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BackgroundKinematic and kinetic analysis have been used to gain an understanding of canine movement and joint loading during gait. By non-invasively predicting muscle activation patterns and forces during gait, musculoskeletal models can further our understanding of normal variability and muscle activation patterns and force profiles characteristic of gait.MethodsPelvic limb kinematics and kinetics were measured for a 2 year old healthy female Dachshund (5.4 kg) during gait using 3-D motion capture and force platforms. A computed tomography scan was conducted to acquire pelvis and pelvic limb morphology. Using the OpenSim modeling platform, a bilateral pelvic limb subject-specific rigid body musculoskeletal computer model was developed. This model predicted muscle activation patterns, muscle forces, and angular kinematics and joint moments during walking.ResultsGait kinematics determined from motion capture matched those predicted by the model, verifying model accuracy. Primary muscles involved in generating joint moments during stance and swing were predicted by the model: at mid-stance the adductor magnus et brevis (peak activation 53.2%, peak force 64.7 N) extended the hip, and stifle flexor muscles (biceps femoris tibial and calcaneal portions) flexed the stifle. Countering vertical ground reaction forces, the iliopsoas (peak activation 37.9%, peak force 68.7 N) stabilized the hip in mid-stance, while the biceps femoris patellar portion stabilized the stifle in mid-stance and the plantar flexors (gastrocnemius and flexor digitorum muscles) stabilized the tarsal joint during early stance. Transitioning to swing, the iliopsoas, rectus femoris and tensor fascia lata flexed the hip, while in late swing the adductor magnus et brevis impeded further flexion as biceps femoris tibial and calcaneal portions stabilized the stifle for ground contact.ConclusionThe musculoskeletal computer model accurately replicated experimental canine angular kinematics associated with gait and was used to predict muscle activation patterns and forces. Thus, musculoskeletal modeling allows for quantification of measures such as muscle forces that are difficult or impossible to measure in vivo.

The Hall thruster is a high-efficiency spacecraft propulsion device that utilizes plasma to generate thrust. The most common variant of the Hall thruster is the stationary plasma thruster (SPT). Erosion of the SPT discharge chamber wall by plasma sputtering degrades thruster performance and ultimately ends thruster life. Many efforts over the past few decades have endeavored to understand wall erosion so that novel thrusters can be designed to operate for the thousands of hours required by many missions. However, due to the challenges presented by the plasma and material physics associated with erosion, a complete understanding has thus far eluded researchers. Sputtering rates are not well quantified, erosion features remain unexplained, and computational models are not yet predictive. This article reviews the physics of plasma-induced SPT erosion, highlights important experimental findings, provides an overview of modeling efforts, and discusses erosion mitigation strategies.

Abstract Aggregation of misfolded α-synuclein (α-syn) is a key characteristic feature of Parkinson’s disease (PD) and related synucleinopathies. The nature of these aggregates and their contribution to cellular dysfunction is still not clearly elucidated. We employed mass spectrometry-based total and phospho-proteomics to characterize the underlying molecular and biological changes due to α-syn aggregation using the M83 mouse primary neuronal model of PD. We identified gross changes in the proteome that coincided with the formation of large Lewy body-like α-syn aggregates in these neurons. We used protein-protein interaction (PPI)-based network analysis to identify key protein clusters modulating specific biological pathways that may be dysregulated and identified several mechanisms that regulate protein homeostasis (proteostasis). The observed changes in the proteome may include both homeostatic compensation and dysregulation due to α-syn aggregation and a greater understanding of both processes and their role in α-syn-related proteostasis may lead to improved therapeutic options for patients with PD and related disorders.

Background In general, the quality of pain care in emergency departments (ED) is poor, despite up to 80% of all ED patients presenting with pain. This may be due to the lack of well-validated patient-reported outcome measures (PROMs) of pain care in the ED setting. The American Pain Society-Patient Outcome Questionnaire-Revised Edition (APS-POQ-R), with slight modification for ED patients, is a potentially useful PROM for the adult ED, however it is yet to be completely validated.Methods Adult patients, who had presented with moderate to severe acute pain, were recruited at two large inner-city EDs in Australia. A modified version of the APS-POQ-R was administered at the completion of their ED care. Responses were randomly split into three groups and underwent multiple rounds of exploratory and confirmatory factor analysis with testing for construct, convergent, divergent validity and internal consistency.Results A total of 646 ED patients (55.6% female), with a median age of 48.3 years, and moderate to severe pain on arrival, completed the ED-modified APS-POQ-R. Psychometric evaluation resulted in a reduced nine-question tool, which measures three constructs (pain relief and satisfaction (α=0.891), affective distress (α=0.823) and pain interference (α=0.908)) and demonstrated construct, convergent, divergent validity, and internal consistency.Conclusions This new tool, which we refer to as the American Pain Society-Patient Outcome Questionnaire-Revised for the ED (APS-POQ-RED), should form the basis for reporting patient-reported outcomes of ED pain care in future quality improvement and research.

With the growing global need for housing and infrastructure, 3D concrete printing (3DCP) has emerged as an innovative construction method offering several potential benefits including design flexibility, speed, and sustainability. However, enhancing the reliability of 3DCP involves managing a variety of parameters that influence various aspects of the 3D printed structure. Process parameters like nozzle velocity, nozzle diameter, nozzle height, and material flow velocity have a major impact on the structural stability and filament shape. This project aimed to develop fast and accurate data-driven models for predicting and classifying filament shape based on process parameters. A print experiment systematically varied process parameters across 144 samples. The resulting filament geometry (width, height, contact width) was measured and classified by quality. Models were trained on this data to predict filament width, contact width, filament height, and classify filaments. These models can be utilized with any buildable material - a material with a high enough yield stress to bear the weight of upper layers without significant deformation. This condition does not restrict this study’s scope as it is a prerequisite for all 3DCP applications. The models’ robustness and generalizability were confirmed through validation on literature data across various printable materials and setups. These data-driven models can aid in optimizing parameters, generating variable width filaments, and printing non-planar layers. By linking print inputs to filament outputs, this comprehensive modeling approach advances 3DCP research for more reliable and versatile concrete printing.

The carbon footprint of a concrete structure is directly affected by the selected concrete mixture proportions. To better understand the influence of different concrete mixtures, data was collected from Environmental Product Declarations (EPDs). Data from 39,213 U.S.A. ready-mix concrete EPDs was obtained from public repositories provided by the American Society for Testing and Materials and the National Ready Mixed Concrete Association. The EPDs in .pdf format were analyzed using a custom Python script to extract useful information for building designers, sustainability practitioners, and researchers including: life cycle assessment (LCA) midpoints (Global Warming Potential, Ozone Depletion Potential, Acidification Potential, Photochemical Ozone Creation Potential, Abiotic Depletion, Total Waste Disposed, and Consumption of Freshwater), concrete strength classes, declared unit, concrete curing time, production components, concrete manufacturers’ company and plant locations, and additional LCA information. Both the dataset and an example of the Python script used to extract the information from the EPDs are provided. This dataset enables users to quickly assess the environmental impacts (including the Global Warming Potential) of different concrete mixtures without the need for extensive data collection and analysis. In summary, this dataset provides environmental information about concrete mixtures to aid civil engineering and architectural researchers, sustainability consultants, building engineering practitioners, and environmental policymakers to make sustainability-informed decisions when specifying concrete in the U.S.A.

Mechanical metamaterials are regularly implemented in engineering applications due to their unique properties derived from their structural geometry and material composition. This study incorporates deep reinforcement learning, a subset of machine learning that teaches an agent to complete a task through interactive experiences, into mechanical metamaterial design. The approach creates a design environment for the reinforcement learning agent to iteratively construct metamaterials with tailorable deformation and hysteretic characteristics. Validation involved producing metamaterials with a thermoplastic polyurethane (TPU) base material that exhibited the deformation response of expanded thermoplastic polyurethane (E-TPU) while maximizing or minimizing hysteresis in cyclic compression. This alignment confirmed the feasibility of tailoring deformation and energy manipulation using mechanical metamaterials. The agent's generalizability was tested by tasking it to create various metamaterials with distinct loading deformation responses and specific hysteresis goals in a simulated setting. The agent consistently delivered metamaterials that met loading curve criteria and demonstrated favorable energy return. This work demonstrates the potential of deep reinforcement learning as a rapid and effective tool for designing mechanical metamaterials with customizable traits. It ushers in the possibility of on-demand metamaterial design solutions, opening avenues across industries like footwear, wearables, and medical equipment.

Interlocking metasurfaces (ILMs) are a new class of mechanical metasurfaces built from architected arrays of interlocking features that can serve as a nonpermanent, robust joining technology. An ILM's strength is governed by the structural material, orientation, and topology of its latching unit cells. The presented work optimized the topologies of ILM unit cells to maximize strength in tensile and shear loading using gradient-based parametric optimization and genetic algorithms. Experimental validation confirmed that the optimized designs achieved considerable strength increases compared to a human intuitive design. In several cases, the optimized designs were approximately double the effective interfacial strength compared to that achieved via expert intuition alone. The strength improvement was seen for isolated unit cells and arrays of interacting unit cells (metasurfaces). An analysis of the topologies of the optimized designs showed that tall dendritic geometric features with large contact surfaces yield robust solutions in tension, while short and broad geometric features with large contact surfaces yield better results in shear loading. This study revealed the importance of shape optimization to maximize ILM effectiveness under single- and multi-objective scenarios.

Dietary flavanols are known for disease preventative properties but are often poorly absorbed. Gut microbiome flavanol metabolites are more bioavailable and may exert protective activities. Using metabolite mixtures extracted from the urine of rats supplemented with flavanols and treated with or without antibiotics, we investigated their effects on INS-1 832/13 β-cell glucose stimulated insulin secretion (GSIS) capacity. We measured insulin secretion under non-stimulatory (low) and stimulatory (high) glucose levels, insulin secretion fold induction, and total insulin content. We conducted treatment-level comparisons, individual-level dose responses, and a responder vs. non-responder predictive analysis of metabolite composition. While the first two analyses did not elucidate treatment effects, metabolites from 9 of the 28 animals demonstrated significant dose responses, regardless of treatment. Differentiation of responders vs. non-responder revealed that levels of native flavanols and valerolactones approached significance for predicting enhanced GSIS, regardless of treatment. Although treatment-level patterns were not discernable, we conclude that the high inter-individual variability shows that metabolite bioactivity on GSIS capacity is less related to flavanol supplementation or antibiotic treatment and may be more associated with the unique microbiome or metabolome of each animal. These findings suggest flavanol metabolite activities are individualized and point to the need for personalized nutrition practices.

Purpose: To report a case of bilateral vision loss as the primary presenting symptom of chronic myeloid leukemia in a young adult. Observations: The 28-year-old male patient presented to clinic with visual acuity of 20/200 in both eyes after several months of episodic bilateral vision loss. Intraretinal and pre-retinal hemorrhages were appreciated, as well as Roth spots and peripheral neovascularization. Initial lab findings were consistent with a diagnosis of acute myeloid leukemia, later, upon bone marrow examination, the diagnosis was edited to chronic myeloid leukemia. Dasatinib therapy resulted in a complete hematologic resolution after six weeks. After intravitreal injections of bevacizumab in both eyes, visual acuity improved to 20/25 in the right eye and 20/20 in the left eye. Conclusion and importance: After review, this is one of only a few reported cases of bilateral blurry vision as the primary presenting symptom of chronic myeloid leukemia in a young adult. Because visual disturbances occur more frequently in acute myeloid leukemia, and lab results may be inconclusive, careful consideration should be given to differentiate myelogenous leukemias, as the acute and chronic subtypes may present similarly.

Increasingly, architects and building engineers use parametric modeling programs to explore design solutions as professionals and as students. However, little is known about their combined efficacy and exploration in these tools when working in mixed design teams. While disciplinarily diverse teams of designers have been shown to develop more creative design solutions, this occurs primarily when there is a conducive environment and a shared understanding of design goals. Because architects and engineers are traditionally taught to use different tools and processes to address their professional goals, indicators of students’ combined efficacy in parametric tools are unclear. In response, this research uses a conceptual design experiment to study aspects of design efficacy and the exploration behavior of student architect-architect, engineer-engineer, and architect-engineer pairs within a live parametric modeling tool. The dimensions of their collaborative exploration within the tool were recorded, and their success at achieving the desired criteria was rated by professionals. Noticeable performance differences between team types were expected, including that the mixed design teams would better balance all goals and that the homogenous teams would better address their own disciplinary criteria. However, this was not the case when working in a shared, multidisciplinary digital environment, as the teams performed similarly despite having different member composition. We discuss several factors, such as the effect of digital design feedback and the still-developing student design process, which may have relationships with the design efficacy of the teams when using the study’s parametric modeling tool. Future research can further investigate the effect of mutually approachable working environments on design team performance.

An origami-based foldable and reconfigurable Reflectarray Antenna (RA) with multiple apertures is proposed for CubeSat applications. The proposed configuration consists of a central RA embedded in folding panels using Lamina Emergent Torsional (LET) joints based on compliant mechanisms. Depending on the folding direction of these panels (forward or backward), a new RA aperture is formed. The proposed RA along with its folding panels and hinges is fabricated using only a single PCB. A prototype of such an RA with two foldable panels is fabricated and measured. This RA operates in the Ku-band at 16 GHz and provides two pencil beams pointing at (8 = 30°, φ = 0°) and (8 = -30°, φ = 0°), and a dual-beam pointing at (8 = +27°, φ = 0°) and (8 = -29°, φ = 0°). The proposed RA provides a gain of 25 dB and 19 dB in its singleand dual-beam operations, respectively. For CubeSat applications, the key advantages of this RA are its small stowed volume, reconfigurable EM performance, beamsteering capabilities, monolithic construction, low fabrication cost, and reduced complexity.

This paper proposes a novel origami-inspired adult diaper design that improves discretion by reducing sag and increasing wicking across the entire diaper pad. While other diapers rely on supporting elastics to reduce the sag of the diaper as a whole, this paper proposes an absorbent core that uses liquid activated shaping to take a specified shape. Origami-based folds are also incorporated into the diaper design to increase wicking performance. The paper introduces a disposable compliant mechanism waistband used to deploy the diaper, making it easier to put onto one’s body.

Introduction We aimed to determine normal thresholds for positive bronchodilator responses for oscillometry in an Australian general population sample aged ≥40 years, to guide clinical interpretation. We also examined relationships between bronchodilator responses and respiratory symptoms, asthma diagnosis, smoking and baseline lung function. Methods Subjects recruited from Sydney, Melbourne and Busselton, Australia, underwent measurements of spirometry, resistance (Rrs6) and reactance (Xrs6) at 6 Hz, before and after inhalation of salbutamol 200 μg. Respiratory symptoms and/or medication use, asthma diagnosis, and smoking were recorded. Threshold bronchodilator responses were defined as the fifth percentile of decrease in Rrs6 and 95th percentile increase in Xrs6 in a healthy subgroup. Results Of 1318 participants, 1145 (570 female) were analysed. The lower threshold for ΔRrs6 was −1.38 cmH2O·s·L−1 (−30.0% or −1.42 Z-scores) and upper threshold for ΔXrs6 was 0.57 cmH2O·s·L−1 (1.36 Z-scores). Respiratory symptoms and/or medication use, asthma diagnosis, and smoking all predicted bronchodilator response, as did baseline oscillometry and spirometry. When categorised into clinically relevant groups according to those predictors, ΔXrs6 was more sensitive than spirometry in smokers without current asthma or chronic obstructive pulmonary disease (COPD), ∼20% having a positive response. Using absolute or Z-score change provided similar prevalences of responsiveness, except in COPD, in which responsiveness measured by absolute change was twice that for Z-score. Discussion This study describes normative thresholds for bronchodilator responses in oscillometry parameters, including intra-breath parameters, as determined by absolute, relative and Z-score changes. Positive bronchodilator response by oscillometry correlated with clinical factors and baseline function, which may inform the clinical interpretation of oscillometry.

Providing pedestrian accessibility to urban services is a big challenge and a key factor in creating more walkable urban areas. Moreover, it is a critical aspect of climate-resilient urban planning as it is broadly assumed that neighborhoods with greater walkability discourage automobile use and reduce CO2 emissions. The idea of 15-minute cities, defined as urban environments where most places that residents need to access are within a 15-minute walk, is gaining increasing attention worldwide. Because aspects of urban performance are increasingly quantifiable, generative, and data-driven design approaches can explore broader sets of potential solutions, while optimization can help identify designs with desired properties. This work demonstrates and tests a new approach that combines shape grammars, a formal method for shape generation that facilitates the elaboration of complex patterns and meaningful solutions, with multi-objective optimization. The goal was to optimize the design of urban fabric layouts and the location of amenities to provide 15-minute neighborhood configurations that minimize infrastructure cost (as estimated by cumulative street length) and the number of amenities, while maximizing pedestrian accessibility to urban services (as assessed by overall integration and the average distance from all plots to nearest amenities).

Abstract Acute lung injury (ALI) leads to progressive loss of breathing capacity and hypoxemia, as well as pulmonary surfactant dysfunction. ALI’s pathogenesis and management are complex, and it is a significant cause of morbidity and mortality worldwide. Exogenous surfactant therapy, even for research purposes, is impractical for adults because of the high cost of current surfactant preparations. Prior in vitro work has shown that poly-N-substituted glycines (peptoids), in a biomimetic lipid mixture, emulate key biophysical activities of lung surfactant proteins B and C at the air-water interface. Here we report good in vivo efficacy of a peptoid-based surfactant, compared with extracted animal surfactant and a synthetic lipid formulation, in a rat model of lavage-induced ALI. Adult rats were subjected to whole-lung lavage followed by administration of surfactant formulations and monitoring of outcomes. Treatment with a surfactant protein C mimic formulation improved blood oxygenation, blood pH, shunt fraction, and peak inspiratory pressure to a greater degree than surfactant protein B mimic or combined formulations. All peptoid-enhanced treatment groups showed improved outcomes compared to synthetic lipids alone, and some formulations improved outcomes to a similar extent as animal-derived surfactant. Robust biophysical mimics of natural surfactant proteins may enable new medical research in ALI treatment.

The novel coronavirus disease 2019 (COVID-19) pandemic is an unprecedented global event that has been challenging governments, health systems, and communities worldwide. Available data from the first months indicated varying patterns of the spread of COVID-19 within American cities, when the spread was faster in high-density and walkable cities such as New York than in low-density and car-oriented cities such as Los Angeles. Subsequent containment efforts, underlying population characteristics, variants, and other factors likely affected the spread significantly. However, this work investigates the hypothesis that urban configuration and associated spatial use patterns directly impact how the disease spreads and infects a population. It follows work that has shown how the spatial configuration of urban spaces impacts the social behavior of people moving through those spaces. It addresses the first 60 days of contagion (before containment measures were widely adopted and had time to affect spread) in 93 urban counties in the United States, considering population size, population density, walkability, here evaluated through walkscore, an indicator that measures the density of amenities, and, therefore, opportunities for population mixing, and the number of confirmed cases and deaths. Our findings indicate correlations between walkability, population density, and COVID-19 spreading patterns but no clear correlation between population size and the number of cases or deaths per 100 k habitants. Although virus spread beyond these initial cases may provide additional data for analysis, this study is an initial step in understanding the relationship between COVID-19 and urban configuration.

BackgroundPerioperative infections, particularly surgical site infections pose significant morbidity and mortality. Phagocytosis is a critical step for microbial eradication. We examined the effect of commonly used anesthetics on macrophage phagocytosis and its mechanism.MethodsThe effect of anesthetics (isoflurane, sevoflurane, propofol) on macrophage phagocytosis was tested using RAW264.7 mouse cells, mouse peritoneal macrophages, and THP-1 human cells. Either opsonized sheep erythrocytes or fluorescent labeled Escherichia coli were used as phagocytic objects. The activation of Rap1, a critical protein in phagocytosis was assessed using the active Rap1 pull-down and detection kit. To examine anesthetic binding site(s) on Rap1, photolabeling experiments were performed using azi-isoflurane and azi-sevoflurane. The alanine scanning mutagenesis of Rap1 was performed to assess the role of anesthetic binding site in Rap1 activation and phagocytosis.ResultsMacrophage phagocytosis was significantly attenuated by the exposure of isoflurane (50% reduction by 1% isoflurane) and sevoflurane (50% reduction by 1.5% sevoflurane), but not by propofol. Photolabeling experiments showed that sevoflurane directly bound to Rap1. Mutagenesis analysis demonstrated that the sevoflurane binding site affected Rap1 activation and macrophage phagocytosis.ConclusionsWe showed that isoflurane and sevoflurane attenuated macrophage phagocytosis, but propofol did not. Our study showed for the first time that sevoflurane served as a novel small GTPase Rap1 inhibitor. The finding will further enrich our understanding of yet-to-be determined mechanism of volatile anesthetics and their off-target effects. The sevoflurane binding site was located outside the known Rap1 functional sites, indicating the discovery of a new functional site on Rap1 and this site would serve as a pocket for the development of novel Rap1 inhibitors.

Orofacial clefting is one of the most common congenital malformations with an incidence of 1 in 500 to 1000 live births. Cleft lip and cleft palate may occur in isolation, in combination or as part of one of the 400 or more syndromes linked to orofacial clefting. Clefting causes problems with feeding, speech, hearing and the dentition. Patients and their families bear a significant emotional burden. Despite the high frequency and significance of the malformation, the aetiology of nonsyndromic cleft lip and palate is poorly understood. It has a complex multifactorial aetiology, both population-based and laboratory-based research has identified numerous genetic and environmental factors as causative agents and the list is continually expanding. Leading scientists are now beginning to question whether this trait is too complex to fully understand. This report provides a comprehensive and up-todate review of research into palatogenesis and non-syndromic oral clefting

Human metabolism is investigated using several in vitro methods. However, the current methodologies are often expensive, tedious and complicated. Over the last decade, the combination of electrochemistry (EC) with mass spectrometry (MS) has a simpler and a cheaper alternative to mimic the human metabolism. This paper describes the development of a disposable microfluidic device with a screen-printed electrode (SPE) for monitoring phase II GSH reactions. The proposed chip has the potential to be used as a primary screening tool, thus complementing the current in vitro methods.

Advances in machine learning algorithms and increased computational efficiencies give engineers new capabilities and tools to apply to engineering design. Machine learning models can approximate complex functions and, therefore, can be useful for various tasks in the engineering design workflow. This paper investigates using reinforcement learning (RL), a subset of machine learning that teaches an agent to complete a task through accumulating experiences in an interactive environment, to automate the designing of 2D discretized topologies. RL agents use past experiences to learn sequential sets of actions to best achieve some objective. In the proposed environment, an RL agent can make sequential decisions to design a topology by removing elements to best satisfy compliance minimization objectives. After each action, the agent receives feedback by evaluating how well the current topology satisfies the design objectives. After training, the agent was tasked with designing optimal topologies under various load cases. The agent's proposed designs had similar or better compliance minimization performance to those produced by traditional gradient-based topology optimization methods. These results show that a deep RL agent can learn generalized design strategies to satisfy multi-objective design tasks and, therefore, shows promise as a tool for arbitrarily complex design problems across many domains.

BACKGROUND:Kinematic gait analysis is an important noninvasive technique used for quantitative evaluation and description of locomotion and other movements in healthy and injured populations. Three dimensional (3D) kinematic analysis offers additional outcome measures including internal-external rotation not characterized using sagittal plane (2D) analysis techniques. METHODS:The objectives of this study were to 1) develop and evaluate a 3D hind limb multiplane kinematic model for gait analysis in cats using joint coordinate systems, 2) implement and compare two 3D stifle (knee) prediction techniques, and 3) compare flexion-extension determined using the multiplane model to a sagittal plane model. Walking gait was recorded in 3 female adult cats (age = 2.9 years, weight = 3.5 ± 0.2 kg). Kinematic outcomes included flexion-extension, internal-external rotation, and abduction-adduction of the hip, stifle, and tarsal (ankle) joints. RESULTS:Each multiplane stifle prediction technique yielded similar findings. Joint angles determined using markers placed on skin above bony landmarks in vivo were similar to joint angles determined using a feline hind limb skeleton in which markers were placed directly on landmarks ex vivo. Differences in hip, stifle, and tarsal joint flexion-extension were demonstrated when comparing the multiplane model to the sagittal plane model. CONCLUSIONS:This multiplane cat kinematic model can predict joint rotational kinematics as a tool that can quantify frontal, transverse, and sagittal plane motion. This model has multiple advantages given its ability to characterize joint internal-external rotation and abduction-adduction. A further, important benefit is greater accuracy in representing joint flexion-extension movements.