Telomeres are the end of eukaryotic chromosomes and shorten with time. Shortened telomeres cause disease in patients with short telomere syndromes. Cancer cells must activate telomerase to maintain telomeres to overcome senescence or apoptosis due to critically shorttelomeres.Telomere length is often measured by Southern Blot (invented in 1973). This method measures the length of all the telomeres in the cell and does not give quantitative information or information about individual telomere lengths. A quantitative PCR method was developed however it has many problems with reproducibility across different labs. The Single Telomere  Length Analysis (STELA) method and the related Telomere Shortest Length Assay (TeSLA) targets individual telomeres, but the use of high cycles of PCR generates bias for shorter products.Additionally, the use of Southern Blotting in STELA and TeSLA makes these techniques laborious and low-throughput. Currently, Telomere Flow FISH is the accepted clinical method for measuring telomere length. This method is highly reproducible, and can report on a specific cell type, howeverit requires fresh blood samples and is not able to measure archival samples or tissues other than blood. The method uses an average of the telomeres without the ability to report the individual lengths of telomeres. Thus, there are currently no methods that provide an accurate, high throughput, and simple approach for quantifying telomere length.

Over the past 5 to 7 years, non-overlapping (NOV) clock phase generators have conventionally relied on downstream, dedicated auxiliary logic—such as cross-coupled SR latch delay blocks. While effective at nominal voltages, these additional stages create severe power overhead and fail under ultra-low-voltage (ULV) constraints due to reduced transistor gain and output swing. Recent advances attempted to integrate NOV phase generation directly within ring oscillators to improve energy efficiency. However, topologies relying on bootstrap architectures or heavy transistor stacking (e.g., laddered inverters) remain restricted to minimum operating voltages ranging from 0.5V to 1.0V. Present implementations lack the ability to directly produce non-overlapping clock phases at sub-100 mV supply levels without incurring high area and power costs from auxiliary phase-generation circuits.

Depositing manganese dioxide (MnO2) onto three-dimensional (3D) porous scaffolds—such as carbon lattices, nickel foam, or graphene frameworks—is a widely used strategy in developing high-performance electrochemical devices like supercapacitors, batteries, and catalysts.The primary objective is to build a hybrid structure that overcomes the natural material limitations of MnO2 while maximizing its high energy storage capabilities. But conventional MnO2 electrodeposition has a number of limitations. Conventional MnO2 electrodeposition tends to form thick coatings only on the outer surfaces of porous substrates. This excessive surface growth leads to pore blockage and poor penetration into the internal structure, impeding electrolyte access and reducing ion transport efficiency. This leads to low utilization of the available surface area and active material, severely limiting electrode performance. Achieving both high mass loading and coating uniformity remains a major challenge. Existing methods such as hydrothermal synthesis often suffer from inherently low loading (typically < 40 mg cm-2), which constrains device energy density and practicality. MnO2 nucleation is dominated by random and large island growth, resulting in thick, non-uniform films with thickness gradients that hinder charge and ion transfer kinetics.  Highly loaded electrodes prepared by conventional methods exhibit thick MnO2 coating often show poor electrochemical kinetics due to poor electron/ion conductivity.Finally, there is a lack of controlled interface regulation during electrodeposition.   

The aim of this work is to target the production of age-specific production of hyperactive platelets as a therapeutic platform to control clot formation that causes thrombosis, stroke, heart attacks, and other cardiovascular disease, as well as platelet overproduction disorders such as essential thrombocytosis. In particular, this effort specifically targets cells that have progressed down an age-specific differentiation pathway. These age specific platelets are hyperactive relative to platelets from younger progenitor cells. These older platelet progenitor cells have been characterized molecularly and functionally characterization and can be targeted using pharmacological, antibody-based, cell based or gene therapy based strategies to control clot formation and platelet activity and numbers.

The transition to 5G and 6G networks has led to a widespread adoption of machine learning (ML) for critical functions like modulation classification, channel estimation, resource management, and spectrum sensing. While ML has enhanced operational efficiency, it has simultaneously expanded the attack surface for adversarial ML at the Physical Layer (PHY), for example, from Generative Adversarial Networks (GANs). While techniques like radio frequency (RF) fingerprinting have emerged as a PHY-level authentication method based on hardware-induced signal traits (such as in-phase/quadrature (I/Q) imbalance and error vector magnitude), GANs can synthesize RF signals to mimic legitimate hardware-induced features up to 95% similarity. This is close enough to evade most detection schemes. Existing defenses to GANs based on convolutional neural networks, deep neural networks, supervised retraining, and/or heuristics do not generalize well across different modulations, protocols, channel conditions, or unseen attack types. Autoencoder and reconstruction-based approaches are often limited to clean reference signals, which are not always available in dynamic wireless environments. While GANs are excellent at mimicking low-order statistics (mean/variance), they fail to replicate complex signal structures.

Methionine (Met) is a common amino acid found in almost all proteins. When it undergoes oxidation (a common process in aging and disease), it transforms into methionine sulfoxide (Met-SO).The challenge is that this chemical reaction creates a new chiral center at the sulfur atom. This means that for every oxidized methionine, two different mirror-image versions (diastereomers) can exist: The (S,S) form and the (S,R) form.Before this invention, researchers struggled to separate these two forms. This resulted in two major technical hurdles:Standard techniques like High Performance Liquid Chromatography (HPLC) or fractional crystallization (a method dating back to 1947) were unreliable, difficult to reproduce, and failed to produce high-purity samplesBecause the two forms were so difficult to separate, almost all previous research on methionine oxidation used a mixture of both. This meant that if one form was toxic and the other was harmless, the results would be averaged out, hiding the true biological mechanism.A core motivation for this invention is the "staggering degree of disagreement" in Alzheimer's Disease research regarding the protein Amyloid beta (Aβ42)Some studies claimed that oxidized Aβ42 increased brain plaque toxicity, while others claimed it decreased itIt is plausible that these contradictions exist because previous researchers didn't know which specific diastereomer—(S,S) or (S,R)—they were testinOnce these two forms are created, they are remarkably stable. The energy barrier to flip from one form to the other is roughly 45.2 kcal/mol, which is significantly higher than other enantiomeric structures. This means that in the human body, the "wrong" version won't just flip back to the "right" one; it stays in that specific shape, potentially causing long-term damage if not properly regulated by specific enzymes (reductases). 

Comprehensive radiation detection across the spectral range requires distinct systems for ionizing and non-ionizing imaging because each technology faces unique architectural hurdles. Modern visible light detection has successfully transitioned from passive plates to digital Active Pixel Sensors (APS) by leveraging Complementary Metal-Oxide-Semiconductor (CMOS) technology to provide every pixel with its own dedicated amplifier and active circuitry. Ionizing radiation detection like X-ray and gamma-ray has relied on exotic scintillators to convert radiation into light, a process prone to lateral light scattering and degraded spatial resolution. Recent advancements in ionizing radiation have shifted toward direct conversion materials like amorphous selenium (a-Se), which transform X-rays directly into electrical charges. However, these direct-conversion devices do not scale to larger areas without significant noise being a factor. This is primarily due to thin-film transistor (TFT) backplanes which, unlike their CMOS counterparts, lack the local amplification necessary to maintain a high signal-to-noise ratio.

Machine learning has transitioned from traditional supervised learning to more resource-efficient sketching and federated techniques. Early compressive learning relied on hand-crafted random projections and task-specific iterative solvers. While these methods reduced data volume, they were inflexible because a change in data distribution or task required a complete redesign of the projection. Concurrently, privacy-preserving needs led to the rise of federated learning and differential privacy. However, these methods often struggled with high communication costs and the inability to merge model updates effectively across different architectures. Until recently, the state of the art remained fairly bifurcated, where one could have either high-accuracy iterative training on raw data, or efficient but brittle and task-specific compressed representations that lacked generalizability across diverse analytical tasks, e.g., Principal Component Analysis (PCA), regression, clustering.