Bayesian hierarchical modeling for signaling pathway inference from single cell interventional data

Recent technological advances have made it possible to simultaneously measure multiple protein activities at the single cell level. With such data collected under different stimulatory or inhibitory conditions, it is possible to infer the causal relationships among proteins from single cell interventional data. In this article we propose a Bayesian hierarchical modeling framework to infer the signaling pathway based on the posterior distributions of parameters in the model. Under this framework, we consider network sparsity and model the existence of an association between two proteins both at the overall level across all experiments and at each individual experimental level. This allows us to infer the pairs of proteins that are associated with each other and their causal relationships. We also explicitly consider both intrinsic noise and measurement error. Markov chain Monte Carlo is implemented for statistical inference. We demonstrate that this hierarchical modeling can effectively pool information from different interventional experiments through simulation studies and real data analysis.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS425 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

The effect of conditional EFNB1 deletion in the T cell compartment on T cell development and function

<p>Abstract</p> <p>Background</p> <p>Eph kinases are the largest family of cell surface receptor tyrosine kinases. The ligands of Ephs, ephrins (EFNs), are also cell surface molecules. Ephs interact with EFNs transmitting signals in both directions, i.e., from Ephs to EFNs and from EFNs to Ephs. EFNB1 is known to be able to co-stimulate T cells <it>in vitro </it>and to modulate thymocyte development in a model of foetal thymus organ culture. To further understand the role of EFNB1 in T cell immunity, we generated T-cell-specific EFNB1 gene knockout mice to assess T cell development and function in these mice.</p> <p>Results</p> <p>The mice were of normal size and cellularity in the thymus and spleen and had normal T cell subpopulations in these organs. The bone marrow progenitors from KO mice and WT control mice repopulated host spleen T cell pool to similar extents. The activation and proliferation of KO T cells was comparable to that of control mice. Naïve KO CD4 cells showed an ability to differentiate into Th1, Th2, Th17 and Treg cells similar to control CD4 cells.</p> <p>Conclusions</p> <p>Our results suggest that the function of EFNB1 in the T cell compartment could be compensated by other members of the EFN family, and that such redundancy safeguards the pivotal roles of EFNB1 in T cell development and function.</p

Iterative Reconstruction Based on Latent Diffusion Model for Sparse Data Reconstruction

Reconstructing Computed tomography (CT) images from sparse measurement is a well-known ill-posed inverse problem. The Iterative Reconstruction (IR) algorithm is a solution to inverse problems. However, recent IR methods require paired data and the approximation of the inverse projection matrix. To address those problems, we present Latent Diffusion Iterative Reconstruction (LDIR), a pioneering zero-shot method that extends IR with a pre-trained Latent Diffusion Model (LDM) as a accurate and efficient data prior. By approximating the prior distribution with an unconditional latent diffusion model, LDIR is the first method to successfully integrate iterative reconstruction and LDM in an unsupervised manner. LDIR makes the reconstruction of high-resolution images more efficient. Moreover, LDIR utilizes the gradient from the data-fidelity term to guide the sampling process of the LDM, therefore, LDIR does not need the approximation of the inverse projection matrix and can solve various CT reconstruction tasks with a single model. Additionally, for enhancing the sample consistency of the reconstruction, we introduce a novel approach that uses historical gradient information to guide the gradient. Our experiments on extremely sparse CT data reconstruction tasks show that LDIR outperforms other state-of-the-art unsupervised and even exceeds supervised methods, establishing it as a leading technique in terms of both quantity and quality. Furthermore, LDIR also achieves competitive performance on nature image tasks. It is worth noting that LDIR also exhibits significantly faster execution times and lower memory consumption compared to methods with similar network settings. Our code will be publicly available

A pathway to the green revolution in emerging economies: how does green technological innovation affect green growth and ecological sustainability?

Green technological innovation (G.T.I.) contributes to making economic growth compatible with ecological sustainability (E.S.). Thus, in light of environmental challenges and attempts of emerging economies’ progress toward a green revolution, this study examines the effects of G.T.I. on green growth (G.G). and E.S. for 25 emerging economies from 1990 to 2018. It also investigates the moderating role of G.T.I. on the impacts of energy intensity and foreign direct investment (F.D.I.) on G.G. and E.S. to illustrate the energy rebound effect and pollution haven hypothesis. The Fully modified least square (F.M.O.L.S.), the Dynamic least square (D.O.L.S.), and the Pooled mean group autoregressive distributed lag (P.M.G./A.R.D.L.) estimators are used. The findings imply that G.T.I. positively impacts G.G. and E.S. in emerging economies. Conversely, F.D.I. and energy intensity have adverse effects on G.G. and E.S. However, the negative effects of F.D.I. and energy intensity on G.G. and E.S. are decreasing with respect to G.T.I., implying that emerging countries promoting G.T.I. minimize the pollution haven effects of F.D.I. and mitigate the negative effect of energy intensity. Therefore, G.T.I. is a vital factor to facilitate the pathway to the green revolution in emerging economies. Policy implications are forwarded based on the findings of the study

Semiconductor saturable absorber mirror passively Q-switched 2.97 μm fluoride fiber laser

A diode-cladding-pumped mid-infrared passively Q-switched Ho 3+-doped fluoride fiber laser using a reverse designed broad band semiconductor saturable mirror (SESAM) was demonstrated. Nonlinear reflectivity of the SESAM was measured using an in-house Yb3+-doped mode-locked fiber laser at 1062 nm. Stable pulse train was produced at a slope efficient of 12.1% with respect to the launched pump power. Maximum pulse energy of 6.65 μJ with a pulse width of 1.68 μs and signal to noise ratio (SNR) of ~50 dB was achieved at a repetition rate of 47.6 kHz and center wavelength of 2.971 μm. To the best of our knowledge, this is the first 3 μm region SESAM based Q-switched fiber laser with the highest average power and pulse energy, as well as the longest wavelength from mid-infrared passively Q-switched fluoride fiber lasers

Wide wavelength selectable all-fiber thulium doped fiber laser between 1925 nm and 2200 nm

We demonstrate an all-fiber Tm3+-doped silica fiber laser operating at a wide selectable wavelength range by using different fiber Bragg gratings (FBGs) as wavelength selection elements. With a specifically designed high reflective (HR) FBG and the fiber end as an output coupler, the lasing in the range from 1975 nm to 2150 nm with slope efficiency of >30% can be achieved. By employing a low reflective (LR) FBG as the output coupler, the obtainable wavelengths were extended to the range between 1925 nm and 2200 nm which is the reported longest wavelength from the Tm3+-doped silica fiber lasers. Furthermore, by employing a FBG array in the laser cavity and inducing bend loss between adjacent FBGs in the array, six switchable lasing wavelengths were achieved. © 2014 Optical Society of America