Voice-Enabled Response Analysis Agent (VERAA): Leveraging Large Language Models to Map Voice Responses in SDoH Survey.

Social Determinants of Health (SDoH) have been shown to have profound impacts on health-related outcomes, yet this data suffers from high rates of missingness in electronic health records (EHR). Moreover, limited English proficiency in the United States can be a barrier to communication with health care providers. In this study, we have designed a multilingual conversational agent capable of conducting SDoH surveys for use in healthcare environments.

Affordable Artificial Intelligence-Assisted Machine Supervision System for the Small and Medium-Sized Manufacturers.

With the rapid concurrent advance of artificial intelligence (AI) and Internet of Things (IoT) technology, manufacturing environments are being upgraded or equipped with a smart and connected infrastructure that empowers workers and supervisors to optimize manufacturing workflow and processes for improved energy efficiency, equipment reliability, quality, safety, and productivity. This challenges capital cost and complexity for many small and medium-sized manufacturers (SMMs) who heavily rely on people to supervise manufacturing processes and facilities. This research aims to create an affordable, scalable, accessible, and portable (ASAP) solution to automate the supervision of manufacturing processes.

Sex-specific differences in the efficacy of traditional low frequency versus high frequency spinal cord stimulation for chronic pain.

Introduction: Spinal cord stimulation (SCS), an FDA-approved therapy for chronic pain, uses paresthesia (low frequency SCS (LF-SCS)) or paresthesia-free (such as high-frequency SCS (HF-SCS)) systems, providing analgesia through partially-elucidated mechanisms, with recent studies indicating a sexual dimorphism in pain pathogenesis (Bretherton et al., Neuromodulation, 2021; Paller et al., Pain Med 10:289-299, 2009; Slyer et al.

Digital healthcare technologies: Modern tools to transform prosthetic care.

Introduction: Digital healthcare technologies are transforming the face of prosthetic care. Millions of people with limb loss around the world do not have access to any form of rehabilitative healthcare. However, digital technologies provide a promising solution to augment the range and efficiency of prosthetists.

Stereoselective Growth of Small Molecule Patches on Nanoparticles.

Patchy nanoparticles featuring tunable surface domains with spatial and chemical specificity are of fundamental interest, especially for creating three-dimensional (3D) colloidal structures. Guided assembly and regioselective conjugation of polymers have been widely used to manipulate such topography on nanoparticles; however, the processes require presynthesized specialized polymer chains and elaborate assembly conditions. Here, we show how small molecules can form 3D patches in aqueous environments in a single step.

A cyclic peptide inhibitor of the SARS-CoV-2 main protease.

This paper presents the design and study of a first-in-class cyclic peptide inhibitor against the SARS-CoV-2 main protease (M). The cyclic peptide inhibitor is designed to mimic the conformation of a substrate at a C-terminal autolytic cleavage site of M. The cyclic peptide contains a [4-(2-aminoethyl)phenyl]-acetic acid (AEPA) linker that is designed to enforce a conformation that mimics a peptide substrate of M.

Determining the Safety Threshold for the Passage of a Ureteral Access Sheath in Clinical Practice Using a Purpose-Built Force Sensor.

Purpose: Ureteral injury is a frequent complication of ureteral access sheath deployment. We sought to define the safe threshold of force for the passage of a ureteral access sheath using a novel ureteral access sheath force sensor.

Materials And Methods: Ureteral access sheath-force sensor measurements were recorded in 210 renal units.

Gastrointestinal Surgery for Inflammatory Bowel Disease Persistently Lowers Microbiome and Metabolome Diversity.

Background: Many studies have investigated the role of the microbiome in inflammatory bowel disease (IBD), but few have focused on surgery specifically or its consequences on the metabolome that may differ by surgery type and require longitudinal sampling. Our objective was to characterize and contrast microbiome and metabolome changes after different surgeries for IBD, including ileocolonic resection and colectomy.

Methods: The UC San Diego IBD Biobank was used to prospectively collect 332 stool samples from 129 subjects (50 ulcerative colitis; 79 Crohn's disease).

Network Hamiltonian models reveal pathways to amyloid fibril formation.

Amyloid fibril formation is central to the etiology of a wide range of serious human diseases, such as Alzheimer's disease and prion diseases. Despite an ever growing collection of amyloid fibril structures found in the Protein Data Bank (PDB) and numerous clinical trials, therapeutic strategies remain elusive. One contributing factor to the lack of progress on this challenging problem is incomplete understanding of the mechanisms by which these locally ordered protein aggregates self-assemble in solution.

Sequence Characterization and Molecular Modeling of Clinically Relevant Variants of the SARS-CoV-2 Main Protease.

The SARS-CoV-2 main protease (M) is essential to viral replication and cleaves highly specific substrate sequences, making it an obvious target for inhibitor design. However, as for any virus, SARS-CoV-2 is subject to constant neutral drift and selection pressure, with new M mutations arising over time. Identification and structural characterization of M variants is thus critical for robust inhibitor design.

Responses to addiction help-seeking from Alexa, Siri, Google Assistant, Cortana, and Bixby intelligent virtual assistants.

We investigated how intelligent virtual assistants (IVA), including Amazon's Alexa, Apple's Siri, Google Assistant, Microsoft's Cortana, and Samsung's Bixby, responded to addiction help-seeking queries. We recorded if IVAs provided a singular response and if so, did they link users to treatment or treatment referral services. Only 4 of the 70 help-seeking queries presented to the five IVAs returned singular responses, with the remainder prompting confusion (e.

#HIV: Alignment of HIV-Related Visual Content on Instagram with Public Health Priorities in the US.

Instagram, with more than 1 billion monthly users, is the go-to social media platform to chronicle one's life via images, but how are people using the platform to present visual content about HIV? We analyzed public Instagram posts containing the hashtag "#HIV" (because they are self-tagged as related to HIV) between January 2017 and July 2018. We described the prevalence of co-occurring hashtags and explored thematic concepts in the images using automated image recognition and topic modeling. Twenty-eight percent of all #HIV posts included hashtags focused on awareness, followed by LGBTQ (24.

De Novo Peptide Sequencing Reveals Many Cyclopeptides in the Human Gut and Other Environments.

Cyclic and branch cyclic peptides (cyclopeptides) represent a class of bioactive natural products that include many antibiotics and anti-tumor compounds. Despite the recent advances in metabolomics analysis, still little is known about the cyclopeptides in the human gut and their possible interactions due to a lack of computational analysis pipelines that are applicable to such compounds. Here, we introduce CycloNovo, an algorithm for automated de novo cyclopeptide analysis and sequencing that employs de Bruijn graphs, the workhorse of DNA sequencing algorithms, to identify cyclopeptides in spectral datasets.

Phylogenomics of 10,575 genomes reveals evolutionary proximity between domains Bacteria and Archaea.

Rapid growth of genome data provides opportunities for updating microbial evolutionary relationships, but this is challenged by the discordant evolution of individual genes. Here we build a reference phylogeny of 10,575 evenly-sampled bacterial and archaeal genomes, based on a comprehensive set of 381 markers, using multiple strategies. Our trees indicate remarkably closer evolutionary proximity between Archaea and Bacteria than previous estimates that were limited to fewer "core" genes, such as the ribosomal proteins.

Understanding the Mechanism of Electronic Defect Suppression Enabled by Nonidealities in Atomic Layer Deposition.

Silicon germanium (SiGe) is a multifunctional material considered for quantum computing, neuromorphic devices, and CMOS transistors. However, implementation of SiGe in nanoscale electronic devices necessitates suppression of surface states dominating the electronic properties. The absence of a stable and passive surface oxide for SiGe results in the formation of charge traps at the SiGe-oxide interface induced by GeO.

Biometric recognition of newborns and infants by non-contact fingerprinting: lessons learned.

Despite years of effort, reliable biometric identification of newborns and young children has remained elusive. In this paper, we review the importance of trusted identification methods, the biometric landscape for infants and adults, barriers and success stories, and we discuss specific failure modes particular to young children. We then describe our approach to infant identification using non-contact optical imaging of fingerprints.

Optimizing sequencing protocols for leaderboard metagenomics by combining long and short reads.

As metagenomic studies move to increasing numbers of samples, communities like the human gut may benefit more from the assembly of abundant microbes in many samples, rather than the exhaustive assembly of fewer samples. We term this approach leaderboard metagenome sequencing. To explore protocol optimization for leaderboard metagenomics in real samples, we introduce a benchmark of library prep and sequencing using internal references generated by synthetic long-read technology, allowing us to evaluate high-throughput library preparation methods against gold-standard reference genomes derived from the samples themselves.

A Wearable Platform for Research in Augmented Hearing.

We have previously reported a realtime, open-source speech-processing platform (OSP) for hearing aids (HAs) research. In this contribution, we describe a wearable version of this platform to facilitate audiological studies in the lab and in the field. The system is based on smartphone chipsets to leverage power efficiency in terms of FLOPS/watt and economies of scale.

Comparative Exploratory Analysis of Intrinsically Disordered Protein Dynamics Using Machine Learning and Network Analytic Methods.

Simulations of intrinsically disordered proteins (IDPs) pose numerous challenges to comparative analysis, prominently including highly dynamic conformational states and a lack of well-defined secondary structure. Machine learning (ML) algorithms are especially effective at discriminating among high-dimensional inputs whose differences are extremely subtle, making them well suited to the study of IDPs. In this work, we apply various ML techniques, including support vector machines (SVM) and clustering, as well as related methods such as principal component analysis (PCA) and protein structure network (PSN) analysis, to the problem of uncovering differences between configurational data from molecular dynamics simulations of two variants of the same IDP.

Predicting Reaction Products and Automating Reactive Trajectory Characterization in Molecular Simulations with Support Vector Machines.

A machine learning-based methodology for the prediction of chemical reaction products, along with automated elucidation of mechanistic details via phase space analysis of reactive trajectories, is introduced using low-dimensional heuristic models and then applied to ab initio computer simulations of the photodissociation of acetaldehyde, an important chemical system in atmospheric chemistry. Our method is centered around training Support Vector Machines (SVMs) to identify optimal separatrices that delineate the regions of phase space that lead to different chemical reaction products. In contrast to the more common "black box" type machine learning methodologies for analyzing chemical simulation data, this SVM-based methodology allows for mechanistic insight to be gleaned from further analysis of the positioning of the phase space points used to train the SVM with respect to the separatrices.

Evaluating Metagenomic Prediction of the Metaproteome in a 4.5-Year Study of a Patient with Crohn's Disease.

Although genetic approaches are the standard in microbiome analysis, proteome-level information is largely absent. This discrepancy warrants a better understanding of the relationship between gene copy number and protein abundance, as this is crucial information for inferring protein-level changes from metagenomic data. As it remains unknown how metaproteomic systems evolve during dynamic disease states, we leveraged a 4.

Metagenomics-Based, Strain-Level Analysis of From a Time-Series of Microbiome Samples From a Crohn's Disease Patient.

Dysbiosis of the gut microbiome, including elevated abundance of putative leading bacterial triggers such as in inflammatory bowel disease (IBD) patients, is of great interest. To date, most studies in IBD patients are focused on clinical isolates, overlooking their relative abundances and turnover over time. Metagenomics-based studies, on the other hand, are less focused on strain-level investigations.

Are microbiome studies ready for hypothesis-driven research?

Hypothesis-driven research has led to many scientific advances, but hypotheses cannot be tested in isolation: rather, they require a framework of aggregated scientific knowledge to allow questions to be posed meaningfully. This framework is largely still lacking in microbiome studies, and the only way to create it is by discovery-driven, tool-driven, and standards-driven research projects. Here we illustrate these issues using several such non-hypothesis-driven projects from our own laboratories, including spatial mapping, the American Gut Project, the Earth Microbiome Project (which is an umbrella project integrating many smaller hypothesis-driven projects), and the knowledgebase-driven tools GNPS and Qiita.