Molecular Sieving in Periodic Free-Energy Landscapes Created by Patterned Nanofilter Arrays

We present an experimental study of Ogston-like sieving process of rodlike DNA in patterned periodic nanofluidic filter arrays. The electrophoretic motion of DNA through the array is described as a biased Brownian motion overcoming periodically modulated free-energy landscape. A kinetic model, constructed based on the equilibrium partitioning theory and the Kramers theory, explains the field-dependent mobility well. We further show experimental evidence of the crossover from Ogston-like sieving to entropic trapping, depending on the ratio between nanofilter constriction size and DNA size

Summary Based Structures with Improved Sublinear Recovery for Compressed Sensing

We introduce a new class of measurement matrices for compressed sensing, using low order summaries over binary sequences of a given length. We prove recovery guarantees for three reconstruction algorithms using the proposed measurements, including $\ell_1$ minimization and two combinatorial methods. In particular, one of the algorithms recovers $k$-sparse vectors of length $N$ in sublinear time $\text{poly}(k\log{N})$, and requires at most $\Omega(k\log{N}\log\log{N})$ measurements. The empirical oversampling constant of the algorithm is significantly better than existing sublinear recovery algorithms such as Chaining Pursuit and Sudocodes. In particular, for $10^3\leq N\leq 10^8$ and $k=100$, the oversampling factor is between 3 to 8. We provide preliminary insight into how the proposed constructions, and the fast recovery scheme can be used in a number of practical applications such as market basket analysis, and real time compressed sensing implementation

Electrochemical characterization of parylene-embedded carbon nanotube nanoelectrode arrays

A novel parylene-embedded carbon nanotube nanoelectrode array is presented for use as an electrochemical detector working electrode material. The fabrication process is compatible with standard microfluidic and other MEMS processing without requiring chemical mechanical polishing. Electrochemical studies of the nanoelectrodes showed that they perform comparably to platinum. Electrochemical pretreatment for short periods of time was found to further improve performance as measured by cathodic and anodic peak separation of K3Fe(CN)6. A lower detection limit below 0.1 µM was measured and with further fabrication improvements detection limits between 100 pM and 10 nM are possible. This makes the nanoelectrode arrays particularly suitable for trace electrochemical analysis

An analog sub-linear time sparse signal acquisition framework based on structured matrices

Advances in compressed-sensing (CS) have sparked interest in designing information acquisition systems that process data at close to the information rate. Initial proposals for CS signal acquisition systems utilized random matrix ensembles in conjunction with convex relaxation based signal reconstruction algorithms. While providing universal performance bounds, random matrix based formulations present several practical problems due to: the difficulty in physically implementing key mathematical operations, and their dense representation. In this paper, we present a CS architecture which is based on a sub-linear time recovery algorithm (with minimum memory requirement) that exploits a novel structured matrix. This formulation allows the use of a reconstruction algorithm based on relatively simple computational primitives making it more amenable to implementation in a fully-integrated form. Theoretical recovery guarantees are discussed and a hypothetical physical CS decoder is described

Two-Year Changes in Diabetic Kidney Disease Phenotype and the Risk of Heart Failure: A Nationwide Population-Based Study in Korea

Background Diabetic kidney disease (DKD) is a risk factor for hospitalization for heart failure (HHF). DKD could be classified into four phenotypes by estimated glomerular filtration rate (eGFR, normal vs. low) and proteinuria (PU, negative vs. positive). Also, the phenotype often changes dynamically. This study examined HHF risk according to the DKD phenotype changes across 2-year assessments. Methods The study included 1,343,116 patients with type 2 diabetes mellitus (T2DM) from the Korean National Health Insurance Service database after excluding a very high-risk phenotype (eGFR <30 mL/min/1.73 m2) at baseline, who underwent two cycles of medical checkups between 2009 and 2014. From the baseline and 2-year eGFR and PU results, participants were divided into 10 DKD phenotypic change categories. Results During an average of 6.5 years of follow-up, 7,874 subjects developed HHF. The cumulative incidence of HHF from index date was highest in the eGFRlowPU– phenotype, followed by eGFRnorPU+ and eGFRnorPU–. Changes in DKD phenotype differently affect HHF risk. When the persistent eGFRnorPU– category was the reference, hazard ratios for HHF were 3.10 (95% confidence interval [CI], 2.73 to 3.52) in persistent eGFRnorPU+ and 1.86 (95% CI, 1.73 to 1.99) in persistent eGFRlowPU–. Among altered phenotypes, the category converted to eGFRlowPU+ showed the highest risk. In the normal eGFR category at the second examination, those who converted from PU– to PU+ showed a higher risk of HHF than those who converted from PU+ to PU–. Conclusion Changes in DKD phenotype, particularly with the presence of PU, are more likely to reflect the risk of HHF, compared with DKD phenotype based on a single time point in patients with T2DM

Learning-Based Near-Optimal Area-Power Trade-offs in Hardware Design for Neural Signal Acquisition

Wireless implantable devices capable of monitoring the electrical activity of the brain are becoming an important tool for understanding and potentially treating mental diseases such as epilepsy and depression. While such devices exist, it is still necessary to address several challenges to make them more practical in terms of area and power dissipation.In this work, we apply Learning Based Compressive Subsampling (LBCS) to tackle the power and area trade-offs in neural wireless devices. To this end, we propose a lowpower and area-effcient system for neural signal acquisition which yields state-of-art compression rates up to 64x with high reconstruction quality, as demonstrated on two human iEEG datasets. This new fully digital architecture handles one neural acquisition channel, with an area of 210x210μm in 90nm CMOS technology, and a power dissipation of only 0:9μW

Resolving catastrophic error bursts from cosmic rays in large arrays of superconducting qubits

Scalable quantum computing can become a reality with error correction, provided coherent qubits can be constructed in large arrays. The key premise is that physical errors can remain both small and sufficiently uncorrelated as devices scale, so that logical error rates can be exponentially suppressed. However, energetic impacts from cosmic rays and latent radioactivity violate both of these assumptions. An impinging particle ionizes the substrate, radiating high energy phonons that induce a burst of quasiparticles, destroying qubit coherence throughout the device. High-energy radiation has been identified as a source of error in pilot superconducting quantum devices, but lacking a measurement technique able to resolve a single event in detail, the effect on large scale algorithms and error correction in particular remains an open question. Elucidating the physics involved requires operating large numbers of qubits at the same rapid timescales as in error correction, exposing the event's evolution in time and spread in space. Here, we directly observe high-energy rays impacting a large-scale quantum processor. We introduce a rapid space and time-multiplexed measurement method and identify large bursts of quasiparticles that simultaneously and severely limit the energy coherence of all qubits, causing chip-wide failure. We track the events from their initial localised impact to high error rates across the chip. Our results provide direct insights into the scale and dynamics of these damaging error bursts in large-scale devices, and highlight the necessity of mitigation to enable quantum computing to scale