Quantum chemistry and charge transport in biomolecules with superconducting circuits.

We propose an efficient protocol for digital quantum simulation of quantum chemistry problems and enhanced digital-analog quantum simulation of transport phenomena in biomolecules with superconducting circuits. Along these lines, we optimally digitize fermionic models of molecular structure with single-qubit and two-qubit gates, by means of Trotter-Suzuki decomposition and Jordan-Wigner transformation. Furthermore, we address the modelling of system-environment interactions of biomolecules involving bosonic degrees of freedom with a digital-analog approach.

Dynamics of inert spheres in active suspensions of micro-rotors.

Inert particles suspended in active fluids of self-propelled particles are known to often exhibit enhanced diffusion and novel coherent structures. Here we numerically investigate the dynamical behavior and self-organization in a system consisting of passive and actively rotating spheres of the same size. The particles interact through direct collisions and the fluid flows generated as they move.

Real-Time Processing of Continuous Physiological Signals in a Neurocritical Care Unit on a Stream Data Analytics Platform.

Continuous high-volume and high-frequency brain signals such as intracranial pressure (ICP) and electroencephalographic (EEG) waveforms are commonly collected by bedside monitors in neurocritical care. While such signals often carry early signs of neurological deterioration, detecting these signs in real time with conventional data processing methods mainly designed for retrospective analysis has been extremely challenging. Such methods are not designed to handle the large volumes of waveform data produced by bedside monitors.

An Efficient Nonlinear Regression Approach for Genome-wide Detection of Marginal and Interacting Genetic Variations.

Genome-wide association studies have revealed individual genetic variants associated with phenotypic traits such as disease risk and gene expressions. However, detecting pairwise interaction effects of genetic variants on traits still remains a challenge due to a large number of combinations of variants (∼10(11) SNP pairs in the human genome), and relatively small sample sizes (typically <10(4)). Despite recent breakthroughs in detecting interaction effects, there are still several open problems, including: (1) how to quickly process a large number of SNP pairs, (2) how to distinguish between true signals and SNPs/SNP pairs merely correlated with true signals, (3) how to detect nonlinear associations between SNP pairs and traits given small sample sizes, and (4) how to control false positives.

Self-Catalyzed Growth and Characterization of In(As)P Nanowires on InP(111)B Using Metal-Organic Chemical Vapor Deposition.

We report the growth of vertical <111>-oriented InAs x P1-x (0.11 ≤ x ≤ 0.27) nanowires via metal-organic chemical vapor deposition in the presence of indium droplets as catalysts on InP(111)B substrates at 375 °C.

Influence of metabolic dysfunction on cardiac mechanics in decompensated hypertrophy and heart failure.

Alterations in energetic state of the myocardium are associated with decompensated heart failure in humans and in animal models. However, the functional consequences of the observed changes in energetic state on mechanical function are not known. The primary aim of the study was to quantify mechanical/energetic coupling in the heart and to determine if energetic dysfunction can contribute to mechanical failure.

A computational method for designing diverse linear epitopes including citrullinated peptides with desired binding affinities to intravenous immunoglobulin.

Background: Understanding the interactions between antibodies and the linear epitopes that they recognize is an important task in the study of immunological diseases. We present a novel computational method for the design of linear epitopes of specified binding affinity to Intravenous Immunoglobulin (IVIg).

Results: We show that the method, called Pythia-design can accurately design peptides with both high-binding affinity and low binding affinity to IVIg.

Nanowire growth kinetics in aberration corrected environmental transmission electron microscopy.

We visualize atomic level dynamics during Si nanowire growth using aberration corrected environmental transmission electron microscopy, and compare with lower pressure results from ultra-high vacuum microscopy. We discuss the importance of higher pressure observations for understanding growth mechanisms and describe protocols to minimize effects of the higher pressure background gas.

High-Performance p-Type Black Phosphorus Transistor with Scandium Contact.

A record high current density of 580 μA/μm is achieved for long-channel, few-layer black phosphorus transistors with scandium contacts after 400 K vacuum annealing. The annealing effectively improves the on-state current and Ion/Ioff ratio by 1 order of magnitude and the subthreshold swing by ∼2.5×, whereas Al2O3 capping significantly degrades transistor performances, resulting in 5× lower on-state current and 3× lower Ion/Ioff ratio.

Predicting colorectal surgical complications using heterogeneous clinical data and kernel methods.

Objective: In this work, we have developed a learning system capable of exploiting information conveyed by longitudinal Electronic Health Records (EHRs) for the prediction of a common postoperative complication, Anastomosis Leakage (AL), in a data-driven way and by fusing temporal population data from different and heterogeneous sources in the EHRs.

Material And Methods: We used linear and non-linear kernel methods individually for each data source, and leveraging the powerful multiple kernels for their effective combination. To validate the system, we used data from the EHR of the gastrointestinal department at a university hospital.

Ag2ZnSn(S,Se)4: A highly promising absorber for thin film photovoltaics.

The growth in efficiency of earth-abundant kesterite Cu2ZnSn(S,Se)4 (CZTSSe) solar cells has slowed, due in part to the intrinsic limitations imposed by the band tailing attributed primarily to I-II antisite exchange. In this study, density functional theory simulations show that when Ag is substituted for Cu to form kesterite Ag2ZnSnSe4 (AZTSe), the I-II isolated antisite formation energy becomes 3.7 times greater than in CZTSSe, resulting in at least an order of magnitude reduction in I-II antisite density.

Adversarial Risk Analysis for Urban Security Resource Allocation.

Adversarial risk analysis (ARA) provides a framework to deal with risks originating from intentional actions of adversaries. We show how ARA may be used to allocate security resources in the protection of urban spaces. We take into account the spatial structure and consider both proactive and reactive measures, in that we aim at both trying to reduce criminality as well as recovering as best as possible from it, should it happen.

Multiscale modeling and simulation of brain blood flow.

The aim of this work is to present an overview of recent advances in multi-scale modeling of brain blood flow. In particular, we present some approaches that enable the study of multi-scale and multi-physics phenomena in the cerebral vasculature. We discuss the formulation of continuum and atomistic modeling approaches, present a consistent framework for their concurrent coupling, and list some of the challenges that one needs to overcome in achieving a seamless and scalable integration of heterogeneous numerical solvers.

Selective Surface Charge Sign Reversal on Metallic Carbon Nanotubes for Facile Ultrahigh Purity Nanotube Sorting.

Semiconducting (semi-) single-walled carbon nanotubes (SWNTs) must be purified of their metallic (met-) counterparts for most applications including nanoelectronics, solar cells, chemical sensors, and artificial skins. Previous bulk sorting techniques are based on subtle contrasts between properties of different nanotube/dispersing agent complexes. We report here a method which directly exploits the nanotube band structure differences.

Physically unclonable cryptographic primitives using self-assembled carbon nanotubes.

Information security underpins many aspects of modern society. However, silicon chips are vulnerable to hazards such as counterfeiting, tampering and information leakage through side-channel attacks (for example, by measuring power consumption, timing or electromagnetic radiation). Single-walled carbon nanotubes are a potential replacement for silicon as the channel material of transistors due to their superb electrical properties and intrinsic ultrathin body, but problems such as limited semiconducting purity and non-ideal assembly still need to be addressed before they can deliver high-performance electronics.

Why neurons mix: high dimensionality for higher cognition.

Neurons often respond to diverse combinations of task-relevant variables. This form of mixed selectivity plays an important computational role which is related to the dimensionality of the neural representations: high-dimensional representations with mixed selectivity allow a simple linear readout to generate a huge number of different potential responses. In contrast, neural representations based on highly specialized neurons are low dimensional and they preclude a linear readout from generating several responses that depend on multiple task-relevant variables.

Closed-Loop Brain Model of Neocortical Information-Based Exchange.

Here we describe an "information-based exchange" model of brain function that ascribes to neocortex, basal ganglia, and thalamus distinct network functions. The model allows us to analyze whole brain system set point measures, such as the rate and heterogeneity of transitions in striatum and neocortex, in the context of neuromodulation and other perturbations. Our closed-loop model is grounded in neuroanatomical observations, proposing a novel "Grand Loop" through neocortex, and invokes different forms of plasticity at specific tissue interfaces and their principle cell synapses to achieve these transitions.

Distinct signaling of Drosophila chemoreceptors in olfactory sensory neurons.

In Drosophila, olfactory sensory neurons (OSNs) rely primarily on two types of chemoreceptors, odorant receptors (Ors) and ionotropic receptors (Irs), to convert odor stimuli into neural activity. The cellular signaling of these receptors in their native OSNs remains unclear because of the difficulty of obtaining intracellular recordings from Drosophila OSNs. Here, we developed an antennal preparation that enabled the first recordings (to our knowledge) from targeted Drosophila OSNs through a patch-clamp technique.

Characterizing redescriptions using persistent homology to isolate genetic pathways contributing to pathogenesis.

Background: Complex diseases may have multiple pathways leading to disease. E.g.

SLOPE-ADAPTIVE VARIABLE SELECTION VIA CONVEX OPTIMIZATION.

We introduce a new estimator for the vector of coefficients in the linear model = + , where has dimensions with possibly larger than . SLOPE, short for Sorted L-One Penalized Estimation, is the solution to [Formula: see text]where λ ≥ λ ≥ … ≥ λ ≥ 0 and [Formula: see text] are the decreasing absolute values of the entries of . This is a convex program and we demonstrate a solution algorithm whose computational complexity is roughly comparable to that of classical ℓ procedures such as the Lasso.

Non-Abelian SU(2) Lattice Gauge Theories in Superconducting Circuits.

We propose a digital quantum simulator of non-Abelian pure-gauge models with a superconducting circuit setup. Within the framework of quantum link models, we build a minimal instance of a pure SU(2) gauge theory, using triangular plaquettes involving geometric frustration. This realization is the least demanding, in terms of quantum simulation resources, of a non-Abelian gauge dynamics.

Quantum entanglement at ambient conditions in a macroscopic solid-state spin ensemble.

Entanglement is a key resource for quantum computers, quantum-communication networks, and high-precision sensors. Macroscopic spin ensembles have been historically important in the development of quantum algorithms for these prospective technologies and remain strong candidates for implementing them today. This strength derives from their long-lived quantum coherence, strong signal, and ability to couple collectively to external degrees of freedom.

Opportunities and challenges in liquid cell electron microscopy.

Transmission electron microscopy offers structural and compositional information with atomic resolution, but its use is restricted to thin, solid samples. Liquid samples, particularly those involving water, have been challenging because of the need to form a thin liquid layer that is stable within the microscope vacuum. Liquid cell electron microscopy is a developing technique that allows us to apply the powerful capabilities of the electron microscope to imaging and analysis of liquid specimens.