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SpeedyTrack: Microsecond Wide-field Single-molecule Tracking

      Single-particle/single-molecule tracking (SPT) is a key tool for quantifying molecular motion in cells and in vitro. Wide-field SPT, in particular, can yield super-resolution mapping of physicochemical parameters and molecular interactions at the nanoscale, especially when integrated with single-molecule localization microscopy techniques like photoactivation and fluorophore exchange. However, wide-field SPT is often limited to the slow (<10 μm2/s) diffusion of molecules bound to membranes, chromosomes, or the small volume of bacteria, in part due to the ~10 ms framerate of common single-molecule cameras like electron-multiplying charge-coupled devices (EM-CCDs); for unbound diffusion in the mammalian cell and in solution, a molecule readily diffuses out of the <1 μm focal range of high-numerical-aperture objective lenses within 10 ms. While recent advances such as ultra-highspeed intensified CMOS cameras, feedback control by locking onto a molecule, trapping, and tandem excitation pulse schemes address the framerate issue, each also introduces drawbacks in light/signal efficiency, speed, uninterrupted diffusion paths, and/or trajectory resolution, e.g., number of time points.      UC Berkeley researchers have overcome these myriad challenges by introducing spatially-encoded dynamics tracking (SpeedyTrack), a strategy to enable direct microsecond wide-field single-molecule tracking/imaging on common microscopy setups. Wide-field tracking is achieved for freely diffusing molecules at down to 50 microsecond temporal resolutions for >30 timepoints, permitting trajectory analysis to quantify diffusion coefficients up to 1,000 um2/s. Concurrent acquisition of single-molecule diffusion trajectories and Forster resonance energy transfer (FRET) time traces further elucidates conformational dynamics and binding states for diffusing molecules. Moreover, spatial and temporal information is deconvolved to map long, fast single-molecule trajectories at the super-resolution level, thus resolving the diffusion mode of a fluorescent protein in live cells with nanoscale resolution. Already substantially outperforming existing approaches, SpeedyTrack stands out further for its simplicity—directly working off the built-in functionalities of EM-CCDs without the need to modify existing optics or electronics.

Spectral Kernel Machines With Electrically Tunable Photodetectors

       Spectral machine vision collects both the spectral and spatial dependence (x,y,λ) of incident light, containing potentially useful information such as chemical composition or micro/nanoscale structure.  However, analyzing the dense 3D hypercubes of information produced by hyperspectral and multispectral imaging causes a data bottleneck and demands tradeoffs in spatial/spectral information, frame rate, and power efficiency. Furthermore, real-time applications like precision agriculture, rescue operations, and battlefields have shifting, unpredictable environments that are challenging for spectroscopy. A spectral imaging detector that can analyze raw data and learn tasks in-situ, rather than sending data out for post-processing, would overcome challenges. No intelligent device that can automatically learn complex spectral recognition tasks has been realized.       UC Berkeley researchers have met this opportunity by developing a novel photodetector capable of learning to perform machine learning analysis and provide ultimate answers in the readout photocurrent. The photodetector automatically learns from example objects to identify new samples. Devices have been experimentally built in both visible and mid-infrared (MIR) bands to perform intelligent tasks from semiconductor wafer metrology to chemometrics. Further calculations indicate 1,000x lower power consumption and 100x higher speed than existing solutions when implemented for hyperspectral imaging analysis, defining a new intelligent photodetection paradigm with intriguing possibilities.

Frequency Programmable MRI Receive Coil

In magnetic resonance imaging (MRI) scanners, the detection of nuclear magnetic resonance (NMR) signals is achieved using radiofrequency, or RF, coils. RF coils are often equivalently called “resonance coils” due to their circuitry being engineered for resonance at a single frequency being received, for low-noise voltage gain and performance. However, such coils are therefore limited to a small bandwidth around the center frequency, restricting MRI systems from imaging more than one type of nucleus at a time (typically just hydrogen-1, or H1), at one magnetic field strength.To overcome the inherent restriction without sacrificing performance, UC Berkeley researchers have developed an MRI coil that can perform low-noise voltage gain at arbitrary relevant frequencies. These frequencies can be programmably chosen and can include magnetic resonance signals from any of various nuclei (e.g., 1H, 13C, 23Na, 31P, etc.), at any magnetic field strength (e.g., 50 mT, 1.5T, 3T, etc.). The multi-frequency resonance can be performed in a single system. The invention has further advantages in terms of resilience due to its decoupled response relative to other coils and system elements.

Self-Supervised Machine-Learning Adaptive Optics For Optical Microscopy

      Image quality and sample structure information from an optical microscope is in large part determined by optical aberrations. Optical aberrations originating from the microscope optics themselves or the sample can degrade the imaging performance of the system. Given the difficulty to find and correct all sources of aberration, a collection of methods termed adaptive optics is used to measure and correct optical aberrations in other ways, to recover imaging performance. However, state-of-the-art adaptive optics systems typically comprise complex hardware and software integration, which has impeded their wide adoption in microscopy. UC Berkeley researchers recently demonstrated how self-supervised machine learning (ML)-based adaptive optics can accurately estimate optical aberrations from a single 3D fluorescence image stack, without requiring external datasets for training. While demonstrated for widefield fluorescence microscopy, many optical microscopy modalities present unique challenges.       In the present technology, UC Berkeley researchers have developed a novel self-supervised ML-based adaptive optics system for two-photon fluorescence microscopy, which should also be extensible to confocal and other modalities. The system can effectively image tissues and samples for cell biology applications. Importantly, the method can address common errors in optical conjugation/alignment in commercial microscopy systems that have yet to be systematically addressed. It can also integrate advanced computational techniques to recover sample structure.

Compact Series Elastic Actuator Integration

      While robots have proven effective in enhancing the precision and time efficiency of MRI-guided interventions across various medical applications, safety remains a formidable challenge for robots operating within MRI environments. As the robots assume full control of medical procedures, the reliability of their operation becomes paramount. Precise control over robot forces is particularly crucial to ensure safe interaction within the MRI environment. Furthermore, the confined space in the MRI bore complicates the safe operation of human-robot interaction, presenting challenges to maneuverability. However, there exists a notable scarcity of force-controlled robot actuators specifically tailored for MRI applications.       To overcome these challenges, UC Berkeley researchers have developed a novel MRI-compatible rotary series elastic actuator module utilizing velocity-sourced ultrasonic motors for force-controlled robots operating within MRI scanners. Unlike previous MRI-compatible SEA designs, the module incorporates a transmission force sensing series elastic actuator structure, while remaining compact in size. The actuator is cylindrical in shape with a length shorter than its diameter and integrates seamlessly with a disk-shaped motor. A precision torque controller enhances the robustness of the invention’s torque control even in the presence of varying external impedance; the torque control performance has been experimentally validated in both 3 Tesla MRI and non-MRI environments, achieving a settling time of 0.1 seconds and a steady-state error within 2% of its maximum output torque. It exhibits consistent performance across low and high external impedance scenarios, compared to conventional controllers for velocity-sourced SEAs that struggle with steady-state performance under low external impedance conditions.

Compact Catadioptric Mapping Optical Sensor For Parallel Goniophotometry

      Goniophotometers measure the luminance distribution of light emitted or reflected from a point in space or a material sample. Increasingly there is a need for such measurements in real-time, and in real-world situations, for example, for daylight monitoring or harvesting in commercial and residential buildings, design and optimization of greenhouses, and testing laser and display components for AR/VR and autonomous vehicles, to name a few. However, current goniophotometers are ill-suited for real-time measurements; mechanical scanning goniophotometers have a large form factor and slow acquisition times. Parallel goniophotometers take faster measurements but suffer from complexity, expense, and limited angular view ranges (dioptric angular mapping systems) or strict form factor and sample positioning requirements (catadioptric angular mapping systems). Overall, current goniophotometers are therefore limited to in-lab environments.      To overcome these challenges, UC Berkeley researchers have invented an optical sensor  for parallel goniophotometry that is compact, cost-effective, and capable of real-time daylight monitoring. The novel optical design addresses key size and flexibility constraints of current state-of-the-art catadioptric angular mapping systems, while maximizing the view angle measurement at 90°. This camera-like, angular mapping device could be deployed at many points within a building to measure reflected light from fenestrations, in agricultural greenhouses or solar farms for real-time monitoring, and in any industry benefitting from real-time daylight data.

Fluorescent Bis-Trifluoromethyl Carborhodamine Compounds

UCB researchers have developed a novel class of bright, fluorescent rhodamine dyes with a novel structural modification resulting in a deep red shift relative to the parent carborhodamine dye, with the new dye absorbing and emitting near-infrared light in the same region as the commercially successful silicon rhodamine dyes. Biological imaging with near-infrared light is advantageous for numerous biological and surgical applications.  Furthermore, bis-trifluoromethyl carborhodamines offer improved properties desirable for biological imaging applications due to their unique physical and electronic properties. 

Any-Nuclei Distributed Active Programmable Transmit MRI Coil

There are 118 known elements. Nearly all of them have NMR active isotopes and at least 39 different nuclei have been shown to have biological relevance. Despite this, most of today’s MRI is based on only one nucleus – 1H. To work towards making use of all potential nuclei, here, UC Berkeley researchers have created a coil enabling the excitation of arbitrary nuclei in human-scale MRI with a single coil. To excite arbitrary nuclei, they developed a completely new type of RF coil, the Any-nuclei Distributed Active Programmable Transmit Coil (ADAPT Coil), that can operate at any relevant frequency. This coil eliminates the need of the expensive traditional RF amplifier by directly converting DC power into RF magnetic fields with frequencies chosen by digital control signals sent to the switches. Semiconductor switch imperfections are overcome by breaking the coil into several segments. The ADAPT Coil presents a scalable and efficient method of exciting arbitrary nuclei in human-scale MRI. This coil concept provides further opportunities for scaling, programmability, lowering coil costs, lowering dead-time, reducing multinuclear MRI workflow complexity, and enabling the study of dozens of biologically relevant nuclei.  

System And Method For Tomographic Fluorescence Imaging For Material Monitoring

Volumetric additive manufacturing and vat-polymerization 3D printing methods rapidly solidify freeform objects via photopolymerization, but problematically raises the local temperature in addition to degree-of-conversion (DOC). The generated heat can critically affect the printing process as it can auto-accelerate the polymerization reaction, trigger convection flows, and cause optical aberrations. Therefore, temperature measurement alongside conversion state monitoring is crucial for devising mitigation strategies and implementing process control. Traditional infrared imaging suffers from multiple drawbacks such as limited transmission of measurement signal, material-dependent absorptions, and high background signals emitted by other objects. Consequently, a viable temperature and DOC monitoring method for volumetric 3D printing doesn’t exist.To address this opportunity, UC Berkeley researchers have developed a tomographic imaging technique that detects the spatiotemporal evolution of temperature and DOC during volumetric printing. The invention lays foundations for the development of volumetric measurement systems that uniquely resolve both temperature and DOC in volumetric printing.This novel Berkeley measurement system is envisaged as an integral tool for existing manufacturing technologies, such as computed axial lithography (CAL, Tech ID #28754), and as a new research tool for commercial biomanufacturing, general fluid dynamics, and more.

System And Method For Noise-Enabled Static Imaging Using Event Cameras

Dynamic Vision Sensors (DVS), also known as event cameras or neuromorphic sensors, enable extremely high temporal resolution and dynamic range compared to traditional sensors. However, DVS pixels only capture changes in intensity, which discards all static information. To overcome this issue, an additional photosensor array is needed either (1) in a two-sensor system or (2) combined into a single sensor with two-pixel technologies (DAVIS346). In both cases, the resulting system is bulkier, more complex to design, and more expensive to manufacture. UC Berkeley researchers have developed an event-based imaging system that can capture static intensity, thereby eliminating the need of such two-pixel technologies by extracting underlying static intensity information directly from DVS pixels. The researchers have also demonstrated the feasibility of this approach through the analysis of noise statistics in event cameras.

Computation Method For 3D Point-Cloud Holography

 The dynamic patterning of 3D optical point clouds has emerged as a key enabling technology in volumetric processing across a number of applications. In the context of biological microscopy, 3D point cloud patterning is employed for non-invasive all-optical interfacing with cell ensembles. In augmented and virtual reality (AR/VR), near-eye display systems can incorporate virtual 3D point cloud-based objects into real-world scenes, and in the realm of material processing, point cloud patterning can be mobilized for 3D nanofabrication via multiphoton or ultraviolet lithography. Volumetric point cloud patterning with spatial light modulators (SLMs) is therefore widely employed across these and other fields. However, existing hologram computation methods, such as iterative, look-up table-based and deep learning approaches, remain exceedingly slow and/or burdensome. Many require hardware-intensive resources and sacrifices to volume quality.To address this problem, UC Berkeley researchers have developed a new, non-iterative point cloud holography algorithm that employs fast deterministic calculations. Compared against existing iterative approaches, the algorithm’s relative speed advantage increases with SLM format, reaching >100,000´ for formats as low as 512x512, and optimally mobilizes time multiplexing to increase targeting throughput. 

Hyperspectral Microscopy Using A Phase Mask And Spectral Filter Array

Hyperspectral imaging, the practice of capturing detailed spectral (color) information from the output of an optical instrument such as a microscope or telescope, is useful in biological and astronomical research and in manufacturing. In addition to being bulky and expensive, existing hyperspectral imagers typically require scanning across a specimen, limiting temporal resolution and preventing dynamic objects from being effectively imaged. Snapshot methods which eliminate scanning are limited by a tradeoff between spatial and spectral resolution.In order to address these problems, researchers at UC Berkeley have developed a hyperspectral imager which can be attached to the output of any benchtop microscope. The imager is compact (about 6-inches), and can achieve a higher spatial resolution than traditional snapshot imagers. Additionally, this imager needs only one exposure to collect measurements for an arbitrary number of spectral filters, giving it unprecedented spectral resolution.

Co-Wiring Method For Primitive Spatial Modulation

Dynamic patterning of light is used in a variety of applications in imaging and projection. This is often done by spatial light modulation, in which a coherent beam of input light is modified at the pixel level to create arbitrary output patterns via later interference. Traditional approaches to spatial light modulation suffer from a high operating burden, especially as the number of pixels increases, and incomplete coverage of the optical surface. This results in high device complexity, and cost, as well as enormous real-time computation requirements, reduced optical performance, and optical artifacts.To address these problems, researchers at UC Berkeley have developed a method for wiring groups of pixels, such as annular rings, parallel strips, or radial strips. This takes advantage of the fact that most spatial light modulation tasks can be accomplished by combining a number of simple “primitive phase profiles”, in which not all pixels need be independent of each other. In this co-wiring method, individual optical elements remain at the pixel level, but are wired together in a way that they move in precisely the coordinated manner to produce one of these primitive phase profiles. This allows for high frame rates, high coverage of the optical plane, and a degree of sensitivity impossible to produce with large, geometric optical elements that exist in prior art.

Pixel And Array Architecture For Spatial Light Modulation

Dynamic patterning of light is used in a variety of applications in imaging and projection. This is often done by spatial light modulation, in which a coherent beam of input light is modified at the pixel level to create arbitrary output patterns via later interference. Traditional approaches to spatial light modulation suffer from a fundamental restriction on frame rate which has led manufacturers to seek the diminishing returns of continually increasing pixel number, resulting in impractical device sizes, complexity, and cost, as well as enormous real-time computation requirements. Additionally, these devices inherently produce monochromatic and speckled frames due to the requirement that the input beam be coherent.To address these problems, researchers at UC Berkeley have developed a device which can perform spatial light modulation with a frame rate ~20 times higher than existing technologies. This allows for a smaller number of pixels to produce high resolution, full color images by interleaving images of different colors and scanning rapidly across a screen in a similar way to the operation of CRT televisions Researchers have also developed an efficient and robust fabrication method, which combined with the smaller pixel number of these devices could cause them to be much more cost effective than existing technologies.

Spectral Fluctuation Raman Spectroscopy (SFRS)

       Our ability to experimentally measure the biomacromolecular structure of proteins and their complexes down to the atomic scale has progressed at a staggering pace in recent years. However, the dynamical conformational changes that affect, to name a few examples, DNA transcription, energy-transfer in photosynthesis and enzyme activity, and the transition from healthy to diseased states, remain difficult to capture. A non-perturbative, label-free approach that is sensitive to individual conformational states is single-protein Raman spectroscopy. However, the time resolution of single-protein Raman spectroscopy is typically limited to milliseconds (10-3 sec), limited by inherent signal strength. Protein conformational dynamics occur over a timescale ranging from tens of seconds down to microseconds (10-6 sec) or even nanoseconds (10-9 sec).       To address these challenges UC Berkeley researchers have developed a novel, high-temporal dynamic range Raman spectrometer capable of measuring sub-microsecond, and even nanosecond, fluctuations in single- and few-molecule spectra. The available dynamic range can be used to study and control of biomolecular dynamics as related to protein-protein interactions, drug discovery, validating computational biophysics capabilities, and many other additional applications. 

Systems For Pulse-Mode Interrogation Of Wireless Backscatter Communication Nodes

Measurement of electrical activity in nervous tissue has many applications in medicine, but the implantation of a large number of sensors is traditionally very risky and costly. Devices must be large due to their necessary complexity and power requirements, driving up the risk further and discouraging adoption. To address these problems, researchers at UC Berkeley have developed devices and methods to allow small, very simple and power-efficient sensors to transmit information by backscatter feedback. That is, a much more complex and powerful external interrogator sends an electromagnetic or ultrasound signal, which is modulated by the sensor nodes and reflected back to the interrogator. Machine learning algorithms are then able to map the reflected signals to nervous activity. The asymmetric nature of this process allows most of the complexity to be offloaded to the external interrogator, which is not subject to the same constraints as implanted devices. This allows for larger networks of nodes which can generate higher resolution data at lower risks and costs than existing devices.

Nuclear Delivery and Transcriptional Repression with a Cell-penetrant MeCP2

Methyl-CpG-binding-protein 2 (MeCP2) is a nuclear protein expressed in all cell types, especially neurons. Mutations in the MECP2 gene cause Rett syndrome (RTT), an incurable neurological disorder that disproportionately affects young girls. Strategies to restore MeCP2 expression phenotypically reverse RTT-like symptoms in male and female MeCP2-deficient mice, suggesting that direct nuclear delivery of functional MeCP2 could restore MeCP2 activity.The inventors have discovered that ZF-tMeCP2, a conjugate of MeCP2(aa13-71, 313-484) and the cell-permeant mini-protein ZF5.3, binds DNA in a methylation-dependent manner and reaches the nucleus of model cell lines intact at concentrations above 700 nM. When delivered to live cells, ZF-tMeCP2 engages the NCoR/SMRT co-repressor complex and selectively represses transcription from methylated promoters. Efficient nuclear delivery of ZF-tMeCP2 relies on a unique endosomal escape portal provided by HOPS-dependent endosomal fusion.In a comparative evaluation, the inventors observed the Tat conjugate of MeCP2 (Tat-tMeCP2) (1) degrades within the nucleus, (2) is not selective for methylated promoters, and (3) traffics in a HOPS-independent manner. These results support the feasibility of a HOPS-dependent portal for delivering functional macromolecules to the cell interior using the cell-penetrant mini-protein ZF5.3. Such a strategy could broaden the impact of multiple families of protein-derived therapeutics.

Type III CRISPR-Cas System for Robust RNA Knockdown and Imaging in Eukaryotes

Type III CRISPR-Cas systems recognize and degrade RNA molecules using an RNA-guided mechanism that occurs widely in microbes for adaptive immunity against viruses. The inventors have demonstrated that this multi-protein system can be leveraged for programmable RNA knockdown of both nuclear and cytoplasmic transcripts in mammalian cells. Using single-vector delivery of the S. thermophilus Csm complex, RNA knockdown was achieved with high efficiency (90-99%) and minimal off-targets, outperforming existing technologies of shRNA- and Cas13-mediated knockdown. Furthermore, unlike Cas13, Csm is devoid of trans-cleavage activity and thus does not induce non-specific transcriptome-wide degradation and cytotoxicity. Catalytically inactivated Csm can also be used for programmable RNA-binding, which the inventors exploit for live-cell RNA imaging. This work demonstrates the feasibility and efficacy of multi-subunit CRISPR-Cas effector complexes as RNA-targeting tools in eukaryotes.

Facile, Excitation-Based Spectral Microscopy For Fast Multicolor Imaging And Quantitative Biosensing

The number of color channels that can be concurrently probed in fluorescence microscopy is severely limited by the broad fluorescence spectral width. Spectral imaging offers potential solutions, yet typical approaches to disperse the local emission spectra notably impede the attainable throughput.    UC Berkeley researchers have discovered methods and systems for simultaneously imaging up to 6 subcellular targets, labeled by common fluorophores of substantial spectral overlap, in live cells at low (~1%) crosstalks and high temporal resolutions (down to ~10 ms), using a single, fixed fluorescence emission detection band. 

Deep Learning Techniques For In Vivo Elasticity Imaging

Imaging the material property distribution of solids has a broad range of applications in materials science, biomechanical engineering, and clinical diagnosis. For example, as various diseases progress, the elasticity of human cells, tissues, and organs can change significantly. If these changes in elasticity can be measured accurately over time, early detection and diagnosis of different disease states can be achieved. Elasticity imaging is an emerging method to qualitatively image the elasticity distribution of an inhomogeneous body. A long-standing goal of this imaging is to provide alternative methods of clinical palpation (e.g. manual breast examination) for reliable tumor diagnosis. The displacement distribution of a body under externally applied forces (or displacements) can be acquired by a variety of imaging techniques such as ultrasound, magnetic resonance, and digital image correlation. A strain distribution, determined by the gradient of a displacement distribution, can be computed (or approximated) from measured displacements. If the strain and stress distributions of a body are both known, the elasticity distribution can be computed using the constitutive elasticity equations. However, there is currently no technique that can measure the stress distribution of a body in vivo. Therefore, in elastography, the stress distribution of a body is commonly assumed to be uniform and a measured strain distribution can be interpreted as a relative elasticity distribution. This approach has the advantage of being easy to implement. The uniform stress assumption in this approach, however, is inaccurate for an inhomogeneous body. The stress field of a body can be distorted significantly near a hole, inclusion, or wherever the elasticity varies. Though strain-based elastography has been deployed on many commercial ultrasound diagnostic-imaging devices, the elasticity distribution predicted based on this method is prone to inaccuracies.To address these inaccuracies, researchers at UC Berkeley have developed a de novo imaging method to learn the elasticity of solids from measured strains. Our approach involves using deep neural networks supervised by the theory of elasticity and does not require labeled data for the training process. Results show that the Berkeley method can learn the hidden elasticity of solids accurately and is robust when it comes to noisy and missing measurements.

Improved guide RNA and Protein Design for CasX-based Gene Editing Platform

The inventors have developed two new CasX gene-editing platforms (DpbCasXv2 and PlmCasXv2) through rationale structural engineering of the CasX protein and gRNA, which yield improved in vitro and in vivo behaviors. These platforms dramatically increase DNA cleavage activity and can be used as the basis for further improving CasX tools.The RNA-guided CRISPR-associated (Cas) protein CasX has been reported as a fundamentally distinct, RNA-guided platform compared to Cas9 and Cpf1. Structural studies revealed structural differences within the nucleotide-binding loops of CasX, with a compact protein size less than 1,000 amino acids, and guide RNA (gRNA) scaffold stem. These structural differences affect the active ternary complex assembly, leading to different in vivo and in vitro behaviors of these two enzymes.

System for Determining Trademark Similarity

Many areas of intellectual property law involve subjective judgments regarding confusion or similarity. For example, in trademark or trade dress lawsuits a key factor considered by the court is the degree of visual similarity between the trademark or product designs under consideration. Such similarity judgments are nontrivial, and may be complicated by cognitive factors such as categorization, memory, and reasoning that vary substantially across individuals. Currently, three forms of evidence are widely accepted: visual comparison by litigants, expert witness testimonies, and consumer surveys. All three rely on subjective reports of human responders, whether litigants, expert witnesses, or consumer panels. Consequently, all three forms of evidence potentially share the criticism that they are subject to overt (e.g. conflict of interest) or covert (e.g. inaccuracy of self-report) biases.To address this situation, researchers at UC Berkeley developed a technology that directly measures the mental state of consumers when they attend to visual images of consumer products, without the need for self-report measures such as questionnaires or interviews. In so doing, this approach reduces the potential for biased reporting.  

Multiphoton Magnetic Resonance Imaging

UC Berkeley researchers have developed novel imaging techniques with the use of a multiphoton magnetic resonance imaging apparatus. By taking a particular rotating frame transformation the researchers found that multiphoton excitations appear just like single‐photon excitations and can also use concepts explored in standard single‐photon excitation. One prototype included a low frequency coil while another prototype included no additional hardware but instead used oscillating gradients as a source of extra photons for excitation.  The methods and multiphoton MRI can be used to transform a standard slice selective adiabatic inversion pulse into a multiband version without modifying the RF pulse itself. The addition of oscillating gradients creates multiphoton resonances at multiple spatial locations and allows for adiabatic inversions at each location.

Materials Platform for Flexible Emissivity Engineering

This materials platform enables flexible engineering of infrared (IR) emissivity and development of thermal radiation devices beyond the Stefan-Boltzmann law. The materials structure is based on thin films of vanadium oxide (VO2) with judiciously designed graded W doping across a thickness less than the skin depth of electromagnetic screening (~100 nm). The infrared emissivity can be engineered to decrease in an arbitrary manner from ~ 0.75 to ~ 0.35 over a temperature range up to 50 C near room temperature. The large range of emissivity tuning and flexible adjustability is beyond the capability of regular materials or structures. This invention provides a new platform for unprecedented manipulation of thermal radiation and IR signals with a wide variety of applications, such as:  The emissivity can be programmed to precisely counteract the T^4 dependence in the Stefan-Boltzmann law and achieve a temperature dependent thermal radiation. Such a design enables a mechanically flexible and power-free infrared camouflage, which is inherently robust and immune to drastic temporal fluctuation and spatial variation of temperature. By tailoring structure and composition, the materials platform can create a surface with robust and arbitrary IR temperature image, regardless of the actual temperature distribution on the targets. This design of infrared "decoy" not only passively conceals the real thermal activity of the object, but also intentionally fools the camera with a counterfeited image. The materials platform can achieve strong temperature dependence of reflectivity over a broad wavelength from near-IR to far-IR, which is promising for high-sensitivity remote temperature sensing by thermoreflectance imaging, or active reflectance modulation of IR signals. 

Strongly Interacting Magnetic Particle Imaging

Nuclear medicine is a diagnostic imaging method that works very well, but it is both expensive and gives off excess radiation. X-rays also are used for diagnostic imaging but have poor contrast. Magnetic Particle Imaging (MPI) is a promising new tracer modality with zero attenuation in tissue, near-ideal contrast and sensitivity, and an excellent safety profile, however, the spatial resolution of MPI is currently the modality’s only weak technical attribute. UC Berkeley and UF researchers have developed a novel, compact, and intuitive MPI scanner that resolves this issue.  The research demonstrated proof-of-concept studies for an MPI modality, referred to herein as strongly-interacting magnetic particle imaging (siMPI) that enables a super-resolution breakthrough. The siMPI provided more than a 6-fold improvement in every dimension of space spatial resolution and 37-fold increase in sensitivity. The MPI can be used for early-stage detection of cancer, gut bleeds, strokes, pulmonary embolism, and tracking immunotherapies and MPI can penetrate any tissue, including bone, lungs, and dense breast tissue.

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