Antibiotic resistance is a major issue in infectious disease treatment and prevention. In bacteria, the type III secretion system (T3SS) secretes effector proteins in the host cell, allowing the pathogen to infect. The T3SS is largely found on pathogens and not beneficial bacteria, so targeting the T3SS might have an advantage over using classic antibiotics, which disturb the beneficial human microbiome.

Combinatorial encoded library technologies can provide a set of tools for discovering protein-targeting ligands (molecules) and for drug discovery. These techniques can accelerate ligand discovery by leveraging chemical diversity achievable through genetically encoded combinatorial libraries, for example, by combinatorial permutation of chemical building blocks. Although display technologies such as mRNA and phage display use biological translation machinery to produce peptide-based libraries, hits from these libraries often lack key drug-like properties, for example, cell permeability. This limitation can arise from the peptide backbone's inherent polarity and the tendency to select compounds with polar/charged side chains. Backbone N-methylation can increase scaffold lipophilicity in mRNA display; however, codon table constraints can necessitate longer sequences to fully utilize the available space.DNA-encoded libraries (DELs) offer an alternative approach towards discovering hits against drug targets. However, like other encoded library techniques, DELs face significant obstacles in affinity selections, which tend to enrich library members bearing polar and/or charged moieties, which can have low (poor) passive cell membrane permeability, especially in larger molecular weight libraries, resulting in hits with poor drug-like properties. This selection bias is especially problematic for larger constructs beyond the rule of 5, where fine-tuning lipophilicity can be critical. Furthermore, DNA-encoded libraries can be of low quality. Although algorithmic predictions of lipophilicity exist, these two-dimensional (2D) atomistic calculations cannot capture conformational effects exhibited by larger molecules like peptide macrocycles. Despite over a decade of DEL technology development, no method exists to measure physical properties of encoded molecules across an entire DNA-encoded library. That is, successful translation of hits from encoded library selections can be impeded by low quality libraries and enrichment of highly polar members which tend to have poor passive cell permeability, especially for larger molecular weight libraries.DELs are produced through split-pool synthesis with DNA barcoding to encode the building block of each chemical step. Although this approach can draw on a large number of building blocks and allow for the formation of non-peptidic libraries with a large number of members, synthetic challenges persist. The formation of DELs can be synthetically inefficient. Truncations multiply ( are compounded) throughout synthesis, reducing the representation of properly synthesized constructs. Although strategies to improve library purity, to enable reaction monitoring for macrocycle formation, and to identify problematic chemistry affecting DNA tag amplification may be applied, a direct method for assessing DEL quality on a library-wide basis has yet to be developed.   

Medium Access Control (MAC) protocols determine how multiple devices share a single communication channel. This started with Additive Links On-Line Hawaii Area (ALOHA) channel protocol and advanced to Carrier Sense Multiple Access (CSMA) protocols, variants of which are used today as WiFi standards. Such random access protocols are generally divided into contention-based methods like ALOHA and CSMA which are simple yet can have collisions at high traffic loads, and contention-free methods like Time Division Multiple Access (TDMA) which offer high efficiency but require complex clock synchronization and inflexible time slotting. While distributed queuing concepts have been pitched to help bridge this gap (e.g., DQDB or DQRAP) they have traditionally relied on physical time slots, dual buses, and/or complex signaling that makes them less suitable for the modern demands of wireless networks.

Historically, radio frequency and microwave switches have historically relied on either electromechanical switches (which suffer from limited speed and reliability) or solid-state switches such as PIN diodes and field-effect transistors (FETs), both of which require continuous bias current to maintain their states, consuming significant power in modern communication systems. In particular, solid-state switches (PIN diodes and FETs) require continuous DC power to maintain their ON or OFF states, leading to substantial energy consumption particularly problematic for battery-operated devices and large-scale systems like 5G/6G base stations and Internet of Things networks. Emerging non-volatile RF switches based on phase-change materials (PCM) and other memristive devices have shown promise but are constrained by large switching energies, limited resistance modulation ratios (typically < three orders of magnitude), volatile behavior requiring thermal maintenance above transition temperatures, and low endurance.

Researchers at UC Santa Cruz have expanded the knowledge on the rippled β-sheet, a protein structural motif formed by certain racemic peptides. Rippled β-sheets already show potential for Alzheimer’s research and drug delivery and leads to formation of hydrogels with enhanced properties. Researchers at UC Santa Cruz have further added to the structural foundation of rippled β-sheets, better understanding how rippled β-sheet formation can be controlled at the molecular level.

Amyloid-β (Aβ) is a protein that is implicated in Alzheimer’s disease. Aβ oligomers aggregate to form amyloid plaques, which are found in the brains of individuals with Alzheimer’s disease. These plaques have high polydispersity; they vary in shape and size. Previously, researchers at UC Santa Cruz demonstrated that using a racemic mixture of Aβ promoted fibril formation, an aggregation that is less neurotoxic than plaques of high polydispersity. Furthermore, these racemic counterparts form rippled β-sheets.

Historically, speech therapy apps have relied primarily on online cloud-based speech recognition systems like those used in digital assistants (Cortana, Siri, Google Assistant), which are designed to best guess speech rather than critically evaluate articulation errors. For children with cleft palate specifically, affecting 1 in 700 babies globally, speech therapy is essential follow-up care after reconstructive surgery. Approximately 25% of children with clefts use compensatory articulation errors, and when these patterns become habituated during ages 3-5, they become particularly resistant to change in therapy. Traditional approaches to mobile speech therapy apps have included storybook-style narratives that proved expensive with low replayability and engagement, as well as fast-paced arcade-style games that failed to maintain user interest. Common speech therapy applications require a facilitator to evaluate speech performance and typically depend on continuous internet connectivity, creating barriers for users in areas with poor network coverage or those concerned about data privacy and roaming costs. The shift toward gamified therapy solutions showed that game elements can serve as powerful motivators for otherwise tedious activities. Speech recognition systems face inherent limitations in accuracy compared to cloud-based solutions and require substantial processing power and memory that can impact device performance and battery life, particularly on older mobile devices. Automatic speech recognition (ASR) models struggle significantly with children's speech due to non-fluent pronunciation and variability in speech patterns, with phoneme error rates reaching almost 12%, and consonant recognition errors affecting the reliability of speech disorder detection. The challenge becomes even more pronounced for populations with speech impairments, as conventional ASR systems are optimized for typical adult speech rather than atypical articulation patterns of cleft palate speech or developmental disabilities. Moreover, maintaining user engagement over extended therapy periods is hard, and many apps fail to provide sufficient motivation for daily practice, which is essential for speech improvement.

Traditional approaches to indoor mapping relied heavily on manual floor plan tracing or rule-based computer vision algorithms, which proved fragile when confronted with the wide variety of graphical representations used in architectural drawings. While Computer-Aided Design (CAD) floor plans in formats like DWG or DWF exist for most modern buildings, these detailed technical drawings are typically proprietary and inaccessible to the public. Mappers often work with low-quality images (JPEG or PDF format) of floor plans, necessitating manual digitization processes. RGB-D cameras, which capture both color and depth information, emerged as promising tools for 3D indoor scanning, though they face limitations including restricted range (typically less than 5 meters), sensitivity to lighting conditions, noisy point clouds at object edges, and computational demands for real-time processing. Automatic floor plan vectorization algorithms remain highly sensitive to image quality and graphical symbol variations, often requiring substantial manual editing even with state-of-the-art deep learning approaches.