Porcine astrovirus (PoAstV) was first detected by electron microscopy in the feces of piglets with diarrhea in 1980. There are five known genotypes of PoAstV, which are thought to be more closely related to other species than to each other. This divergence among genotypes suggests different ancestral origin of PoAstVs. PoAstVs have been detected across the globe including South Africa, Canada, China, Colombia, and Chile. All five genotypes are present in the US with high incidence. It is currently thought to be endemic in commercial swine in the US and potentially elsewhere. One study of fecal samples from 509 pigs from 255 farms across 19 US states showed PoAstV4 was the most prevalent genotype in 62% (317/509) of samples. At least one PoAstV genotype was found in 64% (326/509) of the samples. It is common for multiple astroviruses to be detected in a single pig at once, which could provide opportunity for recombination to occur and lead to the emergence of new strains. Multiple studies have connected PoAstV to a range of disease manifestations, with the virus frequently detected in feces of pigs displaying diarrheal symptoms as well as asymptomatic pigs. PoAstV5 is a cause of clinical enteritis, while PoAstV3 has been identified and characterized in the central nervous system of pigs with neurologic signs and nonsuppurative polioencephalomyelitis. Additionally, PoAstV4 has been identified in nasal samples from pigs with respiratory disease.Researchers have investigated cases of bronchitis and/or tracheitis in pigs where PCR results were negative for influenza virus and other known causes of respiratory virus infection. Next generation sequencing revealed reads of PoAstV4. In a retrospective study of cases of tracheitis and/or bronchitis of unknown etiology, RNA in situ hybridization (ISH) was used to detect PoAstV4 RNA in airway epithelium (trachea, bronchi, or bronchioles), revealing PoAstV4 RNA in 73% (85/117) of cases. So PoAstV4 is strongly associated with lesions of epitheliotropic viral infection in young pigs with clinical respiratory disease.Accurate tests and vaccines for PoAstV4 infection are clearly needed.    

The use of standing surface acoustic waves (SSAWs) in microfluidic channels gained significant momentum when researchers demonstrated size-based cell separation (acoustophoresis) using lateral acoustic forces. Using interdigitated transducers (IDTs) positioned on piezoelectric substrates, SSAWs were found to create pressure nodes along the channel width, allowing larger particles to experience greater acoustic radiation forces and migrate toward these nodes faster than smaller particles. Acoustic-based microfluidic devices were successfully applied to circulating tumor cell (CTC) isolation from clinical blood samples in ~2015, demonstrating recovery rates >80% using tilted-angle standing surface acoustic waves, though these systems relied primarily on size-based separation principles. The integration of acoustic methods with microfluidics offered key advantages including label-free operation, biocompatibility, non-contact manipulation, and preservation of cell viability, addressing limitations of earlier methods like centrifugation, FACS, and magnetic separation that could damage cells or require labeling. Despite these advances in acoustic microfluidics, significant challenges persist in affinity-based rare cell isolation, particularly mass transport limitations in microfluidic channels operating at high Peclet numbers (Pe>10⁶) where convective flow dominates over diffusion. In traditional microfluidic affinity capture systems, cells flow predominantly in the center of laminar flow channels where fluid velocity is highest, resulting in minimal interaction with capture agents immobilized on channel walls and requiring extremely long channels or impractically slow flow rates to achieve adequate capture efficiency. The extremely low concentration of CTCs , combined with their phenotypic heterogeneity and the low diffusion coefficients of cells creates a "needle in a haystack" challenge that existing acoustic separation methods based solely on size discrimination cannot adequately address.

Position-sensitive radiation detection has been used in semiconductor detector development for decades. Traditional approaches have relied on segmented electrodes to achieve spatial resolution. Conventional semiconductor radiation detectors utilize segmented electrodes where each electrode segment is physically separated and individually read out to determine the position of radiation interactions. Traditional segmented electrode designs have long suffered from highly non-uniform electric fields within the detector volume, particularly at electrode edges and corners. These field concentrations can cause premature breakdown and inconsistent charge collection. This non-uniformity can also lead to position-dependent signal variations, pulse time dispersion, and potential electrical connections between adjacent electrodes from radiation damage. Moreover, common approaches to manufacturing of segmented electrodes requires precise mask alignment and complex fabrication processes, resulting in higher production costs and reduced yields.

Chronic wounds affect over 6.5 million people in the United States costing more than $25B annually. 23% of military blast and burn wounds do not close, affecting a military patient's bone, skin, nerves. Moreover, 64% of military trauma have abnormal bone growth into soft tissue. Slow healing of recalcitrant wounds is a known and persistent problem, with incomplete healing, scarring, and abnormal tissue regeneration. Precise control of wound healing depends on physician's evaluation, experience. Physicians generally provide conditions and time for body to either heal itself, or to accept and heal around direct transplantations, and their practice relies a lot on passive recovery. And while newer static approaches have demonstrated enhanced growth of non-regenerative tissue, they do not adapt to the changing state of wound, thus resulting in limited efficacy.

Digital integrated circuit design has evolved significantly over the past several decades, with synthesis becoming increasingly automated and sophisticated. The traditional synthesis flow emerged in the 1980s when commercial logic synthesis packages from companies like Cadence and Synopsys revolutionized chip design by automatically converting hardware description languages (HDL) into gate-level netlists. Electronic design automation (EDA) tools evolved from simple netlist extraction to complex optimization processes, progressing through gate-level optimization, register-transfer-level synthesis, and eventually algorithmic synthesis. However, as designs have grown exponentially in complexity, synthesis times have become a major bottleneck, with full synthesis often taking hours or days for large designs, significantly impacting designer productivity and iteration cycles. Long synthesis runtimes prevent designers from rapid iteration, with typical synthesis taking 3+ days for complex designs, forcing designers to carefully consider when to submit jobs and wait for delayed feedback. The traditional register-transfer level (RTL) design flow suffers from critical limitations including the inability for RTL engineers to identify and resolve top-level timing issues early in the design process, routing congestion problems that cannot be detected until placement is completed, and insufficient feedback on power consumption during early architectural phases. Additionally, even small design changes trigger full re-synthesis of large blocks, wasting computational resources on unchanged portions of the design, while inter-module optimization requirements often degrade quality-of-results (QoR) when designs are artificially partitioned.

Bioluminescence technology offers highly sensitive and non-invasive imaging in living organisms without the need for external excitation. Naturally occurring luciferases, the enzymes responsible for catalyzing light emission, constrained the full potential of luminescence technology for the past several decades due to their poor protein folding, large size, ATP dependency, and low efficiency.Creation of the next generation of luciferases required breaking free of evolutionary constraints. This work describes the creation of novel bioluminescent enzymes that surpass qualities of native luciferase using AI-powered de novo protein design. These designer luciferase catalysts enable genetic labeling across molecular, cellular, and individual levels in a multiplexed manner, using the same underlying technology.This advancement showcases the design of efficient enzymes from scratch in which our de novo luciferases will enable researchers to study complex biological phenomena effectively.In the last three decades, the development of fluorescent protein families has brought a revolution in the way researchers study biological processes in living cells. However, the dependency on external excitation for FPs introduces inherent drawbacks, such as phototoxicity and autofluorescence background. These especially limit the applications for fluorescent proteins in vivo. Bioluminescence technologies, which rely on an enzyme-catalyzed chemiluminescent reaction of a chromophore substrate to emit photons without the need for external light sources, circumvent these limitations and offer several orders-of-magnitude-higher sensitivity than fluorescence for macro-scale imaging.Practically implementing luciferases as general molecular proges has not progressed as far as fluroescent proteins due to a number of factors. Firefly luciferase (FLuc) is used widely for in vivo imaging, but it is dim, large (61 kDa), and ATP dependent. Gaussia luciferase (GLuc) is brighter than FLuc, but has five disulfide bonds and therefore cannot be used intracellularly. It is also prone to misfolding. Engineered variants of Renilla luciferase (RLuc) and Oplophorus Luciferase (NLuc) are brighter and more stable, but they emit blue light and have poor substrate specificity and therefore are difficult to used in multiplexed applications. LuxSit luciferase (Monod Bio Inc.) is the first de novo designed luciferase and has superior folding fidelity and stability to natural luciferases, but more de novo luciferase species are necessary to meet the needs of researchers.  

Kainate receptors, also known as kainic acid receptors are ionotropic receptors that bind to and are responsive to glutamate in neurons. These were originally identified as being activated by the compund kainic acid, orignally isolated from algae. Postsynaptic kainate receptors are involved in excitatory neurotransmission while presynaptic kainate receptors are involved in inhibitory neurotransmission. Kainic acid is a potentially very useful compound but very difficult to synthesize. As a result, there are very few pharmacological tool compounds to study kainate receptors and none that are readily tunable to install labeling compounds. 

The Major Histocompatibility Complex (MHC), is a genomic region that expresses proteins involved in immune system functions and that are important for organ transplantation. In humans, this type of gene is referred to as the Human Leukocyte Antigen (HLA). The HLA region is haplotypic, with all of the region inherited from one parent. HLA is highly polymorphic within the human population, both in terms of protein structure as well as genomic variability.This high genomic diversity makes accurate genotyping difficult using methods such as short-read sequencing. That said, current long-read sequencing methods and analysis can yield incomplete and inaccurate results.