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Abstract Quasars are bright active galactic nuclei powered by the accretion of matter around supermassive black holes at the center of galaxies. Their stochastic brightness variability depends on the physical properties of the accretion disk and black hole. The upcoming Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to observe tens of millions of quasars, so there is a need for efficient techniques like machine learning that can handle the large volume of data. Quasar variability is believed to be driven by an X-ray corona, which is reprocessed by the accretion disk and emitted as UV/optical variability. We are the first to introduce an auto-differentiable simulation of the accretion disk and reprocessing. We use the simulation as a direct component of our neural network to jointly model the driving variability and reprocessing, trained with supervised learning on simulated LSST-like 10 yr quasar light curves. We encode the light curves using a transformer encoder, and the driving variability is reconstructed using latent stochastic differential equations, a physically motivated generative deep learning method that can model continuous-time stochastic dynamics. By embedding the physical processes of the driving signal and reprocessing into our network, we achieve a model that is more robust and interpretable. We demonstrate that our model outperforms a Gaussian process regression baseline and can infer accretion disk parameters and time delays between wave bands, even for out-of-distribution driving signals. Our approach provides a powerful framework that can be adapted to solve other inverse problems in multivariate time series. 

CD4 T lymphocytes play a key role in initiating the adaptive immune response, releasing cytokines that mediate numerous signal transduction pathways across the immune system. Therefore, CD4 T cell counts are widely used as an indicator of overall immunological health. HIV, one of the leading causes of death in the developing world, specifically targets and gradually depletes CD4 cells, making CD4 counts a critical metric for monitoring disease progression. As a result, accurately counting CD4 cells represents a pressing challenge in global healthcare. Flow cytometry remains the gold standard for enumerating CD4 T cells; however, flow cytometers are expensive, difficult to transport, and require skilled medical staff to prepare samples, operate the equipment, and interpret results. This highlights the critical need for novel, rapid, cost-effective, and portable methods of CD4 enumeration that are suitable for deployment in resource-limited countries. This review will survey and analyze emerging research in CD4 counting, with a focus on microfluidic systems, which represent a promising area of investigation. 

Abstract Quasars are bright and unobscured active galactic nuclei (AGN) thought to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The temporal variability of a quasar’s brightness contains valuable information about its physical properties. The UV/optical variability is thought to be a stochastic process, often represented as a damped random walk described by a stochastic differential equation (SDE). Upcoming wide-field telescopes such as the Rubin Observatory Legacy Survey of Space and Time (LSST) are expected to observe tens of millions of AGN in multiple filters over a ten year period, so there is a need for efficient and automated modeling techniques that can handle the large volume of data. Latent SDEs are machine learning models well suited for modeling quasar variability, as they can explicitly capture the underlying stochastic dynamics. In this work, we adapt latent SDEs to jointly reconstruct multivariate quasar light curves and infer their physical properties such as the black hole mass, inclination angle, and temperature slope. Our model is trained on realistic simulations of LSST ten year quasar light curves, and we demonstrate its ability to reconstruct quasar light curves even in the presence of long seasonal gaps and irregular sampling across different bands, outperforming a multioutput Gaussian process regression baseline. Our method has the potential to provide a deeper understanding of the physical properties of quasars and is applicable to a wide range of other multivariate time series with missing data and irregular sampling. 

If agricultural plants are exposed to carbon nanotubes (CNTs), they can potentially take up the CNTs from growth media and translocate them to their different tissues. In addition, agricultural application of CNTs recently attracted increasing attention, as they could promote germination, enhance crop yield, and exhibit other benefits. For evaluating the environmental effects of CNTs and optimizing their agricultural application, it is essential to quantify CNTs in plant tissues. In this study, pristine (p-) and carboxyl-functionalized (c-) multiwall CNTs (MWCNTs) were extracted from plant tissues by a sequential digestion with nitric acid (HNO 3 ) and sulfuric acid (H 2 SO 4 ). The extracted MWCNTs were stabilized with nonionic surfactant Triton X-100 and analyzed with ultraviolet-visible (UV-vis) spectroscopic analysis to measure the concentration of the MWCNTs in plant (lettuce) tissues. The MWCNT concentration was linearly correlated with the absorbance at 800 nm. The detection limit for p- and c-MWCNTs was achieved at 0.10–0.12, 0.070–0.081, 0.019–0.18 μg mg −1 for leaf, stem, and root tissues, respectively. The developed method was applied for lettuce ( Lactuca sativa , cv. black seeded Simpson) hydroponically grown with 5, 10, 20 mg L −1 of p-MWCNTs and c-MWCNTs in the culture solution. We detected 0.21 ± 0.05–4.57 ± 0.39 μg mg −1 p-MWCNTs and 0.20 ± 0.17–0.75 ± 0.25 μg mg −1 c-MWCNTs in the lettuce roots, positively correlated with the dose of CNTs in solution. We have developed a method for rapid quantification of CNTs in plant tissues using a widely-accessible technique, which can enable reliable analysis of CNTs in plant tissues and provide critical information for evaluating the environmental implications and managing agricultural application of CNTs. 

Abstract We present the lens mass model of the quadruply-imaged gravitationally lensed quasar WFI2033−4723, and perform a blind cosmographical analysis based on this system. Our analysis combines (1) time-delay measurements from 14 years of data obtained by the COSmological MOnitoring of GRAvItational Lenses (COSMOGRAIL) collaboration, (2) high-resolution Hubble Space Telescope imaging, (3) a measurement of the velocity dispersion of the lens galaxy based on ESO-MUSE data, and (4) multi-band, wide-field imaging and spectroscopy characterizing the lens environment. We account for all known sources of systematics, including the influence of nearby perturbers and complex line-of-sight structure, as well as the parametrization of the light and mass profiles of the lensing galaxy. After unblinding, we determine the effective time-delay distance to be $$4784_{-248}^{+399}~\mathrm{Mpc}$$, an average precision of $$6.6{{\ \rm per\ cent}}$$. This translates to a Hubble constant $$H_{0} = 71.6_{-4.9}^{+3.8}~\mathrm{km~s^{-1}~Mpc^{-1}}$$, assuming a flat ΛCDM cosmology with a uniform prior on Ωm in the range [0.05, 0.5]. This work is part of the H0 Lenses in COSMOGRAIL’s Wellspring (H0LiCOW) collaboration, and the full time-delay cosmography results from a total of six strongly lensed systems are presented in a companion paper (H0LiCOW XIII). 

Abstract We present a measurement of the Hubble constant (H0) and other cosmological parameters from a joint analysis of six gravitationally lensed quasars with measured time delays. All lenses except the first are analyzed blindly with respect to the cosmological parameters. In a flat ΛCDM cosmology, we find $$H_{0} = 73.3_{-1.8}^{+1.7}~\mathrm{km~s^{-1}~Mpc^{-1}}$$, a $$2.4{{\ \rm per\ cent}}$$ precision measurement, in agreement with local measurements of H0 from type Ia supernovae calibrated by the distance ladder, but in 3.1σ tension with Planck observations of the cosmic microwave background (CMB). This method is completely independent of both the supernovae and CMB analyses. A combination of time-delay cosmography and the distance ladder results is in 5.3σ tension with Planck CMB determinations of H0 in flat ΛCDM. We compute Bayes factors to verify that all lenses give statistically consistent results, showing that we are not underestimating our uncertainties and are able to control our systematics. We explore extensions to flat ΛCDM using constraints from time-delay cosmography alone, as well as combinations with other cosmological probes, including CMB observations from Planck, baryon acoustic oscillations, and type Ia supernovae. Time-delay cosmography improves the precision of the other probes, demonstrating the strong complementarity. Allowing for spatial curvature does not resolve the tension with Planck. Using the distance constraints from time-delay cosmography to anchor the type Ia supernova distance scale, we reduce the sensitivity of our H0 inference to cosmological model assumptions. For six different cosmological models, our combined inference on H0 ranges from ∼73–78 km s−1 Mpc−1, which is consistent with the local distance ladder constraints. 

ABSTRACT We present the measurement of the Hubble constant, H0, with three strong gravitational lens systems. We describe a blind analysis of both PG 1115+080 and HE 0435−1223 as well as an extension of our previous analysis of RXJ 1131−1231. For each lens, we combine new adaptive optics (AO) imaging from the Keck Telescope, obtained as part of the SHARP (Strong-lensing High Angular Resolution Programme) AO effort, with Hubble Space Telescope (HST) imaging, velocity dispersion measurements, and a description of the line-of-sight mass distribution to build an accurate and precise lens mass model. This mass model is then combined with the COSMOGRAIL-measured time delays in these systems to determine H0. We do both an AO-only and an AO + HST analysis of the systems and find that AO and HST results are consistent. After unblinding, the AO-only analysis gives $$H_{0}=82.8^{+9.4}_{-8.3}~\rm km\, s^{-1}\, Mpc^{-1}$$ for PG 1115+080, $$H_{0}=70.1^{+5.3}_{-4.5}~\rm km\, s^{-1}\, Mpc^{-1}$$ for HE 0435−1223, and $$H_{0}=77.0^{+4.0}_{-4.6}~\rm km\, s^{-1}\, Mpc^{-1}$$ for RXJ 1131−1231. The joint AO-only result for the three lenses is $$H_{0}=75.6^{+3.2}_{-3.3}~\rm km\, s^{-1}\, Mpc^{-1}$$. The joint result of the AO + HST analysis for the three lenses is $$H_{0}=76.8^{+2.6}_{-2.6}~\rm km\, s^{-1}\, Mpc^{-1}$$. All of these results assume a flat Λ cold dark matter cosmology with a uniform prior on Ωm in [0.05, 0.5] and H0 in [0, 150] $$\rm km\, s^{-1}\, Mpc^{-1}$$. This work is a collaboration of the SHARP and H0LiCOW teams, and shows that AO data can be used as the high-resolution imaging component in lens-based measurements of H0. The full time-delay cosmography results from a total of six strongly lensed systems are presented in a companion paper.