Advances in biological research have been greatly influenced by the development of organoids, a specialized form of 3D cell culture. Created from pluripotent stem cells, organoids are effective in vitro models in replicating the structure and progression of brain development, providing an exceptional tool for studying the complexities of biology. Among these, cortical organoids, comprising in part of neurons, have been instrumental in providing early insights into brain formation, function, and pathology. Functional characteristics of cortical organoids, such as cellular morphology and electrophysiology, provide physiological insight into cellular states and are crucial for understanding the roles of cell types within their specific niches. And while progress has been made studying engineered neuronal systems, decoding the functional properties of neuronal networks and their role in producing behaviors depends in part on recognizing neuronal cell types, their general locations within the brain, and how they connect.

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Advances in biological research have been greatly influenced by the development of organoids, a specialized form of 3D cell culture. Created from pluripotent stem cells, organoids are effective in vitro models in replicating the structure and progression of organ development, providing an exceptional tool for studying the complexities of biology. Among these, cerebral cortex organoids (hereafter “organoid”) have become particularly instrumental in providing valuable insights into brain formation, function, and pathology. Despite their potential, organoid experiments present several challenges. Organoids require a rigorous, months-long developmental process, demanding substantial resources and meticulous care to yield valuable data on aspects of biology such as neural unit electrophysiology, cytoarchitecture, and transcriptional regulation. Traditionally the data has been difficult to collect on a more frequent and consistent basis, which limits the breadth and depth of modern organoid biology. Generating and measuring organoids depend on media manipulations, imaging, and electrophysiological measurements. Historically these are labor- and skill-intensive processes which can increase risks associated with known human error and contamination.

Researchers at Vanderbilt University and the University of California, Davis have developed MCNC software that significantly compresses large AI models while maintaining their performance using a novel manifold-constrained optimization approach.

       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.

This technology offers a novel mechanical arrangement for lossless, adjustable operation of springs or inerters.

Researchers at the University of California Davis have developed a system designed to predict and prevent ATV rollovers, enhancing rider safety.

Researchers at the University of California Davis have developed a technology that enables the provable editing of DNNs (deep neural networks) to meet specified safety criteria without altering their architecture.

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Researchers at the University of California, Davis have developed an algorithm for designing and identifying complex structures having custom release profiles for controlled drug delivery.

Researchers at the University of California, Davis have developed a haptic interface designed to aid visually impaired individuals in navigating their environment using their portable electronic devices.

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      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.

         While nuclear magnetic resonance (NMR) is one of the most universal synthetic chemistry tools for its ability to measure highly specific kinetic and structural information nondestructively/noninvasively, it is costly and low-throughput primarily due to the small sample-size volumes and expensive equipment needed for stringent magnetic field homogeneity. Conversely, zero-to-ultralow field (ZULF) NMR is an emerging alternative offering similar chemical information but relaxing field homogeneity requirements during detection. ZULF NMR has been further propelled by recent advancements in key componentry, optically pumped magnetometers (OPMs), but suffers in scope due to its low sensitivity and its susceptibility to noise. It has not been possible to detect most organic molecules without resorting to hyperpolarization or 13C enrichment using ZULF NMR.         To overcome these challenges, UC Berkeley researchers have developed a multi-channel ZULF spectrometer that greatly improves on both the sensitivity and throughput abilities of state-of-the art ZULF NMR devices. The novel spectrometer was used in the first reported detection of organic molecules in natural isotopic abundance by ZULF NMR, with sensitivity comparable to current commercial benchtop NMR spectrometers. A proof-of-concept multichannel version of the ZULF spectrometer was capable of measuring three distinct chemical samples simultaneously. The combined sensitivity and throughput distinguish the present ZULF NMR spectrometer as a novel chemical analysis tool at unprecedented scales, potentially enabling emerging fields such as robotic chemistry, as well as meeting the demands of existing fields such as chemical manufacturing, agriculture, and pharmaceutical industries.

The combination of elasticity and rigidity found within mammalian limbs enables dexterous manipulation, agile, and versatile behavior, yet most modern robots are either primarily soft or rigid. Most mammals have ligaments that connect bone to bone, enabling joints to passively redirect forces and softly constrain the range of motion. Hybrid robots, composed of both soft and rigid parts, promote compliance to external forces while maintaining strength and stability provided by rigid robots.Natural manipulators, such as the human arm, have been shaped by the long-term optimization of evolution. They tend to be extremely versatile, having the dexterity to work with various objects and environments. The hybrid composition of rigid and soft components– including bones, muscles, and connective tissues–yield inherent compliance and flexibility. Biological joints often have passive stability and elasticity that create mechanical feedback that benefits disturbance responses. In addition, recent progress in the mechanical complexity of robots has popularized embedding intelligence within the system. Such robots may inherently dampen motion through elastic components or enable complicated movements emerging from simple actuation, e.g., origami robots.In contrast, traditional robot arms tend to feature rigid components that are susceptible to large moments propagating throughout the entire robot. This means the robot’s structural integrity can be compromised by a large unpredictable disturbance. Robotic manipulators involving rigid joints have strictly defined degrees of freedom resulting from the mechanical design. These joints typically fit within three categories: prismatic joints (linear movement on an axis), revolute joints (rotational movement around an axis), or a combination of the two.Rigid robotic joints are often actuated by motors that change their position directly, allowing straightforward kinematic models to calculate the joint's position. Due to their dynamics, traditional feedback control systems, such as a proportional integral derivative (PID) controller, can solve this problem relatively well, with modifications that can adapt to the influences of gravity. One modification to account for nonlinearities involves feed-forward neural networks with PID input features.While these dynamics are effective within controlled environments, rigid robots pose dangers to both themselves and humans because of their intrinsic inability to deal with external forces. In environments where humans are directly interfacing with robots–such as industrial manufacturing or telepresence -- the robot’s lack of compliance can put workers and civilians at risk of injury. Measures have been taken to increase the safety of these robots, but they are not innately safe. Factors including intrinsic safety, human detection, and control techniques influence the overall safety. The risk that a robot will cause physical harm has also been shown to moderate people’s willingness to work with the robot. Strategies such as safety fences and human-detection increase safety but limit the human-robot collaboration.Flexible robots can mitigate these external forces through structural compliance while maintaining morphological similarities with rigid robots. Systems such as soft robots and tensegrity-based robots with elastic components are inherently compliant. Biologically inspired approaches tend to exemplify this behavior. The motion of legged tensegrity structures has been validated by biological simulations while simplifying the underlying bone-ligament architecture. From a bio-mimicry perspective, a human finger has been functionally recreated through oneshot three-dimensional (3D) printing techniques employing both rigid and elastic components. Soft robots can provide safe human interaction, resulting in safer environments. Soft cable driven exo-suits can be compliant while avoiding obstruction to the user’s range of motion. Intelligent design approaches have even resulted in programmable tensegrities. However, due to the non-linearities within the elastic components, these compliant robots tend to require complex models in order to be controlled properly.Soft robotics made from elastic components increase compliance while often sacrificing stability and precision. An accurate model of the system would enable the use of modern control techniques which can provide optimal solutions. Optimal control finds the proper control values which optimize an objective function based on the system model. A Linear-Quadratic Regulator (LQR) solves the problem of minimizing a quadratic cost matrix (encoding weights of errors, energy use, etc.) over a specified time horizon, however it is expensive and demands accurate models. Model predictive control optimizes a finite time-horizon window that is repeatedly solved at each new time-step, reducing computational cost while enabling anticipation of future events. To create the model, a common method involves system identification, which can estimate the dynamics based on measurements.However, noise in the design process can breed inconsistencies in production, and the non-linear nature of flexible robots further complicates modeling. This emphasizes the need for control methods that can learn from data. One potential solution for controlling this variation in robots involves having precisely adjusted models for each physical instance. But these approaches are cumbersome due to the requirement of constructing precise models. Thus, there is a need for a system and method of controlling soft-rigid hybrid robotic joints that overcome the deficiencies of the conventional control methodologies. 

A patent-pending platform technology designed to work in any gravity, which includes in microgravity environments, able to execute advanced molecular biology workflows; representing a paradigm shift in automation for molecular biology.

The use of telemedicine/telehealth increased substantially during the COVID-19 pandemic, leading to its accelerated development, utilization and acceptability. Telehealth momentum with patients, providers, and other stakeholders will likely continue, which will further promote its safe and evidence-based use. Improved healthcare by telehealth has also extended to musculoskeletal care. In a recent study looking at implementation of telehealth physical therapy in response to COVID-19, almost 95% of participants felt satisfied with the outcome they received from the telehealth physical therapy (PT) services, and over 90% expressed willingness to attend another telehealth session. While telehealth has enhanced accessibility by virtual patient visits, certain physical rehabilitation largely depends on physical facility and tools for evaluation and therapy. For example, limb kinematics in PT with respect to the shoulder joint is difficult to evaluate remotely, because the structure of the shoulder allows for tri-planar movement that cannot be estimated by simple single plane joint models. With the emergence of gaming technologies, such as videogames and virtual reality (VR), comes new potential tools for virtual-based physical rehabilitation protocols. Some research has shown digital game environments, and associated peripherals like immersive VR (iVR) headsets, can provide a powerful medium and motivator for physical exercise. And while low-cost motion tracking systems exist to match user movement in the real world to that in the virtual environment, challenges remain in bridging traditional PT tooling and telehealth-friendly physical rehabilitation.

Researchers at the University of California, Davis have developed an automated harvesting system using predictive scheduling for crop-transport robots, reducing manual labor, and increasing harvesting efficiency.

Researchers at the University of California, Davis have developed an autonomous crop transportation robot to aid field workers during harvest.

During initial stages of development, the human brain self assembles from a vast network of billions of neurons into a system capable of sophisticated cognitive behaviors. The human brain maintains these capabilities over a lifetime of homeostasis, and neuroscience helps us explore the brain’s capabilities. The pace of progress in neuroscience depends on experimental toolkits available to researchers. New tools are required to explore new forms of experiments and to achieve better statistical certainty.Significant challenges remain in modern neuroscience in terms of unifying processes at the macroscopic and microscopic scale. Recently, brain organoids, three-dimensional neural tissue structures generated from human stem cells, are being used to model neural development and connectivity. Organoids are more realistic than two-dimensional cultures, recapitulating the brain, which is inherently three-dimensional. While progress has been made studying large-scale brain patterns or behaviors, as well as understanding the brain at a cellular level, it’s still unclear how smaller neural interactions (e.g., on the order of 10,000 cells) create meaningful cognition. Furthermore, systems for interrogation, observation, and data acquisition for such in vitro cultures, in addition to streaming data online to link with these analysis infrastructures, remains a challenge.

 Researchers from UC San Diego developed Pointillism, a system that enables radars to overcome the challenges posed by specular reflections, sparsity and noise in the radar point clouds, to provide high-fidelity perception of the scene with 3D bounding boxes. Pointillism consists of multiple low-resolution radars placed in a optimal fashion to maximize the spatial diversity and scene information. Pointillism combines this spatial diversity with novel multi-radar fusion algorithms to tackle the problem of specular reflections, sparsity and noise in radar point clouds. Building upon the hardware and algorithms, Pointillism also introduces a novel data-driven approach that enables the detection of multiple dynamic objects in the scene, with their accurate location, orientation and 3D dimensions. Furthermore, Pointillism enables such perception even in inclement weather, thereby paving a way for radar to be the main-stream sensor for autonomous perception.

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The inventors have developed a novel 3D printing technique, named Non-Planar Granular 3D Printing (NGP), which selectively deposits a liquid binder into granular particles, enabling rapid fabrication of complex 3-dimensional objects. For this new method, an industrial robotic arm is equipped with a dispenser attached to a long metal needle, called a liquid deposition end-effector, and a container of granular particles, such as sand, beads, or powders. The needle moves freely as it injects the binding liquid into the granular material. Like other 3D printing methods, NGP can use a CAD 3D model and conventional slicing software to produce a robotic toolpath following a desired height and width. However, the advantage of the process lies in its ability to 3D print objects non-planarly, by moving the extruder’s dispensing tip freely within the granular medium. The selective application of the binding liquid causes the particles to bond together, forming parts of the 3D printed object. Meanwhile, the loose particles remaining in the container temporarily support the weight of the wet particles while they cure. This unique approach enables the creation of complex geometric forms without the need for supporting structures that are typical in traditional 3D printing methods, thereby eliminating material waste typically associated with such processes. After the completion of the process, and the binding material has cured, the hard objects can be easily extracted from the container, leaving behind the remaining loose particles, which can be repeatedly re-used.   

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