Clustered regularly interspaced short palindromic repeats (CRISPR) screening is a cornerstone of functional genomics, enabling genome-wide knockout studies to identify genes involved in specific cellular processes or disease pathways. The success of CRISPR screens depends critically on the design of effective guide RNA (gRNA) libraries that maximize on-target activity while minimizing off-target effects. Current CRISPR screening lacks tools that can natively integrate next-generation sequencing (NGS) data for context-specific gRNA design, despite the wealth of genomic and transcriptomic information available from modern sequencing approaches. Traditional gRNA design tools have relied on static libraries with limited genome annotations and outdated scoring methods, lacking the flexibility to incorporate context-specific genomic information. Off-target effects are also a concern, with CRISPR-Cas9 systems tolerating up to three mismatches between single guide RNA (sgRNA) and genomic DNA, potentially leading to unintended mutations that could disrupt essential genes and compromise genomic integrity. Additionally, standard CRISPR library preparation methods can introduce bias through PCR amplification and cloning steps, resulting in non-uniform gRNA representation.

Chinese hamster ovary (CHO) cells are a family of immortalized cells derived from the epithelial cells of the ovary of the Chinese hamster. They are among the most widely used cell systems in cell biology. The cell line is frequently used in biological and medical research. It’s specifically used in the production of recombinant therapeutic proteins. Essentially, the CHO cell is used as a “factory cell,” meaning it can be programmed to produce therapeutic proteins, including vaccines and antibodies.Recombinant protein expression has been used in the development of biologics for many applications including therapeutics and vaccines.  However, one challenge in the development of biologics is the unintended proteolysis associated of expressed recombinant proteins in CHO cells. One example of a biologic that faces supoptimal expression in CHO cells is the HIV envelope glycoprotein, gp120, which is one of the main targets for neutralizing antibodies in HIV infection and a prime candidate for component of an HIV vaccine. When expressed in CHO cells, gp120 undergoes proteolytic clipping by a serine protease at an epitope recognized by neutralizing antibodies. Essentially, the cells produce an enzyme that cuts gp120 into pieces. This proteolysis alters gp120 to the point where it is unrecognizable by the immune system and renders it non-immunogenic. This issue appears frequently with CHO expression of envelope proteins from clade B HIV. Clade B is the most common genetic subtype present in the US and Europe. While this issue has been observed and attributed to CHO cells, it was previously unknown which enzyme was responsible. Without knowledge of the enzyme responsible, the solutions were based on modifying the protein sequence or adjusting the conditions of production, both of which are suboptimal. 

Scaled neural sensing has been pursued for decades. Physical limitations associated with electrical (electrode-based) field recordings hinder advances in both field of view and spatial resolution. Electrochromic plasmonics (electro-plasmonics) has emerged as a rapidly advancing field combining traditional electrochromic materials with plasmonic nanostructures, including recent demonstrations of electrochromic-loaded plasmonic nanoantennas for optical voltage sensing. Existing optical electrophysiology techniques face critical limitations including poor signal-to-noise ratios due to low photon counts from genetically encoded voltage indicators, which have small cross-sections and low quantum yields. Fluorescent voltage indicators suffer from photobleaching, phototoxicity, and require genetic modifications that limit their clinical applicability. Current electrochromic devices also struggle with limited cycling stability, slow switching times, and restricted color options, and conventional plasmonic sensors exhibit inherently low electric field sensitivity due to high electron densities of metals like gold and silver. Current approaches to electro-plasmonics lack stable, high-contrast optical modulators that can operate at sub-millisecond speeds while maintaining human biocompatibility.

Cortisol is an essential steroid hormone that is involved in numerous physiological processes such as the stress response, regulation of blood pressure, immune modulation, and regulation of the sleep cycle. Cortisol levels can vary based on several factors. Cortisol imbalances can indicate adrenal disorders, such as Cushing’s Syndrome and Addison’s Disease. Cortisol imbalance can also lead to disruption in the sleep cycle, increased stress, and metabolic disorders. Given these facts, accurate and accessible cortisol monitoring is crucial for diagnostics and overall health.Standard methods for monitoring cortisol levels involve enzyme-linked immunosorbent assays or liquid chromatography-tandem mass spectrometry. These methods, while reliable are performed in laboratory settings using expensive equipment and take significant time to produce results. Reliable on-site or at-home detection methods of cor are unavailable, but would be important tools

WiFi-based indoor positioning has been a widely researched area for the past five years, with systems traditionally relying on signal telemetry data including Received Signal Strength Indicator (RSSI), Channel State Information (CSI), and Fine Timing Measurement (FTM). However, adoption in practice has remained limited due to environmental challenges including signal fading, multipath effects, and interference that significantly impact positioning accuracy. Existing machine learning approaches typically require extensive manual feature engineering, preprocessing steps like filtering and data scaling, and struggle with missing or incomplete telemetry data while lacking flexibility across heterogeneous environments. Furthermore, there is currently no unified model capable of handling variations in telemetry data formats from different WiFi device vendors, use-case requirements, and environmental conditions, forcing practitioners to develop separate models for each specific deployment scenario.

Pressure injuries (commonly called bedsores or pressure ulcers) represent one of the most persistent and costly challenges in healthcare, affecting over 2.5 million US patients and costing almost $27B in 2019. Hospital-acquired pressure injury events occur in about 3% in general populations and about 6% in intensive care units (ICUs). Current prevention strategies still rely on the Braden Scale risk assessment tool as the gold standard. Developed in the 80s, it is used to stratify patients into risk categories based on factors like sensory perception, moisture, mobility, and friction. The Braden score directly informs turning frequency as the standard of protocol. Unfortunately, medical staff adherence to turning protocols remains low at ~50% nationally, creating a gap between prescribed care and actual implementation. Technologies to help assess by sensing pressure injuries have limitations, including discontinuous monitoring requiring manual interpretation, and lack of objective mobility metrics. These fail to account for the complex interplay between pressure distribution, patient movement patterns, and individual risk factors. The Braden-scoring approach is particularly problematic as it does not account for the presence of existing pressure injuries or patient-specific factors, and has been shown to have inadequate validity for ICU patients. Additionally, current pressure mapping systems are typically large, expensive, and require specialized training, limiting their practical deployment in routine clinical care.

Vehicle platooning technology is an evolving segment within the broader movement towards more intelligent transportation, specifically relating to autonomous vehicles. Some early concepts dates back to the 1970s with projects like Electronic Route Guidance System developed by the U.S. Federal Highway Administration, which used a destination-oriented approach with roadside units to decode vehicle inputs and provide routing instructions. Subsequent initiatives such as the California Partners for Advanced Transportation Technology program demonstrated vehicles traveling in close formation guided by magnets embedded in roadways. The landscape has since evolved from individual vehicle automation concepts to more sophisticated vehicle-to-vehicle (V2V) communication schemes to enable coordinated movements. More recent industry developments have been driven by advancements in 5G technology, V2V communication protocols, and enhanced safety requirements. Current systems face control stability challenges, particularly as platoon size increases, with research showing that system stabilizability degrades and can lose stability entirely in infinite vehicle formations. Moreover, issues with V2V communication reliability persist, including frequent intermittent connectivity problems and wireless interference, limiting wider adoption. Additional challenges include the fundamental trade-off between fuel efficiency and safety margins, where shorter inter-vehicle distances improve aerodynamic benefits but increase collision risk.

Outdoor wireless sensor and camera networks are important for environmental monitoring and public-safety surveillance, yet their real-world deployment still relies heavily on expert intuition and exhaustive simulations that fail to scale in many landscapes. Traditional coverage-maximization techniques evaluate every candidate position for every node while factoring in every other node, the task complexity becomes intractable as node count or terrain granularity grows. The challenge is sharper in three-dimensional topographies where ridges, valleys, and plateaus block line-of-sight and invalidate two-dimensional heuristics. Moreover, once nodes are in the field, relocating them is slow and costly if new blind spots emerge or missions evolve.