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Many bacteria live in natural and clinical environments with abundant macromolecular polymers. Macromolecular fluids commonly display viscoelasticity and non-Newtonian rheological behavior; it is unclear how these complex-fluid properties affect bacterial transport in flows. Here we combine high-resolution microscopy and numerical simulations to study bacterial response to shear flows of various macromolecular fluids. In stark contrast to the case in Newtonian shear flows, we found that flagellated bacteria in macromolecular flows display a giant capacity of upstream swimming (a behavior resembling fish swimming against current) near solid surfaces: The cells can counteract flow washing at shear rates up to ~65 $s^{-1}$, one order of magnitude higher than the limit for cells swimming in Newtonian flows. The significant enhancement of upstream swimming depends on two characteristic complex-fluid properties, namely viscoelasticity and shear-thinning viscosity; meanwhile, increasing the viscosity with a Newtonian polymer can prevent upstream motion. By visualizing flagellar bundles and modeling bacterial swimming in complex fluids, we explain the phenomenon as primarily arising from the augmentation of a "weathervane effect" in macromolecular flows due to the presence of a viscoelastic lift force and a shear-thinning induced azimuthal torque promoting the alignment of bacteria against the flow direction. Our findings shed light on bacterial transport and surface colonization in macromolecular environments, and may inform the design of artificial helical microswimmers for biomedical applications in physiological conditions.

Bacteria often exhibit upstream swimming, which can cause the contamination of biomedical devices and the infection of organs including the urethra or lungs. This process, called rheotaxis, has been studied extensively in Newtonian fluids. However, most microorganisms thrive in non-Newtonian fluids that contain suspended polymers such as mucus and biofilms. Here, we investigate the rheotatic behavior of E. coli near walls in non-Newtonian fluids. Our experiments demonstrate that bacterial upstream swimming is enhanced by an order of magnitude in shear-thinning polymeric fluids relative to Newtonian fluids. This result is explained by direct numerical simulations, revealing a torque that promotes the alignment of bacteria against the flow. From this analysis, we develop a theoretical model that accurately describes experimental rheotatic data in both Newtonian and shear-thinning fluids.

Earth's aquatic environments are inherently stratified layered systems where interfaces between layers serve as ecological niches for microbial swimmers, forming colonies known as Active Carpet (AC). Previous theoretical studies have explored the hydrodynamic fluctuations exerted by ACs in semi-infinite fluid media, demonstrating their capability to enhance thermal diffusion and mass transport in aquatic systems. Yet, little is understood about the fluid dynamics and impact of ACs residing in confined layered environments, like slicks floating on water bodies. In this study, we report novel solutions for the hydrodynamic fluctuations induced by ACs geometrically confined between a free surface and a fluid-fluid interface characterized by a jump in fluid viscosity. Combining theory and numerical experiments, we investigate the topology of the biogenic hydrodynamic fluctuations in a confined, thin fluid environment. We reveal that within this thin layer, ACs gives shape to three characteristic regions: Region I is the closest zone to the AC and the fluid-fluid interface, where hydrodynamic fluctuations are dominantly vertical; Region II is further up from the AC and is characterized by isotropic hydrodynamic fluctuations; Region III is the furthest region, near the free surface and is dominated by horizontal flow fluctuations. We demonstrate that the extent of these regions depends strongly on the degree of confinement, i.e. the layer thickness and the strength of the viscosity jump. Lastly, we show that confinement fosters the emergence of large-scale flow structures within the layer housing the ACs--not previously reported. Our findings shed light on the complex interplay between confinement and hydrodynamics in floating viscous film biological systems, providing valuable insights with implications spanning from ecological conservation to bio-inspired engineering.

Communities of swimming microorganisms often thrive near liquid-air interfaces. We study how such `active carpets' shape their aquatic environment by driving biogenic transport in the water column beneath them. The hydrodynamic stirring that active carpets generate leads to diffusive upward fluxes of nutrients from deeper water layers, and downward fluxes of oxygen and carbon. Combining analytical theory and simulations, we examine the biogenic transport by studying fundamental metrics, including the single and pair diffusivity, the first passage time for particle pair encounters, and the rate of particle aggregation. Our findings reveal that the hydrodynamic fluctuations driven by active carpets have a region of influence that reaches orders of magnitude further in distance than the size of the organisms. These nonequilibrium fluctuations lead to a strongly enhanced diffusion of particles, which is anisotropic and space-dependent. Fluctuations also facilitate encounters of particle pairs, which we quantify by analysing their velocity pair correlation functions as a function of distance between the particles. We found that the size of the particles plays a crucial role in their encounter rates, with larger particles situated near the active carpet being more favourable for aggregation. Overall, this research broadens our comprehension of aquatic systems out of equilibrium and how biologically driven fluctuations contribute to the transport of fundamental elements in biogeochemical cycles.

Highly ambitious initiatives aspire to propel a miniature spacecraft to a neighboring star within a human generation, leveraging the radiation pressure of lasers for propulsion. One of the main challenges to achieving this enormous feat is to build a meter-scale, ultra-low mass lightsail with broadband reflectivity. In this work, we present the design and fabrication of such a lightsail composed of two distinct dielectric layers and patterned with a photonic crystal structure covering a 4" wafer. We overcome the crucial challenge of achieving broad band reflection of >70% spanning over the full Doppler-shifted laser wavelength range during spacecraft acceleration, in combination with low total mass in the range of a few grams when scaled to meter size. Furthermore, we find new paths to reliably fabricate these sub-wavelength structures over macroscopic areas and then systematically characterize their optical performance, confirming their suitability for future lightsail applications. Our innovative device design and precise nanofabrication approaches represent a significant leap toward interstellar exploration.

This study focuses on implementing a real-time control system for a particle accelerator facility that performs high energy physics experiments. A critical operating parameter in this facility is beam loss, which is the fraction of particles deviating from the accelerated proton beam into a cascade of secondary particles. Accelerators employ a large number of sensors to monitor beam loss. The data from these sensors is monitored by human operators who predict the relative contribution of different sub-systems to the beam loss. Using this information, they engage control interventions. In this paper, we present a controller to track this phenomenon in real-time using edge-Machine Learning (ML) and support control with low latency and high accuracy. We implemented this system on an Intel Arria 10 SoC. Optimizations at the algorithm, high-level synthesis, and interface levels to improve latency and resource usage are presented. Our design implements a neural network, which can predict the main source of beam loss (between two possible causes) at speeds up to 575 frames per second (fps) (average latency of 1.74 ms). The practical deployed system is required to operate at 320 fps, with a 3ms latency requirement, which has been met by our design successfully.

Longitudinal MRI is an important diagnostic imaging tool for evaluating the effects of treatment and monitoring disease progression. However, MRI, and particularly longitudinal MRI, is known to be time consuming. To accelerate imaging, compressed sensing (CS) theory has been applied to exploit sparsity, both on single image as on image sequence level. State-of-the-art CS methods however, are generally focused on image reconstruction, and consider analysis (e.g., alignment, change detection) as a post-processing step. In this study, we propose DELTA-MRI, a novel framework to estimate longitudinal image changes {\it directly} from a reference image and subsequently acquired, strongly sub-sampled MRI k-space data. In contrast to state-of-the-art longitudinal CS based imaging, our method avoids the conventional multi-step process of image reconstruction of subsequent images, image alignment, and deformation vector field computation. Instead, the set of follow-up images, along with motion and deformation vector fields that describe their relation to the reference image, are estimated in one go. Experiments show that DELTA-MRI performs significantly better than the state-of-the-art in terms of the normalized reconstruction error., Comment: 5 pages, 4 figures, Submitted to ISBI 2023

A "heart attack" or myocardial infarction (MI), occurs when an artery supplying blood to the heart is abruptly occluded. The "gold standard" method for imaging MI is Cardiovascular Magnetic Resonance Imaging (MRI), with intravenously administered gadolinium-based contrast (late gadolinium enhancement). However, no "gold standard" fully automated method for the quantification of MI exists. In this work, we propose an end-to-end fully automatic system (MyI-Net) for the detection and quantification of MI in MRI images. This has the potential to reduce the uncertainty due to the technical variability across labs and inherent problems of the data and labels. Our system consists of four processing stages designed to maintain the flow of information across scales. First, features from raw MRI images are generated using feature extractors built on ResNet and MoblieNet architectures. This is followed by the Atrous Spatial Pyramid Pooling (ASPP) to produce spatial information at different scales to preserve more image context. High-level features from ASPP and initial low-level features are concatenated at the third stage and then passed to the fourth stage where spatial information is recovered via up-sampling to produce final image segmentation output into: i) background, ii) heart muscle, iii) blood and iv) scar areas. New models were compared with state-of-art models and manual quantification. Our models showed favorable performance in global segmentation and scar tissue detection relative to state-of-the-art work, including a four-fold better performance in matching scar pixels to contours produced by clinicians.

The Main Injector (MI) was commissioned using data acquisition systems developed for the Fermilab Main Ring in the 1980s. New VME-based instrumentation was commissioned in 2006 for beam loss monitors (BLM)[2], which provided a more systematic study of the machine and improved displays of routine operation. However, current projects are demanding more data and at a faster rate from this aging hardware. One such project, Real-time Edge AI for Distributed Systems (READS), requires the high-frequency, low-latency collection of synchronized BLM readings from around the approximately two-mile accelerator complex. Significant work has been done to develop new hardware to monitor the VME backplane and broadcast BLM measurements over Ethernet, while not disrupting the existing operations critical functions of the BLM system. This paper will detail the design, implementation, and testing of this parallel data pathway.

Active emulsions can spontaneously form self-propelled droplets or phoretic micropumps. It has been predicted that the interaction with their self-generated chemical fields can lead to multistable higher-order flows and chemodynamic phenomena. However, it remains unclear how such reaction-advection-diffusion instabilities can emerge from the interplay between chemical reactions and interfacial hydrodynamics. Here, we simultaneously measure the flow fields and the chemical concentration fields using dual-channel microscopy for oil droplets that dynamically solubilize in a supramicellar aqueous surfactant solution. We developed an experimentally tractable setup with micropumps, droplets that are pinned between the top and bottom surfaces of a microfluidic reservoir, which we compare directly to predictions from a Brinkman squirmer model to account for the confinement. With increasing droplet radius, we observe (i) a migration of vortex flows from the posterior to the anterior of the droplet, analogous to a transition from pusher- to puller-type swimmers, (ii) a bistability between dipolar and quadrupolar flow modes, and, eventually, (iii) a transition to multipolar modes. We also investigate how the dynamics evolve over long time periods. Together, our observations suggest that a local build-up of chemical products leads to a saturation of the surface, which controls the propulsion mechanism. These multistable dynamics can be explained by the competing time scales of slow micellar diffusion governing the chemical buildup and faster molecular diffusion powering the underlying transport mechanism. Our results are directly relevant to phoretic micropumps, but also shed light on the interfacial activity dynamics of self-propelled droplets and other active emulsion systems

Innovations in fluid mechanics are leading to better food since ancient history, while creativity in cooking inspires applied and fundamental science. Here, we review how recent advances in hydrodynamics are changing food science, and we highlight how the surprising phenomena that arise in the kitchen lead to discoveries and technologies across the disciplines, including rheology, soft matter, biophysics and molecular gastronomy. This review is structured like a menu, where each course highlights different aspects of culinary fluid mechanics. Our main themes include multiphase flows, complex fluids, thermal convection, hydrodynamic instabilities, viscous flows, granular matter, porous media, percolation, chaotic advection, interfacial phenomena, and turbulence. For every topic, we first provide an introduction accessible to food professionals and scientists in neighbouring fields. We then assess the state-of-the-art knowledge, the open problems, and likely directions for future research. New gastronomic ideas grow rapidly as the scientific recipes keep improving too., Comment: Review paper, 80 pages, 30 figures, 1050 references. Comments/suggestions to improve this arXiv submission are most welcome. Please contact us, and we will try to incorporate them

Missing data is a common issue in many biomedical studies. Under a paired design, some subjects may have missing values in either one or both of the conditions due to loss of follow-up, insufficient biological samples, etc. Such partially paired data complicate statistical comparison of the distribution of the variable of interest between the two conditions. In this paper, we propose a general class of test statistics based on the difference in weighted sample means without imposing any distributional or model assumption. An optimal weight is derived for this class of tests. Simulation studies show that our proposed test with the optimal weight performs well and outperforms existing methods in practical situations. Two cancer biomarker studies are provided for illustration.