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Fluorescence lifetime imaging is an important tool in bioimaging that allows one to detect subtle changes in cell dynamics and their environment. Most time-domain approaches currently involve scanning a single illumination point across the sample, which can make imaging dynamic scenes challenging, while single-shot “rapid lifetime determination” can suffer from large uncertainties when the lifetime is not appropriately sampled. Here, we propose a time-folded fluorescence lifetime imaging microscopy (TFFLIM) approach, whereby a time-folding cavity provides multiple spatially sheared replicas of the lifetime, each shifted temporally with respect to a fixed time gate. This provides a robust, single-shot FLIM approach that we experimentally validate across a broad lifetime range on fluorescent beads and Convallaria samples.

In this paper, we investigate whether fusing depth information on top of normal RGB data for camera-based object detection can help to increase the performance of current state-of-the-art single-shot detection networks. Indeed, depth sensing is easily acquired using depth cameras such as a Kinect or stereo setups. We investigate the optimal manner to perform this sensor fusion with a special focus on lightweight single-pass convolutional neural network (CNN) architectures, enabling real-time processing on limited hardware. For this, we implement a network architecture allowing us to parameterize at which network layer both information sources are fused together. We performed exhaustive experiments to determine the optimal fusion point in the network, from which we can conclude that fusing towards the mid to late layers provides the best results. Our best fusion models significantly outperform the baseline RGB network in both accuracy and localization of the detections.

We evaluate residual aliasing among simultaneously excited and acquired slices in slice accelerated multiband (MB) echo planar imaging (EPI). No in-plane accelerations were used in order to maximize and evaluate achievable slice acceleration factors at 3T. We propose a novel leakage (L-) factor to quantify the effects of signal leakage between simultaneously acquired slices. With a standard 32-channel receiver coil at 3T, we demonstrate that slice acceleration factors of up to eight (MB=8) with blipped controlled aliasing in parallel imaging (CAIPI), in the absence of in-plane accelerations, can be used routinely with acceptable image quality and integrity for whole brain imaging. Spectral analyses of single-shot fMRI time series demonstrate that temporal fluctuations due to both neuronal and physiological sources were distinguishable and comparable up to slice-acceleration factors of nine (MB=9). The increased temporal efficiency could be employed to achieve, within a given acquisition period, higher spatial resolution, increased fMRI statistical power, multiple TEs, faster sampling of temporal events in a resting state fMRI time series, increased sampling of q-space in diffusion imaging, or more quiet time during a scan. [Display omitted] •High slice accelerations using multiband (MB) GRE-EPI with blipped CAIPI.•Acceptable MB factors up to 8 with a 32-channel receiver coil at 3T.•Neuronal and physiological sources are distinguishable at high MB factors.•Leakage (L-) factor evaluates residual aliasing among simultaneously acquired slices.•High temporal efficiency with MB-EPI benefits various applications.

X‐ray holographic microscopy is a three‐dimensional (3D) imaging technique for nanoscale‐resolution imaging of morphological features and phase contrast in biological samples and solid‐state materials. However, it is challenging to recover phase and absorbance information from shot‐noise‐limited (SNL) X‐ray holograms acquired under weak illumination. In this study, we propose a deep learning model, named MorpHoloNet‐X, for single‐shot phase and absorbance retrieval from SNL X‐ray holograms using a physics‐driven neural network. By incorporating physics‐based prior knowledge and wave propagation principles into the neural network, MorpHoloNet‐X can directly reconstruct 3D complex wavefield, phase, and absorbance distributions in a simulated 3D volume. The performance of the proposed MorpHoloNet‐X is validated using synthetic and experimental SNL holograms, and the results are compared with those of conventional methods. The proposed technique would be utilized to reconstruct phase and absorbance information from hard X‐ray holograms acquired under rapid acquisition or weak illumination. A physics‐driven deep learning model enables single‐shot retrieval of 3D phase and absorbance from shot‐noise‐limited hard X‐ray holograms. Validated on both synthetic and experimental data, it provides robust reconstruction under weak illumination.

At the free electron laser FLASH at DESY pulse length measurements can be performed with e.g. THz streaking or an analysis using the PolariX TDS. Since THz streaking examines the XUV pulse directly whereas the PolariX TDS focuses on the energy distribution of the (XUV pulse generating) electron bunch, both techniques are capable of analyzing the same XUV pulse simultaneously. We used a newly installed laser heater to shape the electron bunch and therefore influence the XUV pulse profile and compare the resulting pulse shapes measured by THz streaking and the PolariX TDS. We compare average pulse profiles as well as single-shot examples and discuss the challenges of both types of analysis.

The detection performance of small objects in remote sensing images has not been satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) showed remarkable image enhancement performance, but reconstructed images usually miss high-frequency edge information. Therefore, object detection performance showed degradation for small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we applied a new edge-enhanced super-resolution GAN (EESRGAN) to improve the quality of remote sensing images and used different detector networks in an end-to-end manner where detector loss was backpropagated into the EESRGAN to improve the detection performance. We proposed an architecture with three components: ESRGAN, EEN, and Detection network. We used residual-in-residual dense blocks (RRDB) for both the ESRGAN and EEN, and for the detector network, we used a faster region-based convolutional network (FRCNN) (two-stage detector) and a single-shot multibox detector (SSD) (one stage detector). Extensive experiments on a public (car overhead with context) dataset and another self-assembled (oil and gas storage tank) satellite dataset showed superior performance of our method compared to the standalone state-of-the-art object detectors.

The name of one of the authors in the article by Léveillé et al.[(2022), J. Synchrotron Rad.29, 103–110] is corrected. The article by Léveillé et al.[(2022), J. Synchrotron Rad.29, 103–110] is corrected.

We present a laser reaction chamber that we have developed for in situ/operando X‐ray diffraction measurements at the NSLS‐II 28‐ID‐2 X‐ray powder diffraction beamline. This chamber allows for rapid and dynamic sample heating under specialized gas environments, spanning ambient conditions down to vacuum pressures. We demonstrate the capabilities of this setup through two applications: laser‐driven heating in polycrystalline iron oxide and in single‐crystal WTe2. Our measurements reveal the ability to resolve chemical reaction kinetics over minutes with 1 s time resolution. This setup advances opportunities for in situ/operando X‐ray diffraction studies of both bulk and single‐crystal materials. A laser reaction chamber has been developed for in situ/operando X‐ray diffraction measurements on the NSLS‐II 28‐ID‐2 beamline, allowing for rapid sample heating under various gas and vacuum conditions. This capability has enabled the tracking of chemical reaction kinetics in polycrystalline iron oxide and a reversible phase transformation in WTe2 with 1 s time resolution, expanding the opportunities for dynamic studies in both bulk and single‐crystal materials.

We unravel the nonequilibrium correlated quantum quench dynamics of an impurity traveling through a harmonically confined Bose-Einstein condensate in one-dimension. For weak repulsive interspecies interactions the impurity oscillates within the bosonic gas. At strong repulsions and depending on its prequench position the impurity moves towards an edge of the bosonic medium and subsequently equilibrates. This equilibration being present independently of the initial velocity, the position and the mass of the impurity is inherently related to the generation of entanglement in the many-body system. Focusing on attractive interactions the impurity performs a damped oscillatory motion within the bosonic bath, a behavior that becomes more evident for stronger attractions. To elucidate our understanding of the dynamics an effective potential picture is constructed. The effective mass of the emergent quasiparticle is measured and found to be generically larger than the bare one, especially for strong attractions. In all cases, a transfer of energy from the impurity to the bosonic medium takes place. Finally, by averaging over a sample of simulated in situ single-shot images we expose how the single-particle density distributions and the two-body interspecies correlations can be probed.

Sustained‐release vaccine delivery systems may enhance the immunogenicity of subunit vaccines and reduce the need for multiple vaccinations. The aim of this study was to develop a thermoresponsive hydrogel using poloxamer 407‐chitosan (CP) grafted copolymer as a delivery system for single‐shot sustained‐release vaccines. The CP copolymer was synthesized using 1‐ethyl‐3‐(3‐dimethylaminopropyl)‐carbodiimide and N‐hydroxysuccinimide chemistry. The CP copolymer was a free flowing solution at ambient temperature and transformed rapidly into a gel at body temperature. The hydrogels were loaded with vaccine antigen and adjuvants or the vaccine components were encapsulated in poly (lactic‐co‐glycolic acid) nanoparticles in order to ensure synchronous release. The CP hydrogels were stable for up to 18 days in vitro. Release of both nanoparticles and the individual components was complete, with release of the individual components being modulated by incorporation into nanoparticles. In vivo, a single dose of CP hydrogel vaccine induced strong, long lasting, cellular and humoral responses that could protect against the development of tumors in a murine melanoma model. Grafted poloxamer‐chitosan (CP) thermoresponsive hydrogel‐based single‐shot vaccines were developed that released antigen and adjuvant over 2–3 weeks. In vivo, a single dose of CP hydrogel vaccine induced strong, long lasting, cellular and humoral responses that could protect against the development of tumors in a murine melanoma model.