Signal Filtering Enabled by Spike Voltage-Dependent Plasticity in Metalloporphyrin-Based Memristors.

Neural systems can selectively filter and memorize spatiotemporal information, thus enabling high-efficient information processing. Emulating such an exquisite biological process in electronic devices is of fundamental importance for developing neuromorphic architectures with efficient in situ edge/parallel computing, and probabilistic inference. Here a novel multifunctional memristor is proposed and demonstrated based on metalloporphyrin/oxide hybrid heterojunction, in which the metalloporphyrin layer allows for dual electronic/ionic transport.

Quantifying the Brain Predictivity of Artificial Neural Networks With Nonlinear Response Mapping.

Quantifying the similarity between artificial neural networks (ANNs) and their biological counterparts is an important step toward building more brain-like artificial intelligence systems. Recent efforts in this direction use , or the ability to predict the responses of a biological brain given the information in an ANN (such as its internal activations), when both are presented with the same stimulus. We propose a new approach to quantifying neural predictivity by explicitly mapping the activations of an ANN to brain responses with a non-linear function, and measuring the error between the predicted and actual brain responses.

Impairments in odour detection and hedonic ratings of unpleasant smells in asymptomatic university students as SARS-Cov-2 emerged locally.

Sudden olfactory loss in the absence of concurrent nasal congestion is now a well-recognized symptom of COVID-19. We examined olfaction using standardized objective tests of odour detection, identification and hedonics collected from asymptomatic university students before and as SARS-CoV-2 emerged locally. Olfactory performance of students who were tested when the virus is known to be endemic (n = 22) was compared to students tested in the month prior to viral circulation (n = 25), a normative sample assessed during the previous 4 years (n = 272) and those tested in prior years during the same time period.

The Fermi-Dirac distribution provides a calibrated probabilistic output for binary classifiers.

Binary classification is one of the central problems in machine-learning research and, as such, investigations of its general statistical properties are of interest. We studied the ranking statistics of items in binary classification problems and observed that there is a formal and surprising relationship between the probability of a sample belonging to one of the two classes and the Fermi-Dirac distribution determining the probability that a fermion occupies a given single-particle quantum state in a physical system of noninteracting fermions. Using this equivalence, it is possible to compute a calibrated probabilistic output for binary classifiers.

Prediction of drug metabolites using neural machine translation.

Metabolic processes in the human body can alter the structure of a drug affecting its efficacy and safety. As a result, the investigation of the metabolic fate of a candidate drug is an essential part of drug design studies. Computational approaches have been developed for the prediction of possible drug metabolites in an effort to assist the traditional and resource-demanding experimental route.

Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations.

The de novo design of antimicrobial therapeutics involves the exploration of a vast chemical repertoire to find compounds with broad-spectrum potency and low toxicity. Here, we report an efficient computational method for the generation of antimicrobials with desired attributes. The method leverages guidance from classifiers trained on an informative latent space of molecules modelled using a deep generative autoencoder, and screens the generated molecules using deep-learning classifiers as well as physicochemical features derived from high-throughput molecular dynamics simulations.

Ultrahigh vacuum packaging and surface cleaning for quantum devices.

We describe design, implementation, and performance of an ultra-high vacuum (UHV) package for superconducting qubit chips or other surface sensitive quantum devices. The UHV loading procedure allows for annealing, ultra-violet light irradiation, ion milling, and surface passivation of quantum devices before sealing them into a measurement package. The package retains vacuum during the transfer to cryogenic temperatures by active pumping with a titanium getter layer.

Computational Investigations of the Lithium Superoxide Dimer Rearrangement on Noisy Quantum Devices.

Quantum chemistry studies of biradical systems are challenging due to the required multiconfigurational nature of the wavefunction. In this work, Variational Quantum Eigensolver (VQE) is used to compute the energy profile for the lithium superoxide dimer rearrangement, involving biradical species, on quantum simulators and devices. Considering that current quantum devices can only handle limited number of qubits, we present guidelines for selecting an appropriate active space to perform computations on chemical systems that require many qubits.

Artificial Intelligence for Accelerating Time Integrations in Multiscale Modeling.

We developed a novel data-driven Artificial Intelligence-enhanced Adaptive Time Stepping algorithm (AI-ATS) that can adapt timestep sizes to underlying biophysical dynamics. We demonstrated its values in solving a complex biophysical problem, at multiple spatiotemporal scales, that describes platelet dynamics in shear blood flow. In order to achieve a significant speedup of this computationally demanding problem, we integrated a framework of novel AI algorithms into the solution of the platelet dynamics equations.

A co-design framework of neural networks and quantum circuits towards quantum advantage.

Despite the pursuit of quantum advantages in various applications, the power of quantum computers in executing neural network has mostly remained unknown, primarily due to a missing tool that effectively designs a neural network suitable for quantum circuit. Here, we present a neural network and quantum circuit co-design framework, namely QuantumFlow, to address the issue. In QuantumFlow, we represent data as unitary matrices to exploit quantum power by encoding n = 2 inputs into k qubits and representing data as random variables to seamlessly connect layers without measurement.

Linguistic markers predict onset of Alzheimer's disease.

Background: The aim of this study is to use classification methods to predict future onset of Alzheimer's disease in cognitively normal subjects through automated linguistic analysis.

Methods: To study linguistic performance as an early biomarker of AD, we performed predictive modeling of future diagnosis of AD from a cognitively normal baseline of Framingham Heart Study participants. The linguistic variables were derived from written responses to the cookie-theft picture-description task.

Identifying signals associated with psychiatric illness utilizing language and images posted to Facebook.

Prior research has identified associations between social media activity and psychiatric diagnoses; however, diagnoses are rarely clinically confirmed. Toward the goal of applying novel approaches to improve outcomes, research using real patient data is necessary. We collected 3,404,959 Facebook messages and 142,390 images across 223 participants (mean age = 23.

Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI.

Artificial intelligence (AI) systems hold great promise as decision-support tools, but we must be able to identify and understand their inevitable mistakes if they are to fulfill this potential. This is particularly true in domains where the decisions are high-stakes, such as law, medicine, and the military. In this Perspective, we describe the particular challenges for AI decision support posed in military coalition operations.

Dioxybenzone triggers enhanced estrogenic effect via metabolic activation: in silico, in vitro and in vivo investigation.

Dioxybenzone is widely used in cosmetics and personal care products and frequently detected in multiple environmental media and human samples. However, the current understanding of the metabolic susceptibility of dioxybenzone and the potential endocrine disruption through its metabolites in mimicking human estrogens remains largely unclear. Here we investigated the in vitro metabolism of dioxybenzone, detected the residue of metabolites in rats, and determined the estrogenic disrupting effects of these metabolites toward estrogen receptor α (ERα).

Low-Dose X-ray-Responsive Diselenide Nanocarriers for Effective Delivery of Anticancer Agents.

X-ray-responsive nanocarriers for anticancer drug delivery have shown great promise for enhancing the efficacy of chemoradiotherapy. A critical challenge remains for development of such radiation-controlled drug delivery systems (DDSs), which is to minimize the required X-ray dose for triggering the cargo release. Herein, we design and fabricate an effective DDS based on diselenide block copolymers (as nanocarrier), which can be triggered to release their cargo with a reduced radiation dose of 2 Gy due to their sensitivity to both X-ray and the high level of reactive oxygen species (ROS) in the microenvironment of cancer cells.

Structural cavities are critical to balancing stability and activity of a membrane-integral enzyme.

Packing interaction is a critical driving force in the folding of helical membrane proteins. Despite the importance, packing defects (i.e.

Using Language Processing and Speech Analysis for the Identification of Psychosis and Other Disorders.

Increasingly, data-driven methods have been implemented to understand psychopathology. Language is the main source of information in psychiatry and represents "big data" at the level of the individual. Language and behavior are amenable to computational natural language processing (NLP) analytics, which may help operationalize the mental status examination.

Community Assessment of the Predictability of Cancer Protein and Phosphoprotein Levels from Genomics and Transcriptomics.

Cancer is driven by genomic alterations, but the processes causing this disease are largely performed by proteins. However, proteins are harder and more expensive to measure than genes and transcripts. To catalyze developments of methods to infer protein levels from other omics measurements, we leveraged crowdsourcing via the NCI-CPTAC DREAM proteogenomic challenge.

Reduced odor detection and hedonic changes in asymptomatic university students as SARS-CoV-2 emerged locally.

Aerosol droplets have emerged as the primary mode of SARS-Cov-2 transmission and can be spread by infectious asymptomatic/pre-symptomatic persons rendering indicators of latent viral infection essential. Olfactory impairment is now a recognized symptom of COVID-19 and is rapidly becoming one of the most reliable indicators of the disease. We compared olfaction data from asymptomatic students, who were assessed as SARS-CoV-2 was unknowingly spreading locally, to students tested prior to the arrival of the virus.

In-situ characterization of ultrathin nickel silicides using 3D medium-energy ion scattering.

Epitaxial ultrathin films are of utmost importance for state-of-the-art nanoelectronic devices, such as MOSFET transistors and non-volatile memories. At the same time, as the film thickness is reduced to a few nanometers, characterization of the materials is becoming challenging for commonly used methods. In this report, we demonstrate an approach for in-situ characterization of phase transitions of ultrathin nickel silicides using 3D medium-energy ion scattering.

Learning Interpretable Behavioral Engagement for Care Management.

The health outcomes of high-need patients can be substantially influenced by the degree of patient engagement in their own care. The role of care managers (CMs) includes enrolling patients and keeping them sufficiently engaged in care programs, so that patients complete assigned goals leading to improvement in their health outcomes. Here, we present a data-driven behavioral engagement scoring (BES) pipeline that can compute the patients' engagement level with regards to their interest in: (1) enrolling into a relevant care program, and (2) completing program goals.