect of PI3Kδ inactivation is primarily mediated through the disruption of Treg function, and correlates with tumor dependence on Treg immunosuppression. Compared with Treg-specific PI3Kδ deletion, systemic PI3Kδ inactivation is less effective at conferring resistance to tumors. We show that PI3Kδ deficiency impairs the maturation and reduces the capacity of CD8+ cytotoxic T lymphocytes (CTLs) to kill tumor cells in vitro, and to respond to tumor antigen-specific immunization in vivo. PI3Kδ inactivation antagonized the antitumor effects of tumor vaccines and checkpoint blockade therapies intended to boost the CD8+ T cell response. These findings provide insights into mechanisms by which PI3Kδ inhibition promotes antitumor immunity and demonstrate that the mechanism is distinct from that mediated by immune checkpoint blockade.ELL was supported by the Yousef Jameel Scholarship (Cambridge Trust). DG was funded by a grant from Karus Therapeutics Ltd. RR and KO received institute support from Biotechnology and Biological Sciences Research Council (BBSRC) BBS/E/B/000C0407, - C0409, -C0427 and -C0428 and project grant BB/N007794/1. RR was supported by Wellcome Trust grant 105663/Z/14/Z. KO was also supported by Wellcome Trust grant 095198/Z/10/Z

Training data distribution significantly impacts the estimation of tissue microstructure with machine learning

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

Compensation between CSF1R+ macrophages and Foxp3+ Treg cells drives resistance to tumor immunotherapy.

Redundancy and compensation provide robustness to biological systems but may contribute to therapy resistance. Both tumor-associated macrophages (TAMs) and Foxp3+ regulatory T (Treg) cells promote tumor progression by limiting antitumor immunity. Here we show that genetic ablation of CSF1 in colorectal cancer cells reduces the influx of immunosuppressive CSF1R+ TAMs within tumors. This reduction in CSF1-dependent TAMs resulted in increased CD8+ T cell attack on tumors, but its effect on tumor growth was limited by a compensatory increase in Foxp3+ Treg cells. Similarly, disruption of Treg cell activity through their experimental ablation produced moderate effects on tumor growth and was associated with elevated numbers of CSF1R+ TAMs. Importantly, codepletion of CSF1R+ TAMs and Foxp3+ Treg cells resulted in an increased influx of CD8+ T cells, augmentation of their function, and a synergistic reduction in tumor growth. Further, inhibition of Treg cell activity either through systemic pharmacological blockade of PI3Kδ, or its genetic inactivation within Foxp3+ Treg cells, sensitized previously unresponsive solid tumors to CSF1R+ TAM depletion and enhanced the effect of CSF1R blockade. These findings identify CSF1R+ TAMs and PI3Kδ-driven Foxp3+ Treg cells as the dominant compensatory cellular components of the immunosuppressive tumor microenvironment, with implications for the design of combinatorial immunotherapies.D. Gyori was funded by a research grant from Karus Therapeutics. E.L. Lim was supported by a Yousef Jameel Scholarship (Cambridge Trust). R. Roychoudhuri and K. Okkenhaug received institute support from Biotechnology and Biological Sciences Research Council (BBSRC) (BBS/E/B/000 -C0407, -C0409, -C0427 and -C0428). K. Okkenhaug was also supported by Wellcome Trust grant 095198/Z/10/Z. L.R. Stephens and P.T. Hawkins were supported by and institute grant from the BBSRC (BB/J004456/1). R. Roychoudhuri is supported by the Wellcome Trust/Royal Society (Grant 105663/Z/14/Z), the UK Biotechnology and Biological Sciences Research Council (Grant BB/N007794/1), and Cancer Research UK (Grant C52623/A22597)

Photo(geno)toxicity changes associated with hydroxylation of the aromatic chromophores during diclofenac metabolism

[EN] Diclofenac (DCF) can cause adverse reactions such as gastrointestinal, renal and cardiovascular disorders; therefore, topical administration may be an attractive alternative to the management of local pain in order to avoid these side effects. However, previous studies have shown that DCF, in combination with sunlight, displays capability to induce photosensitivity disorders. In humans, DCF is biotransformed into hydroxylated metabolites at positions 4¿ and 5 (4¿OH-DCF and 5OH-DCF), and this chemical change produces non negligible alterations of the drug chromophore, resulting in a significant modification of its light-absorbing properties. In the present work, 5OH-DCF exhibited higher photo(geno)toxic potential than the parent drug, as shown by several in vitro assays (3T3 NRU phototoxicity, DNA ssb gel electrophoresis and COMET), whereas 4¿OH-DCF did not display significant photo(geno)toxicity. This could be associated, at least partially with their more efficient UV-light absorption by 5OH-DCF metabolite and with a higher photoreactivity. Interestingly, most of the cellular DNA damage photosensitized by DCF and 5OH-DCF was repaired by the cells after several hours, although this effect was not complete in the case of 5OH-DCF.This work was supported by the Carlos III Institute of Health (Grants: RD16/0006/0030, PI16/01877), by the MINECO (Grants: CTQ2013-47872, CTQ2016-78875), and by the Generalitat Valenciana (Prometeo 2017/075).García -Laínez, G.; Ana M Marínez-Reig; Limones Herrero, D.; Jiménez Molero, MC.; Miranda Alonso, MÁ.; Andreu Ros, MI. (2018). Photo(geno)toxicity changes associated with hydroxylation of the aromatic chromophores during diclofenac metabolism. Toxicology and Applied Pharmacology. 341:51-55. https://doi.org/10.1016/j.taap.2018.01.005S515534

Comparative analysis of signal models for microscopic fractional anisotropy estimation using q-space trajectory encoding

Microscopic diffusion anisotropy imaging using diffusion-weighted MRI and multidimensional diffusion encoding is a promising method for quantifying clinically and scientifically relevant microstructural properties of neural tissue. Several methods for estimating microscopic fractional anisotropy (µFA), a normalized measure of microscopic diffusion anisotropy, have been introduced but the differences between the methods have received little attention thus far. In this study, the accuracy and precision of µFA estimation using q-space trajectory encoding and different signal models were assessed using imaging experiments and simulations. Three healthy volunteers and a microfibre phantom were imaged with five non-zero b-values and gradient waveforms encoding linear and spherical b-tensors. Since the ground-truth µFA was unknown in the imaging experiments, Monte Carlo random walk simulations were performed using axon-mimicking fibres for which the ground truth was known. Furthermore, parameter bias due to time-dependent diffusion was quantified by repeating the simulations with tuned waveforms, which have similar power spectra, and with triple diffusion encoding, which, unlike q-space trajectory encoding, is not based on the assumption of time-independent diffusion. The truncated cumulant expansion of the powder-averaged signal, gamma-distributed diffusivities assumption, and q-space trajectory imaging, a generalization of the truncated cumulant expansion to individual signals, were used to estimate µFA. The gamma-distributed diffusivities assumption consistently resulted in greater µFA values than the second order cumulant expansion, 0.1 greater when averaged over the whole brain. In the simulations, the generalized cumulant expansion provided the most accurate estimates. Importantly, although time-dependent diffusion caused significant overestimation of µFA using all the studied methods, the simulations suggest that the resulting bias in µFA is less than 0.1 in human white matter